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13 件
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スライド1:18EN
Matthew Berman· 2026年3月4日NVIDIA’s Alpamayo and the Promise of Simulated Driving DataMatthew Berman · Forward FutureMatthew Berman discusses NVIDIA’s autonomous-driving tools and a Mercedes demonstration, while predicting wider adoption.Autonomous Driving安全性とガバナンスSynthetic data
40:32EN
Matthew Berman· 2025年8月7日Mark Chen on GPT-5, Synthetic Data and the Future of AI ResearchMatthew Berman, Mark Chen · Forward FutureOpenAI’s head of research discusses training, agents, open-weight releases and evaluation with Matthew Berman in August 2025.OpenAIモデルと学習Synthetic data+2
スライド 7 枚15:13EN
Matthew Berman· 2025年5月10日Absolute Zero: Training a Reasoner on Self-Generated Coding TasksMatthew Berman · Forward FutureMatthew Berman examines a 2025 paper in which a pretrained language model proposes and solves code tasks using verifiable feedback.Reinforcement LearningSynthetic dataReasoning & test-time compute+2
スライド 6 枚13:39EN
Matthew Berman· 2024年11月20日What Interview-Based AI Agents Can Predict About PeopleMatthew Berman · Forward FutureMatthew Berman reviews a 2024 study using interviews with 1,052 participants to simulate survey answers and decisions.Multi-agent systemsSynthetic dataPsychology+2
スライド15:42EN
Matthew Berman· 2024年8月28日Strawberry and Orion: What Was Reported About OpenAI’s Next Models in 2024Matthew Berman · Forward FutureMatthew Berman examines reports about a reasoning project, synthetic data and a future flagship model. Later releases clarify some of the story, while many original claims remain attributed to unnamed sources.AI researchReasoning & test-time computeSynthetic data+2
スライド 5 枚10:02EN
Matthew Berman· 2024年7月24日Testing Llama 3.1 405B on Coding, Arithmetic and ReasoningMatthew Berman · Forward FutureMatthew Berman tries Meta's 2024 open-weight model on an informal set of prompts and demonstrates a sponsored dataset workflow.Open-weight modelsモデルと学習評価(Evals)+1
スライド 4 枚15:50EN
Matthew Berman· 2024年7月23日Llama 3.1: Meta’s 405B Model and the Open-Weight EcosystemMatthew Berman · Forward FutureMatthew Berman reviews Meta’s July 2024 release, the smaller models, reported benchmarks, synthetic data and the proposed Llama Stack.Open-weight modelsモデルと学習Benchmarks+2
スライド 5 枚19:37EN
Matthew Berman· 2023年12月13日How Rephrased Test Data Inflated a 13B Model’s Benchmark ScoresMatthew Berman · Forward FutureMatthew Berman examines a 2023 experiment showing how reworded benchmark questions can evade simple contamination checks and inflate model scores.Benchmarks評価(Evals)Data quality & curation+2
スライド 4 枚18:29EN
Matthew Berman· 2023年12月1日OpenAI's 2023 Leadership Crisis, Q* Speculation and Synthetic DataMatthew Berman, Andrew Ross Sorkin, Elon Musk · Forward FutureMatthew Berman connects Sam Altman's return, an Elon Musk interview and Nathan Lambert's discussion of synthetic training data, separating reported events from his own theories.Synthetic dataOpenAIAI Safety+2
スライド 3 枚35:16EN
Matthew Berman· 2023年11月28日What Was Known About Q* in 2023—and What Was SpeculationMatthew Berman, Sam Altman, Andrej Karpathy · Forward FutureMatthew Berman surveys rumors, reasoning papers and self-play analogies after OpenAI’s leadership crisis, with important limits on what was verified.AI researchReasoning & test-time computeOpenAI+2
スライド 2 枚6:28EN
Matthew Berman· 2023年8月29日A 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.ファインチューニングSynthetic dataモデルと学習
スライド 5 枚13:19EN
Matthew Berman· 2023年8月17日Testing WizardLM 70B: Setup, Coding and Reasoning ResultsMatthew Berman · Forward FutureMatthew Berman runs a quantized Llama 2-based model through an informal collection of coding, writing, arithmetic and logic prompts.Foundation ModelsファインチューニングLocal inference+2
スライド 5 枚19:38EN
Matthew Berman· 2023年6月9日Orca: Teaching a Smaller Model with GPT-4 ExplanationsMatthew Berman · Forward FutureMatthew Berman reviews Microsoft Research’s 2023 Orca paper and its approach to training a 13-billion-parameter model on detailed teacher responses.DistillationファインチューニングSynthetic data+2