Fraudsters don’t have governance committees or budget cycles. They just act. New research by @TheACFE + SAS asks whether organizations can move fast enough to keep up with #deepfakes & other AI-charged #fraud threats. Spoiler: most can’t yet.
GENERATIVE AI
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OpenAI Details Its Approach to Model Specification
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More on our approach to the Model Spec: openai.com/index/our-approach-to-the-model-spec/ [Translated from EN to English]
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OpenAI Explains Model Spec and AI Model Behavior
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The more AI can do, the more we need to ask what it should and shouldn't do. OpenAI researcher @w01fe joins host @AndrewMayne to explore the Model Spec, the public framework that defines how models are intended to behave. They break down how it works in practice, from the chain of command that resolves conflicting instructions to the way it evolves over time through real-world use, feedback, and new model capabilities. [Translated from EN to English]
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The AI Scientist Published in Nature with New Scaling Laws
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When we released The AI Scientist, it felt like the far future. Fast forward to today, and the automation of research is on everyone's mind. Thrilled that our foundational work has been published in @Nature! Please check out the paper along with some fun new scaling laws! 😃 Sakana AI (@SakanaAILabs) The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature Nature: nature.com/articles/s41586-0… Blog: sakana.ai/ai-scientist-natur… When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle. From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible. Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process. Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature! This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement. Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable. Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science. This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team! @_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune — https://nitter.net/SakanaAILabs/status/2036840833690071450#m
→ View original post on X — @_yutaroyamada, 2026-03-25 17:11 UTC
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Codex App, Claude Desktop and Chrome Extension Launches
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(Also, the Codex app, Claude desktop and Chrome extension, and OpenClaw)
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Google AI Studio article shared via X platform link
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Read more in our X article: nitter.net/GoogleAIStudio/status/… Google AI Studio (@GoogleAIStudio) x.com/i/article/203683454608… — https://nitter.net/GoogleAIStudio/status/2036845857690313085#m
→ View original post on X — @officiallogank, 2026-03-25 16:52 UTC
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Lyria 3 Pro and Clip Music Models Launch in Gemini API
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Introducing Lyria 3 Pro and Lyria 3 Clip, our full song and 30 second music models, available starting today in the Gemini API and our all new music experience in @GoogleAIStudio!! pic.twitter.com/AFvJRDSAIA
— Logan Kilpatrick (@OfficialLoganK) 25 mars 2026Introducing Lyria 3 Pro and Lyria 3 Clip, our full song and 30 second music models, available starting today in the Gemini API and our all new music experience in @GoogleAIStudio!!
→ View original post on X — @officiallogank, 2026-03-25 16:50 UTC
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User Compares Kling, Veo, and Sora for Video Generation
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I rotate between Kling and Veo. Usually prompting in both and seeing which one does it better. I almost forgot Sora existed until the news that they closed it. Lol
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Memory Systems and RAG Limitations in AI Models
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If I had to guess it's less decay and more that memories have naive RAG-like implementations, so you're at the mercy of whatever happens to retrieve in the top k via embeddings. They don't process you in aggregate and over time (probably compute constraints) so they struggle to
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LLMs Overfitting to RAG Context: A Systemic Training Bias
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(I cycle through all LLMs over time and all of them seem to do this so it's not any particular implementation but something deeper, e.g. maybe during training, a lot of the information in the context window is relevant to the task, so the LLMs develop a bias to use what is given, then at test time overfit to anything that happens to RAG its way there via a memory feature (?))