In interview 21 of The Gradient Podcast, we talk to Percy Liang, an Associate Professor of Computer Science at Stanford University and the director of the Center for Research on Foundation Models. He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), a Microsoft Research Faculty Fellowship (2014), and multiple paper awards at ACL, EMNLP, ICML, and COLT. Removing spurious features can hurt accuracy and affect groups disproportionately.

Consumers have invited AI into their lives with voice-activated personal assistants like Siri and Alexa, but how do they feel about computer vision technologies that can provide visual coaching and feedback in their homes? Sanjay Nichani, vice president of artificial intelligence and computer vision at Peloton Interactive, describes one compelling use case in the at-home fitness space. Sanjay Nichani is vice president of artificial intelligence and computer vision at Peloton Interactive. In that role, he leads an AI/computer vision team focused on human pose estimation, activity recognition, and movement-tracking technologies for the fitness domain. He also leads the ongoing development of Peloton Guide, a new camera-based interactive strength-training product. Previously, Nichani was vice president of the computer vision and machine learning team at Acuant, working on document forensics technologies for detecting fraud. Before that, he was vice president of the Mitek Labs R&D group, where he led the development of a deep learning-based image-processing pipeline for identity verification. He also founded 3D sensor technology company Merakona and cofounded Pelfunc, developer of a photo-sharing app/service. He has advanced degrees in business from Babson College and computer science from the University of South Florida.

So-called energy-based models, which borrow from statistical physics concepts, could lead to deep learning forms of AI that make abstract predictions, says Yann LeCun, Meta's chief scientist. Three decades ago, Yann LeCun, while at Bell Labs, formalized an approach to machine learning called convolutional neural networks that would prove to be profoundly productive in solving tasks such as image recognition. CNNs, as they're commonly known, are a workhorse of AI's deep learning, winning LeCun the prestigious ACM Turing Award, the equivalent of a Nobel for computing, in 2019. These days, LeCun, who is both a professor at NYU and chief scientist at Meta, is the most excited he's been in 30 years, he told ZDNet in an interview last week. The reason: New discoveries are rejuvenating a long line of inquiry that could turn out to be as productive in AI as CNNs are. That new frontier that LeCun is exploring is known as energy-based models. Whereas a probability function is "a description of how likely a random variable or set of random variables is to take on each of its possible states" (see Deep Learning, by Ian Goodfellow, Yoshua Bengio & Aaron Courville, 2019), energy-based models simplify the accordance between two variables. Borrowing language from statistical physics, energy-based models posit that the energy between two variables rises if they're incompatible and falls the more they are in accord. This can remove the complexity that arises in "normalizing" a probability distribution. It's an old idea in machine learning, going back at least to the 1980s, but there has been progress since then toward making energy-based models more workable.

ANJA KASPERSEN: Today I am very pleased to be joined by Pascale Fung. Pascale is a;rofessor in the Department of Electronic and Computer Engineering and Department of Computer Science and Engineering at The Hong Kong University of Science and Technology. She is known globally for her pioneering work on conversational artificial intelligence (AI), computational linguistics, and was one of the earliest proponents of statistical and machine-learning approaches for natural language processing (NLP). She is now leading groundbreaking research on how to build intelligent systems that can understand and empathize with humans. I have really been looking forward to this conversation with you. Your professional accolades are many, most of which we will touch on during our conversation. However, for our listeners to get to know you a bit better, I would like us to go back to your upbringing during what I understand to be a very tenuous political period in China. I was born, spent my childhood, ...

The thriving industry of Data Science is continuously evolving with the technological advancements in Machine Learning and Artificial intelligence. This has opened up whole new avenues for Data Scientists worldwide. Professionals who can handle Big Data and have the necessary knowledge required for understanding, analysing and processing data are in high demand in the job market. However, there is one important thing that also needs to be addressed is the raging problem caused by the gender gap in this sector. As per the statistical report from the Boston Consulting Group, only 15 to 22 per cent of the Data Science-related professional roles are occupied by women.

A version of this article was published in TIME's newsletter Into the Metaverse. You can find past issues of the newsletter here. Technology and fiction have long shared a symbiotic relationship. Just as writers dreamed up fantastical worlds based on imagined technologies, those same worlds have inspired engineers, technologists, and scientists--spurring breakthroughs as well as thorny philosophical questions about their work. The term "metaverse" itself comes from Neal Stephenson's Snow Crash; the comic strip Dick Tracy inspired the cell phone.

Growing up in Spain's Catalonia region, Alberto Rodriguez loved taking things apart and putting them back together. But it wasn't until he joined a robotics lab his last year in college that he realized robotics, and not mathematics or physics, would be his life's calling. "I fell in love with the idea that you could build something and then tell it what to do," he says. "That was my first intense exposure to the magic combo of building and coding, and I was hooked." After graduating from university in Barcelona, Rodriguez looked for a path to study in the United States.

In October 2021, Cynthia Rudin was announced as the winner of the AAAI Squirrel AI award, which was formally presented during an award ceremony at AAAI 2022. Following the presentation, Cynthia delivered an invited talk about her research on interpretable machine learning.

Born to Eritrean parents in Ethiopia, Gebru spoke with The Associated Press recently about how poorly Big Tech's AI priorities -- and its AI-fueled social media platforms -- serve Africa and elsewhere. The new institute focuses on AI research from the perspective of the places and people most likely to experience its harms. She's also co-founder of the group Black in AI, which promotes Black employment and leadership in the field. And she's known for co-authoring a landmark 2018 study that found racial and gender bias in facial recognition software. The interview has been edited for length and clarity.

Most survivors of World War II's Nazi concentration camps are now in their 80s and 90s, and soon there will be no one left who experienced the horrors of the Holocaust firsthand -- no one to answer questions or bear witness to future generations. But as we first reported two years ago, a new and dramatic effort is underway to change that by harnessing the technologies of the present and the future. To keep alive the ability to talk to -- and get answers from -- the past. Our interview with Holocaust survivor Aaron Elster, who spent two years of his childhood hidden in a neighbor's attic, was unlike any interview we have ever done. "Aaron, tell us what your parents did before the war," Stahl asked Elster. "They owned and operated a butcher shop," Elster said. It wasn't the content of the interview that was so unusual. "Where did you live?" Stahl asked. "I was born in a small town in Poland called Sokolów Podlaski," Elster said. It's the fact that this interview was with a man who was no longer alive. Aaron Elster died four years ago.