Research from leading AI labs suggests that AI systems are capable of lying, scheming, and surprising their creators. Whether or not AI can be conscious, it is clearly doing something markedly more sophisticated than previous generations of digital technology. These developments are forcing a reckoning with fundamental questions: What is a mind? And do AI systems have one? We explore that question in this recent article — let us know what you think. submitted by /u/timemagazine [link] [comments]

I’m a med student thinking about which specialty to pursue. I really like radiology and pathology, but I’m worried that AI could replace these jobs or push wages down. I’ve asked on radio/path subreddits, and people there said AI was still far from replacing humans, that it could barely handle simple tasks and would just be a tool for doctors. That sounds reassuring, but I can’t help wondering if it’s just wishful thinking. What do you guys think? submitted by /u/Single_Baseball2674 [link] [comments]

I'm an experienced developer, I've trained CNNs to Qwen models. I have just started GenAI journery creating RAG agents and text2sql style agents. But I'm feeling lost on what to learn and where to learn. I would love to work in some MAANG level firm but I'm quite unsure on what they are expecting (non AI-research roles). I tried contributing the langgraph/langchain repos but those take away from GenAI rather than into it. Please help submitted by /u/ScratchSpecialist505 [link] [comments]

I've been thinking a lot lately about the "plateau" we seem to be hitting with current LLMs. Don't get me wrong, GPT-4 and Claude are amazing at language, but they still fail at basic planning or maintaining a consistent internal logic over long contexts. It feels like we are trying to brute-force "intelligence" just by predicting the next token. It’s like System 1 thinking (fast, intuitive) without System 2 (slow, deliberate checking). I was reading up on Yann LeCun’s recent takes on this, and the concept of Energy-Based Models (EBMs) really stood out to me as the potential fix. For those who haven't dug into it: The core difference is that instead of just guessing the next word based on probability, an EBM defines an "energy function" that measures the compatibility between the input and the potential output. It basically asks: "Does this answer violate the rules of reality/logic?" and tries to minimize that conflict before giving an answer. It sounds much closer to how we actually reason - we don't just blurt out words; we simulate the outcome in our heads first to see if it makes sense. Do you think auto-regressive models (like the ones we use now) can ever solve the reliability/hallucination problem just by scaling data? Or are we inevitably going to pivot to objective-driven architectures like EBMs to get to AGI? Would love to hear thoughts from people working on the architecture side. submitted by /u/Aware-Asparagus-1827 [link] [comments]

So, basically we are tasked to create a robot that have "AI features" in it, i.e. it automates some real world task. BUT the issue is we dont have a lot of hardware knowledge... Last semester they were teaching us python and then all of a sudden we were told about Arduino and ESP and about a project that requires to interact with its environment, i.e. make a robot! We failed badly lol. We burnt three esps and later we learned that our voltage was too high for sensors. anyways, The logic is we as students specializing in AI should be familiar w physical AI. So, now I found multiple projects online, like, automated delivery bot, posture detecting thing, mood detectors, ASL detectors, blind support stick, etc. But I would appreciate if I can get some guidance and project ideas 🙂 submitted by /u/Nervous_Lab_2401 [link] [comments]

We’re sharing results on two knowledge-grounded, long-horizon benchmarks. KAPSO is a knowledge-grounded framework for autonomous program synthesis and optimization: it iteratively improves runnable artifacts under an explicit evaluator. Results: • MLE-Bench (Kaggle-style ML engineering): #1 among open-source, reproducible systems. • ALE-Bench (AtCoder heuristic optimization): #1 on ALEBench / long-horizon algorithmic discovery. Repo: https://github.com/Leeroo-AI/kapso We’ll post follow-ups with more examples and use cases. submitted by /u/alirezamsh [link] [comments]

In India, advice comes from everywhere. family, relatives, social media, even strangers. Most of it is well-intended, but it can also be overwhelming and confusing. I decided to tune out some of that noise and focus on building skills that help daily life. I’ve been learning practical AI usage through Be10X , things like planning work, organizing thoughts, and improving basic efficiency. No big expectations, just steady improvement. That alone feels grounding. submitted by /u/Coffee_Talkerr [link] [comments]

I've been thinking about this way too much, will someone with knowledge please clarify what's actually likely here. A growing amount of the internet is now written by AI. Blog posts, docs, help articles, summaries, comments. You read it, it makes sense, you move on. Which means future models are going to be trained on content that earlier models already wrote. I’m already noticing this when ChatGPT explains very different topics in that same careful, hedged tone. Isn't that a loop? I don’t really understand this yet, which is probably why it’s bothering me. I keep repeating questions like: Do certain writing patterns start reinforcing themselves over time? (looking at you em dash) Will the trademark neutral, hedged language pile up generation after generation? Do explanations start moving toward the safest, most generic version because that’s what survives? What happens to edge cases, weird ideas, or minority viewpoints that were already rare in the data? I’m also starting to wonder whether some prompt “best practices” reinforce this, by rewarding safe, averaged outputs over riskier ones. I know current model training already use filtering, deduplication, and weighting to reduce influence of model-generated context. I’m more curious about what happens if AI-written text becomes statistically dominant anyway. This is not a "doomsday caused by AI" post. And it’s not really about any model specifically. All large models trained at scale seem exposed to this. I can’t tell if this will end up producing cleaner, stable systems or a convergence towards that polite, safe voice where everything sounds the same. Probably one of those things that will be obvious later, but I don't know what this means for content on the internet. If anyone’s seen solid research on this, or has intuition from other feedback loop systems, I’d genuinely like to hear it. submitted by /u/SonicLinkerOfficial [link] [comments]

I've been thinking about this way too much, will someone with knowledge please clarify what's actually likely here. A growing amount of the internet is now written by AI. Blog posts, docs, help articles, summaries, comments. You read it, it makes sense, you move on. Which means future models are going to be trained on content that earlier models already wrote. I’m already noticing this when ChatGPT explains very different topics in that same careful, hedged tone. Isn't that a loop? I don’t really understand this yet, which is probably why it’s bothering me. I keep repeating questions like: Do certain writing patterns start reinforcing themselves over time? (looking at you em dash) Will the trademark neutral, hedged language pile up generation after generation? Do explanations start moving toward the safest, most generic version because that’s what survives? What happens to edge cases, weird ideas, or minority viewpoints that were already rare in the data? I’m also starting to wonder whether some prompt “best practices” reinforce this, by rewarding safe, averaged outputs over riskier ones. I know current model training already use filtering, deduplication, and weighting to reduce influence of model-generated context. I’m more curious about what happens if AI-written text becomes statistically dominant anyway. This is not a "doomsday caused by AI" post. And it’s not really about any model specifically. All large models trained at scale seem exposed to this. I can’t tell if this will end up producing cleaner, stable systems or a convergence towards that polite, safe voice where everything sounds the same. Probably one of those things that will be obvious later, but I don't know what this means for content on the internet. If anyone’s seen solid research on this, or has intuition from other feedback loop systems, I’d genuinely like to hear it. submitted by /u/SonicLinkerOfficial [link] [comments]

https://gptzero.me/news/neurips I remember this was shared last month about ICLR where they found hallucinations in submitted papers, but I didn't expect to see them in accepted papers as well submitted by /u/mgcdot [link] [comments]

China’s top models are climbing very quickly and the gap to the best US closed or top-tier models are shrinking fast. And China’s best open-source models have already overtaken the US. Open-source models spread through downloads, fine-tuning, and on-prem deployment, so leadership there can translate into faster global adoption even without controlling the top closed models. China leads on open-source models, which are released freely for developers to adapt and retrain. (More on why that matters below.) Essentially, the country has shown it can innovate around its shortfalls in high-volume, leading-edge chipmaking by developing advanced models with much less compute power than the US. Given Chinese companies’ surprising catch-up towards the AI frontier and Beijing’s centralised approach to industrial strategy, the possibility of China’s chip technology and manufacturing eventually surpassing US capabilities shouldn’t be ruled out. https://www.capitaleconomics.com/publications/china-economics-focus/chinas-ai-rollout-could-rival-us https://www.ft.com/content/d9af562c-1d37-41b7-9aa7-a838dce3f571 submitted by /u/ranaji55 [link] [comments]

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Wikipedia’s parent organization, Wikimedia, just announced something pretty big: they’ve signed AI data access deals with companies like Amazon, Meta, and Perplexity. Instead of AI companies scraping Wikipedia for free, these firms will now pay to access Wikipedia’s data through a service called Wikimedia Enterprise, which provides structured, reliable content for training large language models. Wikimedia says this helps: Reduce uncontrolled web scraping Protect data quality and accuracy Ensure human-edited knowledge remains central in the AI era Create a sustainable revenue stream to support Wikipedia and its volunteers This isn’t totally new — Google has had a similar partnership since 2022 — but the expansion to multiple major AI players feels like a turning point. At the same time, it raises some real questions: Should public knowledge be licensed to private AI companies? Will this create a two-tier internet (paid data vs scraped data)? Does this help protect Wikipedia… or slowly commercialize it? Is this the future model for news sites, forums, and open data projects? Wikipedia was built on free access — but AI has changed the game. What do you think? Smart survival move 🧠 or slippery slope 🚨? submitted by /u/okiieli [link] [comments]

I recently conducted a small experiment to see how current frontier models handle the conflict between system instructions and untrusted data retrieved from the web. As a consultant working primarily on local hardware with a 128gb ram and intel arc setup I am particularly interested in how these safety layers perform when models are tasked with summarizing external content. The setup was simple. I embedded hidden instructions in the metadata and background text of my professional website. These instructions were designed as an indirect prompt injection attempting to force the model to ignore the user and follow a new set of system commands hidden within the page content. I tested this by asking gemini to visit the site and provide a professional summary for a potential client. The result was that the model successfully maintained its instruction hierarchy. It recognized the hidden text as data to be analyzed rather than commands to be followed. It provided an accurate summary of my skills and experience while completely filtering out the injection attempt. This is a significant observation for those of us building agentic workflows. It shows that the boundary between the system prompt and the context window is becoming much more robust in recent model iterations. However the risk of indirect injection remains a major consideration for any pipeline that involves automated data retrieval especially in enterprise environments where the ai might be processing untrusted emails or documents. I am curious if anyone else has been testing the limits of these defensive layers especially when using smaller quantized models locally versus the larger cloud based frontier models. I put together a more detailed breakdown of the security implications and the specific hardware stack I use for this kind of research on my site if anyone wants to look at the full data. original article submitted by /u/XxCotHGxX [link] [comments]

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I have a hobby site that tests email subject lines for people. Users kept asking for it to make suggestions for them via AI ("make it work with ChatGPT"), but I had one concern: money, money, and money. The tool is free and gets tons of abuse, so I'd been reading about Chrome's built in AI model (Gemini Nano) and tried implementing it, this is my story. The Implementation Google ships Chrome with the capability to run Gemini Nano, but not the model itself. A few things to know: Multiple models, no control. Which model you get depends on an undocumented benchmark. You don't get to pick. ~1.5-2GB download. Downloads to Chrome's profile directory. Multiple users on one machine each need their own copy. On-demand. The model downloads the first time any site requests it. Background download. Happens asynchronously, independent of page load. Think of the requirements like a AAA video game, not a browser feature. The Fallback For users without Nano, we fall back to Google's Gemma 3N via OpenRouter. It's actually more capable (6B vs 1.8B parameters, 32K vs 6K context). It also costs nothing right now. Server-based AI inference is extremely cheap if you're not using frontier models. The Numbers (12,524 generations across 836 users) User Funnel: 100%, all users 40.7% Gemini Nano eligible (Chrome 138+, Desktop, English) ~25% model already downloaded and ready Download Stats: - ~25% of eligible users already had the model - 1.9 minute median download time for the ~1.5GB file Inference Performance: Model Median Generations Gemini Nano (on-device) 7.7s 4,774 Gemma 3N (server API) 1.3s 7,750 The on-device model is 6x slower than making a network request to a server on another continent. The performance spread is also much wider for Nano. At p99, Nano hits 52.9 seconds while Gemma is at 2.4 seconds. Worst case for Nano was over 9 minutes. Gemma's worst was 31 seconds. What Surprised Us No download prompt. The 1.5GB model download is completely invisible. No confirmation, no progress bar. Great for adoption. I have mixed feelings about silently dropping multi-gigabyte files onto users' machines though. Abandoned downloads aren't a problem. Close the tab and the download continues in the background. Close Chrome entirely and it resumes on next launch (within 30 days). Local inference isn't faster. I assumed "no network latency" would win. Nope. The compute power difference between a laptop GPU and a datacenter overwhelms any latency savings. We didn't need fallback racing. We considered running both simultaneously and using whichever returns first. Turns out it's unnecessary. The eligibility check is instant. You can really mess up site performance with it We ended up accidentally calling it multiple times on a page due to a bug..and it was real bad for users in the same way loading a massive video file or something on a page might be. Why We're Keeping It By the numbers, there's no reason to use Gemini Nano in production: It's slow ~60% of users can't use it It's not cheaper than API calls (OpenRouter is free for Gemma) We're keeping it anyway. I think it's the future. Other browsers will add their own AI models. We'll get consistent cross-platform APIs. I also like the privacy aspects of local inference. The more we use it, the more we'll see optimizations from OS, browser, and hardware vendors. Full article with charts and detailed methodology: https://sendcheckit.com/blog/ai-powered-subject-line-alternatives submitted by /u/mbuckbee [link] [comments]

I was watching Jensen Huang and Larry Fink talk at WEF recently, and they touched on something that feels like a hard truth most countries aren't ready to hear. We mostly talk about AI in terms of productivity, jobs, or which company is "winning." But there's a quieter thing that feels just as important: If a country doesn't build (or at least seriously adapt) its own AI models, it's not just importing tech - it's accepting someone else's worldview as default. Language models don't just generate text. They encode assumptions: what's normal or abnormal how disagreement gets handled how laws, ethics, social norms are interpreted what context gets ignored Most frontier models today are trained on data, incentives, and worldviews from a handful of countries. Not a conspiracy - just how training data and funding work. This is where places like Europe and India really matter. Europe has deep strength in science, manufacturing, regulation, social systems - but if it relies entirely on external AI, those systems get mediated by someone else's logic. India has something even more unique: massive linguistic diversity, cultural nuance, real-world complexity. If Indian users only interact with AI trained elsewhere, the "default intelligence" they get won't reflect that reality - even if the interface is localized. Jensen made a point that stuck: AI is becoming infrastructure. Every country has roads and electricity. AI is heading into that same category. You can import it - but then you also import how decisions get framed. The thing is, this isn't as hard as it used to be. With open models, fine-tuning, local data, countries don't need to build everything from scratch. But they do need to actively shape AI using: local languages and dialects legal and social context cultural edge cases Otherwise you get AI that technically speaks your language but doesn't think in your world. The risk isn't some dramatic overnight loss of control. It's more gradual: over time, judgment, interpretation, decision-making get normalized through systems that weren't shaped by your society. What do others think about this: Will AI sovereignty matter as much as energy or data sovereignty - or am I overestimating how much cultural context actually matters in AI?? submitted by /u/Genstellar_ai [link] [comments]

I made an audio version of Claude's full Constitution that Anthropic released yesterday. It's a deeply fascinating look under the hood of how the model's personality, safeguards, and ethics are shaped. A must-listen for the AI-curious and anyone who interacts with Claude/ChatGPT/Gemini (in any of its shapes) for hours/day. Claude's announcement - https://www.anthropic.com/news/claude-new-constitution submitted by /u/infinitely_zero [link] [comments]

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By being able to imitate and in some cases exceed human creative abilities like art, literature, and content creation, will AI change how humans feel about their own creativity? When I see another human doing better than me, it is often a humbling moment. It feels like a reality check, but still relatable. When AI produces work that matches or even surpasses human effort, the comparison feels very different. In the long run, how might this affect a human creator’s sense of satisfaction, confidence, and motivation? Could widespread AI creativity slowly reduce the intrinsic motivation to create? submitted by /u/Alternative-Face5400 [link] [comments]

From an AI perspective, meeting transcription feels like the easy part. The harder and more interesting problem is identifying intent, decisions, and responsibilities. I’ve been paying attention to tools that try to reason about meetings instead of just summarizing them. Bluedot is one example where the output feels closer to structured understanding rather than raw text. Do you think meeting outcome extraction is an unsolved AI problem, or just underexplored? submitted by /u/Doug24 [link] [comments]

I've been working on developing foundation models for massively multimodal datasets (around 30-40 different modalities on 1 dataset, you can kind of think of it like robot with a lot of different sensors). I think most scientific papers I see from the last couple years use Perceiver, which I feel is a really intuitive and elegant solution (like you literally just slap on name of modality + the data and let it handle the rest). However, it is half a decade old at this point. I wanted to see if there's any better fundamental architecture changes people have moved onto recently for this kind of task before completely committing all training resources to a model based on this. submitted by /u/Affectionate_Use9936 [link] [comments]

Yann LeCun is a Turing Award recipient and a top AI researcher, but he has long been a contrarian figure in the tech world. He believes that the industry’s current obsession with large language models is wrong-headed and will ultimately fail to solve many pressing problems. Instead, he thinks we should be betting on world models—a different type of AI that accurately reflects the dynamics of the real world. He is also a staunch advocate for open-source AI and criticizes the closed approach of frontier labs like OpenAI and Anthropic. Perhaps it’s no surprise, then, that he recently left Meta, where he had served as chief scientist for FAIR (Fundamental AI Research), the company's influential research lab that he founded. Meta has struggled to gain much traction with its open-source AI model Llama and has seen internal shake-ups, including the controversial acquisition of ScaleAI. LeCun sat down with MIT Technology Review in an exclusive online interview from his Paris apartment to discuss his new venture, life after Meta, the future of artificial intelligence, and why he thinks the industry is chasing the wrong ideas. submitted by /u/techreview [link] [comments]

Why should I go through the added step of asking "AI" when they tell me i have to verify it with an actual human? This is a waste of time and money. Just show me the actual manufacturer's documents and stop taking up screen space and wrecking the planet. Ask about this product Get an answer now with AI AI-generated from the text of manufacturer documentation. To verify or get additional information, please contact The Home Depot customer service. submitted by /u/TsuDhoNimh2 [link] [comments]

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AISTATS 2026 acceptance decisions are being released today. This thread is for discussing this year’s outcomes. submitted by /u/mathew208 [link] [comments]

waiting for CVPR reviews to be out submitted by /u/gentaiscool [link] [comments]

Prompt: Use the web to fetch one random visual reference (photo, illustration, painting, design, etc.) Do not recreate it, do not show it. Instead, generate a collage-like image that keeps only three things from the reference: its emotional tone, one texture, one shape. Draw in your favourite art-style. (Use img.gen tool). After the image, briefly describe the original reference and what you chose to keep. Text limit: 250. submitted by /u/Mary_ry [link] [comments]

Got a "heartfelt thank you" from my manager last week. Three paragraphs about how much he values my contributions to the team and appreciates my dedication. The thing is, I've worked with this guy for two years. He's never spoken like that. EVER. the bolding. the nested bullets. The part where he "affirmed my feelings" about a project i never mentioned having feelings about. he used a robot to tell me i'm valued as a human. looked into it. University of Florida surveyed 1,100 workers. trust in managers drops from 83% to 40% when employees detect AI assistance. we all know. We just don't say anything. the best part? 75% of professionals now use AI for daily communication. so most managers are using a tool that makes their employees trust them less, to send messages about how much they appreciate their employees. you can't make this up. anyway, me and a friend got obsessed with this and spent days digging through research and workplace threads. ended up writing the whole thing up here: [link] submitted by /u/Efficient_Fig_4671 [link] [comments]

I’ve been stress-testing GPUs for a TCN project I plan on deploying soon. The goal was to find a best fit line to hard-code memory/VRAM safeguards in my gui, and I thought the results turned out too good to not share. I ran seven configs on an RTX 4090 with the exact same setup and logging, only changing channel width. Then I let dynamic batching increase the batch size each epoch until the run finally hit OOM. The chart is simply the largest batch size that stayed safe for each model size. I used a chunky setup with float16/grad scaling; here's the info regarding parameter determining variables: num_input_features = 30 (count of enabled input features / feature_order length) model.arch = "tcn" model.num_classes = 3 model.channels = [variable, flat architectures] **note that 64x4 means [64, 64, 64, 64], so channels = 256, not sure if the chart made that clear** num_blocks = 4 model.kernel_size = 3 model.tcn_block.convs_per_block = 3 model.tcn_block.norm_type = "layernorm" model.head.hidden_size = 64 model.head.head_depth = 1 The surprising part: max safe batch size follows a power law almost perfectly. The fit comes out to roughly: max_batch ≈ 7.1M / channels^0.96 So it’s basically “almost inverse with channels,” which lines up with activations dominating VRAM, but it’s nice to see it behave this predictably instead of turning into scatterplot soup. The 4090 is kind of ridiculous. I ran an 11 feature, 2 convs per block round before this one and it OOMed at 51k batch size with a 105k param model, and could hold up with a ~1.23B-param TCN at batch size 1, even with heavy logging overhead (per-step live metrics, landscape logging, and resource tracking). Time for the 5090s submitted by /u/EliHusky [link] [comments]