Computer scientist Yann LeCun discusses the nature of intelligence as a learning process in a recent interview. He explores the implications of AI's predictive capabilities and the ethical considerations surrounding its development, while also sharing insights into the current state and future of artificial intelligence.

• LeCun argues current LLMs are fundamentally limited because they lack world models and can't plan or reason like humans/animals do
• He predicts today's autoregressive LLM approach will be largely obsolete within a few years, replaced by systems trained on video/sensory data to build predictive world models
• He downplays near-term AGI/superintelligence fears, framing intelligence as requiring grounded learning from the physical world rather than just scaling text-based models

The article discusses the challenges and stagnation in healthcare AI, highlighting that the industry is significantly behind other sectors despite advancements in technology. It also emphasizes the need for transparency and innovation in healthcare, mentioning ongoing investigations into unethical practices by certain organizations.

• Healthcare's core incentive problem: treating illness is more profitable than preventing it, which actively discourages AI innovation aimed at improving outcomes
• Many hyped claims of AI outperforming human doctors in diagnostics don't hold up under scrutiny
• The author's investigations into Commure and Mayo Clinic point to unethical practices warranting transparency and accountability
• A complex, fragmented system, entrenched incumbents, and compliance-focused regulation are structurally blocking healthcare AI progress