Tag: plasma refactoring

Everyone keeps talking about smarter AI. Bigger models. Longer context windows. More autonomous agents. Better reasoning. Better coding. Better memory. But I think we’re missing the real problem. An AI system can sound intelligent… and still operate on completely broken reality. Imagine an AI agent: approving refunds escalating incidents updating records contacting customers changing prices triggering workflows Now ask a simple question: How does the AI know the reality it sees is actually correct? Not “technically accessible.” Actually correct. Because enterprise reality is messy: stale systems conflicting databases outdated approvals missing context silent exceptions contradictory records unclear ownership shifting policies And then there’s an even bigger question: Even if the AI knows something… is it actually allowed to act on it? Under whose authority? With what limits? Who is accountable? Can the action be reversed? What happens if the AI is wrong? That’s why I’m starting to think the future AI stack is not just: data → model → agent → action There are missing runtime layers in between. The mental model I’ve been exploring is: SENSE → reality representation CORE → reasoning DRIVER → governed action And honestly, it feels like the industry is massively overinvested in CORE. We obsess over intelligence. But the real bottlenecks may become: representation quality legitimacy authority boundaries reversibility accountability runtime governance In other words: The biggest AI failures may not come from “bad intelligence.” They may come from machines acting on incomplete reality with unclear authority. And I think this becomes a huge issue once AI moves from: “helping humans” to “acting inside institutions.” Curious what others here are seeing. Are companies actually solving these layers internally? Or are most organizations still mainly focused on model capability and agent demos right now? submitted by /u/raktimsingh22 [link] [comments]

"U.S. artificial intelligence company OpenAI said on Saturday it ​had signed a deal ‌with the government of Malta to give all residents access to ​its ChatGPT Plus service for ​one year after they follow ⁠a course on how ​to use AI." submitted by /u/shikizen [link] [comments]

I’m not at all educated about the subject of AI. I am just an average 9-5 US citizen that doom scrolls Instagram and Reddit after work that has used ChatGPT for a variety of reasons. I’ve used AI to help me to prepare myself for job interviews. I have used it to scan essays or important emails for any grammar or errors I might have missed. I’ve also used it for my hobbies; I create cosplays and I use it to plan out and prepare for creating. I’m quite new so having AI let me know the materials that would work best as well as laying out the most efficient steps in order has taken away so much stress. I also do the NYT crosswords; When I complete the puzzle and find my numerous mistakes I ask AI for the best options and their definitions. It helps me understand and learn crossword “language” better. To sum it up, I’m an average, boring mid 20s human and want to understand more about the complexities of the AI dilemma and the benefits it has. Not just what my feed or news wants to focus on but the dilemma as a whole. submitted by /u/ChipUnfair3345 [link] [comments]

Researchers at Stanford just published a way around this. AsymFlow doesn’t ask you to abandon your latent model or train a pixel model from scratch. It takes what you already have and converts it. And the result beats the latent model it started from. submitted by /u/techzexplore [link] [comments]

The real hallucination going on in the industry right now is not that AI sometimes makes things up, because that's well known. What's really concerning is that companies are acting like these systems are way more mature, reliable, and production-ready than they actually are. In my opinion, there’s a reason this keeps going on, and that reason is that, for a lot of organizations, the downside of being wrong is basically very low. If the AI rollout works out, the leadership gets to brag about innovation, the headlines, the stock bump, the forward-thinking image. If it blows up, they can just dump the fallout onto workers. Suddenly the employee: - wasn’t adapting fast enough - didn’t know how to use the tools - fell behind But the no 1 🏆 most spectacular sentence is: "wasn’t AI-native enough" 🤡 Basically the company gets to push experimental systems into production, spin the wheel, and still come out mostly fine either way. If things go sideways, there’s always somebody lower down the ladder to pin it on, and that's when the gaslighting part kicks in. Workers are being told to downplay what they can clearly see with their own eyes: hallucinations, fragile workflows, agents falling apart, bad outputs wrapped in confident language, hours of cleanup and verification work. Those hours are heavily discounted by a leadership believing AI should already be making us all 100X engineers. If the workers point any of this out too directly, they risk getting painted as outdated, resistant, or somehow incapable, so the vast majority simply stays quiet, pretending the emperor has beautiful clothes. We're all testing somebody else's roadmap, and this is a story about both AI vendors and organizations offloading experimental risk onto individual workers while pretending the technology is already solid enough to bet people’s careers on. submitted by /u/RevolutionStill4284 [link] [comments]

yeah, it is kinda funny but also kinda sad 🙁 is university even worth it anymore, you don't need a degree to use Claude submitted by /u/Complete-Sea6655 [link] [comments]

Hey everyone, Most people build 8-bit computers to run Pong or Tetris. I wanted to see if I could push a custom 8-bit architecture to do something much harder: train a neural network from scratch. I built VirtualPC, an open-source 8-bit computer system simulated from basic NAND gates up to a functional CPU that can train a small neural net from a folder on your computer. Repository: https://github.com/ninjahawk/VirtualPC › The ML Core Instead of importing PyTorch, everything happens at the bare-metal assembly level: Custom ISA: The Instruction Set Architecture was designed to handle the math needed for machine learning. Low-Level Training: The CPU executes forward and backward passes directly through custom assembly code. Matrix Math on 8-bit: Overcoming severe memory limits using disk-backed memory swapping to store weights. › The Architecture Python-Based VM: Runs the entire simulated hardware environment. Custom Assembler: Translates raw assembly files into machine code binary. Full Stack OS: Handles basic I/O and memory management from the ground up. Building this taught me exactly how machine learning math translates into physical CPU cycles. The project is completely open-source and free to mess around with. submitted by /u/TheOnlyVibemaster [link] [comments]

When u/karpathy described the strange shape of modern AI capability, he used a useful word for it. The idea is that the surface of what a model can do is not smooth, the way human ability is roughly smooth, but uneven, with sharp peaks of near-superhuman performance rising directly next to valleys of embarrassing failure. The classic demonstration is to ask a frontier model how many days of the week contain the letter d, and watch it try. Sometimes it answers four. Sometimes six. The answer is seven, because every day of the week ends in "day", which a five-year-old can see in a single glance. The same model, on a different turn, might find a 27-year-old vulnerability in OpenBSD, an operating system whose entire reputation is built on three decades of paranoid code review, and which no human researcher in those three decades had managed to notice was broken. That is what jagged means. The intelligence is real, and the surface of it bears almost no resemblance to the contours of human ability. Most of the conversation since the term was coined has stayed at the level of the model, comparing GPT against Claude or Gemini against Grok and mapping the terrain by benchmark, as if the question were which model is generally smarter rather than where each model's spikes happen to point. Building an attack harness has changed how I see that map, because the jaggedness lives at more than one level, and the level it lives at most powerfully is the one that almost nobody is talking about. The picture I keep coming back to is a wheel with spokes. Each spoke is a direction in capability-space where some combination of people, capital, and data has been invested. Some spokes grew from the model side, by accident or on purpose. Some spokes grew from the harness side, where a team took a generalist model and built the exact scaffolding their domain needed. The durable products of this era will mostly be the combination of both, a model with a natural lean toward the relevant axis paired with a harness that knows how to climb it. Coding is a spike. Legal is a spike. Protein structure is a spike. Clinical reasoning is a spike. Offensive security is a spike. Each of them gets taller every quarter. The reality is though, you do not need to be a frontier lab to sit on the tip of one of these spokes. You need a model with the right natural lean, which is now a commodity available by API, and a harness built by people who know the target domain cold. That is a small team of the right engineers with conviction and a clear thesis about where the spike points. A group of five people, regardless of their moral standing, can climb to the pointiest end of one of these spokes faster than the institutions built to defend against them can react. AI is the great equaliser, and it equalises specifically at the harness layer. The model is the public good, accessible to everyone for roughly the same price. So in my opinion, the harness is where the asymmetry lives, and the harness costs almost nothing to build relative to what it can do once built. Cybersecurity is the cleanest case study for this asymmetry, because the field has more than twenty years of public history showing how the contest between attack and defence plays out under normal conditions. On the defensive side, the industry spent those two decades building infrastructure: endpoint detection and response systems that watch every process on every machine, security information and event management platforms that aggregate logs from across an enterprise, the slow shift toward zero-trust architectures that assume any given network connection is hostile by default, threat intelligence sharing arrangements between companies and governments, mandatory breach disclosure laws, bug bounty programmes that pay researchers to find flaws before criminals do, and the long professionalisation of the security workforce itself. On the offensive side, attackers spent the same two decades under continuous evolutionary pressure, finding new techniques when their old ones got patched and falling back on the old ones whenever defenders failed to learn the lessons of the previous decade, which they routinely did. The equilibrium that emerged was an uneasy one. submitted by /u/theonejvo [link] [comments]

Am I crazy or is AI starting to look economically impossible outside hyperscalers? The deeper I look into capex, power infrastructure, cooling, debt markets, and GPU costs… …the more it feels like only Google, Microsoft, Amazon, and Meta can realistically afford this game long term. submitted by /u/houmanasefiau [link] [comments]

I think AI discussion is still way too obsessed with benchmark scores, model rankings and flashy demos Those things matter, but they are not what will decide whether AI is actually trusted in normal life The real test is boring responsibility Can the model follow instructions without quietly ignoring the awkward parts? Can it admit uncertainty instead of sounding confident? Can it handle edge cases? Can it remember constraints across a long task? Can it stop when it should escalate to a human? Can it produce work that is auditable instead of just impressive-looking? A model can score well on exams and still be dangerous in real use if it invents details, misses exceptions, over-complies, or gives polished answers that hide weak reasoning This matters more for actual deployment than whether one model is slightly better at coding puzzles or abstract reasoning tests For healthcare, education, legal admin, finance, customer support, welfare systems, moderation, HR and public services, the key question is not “how smart is it?” It is “can you safely give it responsibility?” I think we are overvaluing intelligence and undervaluing reliability, restraint, traceability and escalation Curious where people disagree: are benchmarks still the best proxy we have, or are they distracting us from the qualities that actually matter in deployment? submitted by /u/thirdaccountttt [link] [comments]