I love the name “YOLO” for this because it’s single-stage, but I have to laugh that it’s now on its sixth version. You only live once… six times? 😆

Drausin Wulsin, Director of ML at Immunai, joins Daniel & Chris to talk about the role of AI in immunotherapy, and why it is proving to be the foremost approach in fighting cancer, autoimmune disease, and infectious diseases.

The large amount of high dimensional biological data that is available today, combined with advanced machine learning techniques, creates unique opportunities to push the boundaries of what is possible in biology.

To that end, Immunai has built the largest immune database called AMICA that contains tens of millions of cells. The company uses cutting-edge transfer learning techniques to transfer knowledge across different cell types, studies, and even species.

This is a minimal implementation of DALL·E Mini. It has been stripped to the bare essentials necessary for doing inference, and converted to PyTorch. The only third party dependencies are torch for the torch model and flax for the flax model.

How much mini-er can it get from here? 🤔

While scaling up machine learning at Instacart, Montana Low and Lev Kokotov discovered just how much you can do with the Postgres database. They are building on that work with PostgresML, an extension to the database that lets you train and deploy models to make online predictions using only SQL. This is super practical discussion that you don’t want to miss!

While still in its infancy, MLOps has attracted machine learning engineers and software engineers in general. With every new paradigm comes new challenges and opportunities to learn. In this primer, we highlight a few available resources to upskill and inform yourself on the latest in the world of MLOps.

Good resources, regardless of whether you think MLOps is its own thing or should be rolled into DevOps.

Could we create a digital human that processes data in a variety of modalities and detects emotions? Well, that’s exactly what NTT DATA Services is trying to do, and, in this episode, Theresa Kushner joins us to talk about their motivations, use cases, current systems, progress, and related ethical issues.

DALL-E can generate some amazing results, but we’re still in a phase of AI’s progress where having humans involved in the process is just better. Here’s how the authors of this workflow explain it:

Generative art is a creative process. While recent advances of DALL·E unleash people’s creativity, having a single-prompt-single-output UX/UI locks the imagination to a single possibility, which is bad no matter how fine this single result is. DALL·E Flow is an alternative to the one-liner, by formalizing the generative art as an iterative procedure.

dot (aka Deepfake Offensive Toolkit) makes real-time, controllable deepfakes ready for virtual cameras injection. dot is created for performing penetration testing against e.g. identity verification and video conferencing systems, for the use by security analysts, Red Team members, and biometrics researchers.

What’s crazy is dot deepfakes don’t require any additional training. 🤯

In this “fully connected” episode of the podcast, we catch up on some recent developments in the AI world, including a new model from DeepMind called Gato. This generalist model can play video games, caption images, respond to chat messages, control robot arms, and much more. We also discuss the use of AI in the entertainment industry (e.g., in new Top Gun movie).

Hugging Face is increasingly becomes the “hub” of AI innovation. In this episode, Merve Noyan joins us to dive into this hub in more detail. We discuss automation around model cards, reproducibility, and the new community features. If you are wanting to engage with the wider AI community, this is the show for you!

Last week I logged the very impressive Imagen project, which smarter people than me have said is the SOTA for text-to-image synthesis. Now a WIP implementation is just a pip install imagen-pytorch away.

Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pretrained T5 model (attention network). It also contains dynamic clipping for improved classifier free guidance, noise level conditioning, and a memory efficient unet design.

Google researchers are giving DALL-E a run for its money:

Our key discovery is that generic large language models (e.g. T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model.

Don’t all AI methods need a bunch of data to work? How could AI help document and revitalize endangered languages with “human-in-the-loop” or “active learning” methods? Sarah Moeller from the University of Florida joins us to discuss those and other related questions. She also shares many of her personal experiences working with languages in low resource settings.

Big news from our friends at Hugging Face:

Hugging Face is now the fastest growing community & most used platform for machine learning! With 100,000 pre-trained models & 10,000 datasets hosted on the platform for NLP, computer vision, speech, time-series, biology, reinforcement learning, chemistry and more, the Hugging Face Hub has become the Home of Machine Learning to create, collaborate, and deploy state-of-the-art models.

What will they spend the money on? Good stuff:

Thanks to the new funding, we’ll be doubling down on research, open-source, products and responsible democratization of AI.

AI is discovering new drugs. Sound like science fiction? Not at Absci! Sean and Joshua join us to discuss their AI-driven pipeline for drug discovery. We discuss the tech along with how it might change how we think about healthcare at the most fundamental level.

Train and deploy industry-standard or completely custom machine learning models, directly powered by your business data, into your production stack, with an open source Postgres extension.

Is there anything Postgres can’t do?! 😉

OpenAI’s new SOTA text-to-image neural network is producing some “insane results”. The linked repo is an open source implementation of the same technique.

We all hear a lot about MLOps these days, but where does MLOps end and DevOps begin? Our friend Luis from OctoML joins us in this episode to discuss treating AI/ML models as regular software components (once they are trained and ready for deployment). We get into topics including optimization on various kinds of hardware and deployment of models at the edge.

Eric Jang was recently on the job market (finally landing at [Halodi Robotics])(https://halodi.com/) and in this post he shares his process and view of the job market today. He also has some insights on where it’s headed. In brief:

In the future, every successful tech company will use their data moats to build some variant of an Artificial General Intelligence.

Nice to see some efforts around standardizing MLOps. Here’s their high-level selling points:

• 📦 Docker containers without the pain
• 🤬️ No more CUDA hell
• ✅ Define the inputs and outputs for your model with standard Python
• 🎁Automatic HTTP prediction server
• 🥞 Automatic queue worker
• ☁️ Cloud storage
• 🚀 Ready for production

Curious how OpenAI’s new DALL-E 2 manages to generate impressive artwork from short natural language prompts? This article breaks down the steps then focuses in on how it does the image generation step.

In the fourth “AI in Africa” spotlight episode, we welcome Leonida Mutuku and Godliver Owomugisha, two experts in applying advanced technology in agriculture. We had a great discussion about ending poverty, hunger, and inequality in Africa via AI innovation. The discussion touches on open data, relevant models, ethics, and more.

Abubakar Abid joins Daniel and Chris for a tour of Gradio and tells them about the project joining Hugging Face. What’s Gradio? The fastest way to demo your machine learning model with a friendly web interface, allowing non-technical users to access, use, and give feedback on models.

This last week has been a big week for AI news. BigScience is training a huge language model (while the world watches), and NVIDIA announced their latest “Hopper” GPUs. Chris and Daniel discuss these and other topics on this fully connected episode!

What happens when your data operations grow to Internet-scale? How do thousands or millions of data producers and consumers efficiently, effectively, and productively interact with each other? How are varying formats, protocols, security levels, performance criteria, and use-case specific characteristics meshed into one unified data fabric? Chris and Daniel explore these questions in this illuminating and Fully-Connected discussion that brings this new data technology into the light.