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A Day in the Half-Life - Machine Learning

Machine Learning

02/08/21 • 41 min

1 Listener

A Day in the Half-Life

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.

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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.

Next Episode

undefined - Dark Energy

Dark Energy

Twenty years ago, scientists were surprised to discover that the universe’s expansion is accelerating. The unknown and invisible force causing this acceleration was named “dark energy,” and in the years since, researchers learned more about what the phenomenon is not — but have yet to crack the puzzle of what it actually is. Physicists say it could be an as-of-yet undetected form of energy permeating the cosmos, or it could be an unmeasured property of the force of gravity. Either way, the answer will reshape our models of the universe.
In this episode, we speak with Nobel Laureate Saul Perlmutter (the co-discoverer of dark energy) and rising astrophysics instrumentation scientist Claire Poppett about what we know so far, and how new technology could finally shed (metaphorical) light on this fundamental mystery.

A Day in the Half-Life - Machine Learning

Transcript

Aliyah:

Welcome to a Day in the Half Life, a podcast about the incredible and often unexpected ways that science evolves as told by the researchers who led it into its current state and those who are going to bring it into the future. Why focus on this and launch yet another science podcast? Well, when we look around us at the latest technology and medicines available, or read news about advances in our understanding of everything from black holes to gut bacteria, we rar

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