Multi-modal Deep Learning for Complex Document Understanding with Doug Burdick - #541

Multi-modal Deep Learning for Complex Document Understanding with Doug Burdick - #541

Today we’re joined by Doug Burdick, a principal research staff member at IBM Research. In a recent interview, Doug’s colleague Yunyao Li joined us to talk through some of the broader enterprise NLP problems she’s working on. One of those problems is making documents machine consumable, especially with the traditionally archival file type, the PDF. That’s where Doug and his team come in. In our conversation, we discuss the multimodal approach they’ve taken to identify, interpret, contextualize and extract things like tables from a document, the challenges they’ve faced when dealing with the tables and how they evaluate the performance of models on tables. We also explore how he’s handled generalizing across different formats, how fine-tuning has to be in order to be effective, the problems that appear on the NLP side of things, and how deep learning models are being leveraged within the group. The complete show notes for this episode can be found at twimlai.com/go/541

Episoder(782)

Powering AI with the World's Largest Computer Chip with Joel Hestness - #684

Powering AI with the World's Largest Computer Chip with Joel Hestness - #684

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AI for Power & Energy with Laurent Boinot - #683

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Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682

Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682

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 rea...

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GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681

GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681

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 GraphRA...

22 Apr 202447min

Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680

Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680

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 explora...

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Localizing and Editing Knowledge in LLMs with Peter Hase - #679

Localizing and Editing Knowledge in LLMs with Peter Hase - #679

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 ...

8 Apr 202449min

Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678

Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678

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...

1 Apr 202448min

V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran - #677

V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran - #677

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 vi...

25 Mar 202447min

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