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Promega’s top-down adoption of ChatGPT accelerates manufacturing, sales, and marketing

Promega's top-down adoption of ChatGPT accelerates manufacturing, sales, and marketing

2024.10.31읽기
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Introducing SimpleQA

A factuality benchmark called SimpleQA that measures the ability for language models to answer short, fact-seeking questions.

2024.10.30읽기
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Delivering high-performance customer support

Decagon and OpenAI deliver high-performance, fully automated customer support at scale

2024.10.29읽기
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Universal Assisted Generation: Faster Decoding with Any Assistant Model

2024.10.29읽기
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Expert Support case study: Bolstering a RAG app with LLM-as-a-Judge

2024.10.28읽기
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A Deepdive into Aya Expanse: Advancing the Frontier of Multilinguality

2024.10.24읽기
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Simplifying, stabilizing, and scaling continuous-time consistency models

We’ve simplified, stabilized, and scaled continuous-time consistency models, achieving comparable sample quality to leading diffusion models, while using only two sampling steps.

2024.10.23읽기
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CinePile 2.0 - making stronger datasets with adversarial refinement

2024.10.23읽기
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Introducing HUGS - Scale your AI with Open Models

2024.10.23읽기
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Introducing SynthID Text

2024.10.23읽기
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OpenAI and the Lenfest Institute AI Collaborative and Fellowship program

OpenAI and the Lenfest Institute AI Collaborative and Fellowship program

2024.10.22읽기
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Deploying Speech-to-Speech on Hugging Face

2024.10.22읽기
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Releasing Outlines-core 0.1.0: structured generation in Rust and Python

2024.10.22읽기
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Diffusers welcomes Stable Diffusion 3.5 Large

2024.10.22읽기
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Transformers.js v3: WebGPU Support, New Models & Tasks, and More…

2024.10.22읽기
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Hugging Face Teams Up with Protect AI: Enhancing Model Security for the ML Community

2024.10.22읽기
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“Llama 3.2 in Keras”

2024.10.21읽기
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Fixing Gradient Accumulation

2024.10.16읽기
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Evaluating fairness in ChatGPT

We’ve analyzed how ChatGPT responds to users based on their name, using AI research assistants to protect privacy.

2024.10.15읽기
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MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering.

2024.10.10읽기