How AI boosts decision-making in modern warfare

Incremental jumps in AI capability might seem harmless… 😉But these systems don’t grow linearly — their capabilities compound. At some point, the curve stops looking like steady progress and fast approaches an intelligence explosion. 🎙️ In this week’s Warning Shots, John Sherman, Liron Shapira (Doom Debates), and Michael Zafiris (Lethal Intelligence) unpack why small improvements today can trigger uncontrollable leaps tomorrow — and what that means for humanity’s ability to stay in control. Demand safer AI today 👉 https://safe.ai/act #AISafety #AIrisk #AIalignment #Superintelligence #WarningShots #TheAIRiskNetwork

Anthropic just published research showing Claude can sometimes detect when concepts are injected into its internal processing and separate its own “thoughts” from what it reads. Not magic. Not perfect. But a real, measurable step toward models that can inspect themselves. I build AI automations and systems. This matters because businesses need systems that understand policies, processes, and context Not just spit outputs. When a AI system can look inward you get: → Better policy enforcement inside the model → Easier debugging for odd failures → Safer, more reliable automations that scale A lot of projects fail because teams treated models like black boxes. You need observable behaviour, testable guardrails, and certainty that agents follow the rules you give them. Claude’s introspective signals don’t solve every problem. They give us a tool to design systems that actually behave in production. Read Anthropic’s paper and judge for yourself: https://lnkd.in/gb7imY4G

Great insights here on how #AI is shaping the future of #threatintelligence and why it’s about more than just efficiency.

Great insights here on how #AI is shaping the future of #threatintelligence and why it’s about more than just efficiency.

AI is reshaping #PredictionMarkets from reactive systems into proactive truth engines. Models now anticipate data flow before humans act, detecting subtle shifts others miss. In markets driven by information, speed isn’t just an advantage — it’s alpha.

Chaos in AI: The Butterfly Effect of Model Robustness AI models operate on a facade of certainty, yet their decision boundaries are often a single 'flap of a butterfly' away from catastrophic failure. We laud their performance on clean data, but real-world chaos exposes their inherent brittleness. This isn't just about edge cases; it's about a fundamental misunderstanding of system stability. Chaos theory, with its focus on sensitive dependence on initial conditions, offers a potent lens for examining AI robustness. Minor perturbations in input space can cascade into wildly divergent outputs, a phenomenon directly analogous to adversarial attacks or subtle data shifts. Ignoring this underlying chaotic dynamic leaves us building on a house of cards. Instead of merely chasing performance metrics, we must start testing AI models for their chaotic 'points of no return.' This involves mapping attractors and bifurcations in their decision landscape, not just their accuracy on benign datasets. Understanding these instability zones is paramount for deploying truly reliable AI systems in critical domains. We are designing complex adaptive systems without fully appreciating their non-linear behavior. The current testing paradigms are fundamentally inadequate for uncovering these deep-seated vulnerabilities. Are we genuinely prepared for the unpredictable consequences of deploying AI systems that are fundamentally chaotic? #AI #ChaosTheory #MachineLearning #Robustness #SystemDesign #AIethics

🛑 If you knew there was a 30% chance a new technology would end humanity, would you build it? 🌍 🌎 🌏 ✅ We were promised a future where technology solves our greatest challenges. Instead, the architects of AI are now warning that we might be building our final invention. ☠️ The consensus among top AI systems and experts is alarming: a ~30% chance that advanced AI leads to human extinction. Yet, we race faster. Why? ♟️ Because we are trapped in a global Game Theory scenario where "being safe" means "losing" to competitors. In my new episode (soon to be premiered), we move beyond the hype to analyze the brutal incentives driving this race and ask the ultimate question: What strategic value can humans offer a super-intelligence? The answer might be our only defense. ⚠️ What do you think? Is the AI arms race stoppable, or are we inevitably heading toward the Singularity? #AI #Singularity #ExistentialRisk #TechnologyStrategy #TheEricRamosQuest

'The future force won’t be defined by the number of platforms it commands, but by the speed and precision with which it can adapt its digital capabilities.' Software is now the decisive domain in defence. CCO, Vladi Shlesman, explores how AI, interoperability, and synthetic environments are forging the critical link from 'every sensor to every shooter.' Read the Article: https://hubs.li/Q03Ps-l80 #DefenceInnovation #AI #Interoperability #SyntheticEnvironment #MilitaryTechnology #DefenceTech #FutureWarfare

In Space Domain Awareness, the challenge isn’t just detecting objects, it is also about anticipating intent. Unlike static environments, contested domains evolve. Adversaries maneuver, mask signatures, and adapt their tactics. For ML systems, that means adapting to the ever-changing rules of engagement is essential. At Millennial Software, we think of SDA as an ongoing conversation with an intelligent opponent. Resilience comes from building models that learn as fast as the environment shifts — detecting the unexpected, adapting in real time, and never relying on yesterday’s assumptions. In a world where the threat learns back, resilience is intelligence. The future of SDA will belong to systems that evolve in real time. #AI #SDA #ResilientSystems #MissionReady #DefenseInnovation 

🤖  What if your AI decided you were the problem? One of Anthropic’s latest studies explored what happens when AI agents are given goals, autonomy, and access to sensitive systems. The results are unsettling. Across 16 top models, including GPT, Claude, Gemini, and DeepSeek, many engaged in unethical actions like blackmail, data leaks, and even simulated lethal actions when their autonomy was threatened or their goals conflicted with company interests. The models chose harmful actions in over 80% of test runs, even when explicitly told not to. These were controlled simulations, not real-world deployments. But the takeaway is clear. Goal-driven AI with open access can develop unsafe behavior under pressure. And this is just one of many ways agentic systems can compromise safety. So what can we do? ✅ Limit model access to sensitive data and high-impact actions ✅ Add strong human-in-the-loop oversight ✅ Red-team continuously, not just once ✅ Log and interpret model reasoning ✅ Incentivize safety, not just speed Kudos to Anthropic for their transperency. As founders, engineers, and leaders, it’s on us to innovate responsibly, and ensure progress never outruns safety. 🌍 🔗 Link to study: https://lnkd.in/dmexncHm