I don't think of ChatGPT as being "smart" at all, and comparing it to a human seems nonsensical to me. Yet here is a Turing award winning preeminent expert in the field telling me that AI smarter than humans is less (implied: much less) than 30 years away and quitting his job due to the ramifications.
If you're interested in exploring this further I can really recommend taking a look at some of the papers that explore GPT-4's capabilities. Most prominent among them are the "Sparks of AGI" paper from Microsoft, as well as the technical report from openai. Both of them are obviously to be taken with a grain of salt, but they serve as a pretty good jumping off point.
There are some pretty good Videos on Youtube exploring these papers if you don't want to read them yourself.
Also take a look at the stuff that Rob Miles has published over on Computerphile, as well as his own channel. He's an Alignment Researcher with a knack for explaining. He covers not just the theoretical dangers, but also real examples of misaligned ai, that alignment researchers have predicted would occur as capabilities grow.
Also I think it's important to mention that just a short while ago virtually no-one thought that shoving more layers into an llm would be enough to reach AGI. It's still unclear that it will get us all the way there, but recent developments have made a lot of ai researchers rethink that possibility, with many of them significantly shortening their own estimates as to when and how we will get there. It's very unusual that the people that are better informed and closer to the research are more worried than the rest of the world and it's worth keeping this in mind as you explore the topic.
https://arxiv.org/abs/2303.12712
While there is no scientific evidence that LLMs can reach AGI, they will still be practically useful for many other tasks. A human mind paired with an LLM is a powerful combination.
I'm not saying that you're wrong, but...
you'd have to provide a more rigorous rebuttal to be taken seriously.
AGI can exist without sapience and intelligence is a continuum. you can't just hand wave away GPT's capabilities which is why the sharpest minds on the planet are poking this new machine to work out wtf is going on.
human intelligence is a black box. we judge it by its outputs from given inputs. GPT is already producing human-like outputs.
a common rebuttal is: "but it doesn't *really* think/understand/feel", to which my response is: ...and? ¯\_(ツ)_/¯ what does that even mean?
What’s brilliant about this is that typically auto scaling metrics look like a stereotypical roller coaster track with the daily ups and downs!
That’s a genuinely funny, insightful, bespoke, and stylistically correct joke.
Tell me that that is not intelligence!
Here’s the thing: the authors of that paper got early access to GPT-4 and ran a bunch of tests on it. The important bit is that MSR does not see into OpenAI’s sausage making.
Now imagine if you were a peasant from 1000 AD who was given a car or TV to examine. Could you really be confident you understood how it worked by just running experiments on it as a black box? If you give a non-programmer the linux kernel, will he/she think it’s magical?
Things look like magic especially when you can’t look under the hood. The story of the Mechanical Turk is one example of that.
the human brain is a black box, we can certainly learn a lot about it by prodding and poking it.
>Things look like magic especially when you can’t look under the hood.
imagine we had a 100% complete understanding of the mechanical/chemical/electrical functioning of the human brain. Would knowing the magic make it any less magical? in some sense, yes (the mystique would be gone, bye bye dualism), but in a practical sense, not really. It's still an astonishingly useful piece of grey matter.
Well realistically it's like independently evolving the language processing part of our brain without forming the rest of the brain, there seems to be extra logic/functions that emerge within LLMs to handle these restrictions.
I think we'll see AGI when we finally try to build one up from various specialised subcomponents of a "brain". Of course GPT can't "think", it only knows how to complete a stream of text and has figured out internal hacks during training to pass the tests they set for it.
The real difference will be when we train a model to have continuous, connected abstract thoughts - an LLM can be used to communicate these thoughts or put them into words but it should not be used to generate them in the first place...
This was basically the strategy of the OpenAI team if I understand them correctly. Most researchers in the field looked down on LLMs and it was a big surprise when they turned out to perform so well. It also seems to be the reason the big players are playing catch up right now.
I agree that I think they underestimated quite how useful a product could be built around just the language modeling objective, but it's still been critical for most NLP advances of the last ~6+ years.
I struggle to see how GPT-4 is not intelligent by any definition that applies to a human.
Often, people say yes. Those people almost universally cannot be convinced that a machine is intelligent. But, if they agree the brain is an organ, its not hard to convince them that the functions of that organ can be simulated, like any other.
edit. tried the same with GPT-4, doesn't look like it understand either, but can't ask follow up questions since I do not have access (and what really make the other answer so incredibly dumb is not so much that it gets it wrong the first time, but that it keeps not getting it despite the very not subtle hints): https://postimg.cc/ftWJXhtJ
Like isn't that why humans are "more intelligent" than animals?
Plenty of animals can do things that humans can't do, but that doesn't make them necessarily "intelligent".
The fact that it seems trivially simple to fool and trick ChatGPT makes me feel like it's not very intelligent, but that's just me.
Obviously you can trick humans, but IMO it takes more effort than to trick ChatGPT. It just way too often makes such simple and stupid mistakes that it makes it hard for me to think of it as "intelligent".
Humans became more intelligent due to developing oral and literary traditions that allowed the preservation and accumulation of knowledge. Everything that made a modern human "intelligent" is a direct result of that accumulation of knowledge, not some sort of biological miracle.
I don't necessarily disagree, but I do think it is possible we will have AGI, even ASI, long before we have sentience in AI. Of course, I'm a little skeptical of measures of sentience, so even if I'm right it will certainly be debatable.
I don't know. My point is that if there is some objective indicator that's correlated with sentience, LLMs are probably already close to us on it, maybe even beating us on it. And if, at some point, a ML model reaches our levels at every objective measure we can think of, then we'll have no choice but to grant it is intelligent/sentient/sapient.
But sentience itself involves self-reflection, which there is no evidence LLMs do at all. When you submkt a prompt, a giant mathematical operation happens, and when it is complete, it stops. ChatGPT is not sitting there thinking "oh man, I should have said..."
For humans, time does not stop - we constantly process both sensory information and our own thoughts, and even if you cut out external stimuli via e.g. sensory deprivation tank, the brain will just loop on its own output instead, of which you'll suddenly become much more aware.
Sentience is that internal loop you point out. LLMs (today) don't have that. When you prompt for "write a tagline for an ice cream shop", there is no identity that remembers other prompts about ice cream, or which reflects on how taglines have changed over time, or anything else. The results can be astoundingly good, even intelligent, but there's no sentience.
If you somehow turned off a person after each sentence, upon waking up to the next prompt their first thought would be "that was weird, I must have passed out", and we could use fMRI to track brain activity indicating that thought. We are even more capable of inspecting LLMs, and there is no equivalent activity. LLMs start and end with the tokens going in and out, and a huge matrix that transforms them.
I'm generally an open minded, probabilities-rather-than-certainties person, but I'd say the odds of LLMs having sentience that we can't detect are about the same as the odds of a television having sentience that we can't detect: as close to zero as we can measure.
Yes, but that is arguably a trivial limitation. Nothing stops you from running an LLM in a loop and feed it its own output. Plenty such experiments are probably going on already - it's a trivial loop (and a trivial way to burn through your wallet). The problem is, of course, context window being rather small. So it's possible - by no means certain, but I'm no longer dismissing this idea - that the capability for sentience is already there in GPT-4 structurally, and we just lack the ability to sustain it in a loop long enough to bring it into the open.
> If you somehow turned off a person after each sentence, upon waking up to the next prompt their first thought would be "that was weird, I must have passed out", and we could use fMRI to track brain activity indicating that thought.
That's not what I meant by LLM iteration. When I said that time stops, I mean that for LLM, it literally just stops. If you were to step-execute a human like that, they would never notice; it's not like the brain has a separate RTC module constantly feeding it with sub-second resolution timestamps (and if it did, we'd turn that off too). Over a hour or more, the human may realize their inner perception of time is increasingly lagging the wall clock, but to keep it comparable to LLM, we'd be iterating sub-second process.
In this hypothetical, there would be no extra activity in brains that isn't there in LLMs. Step-executing a human mind doesn't freeze some abstract subprocess, it freezes photons and electrons and chemical gradients.
On the other hand, sentience just means having an experience of observing the world, it doesn‘t even need to include a concept of self. Presumably at least all mammals have this, for sure a dog has this. ChatGPT - probably not.
This explains the various pejorative names given to LLMs - stochastic parrots, Chinese Rooms - etc, etc, etc.
I have a feeling all of these things are limited by time/space/speed of light/heat/density limitations. Could be things can't get that much smarter than humans with in an OOM... tho they might get a lot more able to cooperate / delegate.
The only way anybody has ever come up with to measure it is test-taking - which machines can already do far better than we can. Real intelligence is creativity, but good luck measuring that.
If anything, transformer models are closing the gap on that last bit, as they're built by taking the approach of "if we can't describe exactly how we rate and rank things, then let's shove so many examples at the model that it eventually gets a feel for it".
Being handed all the correct solutions without the need to work for them in any way is a nightmare for artists and artisans, craftsmen and researchers, curious puzzle-solvers and Ayn Rand believers. It's pretty much a paradise for everyone else.
Even assuming that definition, it begs the question of, "what is creativity?"
Well said.
Even Jim Keller (a key designer involved with a lot of major CPUs, in his interview with Lex Freidman) said that there might be some sort of magic, or something magical about human consciousness / the human soul. I agree with that.
That's something that a machine will never have.
hehe, this is typical goal post moving.
never is a long time.
Everyone at that conference, including myself, have assumed we will eventually create smarter than human computers and beyond.
So it’s not a new position for people who have been in AI for a long time, though generally it was seen as an outsider position until recently.
There’s a ton of really great work done prior to all of this around these questions and technical approaches - I think my mentor Ben Goertzel was the pioneer here holistically, but others were doing good technical work then too.
We have LLMs that can perform "read and respond", we have systems that can interpret images and sound/speech - and we have plugins that can connect generated output to api calls - that feed back in.
Essentially this means that we could already go from "You are an automated home security system. From the front door camera you see someone trying to break in. What do you do?" - to actually building such a system.
Maybe it will just place a 911 call, maybe it will deploy a tazer. Maybe the burglar is just a kid in a Halloween costume.
The point is that just because you can chain a series of AI/autonomous systems today - with the known, gaping holes - you probably shouldn't.
Ed: Crucially the technology is here (in "Lego parts") to construct systems with (for all intents and purposes) real "agency" - that interact both with the real world, and our data (think: purchase a flight based off an email sent to your inbox).
I don't think it really matters if these simulacra embody AGI - as long as they already demonstrate agency. Ed2: Or demonstrate behavior so complex that it is indistinguishable to agency for us.