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Changelog Interviews Changelog Interviews #541

ANTHOLOGY — Open source AI

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

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

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

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

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

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 🤯

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

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?”

Practical AI Practical AI #217

Accelerated data science with a Kaggle grandmaster

Daniel and Chris explore the intersection of Kaggle and real-world data science in this illuminating conversation with Christof Henkel, Senior Deep Learning Data Scientist at NVIDIA and Kaggle Grandmaster. Christof offers a very lucid explanation into how participation in Kaggle can positively impact a data scientist’s skill and career aspirations. He also shared some of his insights and approach to maximizing AI productivity uses GPU-accelerated tools like RAPIDS and DALI.

Chrome github.com

Automate your browser with GPT-4

Taxy uses GPT-4 to control your browser and perform repetitive actions on your behalf. Currently it allows you to define ad-hoc instructions. In the future it will also support saved and scheduled workflows.

Taxy’s current status is research preview. Many workflows fail or confuse the agent. If you’d like to hack on Taxy to make it better or test it on your own workflows, follow the instructions below to run it locally. If you’d like to know once it’s available for wider usage, you can sign up for our waitlist.

Ok that’s cool… 🤯

Here it is using Google Calendar with the prompt “Schedule standup tomorrow at 10am. Invite david@taxy.ai”

Automate your browser with GPT-4

AI (Artificial Intelligence) futureoflife.org

A petition to pause all AI experiments for at least 6 months

This open letter by the Future of Life institute has been signed by 1380 people (so far) including notable technologists such as Steve Wozniak, Stuart Russell, Emad Mostaque (Stability AI) & Elon Musk.

Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable. This confidence must be well justified and increase with the magnitude of a system’s potential effects. OpenAI’s recent statement regarding artificial general intelligence, states that “At some point, it may be important to get independent review before starting to train future systems, and for the most advanced efforts to agree to limit the rate of growth of compute used for creating new models.” We agree. That point is now.

Therefore, we call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4. This pause should be public and verifiable, and include all key actors. If such a pause cannot be enacted quickly, governments should step in and institute a moratorium.

Practical AI Practical AI #216

Explainable AI that is accessible for all humans

We are seeing an explosion of AI apps that are (at their core) a thin UI on top of calls to OpenAI generative models. What risks are associated with this sort of approach to AI integration, and is explainability and accountability something that can be achieved in chat-based assistants?

Beth Rudden of Bast.ai has been thinking about this topic for some time and has developed an ontological approach to creating conversational AI. We hear more about that approach and related work in this episode.

Steve Yegge about.sourcegraph.com

Cheating is all you need

Steve Yegge is very excited about LLMs and thinks the rest of us should be as well:

There is something legendary and historic happening in software engineering, right now as we speak, and yet most of you don’t realize at all how big it is.

LLMs aren’t just the biggest change since social, mobile, or cloud–they’re the biggest thing since the World Wide Web. And on the coding front, they’re the biggest thing since IDEs and Stack Overflow, and may well eclipse them both.

Steve’s been in the industry a long time. He worked at Amazon back when AWS was just a demo on some engineer’s laptop and he worked at Google when Kubernetes was just a demo on some engineer’s laptop.

The point: when Steve Yegge gets excited about something it probably means more than when most people get excited about something.

Changelog Interviews Changelog Interviews #532

Bringing Whisper and LLaMA to the masses

This week we’re talking with Georgi Gerganov about his work on Whisper.cpp and llama.cpp. Georgi first crossed our radar with whisper.cpp, his port of OpenAI’s Whisper model in C and C++. Whisper is a speech recognition model enabling audio transcription and translation. Something we’re paying close attention to here at Changelog, for obvious reasons. Between the invite and the show’s recording, he had a new hit project on his hands: llama.cpp. This is a port of Facebook’s LLaMA model in C and C++. Whisper.cpp made a splash, but llama.cpp is growing in GitHub stars faster than Stable Diffusion did, which was a rocket ship itself.

Josh Comeau joshwcomeau.com

The end of front-end development

Josh Comeau:

Over the past few months, I’ve spoken with lots of early-career devs who are getting more and more anxious about AI. They’ve seen the increasingly-impressive demos from tools like GPT-4, and they worry that by the time they’re fluent in HTML/CSS/JS, there won’t be any jobs left for them.

I couldn’t disagree more. I don’t think web developer jobs are going anywhere. And I’m getting pretty sick of the FUD? being spread online.

So, in this blog post, I’m going to share my hypothesis for what will happen. Things are going to change, but not in the scary way people are saying.

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