On July 29, 2021 Steve Omohundro was part of the Hive Think Tank’s “AI Reading Group” along with George Gregory, Daniel Goncharov, and Nikesh Kotecha discussing probabilistic programming and its applications to biology:
ABOUT THE SERIES – “AI READING CLUB”: A new quarterly book club focusing on the most relevant and recent AI publications & literature – this first session will focus on Probabilistic Programming. This special quarterly 1-hour event with select guests who pre-select and discuss interesting papers that they have recently read. Attendees are encouraged to share their views or ask questions on the chat and Q&A interface.
ABOUT THE EVENT – “PROBABILISTIC PROGRAMMING”: Deep learning neural networks combined with Bayesian probabilistic models are enabling AI to have a huge impact on science and engineering. Join a group of AI researchers and scientists in a discussion of these trends as they impact biology. AI has recently discovered the antibiotic “halicin”, is making great strides in folding proteins, and is giving biologists new clarity in analyzing cell data. We discuss the role of probability and machine learning and where we see these trends heading in the future.
SPEAKERS: *George Gregory – Co-Founder & CEO, System AI, Inc. [MODERATOR] *Daniel Goncharov – Head of 42 AI & Robotics + Google Developer Expert in ML *Steve Omohundro – Research Scientist, Facebook + Author, “Geometric Perturbation Theory in Physics” *Nikesh Kotecha – Adjunct Prof. Stanford University + frmr Informatics VP Parker Institute for Cancer Immunotherapy
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are “discriminative” in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn’t generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of “generative” AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
KEYNOTE SPEAKER: Steve Omohundro, PhD – Research Scientist @ Facebook + Author, “Geometric Perturbation Theory in Physics” Steve Omohundro has done fundamental research in AI for 35 years and is currently a Research Scientist at Facebook working on AI-based simulation. He has a PhD in physics, was an AI professor at the University of Illinois, and has been a scientist at several research labs and startup companies. He co-founded one of the first complex systems institutes, designed the first data-parallel programming language, invented manifold learning, co-developed the first image recommender system, co-developed the first attention-driven neural nets, co-built the first lip reading system, and developed many other machine learning algorithms. His work on AI’s social impact was featured in the book “Our Final Invention” and he appears in the Universal Pictures documentary “We Need to Talk About AI”. He believes that AI is about to unlock enormous business and social value.