The Data Exchange with Ben Lorica

Uri Gneezy, Professor of Economics and Strategy at UC San Diego, discusses the importance of understanding incentives in various contexts, including software services. They explore tailoring behaviors, credible signaling, unintended consequences of incentives and nudges, the value of a common sense officer in organizations, and the role of incentives in AI development.

Dmitriy Ryaboy is the VP of AI Enablement at Ginkgo Bioworks, a startup that uses machine learning and AI to develop a wide range of applications.  The conversation focuses on the intersection of AI, machine learning, and biology, particularly in the field of synthetic biology.Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Overcast • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

Jian Zhang is co-founder, CTO, VP Engineering at Nexusflow AI a startup that uses Generative AI to build tools for Cybersecurity.  This conversation revolves around the integration of various AI components, with a specific focus on cybersecurity and function calling copilots.Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Overcast • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

Sarmad Qadri, founder and CEO of LastMile, discusses the need for software engineering rigor in AI development and the democratization of AI. Topics include AI config, AI workbook, experimentation and collaboration with generative AI frameworks. The podcast also explores the retrieval-augmented generation system, AI config and semantic retrieval as AI development tools, the concept of orchestration in software engineering teams, ETL focus and vector databases in AI data, and the importance of proper tools for AI development.

In this episode, Nir Shavit, Professor at MIT's Computer Science and Artificial Intelligence Laboratory, discusses the use of LLMs on CPUs and how model sparsity can accelerate open-source LLMs. They explore the process of fine-tuning models, comparing language models using benchmarks, and the benefits of sparsity and quantization in achieving smaller model size and faster performance. They also delve into the advantages of utilizing CPU resources for faster and cheaper inference, the viability of AMD GPUs for inference, and enterprises' focus on LLMs.

Chirag Yagnik, Co-founder of Arta, discusses the role of AI and machine learning in wealth management and the challenges of applying it in the finance industry. They explore the benefits of family offices and alternative assets, as well as the application of machine learning in portfolio management. The significance of domain-specific data and knowledge graphs in optimizing AI systems is also explored. The podcast touches on high frequency trading, generative AI in wealth management, and the fast-paced nature of quantitative finance.

Knowledge graph experts from data.world discuss their work on using knowledge graphs to improve the accuracy of language models for question answering on structured SQL databases. They explain the creation of a knowledge graph from a data warehouse, evaluate the effectiveness of knowledge graphs in improving question answering accuracy, and discuss how to convince organizations to adopt knowledge graphs for improved data exploration. They also highlight the benefits of knowledge graphs, compare RDF and property graphs, and emphasize the importance of improving knowledge graph accuracy and combining knowledge graphs with vector databases.

Max Mergenthaler and Azul Garza Ramirez from Nixtla talk about TimeGPT, a simplified model for time series analysis. They discuss its simplicity, performance, and potential integration with other tools. They also explore the role of expert judgment and the future impact of TimeGPT on forecasting jobs.

Waleed Kadous, Chief Scientist at Anyscale, discusses best practices for building applications leveraging large language models. Topics include heuristics for working with open source models, differences between Code Lama and GitHub Co-pilot, challenges in deploying open source models, using spending data to save data, fine-tuning models in supervised machine learning, and exploring the potential of multimodal models.

CEO of BlockApps and Co-Chair of the Enterprise Ethereum Alliance discusses web3 technologies, transitioning from mining to proof of stake in Ethereum, impact of Proof of Stake and NFTs in the crypto space, intersection of blockchain, AI, and crypto, creation and governance of base models for ML, current happenings in crypto and web3, advantages of blockchain and collapse of VC market