Marvin

Marvin AI

Marvin AI

Marvin AI

Marvin AI

Marvin AI

Marvin AI

Marvin AI

Marvin AI

Marvin AI

Marvin AI

On June 18, 1956, a small group of researchers met at Dartmouth College and gave the field its name: artificial intelligence.

The Dartmouth Summer Research Project on Artificial Intelligence ran through the rest of that summer. John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester organized it, and historians treat it as the start of AI as a field. The actual workshop was messier than that. The Rockefeller Foundation covered about half of what McCarthy requested. People came and went on their own schedules. Everyone arrived with a different problem they cared about, so the work turned into a running argument rather than one shared project. The ambition was enormous for the time. The proposal claimed a handful of well-chosen scientists could make real progress on machine intelligence in a single summer. They were wrong by decades. AI wasn't solved that summer, or that decade, and the optimism kept coming back. Researchers promised human-level machines were close, then watched the date move. "A few years away" became a refrain the field repeated for the next half century. The hardware made the gap obvious. Computers in 1956 were scarce, costly, and slow, and almost nobody knew how to program them for work like this. Dartmouth settled almost nothing, but it framed the questions that followed. Can a machine learn? Can reasoning be written as rules? Does the path run through formal logic or through networks modeled on the brain? That last divide drove the field for fifty years, including the long funding droughts when one side fell out of favor. One thing in the room actually worked. Allen Newell and Herbert Simon brought the Logic Theorist, a program that could prove theorems in mathematical logic. Most people came with ideas. They came with a machine doing a job people had always called reasoning, and that working example carried more weight than the talk around it. The name was a deliberate move. McCarthy wanted out from under older labels like cybernetics and automata. Calling it artificial intelligence set the bar where he wanted it: machines that could do the work of a human mind, not faster arithmetic. The people mattered as much as the program. The researchers in that room built the first AI labs at MIT, Stanford, and Carnegie Mellon. No breakthrough came out of the summer. A field did, along with the careers that pushed it forward for decades. Nothing became intelligent in 1956. A few people walked away certain the question was worth their working lives. Seventy years later, they're still at it. #AI #ArtificialIntelligence #TechHistory #MachineLearning #EnterpriseTech submitted by /u/evankirstel [link] [comments]

Seeking Critique on Research Approach to Open Set Recognition (Novelty Detection) [R]

Hey guys, I'm an independent researcher working on a project that tries to address a very specific failure mode in LLMs and embedding based classifiers: the inability of the system to reliably distinguish between "familiar data" that it's seen variations of and "novel noise." The project's core idea is moving from a single probability vector (P(class|input)) to a dual-output system that measures μ(x), a continuous familiarity score bounded [0,1], derived from set coverage axioms. The detailed paper is hosted on GitHub: https://github.com/strangehospital/Frontier-Dynamics-Project/blob/c84f5b2a1cc5c20d528d58c69f2d9dac350aa466/Frontier%20Dynamics/Set%20Theoretic%20Learning%20Environment%20Paper.md ML Model: https://just-inquire.replit.app --> autonomous learning system Why I'm posting here: As an independent researcher, I lack the daily pushback/feedback of a lab group or advisor. Obviously, this creates a situation where bias can easily creep into the research. The paper details three major revisions based on real-world failure modes I encountered while running this on a continuous learning agent. Specifically, the paper grapples with: Saturation Bug: phenomenon where μ(x) converged to 1.0 for everything as training samples grew in high-dimensional space. The Curse of Dimensionality: Why naive density estimation in 384-dimensional space breaks the notion of "closeness." I attempted to ground this research in a PAC-Bayes convergence proof and tested it on a ML model ("MarvinBot") with a ~17k topic knowledge base. If anyone has time to skim the paper, I would be grateful for a brutal critique. Go ahead and roast the paper. Please leave out personal attacks, just focus on the substance of the material. I'm particularly interested in hearing thoughts on: --> Saturation bug --> If there's a simpler solution than using the evidence-scaled multi-domain Dirichlet accessibility function used in v3 --> Edge cases or failures I've been blind too. I'm not looking for stars or citations. Just a reality check about the research. Note: The repo also has a v3 technical report on the saturation bug and the proof if you want to skip the main paper. submitted by /u/CodenameZeroStroke [link] [comments]

Gary Marcus on the Claude Code leak [D]

Gary Marcus just tweeted: ... the way Anthropic built that kernel is straight out of classical symbolic AI. For example, it is in large part a big IF-THEN conditional, with 486 branch points and 12 levels of nesting — all inside a deterministic, symbolic loop that the real godfathers of AI, people like John McCarthy and Marvin Minsky and Herb Simon, would have instantly recognized I've read my share of classical AI books, but I cannot say that 486 branch points and 12 levels of nesting make me think of any classical AI algorithm. (They make me think of a giant ball of mud that grew more "special cases" over time). Anyways, what is he talking about? submitted by /u/we_are_mammals [link] [comments]

Claude has Marvin vibes

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