Four Key Tools for Robust Enterprise NLP with Yunyao Li - #537

Four Key Tools for Robust Enterprise NLP with Yunyao Li - #537

Today we’re joined by Yunyao Li, a senior research manager at IBM Research. Yunyao is in a somewhat unique position at IBM, addressing the challenges of enterprise NLP in a traditional research environment, while also having customer engagement responsibilities. In our conversation with Yunyao, we explore the challenges associated with productizing NLP in the enterprise, and if she focuses on solving these problems independent of one another, or through a more unified approach. We then ground the conversation with real-world examples of these enterprise challenges, including enabling level document discovery at scale using combinations of techniques like deep neural networks and supervised and/or unsupervised learning, and entity extraction and semantic parsing to identify text. Finally, we talk through data augmentation in the context of NLP, and how we enable the humans in-the-loop to generate high-quality data. The complete show notes for this episode can be found at twimlai.com/go/537

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Jaksot(798)

Your Ranking Model Is Optimizing the Wrong Thing with Zoe Weil - #780

Your Ranking Model Is Optimizing the Wrong Thing with Zoe Weil - #780

In this episode, Zoe Weil, co-founder and CEO of Sequen, joins us to discuss how ranking systems can use live behavioral signals to optimize for purchases rather than clicks or search relevance alone....

15 Loka 52min

Why Jev Is Changing How We Build With AI with Diogo Almeida - #779

Why Jev Is Changing How We Build With AI with Diogo Almeida - #779

In this episode, Diogo Almeida, co-founder and CEO of TypeSafe, joins us to discuss Jev, TypeSafe’s recently released model for bringing fast, reliable intelligence directly into software. We explore ...

6 Loka 1h 30min

From Math Olympiads to Navier-Stokes: How Fast Is AI Progressing? with Greg Burnham - #778

From Math Olympiads to Navier-Stokes: How Fast Is AI Progressing? with Greg Burnham - #778

AI systems have gone from struggling with grade-school math to helping solve research problems that have resisted mathematicians for decades, including Navier-Stokes. In this episode, Greg Burnham, w...

29 Syys 1h 7min

From Voice Agents to AI Avatars with Alexander Smola - #777

From Voice Agents to AI Avatars with Alexander Smola - #777

Voice AI has gotten remarkably good, but natural conversation remains a high bar. Small delays, awkward interruptions, or the wrong tone can quickly break the illusion—and adding vision and visual pre...

17 Syys 1h 4min

Do AI Tokenomics Matter More Than Model Benchmarks? with Chris Potts - #776

Do AI Tokenomics Matter More Than Model Benchmarks? with Chris Potts - #776

As reasoning models consume more tokens and AI systems become more expensive to run, understanding what those tokens actually buy is becoming increasingly important. In this episode, Stanford professo...

9 Syys 59min

World Models and the Future of Spatial AI with Justin Johnson - #775

World Models and the Future of Spatial AI with Justin Johnson - #775

In this episode, Justin Johnson, co-founder of World Labs, joins us to discuss world models and the emerging field of spatial AI. We explore why many researchers see capabilities beyond language as an...

1 Syys 1h 6min

Why the Next AI Breakthrough May Come from Physics with Max Welling - #774

Why the Next AI Breakthrough May Come from Physics with Max Welling - #774

The conventional wisdom in AI is that the next breakthrough will come from more compute, more data, and larger models. But what if the next leap comes from somewhere else? In this episode, Max Wellin...

26 Elo 58min

Why Image Generation Needs More Than Bigger Models with Fatih Porikli - #773

Why Image Generation Needs More Than Bigger Models with Fatih Porikli - #773

Text-to-image models have become remarkably good at producing realistic images. But realism isn’t the same as correctness. Ask for several distinct people, a specific composition, or a high-resolution...

12 Elo 56min

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