Video & Podcasts

First developed about 80 years ago, machine learning (ML) is a type of artificial intelligence centered on programs – called algorithms – that can teach themselves different ways of processing data after they are trained on sample datasets. Now, advanced ML algorithms are everywhere, powering everything from our cars to our voice assistants to the ads appearing on our news feeds. And, in addition to making everyday life easier, ML algorithms are beginning to improve and expedite scientific and medical research in truly dramatic ways.

In addition to making everyday life easier, machine learning (ML) algorithms are beginning to advance scientific and medical research in dramatic ways. In fact, the range of potential applications is so huge that the question has shifted from “Can we use machine learning to solve this?” to “Do we understand these algorithms well enough to use ML for this?” In this podcast, two Berkeley Lab ML experts discuss machine learning and its scientific applications.

How can we improve machine learning to better solve complex scientific problems? By advancing methods that are built to handle sophisticated algorithms and the three-dimensional geometry of physical systems. In this Midday Science Cafe, we’ll learn from two researchers tackling these challenges.

Machine learning approaches, such as the technique recently developed by JBEI scientists, are hamstrung by a lack of large quantities of quality data. New automation capabilities at JBEI will be able to produce these data in a systematic fashion. This video shows a liquid handler coupled with an automated fermentation platform at JBEI, which takes samples automatically to produce data for the machine learning algorithms.

Berkeley Lab computational chemist Bert de Jong explains how the lab is using machine learning to advance the search for carbon capture technologies. Negative emissions technologies (NETs) remove carbon dioxide from the atmosphere or other sources.

Basics2Breakthroughs features Tess Smidt, a Berkeley Lab physicist and computer scientist developing machine learning algorithms that can model the complex geometry of the universe, from atomic arrangements to building designs.