Tag: Reddit

Jeff Bezos: financial bubbles like 2008 are just bad. Industrial bubbles, like biotech in the 90s, can actually benefit society. AI is an industrial bubble, not a financial bubble – and that's an important distinction. Investors may lose money, but when the dust settles, we still get the inventions. submitted by /u/SunAdvanced7940 [link] [comments]

Everyone is chasing larger context windows (1M+), but the retrieval accuracy (Needle In A Haystack) is still sub-optimal for professional use. I’m theorizing that we’re hitting a physical limit of the Transformer architecture. The future isn't a "bigger window," but a better "active memory" management at the infrastructure level. I’d love to hear some thoughts on RAG-Hybrid architectures vs. native long-context models. Which one actually scales for enterprise knowledge bases? submitted by /u/Foreign-Job-8717 [link] [comments]

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

Thrilled to be launching Plano today - delivery infrastructure for agentic apps: An edge and service proxy server with orchestration for AI agents. Plano's core purpose is to offload all the plumbing work required to deliver agents to production so that developers can stay focused on core product logic. Plano runs alongside your app servers (cloud, on-prem, or local dev) deployed as a side-car, and leaves GPUs where your models are hosted. The problem On the ground AI practitioners will tell you that calling an LLM is not the hard part. The really hard part is delivering agentic applications to production quickly and reliably, then iterating without rewriting system code every time. In practice, teams keep rebuilding the same concerns that sit outside any single agent’s core logic: T...

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

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

"Recently, the application of AI tools to Erdos problems passed a milestone: an Erdos problem (#728) was solved more or less autonomously by AI (after some feedback from an initial attempt), in the spirit of the problem (as reconstructed by the Erdos problem website community), with the result (to the best of our knowledge) not replicated in existing literature (although similar results proven by similar methods were located). This is a demonstration of the genuine increase in capability of these tools in recent months, and is largely consistent with other recent demonstrations of AI using existing methods to resolve Erdos problems, although in most previous cases a solution to these problems was later located in the literature, as discussed in https://mathstodon.xyz/deck/@tao/11578826227...

I'm currently in my undergraduate degree and I have been studying AI ethics under one of my professors for a while. I always have been a partisan of Searle's strong AI and I never really found the chinese room argument compelling. Personally I found that the systems argument against the chinese room to make a lot of sense. My first time reading "Minds, Brains, and Programs" I thought Searle's rebuttal was not very well structured and I found it a little logically incorrect. He mentions that if you take away the room and allow the person to internalize all the things inside the system, that he still will not have understanding--and that no part of the system can have understanding since he is the entire system. I always was confused on why he cannot have understanding, since I imagine this ...

submitted by /u/F0urLeafCl0ver [link] [comments]

As we know, one of the godfathers of AI recently left Meta to found his own lab AMI and the the underlying theme is his longstanding focus on world modelling. This is still a relatively underexplored concept however the recent surge of research suggests why it is gaining traction. For example, Marble demonstrates how multimodal models that encode a sense of the world can achieve far greater efficiency and reasoning capability than LLMs, which are inherently limited to predicting the next token. Genie illustrates how 3D interactive environments can be learned and simulated to support agent planning and reasoning. Other recent work includes SCOPE, which leverages world modelling to match frontier LLM performance (GPT-4-level) with far smaller models (millions versus trillions of parameters),...