Linus Torvalds on the state of Linux today and how AI figures in its future
zdnet.com
At Open Source Summit Japan, Linux and Git creator Linus Torvalds talked about Rust in Linux, Linux maintainer fatigue, and AI's future role in Linux and open-source development.
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Exactly.
It is very intelligent though.
It's not simple to come up with coherent statements on such a wide variety of tasks.
It's not just stringing random words together like predictive text. It understands context in a way that is very complex.
It is more knowledgeable than the average person by a huge amount.
For example I asked it to write songs about squidmas, an imaginary holiday I made up to irritate my children. It was able to rewrite Christmas songs but with a squid theme. That's way more complex than predictive text.
You’re conferring a level of agency where none exists.
It appears to “understand.” It appears to be “knowledgeable. “
But LLMs do neither of those things.
Take this note from an OpenAI dev:
It’s that these models have leveraged so much data they’ve been able to map out relationships between words (or images) in way as to be able to generate what seem like new versions of those things.
I grant you that an LLM has more base level knowledge than any one human, but again this is thanks to terrifyingly large dataset and a design that means it can access this data reasonably reliably.
But it is still a prediction model. It just has more context, better design and (most importantly) data to make predictions at a level never before seen.
If you’ve ever had a chance to play with a model at level where you can control some of its basic parameters it offers a glimpse into just how much of a prediction machine it can be.
My favourite game for a while was to give midjourney a wildly vague prompt but crank the chaos up to 100 (literally the chaos flag at the highest level) to see what kind of wild connections exist but are being filtered out during “normal” use.
The same with the GPT-3.5 API in the “early days” - you could return multiple versions of the response and see the sausage being made to a very small degree.
It doesn’t take away from the sense of magic using these tools. It just helps frame what’s going on under the hood.
Given it's an artificial intelligence it stands to reason its understanding and knowledge are artificial.
I don't think there's any relevance pointing that out anymore. No one thinks it's conscious or a general AI.
I also don't see how it's massively different to our ability to parse and output text tbh.
it's different to our ability because we actually know what words are, we know they refer to things.
All an LLM sees is tokens, it has absolutely no concept of what langauge actually is or what things mean, it's literally just "this number seems to occur after these numbers".
I think that is overly simplistic. Embeddings used for LLMs do definitely include a concept of what things mean and the relationship of things to other things.
E.g., compare the embeddings of Paris, Athens, and London to other cities and they will have small cosine distance between them. Compare France, Greece, and England and same. Then very interestingly, look at Paris - France, Athens - Greece, London - England and you'll find the resulting vectors all align (fundamentally the vector operation seems to account for the relationship "is the capital of"). Then go a step further, compare those vector to Paris - US, Athens - US, London - Canada. You'll see the previous set are not aligned with these nearly as much but these are aligned with each other (relationship being something like "is a smaller city in this countrry, named after a famous city in some other country")
The way attention works there is a whole bunch of semantic meaning baked into embeddings, and by comparing embeddings you can get to pragmatic meaning as well.
That's kind of a given though. It's a large language model, so of course its "understanding" can only be in terms of language. In a way, words are its only sense (input), and only way to interact with the world (output). The mechanism isn't really important, imo, since we could reduce our own understanding to chemical reactions.
Homo sapiens have many more dimensions of awareness, dozens maybe including sight, hearing, time, pressure, acceleration, etc., and we've been collecting data from them all 24/7 since embryo, plus instinct (pre-baked weights) from millions of years of evolution. We know that people born without a sense, let's say vision cannot conceptualize visually, even when their sight is restored for a time. I remember reading awhile back that a person born blind had their vision fixed, but they didn't know what "pointy" looked like. They couldn't know. Do they have a lower quality understanding of a word?
My point being, I don't think it's fair to objectively compare understanding between a person and a model without a testable definition of that word. Imo, and feel free to disagree, understanding is no different than merely knowing, it's just implied that the knowledge is deeper, across multiple dimensions of awareness, including subconscious awareness of our own hormones.
It's not intelligent because it's not thinking.
At least my definition of intelligence is thinking. Otherwise a simple pattern matching algorithm like a regexp is also intelligent, or a sorting algorithm that puts things in the right order.
But I agree it's very efficient and has more data than any single person ever could. It's a computer, they are great at storing and processing information.
While I mostly agree, I'd like to point out that GOFAI (good old fashioned AI) exists, and at its core it is basically just pathfinding like a* or something similar. And we still call that AI, because it "intelligently" finds a path quickly.
So my main point is that I agree that it isn't magic or sapient or anything, but in a sense it is definitely intelligent.
I think the defenders of human intellect are heralding our language and thinking to be a much higher standard than for MOST people they are.
A chess champion might be executing critical thinking beyond normal comprehension but I'd say a lot of my interactions with others, my daily experience is just pattern matching the next thing to say or ask.
I think this type of anthropocentrism extends to chess too actually. I'm not an expert on the subject, but I've heard that chess AIs are finding success doing unintuitive things like pushing a and h file pawns in openings. If, 10 years ago, some chess grandmaster was doing the same thing and finding success, I imagine they would have been seen as creative, maybe even groundbreaking.
I think the average person under-rates the sophistication of AI. Maybe as a response to the AI hype. Maybe it's because we're scared of AI, and it's comforting to believe that it's operations are trivial. I see irrationality and anger cropping up in discussions of AI that I think stem from a fundamental fear of its transformative power.
Yes it's going to transform everything. It's about the same as the transformation from typewriter to computer for society. But I still don't think any machine that predicts the next word is intelligent. However, this is only the beginning. We are not going to be able to keep up with AI soon, and it will work around the clock to get better and better.
We will have those high tech societies from the movies where robots are everywhere and people are quite sad.
You say that however we might have stumbled on the groundwork for a GI. Because language is core to our evolutionary advancement. We needed language to build the mental constructs that then enabled logical work.
Imagine if an LLM was able to coordinate the usage of these "logical" AI's like Deep mind etc.
ChatGPT already enabled Internet search and it's better than if I asked someone to Google something for me.
I've heard the argument that we don't really have a good definition of thinking or intelligence and if it can complete a task or do things...what does it matter if it's "thinking" or not if the outcome is the same.
You could say it's artificially intelligent haha
Lol get a load of this fool
Bro the AI neural networks have been shown to be building internal world models to be able to do what they do. How is that not thinking?
I am so sick of this anthrocentrism, as if we are special because we are humans. The computers are now doing the same mathematical processes our brains are doing. The LLMs can be compared to a small subsection of our brains. String enough neural network based AIs together with different tasks and youll get sentience.
Sentience isnt required for "thinking" to happen, thinking is one of the building blocks for sentience.
Bro