Podcast: World Models — A Deep Dive With Andre Franca
Learn about cutting-edge developments in AI and data science from the experts who know them best on ODSC’s Ai X Podcast. Each week we release an interview with a leading expert, core contributor, experienced practitioner, or acclaimed instructor who is helping to shape the future of the AI industry through their work or research.
In this episode, we speak with Andre Franca , Founder of ConnectedFlow, about the concept of World Models. We’ll unpack how they differ from traditional, purely predictive models, and explore key characteristics of World Models and how they empower AI agents to revolutionize the AI landscape.
We’ll delve into the limitations of passive observation and into the power of intervention, expose the pitfalls of curve fitting in machine learning, and explore the “shadow problem” that makes building World Models from data so challenging.
Later, we’ll discuss the powerful role of inductive biases in making World Models tractable. We’ll also unpack the core principles of cutting-edge research areas like Causal AI, Generative Flow Networks, and Active Inference, and explore why these advancements are crucial for achieving Artificial General Intelligence (AGI).
Finally, Andre will share his startup journey and explain why he believes “a world model is all you need.
Start listening now to get the full impact of Pasquale’s extensive knowledge and expertise in evaluating LLM and RAG applications and don’t forget to subscribe to ODSC’s Ai X Podcast to ensure you never miss an episode. Finally, like what you hear? Leave a review or share it with a friend! You can listen on Spotify, Apple, and SoundCloud.
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Show Notes:
More about Andre Franca:
https://www.linkedin.com/in/francaandre/
Resources:
World Model. Paper by David Ha & Jurgen Smithhuber where they explore building generative neural network models of popular reinforcement learning environments
https://arxiv.org/abs/1803.10122
NeurIPS 2018 World Models workshop — Can agents learn inside of their own dreams?
https://worldmodels.github.io/
Open AI GYM (A toolkit for developing and comparing reinforcement learning algorithms ) and Gymnasium
https://github.com/Farama-Foundation/Gymnasium
Atari Game on GYM Retro — a platform for reinforcement learning research on games including 70 Atari games and 30 Sega games and over 1,000 games across a variety of backing emulators
https://openai.com/index/gym-retro/
Definitions:
Curve Fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points
https://en.wikipedia.org/wiki/Curve_fitting
Neuro-symbolic AI — a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling
https://en.wikipedia.org/wiki/Neuro-symbolic_AI
Convolutional neural networks (CNN): a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization
https://en.wikipedia.org/wiki/Convolutional_neural_network
Free Energy Principle: a theoretical framework suggesting that the brain reduces surprise or uncertainty by making predictions based on internal models and updating them using sensory input.
https://en.wikipedia.org/wiki/Free_energy_principle
Bayesian probability
https://en.wikipedia.org/wiki/Bayesian_probability
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Originally posted on OpenDataScience.com
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