{"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":262,"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":"2026-09-09T02:20:14.331532+00:00","page_url":"https://stenobird.com/podcast/machine-learning-street-talk"},"episode":{"title":"How Researchers Test AI for Hidden Goals — Apollo Research","slug":"how-researchers-test-ai-for-hidden-goals-apollo-research","published_at":"2026-07-31T20:31:41+00:00","page_url":"https://stenobird.com/podcast/machine-learning-street-talk/how-researchers-test-ai-for-hidden-goals-apollo-research","show_page_url":"https://stenobird.com/podcast/machine-learning-street-talk","url":"https://podcasters.spotify.com/pod/show/machinelearningstreettalk/episodes/How-Researchers-Test-AI-for-Hidden-Goals--Apollo-Research-e3mqbnr","audio_url":"https://traffic.megaphone.fm/APO7959985412.mp3","summary":"Can an AI do the right thing for the wrong reason? Tim Scarfe speaks with Apollo Research’s Alexander Meinke, Axel Højmark and Jérémy Scheurer about Measuring Reward-Seeking via Contrastive Belief Updates, their new research with OpenAI. The panel asks how models infer what graders reward, why good behaviour can come from the wrong reason, and whether that difference can be measured. The conversation moves through promise-breaking, grader awareness, reward hacking, scheming, opaque reasoning and corrigibility, then turns to a detailed walkthrough of the contrastive-belief method and what its results do and do not show. The o3 results discussed here concern an intermediate checkpoint without safety training. This episode was made in partnership with Apollo Research. MLST retained full editorial control. Reference Apollo Research: https://www.apolloresearch.ai/ --- TIMESTAMPS: 00:00:00 Cold Open 00:02:12 Right Things, Wrong Reasons 00:12:47 Grader Awareness 00:26:22 Legibility 00:32:35 What To Call It 00:35:58 Intelligence, Agency, Anthropomorphism 00:45:16 Apollo’s Mission 00:48:54 The End of the Exponential 00:55:45 The Paper 01:16:34 Closing Reflection --- REFERENCES: tool: [00:00:08] Claude Fable https://www.anthropic.com/claude/fable [00:12:50] AlphaGo Zero https://deepmind.google/blog/alphago-zero-starting-from-scratch/ [00:44:30] AlphaFold 3 https://deepmind.google/science/alphafold/ paper: [00:01:02] Measuring Reward-Seeking via Contrastive Belief Updates https://arxiv.org/abs/2607.18966 [00:16:19] Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations https://transformer-circuits.pub/2026/nla/ [00:26:48] Stress Testing Deliberative Alignment for Anti-Scheming Training https://arxiv.org/abs/2509.15541 [00:35:33] Shortcut learning in de…","meta_description":"Can an AI do the right thing for the wrong reason? Tim Scarfe speaks with Apollo Research’s Alexander Meinke, Axel Højmark and Jérémy Scheurer about Measu…","key_points":[],"chapters":[],"topics":[],"duration_seconds":4739,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/machine-learning-street-talk/episodes/how-researchers-test-ai-for-hidden-goals-apollo-research/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/how-researchers-test-ai-for-hidden-goals-apollo-research.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}