PolyManthanein

Artificial Intelligence could radically reshape geopolitics, according to a new report from Tortoise Intelligence. weforum.org/agenda/2020/02…

A third of Britons used AI for emotional purposes last year. Some even “married” chatbots. If adversaries exploit this social fabric, AI becomes not just a cyber weapon, but a tool of influence ops.

Britain’s AI Security Institute is stress‑testing Anthropic’s “Mythos” tool. The finding? Every system tested had exploitable vulnerabilities. In geopolitics, that’s not just a tech flaw, it’s a national security gap.

Tim Cook steps down as Apple CEO after 1,000% growth; John Ternus named successor investing.com/news/stock-mar…

GenAI isn’t built for forecasting, planning, optimization, or decision intelligence. ML and optimization often deliver more reliable, precise results. Reasoning models and AI agents expand GenAI’s role in autonomy, but traditional methods still lead in planning.

CIO Priorities -Standardize use case prioritization with clear criteria. -Build a high-value AI portfolio aligned to business outcomes. -Define and enforce ROI and performance metrics. -Deploy, track, and automate value capture. -Continuously refine AI systems with feedback loops

AI approaches can be blended for stronger results: ML + GenAI → segmentation, synthetic data, computer vision. Optimization + GenAI → enterprise search. Simulation + GenAI → faster modeling. Graphs + GenAI → knowledge management. Rules + GenAI → chatbots, robo-advisors.

Alternatives to Consider: - Established: nongenerative ML, optimization, simulation, rules/heuristics, knowledge graphs. - Emerging: causal AI, neuro-symbolic AI, first-principles AI. These are often cheaper, less risky, and easier to understand.

- Hardly useful: prediction/forecasting, planning, decision intelligence. - Poor fit if risks (unreliable outputs, privacy, IP, liability, cybersecurity, compliance) cannot be mitigated.

Where GenAI Fits (and Doesn’t) - Highly useful: content generation, conversational interfaces, knowledge discovery. - Somewhat useful: segmentation/classification, recommendations, perception, automation, anomaly detection, autonomous systems.

- Overreliance on generative AI can diminish organizational value. - Hype often leads to overlooking whether GenAI is the right fit, causing complexity and missed opportunities. - GenAI is only one part of the broader AI landscape; most problems need multiple techniques.

Governments shouldn’t just manage the aftermath of AI-driven displacement (e.g., retraining programs). They need to reshape the competitive incentives that drive over-automation in the first place.

Only a targeted tax on automation can internalize the externality—forcing firms to weigh social costs against private gains.

Adjusting wages, offering equity, UBI, or retraining doesn’t break the cycle. These are downstream fixes, not upstream interventions.

Displaced workers mean fewer consumers. That shrinks the very market firms rely on, hurting both labor and capital owners.

Firms automate because rivals do, not because it’s socially optimal. Even if managers know that mass layoffs erode consumer demand, they can’t afford to stop—competition forces their hand.

5/5 Manage and Secure Data for Agentic Workflows Actively: Shift to continuous, automated, code-driven governance to mitigate the increased risk of data misuse, leakage, and manipulation introduced by autonomous systems.

4/5 Ensure Contextual Clarity and Data Provenance: Enhance metadata management, data lineage, and use knowledge graphs to help agents interpret data meaning and trustworthiness.

The 5-Step Data Readiness Checklist: 1/5 Conduct Data Readiness Checks for Each Agent: Implement rigorous, use-case-specific checks to prevent agents from acting on stale or incomplete data.