...would use in energy [$] if it's kept on everyday every hour for a month in Colorado? submitted by /u/ToxicRoserade [link] [Kommentare]

And Charles Hoskinson is a snake oil salesman submitted by /u/LivingShine83 [link] [Kommentare]

How will AI affect our ability to think and judge for ourselves? Our new paper co-authored by 30 experts explores epistemic risks—the threats AI poses to our collective capacity to form beliefs accurately, reason well, and maintain a healthy information environment. We look at how AI can lead to harm through these mechanisms: Persuasion & Manipulation: AI systems are highly persuasive, opening the door for political/economic manipulation, incitement and radicalization, and other misuse, as well as unintentional harms like AI sycophancy and mental health risks. Cognitive Offloading: We may be delegating our thinking to AI at a deeper level than prior technologies, risking long-term degradation of individual and societal cognitive resilience. Feedback Loops: Human-AI and AI-AI interactions are narrowing the epistemic space humans and AIs draw from. This already drives homogenization, and may potentially lead to fragmentation and “lock-in” (a self-referential state that is difficult to reverse). While we believe AI could be an unprecedented lever for improving how humanity processes knowledge, we shouldn’t assume this will happen by default. We outline promising directions to change this trajectory across how AI systems are built, human-AI interaction design, institutional and individual adaptation, and information market incentives. Epistemic risks are self-perpetuating. As they can undermine the individual cognitive and social foundations needed to recognize, prioritize, and govern other threats—including the risks from AI itself—the time to act is now, before our capacity to respond is itself lost. Authors: Mick Yang, Stephen Casper, Jonathan Stray, Jasmine Li, Cameron Jones, Anna Gausen, Natasha Jaques, Brian Christian, Bálint Gyevnár, Hannah Rose Kirk, Zhonghao He, Dan Zhao, Siao Si Looi, Joshua Levy, Kobi Hackenburg, Elizabeth Seger, Matt Kowal, Michelle Malonza, Luke Hewitt, Hause Lin, Maarten Sap, Dylan Hadfield-Menell, Thomas H. Costello, Reihaneh Rabbany, Jean-François Godbout, David G. Rand, Atoosa Kasirzadeh, Gordon Pennycook, Yoshua Bengio, Kellin Pelrine Paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6873005 submitted by /u/KellinPelrine [link] [Kommentare]

Guys i live in pakistan and my family is against all the trading and crypto stuff and i need to know how do i make 5usd or (5×300) pkr because thats the minimum requirement that i can send to phantom wallet. those are the 2 things i know of please any pakistanis help a brother out 🙏🏻🙏🏻 submitted by /u/Tahaisdumbaf [link] [Kommentare]

Hey All, I am currently working on ASR models, and I have gathered some recent literature. From my literature search, it seems like the ASR models are getting more and more powerful due to two main things. Because pseudo-labelled data is growing, supervised models are rising rapidly. Whisper-large-v3 has been trained on 5M hours of weakly supervised data, and Nvidia Parakeet v3 has been trained on 660k hours of labelled data (open-sourced). Funny enough, Nvidia Parakeet v3 actually beats Whisper-large-v3 on almost every benchmark, even though it has a smaller model size and smaller data scale. So clearly, scale is not everything. New architectures are on the rise; We used to have self-supervised + CTC to solve the ASR task, but now it seems like Transducer, and Token-Duration-Transducers are taking off. As well as attention encoder-decoder architectures (Qwen) that are all trained in a supervised manner. Now, given that the labelled data is very huge, and the new architectures are coming up, are we saying bye to the self-supervised learning approaches like Data2Vec2.0, WavLM, etc., for ASR, and will we only use them for general-purpose speech tasks? This is actually not similar to how computer vision operates now. Dinov3 is a self-supervised approach that is extremely performant in segmentation, classification, depth estimation etc but I do not see this in the speech domain now. ASR is dominated by these huge supervised architectures (which is a dense-prediction task), as well as emotion recognition, diarization, and speech seperation are also all dominated by the supervised approaches. Do you think we will have our Dino moment with a new self-supervised architecture? Or supervised learning is the way to go? How would these methods actually perform if we trained a self-supervised model on these huge datasets? submitted by /u/ComprehensiveTop3297 [link] [Kommentare]