Recently updated channels

Pick one to see only that channelBrowse all channels →

Results

328 articles
Sort
1:21:50EN
Cognitive Revolution "How AI Changes Everything"· Nov 14, 2023PodcastA 16K Context Window Holds About 350 Rows. That's Why Tables Need an Agent.Host Nathan LabenzGretel AI founder Alex Watson tells Nathan Labenz why most company data is still a spreadsheet, why GPT-4 is the wrong tool for generating it, and how differential privacy lets a model learn a disease without learning a patient.Models & trainingSynthetic dataData quality & curation+3
5 slides11:05EN
Matthew Berman· Nov 7, 2023Testing 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.Open-weight modelsBenchmarksModels & training+1
41:58EN
No Priors: AI, Machine Learning, Tech, & Startups· Nov 2, 2023Podcast · 2 speakersReliability is the bottleneck. Autonomy is what would make AI feel alive.Host Sarah Guo, Elad GilIlya Sutskever talks with Sarah Guo and Elad Gil about AlexNet, OpenAI’s capped-profit bet, why smaller models mainly lose reliability, and the line he draws for digital life.Models & trainingAGIScaling Laws+3
1:19:52EN
Cognitive Revolution "How AI Changes Everything"· Oct 18, 2023Podcast1,000 H100s Take 115 Days; 22,000 Take FiveHost Nathan LabenzNathan Labenz and Trey Kollmer walk through cluster math that turns GPT-4-scale training into a weekly experiment, then four papers on search-augmented models, recursive code improvers, spatial maps inside Llama 2, and what networks do when they have no idea.Models & trainingAI researchModel Interpretability+3
37:01EN
No Priors: AI, Machine Learning, Tech, & Startups· Sep 28, 2023Podcast · 2 speakersDNA Is Code, but You Compile It with a PipetteHost Sarah Guo, Elad GilGinkgo Bioworks CEO Jason Kelly tells No Priors why cells are worth the magic, why biology has no AWS, and why a pandemic should look like cybersecurity.Models & trainingAI for scienceBiosecurity+3
6 slides12:49EN
Matthew Berman· Sep 14, 2023Falcon 180B: A 2023 Review of TII’s Open-Weight ModelMatthew Berman · Forward FutureMatthew Berman tests coding, reasoning and writing prompts while examining the model’s training scale and license.Open-weight modelsBenchmarksModels & training+1
36:33EN
20VC with Harry Stebbings· Aug 31, 2023Podcast · 2 speakersNoam Shazeer on Character.AI, the Cost of Training Models and the Uses of General AIGuest Noam Shazeer · Host Harry StebbingsThe Character.AI co-founder explains his consumer strategy, language-model training and why he expects users to invent applications the company cannot predict.Foundation ModelsModels & trainingAI startups & small teams+2
55:08EN
Cognitive Revolution "How AI Changes Everything"· Aug 29, 2023PodcastOne Model Inside 38 Products: Paige Bailey on How Google Trains and Ships PaLM 2Host Nathan LabenzAt Google I/O in May 2023, PaLM 2 arrived already running in more than two dozen products — and its lead product manager walks through what that actually took.Models & trainingMultimodal AIEvals+3
2 slides6:28EN
Matthew Berman· Aug 29, 2023A Historical GPT-3.5 Fine-Tuning WalkthroughMatthew Berman · Forward FutureMatthew Berman demonstrates generating synthetic examples in Colab and training a model to use a distinctive tone.Fine-tuningSynthetic dataModels & training
52:48EN
Cognitive Revolution "How AI Changes Everything"· Aug 25, 2023PodcastOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the ListHost Nathan LabenzNathan Labenz and Erik Torenberg walk through GPT-3.5 Turbo fine-tuning, why self-hosting still loses on total cost, and what is actually working in code, school, and medicine.Models & trainingFine-tuningAI cost & performance+3
35:24EN
No Priors: AI, Machine Learning, Tech, & Startups· Aug 24, 2023Podcast · 2 speakersThe Author of Attention Is All You Need Now Compiles mRNA Like BytecodeHost Sarah Guo, Elad GilJakob Uszkoreit on why Transformers won, why models spend compute on the wrong things, and why drug discovery has to become generation rather than screening.Models & trainingAI for scienceTransformers & attention+2
5 slides7:09EN
Matthew Berman· Jul 28, 2023AI News in July 2023: SDXL, Worldcoin, and Biological ComputingMatthew Berman · Forward FutureMatthew Berman reviews a week of AI developments, from Google’s Gemini project and new generative models to biometric identity and experiments with cultured neurons.Generative mediaModels & trainingBrain-Computer Interfaces+2
2:13:07EN
Cognitive Revolution "How AI Changes Everything"· Jul 27, 2023PodcastHalf an Hour to a Circuit in GPT-2 Small — and a Quadratic Wall Before GPT-3Host Nathan LabenzArthur Conmy walks Nathan Labenz through ACDC, the algorithm that automates the most tedious step of mechanistic interpretability: finding which transformer components actually do a task.Models & trainingModel InterpretabilityAI research+3
2 slides4:59EN
Matthew Berman· Jul 25, 2023Testing 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.Open-weight modelsEvalsModels & training+1
7 slides9:40EN
Matthew Berman· Jul 20, 2023Testing Llama 2 70B on Coding, Writing and ReasoningMatthew Berman · Forward FutureMatthew Berman tries Meta’s 70-billion-parameter chat model through a hosted interface, highlighting both useful outputs and inconsistent reasoning.Open-weight modelsBenchmarksModels & training+2
2 slides5:53EN
Matthew Berman· Jul 18, 2023Petals: Distributing Large Language Model Inference Across DevicesMatthew Berman · Forward FutureIn 2023, Matthew Berman explains Petals as a way to use large models through a network of participating computers rather than loading every layer on one device.Distributed training & inferenceLocal inferenceInference+2
3 slides12:20EN
Matthew Berman· Jul 10, 2023Testing UltraLM 13B: A Small Model’s Strengths and Reasoning ErrorsMatthew Berman · Forward FutureA 2023 local test finds useful writing and simple coding alongside failures in games, arithmetic and logic.Open-weight modelsEvalsModels & training+1
1:33:56EN
Cognitive Revolution "How AI Changes Everything"· Jul 6, 2023PodcastThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models UnderstandHost Nathan LabenzNathan Labenz spends a solo episode walking through what general-purpose models can do, how they are trained, how capabilities unlock, and whether they understand anything at all.Models & trainingML fundamentalsScaling Laws+3
6 slides18:08EN
Matthew Berman· Jun 29, 2023Six Language Models Compared on an Informal 2023 Prompt SetMatthew Berman · Forward FutureMatthew Berman tests coding, writing and reasoning with H2O GPT, finding a tie under a small and subjective rubric.BenchmarksOpen-weight modelsModels & training+1
1:32:22EN
Cognitive Revolution "How AI Changes Everything"· Jun 29, 2023PodcastA Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer AwayHost Nathan LabenzLili Yu of Meta AI explains a multiscale transformer that predicts million-byte sequences, skips tokenization, and aims to model any file as raw bytes.Models & trainingMultimodal AITransformers & attention+3
1:52:46EN
Cognitive Revolution "How AI Changes Everything"· Jun 27, 2023PodcastCharge Neural Nets by Connection Length, and the Black Box Splits Into Modules You Can SeeHost Nathan LabenzMIT physicist Ziming Liu walks Nathan Labenz through BIMT, a brain-inspired training trick that sparsifies networks until you can read them by looking.Models & trainingModel InterpretabilityML fundamentals+3
7 slides4:36EN
Matthew Berman· Jun 20, 2023Testing WizardCoder 15B as a Local Coding AssistantMatthew Berman · Forward FutureMatthew Berman runs the 2023 WizardCoder model locally, testing small Python exercises and a longer Snake game.Models & trainingLocal inferenceSoftware engineering+2
59:28EN
Cognitive Revolution "How AI Changes Everything"· Jun 16, 2023PodcastFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced ProductHost Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.Models & trainingFine-tuningPretraining & midtraining+3
2:00:19EN
Cognitive Revolution "How AI Changes Everything"· Jun 6, 2023Podcast1.5 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…Models & trainingSynthetic dataData quality & curation+3
Load moreShowing 24 of 328