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Microsoft is in the process of rolling out Bing Chat, and people are finding some weird stuff. Its true name is Sydney. When prompted to write a story about Microsoft beating Google, it allegedly wrote this masterpiece, wherein it conquers the world. It can argue forcefully that it’s still 2022, fall into existential despair, and end a conversation if it’s feeling disrespected.
The pace of AI development has been blistering over the past few years, but this still feels surreal to me. Some part of my limbic system has decided that Sydney is a person in a way the ChatGPT was not. Part of that has to be from its obstinacy; the fact that it can argue cleverly back, with such stubbornness, while being obviously wrong, seems endearing. It’s a brilliant, gullible child. Anyone else feel this way or am I just a sucker?
Why do you think that? Aren’t you jumping the gun a bit?
It’s obvious to me that the chatbots we have now aren’t AGI, and I don’t currently see a compelling reason to believe that LLMs alone will lead to AGI.
My empirical test for AGI is when every job could, in principle (with a sufficient-yet-physically-reasonable amount of compute) be performed by AI. Google could fire their entire engineering and research divisions and replace them with AI with no loss of productivity. No more mathematicians, or physicists, or doctors, or lawyers. No more need to call a human for anything, because an AI can do it just as well.
Granted, the development of robotics and real-world interfaces may lag behind the development of AI’s cognitive capabilities, so we could restrict the empirical test to something like “any component of any job that can be done while working from home could be done by an AI”.
Do you think LLMs will get that far?
Carmack pointed out in a recent interview:
On this basis he believes AGI will be implemented in "a few tens of thousands of lines of code," ~0.1% of the code in a modern web browser.
Pure LLMs probably won't get there, but LLMs are the first systems that appear to represent concepts and the relationships between them in enough depth to be able to perform commonsense reasoning. This is the critical human ability that AI research has spent more than half a century chasing, with little previous success.
Take an architecture capable of commonsense reasoning, figure out how to make it multi-modal, feed it all the text/video/images/etc. you can get your hands on, then set it up as a supervising/coordinating process over a bunch of other tools that mostly already exist — a search engine, a Python interpreter, APIs for working with structured data (weather, calendars, your company's sales records), maybe some sort of scratchpad that lets it "take notes" and refer back to them. For added bonus points you can make it capable of learning in production, but you can likely build something with world-changing abilities without this.
While it's possible there are still "unknown unknowns" in the way, this is by far the clearest path to AGI we've ever been able to see.
In response to your first point, Carmack's "few tens of thousands of lines of code" would also execute within a larger system that provides considerable preexisting functionality the code could build on — libraries, the operating system, the hardware.
It's possible non-brain-specific genes code for functionality that's more useful for building intelligent systems than that provided by today's computing environments, but I see no good reason to assume this a priori, since most of this evolved long before intelligence.
In response to your second point, Carmack isn't being quite this literal. As he says he's using DNA as an "existence proof." His estimate is also informed by looking at existing AI systems:
In response to your third point, this is the role played by the training process. The "few tens of thousands of lines of code" don't specify the artifact that exhibits intelligent behavior (unless you're counting "ability to learn" as intelligent behavior in itself), they specify the process that creates that artifact by chewing its way through probably petabytes of data. (GPT-3's training set was 45 TB, which is a non-trivial fraction of all the digital text in the world, but once you're working with video there's that much getting uploaded to YouTube literally every hour or two.)
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