The Emergence of Machine Learning as a Rupture Technology for Artificial Intelligence

QUICK INFORMATION
Type
ESRF-ILL colloquium
Start Date
11-02-2022 14:00
End Date
11-02-2022 15:00
Location
Online
Add event to calendar
iCal | vCal
Coordinator contact(s)
Anne-Françoise Maydew

Scientific contact(s)
Patrick Bruno
Ulli Koester

INVITATION

ESRF-ILL Online Colloquium

"The Emergence of Machine Learning as a Rupture Technology for Artificial Intelligence  "

Presented by James Crowley,
Grenoble Institut Polytechnique, Université Grenoble-Alpes

Friday, February 11th at 14:00

Please click here to watch the webinar in replay

Abstract

Turing defined intelligence as human-level performance at interaction. After more than 50 years of research, Machine Learning has provided an enabling technology for constructing intelligent systems with abilities at or beyond human level for interaction with people, with systems, and with the world. In this talk I will review the emergence of Machine Learning as an enabling technology for building systems with human-level intelligence.

Starting with a historical review of the of multi-layer perceptron, I will describe how back-propagation combined with massive computing power and planetary scale data have created the rupture technology known as deep learning, and how this technology enables not only pattern recognition but also signal generation and universal function approximation. I will trace the emergence of deep learning as an enabling technology for computer vision, robotics, and speech recognition and review recent advances such as Generative Adversarial Networks and Deep Reinforcement Learning.

I will then describe how the auto-encoder, originally invented as a distributed algorithm for principal components analysis, has recently empowered the emergence of a revolutionary technology for natural language processing known as Transformers. Transformers, such as Google's BERT and OpenAI's GPT-3, have the potential to unlock all recorded literature as a source for self-supervised machine learning.  I will discuss how transformers can be used to build realistic systems for vision, robotics, and natural language interaction and can potentially enable the emergence of collaborative intelligent systems for scientific discovery.