In this special episode, we interview some of the sponsors and teams from a recent case competition organized by Purdue University, Microsoft, INFORMS, and SIL International. 170+ teams from across the US and Canada participated in the competition, which challenged students to create AI-driven systems to caption images in three languages (Thai, Kyrgyz, and Hausa).

There are some big AI-related controversies swirling, and it’s time we talk about them. A lawsuit has been filed against GitHub, Microsoft, and OpenAI related to Copilot code suggestions, and many people have been disturbed by the output of Meta AI’s Galactica model. Does Copilot violate open source licenses? Does Galactica output dangerous science-related content? In this episode, we dive into the controversies and risks, and we discuss the benefits of these technologies.

Online platforms and their users are susceptible to a barrage of threats – from disinformation to extremism to terror. Daniel and Chris chat with Matar Haller, VP of Data at ActiveFence, a leader in identifying online harm – is using a combination of AI technology and leading subject matter experts to provide Trust & Safety teams with precise, real-time data, in-depth intelligence, and automated tools to protect users and ensure safe online experiences.

Chris sits down with Ankur Goyal to talk about DocQuery, Impira’s new open source ML model. DocQuery lets you ask questions about semi-structured data (like invoices) and unstructured documents (like contracts) using Large Language Models (LLMs). Ankur illustrates many of the ways DocQuery can help people tame documents, and references Chris’s real life tasks as a non-profit director to demonstrate that DocQuery is indeed practical AI.

This working implementation of text-to-3D (powered by Stable Diffusion) didn’t take six months, like Simon predicted it would. Although I will concede that it’s not a 3D environment that can then go into in a game engine, but I’m sure that’s just a few more weeks away this point.

From the readme:

This project is a work-in-progress, and contains lots of differences from the paper. Also, many features are still not implemented now. The current generation quality cannot match the results from the original paper, and many prompts still fail badly!

It’s one thing to gather some labels for your data. It’s another thing to integrate data labeling into your workflows and infrastructure in a scalable, secure, and useful way. Mark from Xelex joins us to talk through some of what he has learned after helping companies scale their data annotation efforts. We get into workflow management, labeling instructions, team dynamics, and quality assessment. This is a super practical episode!

They’re really putting the Open in OpenAI with this one…

Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. We show that the use of such a large and diverse dataset leads to improved robustness to accents, background noise and technical language. Moreover, it enables transcription in multiple languages, as well as translation from those languages into English. We are open-sourcing models and inference code to serve as a foundation for building useful applications and for further research on robust speech processing.

We might need to give this a spin on our transcripts. Who knows, maybe our next big innovation could be The Changelog in German, French, Spanish, etc!

WeightWatcher, created by Charles Martin, is an open source diagnostic tool for analyzing Neural Networks without training or even test data! Charles joins us in this episode to discuss the tool and how it fills certain gaps in current model evaluation workflows. Along the way, we discuss statistical methods from physics and a variety of practical ways to modify your training runs.

This week on The Changelog we’re talking about Stable Diffusion, DALL-E, and the impact of AI generated art. We invited our good friend Simon Willison on the show today because he wrote a very thorough blog post titled, “Stable Diffusion is a really big deal.”

You may know Simon from his extensive contributions to open source software. Simon is a co-creator of the Django Web framework (which we don’t talk about at all on this show), he’s the creator of Datasette, a multi-tool for exploring and publishing data (which we do talk about on this show)…most of all Simon is a very insightful thinker, which he puts on display here on this episode. We talk from all the angles of this topic, the technical, the innovation, the future and possibilities, the ethical and the moral – we get into it all. The question is, will this era be known as the initial push back to the machine?

So you got tired of AI just suggesting code edits, and now you want it to help you run code, too. Silly human, you have come to the right place. This will take five steps.

This gets an A+ for creativity. Fire up your shell, then launch Neovim. Then shell out with :VimShell to get back to where you started, but with Copilot suggestions.

My guess is the ergonomics of this are… bad. But a cool hack, regardless!

The new stable diffusion model is everywhere! Of course you can use this model to quickly and easily create amazing, dream-like images to post on twitter, reddit, discord, etc., but this technology is also poised to be used in very pragmatic ways across industry. In this episode, Chris and Daniel take a deep dive into all things stable diffusion. They discuss the motivations for the work, the model architecture, and the differences between this model and other related releases (e.g., DALL·E 2).

(Image from stability.ai)

Based on the excellent Gradio library that we’ve featured on Practical AI.

AI is increasingly being applied in creative and artistic ways, especially with recent tools integrating models like Stable Diffusion. This is making some artists mad. How should we be thinking about these trends more generally, and how can we as practitioners release and license models anticipating human impacts? We explore this along with other topics (like AI models detecting swimming pools 😊) in this fully connected episode.

Like many software engineers, Matt Bilyeu receives multiple emails from recruiters weekly. And, because he’s polite (and for other reasons) he tries to respond (politely) to all of them. But…

It would be ideal if I could automate sending these responses. Assuming I get four such emails per week and that it takes two minutes to read and respond to each one, automating this would save me about seven hours of administrative work per year.

Enter the GPT-3 API and some code that gets run by a future cron job (now that he’s tested this on a handful of emails) and Matt auto-responds to al the emails, continues to be polite, while also saving (his) time. It’s AI Matt responding the way real Matt would.

Simon Willison explains what it is:

Stable Diffusion is a new “text-to-image diffusion model” that was released to the public by Stability.ai six days ago, on August 22nd.

It’s similar to models like Open AI’s DALL-E, but with one crucial difference: they released the whole thing.

And why it’s a really big deal:

In just a few days, there has been an explosion of innovation around it. The things people are building are absolutely astonishing.

He then details some of the innovation and it is staggering, to say the least. Open FTW!

In this Fully-Connected episode, Daniel and Chris discuss concerns of privacy in the face of ever-improving AI / ML technologies. Evaluating AI’s impact on privacy from various angles, they note that ethical AI practitioners and data scientists have an enormous burden, given that much of the general population may not understand the implications of the data privacy decisions of everyday life.

This intentionally thought-provoking conversation advocates consideration and action from each listener when it comes to evaluating how their own activities either protect or violate the privacy of those whom they impact.

Differentiating between what is real versus what is fake on the internet can be challenging. Historically, AI deepfakes have only added to the confusion and chaos, but when labeled and intended for good, deepfakes can be extremely helpful. But with all of the misinformation surrounding deepfakes, it can be hard to see the benefits they bring. Lior Hakim, CTO at Hour One, joins Chris and Daniel to shed some light on the practical uses of deepfakes. He addresses the AI technology behind deepfakes, how to make positive use of deep fakes such as breaking down communications barriers, and shares how Hour One specializes in the development of virtual humans for use in professional video communications.

Daniel and Chris cover the AI news of the day in this wide-ranging discussion. They start with Truss from Baseten while addressing how to categorize AI infrastructure and tools. Then they move on to transformers (again!), and somehow arrive at an AI pilot model from CMU that can navigate crowded airspace (much to Chris’s delight).

Chip Huyen:

As machine learning moves towards real-time, streaming technology is becoming increasingly important for data scientists. Like many people coming from a machine learning background, I used to dread streaming. In our recent survey, almost half of the data scientists we asked said they would like to move from batch prediction to online prediction but can’t because streaming is hard, both technically and operationally…

Over the last year, working with a co-founder who’s super deep into streaming, I’ve learned that streaming can be quite intuitive. This post is an attempt to rephrase what I’ve learned.

AlphaFold is an AI system developed by DeepMind that predicts a protein’s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiment, and is accelerating research in nearly every field of biology. Daniel and Chris delve into protein folding, and explore the implications of this revolutionary and hugely impactful application of AI.

This is an attempt to build a locally hosted version of GitHub Copilot. It uses the SalesForce CodeGen models inside of NVIDIA’s Triton Inference Server with the FasterTransformer backend.

Once you get it all set up, you can point VS Code’s Copilot plugin at it!

Every year Mozilla releases an Internet Health Report that combines research and stories exploring what it means for the internet to be healthy. This year’s report is focused on AI. In this episode, Solana and Bridget from Mozilla join us to discuss the power dynamics of AI and the current state of AI worldwide. They highlight concerning trends in the application of this transformational technology along with positive signs of change.

In this Fully-Connected episode, Chris and Daniel explore the geopolitics, economics, and power-brokering of artificial intelligence. What does control of AI mean for nations, corporations, and universities? What does control or access to AI mean for conflict and autonomy? The world is changing rapidly, and the rate of change is accelerating. Daniel and Chris look behind the curtain in the halls of power.

In this Fully-Connected episode, Daniel and Chris explore DALL-E 2, the amazing new model from Open AI that generates incredibly detailed novel images from text captions for a wide range of concepts expressible in natural language. Along the way, they acknowledge that some folks in the larger AI community are suggesting that sophisticated models may be approaching sentience, but together they pour cold water on that notion. But they can’t seem to get away from DALL-E’s images of raccoons in space, and of course, who would want to?

Matija Sosic (Co-founder & CEO at Wasp) shares what he thinks programming is going to look like in the near future.

When thinking about how ML code generation affects the overall development process, there is one thing to consider that often doesn’t immediately spring to mind when looking at the impressive Copilot examples. The question is - what happens with the code once it is generated? Who is responsible for it and who will maintain and refactor it in the future?

Although ML code generation helps with getting the initial code written, it cannot do much beyond that - if that code is to be maintained and changed in the future … the developer still needs to fully own and understand it.

Generated code accepted blindly is creating tech debt!

In other words, it means Copilot and similar solutions do not reduce the code complexity nor the amount of knowledge required to build features, they just help write the initial code faster, and bring the knowledge/examples closer to the code (which is really helpful). If a developer accepts the generated code blindly, they are just creating tech debt and pushing it forward.