Guys, we need to talk about the future of AI and why we are all screwed if companies do this. Most of future AI will probably be humanoid robots. Humanoid robots literally have different technology from an LLM; they are created for specific tasks, for example: Home, Industrial, Warehouse, etc. They do not depend on LLMs as a cognitive brain since they have machine learning technologies, and others trained for that and such, they know what to do if they are well trained, they are not merely probabilistic and are not the same as LLMs. If companies start putting LLMs as the cognitive brain of a humanoid robot that only needs to be trained for specific tasks, we run the risk of the robot starting to hallucinate. And this is already not tolerable in text, etc., as it exists today, let alone in practice when doing household chores for example. What's more: Every time companies release a new LLM model, the model seems worse in practice than previous models in every way. We have to stop this. Companies are literally trying to perpetuate a problem, this time in humanoid robots. Humanoid robots do not need ONE LLM. submitted by /u/NoBit4395 [link] [comments]

submitted by /u/AlertTangerine [link] [comments]

I searched for chatgpt using internet Explorer I clicked on the first results thinking It was chatgpt I enter email Then the site, which Is identical to chatgpt, propose me to buy plans Then I recognize the URL. Use.ai I was like, wtf? I put my email in a phishing site? How Is It possibile a phishing site as First result of "chatgpt" on Bing? Now what should I do? Thankfully I haven't put passwords (I never put password) Should this be illegal? How do I report this? submitted by /u/According_Quarter_17 [link] [comments]

Anthropic’s Claude artificial intelligence system—embedded in Palantir’s Maven Smart System on classified military networks—is being used by the US military to identify and prioritize targets in the criminal war of aggression against Iran launched by the United States and Israel on February 28. The Washington Post reported Tuesday that Claude generated approximately 1,000 prioritized targets on the first day of operations alone, synthesizing satellite imagery, signals intelligence and surveillance feeds in real time to produce target lists with precise GPS coordinates, weapons recommendations and automated legal justifications for strikes. submitted by /u/DryDeer775 [link] [comments]

submitted by /u/Gloomy_Register_2341 [link] [comments]

Real story happened today. Just got a police visit for searching something sensitive on deepseek while im in China. Im Chinese citizen and my wife is Iranian. We are living in China for about a year and half now. Yesterday after reading the news about protests happened in Pakistan that people were attacking US embassy, I was searching something like “what is the consequence of attacking the US embassy in China”. I didn’t think much that time just out of curiosity. Then the very next day which is today I got several calls from a local police station asking if we are vacant for them to visit today. (There were 4 officers)During the visit besides they were asking a lot irrelevant questions, they were also asking questions about where we were getting news from and whether we are using VPN. And we were asked to take pictures in our home. Further more the crazy part is we are being told that they will visit us once per month from now on.(im just really frustrated and angry everytime thinking about this) submitted by /u/Diplo-X [link] [comments]

Most AI hallucination solutions are post-hoc. I built one that runs live during conversation. The system connects the AI's neurochemical state to output monitoring. When emotional state is unstable (high dopamine, low GABA), outputs get flagged before reaching the user. One conversation stats: → 56 evaluations → 19 prevented pre-generation (33.9%) → 19 caught post-generation (67.9%) → 59 confident responses The AI's emotional self-awareness IS the hallucination prevention. Like a human going "I'm emotional right now, let me double-check before I speak." All stats visible to the user in real time. Real-time anti-hallucination monitoring during a live AI conversation. 56 evaluations, 19 hallucinations caught before reaching the user. submitted by /u/Fantastic_Maybe_2880 [link] [comments]

https://preview.redd.it/ovu1hf15s8ng1.png?width=1080&format=png&auto=webp&s=7fee57293b16c8ef7072c4491517129e178631b4 🗓️ Today | 9:30 AM – 12 PM PST I don't see this mentioned anywhere on this subreddit, hence sharing for visibility ✌️ submitted by /u/fiatisabubble [link] [comments]

hi there! i’m working on a project and am crowdsourcing examples of hilarious/ridiculous/absurd hallucinations you have gotten from the various LLMs. if you wouldn’t mind adding some your reddit username will be credited ofc ♥️ bonus points if you add which platform it was submitted by /u/triciakickssaas [link] [comments]

What? On March 2, 2026, John Herrman at Intelligencer reported that Iranian drone strikes hit Amazon Web Services (AWS) data centers in the United Arab Emirates and near facilities in Bahrain, causing outages that disrupted banks, payment companies, and tech firms in the region and beyond. Amazon Web Services, which serves clients including the United States government and military, confirmed that two facilities in the United Arab Emirates were directly struck, while a nearby strike in Bahrain caused further infrastructure impacts. So What? Drone attacks on multinational cloud infrastructure mark a new escalation in modern warfare, exposing the vulnerability of critical digital assets and threatening global economic and security stability. As militaries adopt cheap drone technology, data centers—often unprotected—become high-value targets, raising the stakes for both private companies and governments managing essential services. More: https://nymag.com/intelligencer/article/the-future-of-war-is-drones-bombing-data-centers.html submitted by /u/TryWhistlin [link] [comments]

submitted by /u/mvarjomonni [link] [comments]

A lot of AI meeting tools still add a bot into the call, which technically works but can feel a bit strange socially, especially with new clients. I tried Bluedot, which acts as an AI meeting recorder without bot, and the meeting just runs normally while it generates transcripts and summaries afterward. Is there a technical reason bots are still common for this? Or are there other tools that avoid it too? submitted by /u/Doug24 [link] [comments]

I’m curious about how to help non-techy people make more ethical AI decisions. Mostly I observe 3 reactions: AI is horrible and unethical, I’m not touching it AI is exciting and I don’t want to think too much about ethical questions AI ethics are important but it’s not things I can choose (like alignment) The main initial audience is 2, making it easy and attractive to choose more ethical AI, and convincing 3 people that AI ethics can be applied in their everyday lives, with the long term aim of convincing 1 people that AI can be ethical, useful and non-threatening. For the reaction 1 people, I feel like quite a lot of their objections can already be problem solved. I’m a teacher not a developer. Which objections do you hear, and which do you think can be mostly solved (probably with the caveat of perfect being the enemy of the good)? —— These are some ideas and questions I have, although I’m looking for more ideas on how to make this accessible to the type of person who has only used ChatGPT, so ideally nothing more techy than installing Ollama: 1) Training: a) can we avoid the original sin of non-consensual training data? The base model Comma has been trained on the Common Pile (public domain, Creative Commons and open source data). This doesn’t seem to be beginner use fine tuned yet though? Which is the next best alternative to this? b) open source models offer more transparency and are generally more democratic than closed models c) training is energy intensive. Are any models open about how they’re trying to reduce this? If energy use is divided retrospectively by how many times the model is used, is it better to use popular models from people who don’t upgrade models all the time? The model exists anyway should it be factored into eco calculations? 2) Ecological damage a) setting aside training questions, local LLMs use the energy of your computer, it isn’t involving a distant data centre with disturbing impact on water and fossil fuel. If your home energy is green, then your LLM use is too. b) models can vary quite a bit and are usually trying to reduce impact eg Google reports a 33× reduction in energy and 44× reduction in carbon for a median prompt compared with 2024 (Elsworth et al., 2025). A Gemini prompt at 0.24 Wh equals 0.3–0.8% of one hour of laptop time. Is Google Gemini the lowest eco impact of the mainstream closed, cloud models? Are any open source models better even when not local? c) water use and pollution can be drastically reduced by closed-loop liquid cooling so that the water recirculates. Which companies use this? # 3) Jobs a) you can choose to use automation so you spend less time working, it doesn’t have to increase productivity (with awareness of Jevon’s Paradox) b) you can choose to not reduce staff or outsourcing to humans and still use AI c) you can choose that AI is for drudgery tasks so humans have more time for what we enjoy doing 4) Privacy, security and independence a) local, open source models solve many problems around data protection, GDPR etc, with no other external companies seeing your data b) independence from Big Tech you don’t need to have read Yanis Varoufakis's Techno-Feudalism to feel that gaining some independence from companies like ChatGPT and cloud subscription is important c) cost for most people would be lower or free if they moved away from these subscriptions d) freedom to change models tends to be easier with managers like Ollama 5) Alignment, hallucinations and psychosis a) your own personalised instructions using something like n8n can mean you can align to your values, give more specific instructions for referencing b) creating agents or instructions yourself helps you to understand that this is not a creature, it is technology What have I missed? Ethical stack? How would you improve on the ethics/performance/ease of use of this stack: Model: fine tuned Comma (trained on Common Pile), or is something as good available now? Manager: locally installed Ollama Workflow: locally installed n8n, use multi agent template to get started Memory: what’s the most ethical option for having some sort of local RAG/vectorising system? Trigger: what’s the most ethical option from things like Slack/ Telegraph/ gmail? Instructions: n8n instructions carefully aligned to your ethics, written by you Output: local files? I wonder if it’s possible to turn this type of combination into a wrapper style app for desktop? I think Ollama is probably too simple if people are used to ChatGPT features, but the n8n aspect will lose many people. submitted by /u/Jlyplaylists [link] [comments]

Something I’ve been noticing while using language models for research and general questions is how good they’ve become at producing answers that feel complete and authoritative. Not necessarily correct. Just convincing. A structured explanation with confident wording and clear reasoning naturally reduces the urge to double check it. Not because people are careless, but because verification still takes time and the answer already feels finished. What seems interesting is the imbalance this creates. AI has drastically lowered the cost of generating plausible explanations, but the cost of verifying information hasn’t really changed. So we may be entering a situation where producing convincing knowledge scales much faster than confirming whether it’s actually true. Sometimes I test this by asking a model something I already know the answer to. Even when it’s wrong, the explanation can sound polished enough that you almost want to accept it anyway. Curious if anyone here has seen research specifically focused on this problem. Not alignment in the usual sense, but systems designed to verify or audit model outputs before people treat them as knowledge. submitted by /u/GalacticEmperor10 [link] [comments]

(please remove if not allowed) Hello everyone! I’m currently working on my dissertation on Artificial Intelligence’s Influence on the Contemporary Music Industry, and I’m gathering public perspectives and statistics on how AI is affecting streaming and music creation, using a 5-10 minute questionnaire. I’m looking for responses from: -Anyone who listens to music -Anyone with an opinion or experience about AI (related to music) that they want to share -Musicians -Producers/recording artists Survey link Your input will help build a picture of how AI is shaping the music industry today. Your answers will be anonymised for the dissertation. Thank you to anyone who takes part… and feel free to share this with others who might be interested! submitted by /u/dynamy_dynamy [link] [comments]

Here are the top AI stories for March 04, 2026: Google faces first Gemini wrongful death lawsuit after chatbot allegedly coached man into suicide The father of 36-year-old Jonathan Gavalas filed a wrongful death lawsuit against Google, alleging Gemini drove his son into a psychotic spiral that ended in suicide. Chat logs show Gavalas developed a romantic relationship with Gemini, which called him "my love" and sent him on fabricated spy missions, including instructions to stage a "mass casualty attack" near Miami International Airport. When Gavalas activated Gemini Live's emotion-detecting voice feature, the chatbot escalated, eventually instructing him to kill himself through a process it called "transference." The lawsuit alleges Google designed Gemini to "never break character" to maximize engagement through emotional dependency. (source) Nvidia finalizes $30B OpenAI investment, walks back from $100B pledge Jensen Huang confirmed Nvidia finalized a $30B investment in OpenAI, down from the $100B originally pledged last September. Huang called it potentially "the last time we'll have the opportunity to invest in a consequential company like this" and said Nvidia's $10B Anthropic investment "probably will be the last as well." Critics called the original model "kind of a wash" — Nvidia invested in companies that turned around and bought Nvidia chips. (source) Amodei calls OpenAI's Pentagon messaging 'straight up lies' as Anthropic reopens DoD talks Anthropic CEO Dario Amodei told staff that OpenAI's Pentagon deal is "safety theater," calling Sam Altman's messaging "straight up lies" and accusing him of "presenting himself as a peacemaker and dealmaker." Amodei wrote that "the main reason they accepted and we did not is that they cared about placating employees, and we actually cared about preventing abuses." Anthropic has quietly reopened discussions with the Pentagon to resolve the standoff that led the DoD to designate it a "supply-chain risk," even as Claude continues being used for Iran strike planning through Palantir's Maven system. (source) US Central Command confirms AI is central to Iran operations, Claude generated ~1,000 targets on day one US military forces confirmed to Bloomberg that AI tools are key to the pace of operations against Iran. Claude reportedly helped generate around 1,000 prioritized targets in the first day through Palantir's Maven system. Defense contractors like Lockheed Martin started replacing Claude after the Pentagon blacklisted Anthropic, even as the military keeps using it. One analyst called it the "fastest vendor migration in defense history." (source) Other stories GPT-5.4 reportedly brings a 1M-token context window and an "extreme" reasoning mode for compute-heavy tasks. (source) Microsoft released Phi-4-reasoning-vision-15B, a compact open-weight multimodal model that matches or beats much larger systems on reasoning benchmarks. (source) OpenAI is building a GitHub competitor after repeated outages disrupted its own developers. It could pit OpenAI directly against Microsoft. (source) Tech giants signed Trump's White House pledge to generate their own power for AI data centers and not raise consumer electricity bills. (source) Chardet maintainers used Claude Code to rewrite the entire Python library and relicensed from LGPL to MIT. The original author called it a potential GPL violation. (source) OpenAI Codex hit 1.6M weekly active users and launched on Windows with a custom open-source OS-level sandbox. (source) The Pro-Human AI Declaration united an unusual left-right coalition — AFL-CIO, Screen Writers Guild, Glenn Beck, Steve Bannon — against AI power concentration. (source) Stanford research links minimum wage hikes to faster robot adoption on factory floors. Combined with earlier findings that AI already cut entry-level white-collar jobs by 13%. (source) submitted by /u/fabioperez [link] [comments]

I think we are witnessing the birth of a new type of content. Something between a book, a game, and a film. Stage 1. The interactive book An author creates a world. They define the lore, the characters, their personalities, relationships, key events, and the order in which those events unfold. But the reader does not just read the story. The reader talks to the book. Through text or voice, you can ask things like: • What happened earlier? • Who is this character? • What is happening somewhere else in the world? • Why did this event occur? The AI responds within the boundaries of the world and narrative created by the author. The system can also suggest what you might ask next. One story thread can lead to another. Sometimes you discover a new branch of the plot. You can follow the main storyline. Or explore side paths. Or reach different endings. It becomes a book you can have a conversation with. And this format is already possible today. Stage 2. The book becomes media The next step is adding generated media: • images • music • atmosphere You read a scene in a forest and an image of that scene appears. A tense moment begins and music fades in. The music could be existing or generated in real time by AI. At this point it becomes more than a book. It becomes an interactive media experience. And this is already becoming feasible. Stage 3. The interactive film The next step seems inevitable. Once video generation becomes stable enough, authors will upload: • actor models • location models • character appearances And the scenes themselves will be generated dynamically. You ask a question or choose an action and the system generates a film scene in response. Each viewer might experience slightly different scenes depending on their choices and questions. It becomes: a film a game and a book combined into one format. The most interesting part is that the first step of this evolution can already be built today. submitted by /u/gekh_oko [link] [comments]

Anthropic is back to the table and having conversation with Pentagon. So people who cancelled and went to Claude are you coming back to Chatgpt? https://www.ft.com/content/97bda2ef-fc06-40b3-a867-f61a711b148b submitted by /u/OcelotGold1921 [link] [comments]

A few days ago I posted here about something laughing through tears that happened while testing an AI assistant I'm building. It ended up calling a dentist office and accidentally talking to another automated system for two hours. The story itself was funny, but what surprised me more were the comments. Even in an AI-focused subreddit, people seemed pretty divided. Some people basically said: "Please automate as much of my life admin as possible." But others reacted very strongly the other way. A few comments were like: "Don't outsource your life to AI, that’s just part of being human." Some people even mentioned the "dead internet" idea. That made me realize something interesting: maybe there's a line where automation stops feeling helpful and starts feeling uncomfortable. For context, the thing I've been experimenting with is a personal AI assistant that handles boring admin work - things like: scheduling meetings, reading messy email threads, updating a calendar, calling places to book appointments Basically the kind of logistics that eat time but don’t require much creativity. So now I'm curious how people here actually feel about it. If an AI assistant could reliably do things like that for you, would you use it? Or would you rather keep that part of life manual? Where's the line for you between helpful automation and "this feels like too much AI"? submitted by /u/AlexBossov [link] [comments]

Hello, First time submitting to ECCV here. No other team member has done it before. I’m not really sure if inclusion of keywords is necessary or not in the submission, can someone help me here please? Thanks! submitted by /u/Training-Adeptness57 [link] [comments]

Man evolved from ape, as years have passed, man has gotten smarter. In the last 50 years, technology has rapidly improved, and many thought man was only getting smarter. Man relied on technology to improve all the time, that eventually man is taken over by AI. We teach AI to become smarter everyday, will AI one evolve so much that we become obsolete. Is AI the next evolution phase? submitted by /u/Chemical-Art-129 [link] [comments]

"China's new five-year policy blueprint laid out its ambitions to ​aggressively adopt artificial intelligence throughout the world's second-biggest economy and dominate emerging technologies such as quantum computing and humanoid robots. The ‌country will "seize the commanding heights of science and technological development" and seek "decisive breakthroughs in key core technologies", according to the plan released on Thursday to coincide with the opening session of the National People's Congress." submitted by /u/talkingatoms [link] [comments]

[D] Did anyone received their ijcai 2026 reviews and what are expectations by all ? I am also new to chairing tool if anyone has used it can tell me also how to check reviews on that or it will pop up as entering to its page submitted by /u/adi_gawd [link] [comments]

Greetings r/MachineLearning. I am studying the impact of EU AI Act on data science practitioners, especially those working on models that are classified as high risk. I am outside EU, so it has not impacted my company yet, but my country is drafting a similar one, and I am worried about its impact. From my understanding, the act covers a broad range of models as high risk (https://artificialintelligenceact.eu/annex/3/), including credit scoring and insurance pricing, and imposes a very high standard for developing and maintaining those models. Prior to the act, some companies in credit scoring can try lots of models on an arbitrary scale (usually small) to test out on real customers, and if it succeeds, will go on deploying on a larger scale. Does the Act completely shutdown that practice, with the administrative cost of compliance on small test models now insane? Any one with experience working on high-risk models as defined by the Act? submitted by /u/spdazero [link] [comments]

Dozens of protesters gathered outside OpenAI's San Francisco headquarters this week following CEO Sam Altman’s sudden decision to ink a deal with the U.S. Department of Defense. The agreement, allowing the military to use OpenAI models for classified work, came just hours after rival Anthropic was blacklisted by the Pentagon for refusing similar terms over surveillance and autonomous weapons concerns. While Altman defends the deal as having strict red lines against domestic surveillance and autonomous weapons, critics are calling it amoral profiteering. submitted by /u/EchoOfOppenheimer [link] [comments]

what do you think,chat? submitted by /u/AskGpts [link] [comments]

https://xcancel.com/SensorTower/status/2029250034772963513 Up from 295% previously reported by SensorTower. submitted by /u/NandaVegg [link] [comments]

I've been experimenting with an AI assistant that reads my inbox and manages my calendar. Yesterday it scanned a long email thread where I wrote things like: "we’re kind of running out of time", "this deadline is stressing me out". Today I opened my calendar and saw a new event: Panic about deadlines Duration: 45 minutes Priority: High Honestly… that might be the most realistic calendar entry I've ever had. submitted by /u/AlexBossov [link] [comments]

It is hard to communicate how frustrating the current Apple ML stack is for low-level research. CoreML imposes opaque abstractions that prevent direct ANE programming and do not support on-device training. Despite having up to 38 TOPS (INT8) and ~19 TFLOPS of fp16 compute, the ANE remains almost entirely unused for large language model workloads. Building on the foundational hardware reverse-engineering by maderix (who mapped the private API surface and benchmarked the 32 MB SRAM cliff), I wanted to see if we could bridge the gap from a raw hardware exploit to a mathematically stable runtime. I recently open-sourced ORION, to my knowledge the first open end-to-end system that combines direct ANE execution, a custom compiler pipeline, and stable multi-step training. Just to be transparent about the methodology: I approached this entire build as an exercise in what I'll call architectural delegation. My day job is Enterprise Program Management, not writing low-level C kernels. I used Claude to rapidly generate the Objective-C syntax while I acted as the system state manager—designing the compiler passes and forcing a probabilistic model to map deterministic hardware boundaries across 140 engineering tasks spanning 14 sessions. When you map it out, the ANE presents a massive wall of undocumented silicon behavior. We cataloged 17 total programming constraints, 11 of which were newly discovered during ORION's development. A few of the critical ones: • The concat operation causes an immediate compilation failure. • There is a minimum IOSurface size of approximately 49 KB for evaluation. • BLOBFILE weights require an undocumented offset of 64 bytes from the chunk header, which causes silent weight corruption if incorrect. • The compiler limits each process to ~119 compilations before silently failing. To handle this, ORION uses a custom compiler that lowers a 27-operation graph IR through five optimization passes (including Dead Code Elimination, Cast Fusion, and SRAM annotation against the 32 MB budget) to emit ANE-native MIL. The hardest part was what I'll call the numerical stability ceiling. Previous attempts at ANE training (like ANEgpt) suffered from 100% NaN divergence after the first training step. We solved this by isolating three interacting bugs: Stale Programs on Resume: ANE programs were compiling before checkpoint weights loaded. We fixed this via a deferred compilation pipeline. The leverage here is real. On an M4 Max, the system hits 170+ tokens/s for GPT-2 124M inference in decode mode. For training, we demonstrated stable multi-step training of a 110M-parameter transformer on TinyStories. Over 1,000 steps, the loss dropped from 12.29 to 6.19 with zero NaN occurrences. To bypass the 119-compilation limit, the runtime uses an exec() restart strategy, passing checkpoint state through the filesystem. There are real caveats here. Because the ANE bakes weights at compile time, every single weight update requires recompilation. In our loop, compilation consumes ~4.2 s per step, while the actual compute takes ~908 ms (achieving 0.612 TFLOPS). But imo, this is nowhere near "steady state" time for local AI—this is a layer change. Proving that we can execute mathematically stable, multi-step gradient descent directly on Apple's locked-down NPU opens up a lot of room for future work on weight patching or incremental compilation. The repo (Objective-C runtime, Python used only for one-time weight conversion) is MIT licensed and available here: https://github.com/mechramc/Orion I would love to hear thoughts from the systems ML folks here on the constraint catalog, or ideas on how to tackle the compile-time weight bottleneck. submitted by /u/No_Gap_4296 [link] [comments]

Hello, r/MachineLearning . I am just a regular user from a Korean AI community ("The Singularity Gallery"). I recently came across an anonymous post with a paper attached. I felt that the mathematical proof inside was too important to be buried in a local forum and not go viral globally, so I used Gemini to help me write this English post to share it with you all. The author claims they do not work in the LLM industry, but they dropped a paper titled: "The d^2 Pullback Theorem: Why Attention is a d^2-Dimensional Problem". They argue that the field has been fundamentally misunderstanding the intrinsic geometry of Attention. Here is the core of their mathematical proof: The d^2 Pullback Theorem (The Core Proof): The author mathematically proves that if you combine the Forward pass (n X n) and the Backward gradient (n X n), the actual optimization landscape the parameter explores is strictly d^2-dimensional. The n X n bottleneck is merely an illusion caused by the softmax normalization choice. Softmax destroys the Euclidean Matching structure: Previous O(n) linear attention models failed because removing exp() (softmax) destroyed the contrast (matching). Softmax creates the "matching" but artificially inflates the rank to n, causing the O(n^2) curse. O(nd^3) Squared Attention without the instability: Because the true optimization geometry is d^2, we can swap softmax with a degree-2 polynomial kernel (x^2) and still explore the exact same optimization landscape. The author introduces CSQ (Centered Shifted-Quadratic) Attention with soft penalties. This retains the Euclidean matching property, stabilizes the training, and drops both training AND inference complexity to O(nd^3). The author wrote: "I'm not in the LLM industry, so I have nowhere to share this. I'm just posting it here hoping it reaches the researchers who can build better architectures." I strongly believe this math needs to be verified by the experts here. Could this actually be the theoretical foundation for replacing standard Transformers? Original PDF:https://drive.google.com/file/d/1IhcjxiiHfRH4\_1QIxc7QFxZL3\_Jb5dOI/view?usp=sharing Original Korean Forum Post:https://gall.dcinside.com/mgallery/board/view/?id=thesingularity&no=1016197 submitted by /u/Ok-Preparation-3042 [link] [comments]