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슬라이드 5장8:04EN
Matthew Berman· 2023년 7월 11일WizardLM 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.평가(Evals)Open-weight models모델과 학습+1
슬라이드 3장12:20EN
Matthew Berman· 2023년 7월 10일Testing 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 models평가(Evals)모델과 학습+1
1:33:56EN
Cognitive Revolution "How AI Changes Everything"· 2023년 7월 6일팟캐스트The AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models Understand진행자 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.모델과 학습머신러닝 기초Scaling Laws+3
슬라이드 6장18:08EN
Matthew Berman· 2023년 6월 29일Six 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 models모델과 학습+1
1:32:22EN
Cognitive Revolution "How AI Changes Everything"· 2023년 6월 29일팟캐스트A Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer Away진행자 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.모델과 학습Multimodal AITransformers & attention+3
1:52:46EN
Cognitive Revolution "How AI Changes Everything"· 2023년 6월 27일팟캐스트Charge Neural Nets by Connection Length, and the Black Box Splits Into Modules You Can See진행자 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.모델과 학습Model Interpretability머신러닝 기초+3
슬라이드 4장13:50EN
Matthew Berman· 2023년 6월 23일MPT-30B: Running MosaicML’s Model Locally and Testing Its Chat VariantMatthew Berman · Forward FutureMatthew Berman reviews MosaicML’s June 2023 release, demonstrates a local setup, and puts the hosted chat model through an informal prompt rubric.Open-weight modelsLocal inferenceQuantization+2
슬라이드 7장4:36EN
Matthew Berman· 2023년 6월 20일Testing 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.모델과 학습Local inferenceSoftware engineering+2
59:28EN
Cognitive Revolution "How AI Changes Everything"· 2023년 6월 16일팟캐스트From $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced Product진행자 Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.모델과 학습파인튜닝Pretraining & midtraining+3
2:00:19EN
Cognitive Revolution "How AI Changes Everything"· 2023년 6월 6일팟캐스트1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2진행자 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…모델과 학습Synthetic dataData quality & curation+3
슬라이드 6장11:49EN
Matthew Berman· 2023년 6월 1일Testing Wizard-Vicuna 30B: Responses, Reasoning, and CodeMatthew Berman · Forward FutureMatthew Berman examines a model marketed as uncensored, demonstrates a 2023 cloud setup, and runs an informal collection of tests.모델과 학습평가(Evals)AI Safety+1
1:25:35EN
Cognitive Revolution "How AI Changes Everything"· 2023년 5월 21일팟캐스트Two and a Half Years From 33.3% to 86.5%: Google Sent Foundation Models to Medical School진행자 Nathan LabenzVivek Natarajan walks Nathan Labenz through Med-PaLM and Med-PaLM 2: the data-light techniques, the nine-axis clinician evals, and why a high USMLE-style score is the wrong trophy.모델과 학습AI in healthcare파인튜닝+3
42:18EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023년 5월 18일팟캐스트 · 2명Ten Percent of Search Is Health — and the Doctor Is Googling Too진행자 Sarah Guo, Elad GilGoogle researcher Karan Singhal on Med-PaLM 2, the real bar for medical language models, and why medicine is a harder alignment lab than chatbots.모델과 학습AI in healthcare안전과 거버넌스+3
슬라이드 4장11:07EN
Matthew Berman· 2023년 5월 16일Testing MosaicML MPT-7B Chat and StoryWriter in 2023Matthew Berman · Forward FutureMatthew Berman examines MPT-7B model variants, their licenses and a mixed set of local tests.Open-weight modelsBenchmarks모델과 학습+1
슬라이드5:24EN
Matthew Berman· 2023년 5월 12일Google I/O 2023: Bard, PaLM 2 and AI Across Google's ProductsMatthew Berman · Forward FutureMatthew Berman reviews Google's 2023 AI announcements and weighs its focus on improving established products.AI Strategy제품과 UX모델과 학습+1
4:14:33EN
Lex Fridman· 2023년 5월 9일팟캐스트 · 2명The Next Token Is Not a Proof: Stephen Wolfram on ChatGPT and Computational Truth게스트 Stephen Wolfram · 진행자 Lex FridmanIn his fourth conversation with Lex Fridman, Stephen Wolfram contrasts large language models with Wolfram|Alpha, then follows computational irreducibility from ChatGPT through observers, thermodynamics, and the three great theories of twentieth-century physics.모델과 학습PhilosophyAI research+3
슬라이드 4장9:58EN
Matthew Berman· 2023년 4월 26일HuggingChat’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 modelsBenchmarks모델과 학습+2
슬라이드5:47EN
Matthew Berman· 2023년 4월 19일Sam Altman on Scaling AI Models: Matthew Berman Examines OpenAI’s 2023 DirectionMatthew Berman · Forward FutureA 2023 discussion of model scaling, possible ways to improve GPT-4 and the safety priorities OpenAI described at the time.Scaling LawsOpenAI모델과 학습+2
40:25EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023년 4월 6일팟캐스트 · 2명A 6-Billion-Parameter Clone of ChatGPT’s Core Trick진행자 Sarah Guo, Elad GilDatabricks CTO Matei Zaharia tells Sarah Guo and Elad Gil how Dolly learned instruction-following on fifty times less data than GPT-3 — and why he would rather look facts up than bake them into a trillion-parameter network.모델과 학습Open-weight models파인튜닝+3
슬라이드 5장6:24EN
Matthew Berman· 2023년 4월 6일BloombergGPT: Mixing Financial and General Data for a Language ModelMatthew Berman · Forward FutureMatthew Berman examines the 2023 finance-focused research model, its training corpus, evaluation and decision not to release weights.금융 AI모델과 학습평가(Evals)+1
슬라이드7:53EN
Matthew Berman· 2023년 4월 4일HuggingGPT: Using a Language Model to Coordinate AI ToolsMatthew Berman · Forward FutureMatthew Berman explains a research system that routes multimodal tasks to specialist models using a language model as controller.Agent orchestrationMultimodal AI모델과 학습+1
슬라이드 5장8:05EN
Matthew Berman· 2023년 3월 29일A 2023 Guide to Running the Original GPT4All Model LocallyMatthew Berman · Forward FutureMatthew Berman demonstrates an early CPU-based GPT4All release, its terminal installation and the resources available for fine-tuning.Open-weight models모델과 학습인프라와 배포
슬라이드 3장6:50EN
Matthew Berman· 2023년 3월 19일Running LLaMA and Alpaca Locally: A 2023 Dalai TutorialMatthew Berman · Forward FutureMatthew Berman demonstrates an early local-model setup on a MacBook Air and explains the settings that control generation.Local inference모델과 학습파인튜닝+1
슬라이드 5장7:55EN
Matthew Berman· 2023년 2월 20일How to Train a Custom Image Model in Leonardo.aiMatthew Berman · Forward FutureMatthew Berman walks through Leonardo.ai’s 2023 model galleries, training-set guidance, and custom model workflow, then shares results from several experiments.Generative media모델과 학습
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