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.
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.
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!
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).
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.
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.
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.
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?”
This week we’re talking about LLMs with Simon Willison. We can not avoid this topic. Last time it was Stable Diffusion breaking the internet. This time it’s LLMs breaking the internet. Large Language Models, ChatGPT, Bard, Claude, Bing, GitHub Copilot X, Cody…we cover it all.
Mat & Johnny interview everyone’s favorite LLM (Natalie with a special hat on) to see if it’d make a good hire as a Go dev. Also, Mat tries to turn it into his very own creepy robot by asking personal questions about his co-hosts. Things get weird. In a good way?
Try the hosted version if you prefer…
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.
By modifying a classical neural network, we introduced a new model inspired by biology which shows improved memory performance. It helps modeling brain processes and may eventually improve AI-based language tools such as ChatGPT.
This app uses Apple’s Core ML Stable Diffusion implementation to achieve maximum performance and speed on Apple Silicon based Macs while reducing memory requirements. (Also runs on Intel based Macs.)
Jerod & the gang catch you up on what’s new and poppin’ in the web development world. We go deep on GitHub Copilot X and the latest AI advancements, take a bathroom break while Nick talks about TypeScript 5 & continue the debate about the future of React.
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”
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.
The team behind this took LLaMA and trained it on ~800k GPT-3.5-Turbo responses. The result is a sophisticated assistant you can easily run on your laptop by downloading a 4GB file and cloning the repo.
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 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.
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.
This project brings stable diffusion models onto web browsers. Everything runs inside the browser with no server support. To our knowledge, this is the the world’s first stable diffusion completely running on the browser. Please checkout our demo webpage to try it out.
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.
Neural search and chat-based search are all the rage right now. However, You.com has been innovating in these topics long before ChatGPT. In this episode, Bryan McCann from You.com shares insights related to our mental model of Large Language Model (LLM) interactions and practical tips related to integrating LLMs into production systems.
Sualeh Asif from Control (an AI code editor):
We’ve been using GPT-4 for a few months internally, and we thought we’d highlight a few examples](https://github.com/anysphere/gpt-4-for-code) that have been both particularly impressive and really useful to us.
Here it’s converting a Python dict of member functions to esoteric but correct-on-first-try C++ code 👇
