A software engineer questions whether current LLM architecture can achieve AGI, pointing out limitations like autoregressive generation and lack of real-time learning.
I'm a software engineer, not a scientist and I love it. I enjoy solving hard, distributed applied problems at scale. It's what gets me out of bed in the morning, ready go keep learning even after over twenty years of doing this professionally. However, I also love to understand how things work. What makes them tick. How I can bend them to do my bidding, even if that's not what they were originally intended to do. Some may call this a hacker's mindset. Over the last couple of years, this has also applied to the nature of LLMs and where they are heading. Recently, I started peeling back the layers of the LLM black box. Instead of the academic path, I took that of the applied practitioner: Get a solid handle on how to use the thing and then take the knowledge learned from using it and enhance it by digging into how that black box actually works. Something of late has stumped me and I'm looking for those smarter than I to help me understand something: If the definition (as much as one can nail one down) of AGI is something akin to "a hypothetical type of computer software or machine intelligence that can match or surpass human cognitive abilities across any intellectual task", how an it possibly achieve that with current LLM architecture? At its core and at a /very/ high level, it predicts a probability distribution over the next token, conditioned on the tokens that came before it. Autoregressive decoding doesn't give a model an independent mechanism to know when it's wrong and deliberation steps don't fundamentally change that. Harnesses and the "intelligence" or "agentic reasoning" built into them to provide extra context, external feedback and loops work remarkably well in terms of providing what /seems/ to be intelligence, but at its core, it's "just" layers of probabilistic systems providing a magical output. The model itself isn't learning anything new. Ordinary inference doesn't update the model's weights from experience. Autoregressive generation itself doesn't backtrack and revise previously generated tokens. The primitive the model provides is generation, not an explicit reasoning mechanism. A human can have a thought, backtrack and make changes until it comes to the conclusion it wants. All on their own. The current state of LLMs is effectively like having a configurable number of people playing near-lossless telephone, each modifying or enriching the message before passing it along, except every person in the room is a clone (or maybe near clone if you're iterating with different models). Are the AGI claims with the existing architecture legitimately smoke and mirrors, or is there more to this equation that I can't see from my applied engineering standpoint?
This post explores the debate among top AI figures regarding whether LLMs alone can achieve AGI or if additional breakthroughs like world models are required.
The article speculates whether Google's lag behind OpenAI and Anthropic in frontier AI reflects poor execution or a fundamental disagreement with the scaling hypothesis for achieving AGI. The author questions if Google believes LLMs are ultimately a bubble and that AGI requires a fundamentally different approach.
A question seeking fundamental research or papers on whether AGI can or cannot be achieved through large language models, looking to move beyond opinion-based discussion.
This blog post from SenTeGuard discusses the theoretical limits of LLM intelligence, referencing Gödel's incompleteness theorems to argue that current AI architectures cannot achieve general human-level reasoning.