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Tag: benchmarking

  • Simon Willison’s Weblog: Gemini 2.5: Our most intelligent models are getting even better

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    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/May/20/gemini-25/#atom-everything Source: Simon Willison’s Weblog Title: Gemini 2.5: Our most intelligent models are getting even better Feedly Summary: Gemini 2.5: Our most intelligent models are getting even better A bunch of new Gemini 2.5 announcements at Google I/O today. 2.5 Flash and 2.5 Pro are both getting audio output (previously previewed in Gemini…

  • Cloud Blog: Google AI Edge Portal: On-device machine learning testing at scale

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/ai-edge-portal-brings-on-device-ml-testing-at-scale/ Source: Cloud Blog Title: Google AI Edge Portal: On-device machine learning testing at scale Feedly Summary: Today, we’re excited to announce Google AI Edge Portal in private preview, Google Cloud’s new solution for testing and benchmarking on-device machine learning (ML) at scale.  Machine learning on mobile devices enables amazing app experiences. But…

  • CSA: High-Profile AI Failures Teach Us About Resilience

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    Kurt Seifried
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    Source URL: https://cloudsecurityalliance.org/articles/when-ai-breaks-bad-what-high-profile-failures-teach-us-about-resilience Source: CSA Title: High-Profile AI Failures Teach Us About Resilience Feedly Summary: AI Summary and Description: Yes Summary: The text discusses the vulnerabilities of artificial intelligence (AI) highlighted through significant real-world failures, emphasizing a new framework, the AI Resilience Benchmarking Model, developed by the Cloud Security Alliance (CSA). This model delineates methods…

  • Cloud Blog: AI Hypercomputer developer experience enhancements from Q1 25: build faster, scale bigger

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/compute/ai-hypercomputer-enhancements-for-the-developer/ Source: Cloud Blog Title: AI Hypercomputer developer experience enhancements from Q1 25: build faster, scale bigger Feedly Summary: Building cutting-edge AI models is exciting, whether you’re iterating in your notebook or orchestrating large clusters. However, scaling up training can present significant challenges, including navigating complex infrastructure, configuring software and dependencies across numerous…

  • Cloud Blog: Evaluate your gen media models with multimodal evaluation on Vertex AI

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/evaluate-your-gen-media-models-on-vertex-ai/ Source: Cloud Blog Title: Evaluate your gen media models with multimodal evaluation on Vertex AI Feedly Summary: The world of generative AI is moving fast, with models like Lyria, Imagen, and Veo now capable of producing stunningly realistic and imaginative images and videos from simple text prompts. However, evaluating these models is…

  • Slashdot: Study Accuses LM Arena of Helping Top AI Labs Game Its Benchmark

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    Kurt Seifried
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    Source URL: https://slashdot.org/story/25/05/01/0525208/study-accuses-lm-arena-of-helping-top-ai-labs-game-its-benchmark?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Study Accuses LM Arena of Helping Top AI Labs Game Its Benchmark Feedly Summary: AI Summary and Description: Yes Summary: The report highlights significant concerns regarding transparency and fairness in AI benchmarking, particularly focusing on allegations of biased practices within the LM Arena. Such revelations could impact the trustworthiness…

  • Simon Willison’s Weblog: Quoting Mark Zuckerberg

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    Kurt Seifried
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    Source URL: https://simonwillison.net/2025/May/1/mark-zuckerberg/#atom-everything Source: Simon Willison’s Weblog Title: Quoting Mark Zuckerberg Feedly Summary: You also mentioned the whole Chatbot Arena thing, which I think is interesting and points to the challenge around how you do benchmarking. How do you know what models are good for which things? One of the things we’ve generally tried to…

  • Slashdot: Microsoft Researchers Develop Hyper-Efficient AI Model That Can Run On CPUs

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    Kurt Seifried
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    Source URL: https://slashdot.org/story/25/04/17/2224205/microsoft-researchers-develop-hyper-efficient-ai-model-that-can-run-on-cpus?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Microsoft Researchers Develop Hyper-Efficient AI Model That Can Run On CPUs Feedly Summary: AI Summary and Description: Yes Summary: Microsoft has launched BitNet b1.58 2B4T, a highly efficient 1-bit AI model featuring 2 billion parameters, optimized for CPU use and accessible under an MIT license. It surpasses competitors in…