{"podcast":{"title":"Machine Learning Street Talk (MLST)","slug":"machine-learning-street-talk","podcast_index_feed_id":781643,"rss_url":"https://anchor.fm/s/1e4a0eac/podcast/rss","website_url":"https://podcasters.spotify.com/pod/show/machinelearningstreettalk","image_url":"https://d3t3ozftmdmh3i.cloudfront.net/staging/podcast_uploaded_nologo/4981699/4981699-1757416025703-f026fa81b6d04.jpg","author":"Machine Learning Street Talk (MLST)","episode_count":250,"summary":"Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).","last_synced_at":null,"page_url":"https://stenobird.com/podcast/machine-learning-street-talk"},"episode":{"title":"Test-Time Adaptation: the key to reasoning with DL (Mohamed Osman)","slug":"test-time-adaptation-the-key-to-reasoning-with-dl-mohamed-osman","published_at":"2025-03-22T22:48:25+00:00","page_url":"https://stenobird.com/podcast/machine-learning-street-talk/test-time-adaptation-the-key-to-reasoning-with-dl-mohamed-osman","show_page_url":"https://stenobird.com/podcast/machine-learning-street-talk","url":"https://podcasters.spotify.com/pod/show/machinelearningstreettalk/episodes/Test-Time-Adaptation-the-key-to-reasoning-with-DL-Mohamed-Osman-e30hd8t","audio_url":"https://anchor.fm/s/1e4a0eac/podcast/play/100233949/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-2-22%2Fed4795dc-93de-d0ab-94b3-c2face88c2fd.mp3","summary":"Mohamed Osman joins to discuss MindsAI's highest scoring entry to the ARC challenge 2024 and the paradigm of test-time fine-tuning. They explore how the team, now part of Tufa Labs in Zurich, achieved state-of-the-art results using a combination of pre-training techniques, a unique meta-learning strategy, and an ensemble voting mechanism. Mohamed emphasizes the importance of raw data input and flexibility of the network. SPONSOR MESSAGES: *** Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT + REFS: https://www.dropbox.com/scl/fi/jeavyqidsjzjgjgd7ns7h/MoFInal.pdf?rlkey=cjjmo7rgtenxrr3b46nk6yq2e&dl=0 Mohamed Osman (Tufa Labs) https://x.com/MohamedOsmanML Jack Cole (Tufa Labs) https://x.com/MindsAI_Jack How and why deep learning for ARC paper: https://github.com/MohamedOsman1998/deep-learning-for-arc/blob/main/deep_learning_for_arc.pdf TOC: 1. Abstract Reasoning Foundations [00:00:00] 1.1 Test-Time Fine-Tuning and ARC Challenge Overview [00:10:20] 1.2 Neural Networks vs Programmatic Approaches to Reasoning [00:13:23] 1.3 Code-Based Learning and Meta-Model Architecture [00:20:26] 1.4 Technical Implementation with Long T5 Model 2. ARC Solution Architectures [00:24:10] 2.1 Test-Time Tuning and Voting Methods for ARC Solutions [00:27:54] 2.2 Model Generalization and Function Generation Challenges [00:32:53] 2.3 Input Representation and VLM Limitations [00:36:21] 2.4 Architecture Innovation and Cross-Modal Integration [00:40:05] 2.5 Future of ARC Challenge and Program Synthesis Approaches 3. Advanced Systems Integration [00:43:00] 3.1 DreamCoder Evolution and LLM Integration [00:50:07] 3.2…","meta_description":"Mohamed Osman joins to discuss MindsAI's highest scoring entry to the ARC challenge 2024 and the paradigm of test-time fine-tuning. They explore how the t…","key_points":[],"chapters":[],"topics":[],"duration_seconds":3816,"processing_state":"processed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/machine-learning-street-talk/episodes/test-time-adaptation-the-key-to-reasoning-with-dl-mohamed-osman/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/machine-learning-street-talk/test-time-adaptation-the-key-to-reasoning-with-dl-mohamed-osman.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}