Today we’re joined by Subarna Sinha, Machine Learning Engineering Leader at 23andMe.
 23andMe handles a massive amount of genomic data every year from its core ancestry business but also uses that data for disease prediction, which is the core use case we discuss in our conversation.
 Subarna talks us through an initial use case of creating an evaluation of polygenic scores, and how that led them to build an ML pipeline and platform. We talk through the tools and tech stack used for the operationalization of their platform, the use of synthetic data, the internal pushback that came along with the changes that were being made, and what’s next for her team and the platform.
 The complete show notes for this episode can be found at twimlai.com/go/436.

Predictive Disease Risk Modeling at 23andMe with Subarna Sinha - #436

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.

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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 offer a multi-stage workflow for ingesting, processing, retrieving, and generating content using LLMs (like GPT-4) and other Generative AI tech. He shares how the system performs entity extraction to build a knowledge graph and how graph, vector, and object storage are integrated in the system. We dive into how the system uses “prompt compilation” to improve the results it gets from Large Language Models during generation. We conclude by discussing several use cases the approach supports, as well as future agent-based applications it enables.

The complete show notes for this episode can be found at twimlai.com/go/681.

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

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The complete show notes for this episode can be found at twimlai.com/go/680.

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

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The complete show notes for this episode can be found at twimlai.com/go/679.

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

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The complete show notes for this episode can be found at twimlai.com/go/678.

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

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, the video version of Meta’s Joint Embedding Predictive Architecture, aims to bridge the gap between human and machine intelligence by training models to learn abstract concepts in a more efficient predictive manner than generative models. V-JEPA uses a novel self-supervised training approach that allows it to learn from unlabeled video data without being distracted by pixel-level detail. Mido walks us through the process of developing the architecture and explains why it has the potential to revolutionize AI.

The complete show notes for this episode can be found at twimlai.com/go/677.

Predictive Disease Risk Modeling at 23andMe with Subarna Sinha - #436

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