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0 pointsK0balt1y ago0 comments

As a person that has been futzing with "AI" since early expert systems, I can give a little bit of background on the evolution of the term of art 'artificial general intelligence' Back in the days of lisp machines and expert systems, we started playing with early neural networks in an attempt to advance towards 'general intelligence' rather than the highly constrained talents of expert systems.

At that time, general intelligence was imagined like what a mouse, dog, or cat has, in varying degrees. (it was then broadly thought that insects and other "simple" organisms worked from instinct and conditioning only) So intelligence was broadly imagined to be the ability to reason through an arbitrary problem with a combination of insight and trial and error. It had nothing at all to do with reaching human levels of competence.

Gradually, the bar for AGI has slipped skyward to assume human level competence. I think that part of that was because we actually started checking boxes for tasks that were thought to represent "true" AI....

...But then we recognized that nope, it is not yet generally intelligent, it can just easily and accurately do (X task which was thought to require intelligence but really was only pattern matching or stochastic prediction). So, we raise the bar above task X and trivialize it as being less important than we thought.

Meanwhile, -nearly every task- that we thought would represent "true intelligence" has fallen not to some magic AI algorithm, but rather to stochastic models like transformers, pattern matching, or straightforward computation. With no reason to expect otherwise, I expect this trend to continue unabated.

So, what I'm saying is I have come to doubt that there is some secret sauce in "intelligence", but instead believe that "intelligence" is a blend of competencies enabled in animals by specialization of neural computational structures, "REPL" loops, goal seeking behaviors, and other tidbits which will be more of a slog than a eureka. I don't think that LLM's will create human-level intelligence on their own, but I do think that they will be an important component.

I also think that surprisingly, transformers all by them selves exhibit a flaky variety of general intelligence, and they are proving effective in robotic applications (though without a supervisory agent I suspect they will frequently go off the rails, so to speak.)

My original point was more that we expect machine general intelligence to be spectacularly useful. It may be, someday, but it is a kind of fallacy to think that "its not that useful therefore it must not really be intelligence".

Small animals also have limited utility, but with given names, language, tool use, and problem solving skills I think arguing that they do not exhibit "intelligence" would be a tough sell for me.

By my observation, we have made giant leaps in the past 15 years, and we now have perhaps the vast majority of the components required to make artificial general intelligence...but it won't necessarily be all that useful at first, except maybe as a "pet robot" or something like that. Even if we scale it, it might not magically get smarter, just faster. a million hyper-speed squirrels still has a very limited level of utility.

From there, we will incrementally improve if we don't stumble too hard over ourselves on any number of pressing obstacles that we currently face, until we finally succeed in doing what we were apparently born to do - to construct the means of our own irrelevance. Evolution at work, I suppose.

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pdimitar1y ago

Thank you for following up. I'll pedantically respond point by point, hopefully that will not make you bow out.

> Back in the days of lisp machines and expert systems, we started playing with early neural networks in an attempt to advance towards 'general intelligence' rather than the highly constrained talents of expert systems.

Interesting, you must be the first person I "meet" who was "back then and there". Still, if you allow me to point out for the second time, your "we" is really throwing me off because it sounds like "we the council of elders" and "we who self-appointed to determine what an actual true AI is". Positions of implied or directly claimed authority murder my motivation to take people doing them seriously. Hopefully that's a useful piece of feedback for you.

I would think that a random guy like myself who watched Terminator and that was part of his inspiration to become a programmer has just as much "authority" (if we can even call it that but I can't find the right word at this moment) to claim what a general AI should be. Since we don't have it, why not try and dream the ideal AI for us, and then pursue that? It's what the people who wanted humanity on the Moon did after all.

I feel too many people try to define general AI through the lenses of what we have right now -- or we'll have very soon -- and that to me seems very short-sighted and narrow-minded and seems like bronze-age people trying to predict what technology would be. To them it would likely be better carts that shake less while sitting in them. And faster cart-pulling animals.

That's how current AI practitioners trying to enforce their view on what we should expect sound to me.

> Meanwhile, -nearly every task- that we thought would represent "true intelligence" has fallen not to some magic AI algorithm, but rather to stochastic models like transformers, pattern matching, or straightforward computation. With no reason to expect otherwise, I expect this trend to continue unabated.

Sure, but this gets dangerously close to the disingenuous argument of "people who want AI constantly move the goalposts every time we make progress!" which is a stance I can't disagree with more even if I tried. I in fact hate this trope and fight it at every opportunity.

Why? Because to me that looks like AI practitioners are weaseling out of responsibility. It's in fact not that difficult to understand what the common people would want. Take a look at the "I, Robot" movie -- robot butlers that can do many tasks around the house or even assist you when you are outside.

What does that take? Let the practitioners figure it out. I, like yourself, believe LLMs are definitely not that -- but you are also right that it's likely a key ingredient. Being able to semi-informedly and quickly digest and process text is indeed crucial.

The part I hate is the constant chest-pounding: "We practically have general AI now, you plebs don't know our specialized terms and you just don't get it. Stop it with your claims that we don't have it! Nevermind that we don't have robot butlers, that's always in the future, I am telling you!".

And yes that happens even here in this thread, not in that super direct form of course, but it still happens.

> My original point was more that we expect machine general intelligence to be spectacularly useful. It may be, someday, but it is a kind of fallacy to think that "its not that useful therefore it must not really be intelligence".

Here we disagree. It's true that people want useful and don't care how it's achieved; as a fairly disgruntled-by-tech guy I want the same. Put rat brains in my half-intelligent but problem-solving butler for all I care; if it works well people will buy it en masse and ask zero questions.

...But I'd still put strong correlation between a machine being actually intelligent and being able to solve very different problems with the same "brain", and being useful. For the simple reason that a lot of our problems that don't require much intelligence at all have been mostly solved by now.

So it naturally follows that we need intelligent tools for the problems we have not yet solved. Would you not agree with that conclusion?

> Small animals also have limited utility, but with given names, language, tool use, and problem solving skills I think arguing that they do not exhibit "intelligence" would be a tough sell for me.

I agree. Some can actually adapt to conditions that their brain has not had to tackle in generations. But take koalas for example... I actually could easily call these animals not possessing a general intelligence and just being pretty complex bots reacting to stimuli and nothing else (though there's also the possibility that since they ingest such low nutrition food their brains constantly stay in a super low-energy mode where they barely work as problem-solving machines -- topic for another time).

> By my observation, we have made giant leaps in the past 15 years, and we now have perhaps the vast majority of the components required to make artificial general intelligence...but it won't necessarily be all that useful at first, except maybe as a "pet robot" or something like that. Even if we scale it, it might not magically get smarter, just faster. a million hyper-speed squirrels still has a very limited level of utility.

Agree with that as well, just not sure that the path to a better general AI is in scaling higher what we [might] have now. IMO the plateau that the LLMs hit quite quickly partially supports my hypothesis.

As you are alluding to, the path to general AI is to keep adding more and more components to the same amalgam and try and connect them in creative ways. Eventually the spark of artificial life will ignite.

---

To summarize, my problem with the current breed of AI practitioners is that they argue from a position of authority that to me is imaginary; they are working in one of the least clear areas in science and yet they have the audacity to claim superiority to anyone whereas to me it's obvious that a random truck driver might have more interesting ideas than them for their area (exaggerated example but my point is that they lack perspective and become too entrenched in their narrow views, I guess like all scientists).

Yes, LLMs are likely integral part of a future artificial and working brain that can solve general tasks. And no we will not get any further there. Throwing another trillion parameters will achieve nothing but even more elaborate hallucinations. To me it became blindingly obvious that without throwing some symbol logic in there the LLMs will forever have zero concept of what they're talking about so they'll never improve -- because they also rely on truthful sources of info. That's not problem solving; that's regurgitating words with some extra steps.

Time to move on to other stuff -- maybe the transformers? Speaking of which, do you have any material on them that you would recommend for a layman? Just a TL;DR what they do and roughly how? Obviously I can Google it in literal seconds but that sadly does not mean much these days -- so maybe you have a source that's more interesting to read.

K0baltOP1y ago

Point taken on the “we” , it can sound patronising in the wrong light. Just think of that meaning “my immediate colleagues and I” - it is not to mean a universal or authoritative we, rather an anecdotal plurality.

It’s possible that others imagined human level as the base for “generalized intelligence”, but my colleagues and I were taking the term general to mean generalised, as in not narrowly defined (like expert systems are). A type of intelligence that could be applied broadly to different categories of problems, including ones not foreseen by the designer of the system. That these problems might be very basic ones was immaterial.

That this concept of general intelligence is not necessarily life transforming to possess on your smartphone doesn’t mean it isn’t a huge step forward. It is a very hard problem to solve. Transformer networks are the best tool we have for this task, and they work by inferring meaning to patterns and outputting a transform of that meaning. With LLMs, the pattern is the context text string, and the output is the next likely text fragment.

The surprising effectiveness of LLMs is due, I think, not to any characteristic of their architecture that makes them “intelligent”, but rather due to the fundamental nature of language itself, and especially the English language because of its penchant for specificity and its somewhat lower reliance on intonation to convey meaning than most languages. (I’m not a linguist, but I have discussed this with a few and it is interesting to hear their ideas on this)

Language itself captures meanings far beyond the words used in a statement. Only a tiny fraction of information is contained in words, the rest is inferred knowledge based on assumptions about shared experiences and understandings. Transformers tease out this context and imbed it through inferences constructed by billions of textual inputs. They capture the information shadows cast by the words, not just the words themselves.

This way of decoding cultural data, as an n-dimensional matrix of vast proportions rather than just the text of a culture itself, turns out to be a way to access both the explicit and the implicit knowledge imbedded in that culture, especially if that culture is codified using very specific and expressive tokens.

It turns out that this capture of shared experiences and understanding enables a great deal of abstract general problem solving, precisely the kind of problem solving that is vexingly difficult to solve using other methods.

LLMs, not just transformers, are actually a really big deal. In effect, they create a kind of probabilistic expert system where the field of expertise is a significant fraction of the sum total of human thought and experience.

But there are numerous and significant shortcomings to this approach, of course…. Not the least of which is the difficulty to effectively integrate new information or to selectively replace or update existing data in the model. And hallucinations (which are not a malfunction , but rather completely normal operations) are a basically unsolvable problem, though they can be mitigated.

But anyway…

I think the real insight to be had here , or at least my personal takeaway is that we owe a great deal of what we think of as intelligence to our culture than to our individual intellectual prowess. As we solve the problems of general intelligence, we both construct marvellous machines and confront the suggestion that we aren’t nearly as clever as we thought we were.

As for “AGI” I will still call that a crossed threshold , but certainly not to the level that humans have solved “biological GI” with full inculturation.

If people want AGI to mean human level competence, I’m ok with that. It won’t be the first term of art to have drifted in meaning. I would personally choose a more descriptive term for that, but 3 letter terms are not all that expressive, and adding another letter or two just gets awkward, so I get it. And I think it’s a lost battle anyway, it’s mostly old cranks such as myself that are impressed with things that most animals manage easily to do.

  As for the SOTA, I think there is huge room for improvement both in narrowing down competencies for specific tasks, and for expanding capabilities for applications that need human level abstraction and creativity. But I doubt that the next steps toward human level competency will be nearly as fruitful as the ones in the recent past, and I have serious doubts about the ability of current paths to lead to superhuman intelligence. Superhuman capabilities, yes, but there’s nothing new about that. Superhuman intelligence will require things which we don’t understand, by definition.

We have mastered flight, but I’m still waiting for my practical flying car.

Meanwhile, we suffer on with our earthbound wheels and idiot-savant AI.

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