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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.
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%Anime : 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.
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 PerplexityInvité : Aravind Srinivas · Anime : 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%Anime : 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.
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.
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.
2:00:19EN1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2Anime : 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…
9:58ENHuggingChat’s 2023 Debut: Testing Writing, Reasoning and Python CodeMatthew Berman · Forward FutureMatthew Berman tries the early OpenAssistant-powered HuggingChat on writing, arithmetic, logic and a Snake game, then compares a few outputs with GPT-3.5 and GPT-4. The results illustrate both promise and limitations in this small historical test.
1:00:36ENForty games of Diplomacy, and nobody spotted the bot: Noam Brown’s 100,000x lessonAnime : Sarah Guo, Elad GilMeta’s Noam Brown tells Sarah Guo and Elad Gil how Cicero negotiated in English for 40 games on a tiny web community — and why thinking at decision time beat three years of scaling.
1:26:21ENThe Machine Should Never Treat Its Objective as GospelInvité : Stuart Russell · Anime : Lex FridmanStuart Russell talks with Lex Fridman about metareasoning, self-driving reliability, and why uncertainty about human goals is the core of the control problem.
1:45:49ENTrust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the CompanyAnime : Nathan LabenzAndreas Stuhlmüller and Jungwon Byun explain how Elicit keeps frontier models checkable for high-stakes science, and where they think AI reasoning is heading.
15:01ENDeep Learning Is a System 1 Machine: Daniel Kahneman on What AI Still LacksInvité : Daniel Kahneman · Anime : Lex FridmanThe Nobel-winning psychologist tells Lex Fridman how his fast-and-slow thinking framework maps onto deep learning, and why driving among pedestrians may require understanding people.
10:16ENReasoning Without Logic: Yann LeCun on Teaching Neural Networks to ThinkInvité : Yann Lecun · Anime : Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.
58:57ENWhen the Model Learns to Look Around: Apollo Research on Testing o1 for SchemingAnime : Nathan LabenzApollo Research founder Marius Hobbhahn explains how his team probed OpenAI's o1 for deception, what it found, and why the trajectory worries him more than the model itself.
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