Christian Szegedy | The Inverse Mindset of Machine Learning

Virtual London Machine Learning Meetup - 24.05.2021 @ 18:30

We would like to invite you to our next Virtual Machine Learning Meetup.

The discussion will be facilitated by Max Jaderberg, a researcher at DeepMind leading the Open-Ended Learning team, driving the intersection of deep learning, reinforcement learning, and multi-agent systems. His recent work includes creating the first agent to beat human professionals at StarCraft II, and creating algorithms for training teams of agents to play with humans in first-person video games.

Agenda:

• 18:25: Virtual doors open
• 18:30: Talk
• 19:10: Q&A session
• 19:30: Close

Sponsors
https://evolution.ai/ : Machines that Read - Intelligent data extraction from corporate and financial documents.

• Title: The Inverse Mindset of Machine Learning (Christian Szegedy is a machine learning and AI researcher at Google Research, currently focusing on formal reasoning and natural language processing using deep neural networks)

Abstract: Here, in this talk, I will give examples of how large areas of machine learning promotes and requires a mindset different from more to traditional areas of computer science. While most of computer science is focused on disciplined, efficient solutions for most problems, large parts of machine learning and AI is focused on finding good tasks and curricula for certain domains. While machine learning still requires classical optimization and efficient solutions, a lot of the work shifts towards encoding and creating interesting problems and exploring the power and limits of new solutions. For example, transformer networks act as a powerful, self-routing structure and pre-training with the correct, conceptually relevant tasks has become a large area of research. Here we will examine several examples of the working of this mindset in practical examples.

This presentation will assume some familiarity with transformer networks and the basics of contrastive training approaches.

Bio: Christian Szegedy is a machine learning and AI researcher at Google Research, currently focusing on formal reasoning and natural language processing using deep neural networks. He holds a PhD in applied mathematics from the University of Bonn, Germany and worked on algebraic combinatorics, placement, routing and timing optimization of chips, logic synthesis via combinatorial optimization, advertisement pricing optimization and computer vision using deep convolutional networks. He is best known for discovering adversarial examples and co-inventing Batch Normalization. He has designed the vision deep networks of the Inception family and has co-authored the first deep-learning paper on mathematical theorem proving at a large scale.