标签：#artificial

Developer felt "beaten up," with "no choice" but to shrink data center.

Who governs AI agents once they're running in production? I went looking for the answer. It's more complicated than the press releases suggest. This week Cognizant and ServiceNow announced a partnership specifically to close what they're calling the "enforcement gap" in enterprise AI governance. The Everest Group analyst quote from the press release cuts to it: "The hard part of AI governance was never writing the policy. It's enforcing it as systems learn and act." Here's what the enforcement actually looks like. In May, ServiceNow connected AI Control Tower to Amazon Bedrock AgentCore — a single governance layer over every AI agent an enterprise builds on AWS. Cognizant then deploys "Guardian agents" that monitor AI behavior in real time and enforce responsible AI principles throughout the lifecycle. Agents are being governed by other agents. Guardian agents watch the AI agents. The question the press releases don't answer: who watches the Guardian agents? The regulatory picture doesn't help. NIST issued a Request for Information in January specifically on securing AI agent systems — the federal standards body is asking industry how to manage agentic AI risk because the frameworks don't exist yet. The EU AI Act compliance deadline for high-risk AI systems just moved to December 2027. AI Control Tower doesn't hit general availability until August 2026. The enforcement layer is already being sold. The rulebook is still being written. Happy to dig into the primary sources if anyone wants specifics. submitted by /u/roll0ver [link] [留言]

Apparently, talking or mentioning conspiracy theories is enough for Anthropic to ban you from using Claude. submitted by /u/Bitter-Heart7039 [link] [留言]

Is this significant news? submitted by /u/sstiel [link] [留言]

Back in the 1980s a debate raged about whether it was okay to let children use calculators in elementary school. Critics warned that giving kids calculators would lead to the "destruction of student math skills." A similar debate is happening today across a range of areas, including coding, writing and even music. Will using AI lead a brain drain across these and many other areas? One of my favorite authors is Isaac Asimov. He's better known for his Foundation and Robot series of books where he contemplates whether an algorithm can successfully predict (and guide) humankind's development and the relationship between super artificial intelligence and humans. In some ways he predicted what we're experiencing today with AI: the rise of powerful, inscrutable artificial machines that are so complex humans can't understand or maintain them. In the short story, "The Last Question" he wrote: "Multivac was self-adjusting and self-correcting. It had to be, for nothing human could adjust and correct it quickly enough or even adequately enough." We're living an age that was once the stuff of science fiction. The question is: what comes next? submitted by /u/SpiritRealistic8174 [link] [留言]

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been building AI agents for a while and noticing a pattern: the LLM reasoning part works. the part that breaks is everything around accounts, logins, and verification. agent gets to "sign up for this service" and then: - email verification loop breaks - OTP times out while the agent is mid-step - captcha or bot detection fires - session expires between steps the model figured out what to do. the infrastructure around it didn't cooperate. curious if this matches what others are building. where do your agents actually fail in production? is it the reasoning, or is it the plumbing? submitted by /u/kumard3 [link] [留言]

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Disclosure: I work on the benchmark below, so flagging that up front. We've been testing whether LLMs can critique recent science-paper summaries — catch planted flaws, overclaims, and missing evidence — and, separately, how calibrated they are about their own judgments (confidence scored with Brier, a strictly proper rule). The pattern that keeps showing up: the models best at spotting problems are also among the most confidently wrong when they miss. Critique skill and calibration look like different axes, not the same one. There's also a clear gap between raw accuracy and knowing when to abstain. It's open (Apache-2.0) if you want to poke at it: Leaderboard: https://huggingface.co/spaces/BGPT-OFFICIAL/refute-leaderboard Dataset: https://huggingface.co/datasets/BGPT-OFFICIAL/refute Curious how others think about measuring calibration vs. raw capability — is a proper scoring rule enough, or do you need explicit abstention metrics too? submitted by /u/connerpro [link] [留言]

After spending the last few weeks reading through the reasoning literature, I noticed a trend that seems worth discussing. For the past 2–3 years, a large fraction of progress in LLM reasoning came from making models generate more intermediate thoughts. Chain-of-Thought prompting (Wei et al., 2022) pushed PaLM 540B from roughly 18% to 58% on GSM8K. Self-Consistency added another 17.9 percentage points by exploring multiple reasoning paths before committing to an answer. Tree-of-Thoughts later showed that GPT-4's success rate on Game of 24 could jump from 4% to 74% when reasoning was reformulated as search rather than a single chain. DeepSeek-R1 and OpenAI's o1 pushed the idea even further by allocating substantial test-time compute to reasoning itself. Taken together, these results seemed to point in the same direction: giving models additional reasoning trajectories, search paths, or thinking steps often improved outcomes. Recent work increasingly asks whether those traces are actually necessary. Quiet-STaR doesnt treat reasoning traces primarily as explanations for humans. Instead, it trains models to generate internal rationales that improve future token prediction. COCONUT goes a step further and asks a more radical question: why force reasoning to be represented as language at all? Rather than generating reasoning tokens, it feeds continuous hidden states back into the model and performs reasoning directly in latent space. Fast Quiet-STaR then shows that some of the benefits of explicit reasoning can be retained even after removing thought-token generation during inference. This feels like a meaningful shift in research direction. For a while, the field seemed focused on making reasoning more visible. Recent work increasingly explores whether visibility is actually necessary. One way to interpret this is that Chain-of-Thought was never the reasoning process itself. It was a computational scaffold. Transformers perform a fixed amount of computation per generated

PM at a mid-size startup here. Didn’t really notice how bad it got until this week. My workflow now: • Claude for ideation • ChatGPT for rewriting specs • Cursor for implementation • Perplexity for research • Notion AI for docs • Atoms AI for larger tasks None of these tools actually replaced my work. They just redistributed it. I’m still the one dragging context between all of them.Yesterday I literally caught myself pasting the exact same requirement into 4 different tools and thinking… this can’t be how it’s supposed to work. I don’t even think any single tool is bad. It just feels like we hired 6 smart interns and completely forgot to get a manager. submitted by /u/Dangerous-Guava-9232 [link] [留言]

I build and teach this, so here's the honest mechanics, not the hype. Build one consistent AI character (custom-trained, not just prompting), run it as a social presence, monetize on platforms that allow AI. The edge isn't quality vs humans — it's near-zero content cost, no burnout, horizontal scaling. The underrated hard part: consistency is genuinely difficult, and the money is in audience relationship management, not the content. The content's the easy 20%. Broader signal: when content cost hits zero, the bottleneck becomes distribution and trust. Applies way past this niche. Happy to go deeper on any part — it's what I do daily. submitted by /u/PoleTV [link] [留言]

The Air succeeds as a minimalist, reliable fitness tracker, but Google's AI Health Coach feels unnecessary.

I work on a podcast and we wanna do an episode where we have a proponent and opponent of data centers talk. We're looking for a good oppponent voice. Any names or organizations that are intelligent and well spoken and worth checking out? submitted by /u/BikeLaneHero [link] [留言]

Hey guys. I saw a post on Instagram saying that tripo ai is holding a rendering challenge and the theme is “Out There”. This made me think about how AI-generated 3D models might change the rendering challenges. In a traditional rendering challenge most of the work focuses on modeling, resource creation, texture processing and scene setup. However with Tripo AI the process of generating 3D resources can become much faster. This made me think if the real challenges has shifted elsewhere. if everyone could generate models faster then what does the good rendering depend on? Art direction? Composition? Lighting? Camera position? storytelling? atmosphere? or clarity of idea communication? The rule of this challenge not only require to create objects with a beautiful appearance but also to create a scene that is larger, more profound, or more meaningful than what is actually before your eyes. I would really like to hear the opinions of those friends who are interested in AI-generated 3D. Do you think rendering challenge will be more dependent on technical ability or more focused on directionality and creativity? submitted by /u/babyb01 [link] [留言]

Microsoft’s AI products aren’t selling and Github’s been plagued with troubles. WIRED spoke with VP Scott Hanselman about whether the company is in catch-up mode.

Everyone keeps saying we have to use AI, that it’s revolutionary and I totally agree, it saves a ton of time. But there’s a problem with that: it saves so much time that we don’t even pay attention to the data we’re sending to AI, names, passwords, phone numbers, Social Security numbers, we send it all under the pretext of saving time. The problem is that we’re giving it away; we’re sending it to companies whose last concern is our privacy. Imagine you start talking about your eight-year-old child’s health issues to an AI using their full name. You can be sure you’ll get targeted ads about those health issues, and that your son will later see the same hyper-targeted ads. The biggest problem with AI isn’t that it makes us stupid, it’s that it further erodes our privacy. That’s why we created ONYRI Sanitize , the goal is to anonymize your data before sending it to the AI. It’s a project I created with my best friend; it’s taken us two months so far. The detection system has a 95% success rate on data from the United States and France, and we’re working to integrate as many languages as possible while maintaining the highest possible detection rate. I'd love to hear your feedback and thoughts. Thanks, everyone 🙏 Have a great day ☀️ Alex submitted by /u/No_Computer_1247 [link] [留言]

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submitted by /u/Quirky_Inevitable630 [link] [留言]

We've been running AI across a team for about two years. Expected the hard parts to be the models. They weren't. The problem that cost us most early on was context. We had a system making customer-facing recommendations without access to the business-specific knowledge it needed to answer accurately. Spent too long trying to fix it at the prompt level. The context layer didn't exist, and prompting didn't fill that gap, it just made it less obvious until something downstream failed badly enough to trace back to it. That failure pushed us to map the other places where AI builds break structurally rather than technically. We found five more, and they kept showing up across different stacks and different team sizes in roughly the same order. The first is identity, when you move from one person's AI to a team's AI, shared context without role-based permissions either creates noise or recreates the same knowledge silos you were trying to escape. The second is decision memory, records of what was decided aren't the same as memory of why, and that gap compounds quietly until a new team member gets a confident wrong answer from a system referencing reasoning that was abandoned months ago. The third is attention. Dashboards only work if someone looks at them, and the failure mode of every dashboard ever built is the same: critical things slip through when life gets busy. The fourth is write-back. Manual logging is a tax on the busiest moments, and the more important the work, the less likely anyone stops to document it. The fifth is governance, when the same agent that builds something also evaluates it, that's not a check, it's a loop grading its own homework. The sixth is economics, at solo scale AI cost is a rounding error, at team scale you're looking at a vendor invoice with no way to connect spend to specific workflows or outcomes. Which of these have you hit? And did they show up in this order or did something else surface first? If you're interested, we turned these i