Deep Learning

MIT AGI: Building machines that see, learn, and think like people (Josh Tenenbaum)

Lex Fridman

A subset of machine learning, often used in pattern recognition, which has made significant strides but is argued to be insufficient for achieving human-like general intelligence.

David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI | Lex Fridman Podcast #44

Lex Fridman

A subset of machine learning that uses neural networks to learn from large amounts of data, often for prediction tasks.

Artificial intelligence in astrophysics – Public lecture by Dr. Aleksandra Ciprijanovic

Fermilab

A subset of machine learning using neural networks to learn complex tasks, applied in image analysis, speech recognition, and autonomous vehicles.

AI Dev 25 x NYC | Scott Hurrey: Scaling Enterprise AI with MCP and A2A

DeepLearningAI

Regulating Artificial Intelligence: A Conversation with Yoshua Bengio and Scott Wiener(Episode #379)

Sam Harris

A subfield of machine learning that Yoshua Bengio is known for breakthroughs in, contributing to current AI advancements.

Rana el Kaliouby: Emotion AI, Social Robots, and Self-Driving Cars | Lex Fridman Podcast #322

Lex Fridman

Modern AI technology that has simplified certain problems compared to Rana's earlier work with Dynamic Bayesian Networks.

Demis Hassabis: DeepMind - AI, Superintelligence & the Future of Humanity | Lex Fridman Podcast #299

Lex Fridman

Algorithmic advances recently invented in academia around 2010, which DeepMind saw as a key founding tenet.

MIT AGI: Artificial General Intelligence

Lex Fridman

A central method discussed in the course, focusing on its power in representational learning, its limitations, and its comparison to biological neural networks.

MIT 6.S094: Deep Reinforcement Learning

Lex Fridman

A subset of machine learning that uses neural networks with multiple layers to learn representations from data.

Chris Gerdes (Stanford) on Technology, Policy and Vehicle Safety - MIT Self-Driving Cars

Lex Fridman

A machine learning technique that presents challenges for regulators and policymakers in ensuring vehicle safety.

MIT 6.S094: Deep Learning for Human-Centered Semi-Autonomous Vehicles

Lex Fridman

The core technology discussed for analyzing driver behavior, including body pose, gaze, emotion, and cognitive load, to enhance semi-autonomous and fully autonomous vehicles.

Torch Tutorial (Alex Wiltschko, Twitter)

Lex Fridman

A subfield of machine learning based on artificial neural networks with multiple layers or hierarchy, discussed extensively throughout the talk.

19 Uncomfortable Truths About Human Nature - Gurwinder Bhogal

Chris Williamson

Referred to as one of the key advancements in AI leading to powerful models like SeaDance (ByteDance) and Sora (OpenAI).

Jensen Huang: Nvidia's Future, Physical AI, Rise of the Agent, Inference Explosion, AI PR Crisis

All-In Podcast

The foundational revolution that led to current AI advancements, with significant impacts predicted and realized across various scientific and technological fields.

Scott Aaronson: Computational Complexity and Consciousness | Lex Fridman Podcast #130

Lex Fridman

A subset of machine learning using neural networks with multiple layers for tasks like text and image generation.

Pamela McCorduck: Machines Who Think and the Early Days of AI | Lex Fridman Podcast #34

Lex Fridman

A modern AI approach that has led to many applications, contrasting with earlier symbolic AI.

Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI | Lex Fridman Podcast #61

Lex Fridman

A machine learning approach based on large neural networks and big data, which Melanie Mitchell believes has fundamental limits but has achieved surprising success.

Work at a Startup Expo 2019

Y Combinator

Aethelas's test for neutropenia is the first deep-learning based test cleared by the FDA, highlighting its innovative application in blood diagnostics.

François Chollet: ARC-AGI-3, Beyond Deep Learning & A New Approach To ML

Y Combinator

The current dominant approach in machine learning, which the speaker contrasts with their new paradigm, arguing it's nearing its limits for achieving true optimality.