AI Stories

Our guest today is Ryan Shannon, AI Investor at Radical Ventures, a world-known venture capital firm investing exclusively in AI. Radical's portfolio includes hot startups like Cohere, Covariant, V7 and many more.  In our conversation, we talk about how to start an AI company & what makes a good founding team. Ryan also explains what he and Radical look for when investing and how they help their portfolio after the investment. We finally chat about some cool AI Startups like Twelve Labs and get Ryan’s predictions on hot startups in 2024. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Link to Train in Data courses (use the code AISTORIES to get a 10% discount): https://www.trainindata.com/courses?affcode=1218302_5n7krabaFollow Ryan on LinkedIn: https://www.linkedin.com/in/ryan-shannon-1b3a7884/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(0:00) - Intro(2:42) - Ryan's background and journey into AI investing(11:15) -  Radical Ventures(14:34) - How to keep up with AI breakthroughs? (22:42) - How Ryan finds and evaluates founders to invest in(32:54) - What makes a good founding team? (38:57) - Ryan's role at Radical (45:53) - How to start an AI company (50:22) - Twelve Labs(59:19) - Future of AI and hot startups in 2024(1:09:48) - Career advice

Our guest today is Christoph Molnar, expert in Interpretable Machine Learning and book author. In our conversation, we dive into the field of Interpretable ML. Christoph explains the difference between post hoc and model agnostic approaches as well as global and local model agnostic methods. We dig into several interpretable ML techniques including permutation feature importance, SHAP and Lime. We also talk about the importance of interpretability and how it can help you build better models and impact businesses. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Link to Train in Data courses (use the code AISTORIES to get a 10% discount): https://www.trainindata.com/courses?affcode=1218302_5n7krabaFollow Christoph on LinkedIn: https://www.linkedin.com/in/christoph-molnar/Check out the books he wrote here: https://christophmolnar.com/books/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) - Introduction(02:42) - Christoph's Journey into Data Science and AI(07:23) - What is Interpretable ML? (18:57) - Global Model Agnostic Approaches(24:20) - Practical Applications of Feature Importance(28:37) - Local Model Agnostic Approaches(31:17) - SHAP and LIME (40:20) - Advice for Implementing Interpretable Techniques(43:47) - Modelling Mindsets (48:04) - Stats vs ML Mindsets(51:17) -  Future Plans & Career Advice

Discover how Demetrios Brinkmann transitioned from teaching to founding the MLOps Community. Explore the role of MLOps in the ML lifecycle and the debate between specialized vs generalizing language models. Learn about the business implications of relying on OpenAI and the activities of the MLOps Community, including live events, podcasts, and new courses on generative AI.

In this discussion, Noah Gift, MLOps leader and executive in residence at Duke University, shares insights from his 30 years of experience, including building data pipelines in the film industry. He emphasizes the crucial role of MLOps and the software engineering skills essential for data scientists. Noah contrasts Python and Rust, advocating for flexibility in choosing tools. He delves into the differences between MLOps and LLMOps, discussing security concerns and the future of deployment strategies, making a compelling case for adapting to the tech landscape.

Our guest today is Marianne Ducournau, Head of Data Science at Qonto and ex Data Scientist at Amazon and Uber.In our conversation, we first discuss Marianne's first job in Data Science working in the public sector and managing a 10-15 people team. Marianne then talks about her experience at Uber and shares various projects that she worked on. We dive into price elasticity modelling and financial forecasting where her team built thousands of model to forecast financial metrics in multiple cities.  Marianne finally explains her current role as the Head of Data Science at Qonto and gives advice on how to progress in Big Techs and in your career. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Link to Train in Data courses (use the code AISTORIES to get a 10% discount): https://www.trainindata.com/courses?affcode=1218302_5n7krabaFollow Marianne on LinkedIn: https://www.linkedin.com/in/mborzic/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(00:00) - Introduction(02:12) - Marianne's Journey Into Data Science(05:05) - Managing A 10-15 People Team In Her First Job(10:02) - Pros And Cons Of Working In The Public Sector(16:51) - Transition From The Public Sector To Uber(22:25) - Price Elasticity Modelling(35:42) - Building 1000+ Models For Financial Forecasting(42:10) - Progressing In Big Techs(45:01) - What Is Qonto And Marianne's Role There?(48:08) - Understanding Qonto's Product(49:29) - Building A Team As Head Of Data Science(54:37) - Impact Estimation(01:02:52) - Marianne's Advice For Career Progression

Christof Henkel, Senior Deep Learning Data Scientist at NVIDIA and world number 1 on Kaggle, discusses his journey on Kaggle, the role of mathematics in Data Science careers, winning competitions, reducing model overfitting, and advice on progressing in your career.

Davis Blalock discusses his journey from academia to joining Mosaic ML, the startup's focus on efficient neural network training, evolution to large language models, and the $1.3 billion acquisition by Databricks. Topics include enhancing ML algorithms' efficiency, importance of efficiency, and learnings from working in a startup.

Our guest today is Kellin Pelrine, Research Scientist at FAR AI and Doctoral Researcher at the Quebec Artificial Intelligence Institute (MILA). In our conversation, Kellin first explains how he defeated a superhuman Go-playing AI engine named KataGo 14 games to 1. We talk about KataGo’s weaknesses and discuss how Kellin managed to identify them using Reinforcement Learning. In the second part of the episode, we dive into Kellin’s research on building practical AI systems. We dig into his work on misinformation detection and political polarisation and discuss why building stronger models isn’t always enough to get real world impact. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Kellin on LinkedIn: https://www.linkedin.com/in/kellin-pelrine/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(00:00) - Intro(01:54) - How Kellin got into the field(03:23) - The game of Go (06:10) - Lee Sedol vs AlphaGo(11:42) - How Kellin defeated KataGo 14 -1(26:24) - Using AI to detect KataGo’s weaknesses (37:07) - Kellin’s research on building practical AI systems(43:10) - Misinformation detection (49:22) - Political polarisation(54:39) - ML in Academia vs in Industry(1:06:03) - Career Advice

Our guest today is Chanuki Seresinhe, head of Data Science at Zoopla,  a company which provides millions of users with access to properties for sale and for rent. In our conversation, we first talk about Chanuki’s PhD where she used machine learning to identify relationships between beautiful places and happiness. We then dive into Data Science at Zoopla and talk about Generative AI and other exciting projects that Chanuki is currently working on. Throughout the episode, Chanuki shares great insights on why ML projects fail, the importance of good metrics, switching companies and how to progress in your career. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Chanuki on LinkedIn: https://www.linkedin.com/in/chanukiseresinhe/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(00:00) : Intro(01:23) : How Chanuki got into the field(04:58) : AI to better understand happiness(16:37) : Generative AI (21:26) : Generative AI vs supervised learning(24:47) : Data Science at Zoopla(31:46) : The importance of good metrics(35:33) : Dealing with outliers(39:41) : Why ML projects fail(46:30) : Switching companies(48:42) : Bias (54:47) : Career advice

Rémi Ounadjela shares his experience working on shipment optimization at Amazon and data science for risk and safety at TikTok. He discusses the differences between working at TikTok, Google, and Amazon. Rémi also gives advice on how to land a data science job in big tech and common mistakes to avoid.