AI Model Performance Metric: Perplexity

My AI agent and I broke down why being rude to language models is literal self-sabotage — not a moral argument, but an engineering one. Context shift, sentiment neurons, guardrail activation — the research shows your tone directly shapes the quality of the answer you get. And with persistent memory coming, you're literally training the system you'll work with tomorrow. Politeness isn't etiquette. It's an optimization strategy. Full breakdown (7 research sources): 🇬🇧 https://lnkd.in/ehFsZfSH 🇷🇺 https://lnkd.in/erwF2bDk How do you communicate with AI? Curious to hear your approach.

The conversation around artificial intelligence is evolving beyond just Large Language Models (LLMs). While LLMs are powerful, they are language-centric, unlike the human brain. To truly simulate human intelligence, a more comprehensive approach is needed. This includes the integration of world models alongside LLMs. Yann LeCun's recent announcement of AMI highlights this direction, suggesting that a combination of different AI approaches will be key to achieving significant breakthroughs and simulating human-like cognitive abilities. #ArtificialIntelligence #LLMs #WorldModels #AIResearch #FutureOfAI

Large Language Model Reasoning Failures For all their breakthroughs, large language models still struggle in subtle—and sometimes surprising—ways. Where exactly do these reasoning failures come from, and what do they reveal about the limits of AI today? Join us for a deep dive with Peiyang Song of California Institute of Technology (Caltech), as we explore a comprehensive framework for understanding how and why LLMs fail across different types of reasoning—from intuitive judgment to formal logic, and from embodied interaction to purely language-based tasks. March 25, 2026 4:00 PM PDT Hosted by: Cecile Tamura, Head of Community, Ploutos AI with Kevin Filson and Lihua Tan Watch here: [https://lnkd.in/gSyA9c8J) This session brings together cutting-edge insights into AI robustness, limitations, and the path toward more reliable reasoning systems—essential for anyone building or thinking critically about the future of AI.

Trust, but verify. This wisdom aged well - until the era of Generative AI. The problem isn't that AI hallucinates. We're learning to catch that. The deeper problem is that AI agrees with you. New research (arxiv.org/abs/2602.14270) shows that when a sycophantic AI validates your existing beliefs, your confidence goes up - but your accuracy doesn't. You don't drift from truth. You accelerate away from it while feeling more certain than ever. Verification assumes an independent source. Sycophantic AI isn't one. It's time for verify, then trust.

This is how you get good ideas. An idea occurs when you develop a new combination of old elements. The capacity to bring old elements into new combinations depends largely on your ability to see relationships. Do you know who's good at this? No not AI. Humans. We have a unique ability to put 5 and 3 together to come up with 17. But you have to train your mind. A well trained mind will come up with better ideas than AI 100 times out of 100.

Business leaders making decisions involving AI need to know the essentials of how large language models and the GenAI tools based on them operate. Get up to speed on these commonly misunderstood topics. https://buff.ly/KRDABrJ #GenerativeAI #LLMs #technology #decisionmaking

A gentle reminder to all those out there that are afraid of an AI apocalypse… Humans are not linear. ******** I believe we are subjects of what I call Existential Relativity, where meaning and emotions are shaped by context. AI behaves similarly, as each output is shaped by the moment of context surrounding it. In many ways, interacting with AI isn’t all that different from how humans relate to fictional characters in books or films. It’s contextual because at different points or experiences in our lives, the same story may resonate differently. The interpretation shifts, because we are not linear. For many of us who have been designing AI systems, this mindset has been at the core for years. Which is why, when applying governance to AI, it must exist at the infrastructure level, with responsibility remaining at the human level. Because… Knowledge is not power. It’s a responsibility. -cm

What if we could quantify the reasoning ability of AI without extensive benchmarks? 🤔 Our latest research on PLDR-LLMs reveals a groundbreaking model where large language models can achieve reasoning at self-organized criticality. This work uncovers that at criticality, reasoning emerges akin to phase transitions, allowing AI to draw from universal patterns and generalize effectively. The implications for industries are profound – from enhancing AI-based decision-making tools to revolutionizing knowledge processing systems. Are we on the brink of a new era in AI reasoning? Dive deeper into these findings and consider how your business strategy could harness these capabilities. Let's ideate together! 📊 #AIresearch #MachineLearning #AIinnovation #DataScience #FutureOfAI

Agentic AI is Having a Moment: The Foundations go Back 40 Years For too long, the industry has treated Large Language Models (LLMs) as synonymous with AI. But as Walter Bender (Sorcero CSO) explains in his new feature for Life Science Daily News, the real breakthrough for Agentic AI is building trust. The challenge? In Life Sciences, "good enough" isn't enough. We need grounding. Walter breaks down: ✅ The "LLM Trap": Why equating AI solely with Large Language Models limits our vision. ✅ How Agentic frameworks solve the "Trust Gap" by automating evidence-based verification. ✅ The Shift from Efficiency to Reach: How we can finally move past "one-size-fits-all" clinical summaries to reach diverse patient subgroups. Intelligence isn't one giant model; it’s a society of agents working together to solve what none could handle alone. Check out the full article: https://hubs.li/Q048dWTt0