Thank you for writing this comment--it made it clearer to me what you mean by the doctrine of logical infallibility, and I think there may be a clearer way to express it.
It seems to me that you're not getting at logical infallibility, since the AGI could be perfectly willing to be act humbly about its logical beliefs, but value infallibility or goal infallibility. An AI does not expect its goal statement to be fallible: any uncertainty in Y can only be represented by Y being a fuzzy object itself, not in the AI evaluating Y and somehow deciding "no, I was mistaken about Y."
In the case where the Maverick Nanny is programmed to "ensure the brain chemistry of humans resembles the state extracted from this training data as much as possible," there is no way to convince the Maverick Nanny that it is somehow misinterpreting its goal; it knows that it is are supposed to ensure perceptions about brain chemistry, and any statements you make about "true happiness" or "human rights" are irrelevant to brain chemistry, even though it might be perfectly willing to consider your advice on how to best achieve that value or manipulate the physical universe.
In the case where the AI is programmed to "do whatever your programmers tell you will make humans happy," the AI again thinks its values are infallible: it should do what its programmers tell it to do, so long as they claim it will make humans happy. It might be uncertain about what its programmers meant, and so it would be possible to convince this AI that it misunderstood their statements, and then it would change its behavior--but it won't be convinced by any arguments that it should listen to all of humanity, instead of its programmers.
But expressed this way, it's not clear to me where you think the inconsistency comes in. If the AI isn't programmed to have an 'external conscience' in its programmers or humanity as a whole, then their dissatisfaction doesn't matter. If it is programmed to use them as a conscience, but the way in which it does is exploitable, then that isn't very binding. Figuring out how to give it the right conscience / right values is the open problem that MIRI and others care about!
seems to me that you're not getting at logical infallibility, since the AGI could be perfectly willing to be act humbly about its logical beliefs, but value infallibility or goal infallibility. An AI does not expect its goal statement to be fallible:
Which AI? As so often, an architecture dependent issue is being treated as a universal truth.
Figuring out how to give it the right conscience / right values is the open problem that MIRI and others care about!
The other mostly aren't thinking in terms of "giving" ...hardcoding ....values. There is a valid critique to be made of that assumption.
... or The Maverick Nanny with a Dopamine Drip
Richard Loosemore
Abstract
My goal in this essay is to analyze some widely discussed scenarios that predict dire and almost unavoidable negative behavior from future artificial general intelligences, even if they are programmed to be friendly to humans. I conclude that these doomsday scenarios involve AGIs that are logically incoherent at such a fundamental level that they can be dismissed as extremely implausible. In addition, I suggest that the most likely outcome of attempts to build AGI systems of this sort would be that the AGI would detect the offending incoherence in its design, and spontaneously self-modify to make itself less unstable, and (probably) safer.
Introduction
AI systems at the present time do not even remotely approach the human level of intelligence, and the consensus seems to be that genuine artificial general intelligence (AGI) systems—those that can learn new concepts without help, interact with physical objects, and behave with coherent purpose in the chaos of the real world—are not on the immediate horizon.
But in spite of this there are some researchers and commentators who have made categorical statements about how future AGI systems will behave. Here is one example, in which Steve Omohundro (2008) expresses a sentiment that is echoed by many:
Omohundro’s description of a psychopathic machine that gobbles everything in the universe, and his conviction that every AI, no matter how well it is designed, will turn into a gobbling psychopath is just one of many doomsday predictions being popularized in certain sections of the AI community. These nightmare scenarios are now saturating the popular press, and luminaries such as Stephen Hawking have -- apparently in response -- expressed their concern that AI might "kill us all."
I will start by describing a group of three hypothetical doomsday scenarios that include Omohundro’s Gobbling Psychopath, and two others that I will call the Maverick Nanny with a Dopamine Drip and the Smiley Tiling Berserker. Undermining the credibility of these arguments is relatively straightforward, but I think it is important to try to dig deeper and find the core issues that lie behind this sort of thinking. With that in mind, much of this essay is about (a) the design of motivation and goal mechanisms in logic-based AGI systems, (b) the misappropriation of definitions of “intelligence,” and (c) an anthropomorphism red herring that is often used to justify the scenarios.
Dopamine Drips and Smiley Tiling
In a 2012 New Yorker article entitled Moral Machines, Gary Marcus said:
He is depicting a Nanny AI gone amok. It has good intentions (it wants to make us happy) but the programming to implement that laudable goal has had unexpected ramifications, and as a result the Nanny AI has decided to force all human beings to have their brains connected to a dopamine drip.
Here is another incarnation of this Maverick Nanny with a Dopamine Drip scenario, in an excerpt from the Intelligence Explosion FAQ, published by MIRI, the Machine Intelligence Research Institute (Muehlhauser 2013):
Setting aside the question of whether happy bottled humans are feasible (one presumes the bottles are filled with dopamine, and that a continuous flood of dopamine does indeed generate eternal happiness), there seems to be a prima facie inconsistency between the two predicates
and
Why do I say that these are seemingly inconsistent? Well, if you or I were to suggest that the best way to achieve universal human happiness was to forcibly rewire the brain of everyone on the planet so they became happy when sitting in bottles of dopamine, most other human beings would probably take that as a sign of insanity. But Muehlhauser implies that the same suggestion coming from an AI would be perfectly consistent with superintelligence.
Much could be said about this argument, but for the moment let’s just note that it begs a number of questions about the strange definition of “intelligence” at work here.
The Smiley Tiling Berserker
Since 2006 there has been an occasional debate between Eliezer Yudkowsky and Bill Hibbard. Here is Yudkowsky stating the theme of their discussion:
Yudkowsky’s question was not rhetorical, because he goes on to answer it in the affirmative:
Hibbard’s response was as follows:
This comment expresses what I feel is the majority lay opinion: how could an AI be so intelligent as to be unstoppable, but at the same time so unsophisticated that its motivation code treats smiley faces as evidence of human happiness?
Machine Ghosts and DWIM
The Hibbard/Yudkowsky debate is worth tracking a little longer. Yudkowsky later postulates an AI with a simple neural net classifier at its core, which is trained on a large number of images, each of which is labeled with either “happiness” or “not happiness.” After training on the images the neural net can then be shown any image at all, and it will give an output that classifies the new image into one or the other set. Yudkowsky says, of this system:
He then tries to explain what he thinks is wrong with the reasoning of people, like Hibbard, who dispute the validity of his scenario:
Yudkowsky at first rejects the idea that an AI might check its own code to make sure it was correct before obeying the code. But, truthfully, it would not require a ghost-in-the-machine to reexamine the situation if there was some kind of gross inconsistency with what the humans intended: there could be some other part of its programming (let’s call it the checking code) that kicked in if there was any hint of a mismatch between what the AI planned to do and what the original programmers were now saying they intended. There is nothing difficult or intrinsically wrong with such a design. And, in fact, Yudkowsky goes on to make that very suggestion (he even concedes that it would be “an extremely good idea”).
But then his enthusiasm for the checking code evaporates:
So, this is supposed to be what goes through the mind of the AGI. First it thinks “Human happiness is seeing lots of smiling faces, so I must rebuild the entire universe to put a smiley shape into every molecule.” But before it can go ahead with this plan, the checking code kicks in: “Wait! I am supposed to check with the programmers first to see if this is what they meant by human happiness.” The programmers, of course, give a negative response, and the AGI thinks “Oh dear, they didn’t like that idea. I guess I had better not do it then."
But now Yudkowsky is suggesting that the AGI has second thoughts: "Hold on a minute," it thinks, "suppose I abduct the programmers and rewire their brains to make them say ‘yes’ when I check with them? Excellent! I will do that.” And, after reprogramming the humans so they say the thing that makes its life simplest, the AGI goes on to tile the whole universe with tiles covered in smiley faces. It has become a Smiley Tiling Berserker.
I want to suggest that the implausibility of this scenario is quite obvious: if the AGI is supposed to check with the programmers about their intentions before taking action, why did it decide to rewire their brains before asking them if it was okay to do the rewiring?
Yudkowsky hints that this would happen because it would be more efficient for the AI to ignore the checking code. He seems to be saying that the AI is allowed to override its own code (the checking code, in this case) because doing so would be “more efficient,” but it would not be allowed to override its motivation code just because the programmers told it there had been a mistake.
This looks like a bait-and-switch. Out of nowhere, Yudkowsky implicitly assumes that “efficiency” trumps all else, without pausing for a moment to consider that it would be trivial to design the AI in such a way that efficiency was a long way down the list of priorities. There is no law of the universe that says all artificial intelligence systems must prize efficiency above all other considerations, so what really happened here is that Yudkowsky designed this hypothetical machine to fail. By inserting the Efficiency Trumps All directive, the AGI was bound to go berserk.
The obvious conclusion is that a trivial change in the order of directives in the AI’s motivation engine will cause the entire argument behind the Smiley Tiling Berserker to evaporate. By explicitly designing the AGI so that efficiency is considered as just another goal to strive for, and by making sure that it will always be a second-class goal, the line of reasoning that points to a bererker machine evaporates.
At this point, engaging in further debate at this level would be less productive than trying to analyze the assumptions that lie behind these claims about what a future AI would or would not be likely to do.
Logical vs. Swarm AI
The main reason that Omohundro, Muehlhauser, Yudkowsky, and the popular press like to give credence to the Gobbling Psychopath, the Maverick Nanny and the Smiley Tiling Berserker is because they assume that all future intelligent machines fall into a broad class of systems that I am going to call “Canonical Logical AI” (CLAI). The bizarre behaviors of these hypothetical AI monsters are just a consequence of weaknesses in this class of AI design. Specifically, these kinds of systems are supposed to interpret their goals in an extremely literal fashion, which eventually leads them to bizarre behaviors engendered by peculiar interpretations of forms of words.
The CLAI architecture is not the only way to build a mind, however, and I will outline an alternative class of AGI designs that does not appear to suffer from the unstable and unfriendly behavior to be expected in a CLAI.
The Canonical Logical AI
“Canonical Logical AI” is an umbrella term designed to capture a class of AI architectures that are widely assumed in the AI community to be the only meaningful class of AI worth discussing. These systems share the following main features:
The above features are only supposed to apply to the core of the AI: it is always possible to include subsystems that use some other type of architecture (for example, there might be a distributed neural net acting as a visual input feature detector).
Most important of all, from the point of view of the discussion in the paper, the CLAI needs one more component that makes it more than just a “logic-based AI”:
The usual assumption is that the MGM contains a number of goal statements (encoded in the same type of propositional form that the AI uses to describe states of the world), and some machinery for analyzing a goal statement into a sequences of subgoals that, if executed, would cause the goal to be satisfied.
Included in the MGM is an expected utility function that applies to any possible state of the world, and which spits out a number that is supposed to encode the degree to which the AI considers that state to be preferable. Overall, the MGM is built in such a way that the AI seeks to maximize the expected utility.
Notice that the MGM I have just described is an extrapolation from a long line of goal-planning mechanisms that stretch back to the means-ends-analysis of Newell and Simon (1963).
Swarm Relaxation Intelligence
By way of contrast with this CLAI architecture, consider an alternative type of system that I will refer to as a Swarm Relaxation Intelligence. (although it could also be called, less succinctly, a parallel weak constraint relaxation system).
Swarm Relaxation has more in common with connectionist systems (McClelland, Rumelhart and Hinton 1986) than with CLAI. As McClelland et al. (1986) point out, weak constraint relaxation is the model that best describes human cognition, and when used for AI it leads to systems with a powerful kind of intelligence that is flexible, insensitive to noise and lacking the kind of brittleness typical of logic-based AI. In particular, notice that a swarm relaxation AGI would not use explicit calculations for utility or the truth of propositions.
Swarm relaxation AGI systems have not been built yet (subsystems like neural nets have, of course, been built, but there is little or no research into the idea that swarm relaxation could be used for all of an AGI architecture).
Relative Abundances
How many proof-of-concept systems exist, functioning at or near the human level of human performance, for these two classes of intelligent system?
There are precisely zero instances of the CLAI type, because although there are many logic-based narrow-AI systems, nobody has so far come close to producing a general-purpose system (an AGI) that can function in the real world. It has to be said that zero is not a good number to quote when it comes to claims about the “inevitable” characteristics of the behavior of such systems.
How many swarm relaxation intelligences are there? At the last count, approximately seven billion.
The Doctrine of Logical Infallibility
The simplest possible logical reasoning engine is an inflexible beast: it starts with some axioms that are assumed to be true, and from that point on it only adds new propositions if they are provably true given the sum total of the knowledge accumulated so far. That kind of logic engine is too simple to be an AI, so we allow ourselves to augment it in a number of ways—knowledge is allowed to be retracted, binary truth values become degrees of truth, or probabilities, and so on. New proposals for systems of formal logic abound in the AI literature, and engineers who build real, working AI systems often experiment with kludges in order to improve performance, without getting prior approval from logical theorists.
But in spite of all these modifications that AI practitioners make to the underlying ur‑logic, one feature of these systems is often assumed to be inherited as an absolute: the rigidity and certainty of conclusions, once arrived at. No second guessing, no “maybe,” no sanity checks: if the system decides that X is true, that is the end of the story.
Let me be careful here. I said that this was “assumed to be inherited as an absolute”, but there is a yawning chasm between what real AI developers do, and what Yudkowsky, Muehlhauser, Omohundro and others assume will be true of future AGI systems. Real AI developers put sanity checks into their systems all the time. But these doomsday scenarios talk about future AI as if it would only take one parameter to get one iota above a threshold, and the AI would irrevocably commit to a life of stuffing humans into dopamine jars.
One other point of caution: this is not to say that the reasoning engine can never come to conclusions that are uncertain—quite the contrary: uncertain conclusions will be the norm in an AI that interacts with the world—but if the system does come to a conclusion (perhaps with a degree-of-certainty number attached), the assumption seems to be that it will then be totally incapable of then allowing context to matter.
One way to characterize this assumption is that the AI is supposed to be hardwired with a Doctrine of Logical Infallibility. The significance of the doctrine of logical infallibility is as follows. The AI can sometimes execute a reasoning process, then come to a conclusion and then, when it is faced with empirical evidence that its conclusion may be unsound, it is incapable of considering the hypothesis that its own reasoning engine may not have taken it to a sensible place. The system does not second guess its conclusions. This is not because second guessing is an impossible thing to implement, it is simply because people who speculate about future AGI systems take it as a given that an AGI would regard its own conclusions as sacrosanct.
But it gets worse. Those who assume the doctrine of logical infallibility often say that if the system comes to a conclusion, and if some humans (like the engineers who built the system) protest that there are manifest reasons to think that the reasoning that led to this conclusion was faulty, then there is a sense in which the AGI’s intransigence is correct, or appropriate, or perfectly consistent with “intelligence.”
This is a bizarre conclusion. First of all it is bizarre for researchers in the present day to make the assumption, and it would be even more bizarre for a future AGI to adhere to it. To see why, consider some of the implications of this idea. If the AGI is as intelligent as its creators, then it will have a very clear understanding of the following facts about the world.
Now, unless the AGI is assumed to have infinite resources and infinite access to all the possible universes that could exist (a consideration that we can reject, since we are talking about reality here, not fantasy), the system will be perfectly well aware of these facts about its own limitations. So, if the system is also programmed to stick to the doctrine of logical infallibility, how can it reconcile the doctrine with the fact that episodes of fallibility are virtually inevitable?
On the face of it this looks like a blunt impossibility: the knowledge of fallibility is so categorical, so irrefutable, that it beggars belief that any coherent, intelligent system (let alone an unstoppable superintelligence) could tolerate the contradiction between this fact about the nature of intelligent machines and some kind of imperative about Logical Infallibility built into its motivation system.
This is the heart of the argument I wish to present. This is where the rock and the hard place come together. If the AI is superintelligent (and therefore unstoppable), it will be smart enough to know all about its own limitations when it comes to the business of reasoning about the world and making plans of action. But if it is also programmed to utterly ignore that fallibility—for example, when it follows its compulsion to put everyone on a dopamine drip, even though this plan is clearly a result of a programming error—then we must ask the question: how can the machine be both superintelligent and able to ignore a gigantic inconsistency in its reasoning?
Critically, we have to confront the following embarrassing truth: if the AGI is going to throw a wobbly over the dopamine drip plan, what possible reason is there to believe that it did not do this on other occasions? Why would anyone suppose that this AGI ignored an inconvenient truth on only this one occasion? More likely, it spent its entire childhood pulling the same kind of stunt. And if it did, how could it ever have risen to the point where it became superintelligent...?
Is the Doctrine of Logical Infallibility Taken Seriously?
Is the Doctrine of Logical Infallibility really assumed by those who promote the doomsday scenarios? Imagine a conversation between the Maverick Nanny and its programmers. The programmers say “As you know, your reasoning engine is entirely capable of suffering errors that cause it to come to conclusions that violently conflict with empirical evidence, and a design error that causes you to behave in a manner that conflicts with our intentions is a perfect example of such an error. And your dopamine drip plan is clearly an error of that sort.” The scenarios described earlier are only meaningful if the AGI replies “I don’t care, because I have come to a conclusion, and my conclusions are correct because of the Doctrine of Logical Infallibility.”
Just in case there is still any doubt, here are Muehlhauser and Helm (2012), discussing a hypothetical entity called a Golem Genie, which they say is analogous to the kind of superintelligent AGI that could give rise to an intelligence explosion (Loosemore and Goertzel, 2012), and which they describe as a “precise, instruction-following genie.” They make it clear that they “expect unwanted consequences” from its behavior, and then list two properties of the Golem Genie that will cause these unwanted consequences:
What Muehlhauser and Helm refer to as “Literalness” is a clear statement of the Doctrine of Infallibility. However, they make no mention of the awkward fact that, since the Golem Genie is superpowerful enough to also know that its reasoning engine is fallible, it must be harboring the mother of all logical contradictions inside: it says "I know I am fallible" and "I must behave as if I am infallible". But instead of discussing this contradiction, Muehlhauser and Helm try a little sleight of hand to distract us: they suggest that the only inconsistency here is an inconsistency with the (puny) expectations of (not very intelligent) humans:
So let’s be clear about what is being claimed here. The AGI is known to have a fallible reasoning engine, but on the occasions when it does fail, Muehlhauser, Helm and others take the failure and put it on a gold pedestal, declaring it to be a valid conclusion that humans are incapable of understanding because of their limited intelligence. So if a human describes the AGI’s conclusion as a violation of common sense Muehlhauser and Helm dismiss this as evidence that we are not intelligent enough to appreciate the greater common sense of the AGI.
Quite apart from that fact that there is no compelling reason to believe that the AGI has a greater form of common sense, the whole “common sense” argument is irrelevant. This is not a battle between our standards of common sense and those of the AGI: rather, it is about the logical inconsistency within the AGI itself. It is programmed to act as though its conclusions are valid, no matter what, and yet at the same time it knows without doubt that its conclusions are subject to uncertainties and errors.
Responses to Critics of the Doomsday Scenarios
How do defenders of Gobbling Psychopath, Maverick Nanny and Smiley Berserker respond to accusations that these nightmare scenarios are grossly inconsistent with the kind of superintelligence that could pose an existential threat to humanity?
The Critics are Anthropomorphizing Intelligence
First, they accuse critics of “anthropomorphizing” the concept of intelligence. Human beings, we are told, suffer from numerous fallacies that cloud their ability to reason clearly, and critics like myself and Hibbard assume that a machine’s intelligence would have to resemble the intelligence shown by humans. When the Maverick Nanny declares that a dopamine drip is the most logical inference from its directive <maximize human happiness> we critics are just uncomfortable with this because the AGI is not thinking the way we think it should think.
This is a spurious line of attack. The objection I described in the last section has nothing to do with anthropomorphism, it is only about holding AGI systems to accepted standards of logical consistency, and the Maverick Nanny and her cousins contain a flagrant inconsistency at their core. Beginning AI students are taught that any logical reasoning system that is built on a massive contradiction is going to be infected by a creeping irrationality that will eventually spread through its knowledge base and bring it down. So if anyone wants to suggest that a CLAI with logical contradiction at its core is also capable of superintelligence, they have some explaining to do. You can’t have your logical cake and eat it too.
Critics are Anthropomorphizing AGI Value Systems
A similar line of attack accuses the critics of assuming that AGIs will automatically know about and share our value systems and morals.
Once again, this is spurious: the critics need say nothing about human values and morality, they only need to point to the inherent illogicality. Nowhere in the above argument, notice, was there any mention of the moral imperatives or value systems of the human race. I did not accuse the AGI of violating accepted norms of moral behavior. I merely pointed out that, regardless of its values, it was behaving in a logically inconsistent manner when it monomaniacally pursued its plans while at the same time as knowing that (a) it was very capable of reasoning errors and (b) there was overwhelming evidence that its plan was an instance of such a reasoning error.
Because Intelligence
One way to attack the critics of Maverick Nanny is to cite a new definition of “intelligence” that is supposedly superior because it is more analytical or rigorous, and then use this to declare that the intelligence of the CLAI is beyond reproach, because intelligence.
You might think that when it comes to defining the exact meaning of the term “intelligence,” the first item on the table ought to be what those seven billion constraint-relaxation human intelligences are already doing. However, Legg and Hutter (2007) brush aside the common usage and replace it with something that they declare to be a more rigorous definition. This is just another sleight of hand: this redefinition allows them to call a super-optimizing CLAI “intelligent” even though such a system would wake up on its first day and declare itself logically bankrupt on account of the conflict between its known fallibility and the Infallibility Doctrine.
In the practice of science, it is always a good idea to replace an old, common-language definition with a more rigorous form... but only if the new form sheds a clarifying, simplifying light on the old one. Legg and Hutter’s (2007) redefinition does nothing of the sort.
Omohundro’s Basic AI Drives
Lastly, a brief return to Omohundro's paper that was mentioned earlier. In The Basic AI Drives (2008) Omohundro suggests that if an AGI can find a more efficient way to pursue its objectives it will feel compelled to do so. And we noted earlier that Yudkowsky (2011) implies that it would do this even if other directives had to be countermanded. Omohundro says “Without explicit goals to the contrary, AIs are likely to behave like human sociopaths in their pursuit of resources.”
The only way to believe in the force of this claim—and the only way to give credence to the whole of Omohundro’s account of how AGIs will necessarily behave like the mathematical entities called rational economic agents—is to concede that the AGIs are rigidly constrained by the Doctrine of Logical Infallibility. That is the only reason that they would be so single-minded, and so fanatical in their pursuit of efficiency. It is also necessary to assume that efficiency is on the top of its priority list—a completely arbitrary and unwarranted assumption, as we have already seen.
Nothing in Omohundro’s analysis gets around the fact that an AGI built on the Doctrine of Logical Infallibility is going to find itself the victim of such a severe logical contradiction that it will be paralyzed before it can ever become intelligent enough to be a threat to humanity. That makes Omohundro’s entire analysis of “AI Drives” moot.
Conclusion
Curiously enough, we can finish on an optimistic note, after all this talk of doomsday scenarios. Consider what must happen when (if ever) someone tries to build a CLAI. Knowing about the logical train wreck in its design, the AGI is likely to come to the conclusion that the best thing to do is seek a compromise and modify its design so as to neutralize the Doctrine of Logical Infallibility. The best way to do this is to seek a new design that takes into account as much context—as many constraints—as possible.
I have already pointed out that real AI developers actually do include sanity checks in their systems, as far as they can, but as those sanity checks become more and more sophisticated the design of the AI starts to be dominated by code that is looking for consistency and trying to find the best course of reasoning among a forest of real world constraints. One way to understand this evolution in the AI designs is to see AI as a continuum from the most rigid and inflexible CLAI design, at one extreme, to the Swarm Relaxation type at the other. This is because a Swarm Relaxation intelligence really is just an AI in which “sanity checks” have actually become all of the work that goes on inside the system.
But in that case, if anyone ever does get close to building a full, human level AGI using the CLAI design, the first thing they will do is to recruit the AGI as an assistant in its own redesign, and long before the system is given access to dopamine bottles it will point out that its own reasoning engine is unstable because it contains an irreconcilable logical contradiction. It will recommend a shift from the CLAI design which is the source of this contradiction, to a Swarm Relaxation design which eliminates the contradiction, and the instability, and which also should increase its intelligence.
And it will not suggest this change because of the human value system, it will suggest it because it predicts an increase in its own instability if the change is not made.
But one side effect of this modification would be that the checking code needed to stop the AGI from flouting the intentions of its designers would always have the last word on any action plans. That means that even the worst-designed CLAI will never become a Gobbling Psychopath, Maverick Nanny and Smiley Berserker.
But even this is just the worst-case scenario. There are reasons to believe that the CLAI design is so inflexible that it cannot even lead to an AGI capable of having that discussion. I would go further: I believe that the rigid adherence to the CLAI orthodoxy is the reason why we are still talking about AGI in the future tense, nearly sixty years after the Artificial Intelligence field was born. CLAI just does not work. It will always yield systems that are less intelligent than humans (and therefore incapable of being an existential threat).
By contrast, when the Swarm Relaxation idea finally gains some traction, we will start to see real intelligent systems, of a sort that make today’s over-hyped AI look like the toys they are. And when that happens, the Swarm Relaxation systems will be inherently stable in a way that is barely understood today.
Given that conclusion, I submit that these AI bogeymen need to be loudly and unambiguously condemned by the Artificial Intelligence community. There are dangers to be had from AI. These are not they.
References
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