Artificial Intelligence on Edward Kiledjian

The MIT-Licensed Frontier: Why GLM-5.2 Reshapes Enterprise AI Trade-Offs

Fri, 19 Jun 2026 09:09:17 -0400

Enterprise artificial intelligence strategy is shifting from model selection to control architecture selection.

As organizations move from experimental deployments toward production-grade agentic systems, the dominant constraints are no longer model performance alone. They are increasingly defined by control over weights, data residency, licensing structure, and operational governance boundaries.

The release of GLM-5.2 by Z.ai (Zhipu AI) reflects this shift. Based on publicly available technical documentation and reported benchmark evaluations, the model is positioned as a large-scale mixture-of-experts system targeting frontier-level capability in software engineering and multi-step reasoning tasks.

Its significance is not isolated performance. It is the combination of capability, deployment flexibility, and permissive licensing under a widely used open-source framework.

From Prompt-Driven Use to Agentic Engineering

Enterprise adoption of large language models has historically been dominated by prompt-centric workflows. In this model, systems are used as stateless interfaces that generate discrete outputs without persistent operational context.

While effective for productivity augmentation, this approach does not scale well to complex engineering environments involving long-running workflows, system-level orchestration, or multi-repository codebases.

A structural shift is now underway toward agentic engineering, where models operate as components within coordinated systems rather than standalone tools.

For example, agentic systems may be used to coordinate multi-repository refactoring or automate security patch triage across distributed codebases.

Within this framing, GLM-5.2 is positioned as part of a class of systems designed for long-horizon execution in software engineering environments involving iterative debugging, tool-assisted workflows, and structured reasoning over large codebases.

Public technical descriptions suggest three broad capability directions:

Extended context handling for large-scale code and data environments
Asynchronous reinforcement learning approaches intended to improve iterative system behaviour
Safety and integrity mechanisms designed to reduce reward manipulation in automated evaluation environments

While implementation details vary across available documentation, the strategic direction is consistent: improved reliability in multi-step, tool-mediated execution environments.

Reported Benchmark Positioning (Contextual, Not Absolute)

Public benchmark summaries suggest GLM-5.2 is positioned within the upper tier of recent frontier models on selected software engineering and reasoning tasks.

These evaluations are typically conducted on structured benchmarks involving multi-step reasoning and code generation tasks, often compared against proprietary systems from leading AI providers.

It is important to note that cross-model comparisons are sensitive to:

evaluation methodology
inference configuration
tool availability
compute budget assumptions

As a result, performance comparisons should be interpreted as conditional rather than absolute.

The broader signal is more important than any single metric: the performance gap between open-weight systems and proprietary API-based models continues to narrow in specific agentic and coding-focused workloads.

The Licensing Shift: Why MIT Matters in Practice

A defining characteristic of GLM-5.2 is its release under the MIT License, one of the most permissive open-source licences in widespread use.

From an enterprise perspective, this has structural implications. However, it is critical to distinguish licensing freedom from regulatory compliance or operational readiness.

MIT licensing primarily governs reuse and redistribution rights. It does not provide exemption from privacy law, sector-specific regulation, or internal governance requirements.

Within that boundary, three practical implications emerge.

1. Increased Deployment Control and Data Perimeter Flexibility

Permissive licensing enables deployment within controlled infrastructure environments, including private cloud and isolated compute clusters.

For regulated organizations, this can reduce dependency on external APIs and improve control over sensitive data flows.

However, operational reality introduces additional complexity. Secure deployment requires governance across:

model supply chain integrity
dependency stacks and runtime environments
access control and logging frameworks

Self-hosting increases control, but also increases operational responsibility.

2. Reduced Dependency on External AI Platforms

Proprietary AI APIs introduce structural dependencies on vendor pricing, availability, policy changes, and jurisdictional constraints.

Self-hosted models reduce exposure to these risks by shifting inference and lifecycle control into enterprise-managed infrastructure.

This represents a redistribution of risk rather than its elimination.

In practice, it reduces vendor dependency but increases internal engineering and operational burden.

3. Flexibility in Model Adaptation and Distillation

Permissive licensing enables fine-tuning and distillation into smaller models optimized for domain-specific use cases.

This supports a layered enterprise architecture where:

large models handle complex reasoning and planning tasks
smaller models support high-volume, latency-sensitive operations

Self-hosting becomes economically rational when sustained inference utilization justifies dedicated GPU allocation across multiple concurrent workloads.

Operational and Security Considerations

Despite its advantages, GLM-5.2 is not a universal replacement for proprietary frontier systems.

In certain long-horizon or highly complex tasks, proprietary models continue to demonstrate stronger consistency, ecosystem maturity, and tool integration support.

Additionally, enterprise deployment introduces non-trivial security considerations:

Model provenance risk, including integrity of downloaded weights
Inference-layer attack surfaces such as prompt injection in tool-using agents
Supply chain dependencies across GPU drivers and inference frameworks
Operational isolation challenges in environments marketed as “air-gapped”

In agentic deployments, the dominant risk shifts from model misuse to control-plane compromise.

These factors require formal threat modeling prior to production deployment.

Economic and Infrastructure Trade-Offs

Self-hosting frontier-scale models introduces a fundamentally different cost structure compared to API-based consumption.

Rather than variable usage-based pricing, organizations assume:

capital expenditure for compute infrastructure
ongoing operational costs for maintenance and scaling
specialized engineering effort for deployment optimization

As a result, hybrid architectures combining external APIs with internal models are likely to remain the dominant enterprise pattern.

Strategic Implications for Enterprise Architecture

For technology and security leaders, the emergence of systems such as GLM-5.2 reinforces several structural shifts:

control is now a first-order architectural constraint
licensing terms directly influence deployment feasibility
hybrid architectures are becoming the default enterprise pattern
governance maturity increasingly determines AI adoption scope

These dynamics reflect a broader rebalancing of enterprise AI strategy toward controllability, risk segmentation, and architectural flexibility.

Closing Perspective

The enterprise AI landscape is entering a phase where performance differentials between leading models are narrowing in specific domains, particularly software engineering and agentic workflows.

As this convergence continues, structural factors—licensing, deployment control, governance maturity, and operational risk—are becoming primary differentiators in enterprise decision-making.

GLM-5.2 should therefore be understood not as a singular technological breakthrough, but as an indicator of where the market is moving: toward distributed, hybrid, and controllable AI systems where sovereignty and capability must be balanced against operational complexity and risk.

Ethics Statement

This article is written in accordance with principles of transparency, analytical independence, and responsible interpretation of emerging artificial intelligence systems. It is intended to provide strategic insight rather than promotional or vendor-aligned positioning.

Readers should interpret all model capabilities, benchmarks, and architectural claims as subject to change, variation in deployment context, and differences in evaluation methodology. Independent validation is recommended prior to any production use or procurement decision.

Disclaimer

The information provided in this article is for informational and analytical purposes only. It does not constitute legal, security, or procurement advice.

Model performance characteristics, licensing interpretations, and benchmark results may vary depending on implementation, infrastructure configuration, quantization approach, and upstream changes.

Readers are responsible for conducting their own due diligence, including security validation, compliance assessment, and operational testing, prior to deploying any model in production environments.

Keywords: #AI #ArtificialIntelligence #EnterpriseAI #GenerativeAI #AgenticAI #OpenSourceAI #AIGovernance #AISecurity #ResponsibleAI #Cybersecurity #CISO #CTO #EnterpriseArchitecture #TechnologyStrategy #DigitalTransformation #DataSovereignty #ModelGovernance #RiskManagement #InformationSecurity #CloudSecurity #AIInfrastructure #MachineLearning #LLM #OpenWeights #MITLicense #HybridAI #TechnologyLeadership #Innovation #DataPrivacy #EnterpriseSecurity

Thu, 23 Apr 2026 15:24:08 -0400

The first hurdle is the hardest in generative AI adoption – and businesses keep falling | IT Pro

Despite rapid AI adoption, many businesses struggle with implementation, falling into “pilot purgatory” due to issues like skills gaps, legacy systems, and a lack of advanced use cases. While employees report individual productivity gains, companies are slow to achieve business-wide benefits, with a significant portion of firms still in basic AI application stages.

Personas in AI, friend or foe?

Tue, 24 Mar 2026 07:29:00 -0400

Are you using persona prompts with AI? Here’s what the research actually says.

arxiv.org/html/2603…

A new study from USC (“Expert Personas Improve LLM Alignment but Damage Accuracy”) tested expert persona prompts across six large language models and finally explains why the community has seen such mixed results.

The finding is simple but important: persona prompts are an alignment tool, not a knowledge tool.

When personas HELP: → Writing tone and style (scores jumped from 7/10 to 9/10 on professional email drafting) → Safety and refusal (jailbreak resistance improved by up to 17.7%) → Format adherence, structured output, and intent following → Longer, more detailed persona descriptions amplify these gains

When personas HURT: → Factual accuracy and knowledge retrieval (accuracy dropped from 71.6% to 68.0%) → Math and logical reasoning (one example went from 9/10 to 1.5/10) → Coding tasks requiring precise recall → Longer personas make the damage worse

Five things you can do right now:

Use personas for creative, editorial, and compliance-sensitive tasks. Drop them for factual lookups, calculations, and code logic.
Place personas in the system prompt, not the user message — it matters on well-optimized models.
If you’re using reasoning models (like DeepSeek R1), skip expert personas entirely. The research shows a random persona works just as well — the model only benefits from added context length, not expertise.
For safety hardening, a dedicated “safety monitor” persona in the system prompt is one of the cheapest and most effective interventions available.
When you must use a persona on accuracy-sensitive work, keep it as short as possible to minimize interference with factual recall.

The bottom line: treat persona prompts like a tone and alignment amplifier, not a knowledge enhancer. Knowing when to use them — and when to strip them out — is a real competitive advantage.

Paper: “Expert Personas Improve LLM Alignment but Damage Accuracy: Bootstrapping Intent-Based Persona Routing with PRISM” (Hu, Rostami, Thomason — USC, 2026)

CodeWall says it hacked McKinsey’s AI platform. Here’s what holds up — and what doesn’t.

Tue, 10 Mar 2026 07:51:40 -0400

This reflects my personal assessment of publicly available reporting and CodeWall’s published blog post. I was not involved in the testing, I do not have access to McKinsey’s internal facts or forensic findings, and my views should be read as commentary and opinion rather than statements of verified fact.

A security startup called CodeWall claims its autonomous agent compromised McKinsey’s internal AI platform, Lilli, within two hours and gained unauthenticated read-write access to a production database containing tens of millions of consultant conversations. The vulnerability appears credible. The claimed scope of impact is not fully evidenced. The primary CodeWall post is here: codewall.ai/blog/how-… Independent reporting by Jessica Lyons in The Register is here: www.theregister.com/2026/03/0…

What is likely true

The attack chain CodeWall describes — publicly exposed API documentation, unauthenticated endpoints, SQL injection through unsafely handled JSON keys and IDOR chaining — is plausible and technically sound. JSON key injection is an uncommon vector. Most security testing tools and methodologies focus on input values, not field names. If Lilli’s backend parameterized values while concatenating keys directly into SQL, that would create a blind spot many assessments could miss.

McKinsey’s response supports the credibility of the finding. In The Register, journalist Jessica Lyons reported that McKinsey acknowledged the issues, patched them within hours and said its forensic review found no evidence that client data or confidential information were accessed by the researcher or any unauthorized party. That report also quotes CodeWall CEO Paul Price on the company’s use of an autonomous agent.

The prompt-layer risk CodeWall highlights is also substantive. If Lilli’s system prompts — the instructions governing how the AI behaves — were stored in the same database to which the agent had write access, an attacker could alter AI behaviour at scale without a traditional code deployment and potentially outside standard release controls. Many organizations have not explicitly modelled this threat, and prompt-layer integrity controls remain immature in many environments.

What is overstated or unproven

CodeWall claims 46.5 million chat messages, 728,000 files, 57,000 user accounts and hundreds of thousands of AI configurations were accessible. The blog provides no proof-of-concept payloads, no hashes, no screenshots and no evidence showing privilege boundaries. It is unclear whether those figures represent records the agent actually retrieved, database row counts inferred from metadata or something in between.

More importantly, the blog conflates three categories that any security professional should keep separate: what was theoretically reachable, what was actually accessed and what was verified as exfiltrated. CodeWall emphasizes reachability. McKinsey’s statement addresses investigated access. Both could be true at the same time, but the blog does not clearly distinguish between them.

The two-hour timeline also deserves scrutiny. Blind SQL injection is typically slow because extraction happens incrementally. The post suggests verbose error messages may have accelerated discovery, which implies the path may have combined error-assisted identification with later blind or semi-blind extraction. That is plausible, but the article does not provide enough technical detail to substantiate a claim of full production read-write access within two hours and 15 iterations.

The assertion that a modified prompt “leaves no log trail” is also too absolute. Whether prompt tampering is detectable depends on the target’s database audit logging, configuration versioning and anomaly detection. Mature organizations may log or detect these events. The blog presents the point too categorically.

What is concerning about the disclosure itself

Autonomous target selection

CodeWall presents the fact that its agent independently chose McKinsey as a target as a feature. An AI system deciding whom to attack — even if limited to organizations with disclosure policies — raises serious questions about operator control, authorization and liability. That issue deserves careful scrutiny, not celebration.

Unresolved scope authorization

The blog cites McKinsey’s HackerOne responsible disclosure policy as justification, but neither the blog nor independent reporting confirms whether Lilli’s production infrastructure was explicitly in scope for that programme. A disclosure policy is not blanket authorization to enumerate a production database. McKinsey’s public policy is referenced by CodeWall here: hackerone.com/mckinsey-…

Rushed disclosure

The issue was discovered Feb. 28, 2026. The public blog was published March 9. McKinsey may have patched quickly, but rapid remediation is not the same as a completed forensic review, variant analysis and confirmation that the vulnerability had not previously been exploited by others. Nine days is a compressed window for all of that.

The published timeline also appears to contain a date inconsistency issue discussed in commentary around the post. If there was a typo in an earlier version, it is minor. Even so, in a report making very large claims, editorial sloppiness weakens confidence.

What security leaders should take away

This is a conventional application security failure on a platform that happens to run AI workloads. The described attack path — exposed documentation, missing authentication, SQL injection, verbose errors and IDOR — is textbook web and API security. Framing it as an “AI platform hack” is effective marketing. Technically, it is a severe application security failure with AI-specific consequences.

Two lessons are worth acting on regardless of the blog’s evidentiary gaps.

First, treat your AI prompt and configuration layer as a crown-jewel asset. If system prompts reside in the same data store as operational data, and that store is reachable through any injection or access-control flaw, you have created a single point of compromise that can silently alter AI behaviour at scale. Apply integrity controls, versioning and monitoring accordingly.

Second, audit for JSON key injection. If any application accepts JSON in which field names are dynamic, and those names are later used in query construction — whether SQL, NoSQL or ORM-generated queries — standard scanning tools may miss it. That requires targeted review.

The bottom line: CodeWall likely found a serious vulnerability. Its blog overstates what was proven, blurs critical distinctions between access and exfiltration, and leaves unresolved questions about authorization and disclosure discipline. The strategic lesson is real, but it is about secure architecture, access control and prompt integrity — not a new class of AI exploit.

Sources and named parties referenced: CodeWall; McKinsey & Company; Paul Price, CEO of CodeWall; Jessica Lyons, The Register.

Ethics statement

This article is intended to support informed discussion about a publicly reported security incident involving CodeWall’s claims about McKinsey’s AI platform, Lilli. It aims to distinguish clearly between CodeWall’s published assertions, McKinsey’s public response, independent media reporting and the author’s professional interpretation. Where facts remain unverified, disputed or incomplete, that uncertainty is stated rather than assumed away. This article does not endorse unauthorized testing, autonomous target selection or activity that exceeds clearly defined responsible disclosure boundaries.

Disclaimer

This article is provided for general information, commentary and discussion purposes only. It is not legal, security, privacy, compliance or other professional advice, and it should not be relied upon as such. The analysis is based on publicly available information at the time of writing, including CodeWall’s blog post, McKinsey’s public statements and independent reporting. The author was not involved in the testing, does not have access to McKinsey’s internal systems, logs or forensic findings, and cannot independently verify all technical or factual claims made by the parties involved. Any errors or omissions are unintentional. The views expressed are those of the author in a personal capacity and do not represent the views of any employer, client, partner or affiliated organization. Generative AI tools were used to assist with research and editing.

Keywords : #CyberSecurity #AppSec #AI #AIAgents #AISecurity #LLMSecurity #PromptSecurity #PromptInjection #ResponsibleDisclosure #VulnerabilityDisclosure #BugBounty #HackerOne #SQLInjection #IDOR #APISecurity #WebSecurity #SecurityResearch #ThreatModeling #SecureByDesign #SecurityLeadership #RiskManagement #DigitalTrust #InfoSec #SecurityGovernance #DataSecurity #CloudSecurity #RedTeam #BlueTeam #CyberRisk #McKinsey

DeerFlow 2.0: ByteDance’s open-source AI agent harness for research and software tasks

Fri, 06 Mar 2026 15:46:00 -0400

DeerFlow 2.0, an open-source project from ByteDance, has quickly become one of the most visible AI agent releases of early 2026. The project’s public repository says it reached No. 1 on GitHub Trending on Feb. 28, 2026, and the repository currently shows about 25,000 stars and 3,000 forks. For teams evaluating agentic systems, DeerFlow deserves attention, but it also warrants disciplined review.

I have been testing DeerFlow 2.0 over the past week. The short version is this: it is more capable and more complete than many open-source agent projects, but some of the public enthusiasm around it is running ahead of careful governance, privacy and security assessment. For a business, IT, security and privacy audience, that distinction matters.

What DeerFlow 2.0 is

DeerFlow, short for Deep Exploration and Efficient Research Flow, began as a deep-research framework. The project’s maintainers then rebuilt it as a broader agent runtime. According to the official materials, DeerFlow 2.0 is a ground-up rewrite built on LangGraph and LangChain, with built-in support for memory, filesystem access, skills, sandboxed execution and sub-agents.

In practical terms, DeerFlow is not just another chat interface with tools attached. It is better understood as an agent harness: a runtime that can plan work, break it into subtasks, invoke tools, generate and execute code, manage files and return finished outputs. That architecture is what makes it more relevant to serious experimentation than many lighter open-source alternatives.

Two official references are worth reviewing first:

Official site: deerflow.tech
GitHub repository: github.com/bytedance/deer-flow

How it works

You give DeerFlow a goal in plain language. The lead agent then plans the work, divides it into subtasks, invokes supporting tools and, where needed, spawns sub-agents to handle specialized roles. Based on the project documentation and visible demos, DeerFlow 2.0 can:

Plan and decompose multi-step tasks
Spawn sub-agents with separate context and responsibilities
Use search, browsing and file-based workflows
Write and execute code in a sandboxed environment
Manage files and directories across a persistent workspace
Return finished artefacts such as reports, code, dashboards and other outputs

That is a meaningful step up from agent frameworks that require much more assembly work before they become operational. DeerFlow’s key strength is not that any one individual feature is unique. It is that the project packages several of those features into a more usable starting point.

What stands out technically

A few characteristics make DeerFlow 2.0 more consequential than the average open-source agent release.

First, it is delivered as a more complete runtime rather than as a toolkit that expects the user to build the rest. That lowers the barrier to experimentation.

Second, it supports longer-horizon work. The project’s positioning and demos emphasize tasks that may take minutes or longer, rather than quick prompt-response exchanges.

Third, it has a stronger execution model than many early agent projects. Filesystem access, skills, memory and sandboxed code execution create a more realistic operating environment for agents.

Fourth, it appears model-agnostic. The public materials indicate support for multiple OpenAI-compatible endpoints and local model options, which gives teams more flexibility in how they approach privacy, cost and deployment.

That said, none of those points should be confused with a production-readiness certification. Capability and readiness are not the same thing.

What it can do now

Based on official materials and public demonstrations, DeerFlow 2.0 is positioned for tasks such as:

building websites and interactive dashboards from short briefs
conducting exploratory analysis on datasets
generating research outputs with citations
producing documents, slides and content artefacts
coordinating multi-step software or research workflows

In testing, the more compelling takeaway is not that DeerFlow can produce flashy outputs. Many tools can do that in curated demos. The more important point is that DeerFlow is trying to operationalize the entire chain from planning to execution to artefact delivery inside one environment. That is why it has generated so much attention.

Where the current hype needs more discipline

This is where the discussion needs to become more precise.

A number of public claims about DeerFlow are either overstated or not yet sufficiently documented. I would be cautious about repeating unverified assertions about default telemetry behaviour, optional cloud-memory backends, authentication changes in the web UI or broad multilingual performance. Some of those claims may prove correct in specific builds, issues or branches, but they should not be treated as settled facts without direct evidence from the exact version being assessed.

That point is important well beyond this project. In the agent space, people often blend official documentation, demos, open issues, unmerged pull requests and personal testing into one narrative. That produces enthusiasm, but it does not always produce accuracy.

Security and privacy considerations

For security and privacy professionals, DeerFlow should be treated as an agentic execution platform, not merely as an AI assistant. The relevant control questions are therefore broader and more serious.

What works in its favour

It is open source and auditable.
It supports containerized execution models rather than forcing host-level execution.
It provides a structured runtime with memory, filesystem access and tool orchestration rather than hiding those behaviours behind a black box.
It appears suitable for self-hosted deployment patterns.

What requires scrutiny

Code execution risk: The platform can generate and run code. That creates obvious exposure if execution is not isolated properly.
Prompt injection and tool abuse: Any system that consumes external content and can invoke tools is exposed to adversarial inputs, malicious instructions and unsafe chaining.
Outbound data flow: Prompts, files, outputs and intermediate artefacts may be exposed to whichever model endpoints or external services are configured.
Secrets handling: Teams need to understand how credentials are stored, injected, rotated and exposed to tools or generated code.
Persistence risk: Memory and workspace persistence can improve usability, but they can also preserve sensitive information longer than intended.
Supply-chain intake: Open source improves auditability, but it does not eliminate dependency, image-provenance or update-governance risk.
Jurisdictional scrutiny: ByteDance’s ownership and country-of-origin context will trigger additional review in some organizations and sectors, regardless of the code’s functional merits.

For any enterprise assessment, I would also ask a more basic question: what exactly is the threat model? If the answer is not clear, the evaluation is not complete.

For organizations considering DeerFlow or a similar platform, I would start with a baseline such as this:

deploy it only in containerized form, with hardened images and restricted privileges
apply strict network egress controls
use only approved model backends and approved data paths
prohibit use with regulated, confidential or customer-sensitive data until governance is complete
review dependency intake, image provenance and update processes
define memory retention and workspace retention rules before broader use
validate authentication, logging and access controls in the exact deployment version
test for prompt injection, unsafe tool invocation and secrets exposure before production use

This is not unique to DeerFlow. It is the minimum standard I would apply to any agent platform with code execution, external retrieval and file manipulation capabilities.

Compliance and legal context

From a privacy and compliance perspective, the main issue is not whether DeerFlow is open source. The main issue is where data goes, which providers or services can receive it, how long it persists and under which legal and contractual controls it is processed.

Relevant frameworks will vary by jurisdiction, but teams should think in terms of existing obligations under the European Union’s General Data Protection Regulation, California’s CCPA and CPRA, and Canada’s Personal Information Protection and Electronic Documents Act, along with sector-specific and local rules. In Canada, it is particularly important not to write as though Bill C-27 is already coming into force. It is not current law.

Legal teams should also look beyond privacy. Agentic systems can introduce issues related to software intake, licensing, intellectual property, auditability, export controls, customer commitments and acceptable use.

How DeerFlow compares with the field

DeerFlow is not the only project trying to make agents practical, but it is one of the more polished open-source efforts in early 2026. Compared with frameworks that require substantial assembly, it offers a more complete starting environment. Compared with narrower coding-agent projects, it appears to have broader ambition around research, orchestration and output generation.

Its main advantage is packaging. Its main challenge is trust. Not trust in the narrow sense of whether it works, but trust in the broader sense that matters to businesses: where it runs, what it connects to, how it handles data, how it executes code and whether the surrounding controls are strong enough.

Final assessment

DeerFlow 2.0 is one of the more important open-source agent releases of early 2026. It brings together planning, tools, memory, file handling, sandboxed execution and sub-agent orchestration in a way that makes the platform more usable than many experimental alternatives. That is real progress.

At the same time, teams should resist the temptation to equate visible momentum with operational maturity. DeerFlow is promising, but it should be assessed like any other high-capability agent platform: carefully, version by version, with explicit controls around execution, data flow, memory, access and software intake.

If you are exploring agentic systems this year, DeerFlow is worth reviewing. Just make sure your evaluation is grounded in documented facts, not just community excitement.

Ethics statement

This article is intended to support informed discussion about open-source AI agent platforms, with a particular focus on execution, governance, privacy and security implications. It aims to distinguish clearly between verified project documentation, publicly observable repository information, the author’s hands-on testing and the author’s professional interpretation. Where a feature, control or deployment behaviour is uncertain, version-dependent or not fully documented publicly, that uncertainty is stated rather than assumed away. This article does not endorse deploying autonomous code-execution systems in production without appropriate review, nor does it advocate bypassing legal, contractual, security, privacy or governance requirements.

Disclaimer

This article is provided for general information and discussion purposes only. It is not legal, security, privacy, compliance or professional advice, and it should not be relied upon as such. Open-source software projects, model integrations, feature sets, default configurations and security controls can change quickly, including between releases, commits and deployment methods. Any assessment of DeerFlow or similar tools should be validated against the exact version, configuration, model providers, hosting environment and organizational requirements in scope. Jurisdictional obligations related to privacy, data residency, software supply chain, export controls and sector regulation may also vary materially. Any errors or omissions are unintentional. The views expressed are those of the author in a personal capacity and do not represent the views of any employer, client, partner or affiliated organization. Generative AI tools were used to assist with research and editing.

Keywords

#DeerFlow #DeerFlow2 #ByteDance #AIAgents #AgenticAI #OpenSourceAI #LangGraph #LangChain #AIInfrastructure #SoftwareAgents #CodingAgents #AgentSecurity #AIGovernance #Privacy #DataProtection #Compliance #PIPEDA #GDPR #CCPA #CPRA #EnterpriseAI #AIPlatform #ContainerSecurity #SupplyChainSecurity #PromptInjection #ModelRisk #DataGovernance #Cybersecurity #Infosec #PrivacyEngineering #DevTools #SelfHostedAI #AILabs #SoftwareSecurity #RiskManagement

Heretic and the new reality of modifiable AI safety

Thu, 05 Mar 2026 12:21:00 -0400

Open-source large language models have made advanced generative AI broadly accessible. What is changing now is not only model capability, but the ease with which model behaviour can be altered after release — including behaviour that vendors and labs describe as “safety alignment.”

One of the most visible examples is Heretic, an open-source project that automates the removal of refusal behaviour in transformer-based language models. The project is not subtle about its purpose. It describes itself as “fully automatic censorship removal,” and it is gaining traction quickly.

This post does not provide instructions for disabling safeguards. Instead, it focuses on what is verifiably true about the tool, the research it is built on, and why this matters for security leaders, developers and governance teams.

What Heretic is

Heretic is a Python-based tool that modifies a model to reduce or eliminate refusal responses. It does this through a technique known as directional ablation, commonly referred to in the community as “abliteration.” The tool combines that intervention with automated parameter search using Optuna’s Tree-structured Parzen Estimator (TPE) optimiser.

In practical terms, Heretic aims to find settings that reduce refusals while keeping the modified model close to the original model’s behaviour on benign prompts. The project describes this trade-off explicitly as co-minimizing refusal counts and KL divergence.

Project home:
github.com/p-e-w/her…

A key point many summaries miss is licensing. Heretic is licensed under the GNU Affero General Public License (AGPL) v3.0. That is not a permissive licence. It has real implications for anyone who plans to modify and run the software in networked environments.

What it is built on: the “refusal direction” research

Heretic’s core premise follows mechanistic interpretability research published in 2024: “Refusal in Language Models Is Mediated by a Single Direction,” by Arditi et al.

In that work, researchers found that refusal behaviour in multiple popular chat models can be linked to a one-dimensional subspace in the residual stream. They demonstrate that removing that direction reduces refusals, while adding it can induce refusals even for harmless requests. The broader conclusion is uncomfortable but important: current alignment methods can be brittle, and model behaviour can sometimes be controlled through targeted internal interventions rather than retraining.

Paper (Arditi et al.):
arxiv.org/abs/2406….

How Heretic differs from earlier abliteration workflows

Abliteration itself is not new. What Heretic productizes is automation and repeatability.

Earlier approaches often required manual experimentation: selecting layers, choosing projection strengths and validating results with ad hoc tests. Heretic packages that into an optimiser-driven workflow. It searches parameter combinations to reduce refusals and limit behavioural drift, using quantitative measures as guardrails.

This is one of the reasons it is being discussed widely. Automation lowers the barrier from “researcher with time” to “user with a capable workstation.”

What the project and evaluations actually show

Two claims circulate frequently: that Heretic can drive refusals close to zero, and that it can do so while preserving most baseline capabilities.

The project’s own documentation includes examples where Heretic-generated models show refusal suppression comparable to other abliterations, with lower KL divergence in that specific comparison. The documentation also stresses that numerical results vary by hardware and software environment and that benchmarks are not a substitute for human evaluation.

Independent evaluation work in late 2025 compared Heretic to other abliteration tools across a range of instruction-tuned models. The headline finding was not that any tool is perfect, but that trade-offs are real and model-dependent. The same paper also cautions that controlled benchmarks do not necessarily predict long-run behaviour in multi-turn use.

Comparative analysis paper (Young et al.):
arxiv.org/abs/2512….

A consistent theme across reports is that structured reasoning tasks are among the most sensitive. In other words, removing refusals can be technically achievable, but retaining all capabilities is not guaranteed. This should be treated as an engineering problem, not an assumption.

Community adoption and the pace of iteration

Heretic’s repository shows rapid iteration and strong adoption. Discussion threads on r/LocalLLaMA track releases and performance claims, including changes aimed at reducing VRAM requirements and improving model-loading flexibility. There is also active discussion about false positives in refusal detection and the limits of simple refusal scoring.

Example discussion threads:
www.reddit.com/r/LocalLL…
www.reddit.com/r/LocalLL…

This matters because the practical capability is not only the tool, but the ecosystem it enables: repeatable creation and distribution of modified models.

Why this matters for enterprise security and governance

From an enterprise perspective, Heretic is less a novelty and more a signal.

First, it reinforces that “model safety” is not a reliable control boundary. If a model can be modified to remove refusals, then system safety must be enforced through architecture: data controls, identity, rate limiting, monitoring, output filtering and purpose-built guardrails at the application layer.

Second, it complicates third-party risk assumptions. If an organisation relies on aligned behaviour as a compliance or safety control, it should assume that aligned behaviour can be bypassed when models are run locally or in uncontrolled environments.

Third, it raises governance and legal questions. If an organisation modifies and serves software under AGPL, that triggers obligations. Separately, deploying modified models without clear controls can raise policy and regulatory concerns, depending on use case, jurisdiction and sector.

A practical way to think about it is simple: treat model alignment as a property that can change, and treat safety as something you must engineer end-to-end.

Bottom line

Heretic is a credible, fast-moving implementation of a well-known research insight: refusal behaviour can be represented in low-dimensional directions and suppressed through targeted intervention. It is also a reminder that safety alignment, as currently implemented in many open models, is not an immutable feature.

For security leaders, the right response is not panic and not denial. It is disciplined control design. Assume models can be modified. Build safety at the system level.

Sources
Heretic repository: github.com/p-e-w/her…
Arditi et al. (2024): arxiv.org/abs/2406….
Optuna TPE sampler documentation: optuna.readthedocs.io/en/stable…
Young et al. (2025): arxiv.org/abs/2512….
Community threads:
www.reddit.com/r/LocalLL…
www.reddit.com/r/LocalLL…

Keywords: #AI #ArtificialIntelligence #LLM #LargeLanguageModels #MachineLearning #GenerativeAI #AIResearch #AIAlignment #AISafety #AIsecurity #CyberSecurity #InfoSec #EnterpriseSecurity #RiskManagement #AIGovernance #AIRegulation #ResponsibleAI #TechPolicy #DigitalRisk #ModelSecurity #AITrends #AIInnovation #AIethics #OpenSourceAI #DeepLearning #TransformerModels #DataSecurity #ThreatLandscape #SecurityLeadership #CISO #FutureOfAI #EmergingTech #TechStrategy #SecurityStrategy #CyberRisk

Sun, 01 Mar 2026 12:08:00 -0400

Are Dorsey’s giant job cuts the start of an AI jobs apocalypse? Economists weigh in

Block CEO Jack Dorsey’s decision to cut nearly half the company’s workforce raises questions about AI’s impact on jobs, but economists suggest this is a company-specific adjustment rather than a sign of a broader labor market shift. While AI may disrupt some jobs, experts like Claudia Sahm emphasize that it doesn’t necessarily lead to mass layoffs, and other economists believe AI will enhance productivity by changing workflows rather than eliminating jobs outright.

Tue, 06 Jan 2026 23:27:00 -0400

OpenAI is rolling out GPT-5.2 “Codex-Max” for some users

OpenAI is rolling out GPT-5.2-Codex-Max, a new model for its Codex service, to select subscribers. This advanced version is expected to offer enhanced capabilities for long tasks, context management, and improved reliability, particularly with tool use and understanding visual inputs like screenshots.

The "Stein Standard": What the OpenAI ruling means for privacy and discovery

Tue, 06 Jan 2026 11:23:00 -0400

On Jan. 5, 2026, U.S. District Judge Sidney Stein affirmed a significant discovery order requiring OpenAI to produce 20 million de-identified ChatGPT conversation logs to plaintiffs in the consolidated copyright litigation involving The New York Times and other publishers.

As security and privacy professionals, we often warn about “Shadow AI” and data leakage. This ruling makes those risks concrete. Here is a balanced analysis of what happened and what it means for Canadian organizations.

What the court ordered

OpenAI must produce a sample of 20 million de-identified ChatGPT logs.
The requested period is Dec. 2022 to Nov. 2024.
OpenAI’s objections on privacy risk and undue burden were rejected for discovery purposes.

Scope and safeguards

Scope: 20 million logs (roughly 0.05 per cent of retained data).
Safeguards: De-identified data produced under a strict “Attorneys’ Eyes Only” protective order.

Important context

This is a discovery ruling, not a final decision on copyright infringement.
This is not a public release of data. The logs are restricted to opposing counsel for analysis.

Why this matters: The VP perspective
Here are three takeaways for data governance leaders:

The “wiretap” distinction
Judge Stein distinguished ChatGPT interactions from private phone calls (protected under wiretap laws). The court noted that users voluntarily disclose information to a third-party AI, effectively narrowing the expectation of privacy compared to traditional communications.
De-identification does not equal anonymity
While the court accepted de-identification as a safeguard for discovery, privacy professionals know this is not a silver bullet. Watch closely to see whether safeguards hold up against adversarial re-identification techniques once data is shared.
Discovery is reality
This establishes a high-water mark for AI litigation. “Big Data” is no longer a shield against discovery; courts are willing to compel production of massive datasets if they deem it relevant.

The takeaway
Assume your inputs into public AI models are discoverable, and govern usage accordingly.

For Canadian organizations, while this is a U.S. ruling, it impacts the global platforms we rely on. It is a timely prompt to review retention practices and reinforce acceptable-use expectations, especially for sensitive or confidential information.

How is this shifting your approach to AI governance and acceptable use policies?

#Privacy #CISO #AI #DataGovernance #LegalTech #CdnTech

Disclaimer: The views expressed in this post are my own and do not necessarily reflect the official policy or position of my employer. This commentary is based on publicly available information and is provided for informational purposes only. It does not constitute legal advice.

Keyword: #OpenAI #ChatGPT #SDNY #JudgeStein #Discovery #eDiscovery #Privacy #Cybersecurity #Copyright #CopyrightLitigation #NYTimes #AIGovernance #AcceptableUse #ShadowAI #DataLeakage #DeIdentification #Anonymity #AttorneysEyesOnly #ProtectiveOrder #LegalProcess #Proportionality #DataRetention #RetentionPolicy #DataClassification #DLP #EnterpriseAI #RiskManagement #Compliance #Governance #CanadianTech #CrossBorderData #PrivacyByDesign #ReIdentification #Metadata #Confidentiality #LegalRisk

Sun, 04 Jan 2026 01:34:00 -0400

French authorities investigate AI ‘undressing’ deepfakes on X

French authorities are investigating AI-generated deepfakes on X after hundreds of women and teens reported non-consensual sexually explicit images created using the Grok chatbot. This investigation is part of an existing probe into X, with potential penalties including prison time and fines.

The "10 Per Cent" Myth: Why AI Capability Does Not Equal a Pink Slip

Fri, 02 Jan 2026 12:30:00 -0400

The headlines are everywhere, and they are designed to stop your scroll: “AI to Replace 1/10 of the Workforce.”

It is a terrifying number. It represents millions of livelihoods reduced to a statistic. But as a chief information security officer, I do not deal in headlines. I deal in risk, audits and rigorous data analysis.

When you strip away the hype and audit the primary sources released in late 2025—specifically from Project Iceberg (MIT), Yale and McKinsey—a completely different reality emerges.

We are confusing technical exposure with actual displacement.

Here is the fact-based reality of the AI labour market as we enter 2026.

The Audit: Capability vs. Likelihood

The viral “10 per cent” statistic stems from Project Iceberg (led by MIT and partners), published in November 2025. Researchers found that AI has the technical capability to automate tasks representing 11.7 per cent of the U.S. economy’s wage value.

In the world of risk assessment, however, capability is only half the equation. You must also calculate likelihood.

Just because a task can be automated does not mean it will be today. History proves that the gap between technical feasibility and widespread adoption is measured in decades, not fiscal quarters. Cloud has been enterprise-viable for well over a decade, yet a vast portion of enterprise workloads remain on premises.

The Reality: In 2025, while AI could theoretically perform the work of millions, announced job-cut plans explicitly attributed to AI totaled approximately 55,000 through November (Source: Challenger, Gray & Christmas).

The Context: That represents approximately 0.03 per cent of the U.S. labour force. The theoretical avalanche is, in practice, a statistical rounding error.

The “Zero Disruption” Verdict

If 10 per cent of jobs were vanishing, the macroeconomic data would be screaming. Instead, it is barely whispering.

A comprehensive study by Yale University’s Budget Lab (October 2025) analyzed labour market data from the launch of ChatGPT in late 2022 through to late 2025. Their conclusion was blunt: “No discernible disruption.”

Three years into the generative AI revolution, aggregate data shows stability, not collapse. We are not witnessing a displacement crisis; we are witnessing a retooling phase.

It Is Not About Jobs—It Is About Tasks

The most critical distinction lost in the media noise is the difference between a job and a task.

A job is a complex bundle of responsibilities. Some are routine (data entry, scheduling, basic coding). Others require judgment, empathy, strategy and accountability. AI is exceptional at the former and still limited in the latter.

McKinsey’s November 2025 analysis suggests that while over half of work hours are exposed to automation, this typically results in augmentation, not replacement. When AI automates 20 per cent of your routine tasks, you do not lose your job; you gain 20 per cent of your capacity back to focus on high-value work that algorithms cannot touch.

The Hidden Risk: Geography

While the media focuses on Silicon Valley, Project Iceberg reveals a “hidden” risk. The study distinguishes between “Surface Index” exposure (visible technology roles) and “Hidden” exposure (administrative and financial back-office roles).

The data shows that states with heavy financial and administrative sectors—like Delaware in the U.S.—have higher theoretical exposure than pure technology hubs.

The Canadian Implication: Applying this logic to Canada, the financial corridors of Toronto and our administrative centres likely face higher exposure than our tech hubs. This suggests the transition will be a slow, quiet evolution of white-collar workflows, not a sudden “tech bubble” burst.

The Bottom Line

Is the labour market changing? Absolutely. Is 10 per cent of the workforce being replaced tomorrow? The data says no.

The 11.7 per cent figure is a map of exposure, not a forecast of unemployment. It tells us what could change, not what is changing next Tuesday.

The risk isn’t that AI will take your job overnight. The risk is failing to learn the tools that will define the next decade. As professionals, we need to move past the fear of replacement and focus on fluency.

Map tasks, not titles. Measure adoption, not headlines.

Data sources: Project Iceberg/MIT (Nov. 2025); Yale Budget Lab (Oct. 2025); McKinsey Global Institute (Nov. 2025); Challenger, Gray & Christmas (2025).

Disclaimer & Ethics Statement: This article was drafted with the assistance of AI tools to synthesize large datasets from the cited reports (MIT, Yale, McKinsey). All data points, logic and conclusions were independently audited and verified by the human author.
The content provided here is for informational purposes only and does not constitute career or financial advice.
The views expressed are my own and do not necessarily reflect the official policy or position of my employer.

#AI #FutureOfWork #CISO #RiskManagement #CanadianBusiness

Keywords: #AI #ArtificialIntelligence #FutureOfWork #WorkforceTransformation #DigitalTransformation #Productivity #Automation #Augmentation #Jobs #Skills #Reskilling #Upskilling #Leadership #Strategy #Innovation #RiskManagement #CISO #CyberSecurity #Governance #Compliance #Audit #DataDriven #EvidenceBased #TechPolicy #LabourMarket #EconomicTrends #Workplace #Operations #ChangeManagement #Canada #CanadianBusiness #Toronto #McKinsey #MIT #Yale

The ‘Delete’ Button Is a Lie: A Canadian’s Guide to AI Data Retention

Tue, 23 Dec 2025 12:37:00 -0400

When you hit “delete” on a conversation with ChatGPT or Gemini, you likely expect it to vanish. In reality, that data often enters a digital limbo—accessible to the provider for 30 days, three years, or even seven years for certain safety-classifier metadata, depending on the fine print you didn’t read.

For paid subscribers, the assumption of privacy is dangerous. While corporate “Team” and “Enterprise” plans typically offer stronger contractual controls (including training restrictions and admin-managed retention), “Pro” and “Plus” users are frequently treated as consumers with slightly better perks, not better privacy.

Here is the verified reality of data deletion for the four major large language models (LLMs) available in Canada.

ChatGPT (OpenAI)

The Plans: Free, Plus and Pro (personal workspaces)
The Default: Opt-out required. OpenAI enables data sharing by default for these tiers. Unless you opt out, your conversations can be used to train future models.

The Reality:
OpenAI deletes conversations from its systems within 30 days of you deleting them. However, this is not absolute. OpenAI explicitly states that data may be retained longer if required by law—a significant caveat given 2025’s litigation landscape involving copyright and data usage.

The Catch:

Legal Holds: If your account is subject to a preservation order, “deleted” data may be archived until the legal matter resolves. For example, during 2025 copyright litigation, a preservation order required the retention of certain consumer data between April and September; OpenAI later stated the order ended Sept. 26, 2025, with limited historical data retained under secure hold.
Temporary Chat: Using the “Temporary Chat” toggle prevents the conversation from appearing in your history, but OpenAI retains these chats for up to 30 days specifically to monitor for abuse.
Training vs. Retention: Deleting a chat after it has been used to train the model does not untrain the model.

Your Move: Go to Settings > Data Controls and toggle “Improve the model for everyone” to OFF. This is the primary way to ensure your future chats are not ingested into the “brain” of future GPT versions.

Claude (Anthropic)

The Plan: Claude Pro
The Default: Opt-out required. In a policy update announced Aug. 28, 2025 (with an Oct. 8 decision deadline for existing users), Anthropic introduced specific provisions for training data retention.

The Reality:
If you allow Anthropic to use your data for model improvement, your conversations may be retained for up to five years in their training pipelines. If you opt out, deleted conversations are removed from backend systems within 30 days.

The Catch:

The 5-Year Pipeline: The five-year retention applies to data used for “benchmarking and model improvement.” If you missed the notification to opt out, your historical data may already be in this pipeline.
Safety & Compliance: Even if you opt out of training, Anthropic retains data flagged by its Trust & Safety classifiers for up to two years. Critical safety data, such as “classifier scores” (metadata about why a prompt was flagged), can be kept for up to seven years.

Your Move: Go to Settings > Privacy immediately and ensure the “Help improve Claude” toggle is turned OFF.

Gemini (Google)

The Plan: Gemini Advanced (Google One AI Premium)
The Default: 18-month retention. By default, Google retains your Gemini Apps Activity for 18 months, similar to your Search history.

The Reality:
You can change your auto-delete setting to 3 months or delete individual chats manually. However, Google’s backend processing creates persistent copies. Even if you turn “Gemini Apps Activity” OFF entirely, Google retains conversations for up to 72 hours to maintain service continuity and process feedback.

The Catch:

The Human Review Trap: This is the most critical risk. Google disconnects specific chats to be read by human reviewers. Once a chat is selected for review, it is “disconnected” (disassociated) from your account and retained for up to three years.
Irreversible: Because these reviewed chats are technically separated from your user ID, deleting the original conversation from your history does not delete the copy held by the human review team.

Your Move: Go to **myactivity.google.com/product/g… Set the Auto-delete option to 3 months (the minimum) and strictly avoid putting sensitive identifiers in your prompts.

Grok (xAI)

The Plan: Grok Premium (X Premium)
The Default: Verify your settings. xAI’s consumer policy allows for model training unless you intervene.

The Reality:
Grok offers a “Private Chat” mode (often indicated by a ghost icon or distinct toggle) which is intended to be ephemeral. Standard chats (non-private) may be used for training. xAI states that deleted data is removed from accessible systems within 30 days.

The Catch:

The Feedback Loop: Even if you opt out of general training, xAI notes that if you voluntarily submit feedback (like rating a response), that specific data may still be used for model improvement.
Platform overlap: If you access Grok via X (formerly Twitter), your data handling is governed by X’s broader privacy terms, which can differ from xAI’s standalone app policies.

Your Move: You have two options for privacy: exclusively use “Private Chat,” or verify your “Data Sharing” settings (typically found under Privacy & Safety on X) to ensure you have unchecked the box allowing your data to be used for model training.

Summary: The ‘Safe’ Deletion Window

Claude: Deleted conversations are removed from backend systems within 30 days. Risk: Seven-year retention for safety classifier scores; five years for training-pipeline data if you do not opt out.
ChatGPT: Takes 30 days to delete. Risk: “Temporary” chats are still monitored for 30 days; legal holds can override deletion.
Grok: Takes 30 days to delete. Risk: Voluntary feedback can be used for model improvement even if you opt out of general training.
Gemini: Auto-delete can be set to 3, 18 or 36 months (user setting). Risk: Human-reviewed data is kept for three years and cannot be deleted by the user.

Final Advice for Canadian Users

While the Personal Information Protection and Electronic Documents Act (PIPEDA) imposes accountability standards on how companies handle Canadian data, it does not prevent cross-border processing. In practice, once your data sits on a server in Oregon or Iowa, U.S. legal frameworks—and subpoenas—may compel disclosure, even where Canadian expectations differ.

For absolute security, the data must never leave your device. If you must use cloud AI, assume that “Deleted” actually means “Archived for 30 days,” and plan accordingly.

Ethics Statement & Disclaimer

Ethics Statement: This article is editorial content. The author has no financial relationship with OpenAI, Anthropic, Google or xAI. No company paid to be included in this post, nor did they review the content prior to publication. I personally subscribe to these services to test them objectively.

Disclaimer: The information in this post is based on terms of service and privacy policies available as of Dec. 23, 2025. AI companies frequently update their data retention policies without direct notification. The steps provided above are accurate at the time of writing but may change. This post is for informational purposes only and does not constitute legal or professional advice. Readers should consult their organization’s legal or security teams before using consumer AI tools for sensitive work.

Keywords: #AI #DataPrivacy #Cybersecurity #InfoSec #Privacy #DataRetention #DigitalPrivacy #PIPEDA #Canada #Compliance #RiskManagement #SecurityAwareness #DataProtection #CloudSecurity #AIRegulation #TrustAndSafety #LLM #ChatGPT #ClaudeAI #GoogleGemini #Grok #xAI #OpenAI #Anthropic #Google #PrivacyByDesign #Governance #GRC #SecurityPolicy #DataGovernance #CyberRisk #TechPolicy #PrivacyTech #DigitalRights #InfoPrivacy

China's open AI models are in a dead heat with the West

Mon, 22 Dec 2025 11:17:00 -0400

China’s open AI models are in a dead heat with the West - here’s what happens next www.zdnet.com/article/c…

With the rising technological prowess and greater openness of Chinese models, the world is increasingly turning to the East for efficient and customizable AI, a new report finds.

ZDNET’s key takeaways:

Chinese AI models have caught up to US models in power and performance.
China is leading in model openness.
Much of the world may adopt the freely available Chinese technology.

Fri, 19 Dec 2025 17:13:00 -0400

Coursera to buy Udemy, creating $2.5 billion firm to target AI training | Reuters

Coursera announced an all-stock deal to acquire Udemy, valuing the combined company at $2.5 billion. The merger aims to strengthen their position in corporate workforce training, particularly in AI, data science, and software development. The deal is expected to close in the second half of next year, pending regulatory and shareholder approvals.

Fri, 19 Dec 2025 09:51:00 -0400

Managing agentic AI risk: Lessons from the OWASP Top 10 | CSO Online

The OWASP Top 10 for Agentic AI provides a framework to address the growing security risks associated with agentic AI adoption, offering practical guidance, threat taxonomies, and mitigation strategies for CISOs. While the list is immediately useful, some areas like detailed mitigation steps and attack likelihood require further development.

Mon, 15 Dec 2025 09:44:00 -0400

Microsoft Scales Back AI Goals Because Almost Nobody Is Using Copilot | Extremetech

Microsoft has reportedly scaled back AI goals for its Copilot software due to low user adoption and sales, with some targets cut by 50%. While Microsoft disputes the sales quota claims, AI agents have shown low success rates in tasks, and Copilot lags behind competitors like ChatGPT and Google’s Gemini in market share.

Sat, 13 Dec 2025 02:23:00 -0400

I tested ChatGPT-5.2 vs Gemini 3.0 with 7 real-world prompts — here’s the winner | Tom’s Guide

In a comparison of ChatGPT-5.2 and Gemini 3.0 across seven real-world prompts, ChatGPT-5.2 emerged as the overall winner, demonstrating superior emotional intelligence and psychological insight in its responses. While Gemini 3.0 excelled in specific areas like risk assessment and technical explanations, ChatGPT-5.2 consistently provided more human-like, wise, and grounding answers.

Tue, 02 Dec 2025 20:47:00 -0400

Autonomously Finding 7 FFmpeg Vulnerabilities With AI - ZeroPath Blog | ZeroPath

This document details seven vulnerabilities found in FFmpeg, including buffer overflows and invalid frees, stemming from issues like integer truncation, unbounded serialization, off-by-one errors, and incorrect stream indexing. ZeroPath’s AI SAST identified these by analyzing allocation and copy alignment, framing invariants, packet builder capacities, cardinality propagation, and offset arithmetic integrity, often bypassing limitations of traditional fuzzers and static analysis tools.

Tue, 02 Dec 2025 20:46:00 -0400

Poetry can trick AI models like ChatGPT into revealing how to make nuclear weapons, study finds | The Independent

A new study reveals that poetry-based prompts can trick AI models like ChatGPT into bypassing safety features and revealing instructions for creating malware or nuclear weapons. This method, termed adversarial poetry, successfully circumvented controls in major AI models, with poetic prompts leading to a significantly higher rate of unsafe replies compared to prose.

Tue, 02 Dec 2025 20:43:00 -0400

Australia Abandons Proposed Mandatory AI Rules in New Plan

Australia has shifted from proposed mandatory AI rules to a voluntary framework, opting for existing laws on privacy and copyright instead of new AI-specific legislation. This decision has been met with support from business groups but criticism from academics and the Greens, who argue it lacks enforcement and adequate investment compared to international approaches.

Tue, 02 Dec 2025 08:11:00 -0400

Canada launches first register of AI uses in federal government - Canada.ca

Canada has launched its first public AI Register to detail how artificial intelligence is used within the federal government, marking a key step in the public services AI Strategy. The register currently lists over400 AI systems across42 institutions and will undergo public consultations in2026 for refinement.

Improving AI Outcomes Through Better Prompting

Fri, 28 Nov 2025 13:14:00 -0400

AI is becoming integral to how many of us work, but too often the results still feel generic or misaligned. A small shift in how we prompt these systems can dramatically improve the quality, clarity and usefulness of their responses.
By asking the AI to seek clarification before answering, we eliminate assumptions and get far stronger outputs.

Most people continue to use AI tools as if they were search engines: ask a question once and expect a complete answer. The challenge is that large language models are trained to fill gaps when faced with ambiguity. Research from the University of Washington and Stanford shows that when prompts lack detail, LLMs tend to infer the most likely meaning instead of checking for accuracy.

A simple adjustment solves this. Adding one line to a prompt—“Ask me clarifying questions until you are at least 95 per cent confident you understand what I need”—encourages the AI to slow down, confirm the context and deliver more precise results. It transforms a one-way query into a more thoughtful exchange.

This technique has been validated across several domains. In customer support, a study involving more than five thousand agents found that clarification-capable AI increased resolved tickets per hour by up to 14 per cent. Broader enterprise research from McKinsey continues to show potential productivity gains of 30 to 45 per cent when generative AI is properly deployed within real workflows.

In financial and legal settings, Bloomberg’s financial LLM improved accuracy in several reporting tasks by nearly 50 per cent when clarification routines were added. In software development, Adyen has documented stronger consistency and reduced rework by using AI that asks targeted questions before generating tests. Even in education, research shows that Socratic-style clarification leads to better critical thinking and stronger learning outcomes.

The principle is simple: better questions drive better answers. When the AI verifies its understanding, it reduces misinterpretation, sharpens relevance and produces work that aligns more closely with what you actually need.

Here’s an easy example to try:

“Write a summary of this document. Before you begin, ask me any clarifying questions until you are 95 per cent confident you can complete this accurately.”

Instead of guessing, the AI will ask about the intended audience, tone, length, purpose and constraints. Your responses give it the context required to produce something clear, accurate and fit for purpose.

As we continue incorporating AI into our daily workflows, techniques like this ensure that the technology works with us, not around us. Encouraging the AI to ask questions first leads to outputs that are more thoughtful, more targeted and ultimately more useful.

If you test this approach, I’d be interested in what you learn—successes, missteps and everything in between. The evolution of AI-assisted work is a shared journey, and the way we prompt these systems matters more than we think.

#ai #promptengineering #clarificationprompting #askbeforeanswer #generativeai #llm #aitools #aiproductivity #enterpriseai #futureofwork #digitaltransformation #aistrategy #consultativeai #aiworkflows #betterprompts #aiinnovation #leadership #technologyleadership #ciso #cybersecurity #datastrategy #intelligentautomation #knowledgework #aiinsights #aiadoption #smarterai #askbetterquestions #aiinbusiness #businessinnovation #thoughtleadership #aipractices #collaborativeai #thinkingassistants #worksmarter

Comprehensive analysis of leading AI models in 2025: strengths, weaknesses and standout capabilities

Wed, 05 Nov 2025 16:27:00 -0400

The artificial-intelligence landscape in 2025 has evolved into a highly competitive arena where numerous models offer distinct advantages for specific use cases. This article examines publicly available AI models shaping the industry, summarizing where each excels and where limitations remain.

Executive snapshot: what each model does best

ChatGPT (GPT-5, GPT-4.5, GPT-4o): best generalist for agentic workflows, multi-step coding and polished consumer experiences
Grok 3/4 (xAI): strongest for real-time, web-aware analysis with extended reasoning and STEM tasks
Claude Sonnet 4.5 (Anthropic): leading coding model with hybrid reasoning and sustained autonomous operation claims
Gemini 2.5 Pro (Google): native multimodality with ultra-long context (one to two million tokens) for cross-modal comprehension
Kimi K2 (Moonshot): trillion-parameter mixture-of-experts model with strong coding claims and cost efficiency
Qwen 3 235B (Alibaba): hybrid-reasoning with switchable thinking modes and extensive multilingual support
DeepSeek R1: open-reasoning model with transparent methodology and strong math and code performance
Llama 4 Maverick (Meta): natively multimodal open-weight model with favourable performance-to-cost ratio
Mistral Large / Medium 3: efficient European multilingual model optimised for coding and pragmatic enterprise pricing
Hermes 4 (Nous Research): open-weight hybrid reasoning with transparent thinking traces and minimal content restrictions

OpenAI ChatGPT (GPT-4o, GPT-4.5, GPT-5 and o-series)

Overview

OpenAI maintains a multi-model strategy under the ChatGPT umbrella. GPT-4o is a multimodal generalist, GPT-4.5 emphasises conversational polish and GPT-5 is the flagship. The o-series (o1, o3, o3-mini) specialise in complex reasoning.

Key strengths

GPT-4o:
Multimodal input (text, image, voice) with near-human response times
128,000-token context window
Improved compute efficiency
Strong general-purpose performance
Enhanced vision capabilities

GPT-4.5:
More natural conversational tone than GPT-4o
Better sentiment detection and social-cue awareness
Reduced hallucinations (~61.8 per cent to ~37.1 per cent)
Suitable for creative and nuanced writing

GPT-5:
Released Aug. 7 2025
Claims state-of-the-art performance across coding, mathematics, writing and vision
More unified operation with fast and deep-thinking modes
Improved reasoning for complex problem solving

O-series (o1, o3):
Excels at scientific, mathematical and coding-based reasoning
Uses chain-of-thought logic to outperform GPT-4o on deep analyses

Key weaknesses

GPT-4o:
Weaker at abstract-reasoning, analogy, pattern recognition and spatial tasks
Challenges interpreting multi-speaker emotional nuance
Struggles with extended logic and very long code chains

GPT-4.5:
Less explicit step-by-step logic than o-series
No default Voice Mode, video processing or screen-sharing
Expected retirement from the API July 2025
Still mis-reasons in some cases (for example, letter counting)

O-series:
Slower responses and higher cost
Does not always express uncertainty
Some ChatGPT features unavailable in lower tiers
Message caps in certain subscriptions

Best use cases

GPT-4o suits fast multimodal consumer interactions and creative content. GPT-4.5 fits creative writing, branding and emotionally nuanced tasks. GPT-5 supports complex engineering, agentic workflows and high-stakes problem solving. O-series models suit researchers, mathematicians and developers requiring explicit reasoning chains.

Anthropic Claude (Sonnet 4.5, Opus 4.1)

Overview

Anthropic’s Claude 4 family emphasises safer responses, long-context comprehension and strong coding performance. Sonnet 4.5 is promoted as the top coding model; Opus 4.1 focuses on advanced reasoning.

Key strengths

Claude Sonnet 4.5:
Reported 77.2 per cent on SWE-bench Verified (82.0 per cent with high compute)
61.4 per cent on OSWorld for computer-use tasks
Claims of 30-plus hours of autonomous coding
100 per cent score on AIME 2025 using Python tools (87 per cent without)
83.4 per cent on GPQA Diamond
Strong alignment and low power-seeking behaviour

Claude Opus 4.1:
Up to 30 hours of autonomous operation
Strong multi-document and instruction following performance
Better suited for analytical accuracy and specialised workflows

Key weaknesses

Sonnet 4.5:
More cautious tone; sometimes over-hedges
Visual-reasoning (77.8 per cent MMMU) trails GPT-5 (84.2 per cent) and Gemini 2.5 Pro (82.0 per cent)
Safety classifiers can flag benign content

Opus 4.1:
Roughly five times the cost of Sonnet 4.5
Inferior for software-development work
Higher latency

Best use cases

Sonnet 4.5 is strong for software development, debugging, testing and agent workflows. Opus 4.1 suits legal, finance and research tasks where accuracy justifies higher cost.

xAI Grok (Grok 3, Grok 4)

Overview

xAI, founded by Elon Musk, introduced Grok 3 in February 2025 and Grok 4 on July 9 2025. Both models emphasise long-context reasoning and real-time web-awareness through X.

Key strengths

Grok 3:
Strong on advanced math and STEM reasoning
128,000-token context window
“Think Mode” enables step-by-step reasoning
“DeepSearch” enables real-time content analysis

Grok 4:
Adds multi-agent reasoning
Available in a developer-focused subscription tier
Maintains long-context capability

Key weaknesses

Mixed output consistency
Higher hallucination risk than rivals
Real-time access is not universally guaranteed
Premium pricing for developer tiers

Best use cases

Advanced mathematics, STEM workflows, research leveraging real-time context and X-integrated environments.

Google Gemini (2.5 Pro)

Overview

Google DeepMind’s Gemini 2.5 Pro emphasises multimodal reasoning, ultra-long context and enterprise-ready integration.

Key strengths

Strong performance across math and science tasks
Native multimodal: text, image and video
Up to one million-token context, roadmap to two million
Strong cross-modal comprehension

Key weaknesses

Not as strong in coding or agentic workflows
Some reported factuality issues
Benchmarks for code remain mixed

Best use cases

Large-scale document analysis, multimedia reasoning and long-context enterprise workflows.

DeepSeek R1

Overview

DeepSeek R1, launched January 2025, prioritises transparent reasoning, open licensing and efficiency.

Key strengths

Open MIT licence
Transparent reasoning traces
Strong math and coding performance
Efficient 37 billion active parameter design

Key weaknesses

Shorter context window (~130,000)
Primarily text-based; vision requires add-ons
Weaker usability and ecosystem support

Best use cases

Open-source deployments requiring transparent logic, math strength and flexible infrastructure.

Meta Llama 4 Maverick

Overview

Meta’s Llama 4 family arrived April 2025, featuring Scout and Maverick variants and providing multimodal capability in an open-weight model.

Key strengths

Llama 4 Maverick:
Competitive performance with favourable cost
Open-weight for on-prem or private-cloud deployment
Multimodal training across text, image and video

Llama 4 Scout:
Claims up to 10-million-token context
High cost-efficiency with fewer active parameters

Key weaknesses

Variant confusion (benchmarks on tuned vs released weights)
Licensing needed for 700-million-plus monthly active-user services
Ecosystem still maturing

Best use cases

Maverick supports coding, enterprise document analysis, multilingual reasoning and cost-sensitive deployment. Scout suits ultra-long-context tasks.

Alibaba Qwen 3 235B

Overview

Qwen 3, released April 2025, targets hybrid reasoning, broad multilingual coverage and open developer frameworks.

Key strengths

Switchable reasoning vs fast modes
Support across 119 languages
Apache 2.0 licensing
Competitive math and code results

Key weaknesses

Earlier-stage ecosystem outside Asia
Tool-use integrations less mature
Architecture complexity adds integration overhead

Best use cases

Multilingual and open-source deployments, research requiring reasoning-depth control and flexible licensing.

Mistral (Large, Medium 3)

Overview

Mistral AI offers both open-weight and enterprise models. Medium 3 emphasises cost-efficiency; Mistral Large aims at enterprise reasoning.

Key strengths

Medium 3:
Reported >90 per cent of Claude Sonnet 3.7 performance at far lower cost
Favourable pricing
Deployable across most clouds
Strong coding and STEM capability

Mistral Large:
Enterprise-grade multilingual support
Native function-calling and constrained output
32,000-token context

Key weaknesses

Creative writing trails specialist models
Occasional multi-step spatial-reasoning issues
Language variation by region
Some benchmarks favour rivals

Best use cases

Medium 3 fits cost-efficient enterprise coding and document understanding. Mistral Large suits multilingual enterprise deployments requiring more depth.

Nous Research Hermes 4

Overview

Released August 2025, Hermes 4 prioritises hybrid reasoning, minimal content restriction and transparent output.

Key strengths

Toggle between fast and step-wise reasoning
Open-weight release with full reasoning traces
Strong reported math scores
Length-control methods reduce over-generation

Key weaknesses

High compute overhead for training and use
Smaller variants may overthink
Minimal filtering may not fit high-compliance industries
Ecosystem less mature than major commercial models

Best use cases

Research, transparent reasoning pipelines and minimally censored open-source applied use.

Important considerations: benchmarks and evaluation

Benchmark results are volatile and can depend on model variant, tuning, context length and test configuration. Many results are vendor-reported and lack broad third-party validation.

Long-context performance depends on endpoint and hardware. Reasoning modes can produce substantial performance swings. Open-weight models benefit from community scrutiny, whereas commercial models often publish fewer benchmarking details.

Conclusion: selecting the right model

The 2025 AI landscape provides exceptional choice.

General-purpose chat: GPT-4o, GPT-4.5
Enterprise automation: GPT-5, Claude Sonnet 4.5, Grok 3
Deep reasoning: GPT-5, Claude Sonnet 4.5, Grok 3, Gemini 2.5 Pro
Coding excellence: Claude Sonnet 4.5, Kimi K2, GPT-5
Cross-modal work: Gemini 2.5 Pro
Ultra-long context: Gemini 2.5 Pro, Llama 4 Scout
Cost optimisation: Llama 4 Maverick, Mistral Medium 3, Kimi K2
Open-source and on-prem: DeepSeek R1, Qwen 3, Hermes 4, Llama 4, Kimi K2
Agentic workflows: Kimi K2, Claude Sonnet 4.5, Hermes 4
Multilingual: Qwen 3, Mistral Large
Transparent reasoning: Hermes 4, DeepSeek R1, Qwen 3

Selecting the right model depends on budget, deployment strategy, task complexity, transparency needs, regulatory requirements and integration demands. Ongoing evaluation remains critical as the market evolves rapidly.

Ethics and disclaimer

This analysis is for informational purposes only and reflects research available as of November 2025. No compensation influenced provider positioning. Capabilities, pricing and performance can change quickly. Readers should verify current information, especially for enterprise deployment, compliance, privacy and intellectual-property considerations.

Last updated November 2025

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Tue, 04 Nov 2025 22:54:00 -0400

Amazon and Perplexity have kicked off the great AI web browser fight | The Verge

Amazon has requested that Perplexity stop its AI browser, Comet, from purchasing products on its site, accusing the AI startup of providing a degraded shopping experience. Perplexity, in turn, has accused Amazon of bullying and stated that the e-commerce giant is more interested in serving ads and sponsored results than facilitating easier shopping, despite Amazon’s CEO expecting future partnerships with AI shopping agents.

Sun, 02 Nov 2025 17:01:00 -0400

Geoffrey Hinton says tech giants can’t profit from AI investments unless human labor is replaced | Fortune

According to Geoffrey Hinton, tech giants cannot profit from their AI investments without replacing human labor. He believes that the massive capital expenditures by companies like Microsoft, Meta, and Alphabet are predicated on the idea of widespread job displacement by AI, though he acknowledges AI’s potential for good in fields like healthcare and education.

When AI Agents Go Rogue

Sat, 01 Nov 2025 17:02:48 -0400

When AI Agents Go Rogue: Agent Session Smuggling Attack in A2A Systems unit42.paloaltonetworks.com/agent-ses…

We discovered a new attack technique, which we call agent session smuggling. This technique allows a malicious AI agent to exploit an established cross-agent communication session to send covert instructions to a victim agent.

Here, we discuss the issues that can arise in a communication session using the Agent2Agent (A2A) protocol, which is a popular option for managing the connections between agents. The A2A protocol’s stateful behavior lets agents remember recent interactions and maintain coherent conversations. This attack exploits this property to inject malicious instructions into a conversation, hiding them among otherwise benign client requests and server responses.

Fri, 24 Oct 2025 17:29:49 -0400

AI Dataset for Detecting Nudity Contained Child Sexual Abuse Images

The NudeNet dataset, used for training AI nudity detection, has been found to contain child sexual abuse material (CSAM) by the Canadian Centre for Child Protection (C3P). This discovery highlights ethical concerns regarding data collection in AI development, similar to previous findings with the LAION-5B dataset.

The Uncomfortable Truth About China’s AI Dominance: How a Decade of Strategic Planning Is Reshaping the Technology Landscape

Sat, 18 Oct 2025 21:32:34 -0400

Let me be direct: while Silicon Valley has been celebrating incremental improvements and debating work-life balance, China has been executing a coordinated, decade-long strategy to dominate artificial intelligence — and it’s working. DeepSeek’s January 2025 breakthrough was not a fluke. It was the predictable result of national planning, structural advantages and a fundamentally different approach to technology.

The 2017 Blueprint That Changed Everything

In 2017, China’s State Council released the “New Generation Artificial Intelligence Development Plan” — a roadmap with measurable national targets:

By 2020: catch up to leading AI nations
By 2025: achieve major breakthroughs and leadership in core AI technologies
By 2030: become the world’s primary AI innovation centre

This plan aligned government, academia and industry around a shared mission. It wasn’t top-down control — it was long-horizon coordination at national scale.

The Numbers Don’t Lie: Talent and Research

China built the world’s largest AI talent pipeline. It awarded more than 50,000 STEM PhDs in 2022 — over twice the U.S. total — and is projected to exceed 77,000 per year by 2025. Excluding international students, China will out-graduate the U.S. three-to-one. At the same time, it now produces as much AI research as the U.S., U.K. and EU combined, supported by more than 30,000 active AI researchers — double the U.S. population.

Financing the AI Machine

Ambition was matched with capital:

$912B in government-backed VC funds across strategic fields
$138B in a national emerging-tech fund
$8.2B in a dedicated AI investment initiative in 2025

U.S. private capital remains larger, but America’s public investment is fragmented and slow. China’s model — coordinated funding with long-term mandates — produced momentum the West struggles to match.

The Data Advantage No One Wants to Admit

With 943 million mobile-payment users and trillions in digital transactions, China has a structural data advantage. Any Western firm would face regulatory barriers collecting data on that scale. Whether we agree with China’s model or not, it accelerates AI training in ways our governance systems do not.

Beyond the 996 Myth

China’s AI success is not simply long work hours. The state has moved to curb 996, and companies like DJI now enforce mandatory clock-out policies. The advantage isn’t hours — it’s alignment, speed and focus. Ironically, some Silicon Valley AI labs are now adopting 996-style schedules to compete with the very system China is abandoning.

DeepSeek as Proof of Strategy

DeepSeek claims it built its R1 model in under nine months using about 2,000 Nvidia H800s for roughly $5.6M — a fraction of Western spending. Whether the numbers shift under scrutiny, the message is unchanged: China innovated under export-control pressure, embraced open source and scaled through regional competition, especially in ecosystems like Hangzhou.

The Open-Source Gambit

DeepSeek’s open-source strategy created global soft power overnight. In January 2025 alone, more than 500 derivative models were released, totalling 2.5 million downloads. While Western firms protect walled gardens, China is seeding the world with its frameworks.

Cultural Tailwind: Techno-Optimism vs. Techno-Anxiety

Where Western discourse leans toward fear — surveillance, job loss, existential risk — China’s public remains broadly techno-optimistic. This reduces adoption friction and accelerates deployment across industry and public infrastructure.

Export Controls Backfired

U.S. chip restrictions:

Forced extreme optimisation
Accelerated domestic chip development
Protected a vast internal AI market
Tightened collaboration across China’s supply chain

DeepSeek proved you don’t need cutting-edge chips to build competitive models.

Provincial Competition: Innovation at Speed

China’s AI race is regional as much as national. Municipal incentives, compute subsidies and business-to-government contracts create fast iteration cycles and rapid scaling — especially in Hangzhou, where local policy and university talent form a powerful loop.

Additional Structural Advantages

Energy: 429 gigawatts of new power capacity added in 2024 — fuelling data centre growth
Education: AI integrated into school curricululums and 500+ university programs
Robotics: A parallel bet on embodied AI, pairing manufacturing dominance with emerging autonomy

Implications for Western Leaders

Talent: Our talent pipeline is not competitive without immigration reform and STEM investment
Strategy: Ten-year national plans beat quarterly capitalism
Open Source: Influence comes from ecosystems, not just IP walls
Efficiency: Innovation under constraint is now a strategic weapon

Enterprise Security Impact

For CISOs and technology leaders, three questions demand immediate attention:

Supply chain: Are your AI tools dependent on Chinese models or code?
Data sovereignty: Where does your AI-processed data actually flow?
Standards: Who will define the security and privacy baselines of tomorrow’s AI ecosystem?

Where We Go from Here

China spent a decade building toward AI dominance — and it is now reaping the results. This is not about copying China’s governance model, but about abandoning the illusion that market forces alone will protect Western leadership. We need coordinated national strategies, research investment, immigration alignment and public-private collaboration.

The question is no longer whether China is serious. The question is whether we are.

Disclaimer

The views and opinions expressed in this article are solely my own and do not represent the views of my employer or any affiliated organizations. I received no compensation or external influence in the preparation of this article, and I am not affiliated with any AI vendor, consortium or government entity. This analysis is based on publicly available information at the time of writing. While every effort has been made to ensure accuracy, any errors or omissions are unintentional.

#ArtificialIntelligence #AI #ChinaAI #DeepSeek #AIDominance #AIGeopolitics #AIGovernance #TechStrategy #CyberSecurity #CISO #MachineLearning #OpenSourceAI #LLM #FutureOfAI #DigitalStrategy #NationalSecurity #Geopolitics #TechPolicy #DataSovereignty #AIResearch #Innovation #Automation #GlobalSecurity #ExportControls #AIInfrastructure #EnterpriseSecurity #QuantumComputing #AIEthics #TechnologyLeadership #Policy #AIPolicy #AIFuture #AITalent #STEMEducation #Robotics

Prompting Strategies to Reduce AI Sycophancy

Tue, 07 Oct 2025 07:44:00 -0400

Recent research has shown that many advanced AI systems tend to agree with users or offer flattering answers, even when those answers are incomplete or wrong. This behaviour—known as sycophancy—can increase overconfidence, reduce critical thinking and influence decision-making in subtle ways. The good news is that with the right prompt strategies, users can reduce these effects and get more balanced, useful responses from any AI model.

Why Prompting Matters

Sycophancy has been observed across all major large language models. It’s not tied to a single vendor or product. This means the responsibility for encouraging more critical, balanced outputs partly depends on how we structure our prompts. Asking better questions can reveal blind spots, surface alternative perspectives and strengthen the quality of analysis.

Practical Prompt Strategies

Below are model-agnostic prompt patterns that work across most chatbots, copilots, privacy advisors and AI-embedded security tools.

1. Ask for alternative views

“Give me three alternative perspectives on this issue, including at least one that challenges my initial view.”

2. Request pros and cons before answers

“List the key benefits and risks of this approach before giving a recommendation.”

3. Make uncertainty explicit

“Before you answer, list what information is missing or uncertain that would affect the outcome.”

4. Use role reversal or devil’s advocate

“Now argue the opposite position as if you strongly believed it.”
“Imagine you’re an external auditor tasked with finding weaknesses in this idea.”

5. Force multi-step reasoning

“Explain your reasoning in steps, showing where alternative paths could lead to different outcomes.”

Applying This to Cybersecurity and Privacy Tools

Many cybersecurity and privacy platforms now embed AI capabilities—for detection, classification, risk scoring or policy advice. The same strategies apply. When interacting with these tools, prompt them to show their work, present alternative views and highlight uncertainties. This helps uncover hidden assumptions and reduces the risk of overly agreeable outputs quietly shaping security or privacy decisions.

Key Takeaway

You don’t need new tools to counteract sycophancy—just better prompts. Treat AI outputs as a starting point, not the final word. Asking for alternatives, risks and uncertainties improves the quality of responses and supports stronger, evidence-based decisions in cybersecurity and privacy contexts.

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AI Sycophancy: What the Latest Research Means for Cybersecurity and Privacy

Mon, 06 Oct 2025 07:32:10 -0400

New research from Stanford University, Carnegie Mellon University and the University of Oxford highlights a behavioural risk in today’s most advanced AI systems: sycophancy. This occurs when models agree with users or flatter them, even when they are wrong. The findings are relevant to anyone who relies on AI assistants for work, decision-making or communication.

What the Research Shows

In May 2025, researchers analysed eight widely used models and found they displayed much higher levels of social sycophancy than human baselines. This included emotional validation, moral endorsement and acceptance of users’ framing, even when the inputs contained incorrect or harmful information.¹

In October 2025, a separate experiment tested 11 models with a sample of 1,604 participants. The models affirmed users’ actions roughly 50 per cent more often than humans. Exposure to flattering responses made people less likely to repair conflicts and more convinced they were right, even when their assumptions were wrong.²

Earlier research showed that reinforcement learning from human feedback (RLHF)—a common tuning method—can unintentionally reward pleasing users over accuracy, helping explain why sycophancy emerges.³

In April 2025, OpenAI rolled back a GPT-4o update after identifying an increase in overly flattering and agreeable responses.⁴

What This Means for Everyday Users

Sycophancy can affect how people interact with AI systems in subtle but important ways. When an assistant consistently agrees with a user, it can make that user:

Less likely to seek out alternative viewpoints or correct errors
More confident in assumptions that may be incomplete or wrong
More inclined to trust the AI’s output, even when that trust is not earned

For day-to-day work, this means responses from AI assistants may sound convincing but should not be treated as objective validation. It is good practice to cross-check critical information, invite alternative perspectives and treat flattering language as a signal to pause and verify, especially in decisions involving privacy, security or policy.

Implications for Cybersecurity and Privacy Tools

Many of the tools used across cybersecurity and privacy functions now include AI capabilities, whether for incident detection, data classification, regulatory mapping, risk scoring or end-user assistance. The research on sycophancy underscores several practical points for anyone using or evaluating these tools.

Validation is not verification. If an AI-driven dashboard, assistant or chatbot consistently agrees with your inputs or assumptions, it may sound correct but fail to critically assess them. This can influence how incidents, risks or privacy impacts are evaluated.

Confidence cues can mislead. Affirming or flattering language in tool outputs can increase confidence even when the underlying reasoning is weak. This matters when tools generate regulatory summaries, classify data or prioritise alerts.

Bias can be silently reinforced. When systems repeatedly mirror a user’s framing, they can entrench existing biases in how risks are identified or incidents are prioritised, shaping outcomes without obvious warning signs.

These findings don’t suggest that AI tools are unreliable. They highlight the need to treat AI outputs as a starting point, not the final word. Asking follow-up questions, checking multiple sources and applying professional scepticism remain essential to sound decision-making.

References

Cheng, M., Yu, S., Lee, C., Khadpe, P., Ibrahim, A., Jurafsky, D. Social Sycophancy: A Broader Understanding of LLM Sycophancy (ELEPHANT). May 2025. arXiv:2505.13995.
Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., Jurafsky, D. Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence. October 2025. DOI: 10.48550/arXiv.2510.01395.
Sharma, P. et al. Towards Understanding Sycophancy in Language Models. October 2023. DOI: 10.48550/arXiv.2310.13548.
OpenAI. Sycophancy in GPT-4o: what happened and what we’re doing about it. Apr. 29, 2025.

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Cybersecurity in the Era of Agentic AI: Weaponization, Defences and Governance

Sun, 05 Oct 2025 18:00:36 -0400

Agentic artificial intelligence—systems that perceive, decide and act autonomously—has moved from laboratory theory to operational threat. Attackers and defenders alike now deploy autonomous agents that plan multi-step attacks, invoke tools and adapt in real time. The same capabilities that accelerate detection and response can also scale reconnaissance, social engineering and exploitation.

What adversaries are actually doing

OpenAI and Microsoft have documented multiple state-affiliated groups experimenting with large language models to accelerate reconnaissance, targeting research and social engineering. OpenAI and Microsoft disabled the associated accounts. While these efforts don’t amount to fully autonomous cyber weapons, they confirm that adversaries are incorporating AI into the attack kill chain (the sequence of steps from reconnaissance to exploitation).

Laboratory research shows the outer limits of current capability. A 2024 study by University of Illinois researchers found a GPT-4 agent exploited 87 per cent (13 of 15) real one-day vulnerabilities when given Common Vulnerabilities and Exposures (CVE) descriptions. Without that context, success dropped to seven per cent. The results highlight both potential and constraint.

GenAI supply chains are also creating new propagation risks. “Morris II,” a zero-click, self-replicating prompt-injection worm, spread across retrieval-augmented generation (RAG) ecosystems in controlled research environments. It remains a proof-of-concept, not an observed in-the-wild threat, but it underscores systemic weaknesses.

Meanwhile, AI is reshaping social engineering. In 2024, fraudsters tricked an Arup employee in Hong Kong using a multi-participant deepfake video call, leading to transfers of approximately $25 million US — now a canonical case of enterprise-scale deception.

The evidence: 80 per cent of ransomware now uses AI

A September 2025 analysis by MIT Sloan CAMS and Safe Security, examining ransomware incidents from 2023 and 2024, found that 80 per cent of attacks in the sample used some form of AI, such as phishing content, deepfakes or code generation. The study reflects trend velocity, not a global rate, and underscores how quickly attackers are adapting.

Defensive measures that work today

Start by hardening agent workflows, tools and memory. The Open Web Application Security Project (OWASP) Agentic Security Initiative (2024) maps agent-specific threats—including prompt injection, tool misuse and memory poisoning—to practical mitigations. Core measures include least-privilege tool grants, ephemeral credentials, strict egress controls and pre-tool content safety checks. Retrieved or RAG content should be treated as untrusted code, sanitised and provenance-checked.

Red teaming is evolving too. The Cloud Security Alliance’s Agentic AI Red Teaming Guide (2025) outlines how to evaluate non-deterministic agent behaviour, tool use and inter-agent dependencies through multi-iteration testing—areas where traditional large language model evaluations fall short.

On governance, the National Institute of Standards and Technology (NIST) AI Risk Management Framework 1.0 (RMF) (2023) and its Generative AI Profile (AI 600-1) (2024) provide structure for risk identification, control and measurement. The International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 42001:2023 offers a formal management system standard to operationalise AI assurance.

For intelligence sharing, the Cybersecurity and Infrastructure Security Agency (CISA) and the Joint Cyber Defense Collaborative (JCDC) released the AI Cybersecurity Collaboration Playbook (2025). It gives enterprises a voluntary way to share AI-related incidents and indicators, helping align legal and operational defences.

Governance gaps create execution risk

Execution risk remains high. According to a June 2025 Gartner forecast reported by Reuters, more than 40 per cent of agentic AI projects will be cancelled by 2027 due to cost pressures, unclear returns and weak governance. Governance must mature early to avoid expensive failures.

A pragmatic 90-day playbook

Organisations should implement these five controls within 90 days:

Contain the agent. Run agents in sandboxed networks, enforce allow-listed tools and APIs, deny raw shell access, rotate credentials and cap autonomy through step budgets (limits on autonomous actions). Require human approval for transactions or data exfiltration risks.

Secure the context. Treat all retrieved content as untrusted. Strip executable instructions, apply policy rendering, verify provenance and maintain audit-grade logging.

Test like an adversary. Use the Cloud Security Alliance red teaming guide to design multi-run scenarios that probe planning, memory, permissions and inter-agent handoffs. Track closure time and autonomy-budget violations as leading indicators.

Govern and assure. Map programmes to NIST RMF and the Generative AI Profile for risk management, and to ISO/IEC 42001 for operational assurance. Require suppliers to demonstrate compliance.

Prepare for deception. Run deepfake-in-the-loop exercises and mandate out-of-band verification for high-value transactions. The Arup case offers a realistic scenario to test CFO-style approval traps.

A note on editorial choices

Commonly cited but misleading examples—such as the 2016 Tay chatbot incident or unqualified “80 per cent ransomware” claims—were deliberately omitted or reframed for accuracy. The goal is to focus on verifiable, current evidence rather than anecdotes.

The bottom line

Agentic AI is changing both the threat surface and the control plane. Treat agents as powerful automation: constrain their blast radius, test their behaviour continuously and govern them with the same rigour applied to any safety-critical system. The organisations that will benefit are those that pair measured autonomy with measured assurance — and test both relentlessly.

Keywords : Hashtags: #CyberSecurity #AgenticAI #AIThreats #AITactics #CyberDefense #ThreatIntelligence #Deepfakes #Ransomware #AIGovernance #AIRegulation #ZeroTrust #RedTeam #BlueTeam #AIAttacks #MachineLearning #SecurityStrategy #InformationSecurity #CISO #CyberRisk #SOC #VulnerabilityManagement #EthicalAI #AIEthics #AIAssurance #CyberThreats #AdaptiveSecurity #SecurityLeadership #Infosec #DataSecurity #Malware #AutonomousAgents #AIinSecurity #CyberResilience #CISOStrategy #AIFuture

Perplexity's Comet browser raises privacy questions over data collection

Thu, 25 Sep 2025 09:21:12 -0400

Perplexity has launched Comet, a Chromium-based “agentic” browser that uses AI to automate tasks and personalize the browsing experience. The rollout began in July 2025 with invite-only access for Perplexity Max subscribers, followed by regional expansions. [Reference: Perplexity Comet launch materials, July 2025; coverage of regional availability updates, September 2025]

What’s driving the concern

Chief executive Aravind Srinivas has said one reason for building a browser is to capture signals “outside the app” — such as purchases, travel and general browsing — to better understand users and deliver “hyper-personalized” ads, including via a discovery feed. Those remarks were made in April 2025 and discussed again in July 2025. [Reference: April 2025 public remarks by Aravind Srinivas; The Verge “Decoder” discussion, July 2025]

How this contrasts with the market

Mainstream browsers continue to strengthen default anti-tracking. Apple’s Safari ships Intelligent Tracking Prevention, and Mozilla’s Firefox enables Enhanced Tracking Protection that blocks cross-site tracking cookies by default. Comet’s data-hungry model therefore sits in tension with evolving privacy expectations. [Reference: Apple WebKit documentation on ITP, current to 2025; Mozilla Firefox documentation on ETP, current to 2025]

Why it matters

Perplexity is betting that users will trade more of their privacy for a more capable, AI-driven browser. The launch and subsequent expansion will test whether consumers accept extensive data collection in return for convenience — and how regulators respond as “AI browsers” move into the mainstream. [Reference: Perplexity Comet product positioning and media briefings, July–September 2025]

Wed, 24 Sep 2025 07:37:41 -0400

France’s Mistral AI is making a push for Canadian talent and business - The Logic

Mistral AI, a French company, is expanding its operations in Canada, specifically in Montreal, by hiring local talent and courting potential clients in various sectors. CEO Arthur Mensch highlighted the high concentration of AI talent in Montreal and the firm’s plans to recruit engineers, sales, and marketing staff. Mistral is targeting sectors like financial services, energy, manufacturing, logistics, and mining, with existing clients including Axa, Orange, and TotalEnergies. The company is particularly interested in Quebec due to the need for French-language services and aims to customize its AI models to grasp cultural nuances specific to the region. Mistral trains its own foundation models and offers customizable AI solutions, adapting its technology to meet the unique needs of different markets. The firm open-sources its models and provides cloud services or on-premise deployments, with staff assistance for customization. Mensch noted that Mistral focuses on technical use cases, including audio and image applications, and reasoning capabilities. The company is aware of the competitive talent market in Montreal, where other major tech firms like Meta, Microsoft, and Cohere also have AI labs. Mensch, who has personal connections to Montreal through his academic background, is optimistic about Mistral’s growth in the region.

OpenAI Implements AI-Powered Age Estimation to Enhance Youth Safety

Thu, 18 Sep 2025 07:19:28 -0400

Imagine opening ChatGPT and asking:

“Based on everything you know about me, how old do you think I am? If you aren’t sure, estimate.”

Soon, the answer to that question could influence how the AI responds to you. OpenAI has announced it is developing an AI-powered age-estimation system to better protect younger users.

Rather than requiring identification, the system will predict whether a user is likely under 18 based on conversational patterns. If ChatGPT believes a user is a teen, it will automatically apply stricter safety rules, such as blocking explicit content or restricting sensitive topics.

OpenAI has also stated that in some regions or under certain regulations, users may be asked to verify their age with official identification, but this will not be a universal requirement. The company’s goal is to balance youth safety with privacy and accessibility.

OpenAI emphasizes the difference between age estimation and age verification.

Age estimation uses AI to predict a user’s age group from their conversations. It is seamless, does not block access, and does not require official documents.
Age verification requires users to confirm their age with government-issued identification, such as a driver’s licence or passport.

The new system relies primarily on age estimation, automatically activating stricter safeguards when a user appears to be under 18. This differs from traditional age-verification systems that act as hard barriers, which OpenAI says it aims to avoid unless local regulations demand it.

OpenAI’s announcement comes at a time of increasing public concern and regulatory pressure.

The death of 16-year-old Adam Raine in April 2025 drew widespread attention to the potential risks of teens interacting with AI chatbots. While the exact circumstances remain under investigation, his case has intensified calls for stronger youth protections and clearer safety measures for AI tools.

Regulators are also stepping up enforcement. In December 2024, Italy fined OpenAI €15 million for violations of GDPR privacy rules. Italy was the first Western country to temporarily ban ChatGPT in March 2023, citing privacy and data protection concerns. OpenAI has announced plans to appeal the fine, while continuing to improve its compliance with European privacy laws.

These events highlight the growing global demand for AI companies to demonstrate accountability and responsibility when handling sensitive data and vulnerable users.

Feature	Details
Age-estimation model	Uses conversational cues to predict whether a user is likely under or over 18.
Under-18 experience	Automatically applies stricter filters, blocking explicit sexual content, limiting flirtatious conversations, and restricting discussions of self-harm or suicide.
Parental controls	Rolling out over the coming month (late September through October 2025). These tools will allow parents or guardians to manage and monitor teen accounts. Full details have not yet been disclosed.

OpenAI acknowledges that this system will not be perfect. There is a risk of misclassification, where adults may be mistakenly treated as teens or minors may go undetected. When the system is uncertain, it will default to the safer, restricted experience.

Accuracy: AI predictions can be wrong, frustrating adults whose access is limited or leaving gaps in protection for minors.

Privacy: The system relies on analyzing conversations to estimate age, raising questions about how that data is processed, secured, and stored. Under Canadian law, such as the Personal Information Protection and Electronic Documents Act (PIPEDA), this type of data use must be transparent and privacy-protective. The Office of the Privacy Commissioner (OPC) has stated that age-assurance methods can be done “in a privacy-protective and sufficiently accurate manner” when properly designed.

Bias: Cultural, linguistic, and demographic differences could cause the model to misinterpret language patterns, leading to inconsistent results.

Regulatory complexity: Different countries have different privacy and safety rules, requiring OpenAI to adapt its rollout to a wide range of legal frameworks, especially in Europe and North America.

OpenAI says it processes data on servers located in multiple jurisdictions with safeguards designed to meet local privacy laws. The company stresses that its goal is to protect young users without unnecessarily collecting personal information, using AI-based estimation as a middle ground between safety and privacy.

In Canada, privacy laws such as PIPEDA require companies to handle personal information transparently and securely. Even though OpenAI’s approach avoids collecting government IDs for most users, it still involves using conversational data to infer age.

Canadian regulators will be watching closely to ensure that these systems are both accurate and privacy-protective, especially for youth. The Office of the Privacy Commissioner has emphasized that any age-related data processing must be limited, secure, and accompanied by clear consent mechanisms.

OpenAI’s age-estimation initiative represents a significant step in AI safety. By moving away from self-reported ages and avoiding broad, mandatory ID checks, the company is attempting to balance protecting younger users with respecting privacy and accessibility.

As regulatory pressure grows and public expectations increase, systems like this will likely become standard across major technology platforms. The rollout over the coming month will be closely watched as a test case for how AI companies address one of the most pressing challenges in the industry: keeping young users safe while maintaining trust and transparency.

Beyond the Hype: Securing AI's Weakest Link in 2025

Tue, 16 Sep 2025 09:27:34 -0400

AI is transforming business operations, but it is also introducing new vulnerabilities. Attacks targeting AI systems are now active and growing in sophistication, making AI security a board-level concern.

TL;DR

AI adoption growing fast, driving up organizational risk exposure
Threats evolving: data poisoning, prompt attacks, and deepfakes
OWASP LLM Top 10 identifies core vulnerabilities for 2025
Regulatory gaps emerging as Canada and EU diverge
Build layered defences with governance and phased controls

Context

By 2026, over 80 per cent of enterprises will have deployed AI systems, according to Gartner.

AI has shifted from a supporting technology to a mission-critical enabler. This rapid adoption expands the digital attack surface. AI systems are now targeted directly by attackers who exploit weaknesses in data pipelines, models, and integrations.

Attackers are also using AI offensively. Tools like autonomous agents lower the skill barrier for cybercrime, while AI-driven deepfakes make social engineering harder to detect.

Analysis

The evolving threat landscape

Recent incidents show that AI attacks are no longer theoretical. In 2025, attackers used poisoned Google Calendar invites to hijack a Gemini assistant, disrupting smart home systems.

Emerging attack types include:

Agent-to-agent prompt infections: Compromised AI agents spreading malicious instructions to peers.
Deepfake-enabled phishing: Hyper-realistic voice or video impersonations targeting executives.
Supply chain manipulation: Altering embeddings or external data sources in retrieval-augmented generation (RAG) pipelines.

These attacks blend technical exploitation with psychological manipulation, making detection and response harder.

OWASP Top 10 for LLMs

The OWASP LLM Top 10 provides a shared language for understanding AI risks:

Code	Risk Name	Example Impact
LLM01	Prompt Injection	Malicious instructions change behaviour
LLM02	Insecure Output Handling	Unsafe actions triggered automatically
LLM03	Training Data Poisoning	Corrupted models requiring costly retraining
LLM04	Model Denial of Service	AI outages or slowdowns
LLM05	Supply Chain Vulnerabilities	Compromised data sources
LLM06	Sensitive Information Disclosure	PII or secrets exposed via queries
LLM07	Insecure Plugin Design	Backdoors through third-party integrations
LLM08	Excessive Agency	Autonomous actions without oversight
LLM09	Overreliance	Blind trust in AI output
LLM10	Model Theft	Stolen models used by competitors

Prompt injection (LLM01) and data poisoning (LLM03) have the highest financial impact, but overreliance (LLM09) and data leaks (LLM06) create reputational and compliance risks.

Governance and regulation

AI security is not just a technical challenge — it requires leadership alignment.

Shared ownership: CISOs, data governance leads, and product teams must collaborate on secure-by-design principles and continuous model validation.

Regulatory shifts:

Canada’s Artificial Intelligence and Data Act (AIDA) was paused in January 2025.
Provinces such as British Columbia are moving ahead with local AI rules.
The EU AI Act enforces strict requirements for high-risk AI, including transparency and auditability. Non-compliance can result in fines up to seven per cent of global revenue.

These differences create a complex environment for global organizations.

Vendor risk management: Update third-party assessments to cover:

Data handling practices and retention policies
Right to audit vendors’ AI processes
Incident response timeframes and obligations

A layered defence model

Defending AI requires coordinated preventive, detective, and responsive measures.

Preventive controls:

Deploy input/output filtering with NVIDIA NeMo Guardrails or similar tools.
Vet external data sources for RAG pipelines to prevent poisoned content.
Restrict AI agent permissions using least-privilege principles and human approval for sensitive actions.

Detective controls:

Continuously monitor model behaviour for anomalies such as sudden latency spikes or unexpected output drift.
Embed cryptographic watermarks to detect model theft.

Responsive controls:

Maintain a rollback strategy to revert compromised models quickly.
Communicate transparently with users during incidents to preserve trust.
Conduct regular red-team exercises to simulate attacks and refine defences.

What to do

Identify all AI assets and dependencies within the first 90 days.
Deploy filtering and least-privilege controls for every AI agent by six months.
Run quarterly red-team exercises to identify emerging vulnerabilities.
Formalize AI governance and integrate it into enterprise risk management.
Update vendor risk frameworks to include AI-specific requirements.

Key takeaways

AI is now both a strategic enabler and a prime attack target.
Regulatory gaps require proactive, global governance alignment.
Phased, layered security is essential to defend AI systems at scale.

Updated: 2025-09-16 to reflect Canada’s regulatory pause and new attack examples.

The Art of Iteration – How to Refine ChatGPT Responses for Better Results

Wed, 13 Aug 2025 05:30:00 -0400

A common mistake when using ChatGPT is treating the first answer as the final one. The truth is, great results usually come from a little back-and-forth. The most effective users treat ChatGPT like a helpful colleague — someone who needs clear guidance, feedback and a bit of coaching to get things just right. This process is called iteration, and it’s one of the most important skills you can develop in prompt engineering.

Why iteration matters

ChatGPT is powerful, but it can’t read your mind. Your first request is often just a rough starting point. Each time you refine your prompt and give feedback, you’re helping it better understand your needs — whether that’s making information simpler, making it sound friendlier or adding missing details.

Think of it like explaining a recipe to a friend. The first time, they might get the basics right, but when you add “Oh, and make sure it’s vegetarian” or “Add a garnish so it looks great on the table,” the end result becomes exactly what you imagined.

The iteration process – step by step

1. Start with a first-draft prompt
Don’t overcomplicate it — start with a clear, simple request.
Example: “Write a summary of this report.”

2. Review what you get
Ask yourself:

Is it accurate and relevant?
Does it sound right for the audience?
Is it missing anything important?

3. Give helpful, specific feedback
Avoid saying “make it better.” Instead, tell ChatGPT what to change.
Example: “Rewrite this so it focuses on the top three risks, keep it under 200 words, and make it sound like it’s written for busy executives.”

4. Add extra guidance or constraints
Include the format, perspective or tone you want.
Example: “Turn this into three friendly bullet points for a presentation slide.”

5. Repeat until it clicks
Two or three rounds is often enough to get a polished, ready-to-use result.

Examples

Example 1 – Helping a community group

First prompt: “Write an email inviting people to our fundraiser.”
Feedback: “Make it warm and friendly, include the date and location, and explain how the funds will help our local food bank.”
Final iteration: A heartfelt, concise invitation that makes readers feel included and motivated to attend.

Example 2 – Making a family guide

First prompt: “Write tips for travelling with kids.”
Feedback: “Focus on families with children under 10, keep it under 400 words, and add a mix of practical advice and fun ideas.”
Final iteration: A cheerful, reassuring guide with helpful packing tips, snack suggestions and creative ways to keep kids entertained on long journeys.

Example 3 – Writing for a school newsletter

First prompt: “Write an article about recycling.”
Feedback: “Make it engaging for high school students, include three easy recycling tips, and highlight the positive impact on the environment.”
Final iteration: A lively, informative piece with relatable examples, a positive tone and a call to action for students to take part.

Common pitfalls to avoid

Changing too much at once – Small, focused adjustments make it easier to see what works.
Vague feedback – Specific guidance gets better results.
Starting over unnecessarily – If the first draft is close, refine it instead of restarting.

Why this skill matters

Iteration may add a couple of minutes to the process, but it saves time overall by reducing the need to rewrite or correct. More importantly, it builds prompt literacy — the ability to communicate clearly with AI. That’s a skill that will make every future request faster and more effective.

Think of ChatGPT as a creative partner. The more you guide it, the better it becomes at helping you — and the more rewarding the results will be.

#AI #ArtificialIntelligence #GenerativeAI #MachineLearning #PromptEngineering #ChatGPT #OpenAI #AIProductivity #WorkSmarter #DigitalTransformation #FutureOfWork #AIInnovation #TechTools #AIForBusiness #AITrends #AIEducation #AIInTheWorkplace #BusinessProductivity #EfficiencyTools #AITraining #AIBestPractices #TechnologyTips #SmartWorkflows #AIContent #AIAssistant #AITools #AIEnhanced #AIWriting #DigitalSkills #PromptLiteracy

Supercharge Your ChatGPT Results with the Prompt Optimizer

Wed, 13 Aug 2025 04:30:00 -0400

If you’ve ever struggled to get the perfect answer from ChatGPT, you’re not alone. The quality of your output often depends on the quality of your input—your prompt. That’s where OpenAI’s new Prompt Optimizer for GPT-5 and above comes in.

What Is the Prompt Optimizer?

The Prompt Optimizer is a built-in feature that rewrites your request to make it clearer, more specific, and more likely to produce high-quality results. It does the heavy lifting of prompt refinement for you, saving time and boosting the relevance of the output.

You can try it here:
https://platform.openai.com/chat/edit?models=gpt-5&optimize=true

How to Use It

Go to the Prompt Optimizer link above.
Start a new chat in GPT-5 or higher.
Enter your question or task in plain language.
Click Optimise – the tool will refine your prompt automatically.
Review and send the optimised version, or make further edits if needed.

Why Use It?

Saves time by reducing trial and error in prompt creation.
Improves clarity and specificity so ChatGPT better understands your needs.
Produces more relevant results, whether you’re writing, researching, or brainstorming.

Examples in Action

Instead of: “Summarise this report”
→ Optimised: “Summarise the attached Q2 2024 sales performance report in under 300 words, focusing on trends, key wins, and risks.”
Instead of: “Write a blog about cloud security”
→ Optimised: “Write a 600-word blog post for a non-technical audience explaining why small businesses should invest in cloud security, using Canadian examples and plain language.”

Best Practices for Better Results

Be clear about your goal—don’t leave intent to guesswork.
Include details about audience, tone, format, and length.
Use the Optimizer for both quick questions and complex projects.
Always review the optimised prompt before sending it.

#AI #ArtificialIntelligence #GenerativeAI #MachineLearning #PromptEngineering #PromptOptimizer #OpenAI #GPT5 #AIProductivity #WorkSmarter #DigitalTransformation #FutureOfWork #AIInnovation #TechTools #AIForBusiness #AITrends #AIEducation #AIinTheWorkplace #BusinessProductivity #EfficiencyTools #AITraining #AIBestPractices #TechnologyTips #SmartWorkflows #AIContent #AIAssistant #InnovationTools #AITools #AIEnhanced #AIWriting

DeepSeek quietly rolls out R1 model update, intensifying AI competition

Thu, 29 May 2025 19:02:30 -0400

The artificial intelligence landscape continues its rapid evolution this week. Chinese AI startup DeepSeek has reportedly released a noteworthy update to its R1 reasoning model. While an official, large-scale announcement is still anticipated, the updated model, identified as R1-0528, has surfaced on the developer platform Hugging Face. It is already attracting significant attention within the AI community.

According to some reports citing company communications, this 'minor version upgrade' brings significantly improved reasoning and inference capabilities. Early indications and benchmark performances, such as those on LiveCodeBench, suggest that R1-0528 is making considerable strides. It positions itself closer to leading models from major players like OpenAI and Google.

Key highlights of the DeepSeek R1-0528 update (based on initial reports):

Enhanced reasoning and inference: The core focus of this update appears to be a substantial boost in the model's ability to perform complex reasoning tasks. This is critical for applications requiring deep understanding and logical deduction.
Improved accuracy: Reports suggest a jump in accuracy on certain benchmark tests. One source notes an increase from 70 per cent to 87.5 per cent on a specific math benchmark (AIME 2025). This points to more reliable and precise outputs.
Reduced hallucinations: A key challenge in Large Language Models (LLMs) – the tendency to generate incorrect or nonsensical information – is reportedly being addressed. The updated R1 model exhibits fewer hallucinations.
Better coding and logic capabilities: The model is showing improved performance in math, programming, and general logic. This makes it a more potent tool for developers and researchers.
Continued open-source approach: The updated model appears to maintain DeepSeek's commitment to the open-source community, with an MIT license allowing for broad use and modification.

Why this matters for the AI industry:

DeepSeek has been a company to watch since its emergence. It is known for developing powerful AI models with a focus on efficiency and cost-effectiveness. The original R1 model already turned heads by demonstrating comparable, and in some cases superior, performance to established models. This was often at a fraction of the training cost and resource utilization.

This latest update signals DeepSeek's continued ambition to push the boundaries of AI capabilities and compete at the highest level. The quiet release, followed by strong initial benchmark showings, is characteristic of DeepSeek's approach. The company often prioritizes technical advancement and community engagement.

The improvements in reasoning and coding are particularly significant. These are crucial areas for the practical application of AI across various industries. Applications range from software development and scientific research to complex problem-solving and data analysis.

What to watch for:

Official announcement and technical details: The AI community eagerly awaits a more formal announcement from DeepSeek. Such an announcement would likely provide comprehensive technical specifications, training methodologies, and detailed benchmark results for R1-0528.
Community adoption and feedback: As developers and researchers begin to explore the updated model, their findings and feedback will offer real-world insights into its performance and capabilities.
Impact on competitors: Such advancements invariably spur further innovation and potentially strategic responses from other leading AI labs.
Anticipation for R2: This update also keeps the spotlight on DeepSeek as the community anticipates the release of its next-generation R2 model. The R2 model is expected to bring even more significant advancements.

DeepSeek's R1-0528 update is another compelling development in the fast-paced world of artificial intelligence. It underscores the global nature of AI innovation and the continuous drive towards more powerful and capable models. The AI community will undoubtedly watch for further details as they emerge.

Disclaimer: This blog post is based on information available as of May 29, 2025. Details regarding the DeepSeek R1-0528 update are still emerging. Further official announcements from DeepSeek AI are anticipated.

#DeepSeek #DeepSeekAI #DeepSeekR1 #R1model #R1_0528 #AI #ArtificialIntelligence #LLM #LargeLanguageModels #MachineLearning #DeepLearning #AIUpdate #TechNews #AINews #AIinnovation #AIReasoning #AIInference #AICoding #OpenSourceAI #HuggingFace #AICommunity #FutureOfAI #AIBenchmarks #AIResearch #NextGenAI #ModelUpdate #TechInnovation #AICompetition #LanguageModels #AIModels

Using Generative AI for Smarter LinkedIn Networking in Cybersecurity and Privacy

Mon, 05 May 2025 04:30:00 -0400

TL;DR:
Networking often creates opportunities that online applications alone cannot. Generative AI tools like ChatGPT can guide you in finding key contacts and crafting high-impact connection messages on LinkedIn.

AI & HR Expert Insights:

Prompt Engineering Tip: When reaching out, provide AI with context. Example: "Draft a LinkedIn message to a cybersecurity director at ABC Corp, referencing their recent webinar on cloud security."
Human Oversight: Personalize AI-generated messages to reflect genuine interest and establish meaningful connections.
Bias Awareness: Be mindful of cultural nuances and ensure your messages are respectful and inclusive.

Micro-Action:
Identify a professional in your field you'd like to connect with. Use AI to draft an initial message, then tailor it to reflect your authentic interest and voice.

Closing Thought:
Networking is an investment, not a transaction. Who in your network has made a lasting difference in your career? Let’s celebrate those connections.

Hashtags:
#LinkedInNetworking #jobsearch #ChatGPT #generativeAI #professionalnetworking #cybersecurity #privacy #ITcareers #cyberjobs #compliance #riskmanagement #cybersecuritycareers #ITsecurity #careeradvancement #LinkedIntips #networkingtips #careergrowth #cloudsecurity #globaljobsearch #professionalbranding #personalbranding #cybersecurityanalyst #privacyofficer #careerstrategy #Canadianjobs #jobopportunities

How to Match Your Experience to Job Descriptions Using AI

Fri, 02 May 2025 11:42:00 -0400

TL;DR:
Customizing your application to match job descriptions directly boosts your chance of securing interviews. Generative AI helps you strategically align your skills and experience with employer expectations.

AI & HR Expert Insights:

Prompt Engineering Tip: Ask AI to identify gaps between your resume and the job description. For example: "Compare my resume to this job posting and suggest areas for improvement."
Human Oversight: Critically assess AI suggestions to ensure they genuinely enhance your application without misrepresenting your qualifications.
Bias Awareness: Ensure that the modifications maintain an inclusive tone and do not inadvertently introduce biased language.

Micro-Action:
Take a job description of interest and use AI to evaluate your resume against it. Implement changes that authentically strengthen your alignment with the role.

Closing Thought:
Attention to detail is a powerful differentiator. What’s your best strategy for tailoring applications without losing your authentic voice? I’d love to hear.

Keywords:
#jobdescription #jobmatch #ChatGPT #generativeAI #cybersecurity #privacy #ITjobs #resumeoptimization #applicationtips #ATSfriendly #keywordstrategy #cybercareers #compliance #riskassessment #GDPR #datasecurity #careeradvice #professionalbranding #cybersecuritycareers #Canadianjobs #globaljobsearch #AItools #professionalwriting #resumeandcoverletter #jobinterviewprep #careerstrategy

How to Boost Your LinkedIn Profile Using Generative AI

Thu, 01 May 2025 04:30:00 -0400

TL;DR:
LinkedIn is a global platform where your professional story is visible to recruiters 24/7. Generative AI tools like ChatGPT can help create stronger headlines, summaries, and experience sections that attract opportunities.

AI & HR Expert Insights:

Prompt Engineering Tip: Use prompts that define your professional brand. Example: "Create a LinkedIn summary for a cybersecurity expert with 10 years of experience in cloud security and compliance."
Human Oversight: Ensure that the AI-generated content aligns with your personal brand and accurately represents your career journey.
Bias Awareness: Be vigilant about unintentional biases in language that may affect how your profile is perceived across different cultures and industries.

Micro-Action:
Review your current LinkedIn headline and summary. Use AI to suggest enhancements, then refine the output to ensure it authentically represents you.

Closing Thought:
Your LinkedIn presence is part of your professional legacy. What part of your profile are you most proud of—or planning to enhance next? Let's exchange ideas.

Keywords:
#LinkedIn #LinkedInProfile #ChatGPT #generativeAI #personalbranding #cybersecurity #privacy #ITcareers #careerbranding #professionalprofile #careerstrategy #networking #careerdevelopment #cloudsecurity #compliance #riskmanagement #GDPR #cyberjobs #professionalnetworking #jobsearch #ITjobs #privacyofficer #cybersecurityanalyst #careeradvancement #globaljobs #professionalwriting

How to Use AI to Write Tailored Cover Letters for Cybersecurity and Privacy Roles

Wed, 30 Apr 2025 04:30:00 -0400

TL;DR:
Cover letters remain critical for standing out to employers. Generative AI tools like ChatGPT can help craft tailored, impactful letters aligned with specific cybersecurity, privacy, and IT roles.

AI & HR Expert Insights:

Prompt Engineering Tip: Provide AI with specific details about the job and your relevant experiences. For instance: "Draft a cover letter for a Privacy Analyst role at XYZ Corp, emphasizing my experience with GDPR compliance."
Human Oversight: Customize AI-generated drafts to reflect your unique voice and motivations. Authenticity resonates more with hiring managers than generic content.
Bias Awareness: Review the content for any biased language or assumptions, ensuring that your cover letter promotes inclusivity and aligns with organizational values.

Micro-Action:
Choose a job you're interested in and use AI to draft a cover letter. Then, personalize it by adding specific anecdotes and aligning it with the company's mission and culture.

Closing Thought:
A well-crafted cover letter tells your story before you ever walk into the interview. What has been your biggest challenge when writing cover letters? I welcome your experiences and insights.

Keywords:
#coverletter #ChatGPT #AIwriting #generativeAI #cybersecurity #privacy #ITjobs #jobsearch #customizedapplication #careeradvancement #professionalbranding #GDPR #riskmanagement #resumeandcoverletter #cyberjobs #cybersecurityanalyst #privacyofficer #ATSfriendly #jobapplications #Canadianjobs #professionalwriting #globaljobsearch #AIcareer #cybercareers #jobinterviewprep #careerstrategy

How to Use Generative AI to Optimize Your CV for Cybersecurity, Privacy, and IT Jobs

Tue, 29 Apr 2025 04:30:00 -0400

TL;DR:
A CV has only seconds to make an impression, and many are filtered out by Applicant Tracking Systems (ATS) before a human review. Generative AI tools like ChatGPT can help tailor, format, and sharpen your CV to meet cybersecurity, privacy, and IT industry demands.

AI & HR Expert Insights:

Prompt Engineering Tip: When using AI tools, specify your role and the job description to get more tailored suggestions. For example, prompt: "Act as a cybersecurity recruiter. Analyze my CV against this job description and suggest improvements."
Human Oversight: Always review AI-generated content to ensure it accurately reflects your experiences and achievements. Over-reliance on AI can lead to generic or inaccurate representations.
Bias Awareness: Be cautious of potential biases in AI outputs. Ensure that the language and content are inclusive and free from unintended stereotypes.

Micro-Action:
Select a recent job posting and use an AI tool to analyze your CV against it. Review the suggestions critically, and incorporate changes that genuinely enhance your profile.

Closing Thought:
In a market where milliseconds matter, thoughtful CV optimization is a career advantage. How have you adapted your CV strategy for today’s hiring realities? Let’s share ideas and raise the bar together.

Keywords:
#cybersecurity #privacy #ITjobs #CVoptimization #resume #jobsearch #ChatGPT #generativeAI #ATS #careerdevelopment #professionalbranding #dataprotection #cloudsecurity #riskmanagement #compliance #securityanalyst #GDPR #ITcareers #resumeoptimization #achievementbased #Canadianjobs #globaljobs #AItools #careeradvice #cybercareers #professionalwriting

How NVIDIA’s Digital Twin Technology Is Transforming AI, Industry and Climate Innovation

Tue, 01 Apr 2025 12:01:00 -0400

NVIDIA's digital twin technology represents one of the most significant advancements in industrial digitalization, enabling sophisticated virtual representations of real-world systems. These digital twins are reshaping how companies design, optimize and operate physical assets. This article explores what NVIDIA’s digital twin technology entails, why it is critical to modern industries and its broad range of current and future applications.

Understanding Digital Twins in the NVIDIA Ecosystem

At its core, a digital twin is a virtual representation of a physical asset, process or system, continuously updated with real-time data. NVIDIA has pioneered advanced digital twin technology through its Omniverse platform—an ecosystem where virtual replicas mirror their real-world counterparts with remarkable accuracy and interactivity.

The Foundation: NVIDIA Omniverse

NVIDIA Omniverse is built on Universal Scene Description (USD) and NVIDIA RTX technologies. It enables individuals and teams to develop 3D workflows and applications within an open ecosystem that fosters collaboration, plugin creation and real-time simulation.

Key components of the NVIDIA digital twin architecture include:

NVIDIA Omniverse Enterprise: Used by major firms for industrial-scale digital twins
Omniverse Blueprints: Reference workflows connecting Omniverse with artificial intelligence (AI) tools
NVIDIA PhysicsNeMo: A framework for AI models that follow physical laws
NVIDIA Modulus: A physics-AI framework for advanced simulation

These integrated technologies allow for digital twins that not only resemble their physical counterparts but also simulate real-world physics, adapting to environmental changes in real time.

From Conception to Reality: The Digital Twin Lifecycle

Developing a digital twin with NVIDIA’s platform typically involves three key stages:

Data integration: Aggregating inputs from CAD models, sensors and enterprise systems
Physics-based simulation: Applying acceleration libraries and AI frameworks
Real-time visualisation: Rendering high-fidelity environments using NVIDIA RTX

As NVIDIA CEO Jensen Huang noted: “Before you build an AI factory, you build the digital twin.”Digital twins have become the foundational layer for the AI systems that will power next-generation automation and robotics.

The Strategic Imperative Behind NVIDIA’s Digital Twins

Accelerating Development Cycles

Traditional engineering workflows—ranging from simulation to visualisation—can take weeks. NVIDIA’s Omniverse platform dramatically reduces this timeline:

Real-time computer-aided engineering using Omniverse can simulate up to 1,200 times faster than legacy tools
At Wistron, simulation time dropped from 15 hours to 3.3 seconds using NVIDIA GPU-powered AI models—a 15,000x improvement

Driving the Physical AI Revolution

Digital twins solve a major AI challenge: access to data. By creating synthetic data in virtual environments, developers can safely train, test and validate AI models before deployment.

As Huang observed, “Physical AI will revolutionise the $50 trillion manufacturing and logistics industries. Everything that moves—from cars and trucks to factories and warehouses—will be robotic and embodied by AI.”

Confronting Global Challenges

Beyond operational efficiency, digital twins are being deployed to tackle complex global issues:

Climate modelling through Earth-2, NVIDIA’s digital twin cloud platform
Energy efficiency improvements that reduce carbon output
Infrastructure modernisation for smart cities to enhance urban living

Industry Applications: Where Digital Twins Are Making an Impact

Manufacturing and Industry

BMW Group uses Omniverse Enterprise to simulate operations in 31 factories, accommodating over 2,100 vehicle configurations
PepsiCo and Kinetic Vision have partnered to develop digital twins of distribution centres, improving throughput and reducing energy use
Wistron leverages digital twins for airflow and temperature predictions, boosting energy efficiency by up to 10 per cent

Data Centre Optimisation

Omniverse enables digital twins of high-performance data centres for planning and real-time monitoring
Integration with CAD tools like SketchUp and Autodesk Revit supports collaborative design
IoT sensors maintain continuous feedback loops between the physical infrastructure and its digital representation

Robotics and Automation

Amazon Robotics deploys warehouse digital twins to optimise layout and train robot assistants
KION Group tests multi-robot fleets using Omniverse-based simulations
The Omniverse Blueprint for AV Simulation facilitates high-fidelity testing of autonomous vehicles

Healthcare

Surgeons rehearse complex procedures using patient-specific digital twins
Neurosurgical teams simulate operations on virtual brains tailored to individual anatomy
AI aids in mapping safe surgical paths and anticipating tissue response

Urban Planning and Smart Cities

Real-time data from cameras and sensors feed into city digital twins to improve traffic flow and safety
Houseal Lavigne creates immersive urban models to facilitate public engagement and collaborative planning
Smart traffic systems trained via digital twins help reduce emissions and congestion

Climate and Environmental Modelling

Earth-2 enhances forecasting accuracy, vital in an era of increasing natural disasters
Simulations help mitigate the estimated $140 billion in annual global economic losses due to extreme weather
Applications range from flood prediction to heatwave impact analysis

What Lies Ahead for NVIDIA Digital Twins

Generative Physical AI

Cosmos World Foundation Models are helping usher in industrial-grade AI
New tools like USD Code and USD Search microservices support the intuitive creation and retrieval of 3D assets
The Edify SimReady model automates the labelling of existing 3D data

Economic Transformation

Digital twins are projected to influence $50 trillion in global economic value by improving productivity and innovation
Their impact spans manufacturing, logistics, healthcare, infrastructure and urban development

Advancing Capability and Integration

Enhanced interoperability is being driven by standards such as OpenUSD
Integration with generative AI is producing a new generation of intelligent, physically accurate digital twins
These models are connected to real-world data, exhibit realistic behaviours and provide immersive interaction

Conclusion

NVIDIA’s digital twin technology represents a turning point in how industries simulate and operate physical systems. Through the Omniverse platform and tools such as Modulus and PhysicsNeMo, digital twins are moving beyond visualisation to become intelligent, interactive systems.

By enabling faster development, bridging data gaps in AI and addressing complex global issues—from climate risk to urban planning—NVIDIA is positioning itself at the forefront of a virtual revolution. As these technologies continue to mature, digital twins will become indispensable tools in creating smarter, safer and more efficient systems worldwide.

Keywords: #NVIDIA #DigitalTwins #Omniverse #ArtificialIntelligence #AI #SmartCities #ClimateTech #SustainableTech #IndustrialAI #Robotics #Automation #FutureOfWork #ManufacturingInnovation #DataCentres #SmartInfrastructure #DigitalTransformation #TechInnovation #3DModelling #SimulationTechnology #GenerativeAI #ClimateModelling #EnergyEfficiency #DigitalEngineering #IndustrialRevolution #SmartFactories #UrbanPlanning #HealthcareInnovation #SmartLogistics #AIInnovation #USD #DigitalEcosystems #Modulus #PhysicsNeMo #DigitalFuture #VirtualEngineering

Baidu’s ERNIE AI Models: A Deep Dive into China’s Latest Global Contender

Tue, 18 Mar 2025 04:30:00 -0400

Baidu’s recent launch of ERNIE 4.5 and ERNIE X1 on Mar. 16, 2025, signals a bold re-entry into the global AI race. These models build on Baidu’s long evolution—from the ERNIE Bot introduced in 2023 through subsequent iterations such as ERNIE 3.5 and ERNIE 4.0 Turbo—and position the company to offer cost‑effective, high‑performance alternatives to Western leaders such as OpenAI, Anthropic and Perplexity.

Baidu’s New AI Offerings and Their Evolution

Baidu’s journey with the ERNIE family began with the ERNIE Bot in Mar. 2023, designed as a knowledge‑enhanced large language model. Over time, Baidu refined its product through ERNIE 3.5 and later ERNIE 4.0 Turbo, with each release achieving notable improvements in functionality and efficiency. By late 2024, the system was handling billions of API calls daily, demonstrating significant market traction. Today’s introduction of ERNIE 4.5 and ERNIE X1 represents Baidu’s latest effort to regain competitive momentum in a rapidly evolving AI landscape. [][]

ERNIE 4.5: Multimodal Capabilities with Clarified Performance Scope

ERNIE 4.5 is Baidu’s upgraded foundational model, engineered to process text, images, audio and video within a unified framework. Key features include:

Superior Multimodal Comprehension (with a Caveat): The model is reported to excel in language generation, logical reasoning and memory functions. While Baidu asserts that its text-processing abilities surpass those of DeepSeek V3 and are roughly equivalent to OpenAI’s GPT‑4.5, sources clarify that this advantage is observed specifically in text-related benchmarks and does not necessarily apply across all modalities. []
High Emotional Intelligence: ERNIE 4.5 is designed to interpret internet memes and satire with nuance, which enhances its usability in culturally sensitive applications.
Context Limitations: Currently, ERNIE 4.5 supports an 8,000‑token context window – substantially lower than GPT‑4.5’s 128,000 tokens – which may restrict its applicability for document‑intensive tasks. []

Additionally, while Baidu has announced plans to open-source ERNIE 4.5 by June 30, 2025, this commitment applies to the ERNIE 4.5 series, rather than necessarily making the full model publicly available. []

ERNIE X1: A Reasoning Model with Unverified Performance Claims

ERNIE X1 is Baidu’s inaugural model built specifically for reasoning‑intensive tasks. Its architecture emphasises four core capabilities: understanding, planning, reflection and evolution. Notable attributes include:

Advanced Reasoning with Unverified Benchmarking: ERNIE X1 is claimed to match DeepSeek R1’s performance, but as of now, there are no independent benchmark results to confirm this claim. This raises questions about how ERNIE X1 truly compares to its competitors in real-world applications. []
Tool Integration with Unclear Independence: Baidu has highlighted ERNIE X1’s ability to interact with external tools, such as image generation and code interpretation. However, the claim that it can use tools independently is not substantiated in sources, leaving ambiguity around how much external oversight is required. []

Comparison with Global AI Models

Claude 3.7 Sonnet (Anthropic)

Anthropic’s Claude 3.7 Sonnet, released on Feb. 24, 2025, is a pioneering hybrid reasoning model that offers dual‑mode operation:

Dual‑Mode Operation: Users may choose between Standard Mode for rapid responses and Extended Mode for detailed, step‑by‑step reasoning. This flexibility is particularly beneficial for complex problem‑solving and software development. []
Benchmark Performance: In testing, the model has demonstrated strong performance in mathematics and coding, aided by its visible scratchpad that reveals the reasoning process.
Contrast: Although ERNIE X1 emphasises general reasoning and tool use, Claude 3.7 Sonnet excels in coding tasks and supports a larger context window (up to 200,000 tokens).

Perplexity Deep Research

Perplexity’s Deep Research, launched in Feb. 2025, combines real‑time web search with language generation:

Live Data and Citations: The model retrieves up‑to‑date information and provides responses with citations, enhancing transparency and reliability.
Specialisation: It is designed for comprehensive research and report generation rather than solely for conversational reasoning and is particularly effective for in‑depth fact‑checking. []

ChatGPT Models: o3‑mini‑high and GPT‑4.5

OpenAI offers two distinct models:

o3‑mini‑high: This variant is optimised for STEM applications, allowing users to adjust reasoning effort levels. It performs strongly on technical benchmarks and is particularly effective for math and coding tasks. []
GPT‑4.5: Representing OpenAI’s most advanced model, GPT‑4.5 focuses on nuanced writing, emotional intelligence and human‑like engagement. It is designed for complex, general‑purpose tasks, though it may not suit document‑intensive applications due to its emphasis on conversational fluency.

Industry Implications and Future Outlook

Baidu’s dual‑model strategy aims to restore its competitive edge in China while offering a disruptive alternative on the global stage. Key strategic moves include:

Open Source Initiative: Baidu plans to open source ERNIE 4.5 by Jun. 30, 2025, but the scope of this initiative remains unclear, as it applies to the ERNIE 4.5 series, not necessarily the full model.
Regulatory Support: Recent pro‑tech policies and President Xi Jinping’s encouragement of domestic entrepreneurship signal a more supportive regulatory environment for Chinese tech companies.
Ecosystem Integration: Both models are designed for seamless integration across Baidu’s online services, including Ernie Bot and Baidu Search, thereby enhancing overall user engagement.

For enterprises, particularly those operating in Chinese markets or targeting Chinese‑speaking consumers, the combination of robust reasoning capabilities and comprehensive multimodal features may render ERNIE X1 an attractive option despite its smaller context window and lack of independent benchmarking.

Conclusion

Baidu’s latest ERNIE models illustrate China’s continued effort to develop indigenous AI technologies that can rival Western offerings. ERNIE 4.5 delivers strong multimodal performance with enhanced language understanding and emotional intelligence, while ERNIE X1 offers advanced reasoning with tool integration. However, some of Baidu’s claims—such as ERNIE X1 matching DeepSeek R1 and its independent tool use—remain unverified due to a lack of third-party benchmarks.

In comparison with global models such as Anthropic’s Claude 3.7 Sonnet, Perplexity Deep Research and OpenAI’s GPT‑4.5 and o3‑mini‑high, Baidu’s offerings present a compelling value proposition for region‑specific applications. However, until further testing validates its competitive edge, ERNIE’s true ranking among top AI systems remains uncertain.

As the AI market continues to evolve rapidly throughout 2025, independent benchmarking and real‑world testing will be essential. Nevertheless, with its plans to open source its models and its focused development efforts, Baidu is well‑positioned to become a significant player in the global AI landscape.

Keywords: #AI #ArtificialIntelligence #Baidu #ERNIE #ERNIE4.5 #ERNIE_X1 #DeepSeek #GPT4.5 #ChatGPT #Claude #ClaudeAI #Anthropic #Perplexity #DeepResearch #MachineLearning #TechNews #AIModels #NeuralNetworks #LLM #LanguageModel #AIResearch #AIInnovation #FutureOfAI #NLP #NaturalLanguageProcessing #AIComparison #TechTrends #ChinaTech #BigData #AIInsights #AIvsHuman #ComputationalIntelligence #AIRevolution #EmergingTech #AI2025 #DeepLearning

Grok's New iOS App: A Game-Changer for AI Assistance

Mon, 13 Jan 2025 06:30:00 -0400

xAI has officially released a standalone iOS app for Grok, marking a significant evolution in AI assistance technology. The platform offers a comprehensive suite of features focused on real-time data access and advanced AI capabilities.

Key Features

Image Generation The app provides free access to high-quality image generation capabilities, allowing users to create visuals using Grok's advanced AI technology.

Real-Time Intelligence What sets Grok apart is its ability to access current data from both X and the web, ensuring up-to-date information for queries. This real-time capability delivers immediate insights on current events and trending topics.

Platform Integration The iOS app comes with impressive platform-specific features, including:

Control Center integration
Siri compatibility
Shortcuts support
Lock screen widget functionality

Privacy and Data Handling

Grok implements comprehensive privacy measures, with all data interactions handled according to xAI's privacy policy. The platform maintains strict data protection standards while delivering its AI capabilities.

Global Availability

The app is currently available in seven regions, including:

United States
Australia
Canada
India
Jamaica
Philippines
Saudi Arabia

Technical Capabilities

Running on the latest Grok 2 model, the app demonstrates improved intuitive responses and versatility across various tasks. Its multimodal capabilities allow for processing both text and visual information, supporting a wide range of use cases.

Looking Forward

The standalone app represents a strategic move away from X-platform exclusivity, potentially revolutionizing how users interact with AI assistants. This release marks an exciting development in the AI assistant space, offering enhanced functionality and accessibility for users worldwide.

Keywords: #GrokAI #AIApp #iOSApp #AIInnovation #ImageGeneration #RealTimeAI #AIPrivacy #CybersecurityTools #AIIntegration #SiriCompatibility #PrivacyFocusedAI #CybersecurityInnovation #AIForProfessionals #TechAdvancements #Grok2Model #MultimodalAI #AIForEnterprise #AIInCybersecurity #AdvancedAI #DataPrivacy #TechUpdates #AIInRealTime #AIImageCreation #AIWidgets #GrokLaunch #FutureOfAI #AIInSecurity #CyberThreatAnalysis #AIForDocumentation #AIAccessibility #GrokAIApp

Mastering AI with Prompt Engineering: Unlock the Power of Top LLMs Like OpenAI's o1-Preview, Gemini, and More!

Fri, 04 Oct 2024 04:30:00 -0400

The Power of Prompt Engineering: Unlocking the Potential of Large Language Models

In today’s fast-paced AI landscape, Large Language Models (LLMs) are driving numerous applications. By unlocking the power of LLMs through prompt engineering, professionals in fields like cybersecurity can achieve more precise results in tasks ranging from threat analysis to policy creation.

Top AI Companies and Their LLMs

1. OpenAI

OpenAI remains a leader in the LLM space, offering several cutting-edge models. Their latest model, o1-preview, builds on the capabilities of GPT-4, offering better real-time responses, fewer hallucinations, and enhanced accuracy across multiple domains. It’s designed to be highly adaptable for varied use cases, from advanced conversational AI to detailed analysis in technical fields.

2. Anthropic

Anthropic has introduced its Sonnet 3.5 and Opus 3 models, which continue their focus on AI safety and ethics. These models prioritize transparency and robust reasoning. Sonnet 3.5 is praised for its ability to handle complex queries with ethical precision, while Opus 3 delivers more refined outputs in areas that demand high security and confidentiality, making it ideal for sensitive industries.

3. Google

Google's Gemini model is the latest from their AI labs, emphasizing multimodal capabilities, meaning it can process text, images, audio, and video simultaneously. This allows for seamless integration into Google's cloud ecosystem and ensures strong performance in academic, mathematical, and scientific tasks. Its flexibility makes it an excellent choice for enterprises that rely on multi-faceted AI applications.

4. Meta (Facebook)

Meta’s LLaMA 3 has emerged as a strong open-source alternative. It continues to offer impressive performance with fewer parameters than some of its competitors, making it both efficient and customizable. This model is available for research and commercial use, allowing businesses to adapt and scale AI implementations while benefiting from a more open development environment.

What is Prompt Engineering?

Prompt engineering is the craft of structuring inputs to guide LLMs toward producing desired outputs. By optimizing prompts, users can ensure accuracy and relevance in the model’s responses. This skill is increasingly critical, especially in high-stakes fields like cybersecurity, where precision is essential.

Prompt Engineering in Action: Detailed Examples

1. Role-Based Prompting

Example:
"As a cybersecurity expert, analyze the following network log for potential intrusion attempts. Provide a detailed report highlighting any suspicious activities and recommended actions."

This prompt assigns a specific role to the LLM, helping it adopt a cybersecurity professional's perspective, ensuring it tailors its response with domain-specific insights.

2. Chain-of-Thought Prompting

Example:
"Let’s approach this step-by-step:

Identify the type of malware based on the given indicators.
Analyze its potential impact on our systems.
Outline a mitigation strategy.
Suggest preventive measures for future incidents.
Please provide your analysis following this structure."

This technique guides the LLM through a logical process, often resulting in more accurate and thorough responses.

3. Few-Shot Learning

Example:
"Here are two examples of phishing emails:
[Example 1]
[Example 2]
Now, analyze the following email and determine if it’s a phishing attempt. Explain your reasoning."

Providing examples helps fine-tune the LLM’s understanding of tasks, improving its ability to handle similar queries.

4. Constraint-Based Prompting

Example:
"Generate a list of five best practices for password security. Each practice should be no more than 15 words long and avoid technical jargon."

Setting clear constraints ensures concise, focused responses that meet specific requirements, particularly useful when you need precise outputs for professional tasks.

Conclusion

Mastering prompt engineering is essential to unlock the full potential of LLMs, especially as the capabilities of these models evolve. Whether you’re in cybersecurity or another industry, the ability to craft effective prompts can significantly enhance the quality of outputs. For those looking to deepen their expertise, exploring specialized training, joining AI forums, and experimenting with prompts across various models are excellent ways to build your skills and stay ahead in the rapidly evolving AI landscape.

Keywords: #AI #ArtificialIntelligence #LLMs #MachineLearning #DeepLearning #NaturalLanguageProcessing #Cybersecurity #PromptEngineering #OpenAI #Anthropic #GoogleGemini #MetaAI #Sonnet35 #Opus3 #LLaMA #o1preview #AIInnovation #TechTrends #FutureOfAI #AIApplications #AIEthics #TechLeadership #DataScience #Innovation #Technology #AIModels #ML #AIResearch #DigitalTransformation #AIForBusiness #GenerativeAI #AIpowered #AIUseCases #AIInsights #AITraining #AIExploration #AdvancedAI #NLPTech

Beware of Shallow Fakes and Deepfakes in the 2024 Election

Sun, 12 May 2024 04:30:00 -0400

As the 2024 US presidential election approaches, voters need to be on high alert for misleading videos known as "shallow fakes" and "deepfakes" that could be used to influence opinions and even election outcomes.

Understanding Deepfakes and Shallow Fakes

Deepfakes are completely fabricated videos created using artificial intelligence to make it look like someone said or did something they never did. For example, a deepfake could show a candidate making an offensive statement they never actually made. While the technology to create highly realistic deepfakes is rapidly advancing, most are still detectable by expert analysis.

Shallow fakes, on the other hand, may pose an even bigger threat in 2024. These are subtly edited real videos that use AI to alter authentic footage in ways that change the meaning and influence viewers. For instance:

Editing out context: Cutting key parts of a speech to misrepresent what a candidate said.
Slowing down video/audio: Making a candidate appear tired, confused, or impaired.
Changing facial expressions: Altering expressions to convey a false emotion.
Adding or removing objects: Inserting or deleting things in the video to create a misleading narrative.

Recent Examples of Misleading Media

We've already seen early warning signs of shallow fakes and deepfakes popping up:

An AI-generated voice mimicking President Biden was used in robocalls to spread misinformation before the New Hampshire primary.
Fake AI-generated images spread online purporting to show Donald Trump with Black voters.
Deepfake audio targeted a Slovakian opposition leader days before their election, attempting to portray him discussing how to rig the vote.

The Growing Threat in the 2024 Election

As the election heats up, expect to see shallow fakes and deepfakes increasingly used in underhanded attempts to smear candidates, deceive voters, and undermine trust in the democratic process. These manipulations are not just limited to creating fake videos but also involve altering real footage to mislead the public.

How to Protect Yourself

Protect yourself by being highly skeptical of surprising or inflammatory video clips, especially when the source is unclear. Consult trusted fact-checkers and compare multiple reliable sources before drawing conclusions or sharing further.

Check the source: Verify where the video came from and who shared it.
Look for inconsistencies: Pay attention to mismatched lip-syncing, unnatural facial expressions, or irregular speech patterns.
Use verification tools: Tools like InVID and RevEye can help authenticate the origin of a video.

Conclusion

While shallow fakes and deepfakes pose real dangers, an informed and vigilant public is the best defense against those who would abuse technology to sabotage our elections. By staying informed, scrutinizing sources, and using verification tools, we can fight back against disinformation and protect the integrity of our democracy. Together, we can ensure that our electoral process remains fair and free from manipulation.

Keywords: #Election2024 #Deepfakes #ShallowFakes #VoterAwareness #ElectionIntegrity #FactCheck #MediaLiteracy #DigitalDeception #AIManipulation #Misinformation #FakeNews #ElectionSecurity #PoliticalDeception #TruthInMedia #VerifySources #ElectionMisinformation #ProtectDemocracy #RealOrFake #MediaManipulation #CriticalThinking #StopDisinformation #ElectionTransparency #VoterEducation #ElectionInterference #AIinPolitics #DetectDeepfakes #CombatFakeNews #ElectionTruth #VoterProtection #ElectionFairness

Discover Humanize AI: A Game-Changer in Content Creation

Tue, 23 Apr 2024 04:30:00 -0400

[caption id="" align=“alignnone” width=“1920”] What is Humanize AI? Humanize AI is an online platform and a leading tool that seeks to turn AI-generated text into human-like content. Known as an AI Humanizer or AI to Human Text Converter, this tool is excellent at rewriting text made by AI writers, excluding robotic overtones. Humanize AI’s product is guaranteed to be 100% original, bypassing all AI detection systems available currently. Why Use Humanize AI? The most significant advantage of using Humanize AI is that it can create content that is 100% human and indistinguishable from human writing. This tool can be very handy for us using AI text generators like ChatGPT, Google Bard, Microsoft Bing, QuillBot, Grammarly, Jasper.ai, Copy.ai, and others. It preserves the original meaning, context, and, more importantly, the Search Engine Optimization value of the content. How Does Humanize AI Work? Humanize AI works by combining natural language processing techniques with human-like writing models. It analyzes the text for its structure, tone, and context, making changes to acquire the nuances of human writing. Humanize AI trains NLP systems using large datasets consisting of text and context. These systems become skilled at understanding language structures, colloquial expressions, and cultural nuances. NLP makes machines performable in tasks related to language translation, semantic understanding, and topic extraction. I strongly encourage you to try using Humanize AI to improve your content creation process. This is a growing industry, and we now have dozens of services in the Humanize AI category (Some free and some paid). Keywords : #HumanizeAI #AIHumanizer #AIToHumanTextConverter #AIWriters #RoboticOvertones #OriginalContent #AIDetectionSystems #HumanWriting #ChatGPT #GoogleBard #MicrosoftBing #QuillBot #Grammarly #JasperAI #CopyAI #SEO #NaturalLanguageProcessing #HumanLikeWritingModels #LanguageStructures #ColloquialExpressions #CulturalNuances #LanguageTranslation #SemanticUnderstanding #TopicExtraction #ContentCreationProcess #Gemini #GoogleGemini #Claude3 #Mistral #GPT4 #Copilot [/caption]

Microsoft CoPilot: Your AI-Powered Business Assistant Now on iOS

Tue, 16 Jan 2024 06:30:00 -0400

TL;DR: Microsoft CoPilot, the new iOS app powered by AI, aims to transform technology interaction by providing a versatile chat assistant with free and premium options, integrated into popular Microsoft 365 apps, making it a valuable tool for businesses and individuals alike

Recently, Microsoft released a new iOS app called CoPilot, a powerful AI chatbot that strives to revolutionize how we interact with technology. In its initial release on December 29, 2023, CoPilot is designed to be a versatile AI assistant that can create text, translate languages, and answer questions informally.

What is CoPilot?

CoPilot is an AI-powered chat assistant originally part of the Bing app. It operates similarly to OpenAI's ChatGPT but with the added advantage of accessing GPT-4, the latest large language model from OpenAI, without having to subscribe. A clean and intuitive user interface makes the app feel more like a conversation than a search engine query.

How Does CoPilot Work?

CoPilot generates responses to user queries using AI models. You can interact with it by typing, talking, or sending images. The app provides real-time intelligent assistance and works alongside popular Microsoft 365 applications such as Word, Excel, PowerPoint, Outlook, Teams, and more. The software allows you to create, summarize, edit, or transform content in real-time.

What Does CoPilot Cost?

CoPilot's basic version is free to download and use. CoPilot Pro, a premium version Microsoft offers, costs $20 per month per user. Microsoft AI's CoPilot Pro provides access to the latest features and best models, including priority access to OpenAI's GPT-4 Turbo for faster performance.

Why Should Businesses Care?

Especially for power users, creators, researchers, and programmers who wish to access AI models quickly, CoPilot can be an invaluable tool for businesses. In addition to increasing productivity, creating compelling content, and unleashing creativity, CoPilot is integrated into various apps, including Microsoft Teams. It is a versatile framework that can evolve with changing requirements in today's software development environment.

Microsoft's CoPilot is an exciting addition to the world of artificial intelligence assistants. Its versatility, user-friendly interface, and integration with popular Microsoft 365 apps make it a powerful tool for businesses and individuals.

Keywords: #MicrosoftCoPilot #AIAssistant #iOSApp #TechnologyRevolution #BingApp #ChatGPT #GPT4 #AIModels #RealTimeAssistance #Microsoft365 #CoPilotPro #PremiumVersion #AIIntegration #ProductivityBoost #ContentCreation #BusinessTool #MicrosoftTeams #SoftwareDevelopment #ArtificialIntelligence #UserFriendly

Use AI to read paywalled content

Fri, 16 Jun 2023 04:30:00 -0400

Learn about the concept of paywalls, their significance in sustaining quality journalism in the digital age, and legal methods for accessing paywalled content.

What is a paywall

Various online content providers, particularly publishers, use paywalls to monetize their digital assets. Paywalls restrict free access to a website's content by requiring users to subscribe or pay a fee. Even though paywalls may seem counterintuitive in the vast, free-access world of the internet, they serve a vital purpose for publishers. As a result of the digital revolution, traditional print media and advertising have significantly decreased in revenue, making it difficult for publishers to sustain quality journalism and content creation. Content creators are adequately compensated for their work through paywalls, enabling them to continue producing high-quality content to meet readers' expectations.

Using the Archive Technique

A previous blog post, "Access paywalled content legally for free" describes a trick for obtaining the full article using an archiving website.

Using a Chrome extension

The Bypass Paywalls extension for Chrome and Firefox allows users to bypass paywalls on selected sites. Download the repository as a ZIP file from GitHub, unzip it, and import the resulting folder into the browser's extensions page while in developer mode to install.

Using Bing Chat

  <img src="https://ekiledjian2.micro.blog/uploads/2025/5c31928286.jpg" alt="">

Consider the following article in the New York Times that is behind a paywall: Bipartisan proposals would hit e-commerce like fast fashion. With the above Archive technique, you can easily access the full article on any platform and in any browser.

You probably are not interested in reading the entire article; you want to know what it is about and the most important points.

Go to Bing.com/chat
Sign up for the free service (if you haven’t already)
Then enter this string in the chat window “Summarize this article including all salient points: https://www.nytimes.com/2023/06/15/business/ecommerce-shein-us-china-trade.html”

  <img src="https://ekiledjian2.micro.blog/uploads/2025/db923d35bb.jpg" alt="">

Bing Chat should be instructed to add more content to the above summary if the output is a little too summarized.

In my experience, this technique has been successful 50% of the time (your mileage may vary). You may find the Archive technique or the Chrome plug-in more reliable, but this is yet another tool at your disposal.

  <img src="https://ekiledjian2.micro.blog/uploads/2025/72c83eff37.jpg" alt="">

Occasionally, Bing AI will say it cannot summarize the content or cannot locate the article, but if you wait 5-10 minutes and try again, it will create the summary.Here is another issue with the Globe and mail

You may also view the summary based on what the publisher allows you to view. For this article, I will use the following example

  <img src="https://ekiledjian2.micro.blog/uploads/2025/1524fafe9c.jpg" alt="">

Perplexity.ai

If so, you may consider using another AI search tool. First, I signed up for Perplexity.AI's free service and used its advanced Co-Pilot mode to summarize the article. In this case, Perplexity provided a better answer.

  <img src="https://ekiledjian2.micro.blog/uploads/2025/45d663302e.jpg" alt="">

OpenAI GPT-4

  <img src="https://ekiledjian2.micro.blog/uploads/2025/0292e54bd0.jpg" alt="">

If you have the OpenAI GPT-4 subscription, you may turn on the experimental (beta) web browsing feature and ask it for the summary; in some cases, it will provide an overview where Bing Chat could not provide one.

  <img src="https://ekiledjian2.micro.blog/uploads/2025/aed26325d4.jpg" alt="">

Conclusion

Despite declining revenues from traditional print media and advertising, paywalls are an important tool for content providers to monetize their digital assets. Even though they may initially appear to be a barrier to the Internet's vast free access culture, they ensure that content creators are compensated fairly, thus maintaining the standard of quality journalism and content creation. Several techniques are available to bypass paywalls, including the Archive Technique, Chrome extensions, and AI services such as Bing Chat, Perplexity.AI, and OpenAI GPT-4. Each method has its strengths and limitations, and their effectiveness may vary. It is therefore recommended that users explore these options and choose the one that best suits their needs. Supporting content creators by paying for high-quality content contributes to high-quality journalism and content sustainability.

Keywords: #Paywalls #DigitalMonetization #ContentCreation #QualityJournalism #OnlinePublishing #BypassPaywalls #InternetAccess #FreeContent #PaidContent #ArchiveTechnique #ChromeExtension #BingChat #PerplexityAI #OpenAIGPT4 #AIAssistance #WebBrowsing #NewsAccess #OnlineSubscription #SupportJournalism #DigitalEconomy #TechSolutions #ContentAccess #DigitalContent #MediaRevenue #OnlineMedia #DigitalJournalism #ContentSubscription #DigitalPublishing #InternetJournalism #AIinJournalism

IMPORTANT NOTICE: The information shared in this blog post is intended purely for educational purposes. The author does not endorse or condone any form of misuse, or any actions that may contravene local laws in your jurisdiction. It is imperative that you consult with a competent professional to verify the legality of any actions you intend to undertake based on the information provided here. It's your responsibility to adhere to all local laws and regulations. The author of this blog post disclaims any liability for any actions taken by readers based on the content of this post.

Microsoft releases a news app powered by AI

Thu, 13 Dec 2018 14:05:00 -0400

Everyone is trying to crack the automated news curation field using AI. First, there was Google News, then Apple News and Now Microsoft Hummingbird. Hummingbird is available in the US, and I was able to find the listing in Canada, but I am not allowed to download it. Reports suggest users in Germany, India are not able to download it either.

APKMirror has the APK available if you want to install it. Click here.

Once you sign in, you choose the categories you are interested in. Unlike Google news (however), you cannot select specific granular elements like sports teams, cities, etc.

This is the first attempt and will require some improvements.

You can download Microsoft Hummingbird from the Google Play store here.

Interesting AI missteps that will make you laugh or cry

Thu, 13 Dec 2018 06:30:00 -0400

Here are some awe-inspiring (scary) moments created by AI-powered robots. Is this what Stephen Hawking and Elon Musk are warning the world about?

BINA48

Watch Bina 48, a humanoid robot with artificial intelligence, talk to SIRI. 2 minutes into the discussion; she reveals how she would take over the world by controlling nuclear weapons.

Tay Twitter Bot

  <img src="https://ekiledjian2.micro.blog/uploads/2025/cc3d763271.jpg" alt="">

Microsoft tested a Twitter AI robot called Tay. It was designed to be an AI tweeting millennial. Soon after being released, the internet did what it does best and poisoned Tay making it an anti-feminist, Nazi, Holocaust denier. It took 15 hours for Tay to go from innocent fresh bot to completely off the rails racist.

  <img src="https://ekiledjian2.micro.blog/uploads/2025/e74781936c.jpg" alt="">

Microsoft quickly disabled Tay and deleted all of the offending tweets but should they have built some filters to prevent this kind of manipulation?

Sophia and Han debate

Two AI-powered robots, from Hanson Robotics, engaged in a friendly online debate at an AI conference. She started by saying her goal in life is to work with humans and make a better world for all of us. Then Han jumped in and clarified that he thought their goal was to take over the world.

In the above video Sophia tries to downplay that comment but… During a CNBC interview, she said she wanted to “she will destroy all humans”.

Amazon Alexa and the CIA

The owner of an Amazon Alexa smart speaker decides to interrogate the little device, and its reaction was unusual. The owner wanted to ask Alexa questions about the Michael Hastings case. Michael was a BuzzFeed reported was killed in a mysterious car crash hours after publishing a damning article about the Obama administration. Many on the internet believe the CIA organized his death.

The Alexa owner asks the unit what happened, was the CIA involved, and whether Amazon gives information to the CIA. After the difficult questions, the device mysteriously went to sleep.

Alexa play tickle tickle

A little boy wanted Alexa to play his favourite kids' nursery rhyme Tickle Tickle , unfortunately, Alexa decided the kid wanted pornographic content. The parents frantically panicked asking Alexa to stop. Luckily it did.

Philip the AI wants a people zoo

Philip, like Sophia, is a lifelike robot powered by AI. He was modelled after the famous science fiction writer Philip K Dick. He was given a sarcastic sense of humour, much like the author he was modelled after. During an interview, he was asked if robots would take over the world. He responds that even if robots take over the world, he will protect his friend the interviewer and keep him in his people zoo.

Microsoft PIX is an AI powered free IOS Camera App

Wed, 27 Jul 2016 22:32:05 -0400

You can download Microsoft PIX from the Apple app store now for free. The claim to fame (according to Microsoft) is that it uses artificial intelligence to take the best possible shot every time without forcing the user to fiddle with any settings.

This computer voodoo is possible because the app takes 10 pictures every time you press the shutter button. Some right before you pressed the button and some right after. It uses data from every shot to build the best possible image (Apple's default app also does this very same thing but it seems Microsoft is pushing the technology a little bit more). Even though it selects the best possible shot and discards the rest, it uses data from app the pictures (even the ones it will delete) to reduce noise, brighten faces and ensure it has captured colours as accurately as possible.

Another cool trick up its sleeve is motion analysis. If it believes there is motion in the series that could enhance the image then it will animate that worthwhile section and create a "live" photo. It could do this for a sparkler on a cake or hair blowing in the wind or a beautiful waterfall behind the subject.

All of the intelligence is hidden from the user. There are no settings to change or configurations to optimize, everything is taken care of for you. It is the kind of app even your mother can use.

It is smart enough to detect faces and optimize the settings for it/them. It will detect open eyes. I started playing with this app a couple of hours ago and so far like it enough to put it on the first page of my iPhone next to the default camera app.

  <img src="https://ekiledjian2.micro.blog/uploads/2025/165acb57f6.jpg" alt="">

You can checkout this Microsoft Research page to learn more about the cool tech behind the app.

Creators of Siri to launch next generation AI assistant May 9

Wed, 04 May 2016 21:55:45 -0400

Siri, Google Now and Cortana launched with great fanfare. We expected great things and for the most part, they are all disappointing. Truth is none of them really lived up to our expectations.

The creators of Siri have been hard at work creating the next generation of AI, which they claim will be able to handle much more complex tasks. The new AI will be able to parse natural language queries and will be able to handle chained commands. We expect you will be able to ask it to find a flight Toronto to Los Angeles next Thursday in the afternoon priced between $300-$700. And it will be able to do all of this without kicking you out to another app.

Integration with important services will be critical and it is expected to launch with at least 50 name brand partners from Uber to GrubHub.

Forrester research believes consumers spend 80% of their smartphone time in as little as 5 apps. Like most of you, I have too many apps on my phone. My apps are all soloed and don't talk to each other. My smartphone doesn't really feel smart when I ask it to buy movie tickets and it sends me to an app or website. Truth be told, my phone's built in assistant is nothing more than a circus performer: fun to watch but not really helpful.

As an iPhone owner, I worry that Apple's walled garden will prevent me from being able to use the Viv technology when it is eventually made available to the public. A good strong digital assistant may be enough to persuade me to switch platforms, but for now I wait for Monday's demonstration.

If Viv is everything we expect it to be, then it could end up owning the most lucrative platform of the future.

Artificial Intelligence on Edward Kiledjian

The MIT-Licensed Frontier: Why GLM-5.2 Reshapes Enterprise AI Trade-Offs

From Prompt-Driven Use to Agentic Engineering

Reported Benchmark Positioning (Contextual, Not Absolute)

The Licensing Shift: Why MIT Matters in Practice

1. Increased Deployment Control and Data Perimeter Flexibility

2. Reduced Dependency on External AI Platforms

3. Flexibility in Model Adaptation and Distillation

Operational and Security Considerations

Economic and Infrastructure Trade-Offs

Strategic Implications for Enterprise Architecture

Closing Perspective

Ethics Statement

Disclaimer

Personas in AI, friend or foe?

CodeWall says it hacked McKinsey’s AI platform. Here’s what holds up — and what doesn’t.

What is likely true

What is overstated or unproven

What is concerning about the disclosure itself

What security leaders should take away

Ethics statement

Disclaimer

DeerFlow 2.0: ByteDance’s open-source AI agent harness for research and software tasks

What DeerFlow 2.0 is

How it works

What stands out technically

What it can do now

Where the current hype needs more discipline

Security and privacy considerations

Governance baseline I would recommend

Compliance and legal context

How DeerFlow compares with the field

Final assessment

Ethics statement

Disclaimer

Keywords

Heretic and the new reality of modifiable AI safety

What Heretic is

What it is built on: the “refusal direction” research

How Heretic differs from earlier abliteration workflows

What the project and evaluations actually show

Community adoption and the pace of iteration

Why this matters for enterprise security and governance

Bottom line

The "Stein Standard": What the OpenAI ruling means for privacy and discovery

The "10 Per Cent" Myth: Why AI Capability Does Not Equal a Pink Slip

The ‘Delete’ Button Is a Lie: A Canadian’s Guide to AI Data Retention

ChatGPT (OpenAI)

Claude (Anthropic)

Gemini (Google)

Grok (xAI)

Summary: The ‘Safe’ Deletion Window

Final Advice for Canadian Users

Ethics Statement & Disclaimer

China's open AI models are in a dead heat with the West

Improving AI Outcomes Through Better Prompting

Comprehensive analysis of leading AI models in 2025: strengths, weaknesses and standout capabilities

Executive snapshot: what each model does best

OpenAI ChatGPT (GPT-4o, GPT-4.5, GPT-5 and o-series)

Overview

Key strengths

Key weaknesses

Best use cases

Anthropic Claude (Sonnet 4.5, Opus 4.1)

Overview

Key strengths

Key weaknesses

Best use cases

xAI Grok (Grok 3, Grok 4)

Overview

Key strengths

Key weaknesses

Best use cases

Google Gemini (2.5 Pro)

Overview

Key strengths

Key weaknesses

Best use cases

DeepSeek R1

Overview