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39:05ZH → EnglishWang Xiaochuan on o1, Reinforcement Learning, and Slow ThinkingGuest 王小川 · Host 张小珺Baichuan Intelligence’s founder discusses possible technical paths behind o1, healthcare applications, and choices for AI startups.
10:57ENTesting OpenAI o1-preview: Tetris, Physical Reasoning and a North Pole ErrorMatthew Berman · Forward FutureIn September 2024, Matthew Berman runs the new reasoning-model preview through a personal rubric and examines where its answers succeed or fail.
21:21ENOpenAI o1’s 2024 Launch: Reasoning Benchmarks and Early TestsMatthew Berman · Forward FutureMatthew Berman reviews o1-preview and o1-mini, OpenAI’s reported evaluations, and his own early coding experiments.
51:15ENAGI Has One Problem Left: Spend a Million Times More on a TokenMagic founder Eric Steinberger tells Sequoia’s Training Data why he trains his own models, why a software engineer is the MVP of AGI, and why 96% still is not a colleague.
12:03ENReflection 70B: Self-Correction Claims and the Limits of a 2024 DemoMatthew Berman · Forward FutureMatthew Berman reviews a model trained to generate reflection steps. Its initial benchmark claims later proved difficult for independent evaluators to reproduce.
15:42ENStrawberry and Orion: What Was Reported About OpenAI’s Next Models in 2024Matthew Berman · Forward FutureMatthew Berman examines reports about a reasoning project, synthetic data and a future flagship model. Later releases clarify some of the story, while many original claims remain attributed to unnamed sources.
15:24ENTesting the Anonymous sus-column-r Model, Later Identified as Grok-2Matthew Berman · Forward FutureMatthew Berman tests a mysterious 2024 Arena model on coding and reasoning; its verbose answers prompted speculation about OpenAI that the later identification contradicted.
12:17ENTesting Gemini 1.5 Pro on Code, Reasoning, Images and VideoMatthew Berman · Forward FutureMatthew Berman compares standard and experimental Gemini versions in an informal 2024 review, finding mixed results and repeated output errors.
14:15ENOpenAI’s Reported Strawberry Project and a Five-Level AGI FrameworkMatthew Berman · Forward FutureMatthew Berman examines 2024 reporting on a reasoning project, long-horizon tasks and a proposed scale for AI capabilities.
1:56:03ENHumans Near a Perfect Score. The Best AI Still Gets 39%Host Nathan LabenzZapier co-founder Mike Knoop and François Chollet put $1 million on ARC-AGI, a 2019 test of sample-efficient intelligence that language models still cannot crack under a one-cent-per-puzzle budget.
55:13ENThe Best AI Scores 39% on This Test. Zapier’s Co-Founder Put $1 Million on Beating ItFrançois Chollet designed ARC to measure skill-acquisition efficiency; five years later humans still crush it and frontier models do not.
12:55ENTesting Mixture-of-Agents on Coding, Arithmetic and ReasoningMatthew Berman · Forward FutureMatthew Berman tries a multi-model setup on his informal LLM rubric and finds strong individual answers alongside coding and counting failures.
17:03ENQwen2 0.5B and 72B: A Hands-On ComparisonMatthew Berman · Forward FutureMatthew Berman tests coding, arithmetic and reasoning prompts on the smallest and largest Qwen2 models in a 2024 demonstration.
1:03:12ENAravind Srinivas on Reasoning, Model Economics and Building PerplexityGuest Aravind Srinivas · Host Harry StebbingsThe Perplexity co-founder discusses AI scaling, foundation-model competition, advertising and the challenges of building a search product.
17:12ENTesting the Mystery 'gpt2-chatbot' Model in April 2024Matthew Berman · Forward FutureMatthew Berman tries an anonymous model on coding, arithmetic and reasoning tasks while its identity remains uncertain.
22:17ENQ* Rumors, Energy-Based Models, and Research on ReasoningMatthew Berman, Yann Lecun · Forward FutureMatthew Berman examines an anonymous, unverified account of Q* and compares its ideas with published research on teaching language models to reason.
14:04ENMistral Medium in 2023: API Pricing and Informal Model TestsMatthew Berman · Forward FutureMatthew Berman compares the original Mistral Medium prototype with Mixtral and GPT-4, then tests coding, writing and reasoning through the API.
1:22:59ENIf Your Agent Is Right 80% of the Time, Ten Steps Leave You With 10%Host Nathan LabenzImbue CTO Josh Albrecht tells Nathan Labenz how a 27-person, pre-product research company is putting $200 million and 10,000 H100s toward agents that can actually finish a long job.
35:16ENWhat Was Known About Q* in 2023—and What Was SpeculationMatthew Berman, Sam Altman, Andrej Karpathy · Forward FutureMatthew Berman surveys rumors, reasoning papers and self-play analogies after OpenAI’s leadership crisis, with important limits on what was verified.
11:05ENTesting Dolphin 2.2, a Mistral 7B Fine-TuneMatthew Berman · Forward FutureMatthew Berman tries a small open-weight model on coding, formatting and reasoning prompts, with mixed results.
4:59ENTesting Airoboros L2 70B on Coding, Writing and ReasoningMatthew Berman · Forward FutureMatthew Berman runs a small set of prompts against a Llama 2 fine-tune, finding useful structured output alongside failures on code and logic.
9:46ENOrca Mini V2 vs. OpenOrca: A 2023 Informal Model ComparisonMatthew Berman · Forward FutureMatthew Berman tests two Orca-inspired models on coding, writing and reasoning, with results shaped by prompt setup and subjective scoring.
8:04ENWizardLM 13B v1.1 Tested on Coding, Writing and LogicMatthew Berman · Forward FutureMatthew Berman tries an instruction-tuned model on a small set of prompts, with mixed results and several subjective scoring choices.
2:00:19EN1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2Host Nathan LabenzTry this fill-in: Lily likes cats and dogs. She asked her mom for a dog. Mom said no, so she asked for a…
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