We seem to be experiencing a surge of video generation tools, models, and applications. However, video generation models generally struggle with some basic physics, like realistic walking motion. This leaves some generated videos lacking true motion with disappointing, simplistic panning camera views. Genmo is focused on the motion side of video generation and has released some of the best open models. Paras joins us to discuss video generation and their journey at Genmo.

Nous Research has been pumping out some of the best open access LLMs using SOTA data synthesis techniques. Their Hermes family of models is incredibly popular! In this episode, Karan from Nous talks about the origins of Nous as a distributed collective of LLM researchers. We also get into fine-tuning strategies and why data synthesis works so well.

Tivadar Danka is an educator and content creator in the machine learning space, and he is writing a book to help practitioners go from high school mathematics to mathematics of neural networks. His explanations are lucid and easy to understand. You have never had such a fun and interesting conversation about calculus, linear algebra, and probability theory before!

From MIT researchers who have an AI system that rapidly predicts how two proteins will attach, to Facebook’s first high-performance self-supervised algorithm that works for speech, vision, and text, Daniel and Chris survey the AI landscape for notable milestones in the application of AI in industry and research.

We’re super excited to welcome Jay Alammar to the show. Jay is a well-known AI educator, applied NLP practitioner at co:here, and author of the popular blog, “The Illustrated Transformer.” In this episode, he shares his ideas on creating applied NLP solutions, working with large language models, and creating educational resources for state-of-the-art AI.

We’ve all experienced pain moving from local development, to testing, and then on to production. This cycle can be long and tedious, especially as AI models and datasets are integrated. Modal is trying to make this loop of development as seamless as possible for AI practitioners, and their platform is pretty incredible!

Erik from Modal joins us in this episode to help us understand how we can run or deploy machine learning models, massively parallel compute jobs, task queues, web apps, and much more, without our own infrastructure.

Chris and Daniel dive into what Trump’s impending second term could mean for AI companies, model developers, and regulators, unpacking the potential shifts in policy and innovation. Next, they discuss the latest models, like Qwen, that blur the performance gap between open and closed systems. Finally, they explore new AI tools for meeting clones and AI-driven commerce, sparking a conversation about the balance between digital convenience and fostering genuine human connections.

In addition to being a Developer Advocate at Hugging Face, Thomas Simonini is building next-gen AI in games that can talk and have smart interactions with the player using Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP). He also created a Deep Reinforcement Learning course that takes a DRL beginner to from zero to hero. Natalie and Chris explore what’s involved, and what the implications are, with a focus on the development path of the new AI data scientist.

Recently, Intel’s Liftoff program for startups and Prediction Guard hosted the first ever “Advent of GenAI” hackathon. 2,000 people from all around the world participated in Generate AI related challenges over 7 days. In this episode, we discuss the hackathon, some of the creative solutions, the idea behind it, and more.

First there was Mamba… now there is Jamba from AI21. This is a model that combines the best non-transformer goodness of Mamba with good ‘ol attention layers. This results in a highly performant and efficient model that AI21 has open sourced! We hear all about it (along with a variety of other LLM things) from AI21’s co-founder Yoav.

Recently, we briefly mentioned the concept of “Activation Hacking” in the episode with Karan from Nous Research. In this fully connected episode, Chris and Daniel dive into the details of this model control mechanism, also called “representation engineering”. Of course, they also take time to discuss the new Sora model from OpenAI.

We’ve been hearing about “serverless” CPUs for some time, but it’s taken a while to get to serverless GPUs. In this episode, Erik from Banana explains why its taken so long, and he helps us understand how these new workflows are unlocking state-of-the-art AI for application developers. Forget about servers, but don’t forget to listen to this one!

The term “foundation” model has been around since about the middle of last year when a research group at Stanford published the comprehensive report On the Opportunities and Risks of Foundation Models. The naming of these models created some strong reactions, both good and bad. In this episode, Chris and Daniel dive into the ideas behind the report.

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)

Worlds are colliding! This week we join forces with the hosts of the MLOps.Community podcast to discuss all things machine learning operations. We talk about how the recent explosion of foundation models and generative models is influencing the world of MLOps, and we discuss related tooling, workflows, perceptions, etc.

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.

We have all seen how AI models fail, sometimes in spectacular ways. Yaron Singer joins us in this episode to discuss model vulnerabilities and automatic prevention of bad outcomes. By separating concerns and creating a “firewall” around your AI models, it’s possible to secure your AI workflows and prevent model failure.

Daniel and Chris talk with Lukas Egger, Head of Innovation Office and Strategic Projects at SAP Business Process Intelligence. Lukas describes what it takes to bring a culture of innovation into an organization, and how to infuse product development with that innovation culture. He also offers suggestions for how to mitigate challenges and blockers.

Alon from Greeneye and Moses from ClearML blew us away when they said that they are training 1000’s of models a year that get deployed to Kubernetes clusters on tractors. Yes… we said tractors, as in farming! This is a super cool discussion about MLOps solutions at scale for interesting use cases in agriculture.

In the second of the “AI in Africa” spotlight episodes, we welcome guests from Radiant Earth to talk about machine learning for earth observation. They give us a glimpse into their amazing data and tooling for working with satellite imagery, and they talk about use cases including crop identification and tropical storm wind speed estimation.

What’s the current reality and practical implications of using 3D environments for simulation and synthetic data creation? In this episode, we cut right through the hype of the Metaverse, Multiverse, Omniverse, and all the “verses” to understand how 3D assets and tooling are actually helping AI developers develop industrial robots, autonomous vehicles, and more. Beau Perschall is at the center of these innovations in his work with NVIDIA, and there is no one better to help us explore the topic!

You might know about MLPerf, a benchmark from MLCommons that measures how fast systems can train models to a target quality metric. However, MLCommons is working on so much more! David Kanter joins us in this episode to discuss two new speech datasets that are democratizing machine learning for speech via data scale and language/speaker diversity.

From drug discovery at the Quebec AI Institute to improving capabilities with low-resourced languages at the Masakhane Research Foundation and Google AI, Bonaventure Dossou looks for opportunities to use his expertise in natural language processing to improve the world - and especially to help his homeland in the Benin Republic in Africa.

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.

Recently, GitHub released Copilot, which is an amazing AI pair programmer powered by OpenAI’s Codex model. In this episode, Natalie Pistunovich tells us all about Codex and helps us understand where it fits in our development workflow. We also discuss MLOps and how AI is influencing software engineering more generally.

Creating and sharing reproducible development environments for AI experiments and production systems is a huge pain. You have all sorts of weird dependencies, and then you have to deal with GPUs and NVIDIA drivers on top of all that! brev.dev is attempting to mitigate this pain and create delightful GPU dev environments. Now that sounds practical!

In the third of the “AI in Africa” spotlight episodes, we welcome Kathleen Siminyu, who is building Kiswahili voice tools at Mozilla. We had a great discussion with Kathleen about creating more diverse voice and language datasets, involving local language communities in NLP work, and expanding grassroots ML/AI efforts across Africa.

José and Ricardo joined Daniel at EMNLP 2022 to discuss state-of-the-art machine translation, the WMT shared tasks, and quality estimation. Among other things, they talk about Unbabel’s innovations in quality estimation including COMET, a neural framework for training multilingual machine translation (MT) evaluation models.

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!

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.