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AI (Artificial Intelligence)

Machines simulating human characteristics and intelligence.
284 episodes
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Practical AI Practical AI #238

Fine-tuning vs RAG

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2023-09-06T12:30:00Z #ai +1 🎧 36,594

In this episode we welcome back our good friend Demetrios from the MLOps Community to discuss fine-tuning vs. retrieval augmented generation. Along the way, we also chat about OpenAI Enterprise, results from the MLOps Community LLM survey, and the orchestration and evaluation of generative AI workloads.

Practical AI Practical AI #237

Automating code optimization with LLMs

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2023-08-29T21:30:00Z #ai +1 🎧 32,618

You might have heard a lot about code generation tools using AI, but could LLMs and generative AI make our existing code better? In this episode, we sit down with Mike from TurinTech to hear about practical code optimizations using AI “translation” of slow to fast code. We learn about their process for accomplishing this task along with impressive results when automated code optimization is run on existing open source projects.

Practical AI Practical AI #236

The new AI app stack

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2023-08-23T12:00:00Z #ai +2 🎧 34,273

Recently a16z released a diagram showing the “Emerging Architectures for LLM Applications.” In this episode, we expand on things covered in that diagram to a more general mental model for the new AI app stack. We cover a variety of things from model “middleware” for caching and control to app orchestration.

Practical AI Practical AI #235

Blueprint for an AI Bill of Rights

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2023-08-09T16:20:00Z #ai +3 🎧 32,236

In this Fully Connected episode, Daniel and Chris kick it off by noting that Stability AI released their SDXL 1.0 LLM! They discuss its virtues, and then dive into a discussion regarding how the United States, European Union, and other entities are approaching governance of AI through new laws and legal frameworks. In particular, they review the White House’s approach, noting the potential for unexpected consequences.

Practical AI Practical AI #234

Vector databases (beyond the hype)

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2023-08-01T18:30:00Z #ai +1 🎧 36,363

There’s so much talk (and hype) these days about vector databases. We thought it would be timely and practical to have someone on the show that has been hands on with the various options and actually tried to build applications leveraging vector search. Prashanth Rao is a real practitioner that has spent and huge amount of time exploring the expanding set of vector database offerings. After introducing vector database and giving us a mental model of how they fit in with other datastores, Prashanth digs into the trade offs as related to indices, hosting options, embedding vs. query optimization, and more.

Practical AI Practical AI #233

There's a new Llama in town

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2023-07-25T19:00:00Z #ai 🎧 30,790

It was an amazing week in AI news. Among other things, there is a new NeRF and a new Llama in town!!! Zip-NeRF can create some amazing 3D scenes based on 2D images, and Llama 2 from Meta promises to change the LLM landscape. Chris and Daniel dive into these and they compare some of the recently released OpenAI functionality to Anthropic’s Claude 2.

Changelog Interviews Changelog Interviews #549

Storytime with Steve Yegge

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2023-07-20T21:00:00Z #ai +3 🎧 32,355

This week it’s storytime with Steve Yegge! Steve came out of retirement to join Sourcegraph as Head of Engineering. Their next frontier is Cody, their AI coding assistant that answers code questions and writes code for you by reading your entire codebase and the code graph. But, we really spent a lot of time talking with Steve about his time at Amazon, Google, and Grab. Ok, it’s storytime!

Practical AI Practical AI #232

Legal consequences of generated content

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2023-07-18T18:30:00Z #ai +2 🎧 30,925

As a technologist, coder, and lawyer, few people are better equipped to discuss the legal and practical consequences of generative AI than Damien Riehl. He demonstrated this a couple years ago by generating, writing to disk, and then releasing every possible musical melody. Damien joins us to answer our many questions about generated content, copyright, dataset licensing/usage, and the future of knowledge work.

Practical AI Practical AI #231

A developer's toolkit for SOTA AI

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2023-07-12T21:00:00Z #ai +2 🎧 28,651

Chris sat down with Varun Mohan and Anshul Ramachandran, CEO / Cofounder and Lead of Enterprise and Partnership at Codeium, respectively. They discussed how to streamline and enable modern development in generative AI and large language models (LLMs). Their new tool, Codeium, was born out of the insights they gleaned from their work in GPU software and solutions development, particularly with respect to generative AI, large language models, and supporting infrastructure. Codeium is a free AI-powered toolkit for developers, with in-house models and infrastructure - not another API wrapper.

Practical AI Practical AI #230

Cambrian explosion of generative models

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2023-07-06T17:30:00Z #ai +3 🎧 29,571

In this Fully Connected episode, Daniel and Chris explore recent highlights from the current model proliferation wave sweeping the world - including Stable Diffusion XL, OpenChat, Zeroscope XL, and Salesforce XGen. They note the rapid rise of open models, and speculate that just as in open source software, open models will dominate the future. Such rapid advancement creates its own problems though, so they finish by itemizing concerns such as cybersecurity, workflow productivity, and impact on human culture.

Practical AI Practical AI #229

Automated cartography using AI

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2023-06-28T14:30:00Z #ai +1 🎧 28,725

Your feed might be dominated by LLMs these days, but there are some amazing things happening in computer vision that you shouldn’t ignore! In this episode, we bring you one of those amazing stories from Gabriel Ortiz, who is working with the government of Cantabria in Spain to automate cartography and apply AI to geospatial analysis. We hear about how AI tooling fits into the GIS workflow, and Gabriel shares some of his recent work (including work that can identify individual people, invasive plant species, building and more from aerial survey data).

Practical AI Practical AI #228

From ML to AI to Generative AI

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2023-06-21T13:30:00Z #ai +2 🎧 35,069

Chris and Daniel take a step back to look at how generative AI fits into the wider landscape of ML/AI and data science. They talk through the differences in how one approaches “traditional” supervised learning and how practitioners are approaching generative AI based solutions (such as those using Midjourney or GPT family models). Finally, they talk through the risk and compliance implications of generative AI, which was in the news this week in the EU.

Practical AI Practical AI #226

Accidentally building SOTA AI

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2023-06-06T20:45:00Z #ai +2 🎧 27,398

Lately.AI has been working for years on content generation systems that capture your unique “voice” and are tailored to your unique audience. At first, they didn’t know that they were going to build an AI system, but now they have a state-of-the-art generative platform that provides much more than “prompting” out of thin air. Lately.AI’s CEO Kate explain their journey, her perspective on generative AI in marketing, and much more in this episode!

Practical AI Practical AI #225

Controlled and compliant AI applications

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2023-05-31T17:00:00Z #ai +2 🎧 27,696

You can’t build robust systems with inconsistent, unstructured text output from LLMs. Moreover, LLM integrations scare corporate lawyers, finance departments, and security professionals due to hallucinations, cost, lack of compliance (e.g., HIPAA), leaked IP/PII, and “injection” vulnerabilities.

In this episode, Chris interviews Daniel about his new company called Prediction Guard, which addresses these issues. They discuss some practical methodologies for getting consistent, structured output from compliant AI systems. These systems, driven by open access models and various kinds of LLM wrappers, can help you delight customers AND navigate the increasing restrictions on “GPT” models.

Changelog Interviews Changelog Interviews #541

ANTHOLOGY — Open source AI

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2023-05-24T21:00:00Z #oss +1
🎧 26,431

This week on The Changelog we’re taking you to the hallway track of The Linux Foundation’s Open Source Summit North America 2023 in Vancouver, Canada. Today’s anthology episode features: Beyang Liu (Co-founder and CTO at Sourcegraph), Denny Lee (Developer Advocate at Databricks), and Stella Biderman (Executive Director and Head of Research at EleutherAI).

Special thanks to our friends at GitHub for sponsoring us to attend this conference as part of Maintainer Month.

Practical AI Practical AI #224

Data augmentation with LlamaIndex

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2023-05-23T16:15:00Z #ai +1 🎧 29,098

Large Language Models (LLMs) continue to amaze us with their capabilities. However, the utilization of LLMs in production AI applications requires the integration of private data. Join us as we have a captivating conversation with Jerry Liu from LlamaIndex, where he provides valuable insights into the process of data ingestion, indexing, and query specifically tailored for LLM applications. Delving into the topic, we uncover different query patterns and venture beyond the realm of vector databases.

Practical AI Practical AI #223

Creating instruction tuned models

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2023-05-16T18:20:00Z #ai +1 🎧 27,489

At the recent ODSC East conference, Daniel got a chance to sit down with Erin Mikail Staples to discuss the process of gathering human feedback and creating an instruction tuned Large Language Models (LLM). They also chatted about the importance of open data and practical tooling for data annotation and fine-tuning. Do you want to create your own custom generative AI models? This is the episode for you!

Practical AI Practical AI #222

The last mile of AI app development

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2023-05-11T13:00:00Z #ai +3 🎧 29,427

There are a ton of problems around building LLM apps in production and the last mile of that problem. Travis Fischer, builder of open AI projects like @ChatGPTBot, joins us to talk through these problems (and how to overcome them). He helps us understand the hierarchy of complexity from simple prompting to augmentation, agents, and fine-tuning. Along the way we discuss the frontend developer community that is rapidly adopting AI technology via Typescript (not Python).

Practical AI Practical AI #221

Large models on CPUs

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2023-05-02T16:00:00Z #ai +1 🎧 30,050

Model sizes are crazy these days with billions and billions of parameters. As Mark Kurtz explains in this episode, this makes inference slow and expensive despite the fact that up to 90%+ of the parameters don’t influence the outputs at all.

Mark helps us understand all of the practicalities and progress that is being made in model optimization and CPU inference, including the increasing opportunities to run LLMs and other Generative AI models on commodity hardware.

Practical AI Practical AI #220

Causal inference

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2023-04-25T16:35:00Z #ai 🎧 28,774

With all the LLM hype, it’s worth remembering that enterprise stakeholders want answers to “why” questions. Enter causal inference. Paul Hünermund has been doing research and writing on this topic for some time and joins us to introduce the topic. He also shares some relevant trends and some tips for getting started with methods including double machine learning, experimentation, difference-in-difference, and more.

Practical AI Practical AI #219

Capabilities of LLMs 🤯

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2023-04-19T21:00:00Z #ai +1 🎧 34,682

Large Language Model (LLM) capabilities have reached new heights and are nothing short of mind-blowing! However, with so many advancements happening at once, it can be overwhelming to keep up with all the latest developments. To help us navigate through this complex terrain, we’ve invited Raj - one of the most adept at explaining State-of-the-Art (SOTA) AI in practical terms - to join us on the podcast.

Raj discusses several intriguing topics such as in-context learning, reasoning, LLM options, and related tooling. But that’s not all! We also hear from Raj about the rapidly growing data science and AI community on TikTok.

Practical AI Practical AI #218

Computer scientists as rogue art historians

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2023-04-12T13:30:00Z #ai +2 🎧 25,016

What can art historians and computer scientists learn from one another? Actually, a lot! Amanda Wasielewski joins us to talk about how she discovered that computer scientists working on computer vision were actually acting like rogue art historians and how art historians have found machine learning to be a valuable tool for research, fraud detection, and cataloguing. We also discuss the rise of generative AI and how we this technology might cause us to ask new questions like: “What makes a photograph a photograph?”

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