The MAD Podcast with Matt Turck

Making AI Work: Fine-Tuning, Inference, Memory | Sharon Zhou, CEO, Lamini

Jul 25, 2024

Sharon Zhou, CEO of Lamini, discusses the evolving AI market, Inference, Memory Tuning, AI agents, real-time challenges, and Lamini's future. Topics include GPU shortage, AMD vs Nvidia, LoRA, and reasoning vs pure compute in AI.

43:55

Creator website

Episode guests

Sharon Zhou

AI Summary

Highlights

AI Chapters

Episode notes

Podcast summary created with Snipd AI

Quick takeaways

Enterprises are shifting towards deeper AI use cases for industry breakthroughs.

Memory tuning optimizes AI models for accurate recall and improved performance.

Deep dives

Enterprise AI Trends in 2024

Enterprise AI in 2024 is transitioning from tackling shallow use cases like composing emails to addressing deeper challenges. Organizations are focusing on advancing AI applications beyond basic functions to achieve industry breakthroughs, such as enhancing workforce training and accelerating drug discovery processes. The sentiment in the market reflects a shift towards deeper use cases that require collaboration across data science, engineering, and infrastructure teams.

Memory Tuning for Model Accuracy

01:28

Achieving Success with AI in Enterprises

01:27

Enhanced Model Accuracy Leading to New Use Cases

01:38

Key Differentiators in Inference Market Mapping

01:34

Intro

2min

Evolution of Enterprise AI Applications and Infrastructure

18min

Understanding AI Model Training and Tuning Process

2min

Discussion on LEMONI's Output, GPU Optimization, and Inference Differentiation

3min

Memory Tuning for Improved AI Accuracy

10min

Agents in AI: Leveraging LLM and Case Study on SQL Agent

6min

Debating the Role of Reasoning and Symbolic Components in AI Models

2min

In this episode, we reconnect with Sharon Zhou, co-founder and CEO of Lamini, to dive deep into the ever-evolving world of enterprise AI.

We discuss how the AI hype is evolving and what enterprises are doing to stay ahead, break down the different players in the Inference market, explore how Memory Tuning is reducing hallucinations in AI models, the role of agents in enterprise AI, and the challenges of making them real-time and reliable.

Lamini

Website - https://www.lamini.ai

Twitter - https://x.com/laminiai

Sharon Zhou

LinkedIn - https://www.linkedin.com/in/zhousharon

Twitter - https://x.com/realsharonzhou

FIRSTMARK

Website - https://firstmark.com

Twitter - https://twitter.com/FirstMarkCap

Matt Turck (Managing Director)

LinkedIn - https://www.linkedin.com/in/turck/

Twitter - https://twitter.com/mattturck

(00:00) Intro

(02:18) The state of the AI market in July, 2024

(10:51) What is Lamini?

(11:43) What is Inference?

(15:36) GPU shortage in the enterprise

(18:06) AMD vs Nvidia

(22:10) What is Lamini's final product?

(25:30) What is Memory Tuning?

(29:01) What is LoRA?

(32:39) More on Memory Tuning

(35:51) Sharon's perspective on AI agents

(40:01) What is next for Lamini?

(41:54) Reasoning vs pure compute in AI

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The MAD Podcast with Matt Turck

Making AI Work: Fine-Tuning, Inference, Memory | Sharon Zhou, CEO, Lamini

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Enterprise AI Trends in 2024

Organizational Structure and AI Development

Hybrid AI Model Deployment Strategies

Memory Tuning for Enhanced AI Performance

Memory Tuning for Model Accuracy

Achieving Success with AI in Enterprises

Enhanced Model Accuracy Leading to New Use Cases

Key Differentiators in Inference Market Mapping

Remember Everything You Learn from Podcasts