Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

KDD 2020: Physics Inspired Models in Artificial Intelligence

Association for Computing Machinery (ACM) via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore the intersection of physics and artificial intelligence in this comprehensive conference talk from KDD 2020. Delve into the four paradigms of scientific discovery, examining the interplay between theory and data. Investigate the limitations of big data approaches and the importance of generalization in both physics and AI. Analyze computational complexity classes and their relevance to AI and physics problems. Discover physics-informed neural networks (PINN) and physics-guided neural networks (PGNN), and their applications in explainable AI. Examine open questions in neural networks, including the potential for a statistical physics theory of deep learning. Gain insights into information bottlenecks and their role in both physics and neural networks. Enhance your understanding of the evolving relationship between physics and artificial intelligence through this in-depth presentation.

Syllabus

Intro
Terminology
Motivation: Why Physics & AI?
Why this Tutorial?
Tutorial Goals
Interplay of Physics and Al
The Four Paradigms
Theory vs. Data?
Limitations of the 4th Paradigm
Cautionary Tale: Problems with Big Data
Parameters Galore!
Physics: Tycho Brahe to Kepler to Newton
A Brief History of Physics & Al
Generalization in Physics & Al
Generalization in Neural Nets
Generalization: Observations
Computational Complexity, Al & Physics
Complexity Classes
3-SAT and Phase Transitions
Problems: Complexity
Interpretability & Explainability in Al/ML
Properties of XAI
Physics Informed Neural Nets (PINN)
Physics-guided Neural Network (PGNN)
Physics & Explainable Al: An Illustration
Results Summary
Open Questions in Neural Networks
Statistical physics theory of Deep Learning?
Information Bottleneck & Neural Nets
Information Bottlenecks & Physics
The Committee Machine

Taught by

Association for Computing Machinery (ACM)

Reviews

5.0 rating, based on 1 Class Central review

Start your review of KDD 2020: Physics Inspired Models in Artificial Intelligence

  • Nir Levanon
    Completed viewing course on 26/9/24.
    This course helped to introduce these new concepts to me: regularization (methods for correcting model overfitting), 3-SAT problems, Explainable AI (XAI), Physics Informed Neural Networks (PINN), Physics Guided Neural Networks (PGNN), Information bottlenecks in Neural Networks.

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.