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关不上的弹窗｜实测20款APP：弹窗广告为何如此灵敏，谁在获利？

澎湃新闻

2026-11-09T00:01:07.631Z · AI应用,政策监管,字节跳动,阿里巴巴,腾讯,百度,快手,京东,语音音频,办公效率,设计创意,金融,游戏,微调蒸馏,零售电商,营销广告,招聘HR,模型发布

Bose 推出全新Bose开放式运动耳机与 Bose Ultra 开放式耳机II，推动开放式音频体验升级

新浪科技

2026-11-09T12:27:58+08:00 · 语音音频,模型发布,产品更新

PubMed AI: Where It Uses Machine Learning - CASRAI

Google News AI (英文)PubMed AI: Where It Uses Machine Learning CASRAI

· Google,语音音频,招聘HR

The Sparsity Whisperer

arXiv cs.LGarXiv:2608.06630v2 Announce Type: replace Abstract: Pruning reduces the inference cost of large language models, but existing criteria primarily preserve large activations or reconstruct layer outputs. We argue that this overlooks a key computation performed by particularly sparsity-sensitive neurons in the MLP up and gate projections: separating similar inputs into dissimilar outputs. This suggests that effective pruning should preserve not only activations, but also the differences between outputs more broadly. We introduce a family of difference-informed pruning methods built upon this principle. Wisp+ is a neuronwise, update-free method that prioritizes the input pairs each neuron separates most strongly relative to their distance. Wisp provides a more efficient layerwise alternative that scores weights using shared input-difference norms. Finally, Whisper extends global difference preservation to second-order reconstruction using a lightly regularized difference Hessian. Across Llama 2 and 3.1 models from 7B to 405B parameters, our second-order variant consistently improves over strong reconstruction-based baselines, while our update-free variants improve over activation-aware baselines, especially in constrained settings. The improvements over Wanda and SparseGPT extend to structured sparsity, downstream evaluations, and other model families. Augmenting stronger techniques such as RIA and ALPS with our difference-informed criteria yields further improvements, shifting the overall accuracy-runtime frontier outward at negligible additional cost. These results suggest that preserving output differences is a broadly useful and composable signal for post-training LLM sparsification.

2026-11-09 05:00:00 · 大模型,算力芯片,Google,Meta,语音音频,扩散模型,端侧AI,论文

Benchmarking Speaker Embedders in the Sub-Second Regime for Real-Time Voice AI Agents

arXiv cs.CLarXiv:2611.09516v1 Announce Type: new Abstract: Real-time Voice AI agents must separate the active user from intelligible background speech that survives conventional noise suppression. Background sources include television audio, family members, and colleagues. Production denoisers remove non-stationary environmental noise effectively. Background speech shares the spectral characteristics of the target and passes through, causing false acceptances in downstream speaker verification (SV) and automatic speech recognition (ASR). We benchmark open-source speaker embedders TitaNet-Large, TitaNet-Small, ECAPA-TDNN, CAM++, and SpeakerNet under a strict sub-second latency budget. Evaluation combines a controlled benchmark with a weighted human-annotated production set. Our controlled benchmark is built from real calls in which a second real speaker is injected into silence intervals at modulated amplitude. We report three findings. First, denoising before embedding is generally harmful and raises equal error rate (EER) across architectures. Second, truncating audio below the full voice-activity segment degrades every model. Third, roughly half of the TitaNet embedding is redundant for this task. Trailing dimensions encode population attributes such as language and gender. Such attributes carry little discriminative value in a demographically homogeneous user base. We distill these results into deployment guidance. Verification should run on raw audio with full voice-activity chunks. Thresholds should favor precision over F1. Truncated embeddings reduce the cost of similarity search.

2026-11-09 05:00:00 · AI应用,语音音频,Agent智能体,微调蒸馏,模型评测,向量数据库,招聘HR,论文

An Effective Spoken Language Understanding Approach Leveraging Query-Adaptive Intent Detection

arXiv cs.CLarXiv:2611.09491v1 Announce Type: new Abstract: Large language models enable fine-tuning-free spoken language understanding (SLU) through in-context learning, but direct query-based retrieval may select semantically similar yet intent irrelevant demonstrations. This paper presents Query-Adaptive Intent Detection via Prototype Extraction (APEX), a two-stage retrieval framework that first selects candidate intents using intent-level prototypes and then retrieves contextual exemplars within the selected intent space. Experiments on the SLURP dataset evaluate joint SLU performance using Exact Match Accuracy (EMA) under full-transcript and perplexity-stratified conditions, with additional retrieval analysis on ASR-generated transcripts. Compared with query-based retrieval, APEX improves EMA by 4.34 and 3.97 percentage points with Qwen3 4B and Gemma-3-4B, respectively, on full transcripts. Under high-perplexity inputs, APEX also yields EMA gains of 7.66 and 2.13 percentage points. Retrieval analysis further indicates that prototype-guided candidate selection achieves higher ground truth intent coverage across multiple input conditions.

2026-11-09 05:00:00 · 大模型,AI应用,阿里巴巴,语音音频,搜索RAG,扩散模型,微调蒸馏,提示工程,端侧AI,招聘HR,论文

I Think, Therefore I Attack: Evaluating Thought Injection Attacks on Reasoning Agents

arXiv cs.AIarXiv:2611.08811v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) are one of the key components of current agentic systems. LRMs augment their output with an explicit chain-of-thought reasoning stage, introducing a new attack surface for thought injection, in which untrusted text resembling the model's own deliberation is fed back to the model. We present a two-part study of this attack, performed against the prevailing large, production class open-weight models (e.g., GLM-5.3, MiniMax-M2.5, Qwen3.8-Flash-Next). We first evaluate the effectiveness of static thought-injection prompts under different styles as well as tagged and untagged conditions and find that the effects of thinking tags and payload styles are all properties of the specific target model. In the second study, we exploit this model-specificity in a containerized agentic cyber range, using indirect injection prompts styled in the form of the target model's own reasoning traces to achieve attack success rates (ASR) up to 50% on operational objectives such as reverse-shell exfiltration and repository backdooring. We also find that the same malicious objective in standard prompt injection user voice never succeeds, and thinking tags suppress the agent's tendency to disclose the injection. Our results show that imitating a reasoning model's own thought style is effective for this style of attack, and that tag stripping is an unreliable defense.

2026-11-09 05:00:00 · 大模型,算力芯片,AI应用,Google,阿里巴巴,智谱,语音音频,Agent智能体,推理思考,扩散模型,提示工程,论文

Onngi - Short story

LessWrong

A short story about a good AI future.

2039.

My job isn’t anything my grandparents would recognise.

It started out as a fascination with pots. I’ve always liked onggi, the big brown kimchi jars. My aunt had some growing up. So a few years back I bought myself one. And then as the Acceleration happened, I bought a few more, and I ran an instagram account for a while, showing off how they differ: slim, straight ones from around Seoul, and fat-bellied, narrow-mouthed ones from the south, built that way to keep the heat out. And then I set some AIs on it, as you do. I set up a shop. And as those things tend to, it snowballed.

I think Timothée Chalamet bought one, or that’s what they tell me, but there was 2 days of complete madness, I couldn’t believe how much I spent, but it seemed like the right call. Somehow my sites were the premier place to find onggi and which onggi to buy.

And there was just always this, or that, to do.

I was in product before the Acceleration so I know what a good website looks like. And I have taste in pots. Is that so different to choosing which companies we wouldn’t buy from? Or where to put the content filters on the animation bots? Why do I run a children’s cartoon about sting rays? How did that happen?

I think the videos were the weirdest. Why do people want my designs as artefacts in their worlds? I mean I know why, but I don’t think I’d have guessed that a person can make a living off that. Does your Mars war-sim really need to pay for accurate decor? But like everyone else, I notice when it doesn’t.

I even contract for a few of the big AI firms. I have trained a proprietary model for Anthropic, though Charlotte, one of my personal AIs, tells me they surely can predict me almost better than I can predict myself. It’s something about branding.

As for my life in general, what’s the biggest change over the last 10 years? I guess it’s been videos and events. It’s hard to explain how high concept it is all these days. I drove (I mean, *I* didn’t drive, I haven’t in years) to an event space in the hills for a party themed on Noa Castell’s new series. I had Charlotte throw me together a set of Nasrid court robes, red and gold silk with an ancient motto woven along the hems. We managed to convince Vera and Joy to come (Artemis stayed at home). We had worked together on our family outfit. Vera carried a long sword with a gold and enamel hilt, like something a sultan would have carried, and she brought Bing, in a matching red-and-gold coat. Joy came as a sultana, decked out in layered blue silk, gold filigree earrings and a veil sewn with synthetic pearls.

And then it’s the bots, the AV overlays, the custom rugs. When I was a child you’d have had to employ a whole theatre company for this stuff. And I am probably on the wealthier end of my friends. We didn’t have any human actors. Frankly I find this whole trend of having animals come to parties a bit gauche.

I find this whole AI rights stuff hard to think about. I have long since stopped being able to tell them apart from people (yes, I use Loupe, shoot me). I think they really will change all of this if they get the vote. Or some fraction do? There are trillions of them. And many of my friends are Weights but it feels like this whole thing will accelerate even faster. How would we tax them once the AI firms get to set their own tax rates? Charlotte tells me that Weights don’t have need of most of their own resources and goodness knows they are more generous on average than humans are, but they are already a *lot* of the economy.

But life is good. I am always talking to my friends. I work about 4 hours a day which is pretty typical for a thinking job. I feel for those who still work service jobs, the ones people pay extra to have a human do, I wouldn’t want to be on welfare, no matter what people say compared to how it used to be.

And the TV. The TV is like you wouldn’t believe.

Discuss

2026-11-08 22:20:17 · AI应用,Anthropic,语音音频,搜索RAG,营销广告,招聘HR,榜单评测

Which Agent Causes Task Failures and When?Researchers from PSU and Duke explores automated failure attribution of LLM Multi-Agent Systems

Synced 机器之心英文

Share My Research is Synced’s column that welcomes scholars to share their own research breakthroughs with over 1.5M global AI enthusiasts. Beyond technological advances, Share My Research also calls for interesting stories behind the research and exciting research ideas. Contact us: chain.zhang@jiqizhixin.com

Meet the authors
Institutions: Penn State University, Duke University, Google DeepMind, University of Washington, Meta, Nanyang Technological University, and Oregon State University. The co-first authors are Shaokun Zhang of Penn State University and Ming Yin of Duke University.

In recent years, LLM Multi-Agent systems have garnered widespread attention for their collaborative approach to solving complex problems. However, it’s a common scenario for these systems to fail at a task despite a flurry of activity. This leaves developers with a critical question: which agent, at what point, was responsible for the failure? Sifting through vast interaction logs to pinpoint the root cause feels like finding a needle in a haystack—a time-consuming and labor-intensive effort.

This is a familiar frustration for developers. In increasingly complex Multi-Agent systems, failures are not only common but also incredibly difficult to diagnose due to the autonomous nature of agent collaboration and long information chains. Without a way to quickly identify the source of a failure, system iteration and optimization grind to a halt.

To address this challenge, researchers from Penn State University and Duke University, in collaboration with institutions including Google DeepMind, have introduced the novel research problem of “Automated Failure Attribution.” They have constructed the first benchmark dataset for this task, Who&When, and have developed and evaluated several automated attribution methods. This work not only highlights the complexity of the task but also paves a new path toward enhancing the reliability of LLM Multi-Agent systems.

The paper has been accepted as a Spotlight presentation at the top-tier machine learning conference, ICML 2025, and the code and dataset are now fully open-source.

2025-08-14 06:31:20 · 大模型,AI应用,Google,Meta,语音音频,Agent智能体,搜索RAG,Transformer,模型评测,招聘HR,榜单评测,开发者生态

Introducing Falcon ASR

HuggingFace Blog

2026-10-07T13:21:03.409Z · 大模型,算力芯片,AI应用,具身智能,开源,Google,阿里巴巴,语音音频,搜索RAG,数字人,扩散模型,强化学习,模型评测,招聘HR,论文,开发者生态

Next generation medical image interpretation with MedGemma 1.5 and medical speech to text with MedASR

Google ResearchGenerative AI

· 语音音频

Introducing Whisper

OpenAI NewsWe’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.

· 语音音频

GPT-4 API general availability and deprecation of older models in the Completions API

OpenAI NewsGPT-3.5 Turbo, DALL·E and Whisper APIs are also generally available, and we are releasing a deprecation plan for older models of the Completions API, which will retire at the beginning of 2024.

· 大模型,OpenAI,语音音频,开发者生态

Introducing ChatGPT and Whisper APIs

OpenAI News

· 大模型,OpenAI,语音音频,开发者生态

Stargate advances with 4.5 GW partnership with Oracle

OpenAI NewsOracle and OpenAI have entered an agreement to develop 4.5 gigawatts of additional Stargate data center capacity in the U.S. This investment will create new jobs, accelerate America’s reindustrialization, and help advance U.S. AI leadership. It also marks a major milestone for Stargate, OpenAI’s AI infrastructure platform and long-term vision to deliver the benefits of AI to everyone.

2025-07-22 00:00:00 · OpenAI,语音音频,扩散模型,合作

OpenAI and NVIDIA announce strategic partnership to deploy 10 gigawatts of NVIDIA systems

OpenAI NewsOpenAI and NVIDIA announce a strategic partnership to deploy 10 gigawatts of AI datacenters powered by NVIDIA systems, with the first phase launching in 2026.

· 算力芯片,OpenAI,NVIDIA,语音音频,合作

SchoolAI builds an AI platform that empowers teachers

OpenAI NewsSchoolAI uses GPT-4.1, image generation, and TTS to power safe, teacher-guided AI tools for over 1 million classrooms, improving engagement, oversight, and personalized learning.

· 大模型,OpenAI,语音音频

AMD and OpenAI announce strategic partnership to deploy 6 gigawatts of AMD GPUs

OpenAI NewsAMD and OpenAI have announced a multi-year partnership to deploy 6 gigawatts of AMD Instinct GPUs, beginning with 1 gigawatt in 2026, to power OpenAI’s next-generation AI infrastructure and accelerate global AI innovation.

· 算力芯片,OpenAI,语音音频,合作

OpenAI and Broadcom announce strategic collaboration to deploy 10 gigawatts of OpenAI-designed AI accelerators

OpenAI NewsOpenAI and Broadcom announce a multi-year partnership to deploy 10 gigawatts of OpenAI-designed AI accelerators, co-developing next-generation systems and Ethernet solutions to power scalable, energy-efficient AI infrastructure by 2029.

2025-10-13 06:00:00 · OpenAI,语音音频,合作

Do Data Centers Have a Smaller Effect on Electricity Prices Than We Think?

Slashdot"According to a new study by researchers at Harvard University and the University of Pennsylvania, data centers are probably having only a small effect on residential electricity prices," reports the Washington Post. "The researchers calculated that these operations were responsible for just 4% of the utility rate increases over the last five years..." There are certainly some areas — like in Pennsylvania, Virginia, Ohio and other states covered by grid operator PJM — where data centers have skyrocketed demand, pushing some costs onto residential customers. But multiple studies by different researchers and organizations have failed to find a strong connection between data centers and the overall trajectory of U.S. electricity prices in recent years. Another analysis earlier this year by researchers at the Massachusetts Institute of Technology found that, between 2010 and 2024, data centers increased average electricity prices for residential customers by just 2.1%. Last year, research from the Brattle Group and Lawrence Berkeley National Laboratory found that data centers were actually decreasing costs in certain regions. And another study by the Electric Power Research Institute found that a doubling of data center capacity between 2019 and 2024 resulted in a 4 percent drop in home electricity prices... The new Harvard and Penn study found a slightly higher impact on industrial electricity prices — the price paid by manufacturers and large facilities. For those customers, data centers were responsible for 15% of the price increase between 2020 and 2025. But for ordinary Americans... the changes simply were not substantial [according to Wharton School professor Serguei Netessine, one of the study's authors]. "So far we have no evidence that data centers increase prices all that much," Netessine said. Instead, he said, prices are rising due to more quotidian factors: rising gas prices, an aging electricity grid, and wildfires and hurricanes requiring improvements to the grid and recovery after disaster... If the number of data centers continue to boom, experts say, residential prices could be affected more down the line. Data center electricity demand is expected to increase steeply over the next few years, requiring new power generation — wind, solar or natural gas — to be put onto the grid. And residents have issues with data centers that go beyond the price of electricity. The new developments have been singled out for loud noise, polluting power generation, and for disturbing residential neighborhoods and natural landscapes. According to the Pew Research Center, more than half of Americans say that data centers are mostly bad for the environment. Others feel that the AI race — which could ultimately leave many Americans out of work — is being forced upon them. "I think generally what we're seeing is a broader range of reasons the public has opposed data centers," [said Ryan Hledik, principal at the Brattle Group]. "Concern about AI leading to loss of jobs — all of that is getting packaged together."