Ergebnisse
94 ArtikelSortieren
9:46EN
Matthew Berman· 17. Juli 2023Orca 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.
8:04ENMatthew Berman· 11. Juli 2023WizardLM 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.
2:00:19EN
Cognitive Revolution "How AI Changes Everything"· 6. Juni 2023Podcast1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2Moderation: 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…
9:58ENMatthew Berman· 26. Apr. 2023HuggingChat’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.
1:00:36EN
No Priors: AI, Machine Learning, Tech, & Startups· 2. Feb. 2023Podcast · 2 StimmenForty games of Diplomacy, and nobody spotted the bot: Noam Brown’s 100,000x lessonModeration: 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.
1:26:21EN
Lex Fridman· 9. Dez. 2018Podcast · 2 StimmenThe Machine Should Never Treat Its Objective as GospelGast: Stuart Russell · Moderation: 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.
1:45:49ENCognitive Revolution "How AI Changes Everything"PodcastTrust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the CompanyModeration: 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.
15:01ENLex FridmanPodcast · 2 StimmenDeep Learning Is a System 1 Machine: Daniel Kahneman on What AI Still LacksGast: Daniel Kahneman · Moderation: 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.
10:16ENLex FridmanPodcast · 2 StimmenReasoning Without Logic: Yann LeCun on Teaching Neural Networks to ThinkGast: Yann Lecun · Moderation: Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.
58:57ENCognitive Revolution "How AI Changes Everything"PodcastWhen the Model Learns to Look Around: Apollo Research on Testing o1 for SchemingModeration: 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.
57:25EN
Unsupervised Learning: With Jacob EffronPodcastFrom RAG to Reasoning: Why the Future of AI Is a System, Not a ModelModeration: 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.
10:34ENLex FridmanPodcast · 2 StimmenWhy Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AIGast: François Chollet · Moderation: 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.
1:03:10ENCognitive Revolution "How AI Changes Everything"PodcastPause Tokens, Analogical Prompts and Ring Attention: A Tour of the Transformer's Next UpgradesModeration: 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.