GenAI is often what people think of when someone mentions AI. However, AI is much more. In this episode, Daniel breaks down a history of developments in data science, machine learning, AI, and GenAI in this episode to give listeners a better mental model. Don’t miss this one if you are wanting to understand the AI ecosystem holistically and how models, embeddings, data, prompts, etc. all fit together.
Daniel & Chris explore the advantages of vector databases with Roie Schwaber-Cohen of Pinecone. Roie starts with a very lucid explanation of why you need a vector database in your machine learning pipeline, and then goes on to discuss Pinecone’s vector database, designed to facilitate efficient storage, retrieval, and management of vector data.
At the age of 72, U.S. Representative Don Beyer of Virginia enrolled at GMU to pursue a Master’s degree in C.S. with a concentration in Machine Learning.
Rep. Beyer is Vice Chair of the bipartisan Artificial Intelligence Caucus & Vice Chair of the NDC’s AI Working Group. He is the author of the AI Foundation Model Transparency Act & a lead cosponsor of the CREATE AI Act, the Federal Artificial Intelligence Risk Management Act & the Artificial Intelligence Environmental Impacts Act.
We hope you tune into this inspiring, nonpartisan conversation with Rep. Beyer about his decision to dive into the deep end of the AI pool & his leadership in bringing that expertise to Capitol Hill.
Daniel & Chris delight in conversation with “the funniest guy in AI”, Demetrios Brinkmann. Together they explore the results of the MLOps Community’s latest survey. They also preview the upcoming AI Quality Conference.
Chris & Daniel explore AI in national security with Lt. General Jack Shanahan (USAF, Ret.). The conversation reflects Jack’s unique background as the only senior U.S. military officer responsible for standing up and leading two organizations in the United States Department of Defense (DoD) dedicated to fielding artificial intelligence capabilities: Project Maven and the DoD Joint AI Center (JAIC).
Together, Jack, Daniel & Chris dive into the fascinating details of Jack’s recent written testimony to the U.S. Senate’s AI Insight Forum on National Security, in which he provides the U.S. government with thoughtful guidance on how to achieve the best path forward with artificial intelligence.
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.
We scoured the internet to find all the AI related predictions for 2024 (at least from people that might know what they are talking about), and, in this episode, we talk about some of the common themes. We also take a moment to look back at 2023 commenting with some distance on a crazy AI year.
Daniel & Chris conduct a retrospective analysis of the recent OpenAI debacle in which CEO Sam Altman was sacked by the OpenAI board, only to return days later with a new supportive board. The events and people involved are discussed from start to finish along with the potential impact of these events on the AI industry.
On Monday, October 30, 2023, the U.S. White House issued its Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Two days later, a policy paper was issued by the U.K. government entitled The Bletchley Declaration by Countries Attending the AI Safety Summit, 1-2 November 2023. It was signed by 29 countries, including the United States and China, the global leaders in AI research.
In this Fully Connected episode, Daniel and Chris parse the details and highlight key takeaways from these documents, especially the extensive and detailed executive order, which has the force of law in the United States.
In this episode we welcome back our good friend Demetrios from the MLOps Community to discuss fine-tuning vs. retrieval augmented generation. Along the way, we also chat about OpenAI Enterprise, results from the MLOps Community LLM survey, and the orchestration and evaluation of generative AI workloads.
As a technologist, coder, and lawyer, few people are better equipped to discuss the legal and practical consequences of generative AI than Damien Riehl. He demonstrated this a couple years ago by generating, writing to disk, and then releasing every possible musical melody. Damien joins us to answer our many questions about generated content, copyright, dataset licensing/usage, and the future of knowledge work.
Chris and Daniel take a step back to look at how generative AI fits into the wider landscape of ML/AI and data science. They talk through the differences in how one approaches “traditional” supervised learning and how practitioners are approaching generative AI based solutions (such as those using Midjourney or GPT family models). Finally, they talk through the risk and compliance implications of generative AI, which was in the news this week in the EU.
You can’t build robust systems with inconsistent, unstructured text output from LLMs. Moreover, LLM integrations scare corporate lawyers, finance departments, and security professionals due to hallucinations, cost, lack of compliance (e.g., HIPAA), leaked IP/PII, and “injection” vulnerabilities.
In this episode, Chris interviews Daniel about his new company called Prediction Guard, which addresses these issues. They discuss some practical methodologies for getting consistent, structured output from compliant AI systems. These systems, driven by open access models and various kinds of LLM wrappers, can help you delight customers AND navigate the increasing restrictions on “GPT” models.
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).
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.
With the recent proliferation of generative AI models (from OpenAI, co:here, Anthropic, etc.), practitioners are racing to come up with best practices around prompting, grounding, and control of outputs.
Chris and Daniel take a deep dive into the kinds of behavior we are seeing with this latest wave of models (both good and bad) and what leads to that behavior. They also dig into some prompting and integration tips.
Why is ML is so poorly adopted in small organizations (hint: it’s not because they don’t have enough data)? In this episode, Kirsten Lum from Storytellers shares the patterns she has seen in small orgs that lead to a successful ML practice. We discuss how the job of a ML Engineer/Data Scientist is different in that environment and how end-to-end project management is key to adoption.
Daniel and Chris do a deep dive into OpenAI’s ChatGPT, which is the first LLM to enjoy direct mass adoption by folks outside the AI world. They discuss how it works, its effect on the world, ramifications of its adoption, and what we may expect in the future as these types of models continue to evolve.
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)
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.
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.
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.
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!
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.
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.
Each year we discuss the latest insights from the Stanford Institute for Human-Centered Artificial Intelligence (HAI), and this year is no different. Daniel and Chris delve into key findings and discuss in this Fully-Connected episode. They also check out a study called ‘Delphi: Towards Machine Ethics and Norms’, about how to integrate ethics and morals into AI models.
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!
How did we get from symbolic AI to deep learning models that help you write code (i.e., GitHub and OpenAI’s new Copilot)? That’s what Chris and Daniel discuss in this episode about the history and future of deep learning (with some help from an article recently published in ACM and written by the luminaries of deep learning).
Chris and Daniel sit down to chat about some exciting new AI developments including wav2vec-u (an unsupervised speech recognition model) and meta-learning (a new book about “How To Learn Deep Learning And Thrive In The Digital World”). Along the way they discuss engineering skills for AI developers and strategies for launching AI initiatives in established companies.
Nhung Ho joins Daniel and Chris to discuss how data science creates insights into financial operations and economic conditions. They delve into topics ranging from predictive forecasting to aid small businesses, to learning about the economic fallout from the COVID-19 Pandemic.
