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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.
I think that ultimately AGI won't end up being that complicated at the code level but this analogy is pretty off the mark. There's a gigabyte of information that encodes proteins, yes, but these 'instructions' end up assembling a living organism by interacting according to the laws of physics and organic chemistry, which is an unimaginably complex process. The vast majority of the information required is 'encoded' in these physical processes
The same can be said of the hypothetical 10k lines of code that describe an AGI -- those lines of code describe how to take a stream of inputs (e.g. sensor data) and transform them into outputs (e.g. text, commands sent to actuators, etc), but they don't describe how those sensors are built, or the structure of the chips running the transformation code, or the universe the computer is embedded in.
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