Learning Transformer Programs with Dan Friedman - #667

Learning Transformer Programs with Dan Friedman - #667

Today, we continue our NeurIPS series with Dan Friedman, a PhD student in the Princeton NLP group. In our conversation, we explore his research on mechanistic interpretability for transformer models, specifically his paper, Learning Transformer Programs. The LTP paper proposes modifications to the transformer architecture which allow transformer models to be easily converted into human-readable programs, making them inherently interpretable. In our conversation, we compare the approach proposed by this research with prior approaches to understanding the models and their shortcomings. We also dig into the approach’s function and scale limitations and constraints. The complete show notes for this episode can be found at twimlai.com/go/667.

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Explaining Black Box Predictions with Sam Ritchie - TWiML Talk #73

Explaining Black Box Predictions with Sam Ritchie - TWiML Talk #73

This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this episode, I speak with Sam Ritchie, a software engineer at Stripe. I caught up with Sam RIGHT after his talk at the conference, where he covered his team’s work on explaining black box predictions. In our conversation, we discuss how Stripe uses black box predictions for fraud detection, and he gives a few use case scenarios. We discuss Stripe’s approach for explaining those predictions as well as other approaches, and briefly mention Carlos Guestrin’s work on LIME paper, which he and I discuss in TWiML Talk #7. The notes for this show can be found at twimlai.com/talk/73 For more series info, visit twimlai.com/STLoop

25 Nov 201738min

Experimental Creative Writing with the Vectorized Word - Allison Parish - TWIML Talk #72

Experimental Creative Writing with the Vectorized Word - Allison Parish - TWIML Talk #72

This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this episode, I speak with Allison Parrish, Poet and Professor at NYU in the Interactive Telecommunications dept. Allison’s work centers around generated poetry, via artificial intelligence and machine learning. She joins me prior to her conference talk on “Experimental Creative Writing with the Vectorized Word”. In our time together, we discuss some of her research into computational poetry generation, actually performing AI-produced poetry, and some of the methods and processes she uses for generating her work. Allison’s work centers around generated poetry, via artificial intelligence and machine learning. She joins me prior to her conference talk on “Experimental Creative Writing with the Vectorized Word”. In our time together, we discuss some of her research into computational poetry generation, actually performing AI-produced poetry, and some of the methods and processes she uses for generating her work. The notes for this show can be found at twimlai.com/talk/72 For more series info, visit twimlai.com/STLoop

24 Nov 201728min

The Biological Path Towards Strong AI - Matthew Taylor - TWiML Talk #71

The Biological Path Towards Strong AI - Matthew Taylor - TWiML Talk #71

This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this episode, I speak with Matthew Taylor, Open Source Manager at Numenta. You might remember hearing a bit about Numenta from an interview I did with Francisco Weber of Cortical.io, for TWiML Talk #10, a show which remains the most popular show on the podcast. Numenta is basically trying to reverse-engineer the neocortex, and use what they learn to develop a neocortical theory for biological and machine intelligence called Hierarchical Temporal Memory. Matt joined me at the conference to discuss his talk “The Biological Path Towards Strong AI”. In our conversation, we discuss the basics of HTM, it’s biological inspiration, and how it differs from traditional neural network models including deep learning. This is a Nerd Alert show, and after you listen I would encourage you to check out the conversation with Francisco which we’ll link to in the show notes. The notes for this show can be found at twimlai.com/talk/71 For series information, visit twimlai.com/stloop

22 Nov 201737min

Pytorch: Fast Differentiable Dynamic Graphs in Python with Soumith Chintala - TWiML Talk #70

Pytorch: Fast Differentiable Dynamic Graphs in Python with Soumith Chintala - TWiML Talk #70

This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this show I speak with Soumith Chintala, a Research Engineer in the Facebook AI Research Lab (FAIR). Soumith joined me at Strange Loop before his talk on Pytorch, the deep learning framework. In this talk we discuss the market evolution of deep learning frameworks and tools, different approaches to programming deep learning frameworks, Facebook’s motivation for investing in Pytorch, and much more. This was a fun interview, I hope you enjoy! The notes for this show can be found at twimlai.com/talk/70 For series information, visit twimlai.com/stloop

21 Nov 201742min

Accessible Machine Learning for the Enterprise Developer with Ryan Sevey & Jason Montgomery

Accessible Machine Learning for the Enterprise Developer with Ryan Sevey & Jason Montgomery

This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this show you'll hear from Nexosis founders Ryan Sevey and Jason Montgomery. Ryan, Jason and I discuss how they got their start by applying ML to identify cheaters in video games, the application of ML for time-series data analysis, and of course the Nexosis Machine Learning API. Of course, if you like what you hear, they invite you to get your free Nexosis API key and discover what they can bring to your next project at nexosis.com/twiml. The notes for this show can be found at twimlai.com/talk/69 For series information, visit twimlai.com/stloop

20 Nov 201745min

Bridging the Gap Between Academic and Industry Careers with Ross Fadely - TWiML Talk #68

Bridging the Gap Between Academic and Industry Careers with Ross Fadely - TWiML Talk #68

We close out our NYU Future Labs AI Summit interview series with Ross Fadely, a New York based AI lead with Insight Data Science. Insight is an interesting company offering a free seven week post-doctoral training fellowship helping individuals to bridge the gap between academia and careers in data science, data engineering and AI. Ross joined me backstage at the Future Labs Summit after leading a Machine Learning Primer for attendees. Our conversation explores some of the knowledge gaps that Insight has identified in folks coming out of academia, and how they structure their program to address them. If you find yourself looking to make this transition, you’ll definitely want to check out this episode. The notes for this show can be found at twimlai.com/talk/68 For series information, visit twimlai.com/ainexuslab2

16 Nov 201719min

The Limitations of Human-in-the-Loop AI with Dennis Mortensen - TWiML Talk #67

The Limitations of Human-in-the-Loop AI with Dennis Mortensen - TWiML Talk #67

We continue our NYU Future Labs AI Summit interview series with Dennis Mortensen, founder and CEO of X.ai, a company whose AI-based personal assistant Amy helps users with scheduling meetings. I caught up with Dennis backstage at the Future Labs event a few weeks ago, right before he went on stage to talk about “Investing in AI from the Startup POV.” Dennis gave shares some great insight into building an AI-first company, not to mention his vision for the future of scheduling, something no one actually enjoys doing, and his thoughts on the future of human-AI interaction. This was a fun interview, which I’m sure you’ll enjoy. A quick warning though… This might not be a show to listen to in the car with the kiddos, as this episode does contain a few expletives. The notes for this show can be found at twimlai.com/talk/67 For series information, visit twimlai.com/ainexuslab2

13 Nov 201735min

Nexus Lab Cohort 2 - Second Mind - TWiML Talk #66

Nexus Lab Cohort 2 - Second Mind - TWiML Talk #66

The podcast you’re about to hear is the fourth of a series of shows recorded at the NYU Future Labs AI Summit last week in New York City. In this show, I speak with Kul Singh, CEO and Founder of Second Mind. Second Mind is building an integration platform for businesses that allows them to bring augmented intelligence to voice conversations. We talk to Kul about the concept behind Second Mind, and how the company combines ambient listening with a low-latency matching system to help users eliminate an estimated 2.5 hours of manual searches per day! The notes for this show can be found at twimlai.com/talk/66 For series information, visit twimlai.com/ainexuslab2

9 Nov 201721min

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