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94 件
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スライド 3 枚9:46EN
Matthew Berman· 2023年7月17日Orca 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.BenchmarksOpen-weight modelsReasoning & test-time compute+2
スライド 5 枚8:04EN
Matthew Berman· 2023年7月11日WizardLM 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.評価(Evals)Open-weight modelsモデルと学習+1
2:00:19EN
Cognitive Revolution "How AI Changes Everything"· 2023年6月6日ポッドキャスト1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2ホスト 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…モデルと学習Synthetic dataData quality & curation+3
スライド 4 枚9:58EN
Matthew Berman· 2023年4月26日HuggingChat’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.Open-weight modelsBenchmarksモデルと学習+2
1:00:36EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年2月2日ポッドキャスト · 2 人Forty games of Diplomacy, and nobody spotted the bot: Noam Brown’s 100,000x lessonホスト 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.エージェントAI researchReasoning & test-time compute+3
1:26:21EN
Lex Fridman· 2018年12月9日ポッドキャスト · 2 人The Machine Should Never Treat Its Objective as Gospelゲスト Stuart Russell · ホスト 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.セキュリティと安全性安全性とガバナンスAutonomous Driving+3
1:45:49EN
Cognitive Revolution "How AI Changes Everything"ポッドキャストTrust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the Companyホスト 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.エージェントWorld modelsReasoning & test-time compute+3
15:01EN
Lex Fridmanポッドキャスト · 2 人Deep Learning Is a System 1 Machine: Daniel Kahneman on What AI Still Lacksゲスト Daniel Kahneman · ホスト 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.モデルと学習Deep LearningAI research+3
10:16EN
Lex Fridmanポッドキャスト · 2 人Reasoning Without Logic: Yann LeCun on Teaching Neural Networks to Thinkゲスト Yann Lecun · ホスト Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.モデルと学習機械学習の基礎Deep Learning+3
58:57EN
Cognitive Revolution "How AI Changes Everything"ポッドキャストWhen the Model Learns to Look Around: Apollo Research on Testing o1 for Schemingホスト 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.セキュリティと安全性AI security安全性とガバナンス+3
57:25EN
Unsupervised Learning: With Jacob EffronポッドキャストFrom RAG to Reasoning: Why the Future of AI Is a System, Not a Modelホスト Jacob EffronDouwe Kiela explains what OpenAI's o1 reveals about test-time compute, why enterprise AI must be specialized, and where post-training and evaluations are heading.モデルと学習Reasoning & test-time computePost-training+3
10:34EN
Lex Fridmanポッドキャスト · 2 人Why Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AIゲスト François Chollet · ホスト Lex FridmanFrançois Chollet explains why neural networks only interpolate between points they have seen, and why the best AI systems pair them with symbolic rules.モデルと学習機械学習の基礎Deep Learning+2
1:03:10EN
Cognitive Revolution "How AI Changes Everything"ポッドキャストPause Tokens, Analogical Prompts and Ring Attention: A Tour of the Transformer's Next Upgradesホスト Nathan LabenzNathan Labenz and Trey Kollmer walk through five recent papers on pausing, backspacing, prompting, streaming and long-context training, and sketch how they might combine into a more capable model.モデルと学習Transformers & attention機械学習の基礎+3