AI Stories

Lewis Tunstall, an LLM Engineer at Hugging Face and co-author of "Natural Language Processing with Transformers," dives into captivating discussions on topological machine learning and its applications. He contrasts open source and closed source LLMs, shedding light on their implications for security and collaboration. Tunstall shares insights on fine-tuning language models, innovative training techniques, and the importance of community-driven advancements in AI. His journey from Kaggle competitions to real-world applications offers valuable lessons for aspiring data scientists.

Guest Maria Vechtomova is a skilled ML Engineering Manager at Ahold Delhaize and co-founder of the Marvelous MLOps blog. She shares essential coding best practices for data scientists, emphasizing modularity and CI/CD pipelines. Maria discusses her experience deploying a fraud detection algorithm, highlighting the necessity of collaboration and infrastructure monitoring. Additionally, she dives into the distinct roles of ML and MLOps engineers and shares her journey in content creation, offering insights into building a community around MLOps.

Reah Miyara, an expert in LLMs evaluation at OpenAI with a rich background at Google and IBM, shares his career journey from software engineering to product leadership. He discusses the evolution of AI, focusing on the importance of validating innovations in real-world applications. Reah delves into the complexities of LLM evaluation and the significance of safety metrics in AI models. He emphasizes the vital role of feedback in career growth and offers insights into the future landscape of generative AI and its implications for society.

Erwin Huizenga, Machine Learning Lead at Google, discusses his journey from SAS and IBM to Google. Topics include early days of cloud computing, Gemini vs other LLMs, LLMOps, evaluating and monitoring LLMs, and deploying LLMs vs traditional ML models.

Our guest today is Andras Palffy, Co-Founder of Perciv AI: a startup offering AI based software solutions to build robust and affordable autonomous systems. In our conversation, we first talk about Andras' PhD focusing on road users detection. We dive into AI applied to autonomous driving and discuss the pros and cons of the most common pieces of hardware: cameras, lidars and radars. We then focus on Perciv AI. Andras explains why he decided to focus on radars and how he uses Deep Learning algorithms to enable autonomous systems. He finally gives his take on the future of autonomous vehicles and shares learnings from his experience in the field.  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_5n7krabaTo learn more about Perciv AI: https://www.perciv.ai/ Follow Andras on LinkedIn: https://www.linkedin.com/in/andraspalffy/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) - Intro(02:57) - Andras' Journey into AI (06:11) - Getting into Robotics (10:15) - Evolution of Computer Vision Algorithms(13:38) - PhD on Autonomous Driving & Road Users Detection(28:01) - Launching Perciv AI(35:19) - Augmenting Radars Performance with AI(44:45) - Inside Perciv AI: Roles, Challenges, and Stories(48:43) - Future of Autonomous Vehicles and Road Safety(51:46) - Solving a Technical Challenge with Camera Calibration(54:12) - Andras' First Self-Driving Car Experience(56:09) - Career Advice

Franziska Kirschner, Co-Founder of Intropy AI and former AI Lead at Tractable, discusses her impressive journey from physics to AI product management. She shares insights on launching AI tools for scrapyards and how these innovations enhance vehicle recycling. Franziska reflects on deep learning's impact in accident recovery and the complexities of bringing AI products to market. She also emphasizes building trust in AI adoption by engaging non-technical users, illustrating her passion for problem-solving and personal growth through unique experiences.

In this podcast, Maxime Labonne discusses building 7B params LLMs, steps to create LLMs, RAG vs fine-tuning, DPO vs RLHF, and deploying LLMs in production. He shares insights on merging models for enhanced performance, getting into GenAI, and using ChatGPT for various applications. From cybersecurity to AI, Maxime's journey and career advice offer valuable perspectives on entering the field of AI.

Our guest today is Harpreet Sahota, Deep Learning Developer Relations Manager at Deci AI. In our conversation, we first talk about Harpreet’s work as a Biostatistician and dive into A/B testing. We then talk about Deci AI and Neural Architecture Search (NAS): the algorithm used to build powerful deep learning models like YOLO-NAS. We finally dive into GenAI where Harpreet shares 7 prompting tips and explains how Retrieval Augmented Generation (RAG) works.  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 Harpreet on LinkedIn: https://www.linkedin.com/in/harpreetsahota204/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) - Intro(02:34) - Harpreet's Journey into Data Science(07:00) - A/B Testing (17:50) - DevRel at Deci AI(26:25) - Deci AI:  Products and Services(32:22) - Neural Architecture Search (NAS)(36:58) - GenAI(39:53) - Tools for Playing with LLMs(42:56) - Mastering Prompt Engineering(46:35) - Retrieval Augmented Generation (RAG)(54:12) - Career Advice

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