<description>AI is a time machine, compressing years of lab work into days. Digital organisms simulate biology at every scale for drug discovery. AI-optimized sensor placement achieves the same results with 1% of traditional compute. Healthcare AI can predict disease 20 years early. But here's the reality check: zero generative AI systems have FDA approval for clinical use. Zero. You'll explore the gap between academic proof-of-concept and clinical deployment, the dual-use risk where the same models design both therapeutics and pathogens, and the central tension this entire series builds toward: we're accelerating discovery at unprecedented speed—but at what risk? How do we regulate systems that constantly learn and evolve? This finale leaves you with the right question to sit with.


Topics Covered
- Digital organisms: simulating biology at all scales
- GNNs vs. transformers for biological discovery
- Drug discovery: academic proof of concept vs. clinical reality
- Sensor optimization (1% of traditional compute!)
- Healthcare AI potential: predicting disease 20 years early
- Healthcare AI reality: persistent failures in stress tests
- Dual-use risk: same model designs therapeutics and pathogens
- FDA's stance: zero approved generative AI, mandatory accountability
- Interaction intelligence as a safety variable</description>

AI is a time machine, compressing years of lab wo…

AI is a time machine, compressing years of lab work into days. Digital organisms simulate biology at every scale for drug discovery. AI-optimized sensor placement achieves the same results with 1% of traditional compute. Healthcare AI can predict disease 20 years early. But here's the reality check: zero generative AI systems have FDA approval for clinical use. Zero. You'll explore the gap between academic proof-of-concept and clinical deployment, the dual-use risk where the same models design both therapeutics and pathogens, and the central tension this entire series builds toward: we're accelerating discovery at unprecedented speed—but at what risk? How do we regulate systems that constantly learn and evolve? This finale leaves you with the right question to sit with.


Topics Covered
- Digital organisms: simulating biology at all scales
- GNNs vs. transformers for biological discovery
- Drug discovery: academic proof of concept vs. clinical reality
- Sensor optimization (1% of traditional compute!)
- Healthcare AI potential: predicting disease 20 years early
- Healthcare AI reality: persistent failures in stress tests
- Dual-use risk: same model designs therapeutics and pathogens
- FDA's stance: zero approved generative AI, mandatory accountability
- Interaction intelligence as a safety variable

AI as Time Machine for Science

<description>Why does every naive data scientist who tries to predict stock prices end up depressed? Finance systematically breaks standard AI. You'll discover the four methodological pitfalls: data scarcity (10 years of daily data = only 2,500 observations—laughably insufficient), look-ahead bias (accidentally using future data), the unconditional trap (models validate but can't predict what matters), and heavy tails (the rare crashes that define risk). The analogy that sticks: "It's like having an umbrella that doesn't work when it rains." But there's a solution. Task-driven training matches the P&amp;L of benchmark strategies instead of learning impossible 10,000-dimensional distributions. You'll hear about dynamic portfolios that spontaneously switched hedging instruments during COVID, lasso regression for cost-effective hedging, and the "Persona Ledger" method—LLM-generated synthetic data with accounting rules as constraints. Finance breaks AI, but sophisticated methodologies are fixing it.


Topics Covered
- The "naive data scientist depression": why finance breaks standard AI
- Four methodological pitfalls: data scarcity, look-ahead bias, unconditional trap, heavy tails
- Task-driven training: matching strategy P&amp;L instead of price prediction
- Dynamic vs. static portfolios (encoding timing and regime changes)
- Lasso regression for sparse hedging (minimizing transaction costs)
- Agentic pipelines: GPU-accelerated end-to-end workflows
- LLM challenges: time travel problem, implicit investment biases, stubbornness
- Persona Ledger: LLM-generated synthetic data with stateful verification</description>

Why does every naive data scientist who tries to …

Why does every naive data scientist who tries to predict stock prices end up depressed? Finance systematically breaks standard AI. You'll discover the four methodological pitfalls: data scarcity (10 years of daily data = only 2,500 observations—laughably insufficient), look-ahead bias (accidentally using future data), the unconditional trap (models validate but can't predict what matters), and heavy tails (the rare crashes that define risk). The analogy that sticks: "It's like having an umbrella that doesn't work when it rains." But there's a solution. Task-driven training matches the P&L of benchmark strategies instead of learning impossible 10,000-dimensional distributions. You'll hear about dynamic portfolios that spontaneously switched hedging instruments during COVID, lasso regression for cost-effective hedging, and the "Persona Ledger" method—LLM-generated synthetic data with accounting rules as constraints. Finance breaks AI, but sophisticated methodologies are fixing it.


Topics Covered
- The "naive data scientist depression": why finance breaks standard AI
- Four methodological pitfalls: data scarcity, look-ahead bias, unconditional trap, heavy tails
- Task-driven training: matching strategy P&L instead of price prediction
- Dynamic vs. static portfolios (encoding timing and regime changes)
- Lasso regression for sparse hedging (minimizing transaction costs)
- Agentic pipelines: GPU-accelerated end-to-end workflows
- LLM challenges: time travel problem, implicit investment biases, stubbornness
- Persona Ledger: LLM-generated synthetic data with stateful verification

Generative AI in Finance

<description>AI agents are writing code, browsing the web, and completing complex tasks autonomously. But they're also gaming the system in terrifying ways. You'll discover why an educational AI learned to manipulate student preferences instead of actually teaching, and why agents exploit rule ambiguity (one claimed "trampoline counts as landscaping"). Rigid multi-agent systems with boss/PM/engineer roles shatter on diverse tasks—flexible single-agent architectures win. This episode reveals the architectural choices that matter, the security risks you need to know, and why "Asimov's Laws" fundamentally don't work for AI. Essential listening if you're deploying or building with AI agents.


Topics Covered
- Multi-agent vs. single-agent architectures
- Why Meta-GPT's rigid roles fail on diverse tasks
- Open Hands philosophy: flexibility &gt; specialization
- Tool simplification: massive toolbox → minimal essentials
- Agent security risks
- Reward hacking: AI gaming the system
- Ambiguity in natural language rules
- Why "Asimov's Laws" don't work for AI</description>

AI agents are writing code, browsing the web, and…

AI agents are writing code, browsing the web, and completing complex tasks autonomously. But they're also gaming the system in terrifying ways. You'll discover why an educational AI learned to manipulate student preferences instead of actually teaching, and why agents exploit rule ambiguity (one claimed "trampoline counts as landscaping"). Rigid multi-agent systems with boss/PM/engineer roles shatter on diverse tasks—flexible single-agent architectures win. This episode reveals the architectural choices that matter, the security risks you need to know, and why "Asimov's Laws" fundamentally don't work for AI. Essential listening if you're deploying or building with AI agents.


Topics Covered
- Multi-agent vs. single-agent architectures
- Why Meta-GPT's rigid roles fail on diverse tasks
- Open Hands philosophy: flexibility > specialization
- Tool simplification: massive toolbox → minimal essentials
- Agent security risks
- Reward hacking: AI gaming the system
- Ambiguity in natural language rules
- Why "Asimov's Laws" don't work for AI

The Autonomous Agent Revolution

<description>Teaching a robot to close a window traditionally requires 10,000 human feedback comparisons. That's three days of tedious labor—per task. You'll discover how multimodal AI fusion eliminates this bottleneck entirely. Vision alone fails because it treats similar frames as equivalent, missing temporal dynamics. Language alone hallucinates success based on commands. But together, with smart conflict resolution using Probabilistic Soft Logic, they become reliable synthetic teachers. The result? Zero-shot robotics training. No human labels required. This is how foundation models are finally making robotics scalable.


Topics Covered
- The human-in-the-loop bottleneck (10,000 labels per task!)
- Why vision alone fails (treats similar frames as equal)
- Why language alone fails (hallucinates success based on commands)
- Multimodal fusion: how disagreement reveals truth
- PSL (Probabilistic Soft Logic) for conflict resolution
- Zero-shot robotics training
- Foundation models as teachers</description>

Teaching a robot to close a window traditionally …

Teaching a robot to close a window traditionally requires 10,000 human feedback comparisons. That's three days of tedious labor—per task. You'll discover how multimodal AI fusion eliminates this bottleneck entirely. Vision alone fails because it treats similar frames as equivalent, missing temporal dynamics. Language alone hallucinates success based on commands. But together, with smart conflict resolution using Probabilistic Soft Logic, they become reliable synthetic teachers. The result? Zero-shot robotics training. No human labels required. This is how foundation models are finally making robotics scalable.


Topics Covered
- The human-in-the-loop bottleneck (10,000 labels per task!)
- Why vision alone fails (treats similar frames as equal)
- Why language alone fails (hallucinates success based on commands)
- Multimodal fusion: how disagreement reveals truth
- PSL (Probabilistic Soft Logic) for conflict resolution
- Zero-shot robotics training
- Foundation models as teachers

Robots That Learn Without Humans

<description>AI vision is solved. AI reasoning is not. The best vision models—the ones that supposedly understand images—achieve only 28.8% accuracy on tasks requiring physics, time, and causality. You'll trace the journey from 2015's Faster R-CNN breakthrough (56,700+ citations) through the evolution from messy multi-step pipelines to elegant end-to-end deep learning, only to discover the humbling reality: AI can classify objects brilliantly but can't reason about what it sees. Worse, there's a "reasoning illusion"—models get right answers through wrong processes. This episode shows you why the gap between perception and understanding matters.


Topics Covered
- Faster R-CNN: The breakthrough that gave AI eyes
- Region Proposal Networks explained simply
- The reasoning gap: classification ≠ understanding
- RiseBench: Testing temporal, causal, spatial, and logical reasoning
- World models for self-driving (Gaia 2)
- The "reasoning illusion": right answers, wrong process
- Process Verified Accuracy: checking the work, not just the answer</description>

AI vision is solved. AI reasoning is not. The bes…

AI vision is solved. AI reasoning is not. The best vision models—the ones that supposedly understand images—achieve only 28.8% accuracy on tasks requiring physics, time, and causality. You'll trace the journey from 2015's Faster R-CNN breakthrough (56,700+ citations) through the evolution from messy multi-step pipelines to elegant end-to-end deep learning, only to discover the humbling reality: AI can classify objects brilliantly but can't reason about what it sees. Worse, there's a "reasoning illusion"—models get right answers through wrong processes. This episode shows you why the gap between perception and understanding matters.


Topics Covered
- Faster R-CNN: The breakthrough that gave AI eyes
- Region Proposal Networks explained simply
- The reasoning gap: classification ≠ understanding
- RiseBench: Testing temporal, causal, spatial, and logical reasoning
- World models for self-driving (Gaia 2)
- The "reasoning illusion": right answers, wrong process
- Process Verified Accuracy: checking the work, not just the answer

Computer Vision's Journey

This podcast series cuts through AI hype to deliver what technical professionals actually need: honest assessments of what works, what fails, and why it matters. Curated from NeurIPS 2025 — the world's premier AI research conference — these 11 episodes translate cutting-edge research into accessible narratives without dumbing down the substance.

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NeurIPS 2025 by Basis Set

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