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This paper critiques the limitations of current AI and introduces a new learning model inspired by biological brains. The authors propose a framework that combines two key methods: System A, which learns by watching, and System B, which learns by doing. To manage these, they include System M, a control unit that decides which learning style to use based on the situation. By mimicking how animals and humans adapt to the real world over time, the authors aim to create AI that can learn more independently. submitted by /u/ViKKed [link] [comments]

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

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

Last week's post about Meta buying Moltbook got a lot of discussion here. I think most of the coverage (and the comments) missed what Meta is actually doing with it. I read a lot of patent filings because LLMs make them surprisingly accessible now, and one filed by Meta's CTO Andrew Bosworth connects directly to the Moltbook acquisition in a way I haven't seen anyone talk about. In December 2025, Meta was granted patent US 12513102B2 for a system that trains a language model on a user's historical interactions (posts, comments, likes, DMs, voice messages) and deploys it to simulate that user's social media behavior autonomously. The press covered it as "Meta wants to post for you after you die." The actual patent text describes simulating any user who is "absent from the social networking ...

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

I've been building long-running agentic workflows and kept hitting the same problem: the AI forgets instructions from earlier in the conversation, rushes to produce output, and skips boring middle steps. The research explains why: "Lost in the Middle" (Stanford 2023) showed a 30%+ performance drop when critical information is in the middle of the context window. Accuracy is high at the start and end, drops in the middle. Exactly like working memory overflow. "LLMs Get Lost in Multi-Turn Conversation" (Laban et al. 2025) showed that instructions from early turns get diluted by later content. The more turns, the worse the recall. 65% of enterprise AI failures in 2025 were attributed to context drift during multi-step reasoning. The parallel to ADHD executive dysfunction isn't metaphorical. ...

submitted by /u/sksarkpoes3 [link] [comments]

I work as a developer, and before this I was copium about AI, it was a form of self defense. But in Dec 2025 I bought subscriptions to gpt codex and claude. And honestly the impact was so strong that I still haven't recovered, I've barely written any code by hand since I bought the subscription And it's not that AI is better code than me. The point is that AI is replacing intellectual activity itself. This is absolutely not the same as automated machines in factories replacing human labor Neural networks aren't just about automating code, they're about automating intelligence as a whole. This is what AI really is. Any new tasks that arise can, in principle, be automated by a neural network. It's not a machine, not a calculator, not an assembly line, it's automation of intelligence in the b...

I built a pipeline where 5 AI models (Claude, GPT-4o, Gemini, Grok, DeepSeek) independently assess the probability of 30+ crisis scenarios twice daily. None of them see the others' outputs. An orchestrator synthesizes their reasoning into final projections. Some observations after 15 days of continuous operation: The models frequently disagree, sometimes by 25+ points. Grok tends to run hot on scenarios with OSINT signals. The orchestrator has to resolve these tensions every cycle. The models anchored to their own previous outputs when shown current probabilities, so I made them blind. Named rules in prompts became shortcuts the models cited instead of actually reasoning. Google Search grounding prevented source hallucination but not content hallucination, the model fabricated a $138...

Open source: https://github.com/htdt/godogen submitted by /u/crush-name [link] [comments]