Today we’re joined by friend of the show John Bohannon, the director of science at Primer AI, to help us showcase all of the great achievements and accomplishments in NLP in 2021! In our conversation, John shares his two major takeaways from la

Machine Learning for Equitable Healthcare Outcomes with Irene Chen

1 Apr 29th, 2021 36m 59s

Today we’re joined by Irene Chen, a Ph.D. student at MIT. Irene’s research is focused on developing new machine learning methods specifically for healthcare, through the lens of questions of equity and inclusion. In our conversation, we explo

Scalable Differential Privacy for Deep Learning with Nicolas Papernot - TWiML Talk #134

1 May 3rd, 2018 59m 28s

In this episode of our Differential Privacy series, I'm joined by Nicolas Papernot, Google PhD Fellow in Security and graduate student in the department of computer science at Penn State University. Nicolas and I continue this week’s look into

Managing Data Labeling Ops for Success with Audrey Smith

Jul 18th, 2022 47m 26s

Today we continue our Data-Centric AI Series joined by Audrey Smith, the COO at MLtwist, and a recent participant in our panel on DCAI. In our conversation, we do a deep dive into data labeling for ML, exploring the typical journey for an organ

Differential Privacy Theory & Practice with Aaron Roth - TWiML Talk #132

1 Apr 30th, 2018 42m 55s

In the first episode of our Differential Privacy series, I'm joined by Aaron Roth, associate professor of computer science and information science at the University of Pennsylvania. Aaron is first and foremost a theoretician, and our conversati

A Quantum Computing Primer and Implications for AI with Davide Venturelli - TWiML Talk #93

1 Jan 8th, 2018 34m 11s

Today, I'm joined by Davide Venturelli, science operations manager and quantum computing team lead for the Universities Space Research Association’s Institute for Advanced Computer Science at NASA Ames. Davide joined me backstage at the NYU Fut

How Microsoft Scales Testing and Safety for Generative AI with Sarah Bird

4 days ago 57m 9s

Today, we're joined by Sarah Bird, chief product officer of responsible AI at Microsoft. We discuss the testing and evaluation techniques Microsoft applies to ensure safe deployment and use of generative AI, large language models, and image gen

Long Context Language Models and their Biological Applications with Eric Nguyen

10 days ago 45m 11s

Today, we're joined by Eric Nguyen, PhD student at Stanford University. In our conversation, we explore his research on long context foundation models and their application to biology particularly Hyena, and its evolution into Hyena DNA and Evo

Accelerating Sustainability with AI with Andres Ravinet

17 days ago 48m 1s

Today, we're joined by Andres Ravinet, sustainability global black belt at Microsoft, to discuss the role of AI in sustainability. We explore real-world use cases where AI-driven solutions are leveraged to help tackle environmental and societal

Gen AI at the Edge: Qualcomm AI Research at CVPR 2024 with Fatih Porikli

25 days ago 1h 10m

Today we’re joined by Fatih Porikli, senior director of technology at Qualcomm AI Research. In our conversation, we covered several of the Qualcomm team’s 16 accepted main track and workshop papers at this year’s CVPR conference. The papers spa

Energy Star Ratings for AI Models with Sasha Luccioni

Jun 3rd, 2024 47m 56s

Today, we're joined by Sasha Luccioni, AI and Climate lead at Hugging Face, to discuss the environmental impact of AI models. We dig into her recent research into the relative energy consumption of general purpose pre-trained models vs. task-sp

Language Understanding and LLMs with Christopher Manning

May 27th, 2024 55m 40s

Today, we're joined by Christopher Manning, the Thomas M. Siebel professor in Machine Learning at Stanford University and a recent recipient of the 2024 IEEE John von Neumann medal. In our conversation with Chris, we discuss his contributions t

Chronos: Learning the Language of Time Series with Abdul Fatir Ansari

May 20th, 2024 42m 35s

Today we're joined by Abdul Fatir Ansari, a machine learning scientist at AWS AI Labs in Berlin, to discuss his paper, "Chronos: Learning the Language of Time Series." Fatir explains the challenges of leveraging pre-trained language models for

Powering AI with the World's Largest Computer Chip with Joel Hestness

May 13th, 2024 54m 36s

Today we're joined by Joel Hestness, principal research scientist and lead of the core machine learning team at Cerebras. We discuss Cerebras’ custom silicon for machine learning, Wafer Scale Engine 3, and how the latest version of the company’

AI for Power & Energy with Laurent Boinot

May 7th, 2024 49m 55s

Today we're joined by Laurent Boinot, power and utilities lead for the Americas at Microsoft, to discuss the intersection of AI and energy infrastructure. We discuss the many challenges faced by current power systems in North America and the ro

Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand

Apr 29th, 2024 42m 9s

Today we're joined by Azarakhsh (Aza) Jalalvand, a research scholar at Princeton University, to discuss his work using deep reinforcement learning to control plasma instabilities in nuclear fusion reactors. Aza explains his team developed a mod

GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple

Apr 22nd, 2024 3m 59s

Today we're joined by Kirk Marple, CEO and founder of Graphlit, to explore the emerging paradigm of "GraphRAG," or Graph Retrieval Augmented Generation. In our conversation, Kirk digs into the GraphRAG architecture and how Graphlit uses it to o

Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla

Apr 16th, 2024 45m 54s

Today we're joined by Alex Havrilla, a PhD student at Georgia Tech, to discuss "Teaching Large Language Models to Reason with Reinforcement Learning." Alex discusses the role of creativity and exploration in problem solving and explores the opp

Localizing and Editing Knowledge in LLMs with Peter Hase

Apr 8th, 2024 49m 16s

Today we're joined by Peter Hase, a fifth-year PhD student at the University of North Carolina NLP lab. We discuss "scalable oversight", and the importance of developing a deeper understanding of how large neural networks make decisions. We lea

Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping

Apr 1st, 2024 47m 57s

Today we're joined by Jonas Geiping, a research group leader at the ELLIS Institute, to explore his paper: "Coercing LLMs to Do and Reveal (Almost) Anything". Jonas explains how neural networks can be exploited, highlighting the risk of deployi

V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran

Mar 25th, 2024 47m 17s

Today we’re joined by Mido Assran, a research scientist at Meta’s Fundamental AI Research (FAIR). In this conversation, we discuss V-JEPA, a new model being billed as “the next step in Yann LeCun's vision” for true artificial reasoning. V-JEPA,

Video as a Universal Interface for AI Reasoning with Sherry Yang

Mar 18th, 2024 49m 4s

Today we’re joined by Sherry Yang, senior research scientist at Google DeepMind and a PhD student at UC Berkeley. In this interview, we discuss her new paper, "Video as the New Language for Real-World Decision Making,” which explores how genera

Assessing the Risks of Open AI Models with Sayash Kapoor

Mar 11th, 2024 40m 26s

Today we’re joined by Sayash Kapoor, a Ph.D. student in the Department of Computer Science at Princeton University. Sayash walks us through his paper: "On the Societal Impact of Open Foundation Models.” We dig into the controversy around AI saf

OLMo: Everything You Need to Train an Open Source LLM with Akshita Bhagia

Mar 4th, 2024 32m 12s

Today we’re joined by Akshita Bhagia, a senior research engineer at the Allen Institute for AI. Akshita joins us to discuss OLMo, a new open source language model with 7 billion and 1 billion variants, but with a key difference compared to simi

Training Data Locality and Chain-of-Thought Reasoning in LLMs with Ben Prystawski

Feb 26th, 2024 25m 3s

Today we’re joined by Ben Prystawski, a PhD student in the Department of Psychology at Stanford University working at the intersection of cognitive science and machine learning. Our conversation centers on Ben’s recent paper, “Why think step by