A Day in the Half Life is a podcast from Lawrence Berkeley National Laboratory (Berkeley Lab) about the incredible and often unexpected ways that science evolves over time, as told by the researchers who led it into its current state and those who are going to bring it into the future.

In our very first episode, we discuss machine learning. 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.

In the early days of ML, the technology was used for simple tasks such as voice recognition or identifying a specific type of object in images, and was only found in high-end academic, government, or military devices. But 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 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 the way these algorithms work well enough to feel comfortable using ML for this?” 

Our two ML expert guests are:

John Dagdelen, a materials science graduate student researcher at Berkeley Lab and UC Berkeley. John is part of several scientific teams using ML to discover new materials and material properties, as well as using ML to make discoveries in COVID-19 research.

Prabhat, the former leader of the Data and Analytics Services group at NERSC, Berkeley Lab’s world-renown supercomputing center. Prabhat has been using and developing ML for decades, including for use in climate research. He is now at Microsoft.