While I agree, I can't help but wonder: if such a "super search engine" were to have the knowledge on how to solve individual steps of problems, how different would that be from an "intelligent" thing? I mean that, instead of "searching" for the next line of code, it searches for the next solution or implementation detail, then using it as the query that eventually leads to code.

Having knowledge isn't the same as knowing. I can hold a stack of physics papers in my hand but that doesn't make me a physics professor.

LLMs possess and can retrieve knowledge but they don't understand it, and when people try to get them to do that it's like talking to a non-expert who has been coached to smalltalk with experts. I remember reading about a guy who did this with his wife so she could have fun when travelling to conferences with him!

I've spent a lot of time thinking about that - what if the realization that we need is not that LLMs are intelligent, but that our own brains work in the same way as the LLMs. There is certainly a cognitive bias to believe that humans are somehow special and that our brains are not simply machinery.

The difference, to me, is that an LLM can very efficiently recall information, or more accurately, a statistical model of information. However, they seem to be unable to actually extrapolate from it or rationalize about it (they can create the illusion of rationalization be knowing what the rationalization would look like). A human would never be able to ingest and remember the amount of information that an LLM can, but we seem to have the incredible ability of extrapolation - to reach new conclusions by deeply reasoning about old ones.

This is much like the difference in being "book smart" and "actually smart" that some people use to describe students. Some students can memorize vast amounts of information, pass all tests with straight A's, only to fail when they're tasked with thinking on their own. Others perform terribly on memorization tasks, but naturally are gifted at understanding things in a more intuitive sense.

I have seen heaps of evidence that LLMs have zero ability to reason, so I believe that there's something very fundamental missing. Perhaps the LLM is a small part of the puzzle, but there doesn't seem to be any breakthroughs that seem like we might be moving towards actual reasoning. I do think that the human brain can very likely be emulated if we cracked the technology. I just don't believe we're close.

Even though I think it's true that it's lossy, I think there is more going on in an LLM neural net. Namely that when it uses tokens to produce output, you essentially split the text into millions or billions of chunks, each with probability of those chunks. So in essence the LLM can do a form of pattern recognition where the patterns are the chunks and it also enables basic operations on those chunks.

That's why I think you can work iteratively on code and change parts of the code while keeping others, because the code gets chunked and "probabilitized'. It can also do semantic processing and understanding where it can apply knowledge about one topic (like 'swimming') to another topic (like a 'swimming spaceship', it then generates text about what a swimming spaceship would be which is not in the dataset). It chunks it into patterns of probability and then combines them based on probability. I do think this is a lossy process though which sucks.

Maybe it's looked down upon to complain about downvotes but I have to say I'm a little disappointed that there is a downvote with no accompanying post to explain that vote, especially to a post that is factually correct and nothing obviously wrong with it.

> Is such a thing intellegent [sic]? Certainly NO!

A proofreader would have caught this humorous gaffe. In fact, one just did.

I personally had the completely opposite takeaway: Intelligence, at its core, really might just be a bunch of extremely good and self-adapting search heuristics.

I don't blurt out different answers to the same question using different phrasing, I doubt any human does.

We actually do, and often - depending on who our speaker is, our relationship with them, the tone of the message, etc. Maybe our intellect is not fully an LLM, but I truly wonder how much of our dialectical skills are.

You're describing the same answer with different phrasing.

Humans do that, LLMs regularly don't.

If you phrase the question "what color is your car?" a hundred different ways, a human will get it correct every time. LLMs randomly don't, if the token prediction veers off course.

Edit:

A human also doesn't get confused at fundamental priors after a reasonable context window. I'm perplexed that we're still having this discussion after years of LLM usage. How is it possible that it's not clear to everyone?

Don't get me wrong, I use it daily at work and at home and it's indeed useful, but there's is absolutely 0 illusion of intelligence for me.

> …can return the results in natural language.

That’s one of the most important features, though. For example, LLMs can analyze a code base and tell you how it works in natural language. That demonstrates functional understanding and intelligence - in addition to exceeding the abilities of the majority of humans in this area.

You’d need a very no-true-Scotsmanned definition of intelligence to be able exclude LLMs. That’s not to say that they’re equivalent to human intelligence in all respects, but intelligence is not an all-or-nothing property. (If it were, most humans probably wouldn’t qualify.)

LLMs being intelligence or not is not really that interesting. It's just matter of how you define intelligence. It matters maybe to the AI CEOs and their investors because of marketing.

What matters is how useful LLMs actually are. Many people here say it is useful as advanced search engine and not that useful as your coworker. That is very useful but most likely not something the AI companies want to hear.

> You’d need a very no-true-Scotsmanned definition of intelligence to be able exclude LLMs.

The thing is, that intelligence is an anthropocentric term. And has always been defined in a no-true-Scotsman way. When we describe the intelligence of other species we do so in extremely human terms (except for dogs). For example we consider dolphins smart when we see them play with each other, talk to each other, etc. We consider chimpanzees when we see them use a tool, row a boat, etc. We don’t consider an ant colony smart when they optimize a search for food sources, only because humans don’t normally do that. The only exception here are dogs, who we consider smart when they obey us more easily.

Personally, my take on this is that intelligence is not a useful term in philosophy nor science. Describing a behavior as intelligent is kind of like calling a small creature a bug. It is useful in our day to day speech, but fails when we want to build any theory around it.

In the context of "AI", the use of the word "intelligence" has referred to human-comparable intelligence for at least the last 75 years, when Alan Turing described the Turing Test. That test was explicitly intended to test for a particular kind of human equivalent intelligence. No other animal has come close to passing the Turing Test. As such, the distinction you're referring to isn't relevant to this discussion.

> Personally, my take on this is that intelligence is not a useful term in philosophy nor science.

Hot take.

The Turing test was debunked by John Searle in 1980 with the Chinese room thought experiment. And even looking past that, the existence, and the pervasiveness, of the Turing test proves my point that this term is and always has been extremely anthropocentric.

In statistics there has been a prevailing consensus for a really long time that artificial intelligence is not only a misnomer, but also rather problematic, and maybe even confusing. There has been a concerted effort the past 15 years to move away from this term onto something like machine learning (machine learning is not without its own set of downsides, but is still miles better then AI). So honestly my take is not that hot (at least not in statistics; maybe in psychology and philosophy).

But I want to justify my take in psychology. Psychometricians have been doing intelligence testing for well over a century now, and the science is not much further along then it was a century ago. No new prediction, no new subfields, etc. This is a hallmark of a scientific dead end. And on the flip side, psychological theories that don‘t use intelligence at all are doing just fine.