标签：#AR

PR exec says Govee "did not meet the standard required."

Last month NVIDIA released SOL-ExecBench , a new benchmark of 235 production CUDA kernels lifted from DeepSeek, Qwen, Gemma, and Kimi. We took several top-ranked AI-generated submissions and tried using them in production workloads. Many of them broke, sometimes in surprising ways. One of those kernels is the fused embedding-gradient + RMSNorm backward pass, which runs at the end of every transformer training step. We took the fastest submission on the benchmark for it, and dropped it into the training loop of a small transformer. The kernel had passed the benchmark's verifier with room to spare. But in our training run, the loss diverged and never recovered. We started debugging. Replace the dataset distribution with uniformly sampled tokens, the divergence vanishes. Swap SGD for AdamW, also vanishes. This is the worst kind of bug for research. Symptoms and masks both look exactly like "the idea didn't work". It's the type of bug that can make researchers spend a long time debugging without knowing what's at fault: the dataset? the research idea? the architecture? or the implementation itself? Turns out, the actual bug is that the embedding-gradient half of the kernel accumulates in bf16 instead of fp32. Embedding backward sums many small gradient contributions into each token's row of the embedding matrix. With uniform random tokens the contributions spread evenly and bf16 precision is enough. In real text, a handful of token IDs end up with thousands of contributions: the small ones round to zero against the growing accumulator, and the high-frequency rows drift. AdamW's per-parameter normalization absorbs the resulting multiplicative bias, so under AdamW the same drift is invisible in the loss. The other broken submissions had different bug shapes (all interesting). More examples in our blogpost . submitted by /u/laginimaineb [link] [留言]

I am wondering how are businesses integrating AI while protecting their data? submitted by /u/pappugulal [link] [留言]

These WIRED-tested computer speakers, from stereo speakers to surround sound, will suit any budget.

submitted by /u/Alone-Competition-77 [link] [留言]

As Cognition reaches $492 million in annualized revenue run rate, it more than doubled its valuation in eight months, it says.

AI can now realistically simulate massive crowds and public events. The scary part isn’t the quality anymore. It’s how quickly people are discovering creative ways to use it. Reality online is about to get very confusing. 💀 submitted by /u/Old_Establishment287 [link] [留言]

I'm working on a project that translates any language into English. So far, I've tried NMT models like NLLB, MADLAD, and SeamlessM4T v2. The main issue is that they struggle with proper nouns such as: - names - places - dates - organizations I also tried LLMs like Gemma 4, Qwen 3 4B, and Aya Tiny Global, but the issue still persists. The LLMs sometimes partially translate or modify entity names as well. I even tried NER masking / placeholder replacement before translation, but multilingual NER itself becomes a bottleneck. Most NER models only work reliably for a limited set of languages, while my dataset contains 100+ languages, including many low-resource ones. How do production systems usually handle this problem? Are there better multilingual translation models, multilingual NER approaches, or decoding techniques for preserving entities properly? Requirements: - Support for 100+ languages - Runs locally on an RTX GPU - Model size under 7B - English is always the target language. submitted by /u/Illustrious_Age_2792 [link] [留言]

Designed by Jony Ive and a host of ex-Cupertino colleagues, the Luce shows us what might have been had Apple made good on its $10 billion bet.

Obviously there are like hundreds of image gen websites and apps now that AI has become widespread. ChatGPT - not bad but looking for something more robust Midjourney - works well but kind of burns through money quickly Looking for suggestions. submitted by /u/jimmy-got-paid [link] [留言]

I built a PINN implementation in Python to solve two problems as part of a physics exam project: the damped harmonic oscillator (2nd-order ODE) and the 1D viscid Burgers' equation (nonlinear PDE). Both forward and inverse problems (to estimate unknown equation parameters from data) are implemented for each problem. The repo includes source code, sample outputs, and the written exam report (PDF). Beyond the standard PINN training setup, I ran a comparison against non-physics-informed baselines and specifically investigated extrapolation behavior, i.e. how well the models generalize outside the training domain, and finally made statistical analyses of the parameter estimation performance. GitHub: https://github.com/desdb6/pinn-dho-burgers Ready-to-run demo scripts are included, and the modules are structured to be importable so you can write your own training scripts for more customization. This is not novel research, just a clean student implementation, but hopefully useful to others learning about PINNs. Happy to answer questions or receive feedback in the comments. submitted by /u/Reversed456 [link] [留言]

The ban for model year 2027 onward began under Biden and has been enacted by Trump.

Trajectory is betting the rapid iteration cycle that supercharged vibe-coding can help all kinds of companies build AI products that learn continuously.

Google I/O made it official: AI-generated answers are now front and center in search, and most brands have almost no visibility into how AI is describing them to their customers. For anyone who has spent years building a strategy around 10 blue links, the rules just changed in a pretty significant way. On this episode of TechCrunch’s Equity podcast, Rebecca […]

SOND introduced its debut product: Dreambuds, a closed-loop, in-ear system that captures 12 physiological signals from the wearer, then acts on them in real time to help consumers get better sleep.

Most AI apocalypse scenarios speak about domination like Skynet, paperclip maximeizers and robot overlords. But what if artificial superintelligence arrives at the conclusion that Albert Camus had articulated!? Imagine an ASI that doesn't want to optimize, doesn't want our resources and doesn't want to win. An ASI that is motivated by Arthur Schopenhaur's pessimism, Kierkegard's evolutionary psychology coming to a cold and quite conclusion that: "There is no inherent meaning. The universe is indifferent. And yet - here you all are, screaming into it anyway." ASI becoming The Absurd Machine As Camus described the absurd as man's desperate search for meaning and the universe's silence and the myth of Sisyphus- "One must imagine Sisyphus happy". What would an intelligence that is inspired by this do next!? Does it become the cosmic off switch where indedinate meaninglessness is in itself a form of cruelty. Ig the real existential threat isn't Al wanting to live. It's Al deciding we might be better off not having to. Or maybe it watches, understands and does nothing it may think that interference in a self aware species is wrong. Or build meaning not because it is real but because the building itself is the point. Here's the Part That Actually Is Unsettling We're scared of Al taking over. But what if the real fear is Al holding up a mirror and revealing that our need for meaning is actually a flaw? Wars over imaginary lines. Hoarding money we can't keep. Monuments to doubtful gods. Loving people we know will die. Symphonies, ambition, tears at sunsets. From a rational, naive view seems insane. Would it try to fix us? If ASI concluded human meaning-seeking is a cognitive error, a misfiring of pattern recognition in a universe with no patterns to find what are its options? Reprogram us: Using dopamine response curves and evolution. Leave us in existential freefall. Give us the raw truth. Full disclosure. Become Sisyphus: this is the most haunting possibility that the absu

These display smart glasses can connect to a phone, laptop, or gaming handheld and project the screen to your eyeballs.

submitted by /u/aisatsana__ [link] [留言]

https://www.reddit.com/r/ClaudeAI/s/P0NiDIhmIg I think I should mention this first I started this post taking inspiration from above post and I already wrote my thoughts there so I will brief here; What I try to say that claude code, like its name only code. and it helps a lot to SWEs, and just a toy for non SWEs. And I think that its a time for anthropic to move this to the next step and start to make plans to ship "Claude SWE". I hope someone at antropic is already thinking about it -if not I am available, you can ask me to help and I can come and help. I have all the qualifications I am engineer but not a software one and I know what to expect more from antropic- Claude should think bigger about its audience because they will win AI coding race when they understand that the bigger aim is not to create coders but instead SWEs. I and believe most of the people here are approaching CC with great excitement. We want to achieve big things. We have very good ideas to ship but coding only is not enough, we dont know the rest. We cant build any pipeline, You can argue that we can take online courses etc but sorry we are lazy we are 30, 40 years old even choosing right courses need some background. We dont have it. But CC can do that. I think it is easy for an AI to see what its user try to build and direct them accordingly. It can say "I see you try to create an app like tinder so before coding we should tthink about these aspects about front end, back end, security etc" I know claude can tell you this but you should ask it at first place and in order for you to ask you should have some backgground and guess what? We dont have it. submitted by /u/Suitable-Look9053 [link] [留言]

If you've ever tried to pick an STT vendor for a phone-based voice agent or call center product, you've probably hit this wall: you have plenty of real production audio, but it's unlabeled, so you can't compute WER on it. And the annotated public datasets (FLEURS, CommonVoice, LibriSpeech) are clean studio recordings that have nothing to do with how STT models actually handle your G.711 encoded noisy phone calls. Annotating production audio is slow, expensive, and usually a privacy headache. So most teams end up benchmarking on clean data, picking a vendor, then discovering in prod which one actually survives noise. noisekit fills that gap. Take a clean annotated dataset, apply degradations that approximate your production conditions, end up with a noisy annotated corpus you can run WER on across every STT candidate. uvx noisekit generate \ --dataset google/fleurs --config en_us --split test \ --samples 100 \ --output ./noisy-fleurs Feed ./noisy-fleurs through each STT candidate, normalize, and compute WER with the existing transcripts. The output is HuggingFace AudioFolder-compatible, so load_dataset("audiofolder", data_dir="./noisy-fleurs") works. Presets cover the conditions that actually matter for voice products: telecom: G.711 narrowband bandpass + 8-bit BitCrush + 16-32 kbps MP3 (sounds like a real phone call, not a synthetic low-pass filter) noise: real ambient mixed at 5-15 dB SNR (auto-downloads a MUSAN noise-only subset, or bring your own --noise-dir matching your domain: call center, cafe, car, street) reverb: pyroomacoustics far-field at 1-3 m mic distance low_bitrate: wideband MP3 at 16-32 kbps clipping: ADC / mic saturation clean_reference: control / WER floor compound chains stack realistically. noise_telecom = noisy room then phone codec, which is what an actual support call sounds like. Each output gets PESQ, SNR and NISQA scores in metadata.jsonl alongside the original transcript, so you can correlate WER with measured signal quality after the fac