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(11 Jul) AI therapy bots fuel delusions and give dangerous advice, Stanford study finds

s.faithcollapsing.com/i46py

Archive: ais: archive.md/wip/BLlN8 ia: s.faithcollapsing.com/ywpww

#7cups #ai #ai-behavior #ai-ethics #ai-regulation #ai-safety #ai-sycophancy #biz-&-it #character.ai #chatgpt #clinical-psychology #delusions #jared-moore #machine-learning #mental-health #nick-haber #openai #science #stanford-university #stigma #suicidal-ideation #therapy

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@Em0nM4stodon
My #NixOS #setup. Even when I #ported it once to #another #machine, I #forgot to add the #user #password. So I kind of went into a #ghost user #session. All i did is #reboot and chose previous #generation corrected my #config and that's all. No need for #backup, #rescue #disk etc. I corrected my configs, now it's truly portable. All my setup is #rock #solid #stable and never #broke it though I use a lot of #unstable #packages. #Nix and #NixOS are just 🔥 ❤️

WorldVLA: Towards Autoregressive Action World Model

arxiv.org/abs/2506.21539

arXiv.orgWorldVLA: Towards Autoregressive Action World ModelWe present WorldVLA, an autoregressive action world model that unifies action and image understanding and generation. Our WorldVLA intergrates Vision-Language-Action (VLA) model and world model in one single framework. The world model predicts future images by leveraging both action and image understanding, with the purpose of learning the underlying physics of the environment to improve action generation. Meanwhile, the action model generates the subsequent actions based on image observations, aiding in visual understanding and in turn helps visual generation of the world model. We demonstrate that WorldVLA outperforms standalone action and world models, highlighting the mutual enhancement between the world model and the action model. In addition, we find that the performance of the action model deteriorates when generating sequences of actions in an autoregressive manner. This phenomenon can be attributed to the model's limited generalization capability for action prediction, leading to the propagation of errors from earlier actions to subsequent ones. To address this issue, we propose an attention mask strategy that selectively masks prior actions during the generation of the current action, which shows significant performance improvement in the action chunk generation task.
Continued thread

The #law also refers to other elements that can’t be attributed to a #machine, Millet said, when it requires an #author’s “signature” to transfer, talks about co-authors’ “intention,” & refers to an author’s “nationality or domicile.”

“Machines do not have property, traditional human lifespans, family members, domiciles, nationalities, mentes reae, or signatures,” Millet said, later adding that “the #Copyright Act makes no sense if an ‘author’ is not a human being.”

arXiv.orgEnhancing Frame Detection with Retrieval Augmented GenerationRecent advancements in Natural Language Processing have significantly improved the extraction of structured semantic representations from unstructured text, especially through Frame Semantic Role Labeling (FSRL). Despite this progress, the potential of Retrieval-Augmented Generation (RAG) models for frame detection remains under-explored. In this paper, we present the first RAG-based approach for frame detection called RCIF (Retrieve Candidates and Identify Frames). RCIF is also the first approach to operate without the need for explicit target span and comprises three main stages: (1) generation of frame embeddings from various representations ; (2) retrieval of candidate frames given an input text; and (3) identification of the most suitable frames. We conducted extensive experiments across multiple configurations, including zero-shot, few-shot, and fine-tuning settings. Our results show that our retrieval component significantly reduces the complexity of the task by narrowing the search space thus allowing the frame identifier to refine and complete the set of candidates. Our approach achieves state-of-the-art performance on FrameNet 1.5 and 1.7, demonstrating its robustness in scenarios where only raw text is provided. Furthermore, we leverage the structured representation obtained through this method as a proxy to enhance generalization across lexical variations in the task of translating natural language questions into SPARQL queries.