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344개정렬
13:57ENMistral Large: A 2024 Review of Coding, Reasoning and Safety ResponsesMatthew Berman · Forward FutureMatthew Berman reviews Mistral’s launch claims and tests the model with a small set of prompts.
23:00ENTesting Google Gemma 7B at Launch: Setup MattersMatthew Berman · Forward FutureMatthew Berman compares local and hosted tests of Google’s early open-weight model.
1:58:43ENA Worm With 302 Neurons Already Makes Trade-offs. AI Is Still on the Same Ladder.진행자 Nathan LabenzMax Bennett maps 600 million years of brain evolution onto five breakthroughs — and asks which rungs today's models have actually reached.
0:42ENLLaVA 1.6 Adds Higher-Resolution Image UnderstandingMatthew Berman · Forward FutureMatthew Berman reviews the January 2024 release of LLaVA 1.6 and its local availability through Ollama.
40:58EN99% of Factory Robots Are Dumb. Covariant Wants the Smart Ones Working in Warehouses First진행자 Sarah Guo, Elad GilFormer OpenAI researcher Peter Chen explains why robot foundation models have to be trained on production floors, not in labs.
35:28ENSam Altman on Future AI Models, Copyright and the Limits of DeploymentMatthew Berman, Ina Fried, Sam Altman · Forward FutureIn a January 2024 Davos interview, OpenAI’s CEO discusses model progress, election risks, military use and customization. Matthew Berman responds to the claims and their implications.
2:30:06ENTrained on a Thirtieth of GPT-4's Tokens, Mamba Beat Transformers on Home Turf진행자 Nathan LabenzIn a solo emergency episode, Nathan Labenz argues that Albert Gu and Tri Dao's selective state space model is the first architecture since 2017 that deserves to share the Transformer’s throne.
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.
20:49ENMixtral 8x7B: Mixture-of-Experts Architecture, Setup and Early TestsMatthew Berman · Forward FutureMatthew Berman reviews Mistral AI’s December 2023 open-weight model, demonstrates a cloud setup and tests its coding and reasoning responses.
1:19:24ENA Toy Paper Did Not Prove LLMs Cannot Generalize. The Discourse Still Ran With It.진행자 Nathan LabenzNathan Labenz and Erik Torenberg unpack a viral DeepMind result, a weekend reproduction, and a premature war over voluntary AI commitments.
1:21:50ENA 16K Context Window Holds About 350 Rows. That's Why Tables Need an Agent.진행자 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.
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.
41:58ENReliability is the bottleneck. Autonomy is what would make AI feel alive.진행자 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.
1:19:52EN1,000 H100s Take 115 Days; 22,000 Take Five진행자 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.
13:11ENTesting Mistral 7B OpenOrca: A 2023 Cloud Setup and Prompt TrialMatthew Berman · Forward FutureMatthew Berman runs a quantized OpenOrca variant on a rented GPU and reviews coding, writing and reasoning examples.
37:01ENDNA Is Code, but You Compile It with a Pipette진행자 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.
12:49ENFalcon 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.
4:35ENRunning WizardCoder on a Cloud GPU: A 2023 RunPod WalkthroughMatthew Berman · Forward FutureMatthew Berman demonstrates downloading a quantized 13B coding model into a hosted text-generation interface, then explains how to stop the pod and avoid ongoing charges.
36:33ENNoam Shazeer on Character.AI, the Cost of Training Models and the Uses of General AI게스트 Noam Shazeer · 진행자 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.
55:08ENOne Model Inside 38 Products: Paige Bailey on How Google Trains and Ships PaLM 2진행자 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.
6:28ENA 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.
52:48ENOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the List진행자 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.
35:24ENThe Author of Attention Is All You Need Now Compiles mRNA Like Bytecode진행자 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.
7:09ENAI 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.
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