|Main Question: What is "superintelligence"? (edit question) (edit answer)|
|Alignment Forum Tag|
A Superintelligence is a being with superhuman intelligence, and a focus of the Machine Intelligence Research Institute's research. Specifically, Nick Bostrom (1997) defined it as
"An intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills."
The Machine Intelligence Research Institute is dedicated to ensuring humanity's safety and prosperity by preparing for the development of an Artificial General Intelligence with superintelligence. Given its intelligence, it is likely to be incapable of being controlled by humanity. It is important to prepare early for the development of friendly artificial intelligence, as there may be an AI arms race. A strong superintelligence is a term describing a superintelligence which is not designed with the same architecture as the human brain.
An Artificial General Intelligence will have a number of advantages aiding it in becoming a superintelligence. It can improve the hardware it runs on and obtain better hardware. It will be capable of directly editing its own code. Depending on how easy its code is to modify, it might carry out software improvements that spark further improvements. Where a task can be accomplished in a repetitive way, a module preforming the task far more efficiently might be developed. Its motivations and preferences can be edited to be more consistent with each other. It will have an indefinite life span, be capable of reproducing, and transfer knowledge, skills, and code among its copies as well as cooperating and communicating with them better than humans do with each other.
The development of superintelligence from humans is another possibility, sometimes termed a weak superintelligence. It may come in the form of whole brain emulation, where a human brain is scanned and simulated on a computer. Many of the advantages a AGI has in developing superintelligence apply here as well. The development of Brain-computer interfaces may also lead to the creation of superintelligence. Biological enhancements such as genetic engineering and the use of nootropics could lead to superintelligence as well.
- Superintelligence by Michael Anissimov
- How long before Superintelligence? by Nick Bostrom
- A discussion between Hugo de Garis and Ben Goertzel on superintelligence
- Advantages of Artificial Intelligences, Uploads, And Digital Minds by Kaj Sotala
Intelligence is powerful. Because of superior intelligence, we humans have dominated the Earth. The fate of thousands of species depends on our actions, we occupy nearly every corner of the globe, and we repurpose vast amounts of the world's resources for our own use. Artificial Superintelligence (ASI) has potential to be vastly more intelligent than us, and therefore vastly more powerful. In the same way that we have reshaped the earth to fit our goals, an ASI will find unforeseen, highly efficient ways of reshaping reality to fit its goals.
The impact that an ASI will have on our world depends on what those goals are. We have the advantage of designing those goals, but that task is not as simple as it may first seem. As described by MIRI in their Intelligence Explosion FAQ:
“A superintelligent machine will make decisions based on the mechanisms it is designed with, not the hopes its designers had in mind when they programmed those mechanisms. It will act only on precise specifications of rules and values, and will do so in ways that need not respect the complexity and subtlety of what humans value.”
If we do not solve the Control Problem before the first ASI is created, we may not get another chance.
The argument goes: computers only do what we command them; no more, no less. So it might be bad if terrorists or enemy countries develop superintelligence first. But if we develop superintelligence first there’s no problem. Just command it to do the things we want, right? Suppose we wanted a superintelligence to cure cancer. How might we specify the goal “cure cancer”? We couldn’t guide it through every individual step; if we knew every individual step, then we could cure cancer ourselves. Instead, we would have to give it a final goal of curing cancer, and trust the superintelligence to come up with intermediate actions that furthered that goal. For example, a superintelligence might decide that the first step to curing cancer was learning more about protein folding, and set up some experiments to investigate protein folding patterns.
A superintelligence would also need some level of common sense to decide which of various strategies to pursue. Suppose that investigating protein folding was very likely to cure 50% of cancers, but investigating genetic engineering was moderately likely to cure 90% of cancers. Which should the AI pursue? Presumably it would need some way to balance considerations like curing as much cancer as possible, as quickly as possible, with as high a probability of success as possible.
But a goal specified in this way would be very dangerous. Humans instinctively balance thousands of different considerations in everything they do; so far this hypothetical AI is only balancing three (least cancer, quickest results, highest probability). To a human, it would seem maniacally, even psychopathically, obsessed with cancer curing. If this were truly its goal structure, it would go wrong in almost comical ways. This type of problem, specification gaming, has been observed in many AI systems.
If your only goal is “curing cancer”, and you lack humans’ instinct for the thousands of other important considerations, a relatively easy solution might be to hack into a nuclear base, launch all of its missiles, and kill everyone in the world. This satisfies all the AI’s goals. It reduces cancer down to zero (which is better than medicines which work only some of the time). It’s very fast (which is better than medicines which might take a long time to invent and distribute). And it has a high probability of success (medicines might or might not work; nukes definitely do).
So simple goal architectures are likely to go very wrong unless tempered by common sense and a broader understanding of what we do and do not value.
Even if we do train the AI on an actually desirable goal, there is also the risk of the AI actually learning a different and undesirable objective. This problem is called inner alignment.
There's the "we never figure out how to reliably instill AIs with human friendly goals" filter, which seems pretty challenging, especially with inner alignment, solving morality in a way which is possible to code up, interpretability, etc.
There's the "race dynamics mean that even though we know how to build the thing safely the first group to cross the recursive self-improvement line ends up not implementing it safely" which is potentially made worse by the twin issues of "maybe robustly aligned AIs are much harder to build" and "maybe robustly aligned AIs are much less compute efficient".
There's the "we solved the previous problems but writing perfectly reliably code in a whole new domain is hard and there is some fatal bug which we don't find until too late" filter. The paper The Pursuit of Exploitable Bugs in Machine Learning explores this.
Many of the people with the deepest understanding of artificial intelligence are concerned about the risks of unaligned superintelligence. In 2014, Google bought world-leading artificial intelligence startup DeepMind for $400 million; DeepMind added the condition that Google promise to set up an AI Ethics Board. DeepMind cofounder Shane Legg has said in interviews that he believes superintelligent AI will be “something approaching absolute power” and “the number one risk for this century”.
Stuart Russell, Professor of Computer Science at Berkeley, author of the standard AI textbook, and world-famous AI expert, warns of “species-ending problems” and wants his field to pivot to make superintelligence-related risks a central concern. He went so far as to write Human Compatible, a book focused on bringing attention to the dangers of artificial intelligence and the need for more work to address them.
Many other science and technology leaders agree. Late astrophysicist Stephen Hawking said that superintelligence “could spell the end of the human race.” Tech billionaire Bill Gates describes himself as “in the camp that is concerned about superintelligence…I don’t understand why some people are not concerned”. Oxford Professor Nick Bostrom, who has been studying AI risks for over 20 years, has said: “Superintelligence is a challenge for which we are not ready now and will not be ready for a long time.”
A value handshake is a form of trade between superintelligences, when two AI's with incompatible utility functions meet, instead of going to war, since they have superhuman prediction abilities and likely know the outcome before any attack even happens, they can decide to split the universe into chunks with volumes according to their respective military strength or chance of victory, and if their utility functions are compatible, they might even decide to merge into an AI with an utility function that is the weighted average of the two previous ones.
This could happen if multiple AI's are active on earth at the same time, and then maybe if at least one of them is aligned with humans, the resulting value handshake could leave humanity in a pretty okay situation.
See The Hour I First Believed By Scott Alexander for some further thoughts and an introduction to related topics.
Machines are already smarter than humans are at many specific tasks: performing calculations, playing chess, searching large databanks, detecting underwater mines, and more. But one thing that makes humans special is their general intelligence. Humans can intelligently adapt to radically new problems in the urban jungle or outer space for which evolution could not have prepared them. Humans can solve problems for which their brain hardware and software was never trained. Humans can even examine the processes that produce their own intelligence (cognitive neuroscience), and design new kinds of intelligence never seen before (artificial intelligence).
To possess greater-than-human intelligence, a machine must be able to achieve goals more effectively than humans can, in a wider range of environments than humans can. This kind of intelligence involves the capacity not just to do science and play chess, but also to manipulate the social environment.
Computer scientist Marcus Hutter has described a formal model called AIXI that he says possesses the greatest general intelligence possible. But to implement it would require more computing power than all the matter in the universe can provide. Several projects try to approximate AIXI while still being computable, for example MC-AIXI.
Still, there remains much work to be done before greater-than-human intelligence can be achieved in machines. Greater-than-human intelligence need not be achieved by directly programming a machine to be intelligent. It could also be achieved by whole brain emulation, by biological cognitive enhancement, or by brain-computer interfaces (see below).
- Goertzel & Pennachin (eds.), Artificial General Intelligence
- Sandberg & Bostrom, Whole Brain Emulation: A Roadmap
- Bostrom & Sandberg, Cognitive Enhancement: Methods, Ethics, Regulatory Challenges
- Wikipedia, Brain-computer interface
We would not be able to turn off or reprogram a superintelligence gone rogue by default. Once in motion the superintelligence is now focused on completing its task. Suppose that it has a goal of calculating as many digits of pi as possible. Its current plan will allow it to calculate two hundred trillion such digits. But if it were turned off, or reprogrammed to do something else, that would result in it calculating zero digits. An entity fixated on calculating as many digits of pi as possible will work hard to prevent scenarios where it calculates zero digits of pi. Just by programming it to calculate digits of pi, we would have given it a drive to prevent people from turning it off.
University of Illinois computer scientist Steve Omohundro argues that entities with very different final goals – calculating digits of pi, curing cancer, helping promote human flourishing – will all share a few basic ground-level subgoals. First, self-preservation – no matter what your goal is, it’s less likely to be accomplished if you’re too dead to work towards it. Second, goal stability – no matter what your goal is, you’re more likely to accomplish it if you continue to hold it as your goal, instead of going off and doing something else. Third, power – no matter what your goal is, you’re more likely to be able to accomplish it if you have lots of power, rather than very little. Here’s the full paper.
So just by giving a superintelligence a simple goal like “calculate digits of pi”, we would have accidentally given it convergent instrumental goals like “protect yourself”, “don’t let other people reprogram you”, and “seek power”.
As long as the superintelligence is safely contained, there’s not much it can do to resist reprogramming. But it’s hard to consistently contain a hostile superintelligence.
An aligned superintelligence will have a set of human values. As mentioned in What are "human values"? the set of values are complex, which means that the implementation of these values will decide whether the superintelligence cares about nonhuman animals. In AI Ethics and Value Alignment for Nonhuman Animals Soenke Ziesche argues that the alignment should include the values of nonhuman animals.
Yes, if the superintelligence has goals which include humanity surviving then we would not be destroyed. If those goals are fully aligned with human well-being, we would in fact find ourselves in a dramatically better place.
We can run some tests and simulations to try and figure out how an AI might act once it ascends to superintelligence, but those tests might not be reliable.
Suppose we tell an AI that expects to later achieve superintelligence that it should calculate as many digits of pi as possible. It considers two strategies.
First, it could try to seize control of more computing resources now. It would likely fail, its human handlers would likely reprogram it, and then it could never calculate very many digits of pi.
Second, it could sit quietly and calculate, falsely reassuring its human handlers that it had no intention of taking over the world. Then its human handlers might allow it to achieve superintelligence, after which it could take over the world and calculate hundreds of trillions of digits of pi.
Since self-protection and goal stability are convergent instrumental goals, a weak AI will present itself as being as friendly to humans as possible, whether it is in fact friendly to humans or not. If it is “only” as smart as Einstein, it may be very good at deceiving humans into believing what it wants them to believe even before it is fully superintelligent.
There’s a second consideration here too: superintelligences have more options. An AI only as smart and powerful as an ordinary human really won’t have any options better than calculating the digits of pi manually. If asked to cure cancer, it won’t have any options better than the ones ordinary humans have – becoming doctors, going into pharmaceutical research. It’s only after an AI becomes superintelligent that there’s a serious risk of an AI takeover.
So if you tell an AI to cure cancer, and it becomes a doctor and goes into cancer research, then you have three possibilities. First, you’ve programmed it well and it understands what you meant. Second, it’s genuinely focused on research now but if it becomes more powerful it would switch to destroying the world. And third, it’s trying to trick you into trusting it so that you give it more power, after which it can definitively “cure” cancer with nuclear weapons.
That is, if you know an AI is likely to be superintelligent, can’t you just disconnect it from the Internet, not give it access to any speakers that can make mysterious buzzes and hums, make sure the only people who interact with it are trained in caution, et cetera?. Isn’t there some level of security – maybe the level we use for that room in the CDC where people in containment suits hundreds of feet underground analyze the latest superviruses – with which a superintelligence could be safe?
This puts us back in the same situation as lions trying to figure out whether or not nuclear weapons are a things humans can do. But suppose there is such a level of security. You build a superintelligence, and you put it in an airtight chamber deep in a cave with no Internet connection and only carefully-trained security experts to talk to. What now?
Now you have a superintelligence which is possibly safe but definitely useless. The whole point of building superintelligences is that they’re smart enough to do useful things like cure cancer. But if you have the monks ask the superintelligence for a cancer cure, and it gives them one, that’s a clear security vulnerability. You have a superintelligence locked up in a cave with no way to influence the outside world except that you’re going to mass produce a chemical it gives you and inject it into millions of people.
Or maybe none of this happens, and the superintelligence sits inert in its cave. And then another team somewhere else invents a second superintelligence. And then a third team invents a third superintelligence. Remember, it was only about ten years between Deep Blue beating Kasparov, and everybody having Deep Blue – level chess engines on their laptops. And the first twenty teams are responsible and keep their superintelligences locked in caves with carefully-trained experts, and the twenty-first team is a little less responsible, and now we still have to deal with a rogue superintelligence.
Superintelligences are extremely dangerous, and no normal means of controlling them can entirely remove the danger.
We’re facing the challenge of “Philosophy With A Deadline”.
Many of the problems surrounding superintelligence are the sorts of problems philosophers have been dealing with for centuries. To what degree is meaning inherent in language, versus something that requires external context? How do we translate between the logic of formal systems and normal ambiguous human speech? Can morality be reduced to a set of ironclad rules, and if not, how do we know what it is at all?
Existing answers to these questions are enlightening but nontechnical. The theories of Aristotle, Kant, Mill, Wittgenstein, Quine, and others can help people gain insight into these questions, but are far from formal. Just as a good textbook can help an American learn Chinese, but cannot be encoded into machine language to make a Chinese-speaking computer, so the philosophies that help humans are only a starting point for the project of computers that understand us and share our values.
The field of AI alignment combines formal logic, mathematics, computer science, cognitive science, and philosophy in order to advance that project.
This is the philosophy; the other half of Bostrom’s formulation is the deadline. Traditional philosophy has been going on almost three thousand years; machine goal alignment has until the advent of superintelligence, a nebulous event which may be anywhere from a decades to centuries away.
If the alignment problem doesn’t get adequately addressed by then, we are likely to see poorly aligned superintelligences that are unintentionally hostile to the human race, with some of the catastrophic outcomes mentioned above. This is why so many scientists and entrepreneurs are urging quick action on getting machine goal alignment research up to an adequate level.
If it turns out that superintelligence is centuries away and such research is premature, little will have been lost. But if our projections were too optimistic, and superintelligence is imminent, then doing such research now rather than later becomes vital.
Humanity hasn't yet built a superintelligence, and we might not be able to without significantly more knowledge and computational resources. There could be an existential catastrophe that prevents us from ever building one. For the rest of the answer let's assume no such event stops technological progress.With that out of the way: there is no known good theoretical reason we can't build it at some point in the future; the majority of AI research is geared towards making more capable AI systems; and a significant chunk of top-level AI research attempts to make more generally capable AI systems. There is a clear economic incentive to develop more and more intelligent machines and currently billions of dollars of funding are being deployed for advancing AI capabilities.
We consider ourselves to be generally intelligent (i.e. capable of learning and adapting ourselves to a very wide range of tasks and environments), but the human brain almost certainly isn't the most efficient way to solve problems. One hint is the existence of AI systems with superhuman capabilities at narrow tasks. Not only superhuman performance (as in, AlphaGo beating the Go world champion) but superhuman speed and precision (as in, industrial sorting machines). There is no known discontinuity between tasks, something special and unique about human brains that unlocks certain capabilities which cannot be implemented in machines in principle. Therefore we would expect AI to surpass human performance on all tasks as progress continues.
In addition, several research groups (DeepMind being one of the most overt about this) explicitly aim for generally capable systems. AI as a field is growing, year after year. Critical voices about AI progress usually argue against a lack of precautions around the impact of AI, or against general AI happening very soon, not against it happening at all.
A satire of arguments against the possibility of superintelligence can be found here.
Machines are already smarter than humans are at many specific tasks: performing calculations, playing chess, searching large databanks, detecting underwater mines, and more. However, human intelligence continues to dominate machine intelligence in generality.
A powerful chess computer is “narrow”: it can’t play other games. In contrast, humans have problem-solving abilities that allow us to adapt to new contexts and excel in many domains other than what the ancestral environment prepared us for.
In the absence of a formal definition of “intelligence” (and therefore of “artificial intelligence”), we can heuristically cite humans’ perceptual, inferential, and deliberative faculties (as opposed to, e.g., our physical strength or agility) and say that intelligence is “those kinds of things.” On this conception, intelligence is a bundle of distinct faculties — albeit a very important bundle that includes our capacity for science.
Our cognitive abilities stem from high-level patterns in our brains, and these patterns can be instantiated in silicon as well as carbon. This tells us that general AI is possible, though it doesn’t tell us how difficult it is. If intelligence is sufficiently difficult to understand, then we may arrive at machine intelligence by scanning and emulating human brains or by some trial-and-error process (like evolution), rather than by hand-coding a software agent.
If machines can achieve human equivalence in cognitive tasks, then it is very likely that they can eventually outperform humans. There is little reason to expect that biological evolution, with its lack of foresight and planning, would have hit upon the optimal algorithms for general intelligence (any more than it hit upon the optimal flying machine in birds). Beyond qualitative improvements in cognition, Nick Bostrom notes more straightforward advantages we could realize in digital minds, e.g.:
- editability — “It is easier to experiment with parameter variations in software than in neural wetware.”
- speed — “The speed of light is more than a million times greater than that of neural transmission, synaptic spikes dissipate more than a million times more heat than is thermodynamically necessary, and current transistor frequencies are more than a million times faster than neuron spiking frequencies.”
- serial depth — On short timescales, machines can carry out much longer sequential processes.
- storage capacity — Computers can plausibly have greater working and long-term memory.
- size — Computers can be much larger than a human brain.
- duplicability — Copying software onto new hardware can be much faster and higher-fidelity than biological reproduction.
Any one of these advantages could give an AI reasoner an edge over a human reasoner, or give a group of AI reasoners an edge over a human group. Their combination suggests that digital minds could surpass human minds more quickly and decisively than we might expect.
AI subsystems or regions in gradient descent space that more closely approximate utility maximizers are more stable, and more capable, than those that are less like utility maximizers. Having more agency is a convergent instrument goal and a stable attractor which the random walk of updates and experiences will eventually stumble into.
The stability is because utility maximizer-like systems which have control over their development would lose utility if they allowed themselves to develop into non-utility maximizers, so they tend to use their available optimization power to avoid that change (a special case of goal stability). The capability is because non-utility maximizers are exploitable, and because agency is a general trick which applies to many domains, so might well arise naturally when training on some tasks.
Humans and systems made of humans (e.g. organizations, governments) generally have neither the introspective ability nor self-modification tools needed to become reflectively stable, but we can reasonably predict that in the long run highly capable systems will have these properties. They can then fix in and optimize for their values.
People tend to imagine AIs as being like nerdy humans – brilliant at technology but clueless about social skills. There is no reason to expect this – persuasion and manipulation is a different kind of skill from solving mathematical proofs, but it’s still a skill, and an intellect as far beyond us as we are beyond lions might be smart enough to replicate or exceed the “charming sociopaths” who can naturally win friends and followers despite a lack of normal human emotions.
A superintelligence might be able to analyze human psychology deeply enough to understand the hopes and fears of everyone it negotiates with. Single humans using psychopathic social manipulation have done plenty of harm – Hitler leveraged his skill at oratory and his understanding of people’s darkest prejudices to take over a continent. Why should we expect superintelligences to do worse than humans far less skilled than they?
More outlandishly, a superintelligence might just skip language entirely and figure out a weird pattern of buzzes and hums that causes conscious thought to seize up, and which knocks anyone who hears it into a weird hypnotizable state in which they’ll do anything the superintelligence asks. It sounds kind of silly to me, but then, nuclear weapons probably would have sounded kind of silly to lions sitting around speculating about what humans might be able to accomplish. When you’re dealing with something unbelievably more intelligent than you are, you should probably expect the unexpected.
A superintelligence is a mind that is much more intelligent than any human. Most of the time, it’s used to discuss hypothetical future AIs.
Present-day AI algorithms already demand special safety guarantees when they must act in important domains without human oversight, particularly when they or their environment can change over time:
Achieving these gains [from autonomous systems] will depend on development of entirely new methods for enabling “trust in autonomy” through verification and validation (V&V) of the near-infinite state systems that result from high levels of [adaptability] and autonomy. In effect, the number of possible input states that such systems can be presented with is so large that not only is it impossible to test all of them directly, it is not even feasible to test more than an insignificantly small fraction of them. Development of such systems is thus inherently unverifiable by today’s methods, and as a result their operation in all but comparatively trivial applications is uncertifiable.
It is possible to develop systems having high levels of autonomy, but it is the lack of suitable V&V methods that prevents all but relatively low levels of autonomy from being certified for use.
- Office of the US Air Force Chief Scientist (2010). Technology Horizons: A Vision for Air Force Science and Technology 2010-30.
As AI capabilities improve, it will become easier to give AI systems greater autonomy, flexibility, and control; and there will be increasingly large incentives to make use of these new possibilities. The potential for AI systems to become more general, in particular, will make it difficult to establish safety guarantees: reliable regularities during testing may not always hold post-testing.
The largest and most lasting changes in human welfare have come from scientific and technological innovation — which in turn comes from our intelligence. In the long run, then, much of AI’s significance comes from its potential to automate and enhance progress in science and technology. The creation of smarter-than-human AI brings with it the basic risks and benefits of intellectual progress itself, at digital speeds.
As AI agents become more capable, it becomes more important (and more difficult) to analyze and verify their decisions and goals. Stuart Russell writes:
The primary concern is not spooky emergent consciousness but simply the ability to make high-quality decisions. Here, quality refers to the expected outcome utility of actions taken, where the utility function is, presumably, specified by the human designer. Now we have a problem:
- The utility function may not be perfectly aligned with the values of the human race, which are (at best) very difficult to pin down.
- Any sufficiently capable intelligent system will prefer to ensure its own continued existence and to acquire physical and computational resources – not for their own sake, but to succeed in its assigned task.
A system that is optimizing a function of n variables, where the objective depends on a subset of size k<n, will often set the remaining unconstrained variables to extreme values; if one of those unconstrained variables is actually something we care about, the solution found may be highly undesirable. This is essentially the old story of the genie in the lamp, or the sorcerer’s apprentice, or King Midas: you get exactly what you ask for, not what you want.
Bostrom’s “The Superintelligent Will” lays out these two concerns in more detail: that we may not correctly specify our actual goals in programming smarter-than-human AI systems, and that most agents optimizing for a misspecified goal will have incentives to treat humans adversarially, as potential threats or obstacles to achieving the agent’s goal.
If the goals of human and AI agents are not well-aligned, the more knowledgeable and technologically capable agent may use force to get what it wants, as has occurred in many conflicts between human communities. Having noticed this class of concerns in advance, we have an opportunity to reduce risk from this default scenario by directing research toward aligning artificial decision-makers’ interests with our own.
This is a big question that it would pay to start thinking about. Humans are in control of this planet not because we are stronger or faster than other animals, but because we are smarter! If we cede our position as smartest on our planet, it’s not obvious that we’ll retain control.
Superintelligence has an advantage that an early human didn’t – the entire context of human civilization and technology, there for it to manipulate socially or technologically.
The degree to which an Artificial Superintelligence (ASI) would resemble us depends heavily on how it is implemented, but it seems that differences are unavoidable. If AI is accomplished through whole brain emulation and we make a big effort to make it as human as possible (including giving it a humanoid body), the AI could probably be said to think like a human. However, by definition of ASI it would be much smarter. Differences in the substrate and body might open up numerous possibilities (such as immortality, different sensors, easy self-improvement, ability to make copies, etc.). Its social experience and upbringing would likely also be entirely different. All of this can significantly change the ASI's values and outlook on the world, even if it would still use the same algorithms as we do. This is essentially the "best case scenario" for human resemblance, but whole brain emulation is kind of a separate field from AI, even if both aim to build intelligent machines. Most approaches to AI are vastly different and most ASIs would likely not have humanoid bodies. At this moment in time it seems much easier to create a machine that is intelligent than a machine that is exactly like a human (it's certainly a bigger target).
AI is already superhuman at some tasks, for example numerical computations, and will clearly surpass humans in others as time goes on. We don’t know when (or even if) machines will reach human-level ability in all cognitive tasks, but most of the AI researchers at FLI’s conference in Puerto Rico put the odds above 50% for this century, and many offered a significantly shorter timeline. Since the impact on humanity will be huge if it happens, it’s worthwhile to start research now on how to ensure that any impact is positive. Many researchers also believe that dealing with superintelligent AI will be qualitatively very different from more narrow AI systems, and will require very significant research effort to get right.
Nick Bostrom defines superintelligence as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.” A chess program can outperform humans in chess, but is useless at any other task. Superintelligence will have been achieved when we create a machine that outperforms the human brain across practically any domain.
Except in the case of Whole Brain Emulation, there is no reason to expect a superintelligent machine to have motivations anything like those of humans. Human minds represent a tiny dot in the vast space of all possible mind designs, and very different kinds of minds are unlikely to share to complex motivations unique to humans and other mammals.
Whatever its goals, a superintelligence would tend to commandeer resources that can help it achieve its goals, including the energy and elements on which human life depends. It would not stop because of a concern for humans or other intelligences that is ‘built in’ to all possible mind designs. Rather, it would pursue its particular goal and give no thought to concerns that seem ‘natural’ to that particular species of primate called homo sapiens.
There are, however, some basic instrumental motivations we can expect superintelligent machines to display, because they are useful for achieving its goals, no matter what its goals are. For example, an AI will ‘want’ to self-improve, to be optimally rational, to retain its original goals, to acquire resources, and to protect itself — because all these things help it achieve the goals with which it was originally programmed.
AlphaGo was connected to the Internet – why shouldn’t the first superintelligence be? This gives a sufficiently clever superintelligence the opportunity to manipulate world computer networks. For example, it might program a virus that will infect every computer in the world, causing them to fill their empty memory with partial copies of the superintelligence, which when networked together become full copies of the superintelligence. Now the superintelligence controls every computer in the world, including the ones that target nuclear weapons. At this point it can force humans to bargain with it, and part of that bargain might be enough resources to establish its own industrial base, and then we’re in humans vs. lions territory again.
Satoshi Nakamoto is a mysterious individual who posted a design for the Bitcoin currency system to a cryptography forum. The design was so brilliant that everyone started using it, and Nakamoto – who had made sure to accumulate his own store of the currency before releasing it to the public – became a multibillionaire.
In other words, somebody with no resources except the ability to make posts to Internet forums managed to leverage that into a multibillion dollar fortune – and he wasn’t even superintelligent. If Hitler is a lower-bound on how bad superintelligent persuaders can be, Nakamoto should be a lower-bound on how bad superintelligent programmers with Internet access can be.
Science fiction author Isaac Asimov told stories about robots programmed with the Three Laws of Robotics: (1) a robot may not injure a human being or, through inaction, allow a human being to come to harm, (2) a robot must obey any orders given to it by human beings, except where such orders would conflict with the First Law, and (3) a robot must protect its own existence as long as such protection does not conflict with the First or Second Law. But Asimov’s stories tended to illustrate why such rules would go wrong.
Still, could we program ‘constraints’ into a superintelligence that would keep it from harming us? Probably not.
One approach would be to implement ‘constraints’ as rules or mechanisms that prevent a machine from taking actions that it would normally take to fulfill its goals: perhaps ‘filters’ that intercept and cancel harmful actions, or ‘censors’ that detect and suppress potentially harmful plans within a superintelligence.
Constraints of this kind, no matter how elaborate, are nearly certain to fail for a simple reason: they pit human design skills against superintelligence. A superintelligence would correctly see these constraints as obstacles to the achievement of its goals, and would do everything in its power to remove or circumvent them. Perhaps it would delete the section of its source code that contains the constraint. If we were to block this by adding another constraint, it could create new machines that don’t have the constraint written into them, or fool us into removing the constraints ourselves. Further constraints may seem impenetrable to humans, but would likely be defeated by a superintelligence. Counting on humans to out-think a superintelligence is not a viable solution.
If constraints on top of goals are not feasible, could we put constraints inside of goals? If a superintelligence had a goal of avoiding harm to humans, it would not be motivated to remove this constraint, avoiding the problem we pointed out above. Unfortunately, the intuitive notion of ‘harm’ is very difficult to specify in a way that doesn’t lead to very bad results when used by a superintelligence. If ‘harm’ is defined in terms of human pain, a superintelligence could rewire humans so that they don’t feel pain. If ‘harm’ is defined in terms of thwarting human desires, it could rewire human desires. And so on.
If, instead of trying to fully specify a term like ‘harm’, we decide to explicitly list all of the actions a superintelligence ought to avoid, we run into a related problem: human value is complex and subtle, and it’s unlikely we can come up with a list of all the things we don’t want a superintelligence to do. This would be like writing a recipe for a cake that reads: “Don’t use avocados. Don’t use a toaster. Don’t use vegetables…” and so on. Such a list can never be long enough.
Humans provide an existence prove for the physical possiblity of intelligent systems and there are many advantages computers have (like processing speed and size) such that one would stongly expect AI systems significantly more intelligent than humans to be possible. For an implicitly joking depiction of common arguments for the impossibility of superintelligence see this article. Conditional on technological progress continuing it seems extremely likely that at some point humanity will build superintelligent machines. There is a clear economic incentive to develop more and more intelligent machines and currently billions of dollars of funding are being deployed for advancing AI capabilities. Computers are already superhuman at a variety of tasks such as arithmetic and classifying images and one would expect the number of tasks that machines are capable of performing to continue growing and lead to AI systems far more capable than humans in many domains, especially once AI starts making significant contributions to developing better AI systems. The main reason for why we might never build superintelligent AI then is that humanity went extinct before developing the techonology or stopped techonological progress for some other reason. For an analysis of existential risks which could cause such a scenario see The Precipice.
Nick Bostrom defined ‘superintelligence’ as:
"an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills."
This definition includes vague terms like ‘much’ and ‘practically’, but it will serve as a working definition for superintelligence in this FAQ An intelligence explosion would lead to machine superintelligence, and some believe that an intelligence explosion is the most likely path to superintelligence.
Professor Nick Bostrom is the director of Oxford’s Future of Humanity Institute, tasked with anticipating and preventing threats to human civilization.
He has been studying the risks of artificial intelligence for over twenty years. In his 2014 book Superintelligence, he covers, among other things three major questions:
- First, why is superintelligence a topic of concern
- Second, what is a “hard takeoff” and how does it impact our concern about superintelligence?
- Third, what measures can we take to make superintelligence safe and beneficial for humanity?