For decades, the most exalted goal of artificial intelligence has been the creation of an artificial general intelligence, or AGI, capable of matching or even outperforming human beings on any intellectual task. It’s an ambitious goal long regarded with a mixture of awe and apprehension, because of the likelihood of massive social disruption any such AGI would undoubtedly cause. For years, though, such discussions were theoretical. Specific predictions forecasting AGI’s arrival were hard to come by.
But now, thanks to the latest large language models from the AI research firm OpenAI, the concept of an artificial general intelligence suddenly seems much less speculative. OpenAI’s latest LLMs—GPT-3.5, GPT-4, and the chatbot/interface ChatGPT—have made believers out of many previous skeptics. However, as spectacular tech advances often do, they seem also to have unleashed a torrent of misinformation, wild assertions, and misguided dread. Speculation has erupted recently about the end of the world-wide web as we know it, end-runs around GPT guardrails, and AI chaos agents doing their worst (the latter of which seems to be little more than clickbait sensationalism). There were scattered musings that GPT-4 is a step towards machine consciousness, and, more ridiculously, that GPT-4 is itself “slightly conscious.” There were also assertions that GPT-5, which OpenAI’s CEO Sam Altman said last week is not currently being trained, will itself be an AGI.
“The number of people who argue that we won’t get to AGI is becoming smaller and smaller.”
—Christof Koch, Allen Institute
To provide some clarity, IEEE Spectrum contacted Christof Koch, chief scientist of the Mindscope Program at Seattle’s Allen Institute. Koch has a background in both AI and neuroscience and is the author of three books on consciousness as well as hundreds of articles on the subject, including features for IEEE Spectrum and Scientific American.
Christof Koch on…
- Superintelligence and the Singularity
- Does an AGI Need to be Conscious?
- Should We Fear the Emergence of AGIs?
What would be the important characteristics of an artificial general intelligence as far as you’re concerned? How would it go beyond what we have now?
Christof Koch: AGI is ill defined because we don’t know how to define intelligence. Because we don’t understand it. Intelligence, most broadly defined, is sort of the ability to behave in complex environments that have multitudes of different events occurring at a multitude of different time scales, and successfully learning and thriving in such environments.
Christof KochPhoto: Erik Dinnel/Allen Institute
I’m more interested in this idea of an artificial general intelligence. And I agree that even if you’re talking about AGI, it’s somewhat nebulous. People have different opinions….
Koch: Well, by one definition, it would be like an intelligent human, but vastly quicker. So you can ask it—like Chat GPT—you can ask it any question, and you immediately get an answer, and the answer is deep. It’s totally researched. It’s articulated and you can ask it to explain why. I mean, this is the remarkable thing now about Chat GPT, right? It can give you its train of thought. In fact, you can ask it to write code, and then you can ask it, please explain it to me. And it can go through the program, line by line, or module by module, and explain what it does. It’s a train-of-thought type of reasoning that’s really quite remarkable.
You know, that’s one of the things that has emerged out of these large language models. Most people think about AGI in terms of human intelligence, but with infinite memory and with totally rational abilities to think—unlike us. We have all these biases. We’re swayed by all sorts of things that we like or dislike, given our upbringing and culture, etcetera, and supposedly AGI would be less amenable to that. And maybe able to do it vastly faster, right? Because if it just depends on the underlying hardware and the hardware keeps on speeding up and you can go into the cloud, then of course you could be like a human except a hundred times faster. And that’s what Nick Bostrom called a superintelligence.
“What GPT-4 shows, very clearly, is that there are different routes to intelligence.”
—Christof Koch, Allen Institute
You’ve touched on this idea of superintelligence. I’m not sure what this would be, except something that would be virtually indistinguishable from a human—a very, very smart human—except for its enormous speed. And presumably, accuracy. Is this something you believe?
Koch: That’s one way to think about it. It’s just like very smart people. But it can take those very smart people, like Albert Einstein, years to complete their insights and finish their work. Or to think and reason through something, it may take us, say, half an hour. But an AGI may be able to do this in one second. So if that’s the case, and its reasoning is effective, it may as well be superintelligent.
So this is basically the singularity idea, except for the self-creation and self-perpetuation.
Koch: Well, yeah, I mean the singularity… I’d like to stay away from that, because that’s yet another sort of more nebulous idea: that machines will be able to design themselves, each successive generation better than the one before, and then they just take off and totally escape our control. I don’t find that useful to think about in the real world. But if you return to where we are today, we have today amazing networks, amazing algorithms, that anyone can log on to and use, that already have emergent abilities that are unpredictable. They have become so large that they can do things that they weren’t directly trained for.
Let’s go back to the basic way these networks are trained. You give them a string of text or tokens. Let’s call it text. And then the algorithm predicts the next word, and the next word, and the next word, ad infinitum. And everything we see now comes just out of this very simple thing applied to vast reams of human-generated writing. You feed it all text that people have written. It’s read all of Wikipedia. It’s read all of, I don’t know, the Reddits and Subreddits and many thousands of books from Project Gutenberg and all of that stuff. It has ingested what people have written over the last century. And then it mimics that. And so, who would have thought that that leads to something that could be called intelligent? But it seems that it does. It has this emergent, unpredictable behavior.
For instance, although it wasn’t trained to write love letters, it can write love letters. It can do limericks. It can generate jokes. I just asked it to generate some trivia questions. You can ask it to generate computer code. It was also trained on code, on GitHub. It speaks many languages—I tested it in German.
So you just mentioned that it can write jokes. But it has no concept of humor. So it doesn’t know why a joke works. Does that matter? Or will it matter?
Koch: It may not matter. I think what it shows, very clearly, is that there are different routes to intelligence. One way you get to intelligence, is human intelligence. You take a baby, you expose this baby to its family, its environment, the child goes to school, it reads, etc. And then it understands in some sense, right?
“In the long term, I think everything is on the table. And yes, I think we need to worry about existential threats.”
—Christof Koch, Allen Institute
Although many people, if you ask them why a joke is funny, they can’t really tell you, either. The ability of many people to understand things is quite limited. If you ask people, well, why is this joke funny? Or how does that work? Many people have no idea. And so [GPT-4] may not be that different from many people. These large language models demonstrate quite clearly that you do not have to have a human-level type of understanding in order to compose text that to all appearances was written by somebody who has had a secondary or tertiary education.
IEEE Spectrum prompted OpenAI’s DALL·E to help create a series of portraits of AI telling jokes.DALL·E/IEEE Spectrum
Chat GPT reminds me of a widely read, smart, undergraduate student who has an answer for everything, but who’s also overly confident of his answers and, quite often, his answers are wrong. I mean, that’s a thing with Chat GPT. You can’t really trust it. You always have to check because very often it gets the answer right, but you can ask other questions, for example about math, or attributing a quote, or a reasoning problem, and the answer is plainly wrong.
This is a well-known weakness you’re referring to, a tendency to hallucinate or make assertions that seem semantically and syntactically correct, but are actually completely incorrect.
Koch: People do this constantly. They make all sorts of claims and often they’re simply not true. So again, this is not that different from humans. But I grant you, for practical applications right now, you can not depend on it. You always have to check other sources—Wikipedia, or your own knowledge, etc. But that’s going to change.
The elephant in the room, it seems to me that we’re kind of dancing around, all of us, is consciousness. You and Francis Crick, 25 years ago, among other things, speculated that planning for the future and dealing with the unexpected may be part of the function of consciousness. And it just so happens that that’s exactly what GPT-4 has trouble with.
Koch: So, consciousness and intelligence. Let’s think a little bit about them. They’re quite different. Intelligence ultimately is about behaviors, about acting in the world. If you’re intelligent, you’re going to do certain behaviors and you’re not going to do some other behaviors. Consciousness is very different. Consciousness is more a state of being. You’re happy, you’re sad, you see something, you smell something, you dread something, you dream something, you fear something, you imagine something. Those are all different conscious states.
Now, it is true that with evolution, we see in humans and other animals and maybe even squids and birds, etc., that they have some amount of intelligence and that goes hand in hand with consciousness. So at least in biological creatures, consciousness and intelligence seem to go hand in hand. But for engineered artifacts like computers, that does not have to be at all the case. They can be intelligent, maybe even superintelligent, without feeling like anything.
“It’s not consciousness that we need to be concerned about. It’s their motivation and high intelligence that we need to be concerned with.”
—Christof Koch, Allen Institute
And certainly there’s one of the two dominant theories of consciousness, the Integrated Information Theory of consciousness, that says you can never simulate consciousness. It can’t be computed, can’t be simulated. It has to be built into the hardware. Yes, you will be able to build a computer that simulates a human brain and the way people think, but it doesn’t mean it’s conscious. We have computer programs that simulate the gravity of the black hole at the center of our galaxy, but funny enough, no one is concerned that the astrophysicist who runs the computer simulation on a laptop is going to be sucked into the laptop. Because the laptop doesn’t have the causal power of a black hole. And same thing with consciousness. Just because you can simulate the behavior associated with consciousness, including speech, including speaking about it, doesn’t mean that you actually have the causal power to instantiate consciousness. So by that theory, it would say, these computers, while they might be as intelligent or even more intelligent than humans, they will never be conscious. They will never feel.
Which you don’t really need, by the way, for anything practical. If you want to build machines that help us and serve our goals by providing text and predicting the weather or the stock market, writing code, or fighting wars, you don’t really care about consciousness. You care about reasoning and motivation. The machine needs to be able to predict and then based on that prediction, do certain things. And even for the doomsday scenarios, it’s not consciousness that we need to be concerned about. It’s their motivation and high intelligence that we need to be concerned with. And that can be independent of consciousness.
Why do we need to be concerned about those?
Koch: Look, we’re the dominant species on the planet, for better or worse, because we are the most intelligent and the most aggressive. Now we are building creatures that are clearly getting better and better at mimicking one of our unique hallmarks—intelligence. Of course, some people, the military, independent state actors, terrorist groups, they will want to marry that advanced intelligent machine technology to warfighting capability. It’s going to happen sooner or later. And then you have machines that might be semiautonomous or even fully autonomous and that are very intelligent and also very aggressive. And that’s not something that we want to do without very, very careful thinking about it.
But that kind of mayhem would require both the ability to plan and also mobility, in the sense of being embodied in something, a mobile form.
Koch: Correct, but that’s already happening. Think about a car, like a Tesla. Fast forward another ten years. You can put the capability of something like a GPT into a drone. Look what the drone attacks are doing right now. The Iranian drones that the Russians are buying and launching into Ukraine. Now imagine, that those drones can tap into the cloud and gain superior, intelligent abilities.
There’s a recent paper by a team of authors at Microsoft, and they theorize about whether GPT-4 has a theory of mind.
Koch: Think about a novel. Any novels about what the protagonist thinks, and then what he or she imputes what others think. Much of modern literature is about, what do people think, believe, fear, or desire. So it’s not surprising that GPT-4 can answer such questions.
Is that really human-level understanding? That’s a much more difficult question to grok. “Does it matter?” is a more relevant question. If these machines behave like they understand us, yeah, I think it’s a further step on the road to artificial generalized intelligence, because then they begin to understand our motivation—including maybe not just generic human motivations, but the motivation of a specific individual in a specific situation, and what that implies.
“When people say in the long term this is dangerous, that doesn’t mean, well, maybe in 200 years. This could mean maybe in three years, this could be dangerous.”
—Christof Koch, Allen Institute
Another risk, which also gets a lot of attention, is the idea that these models could be used to produce disinformation on a staggering scale and with staggering flexibility.
Koch: Totally. You see it already. There were already some deep fakes around the Donald Trump arrest, right?
So it would seem that this is going to usher in some kind of new era, really. I mean, into a society that is already reeling with disinformation spread by social media. Or amplified by social media, I should say.
Koch: I agree. That’s why I was one of the early signatories on this proposal that was circulating from the Future of Life Institute, that calls on the tech industry to pause for at least for half a year before releasing the next, more powerful large language model. This isn’t a plea to stop the development of ever more powerful models. We’re just saying, “let’s just hit pause here in order to try to understand and safeguard. Because it’s changing so very rapidly.” The basic invention that made this possible are transformer networks, right? And they were only published in 2017, in a paper by Google Brain, “Attention Is All You Need.” And then GPT, the original GPT, was born the next year, in 2018. GPT-2 in 2019, I think, and last year, GPT-3 and ChatGPT. And now GPT-4. So where are we going to be ten years from now?
Do you think the upsides are going to outweigh whatever risks we will face in the shorter term? In other words, will it ultimately pay off?
Koch: Well, it depends what your long-term view is on this. If it’s existential risk, if there’s a possibility of extinction, then, of course, nothing can justify it. I can’t read the future, of course. There’s no question that these methods—I mean, I see it already in my own work—these large language models make people more powerful programmers. You can more quickly gain new knowledge or take existing knowledge and manipulate it. They are certainly force multipliers for people that have knowledge or skills.
Ten years ago, this wasn’t even imaginable. I remember even six or seven years ago people arguing, “well, these large language models are very quickly going to saturate. If you scale them up, you can’t really get much farther this way.” But that turned out to be wrong. Even the inventors themselves have been surprised, particularly, by this emergence of these new capabilities, like the ability to tell jokes, explain a program, and carrying out a particular task without having been trained on that task.
Well, that’s not very reassuring. Tech is releasing these very powerful model systems. And the people themselves that program them say, we can’t predict what new behaviors are going to emerge from these very large models. Well, gee, that makes me worry even more. So in the long term, I think everything is on the table. And yes, I think we need to worry about existential threats. Unfortunately, when you talk to AI people at AI companies, they typically say, oh, that’s just all laughable. That’s all hysterics. Let’s talk about the practical things right now. Well, of course, they would say that because they’re being paid to advance this technology and they’re being paid extraordinarily well. So, of course, they’re always going to push it.
I sense that the consensus has really swung because of GPT-3.5 and GPT-4. Has really swung that it’s only a matter of time before we have an AGI. Would you agree with that?
Koch: Yes. I would put it differently though: the number of people who argue that we won’t get to AGI is becoming smaller and smaller. It’s a rear-guard action, fought by people mostly in the humanities: “Well, but they still can’t do this. They still can’t write Death in Venice.” Which is true. Right now, none of these GPTs has produced a novel. You know, a 100,000-word novel. But I suspect it’s also just going to be a question of time before they can do that.
If you had to guess, how much time would you say that that’s going to be?
Koch: I don’t know. I’ve given up. It’s very difficult to predict. It really depends on the available training material you have. Writing a novel requires long-term character development. If you think about War and Peace or Lord of the Rings, you have characters developing over a thousand pages. So the question is, when can AI get these sorts of narratives? Certainly it’s going to be faster than we think.
So as I said, when people say in the long term this is dangerous, that doesn’t mean, well, maybe in 200 years. This could mean maybe in three years, this could be dangerous. When will we see the first application of GPT to warlike endeavors? That could happen by the end of this year.
But the only thing I can think of that could happen in 2023 using a large language model is some sort of concerted propaganda campaign or disinformation. I mean, I don’t see it controlling a lethal robot, for example.
Koch: Not right now, no. But again, we have these drones, and drones are getting very good. And all you need, you need a computer that has access to the cloud and can access these models in real time. So that’s just a question of assembling the right hardware. And I’m sure this is what militaries, either conventional militaries or terrorists organizations, are thinking about and will surprise us one day with such an attack. Right now, what could happen? You could get deep fakes of—all sorts of nasty deep fakes or people declaring war or an imminent nuclear attack. I mean, whatever your dark fantasy gives rise to. It’s the world we now live in.
Well, what are your best-case scenarios? What are you hopeful about?
Koch: We’ll muddle through, like we’ve always muddled through. But the cat’s out of the bag. If you extrapolate these current trends three or five years from now, and given this very steep exponential rise in the power of these large language models, yes, all sorts of unpredictable things could happen. And some of them will happen. We just don’t know which ones.
Source: IEEE Spectrum Computing