Large language models (LLMs) Icon

Large language models (LLMs)

A language model is a probability distribution over sequences of words. Given any sequence of words of length m, a language model assigns a probability P to the whole sequence. Language models generate probabilities by training on text corpora in one or many languages. Whew!
47 episodes
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Practical AI Practical AI #300

Mozart to Megadeath at CHRP

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2024-12-19T16:30:00Z #ai +2 🎧 22,947

Daniel and Chris groove with Jeff Smith, Founder and CEO at CHRP.ai. Jeff describes how CHRP anonymously analyzes emotional wellness data, derived from employees’ music preferences, giving HR leaders actionable insights to improve productivity, retention, and overall morale. By monitoring key trends and identifying shifts in emotional health across teams, CHRP.ai enables proactive decisions to ensure employees feel supported and engaged.

Practical AI Practical AI #299

Sidekick is an AI Shopify expert

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2024-12-11T22:15:00Z #ai +2 🎧 21,489

Today, Chris explores Shopify Magic and other AI offerings with Mike Tamir, Distinguished ML Engineer and Head of Machine Learning, and Matt Colyer, Director of Product Management for Sidekick. They talk about how Shopify uses generative AI and LLMs to enhance their products, and they take a deeper dive into Sidekick, a first-of-its-kind, AI-enabled commerce assistant that understands a merchant’s business (products, orders, customers) and has been trained to know all about Shopify.

Practical AI Practical AI #294

AI is changing the cybersecurity threat landscape

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2024-11-05T19:40:00Z #ai +3 🎧 25,850

This week, Chris is joined by Gregory Richardson, Vice President and Global Advisory CISO at BlackBerry, and Ismael Valenzuela, Vice President of Threat Research & Intelligence at BlackBerry. They address how AI is changing the threat landscape, why human defenders remain a key part of our cyber defenses, and the explain the AI standoff between cyber threat actors and cyber defenders.

Practical AI Practical AI #293

The path towards trustworthy AI

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2024-10-29T19:00:00Z #ai +2 🎧 25,616

Elham Tabassi, the Chief AI Advisor at the U.S. National Institute of Standards & Technology (NIST), joins Chris for an enlightening discussion about the path towards trustworthy AI. Together they explore NIST’s ‘AI Risk Management Framework’ (AI RMF) within the context of the White House’s ‘Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence’.

Practical AI Practical AI #289

Understanding what's possible, doable & scalable

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2024-10-03T15:45:00Z #ai +1 🎧 28,732

We are constantly hearing about disillusionment as it relates to AI. Some of that is probably valid, but Mike Lewis, an AI architect from Cincinnati, has proven that he can consistently get LLM and GenAI apps to the point of real enterprise value (even with the Big Cos of the world). In this episode, Mike joins us to share some stories from the AI trenches & highlight what it takes (practically) to show what is possible, doable & scalable with AI.

Practical AI Practical AI #288

GraphRAG (beyond the hype)

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2024-09-25T18:30:00Z #ai +1 🎧 31,265

Seems like we are hearing a lot about GraphRAG these days, but there are lots of questions: what is it, is it hype, what is practical? One of our all time favorite podcast friends, Prashanth Rao, joins us to dig into this topic beyond the hype. Prashanth gives us a bit of background and practical use cases for GraphRAG and graph data.

Practical AI Practical AI #284

Metrics Driven Development

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2024-08-29T20:45:00Z #ai +1 🎧 30,459

How do you systematically measure, optimize, and improve the performance of LLM applications (like those powered by RAG or tool use)? Ragas is an open source effort that has been trying to answer this question comprehensively, and they are promoting a “Metrics Driven Development” approach. Shahul from Ragas joins us to discuss Ragas in this episode, and we dig into specific metrics, the difference between benchmarking models and evaluating LLM apps, generating synthetic test data and more.

Practical AI Practical AI #283

Threat modeling LLM apps

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2024-08-22T13:30:00Z #ai +2 🎧 29,184

If you have questions at the intersection of Cybersecurity and AI, you need to know Donato at WithSecure! Donato has been threat modeling AI applications and seriously applying those models in his day-to-day work. He joins us in this episode to discuss his LLM application security canvas, prompt injections, alignment, and more.

JS Party JS Party #331

Building LLM agents in JS

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2024-07-18T18:30:00Z #javascript +2 🎧 13,282

KBall and returning guest Tejas Kumar dive into the topic of building LLM agents using JavaScript. What they are, how they can be useful (including how Tejas used home-built agents to double his podcasting productivity) & how to get started building and running your own agents, even all on your own device with local models.

Practical AI Practical AI #277

Vectoring in on Pinecone

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2024-07-10T17:30:00Z #ai +2 🎧 26,316

Daniel & Chris explore the advantages of vector databases with Roie Schwaber-Cohen of Pinecone. Roie starts with a very lucid explanation of why you need a vector database in your machine learning pipeline, and then goes on to discuss Pinecone’s vector database, designed to facilitate efficient storage, retrieval, and management of vector data.

Practical AI Practical AI #274

The perplexities of information retrieval

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2024-06-19T16:30:00Z #ai +2 🎧 27,133

Daniel & Chris sit down with Denis Yarats, Co-founder & CTO at Perplexity, to discuss Perplexity’s sophisticated AI-driven answer engine. Denis outlines some of the deficiencies in search engines, and how Perplexity’s approach to information retrieval improves on traditional search engine systems, with a focus on accuracy and validation of the information provided.

Practical AI Practical AI #272

Rise of the AI PC & local LLMs

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2024-06-04T18:45:00Z #ai +2 🎧 34,389

We’ve seen a rise in interest recently and a number of major announcements related to local LLMs and AI PCs. NVIDIA, Apple, and Intel are getting into this along with models like the Phi family from Microsoft. In this episode, we dig into local AI tooling, frameworks, and optimizations to help you navigate this AI niche, and we talk about how this might impact AI adoption in the longer term.

Practical AI Practical AI #271

AI in the U.S. Congress

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2024-05-29T14:30:00Z #ai +2 🎧 25,410

At the age of 72, U.S. Representative Don Beyer of Virginia enrolled at GMU to pursue a Master’s degree in C.S. with a concentration in Machine Learning.

Rep. Beyer is Vice Chair of the bipartisan Artificial Intelligence Caucus & Vice Chair of the NDC’s AI Working Group. He is the author of the AI Foundation Model Transparency Act & a lead cosponsor of the CREATE AI Act, the Federal Artificial Intelligence Risk Management Act & the Artificial Intelligence Environmental Impacts Act.

We hope you tune into this inspiring, nonpartisan conversation with Rep. Beyer about his decision to dive into the deep end of the AI pool & his leadership in bringing that expertise to Capitol Hill.

Practical AI Practical AI #267

Private, open source chat UIs

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2024-04-30T20:45:00Z #ai +2 🎧 26,948

We recently gathered some Practical AI listeners for a live webinar with Danny from LibreChat to discuss the future of private, open source chat UIs. During the discussion we hear about the motivations behind LibreChat, why enterprise users are hosting their own chat UIs, and how Danny (and the LibreChat community) is creating amazing features (like RAG and plugins).

Practical AI Practical AI #266

Mamba & Jamba

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2024-04-24T15:45:00Z #ai +1 🎧 24,397

First there was Mamba… now there is Jamba from AI21. This is a model that combines the best non-transformer goodness of Mamba with good ‘ol attention layers. This results in a highly performant and efficient model that AI21 has open sourced! We hear all about it (along with a variety of other LLM things) from AI21’s co-founder Yoav.

Practical AI Practical AI #263

Should kids still learn to code?

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2024-04-02T20:00:00Z #ai +3 🎧 26,959

In this fully connected episode, Daniel & Chris discuss NVIDIA GTC keynote comments from CEO Jensen Huang about teaching kids to code. Then they dive into the notion of “community” in the AI world, before discussing challenges in the adoption of generative AI by non-technical people. They finish by addressing the evolving balance between generative AI interfaces and search engines.

Practical AI Practical AI #261

Prompting the future

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2024-03-20T13:45:00Z #ai +2 🎧 31,298

Daniel & Chris explore the state of the art in prompt engineering with Jared Zoneraich, the founder of PromptLayer. PromptLayer is the first platform built specifically for prompt engineering. It can visually manage prompts, evaluate models, log LLM requests, search usage history, and help your organization collaborate as a team. Jared provides expert guidance in how to be implement prompt engineering, but also illustrates how we got here, and where we’re likely to go next.

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