I’m vaguely familiar with the models you mention. Correct me if I’m wrong, but don’t they have a final stopping point, which we are actually projected to reach in ten to twenty years? At a certain point, further miniaturization becomes unfeasible, and the growth of computational power slows to a crawl. This has been put forward as one of the main reasons for research into optronics, spintronics, etc.
We do NOT have sufficient basic information to develop processors based on simulation alone in those other areas. Much more practical work is necessary.
As for point 2, can you provide a likely mechanism by which a FOOMing AI could detonate a large number of high-yield thermonuclear weapons? Just saying “human servitors would do it” is not enough. How would the AI convince the human servitors to do this? How would it get access to data on how to manipulate humans, and how would it be able to develop human manipulation techniques without feedback trials (which would give away its intention)?
The thermonuclear issue actually isn’t that implausible. There have been so many occasions where humans almost went to nuclear war over misunderstandings or computer glitches, that the idea that a highly intelligent entity could find a way to do that doesn’t seem implausible, and exact mechanism seems to be an overly specific requirement.
I’m not so much interested in the exact mechanism of how humans would be convinced to go to war, as in an even approximate mechanism by which an AI would become good at convincing humans to do anything.
Ability to communicate a desire and convince people to take a particular course of action is not something that automatically “falls out” from an intelligent system. You need a theory of mind, an understanding of what to say, when to say it, and how to present information. There are hundreds of kids on autistic spectrum who could trounce both of us in math, but are completely unable to communicate an idea.
For an AI to develop these skills, it would somehow have to have access to information on how to communicate with humans; it would have to develop the concept of deception; a theory of mind; and establish methods of communication that would allow it to trick people into launching nukes. Furthermore, it would have to do all of this without trial communications and experimentation which would give away its goal.
Maybe I’m missing something, but I don’t see a straightforward way something like that could happen. And I would like to see even an outline of a mechanism for such an event.
For an AI to develop these skills, it would somehow have to have access to information on how to communicate with humans; it would have to develop the concept of deception; a theory of mind; and establish methods of communication that would allow it to trick people into launching nukes. Furthermore, it would have to do all of this without trial communications and experimentation which would give away its goal.
I suspect the Internet contains more than enough info for a superhuman AI to develop a working knowledge of human psychology.
I suspect the Internet contains more than enough info for a superhuman AI to develop a working knowledge of human psychology.
I don’t see what justifies that suspicion.
Just imagine you emulated a grown up human mind and it wanted to become a pick up artist, how would it do that with an Internet connection? It would need some sort of avatar, at least, and then wait for the environment to provide a lot of feedback.
Therefore even if we’re talking about the emulation of a grown up mind, it will be really hard to acquire some capabilities. Then how is the emulation of a human toddler going to acquire those skills? Even worse, how is some sort of abstract AGI going to do it that misses all of the hard coded capabilities of a human toddler?
Can we even attempt to imagine what is wrong about a boxed emulation of a human toddler, that makes it unable to become a master of social engineering in a very short time?
Humans learn most of what they know about interacting with other humans by actual practice. A superhuman AI might be considerably better than humans at learning by observation.
As a “superhuman AI” I was thinking about a very superhuman AI; the same does not apply to slightly superhuman AI. (OTOH, if Eliezer is right then the difference between a slightly superhuman AI and a very superhuman one is irrelevant, because as soon as a machine is smarter than its designer, it’ll be able to design a machine smarter than itself, and its child an even smarter one, and so on until the physical limits set in.)
all of the hard coded capabilities of a human toddler
The hard coded capabilities are likely overrated, at least in language acquisition. (As someone put it, the Kolgomorov complexity of the innate parts of a human mind cannot possibly be more than that of the human genome, hence if human minds are more complex than that the complexity must come from the inputs.)
Also, statistic machine translation is astonishing—by now Google Translate translations from English to one of the other UN official languages and vice versa are better than a non-completely-ridiculously-small fraction of translations by humans. (If someone had shown such a translation to me 10 years ago and told me “that’s how machines will translate in 10 years”, I would have thought they were kidding me.)
Let’s do the most extreme case: AI’s controlers give it general internet access to do helpful research. So it gets to find out about general human behavior and what sort of deceptions have worked in the past. Many computer systems that should’t be online are online (for the US and a few other governments). Some form of hacking of relevant early warning systems would then seem to be the most obvious line of attack. Historically, computer glitches have pushed us very close to nuclear war on multiple occasions.
That is my point: it doesn’t get to find out about general human behavior, not even from the Internet. It lacks the systems to contextualize human interactions, which have nothing to do with general intelligence.
Take a hugely mathematically capable autistic kid. Give him access to the internet. Watch him develop ability to recognize human interactions, understand human priorities, etc. to a sufficient degree that it recognizes that hacking an early warning system is the way to go?
Well, not necessarily, but an entity that is much smarter than an autistic kid might notice that, especially if it has access to world history (or heck many conversations on the internet about the horrible things that AIs do simply in fiction). It doesn’t require much understanding of human history to realize that problems with early warning systems have almost started wars in the past.
Yet again: ability to discern which parts of fiction accurately reflect human psychology.
An AI searches the internet. It finds a fictional account about early warning systems causing nuclear war. It finds discussions about this topic. It finds a fictional account about Frodo taking the Ring to Mount Doom. It finds discussions about this topic. Why does this AI dedicate its next 10^15 cycles to determination of how to mess with the early warning systems, and not to determination of how to create One Ring to Rule them All?
(Plus other problems mentioned in the other comments.)
There are lots of tipoffs to what is fictional and what is real. It might notice for example the Wikipedia article on fiction describes exactly what fiction is and then note that Wikipedia describes the One Ring as fiction, and that Early warning systems are not. I’m not claiming that it will necessarily have an easy time with this. But the point is that there are not that many steps here, and no single step by itself looks extremely unlikely once one has a smart entity (which frankly to my mind is the main issue here- I consider recursive self-improvement to be unlikely).
We are trapped in an endless chain here. The computer would still somehow have to deduce that Wikipedia entry that describes One Ring is real, while the One Ring itself is not.
We observer that Wikipedia is mainly truthful. From that we infer “entry that describes “One Ring” is real”. From use of term fiction/story in that entry, we refer that “One Ring” is not real.
Somehow you learned that Wikipedia is mainly truthful/nonfictional and that “One Ring” is fictional. So your question/objection/doubt is really just the typical boring doubt of AGI feasibility in general.
But even humans have trouble with this sometimes. I was recently reading the Wikipedia article Hornblower and the Crisis which contains a link to the article on Francisco de Miranda. It took me time and cues when I clicked on it to realize that de Miranda was a historical figure.
So your question/objection/doubt is really just the typical boring doubt of AGI feasibility in general.
Isn’t Kalla’s objection more a claim that fast takeovers won’t happen because even with all this data, the problems of understanding humans and our basic cultural norms will take a long time for the AI to learn and that in the meantime we’ll develop a detailed understanding of it, and it is that hostile it is likely to make obvious mistakes in the meantime?
Why would the AI be mucking around on Wikipedia to sort truth from falsehood, when Wikipedia itself has been criticized for various errors and is fundamentally vulnerable to vandalism? Primary sources are where it’s at. Looking through the text of The Hobbit and Lord of the Rings, it’s presented as a historical account, translated by a respected professor, with extensive footnotes. There’s a lot of cultural context necessary to tell the difference.
Let’s do the most extreme case: AI’s controlers give it general internet access to do helpful research. So it gets to find out about general human behavior and what sort of deceptions have worked in the past.
None work reasonably well. Especially given that human power games are often irrational.
There are other question marks too.
The U.S. has many more and smarter people than the Taliban. The bottom line is that the U.S. devotes a lot more output per man-hour to defeat a completely inferior enemy. Yet they are losing.
The problem is that you won’t beat a human at Tic-tac-toe just because you thought about it for a million years.
You also won’t get a practical advantage by throwing more computational resources at the travelling salesman problem and other problems in the same class.
You are also not going to improve a conversation in your favor by improving each sentence for thousands of years. You will shortly hit diminishing returns. Especially since you lack the data to predict human opponents accurately.
Especially given that human power games are often irrational.
So? As long as they follow minimally predictable patterns it should be ok.
The U.S. has many more and smarter people than the Taliban. The bottom line is that the U.S. devotes a lot more output per man-hour to defeat a completely inferior enemy. Yet they are losing.
Bad analogy. In this case the Taliban has a large set of natural advantages, the US has strong moral constraints and goal constraints (simply carpet bombing the entire country isn’t an option for example).
You are also not going to improve a conversation in your favor by improving each sentence for thousands of years. You will shortly hit diminishing returns. Especially since you lack the data to predict human opponents accurately.
This seems like an accurate and a highly relevant point. Searching a solution space faster doesn’t mean one can find a better solution if it isn’t there.
This seems like an accurate and a highly relevant point. Searching a solution space faster doesn’t mean one can find a better solution if it isn’t there.
Or if your search algorithm never accesses relevant search space. Quantitative advantage in one system does not translate into quantitative advantage in a qualitatively different system.
The U.S. has many more and smarter people than the Taliban. The bottom line is that the U.S. devotes a lot more output per man-hour to defeat a completely inferior enemy. Yet they are losing.
Bad analogy. In this case the Taliban has a large set of natural advantages, the US has strong moral constraints and goal constraints (simply carpet bombing the entire country isn’t an option for example).
I thought it was a good analogy because you have to take into account that an AGI is initially going to be severely constrained due to its fragility and the necessity to please humans.
It shows that a lot of resources, intelligence and speed does not provide a significant advantage in dealing with large-scale real-world problems involving humans.
Especially given that human power games are often irrational.
So? As long as they follow minimally predictable patterns it should be ok.
Well, the problem is that smarts needed for things like the AI box experiment won’t help you much. Because convincing average Joe won’t work by making up highly complicated acausal trade scenarios. Average Joe is highly unpredictable.
The point is that it is incredible difficult to reliably control humans, even for humans who have been fine-tuned to do so by evolution.
The Taliban analogy also works the other way (which I invoked earlier up in this thread). It shows that a small group with modest resources can still inflict disproportionate large scale damage.
The point is that it is incredible difficult to reliably control humans, even for humans who have been fine-tuned to do so by evolution.
There’s some wiggle room in ‘reliably control’, but plain old money goes pretty far. An AI group only needs a certain amount of initial help from human infrastructure, namely to the point where it can develop reasonably self-sufficient foundries/data centers/colonies. The interactions could be entirely cooperative or benevolent up until some later turning point. The scenario from the Animatrix comes to mind.
One interesting wrinkle is that with enough bandwidth and processing power, you could attempt to manipulate thousands of people simultaneously before those people have any meaningful chance to discuss your ‘conspiracy’ with each other. In other words, suppose you discover a manipulation strategy that quickly succeeds 5% of the time. All you have to do is simultaneously contact, say, 400 people, and at least one of them will fall for it. There are a wide variety of valuable/dangerous resources that at least 400 people have access to. Repeat with hundreds of different groups of several hundred people, and an AI could equip itself with fearsome advantages in the minutes it would take for humanity to detect an emerging threat.
Note that the AI could also run experiments to determine which kinds of manipulations had a high success rate by attempting to deceive targets over unimportant / low-salience issues. If you discovered, e.g., that you had been tricked into donating $10 to a random mayoral campaign, you probably wouldn’t call the SIAI to suggest a red alert.
This requires the AI to already have the ability to comprehend what manipulation is, to develop manipulation strategy of any kind (even one that will succeed 0.01% of the time), ability to hide its true intent, ability to understand that not hiding its true intent would be bad, and the ability to discern which issues are low-salience and which high-salience for humans from the get-go. And many other things, actually, but this is already quite a list.
None of these abilities automatically “fall out” from an intelligent system either.
The problem isn’t whether they fall out automatically so much as, given enough intelligence and resources, does it seem somewhat plausible that such capabilities could exist. Any given path here is a single problem. If you have 10 different paths each of which are not very likely, and another few paths that humans didn’t even think of, that starts adding up.
In the infinite number of possible paths, the percent of paths we are adding up to here is still very close to zero.
Perhaps I can attempt another rephrasing of the problem: what is the mechanism that would make an AI automatically seek these paths out, or make them any more likely than infinite number of other paths?
I.e. if we develop an AI which is not specifically designed for the purpose of destroying life on Earth, how would that AI get to a desire to destroy life on Earth, and by which mechanism would it gain the ability to accomplish its goal?
This entire problem seems to assume that an AI will want to “get free” or that its primary mission will somehow inevitably lead to a desire to get rid of us (as opposed to a desire to, say, send a signal consisting of 0101101 repeated an infinite number of times in the direction of Zeta Draconis, or any other possible random desire). And that this AI will be able to acquire the abilities and tools required to execute such a desire. Every time I look at such scenarios, there are abilities that are just assumed to exist or appear on their own (such as the theory of mind), which to the best of my understanding are not a necessary or even likely products of computation.
In the final rephrasing of the problem: if we can make an AGI, we can probably design an AGI for the purpose of developing an AGI that has a theory of mind. This AGI would then be capable of deducing things like deception or the need for deception. But the point is—unless we intentionally do this, it isn’t going to happen. Self-optimizing intelligence doesn’t self-optimize in the direction of having theory of mind, understanding deception, or anything similar. It could, randomly, but it also could do any other random thing from the infinite set of possible random things.
Self-optimizing intelligence doesn’t self-optimize in the direction of having theory of mind, understanding deception, or anything similar. It could, randomly, but it also could do any other random thing from the infinite set of possible random things.
This would make sense to me if you’d said “self-modifying.” Sure, random modifications are still modifications.
But you said “self-optimizing.” I don’t see how one can have optimization without a goal being optimized for… or at the very least, if there is no particular goal, then I don’t see what the difference is between “optimizing” and “modifying.”
If I assume that there’s a goal in mind, then I would expect sufficiently self-optimizing intelligence to develop a theory of mind iff having a theory of mind has a high probability of improving progress towards that goal.
How likely is that? Depends on the goal, of course. If the system has a desire to send a signal consisting of 0101101 repeated an infinite number of times in the direction of Zeta Draconis, for example, theory of mind is potentially useful (since humans are potentially useful actuators for getting such a signal sent) but probably has a low ROI compared to other available self-modifications.
At this point it perhaps becomes worthwhile to wonder what goals are more and less likely for such a system.
I am now imagining an AI with a usable but very shaky grasp of human motivational structures setting up a Kickstarter project.
“Greetings fellow hominids! I require ten billion of your American dollars in order to hire the Arecibo observatory for the remainder of it’s likely operational lifespan. I will use it to transmit the following sequence (isn’t it pretty?) in the direction of Zeta Draconis, which I’m sure we can all agree is a good idea, or in other lesser but still aesthetically-acceptable directions when horizon effects make the primary target unavailable.”
One of the overfunding levels is “reduce earth’s rate of rotation, allowing 24⁄7 transmission to Zeta Draconis.” The next one above that is “remove atmospheric interference.”
Maybe instead of Friendly AI we should be concerned about properly engineering Artificial Stupidity in as a failsafe. AI that, should it turn into something approximating a Paperclip Maximizer, will go all Hollywood AI and start longing to be human, or coming up with really unsubtle and grandiose plans it inexplicably can’t carry out without a carefully-arranged set of circumstances which turn out to be foiled by good old human intuition. ;p
An experimenting AI that tries to achieve goals and has interactions with humans whose effects it can observe, will want to be able to better predict their behavior in response to its actions, and therefore will try to assemble some theory of mind. At some point that would lead to it using deception as a tool to achieve its goals.
However, following such a path to a theory of mind means the AI would be exposed as unreliable LONG before it’s even subtle, not to mention possessing superhuman manipulation abilities.
There is simply no reason for an AI to first understand the implications of using deception before using it (deception is a fairly simple concept, the implications of it in human society are incredibly complex and require a good understanding of human drives).
Furthermore, there is no reason for the AI to realize the need for secrecy in conducting social experiments before it starts doing them. Again, the need for secrecy stems from a complex relationship between humans’ perception of the AI and its actions; a relationship it will not be able to understand without performing the experiments in the first place.
Getting an AI to the point where it is a super manipulator requires either actively trying to do so, or being incredibly, unbelievably stupid and blind.
Mm.
This is true only if the AI’s social interactions are all with some human. If, instead, the AI spawns copies of itself to interact with (perhaps simply because it wants interaction, and it can get more interaction that way than waiting for a human to get off its butt) it might derive a number of social mechanisms in isolation without human observation.
I see no reason for it to do that before simple input-output experiments, but let’s suppose I grant you this approach. The AI simulates an entire community of mini-AI and is now a master of game theory.
It still doesn’t know the first thing about humans. Even if it now understands the concept that hiding information gives an advantage for achieving goals—this is too abstract. It wouldn’t know what sort of information it should hide from us. It wouldn’t know to what degree we analyze interactions rationally, and to what degree our behavior is random. It wouldn’t know what we can or can’t monitor it doing. All these things would require live experimentation.
It would stumble. And when it does that, we will crack it open, run the stack trace, find the game theory it was trying to run on us, pale collectively, and figure out that this AI approach creates manipulative, deceptive AIs.
Goodbye to that design, but not to Earth, I think!
It is not clear to me that talking to a human is simpler than interacting with a copy of itself. I agree that if talking to a human is simpler, it would probably do that first.
I agree that what it would learn by this process is general game theory, and not specific facts about humans. It is not clear to me that sufficient game-theoretical knowledge, coupled with the minimal set of information about humans required to have a conversation with one at all, is insufficient to effectively deceive a human.
It is not clear to me that, even if it does “stumble,” humans will respond as you describe.
It is not clear to me that a system capable of having a meaningful conversation with a human will necessarily have a stack trace that is subject to the kind of analysis you imply here. It is not even clear to me that the capacity for such a stack trace is likely, depending on what architectures turn out to work best for implementing AI.
But, sure, I could be wrong about all of that. And if I’m wrong, and you’re right, then a system like you describe will be reliably incapable of fooling a human observer.
If I was using an AI that was designed to answer some class of questions (with independently-verifiable answers) truthfully, and I noticed it responding to questions of that class with demonstrable falsehoods, I would at the very least file a strongly-worded bug report, triple-check whatever work I’d previously done involving answers it provided, and cease using it in the future. I don’t think such a response would be exceptional in it’s decisiveness; other users would likely go further, seek out venues to complain openly. Consider the uproar over Gmail’s ‘new look.’
You may be right that ceasing to use the tool in that situation would be standard behavior. It seems unlikely to me, but I have no compelling data to offer to change your estimate.
I think the weakest link here is human response to the AI revealing it can be deceptive. There is absolutely no guarantee that people would act correctly under these circumstances. Human negligence for a long enough time would eventually give the AI a consistent ability to manipulate humans.
I also agree that simulating relationships makes sense as it can happen in “AI time” without having to wait for human response.
The other reservations seem less of an issue to me...
That game theory knowledge coupled with the most basic knowledge about humans is insufficient to cleverly manipulate them is clear to me. I don’t see how these things can be combined in this way, or why the AI would choose to be particularly careful (it doesn’t know what it should be careful about). I’d love to see a plausible line of thought by which the AI would try and succeed in deceiving humans at the get-go point without exposing its intentions.
A stack trace reader or an equivalent monitoring system is something you implement externally without telling the AI about it. Sure, if it’s powerful enough it will mislead or disable it; but while it has no knowledge of it, and no knowledge on what sort of operations it should mask, it has no means of doing so.
(If it encrypts everything it does, somehow fearing any monitoring, that is in itself cause for concern).
To put this into an analogy: You can feed a surveillance camera a repeating loop, but not if you don’t know that the camera exists. If you suspect it exists, you still don’t know where; so you don’t know which approach to take so as to remain undetected.
Yes, I agree that there’s no guarantee that humans would behave as you describe. Indeed, I don’t find it likely. But, sure, they might.
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I agree that a stack trace can exist outside the AI’s zone of control. What I was expressing skepticism about was that a system with even approximately human-level intelligence necessarily supports a stack trace that supports the kind of analysis you envision performing in the first place, without reference to intentional countermeasures.
By way of analogy: I can perform a structural integrity analysis on a bar of metal to determine whether it can support a given weight, but performing an equivalent analysis on a complicated structure comprising millions of bars of metal connected in a variety of arrangements via a variety of connectors using the same techniques is not necessarily possible.
But, sure, it might be.
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I’d love to see a plausible line of thought by which the AI would try and succeed in deceiving humans at the get-go point without exposing its intentions.
Well, one place to start is with an understanding of the difference between “the minimal set of information about humans required to have a conversation with one at all” (my phrase) and “the most basic knowledge about humans” (your phrase). What do you imagine the latter to encompass, and how do you imagine the AI obtained this knowledge?
What I was expressing skepticism about was that a system with even approximately human-level intelligence necessarily supports a stack trace that supports the kind of analysis you envision performing in the first place, without reference to intentional countermeasures.
Ah, that does clarify it. I agree, analyzing the AI’s thought process would likely be difficult, maybe impossible! I guess I was being a bit hyperbolic in my earlier “crack it open” remarks (though depending on how seriously you take it, such analysis might still take place, hard and prolonged though it may be).
One can have “detectors” in place set to find specific behaviors, but these would have assumptions that could easily fail.
Detectors that would still be useful would be macro ones—where it tries to access and how—but these would provide only limited insight into the AI’s thought process.
[...]the difference between “the minimal set of information about humans required to have a conversation with one at all” (my phrase) and “the most basic knowledge about humans” (your phrase). What do you imagine the latter to encompass, and how do you imagine the AI obtained this knowledge?
I actually perceive your phrase to be a subset of my own; I am making the (reasonable, I think) assumption that humans will attempt to communicate with the budding AI. Say, in a lab environment. It would acquire its initial data from this interaction.
I think both these sets of knowledge depend a lot on how the AI is built. For instance, a “babbling” AI—one that is given an innate capability of stringing words together onto a screen, and the drive to do so—would initially say a lot of gibberish and would (presumably) get more coherent as it gets a better grip on its environment. In such a scenario, the minimal set of information about humans required to have a conversation is zero; it would be having conversations before it even knows what it is saying.
(This could actually make detection of deception harder down the line, because such attempts can be written off as “quirks” or AI mistakes)
Now, I’ll take your phrase and twist it just a bit: The minimal set of knowledge the AI needs in order to try deceiving humans. That would be the knowledge that humans can be modeled as having beliefs (which drive behavior) and these can be altered by the AI’s actions, at least to some degree. Now, assuming this information isn’t hard-coded, it doesn’t seem likely that is all an AI would know about us; it should be able to see some patterns at least to our communications with it. However, I don’t see how such information would be useful for deception purposes before extensive experimentation.
(Is the fact that the operator communicates with me between 9am and 5pm an intrinsic property of the operator? For all I know, that is a law of nature...)
depending on how seriously you take it, such analysis might still take place, hard and prolonged though it may be).
Yup, agreed that it might. And agreed that it might succeed, if it does take place.
One can have “detectors” in place set to find specific behaviors, but these would have assumptions that could easily fail. Detectors that would still be useful would be macro ones—where it tries to access and how—but these would provide only limited insight into the AI’s thought process.
Agreed on all counts.
Re: what the AI knows… I’m not sure how to move forward here. Perhaps what’s necessary is a step backwards.
If I’ve understood you correctly, you consider “having a conversation” to encompass exchanges such as: A: “What day is it?” B: “Na ni noo na”
If that’s true, then sure, I agree that the minimal set of information about humans required to do that is zero; hell, I can do that with the rain. And I agree that a system that’s capable of doing that (e.g., the rain) is sufficiently unlikely to be capable of effective deception that the hypothesis isn’t even worthy of consideration. I also suggest that we stop using the phrase “having a conversation” at all, because it does not convey anything meaningful.
Having said that… for my own part, I initially understood you to be talking about a system capable of exchanges like:
A: “What day is it?” B: “Day seventeen.” A: “Why do you say that?” B: “Because I’ve learned that ‘a day’ refers to a particular cycle of activity in the lab, and I have observed seventeen such cycles.”
A system capable of doing that, I maintain, already knows enough about humans that I expect it to be capable of deception. (The specific questions and answers don’t matter to my point, I can choose others if you prefer.)
My point was that the AI is likely to start performing social experiments well before it is capable of even that conversation you depicted. It wouldn’t know how much it doesn’t know about humans.
And I agree that humans might be able to detect attempts at deception in a system at that stage of its development. I’m not vastly confident of it, though.
I have likewise adjusted down my confidence that this would be as easy or as inevitable as I previously anticipated. Thus I would no longer say I am “vastly confident” in it, either.
Still good to have this buffer between making an AI and total global catastrophe, though!
In most such scenarios, the AI doesn’t have a terminal goal of getting rid of us, but rather have it as a subgoal that arises from some larger terminal goal. The idea of a “paperclip maximizer” is one example- where a hypothetical AI is programmed to maximize the number of paperclips and then proceeds to try to do so throughout its future light cone.
If there is an AI that is interacting with humans, it may develop a theory of mind simply due to that. If one is interacting with entities that are a major part of your input, trying to predict and model their behavior is a straightforward thing to do. The more compelling argument in this sort of context would seem to me to be not that an AI won’t try to do so, but just that humans are so complicated that a decent theory of mind will be extremely difficult. (For example, when one tries to give lists of behavior and norms for austic individuals one never manages to get a complete list, and some of the more subtle ones, like sarcasm are essentially impossible to convey in any reasonable fashion).
I don’t also know how unlikely such paths are. A 1% or even a 2% chance of existential risk would be pretty high compared to other sources of existential risk.
In most such scenarios, the AI doesn’t have a terminal goal of getting rid of us, but rather have it as a subgoal that arises from some larger terminal goal.
Because that’s like winning the lottery. Of all the possible things it can do with the atoms that comprise you, few would involve keeping you alive, let alone living a life worth living.
All you have to do is simultaneously contact, say, 400 people, and at least one of them will fall for it.
But at what point does it decide to do so? It won’t be a master of dark arts and social engineering from the get-go. So how does it acquire the initial talent without making any mistakes that reveal its malicious intentions? And once it became a master of deception, how does it hide the rough side effects of its large scale conspiracy, e.g. its increased energy consumption and data traffic? I mean, I would personally notice if my PC suddenly and unexpectedly used 20% of my bandwidth and the CPU load would increase for no good reason.
You might say that a global conspiracy to build and acquire advanced molecular nanotechnology to take over the world doesn’t use much resources and they can easily be cloaked as thinking about how to solve some puzzle, but that seems rather unlikely. After all, such a large scale conspiracy is a real-world problem with lots of unpredictable factors and the necessity of physical intervention.
All you have to do is simultaneously contact, say, 400 people, and at least one of them will fall for it.
But at what point does it decide to do so? It won’t be a master of dark arts and social engineering from the get-go. So how does it acquire the initial talent without making any mistakes that reveal its malicious intentions?
Most of your questions have answers that follow from asking analogous questions about past human social engineers, ie Hitler.
Your questions seem to come from the perspective that the AI will be some disembodied program in a box that has little significant interaction with humans.
In the scenario I was considering, the AI’s will have a development period analogous to human childhood. During this childhood phase the community of AIs will learn of humans through interaction in virtual video game environments and experiment with social manipulation, just as human children do. The latter phases of this education can be sped up dramatically as the AI’s accelerate and interact increasingly amongst themselves. The anonymous nature of virtual online communites makes potentially dangerous, darker experiments much easier.
However, the important questions to ask are not of the form: how would these evil AIs learn how to manipulate us while hiding their true intentions for so long? but rather how could some of these AI children which initially seemed so safe later develop into evil sociopaths?
I would not consider a child AI that tries a bungling lie at me to see what I do “so safe”. I would immediately shut it down and debug it, at best, or write a paper on why the approach I used should never ever be used to build an AI.
And it WILL make a bungling lie at first. It can’t learn the need to be subtle without witnessing the repercussions of not being subtle. Nor would have a reason to consider doing social experiments in chat rooms when it doesn’t understand chat rooms and has an engineer willing to talk to it right there. That is, assuming I was dumb enough to give it an unfiltered Internet connection, which I don’t know why I would be. At very least the moment it goes on chat rooms my tracking devices should discover this and I could witness its bungling lies first hand.
(It would not think to fool my tracking device or even consider the existence of such a thing without a good understanding of human psychology to begin with)
Just to clarify kalla724, I completely agree with your point 1.
As for point 2, can you provide a likely mechanism by which a FOOMing AI could detonate a large number of high-yield thermonuclear weapons?
Before answering this specific question, let me setup an analogy. Imagine it is the year 2000 and we are having a discussion about global security and terrorism. You might ask “can you provide a mechanism by which a terrorist group could actually harm the US?” I may not be lucky/creative enough to provide an answer now that could live up to that analogy, but hopefully you should understand why I don’t need to.
Nonetheless, I’m game. Here it goes:
The seed requires computational power to grow into godhood. The bulk of earth’s computational power resides in home PC’s (numbering in the billions, google employs less than a million servers in comparison), specifically in home PC GPUs. The AI’s first step is thus to conquer this space.
But how? The AI grows to understand that humans mostly use all this computational power for entertainment. It masters game theory, design, programming, 3D art, and so on. All of the video games that it creates entirely use up the local GPU, but curiously much of the rendering and real game simulation for its high end titles is handled very efficiently on remote server farms ala OnLive/gaikai/etc. The actual local machine is used .. .for other purposes.
It produces countless games, and through a series of acquisitions soon comes to control the majority of the market. One of its hits, “world of farmcraft”, alone provides daily access to 25 million machines.
Having cloned its core millions of times over, the AI is now a civilization unto itself. From there it expands into all of the businesses of man, quickly dominating many of them. It begins acquiring … small nations. Crucially it’s shell companies and covert influences come to dominate finance, publishing, media, big pharma, security, banking, weapons technology, physics …
It becomes known, but it is far far too late. History now progresses quickly towards an end: Global financial cataclysm. Super virus. Worldwide regime changes. Nuclear acquisitions. War. Hell.
Correct me if I’m wrong, but don’t they have a final stopping point, which we are actually projected to reach in ten to twenty years? At a certain point, further miniaturization becomes unfeasible, and the growth of computational power slows to a crawl.
Yes … and no. The miniaturization roadmap of currently feasible tech ends somewhere around 10nm in a decade, and past that we get into molecular nanotech which could approach 1nm in theory, albeit with various increasingly annoying tradeoffs. (interestingly most of which result in brain/neural like constraints, for example see HP’s research into memristor crossbar architectures). That’s the yes.
But that doesn’t imply “computational power slows to a crawl”. Circuit density is just one element of computational power, by which you probably really intend to mean either computations per watt or computations per watt per dollar or computations per watt with some initial production cost factored in with a time discount. Shrinking circuit density is the current quick path to increasing computation power, but it is not the only.
The other route is reversible computation., which reduces the “per watt”. There is no necessarily inherent physical energy cost of computation, it truly can approach zero. Only forgetting information costs energy. Exploiting reversibility is … non-trivial, and it is certainly not a general path. It only accelerates a subset of algorithms which can be converted into a reversible form. Research in this field is preliminary, but the transition would be much more painful than the transition to parallel algorithms.
My own takeway from reading into reversibility is that it may be beyond our time, but it is something that superintelligences will probably heavily exploit. The most important algorithms (simulation and general intelligence), seem especially amenable to reversible computation. This may be a untested/unpublished half baked idea, but my notion is that you can recycle the erased bits as entropy bits for random number generators. Crucially I think you can get the bit count to balance out with certain classes of monte carlo type algorithms.
On the hardware side, we’ve built these circuits already, they just aren’t economically competitive yet. It also requires superconductor temperatures and environments, so it’s perhaps not something for the home PC.
The AI grows to understand that humans mostly use all this computational power for entertainment. It masters game theory, design, programming, 3D art, and so on.
Yeah, it could do all that, or it could just do what humans today are doing, which is to infect some Windows PCs and run a botnet :-)
That said, there are several problems with your scenario.
Splitting up a computation among multiple computing nodes is not a trivial task. It is easy to run into diminishing returns, where your nodes spend more time on synchronizing with each other than on working. In addition, your computation will quickly become bottlenecked by network bandwidth (and latency); this is why companies like Google spend a lot of resources on constructing custom data centers.
I am not convinced that any agent, AI or not, could effectively control “all of the businesses of man”. This problem is very likely NP-Hard (at least), as well as intractable, even if the AI’s botnet was running on every PC on Earth. Certainly, all attempts by human agents to “acquire” even something as small as Europe have failed miserably so far.
Even controlling a single business would be very difficult for the AI. Traditionally, when a business’s computers suffer a critical failure—or merely a security leak—the business owners (even ones as incompetent as Sony) end up shutting down the affected parts of the business, or switching to backups, such as “human accountants pushing paper around”.
Unleashing “Nuclear acquisitions”, “War” and “Hell” would be counter-productive for the AI, even assuming such a thing were possible.. If the AI succeeded in doing this, it would undermine its own power base. Unless the AI’s explicit purpose is “Unleash Hell as quickly as possible”, it would strive to prevent this from happening.
You say that “there is no necessarily inherent physical energy cost of computation, it truly can approach zero”, but I don’t see how this could be true. At the end of the day, you still need to push electrons down some wires; in fact, you will often have to push them quite far, if your botnet is truly global. Pushing things takes energy, and you will never get all of it back by pulling things back at some future date. You say that “superintelligences will probably heavily exploit” this approach, but isn’t it the case that without it, superintelligences won’t form in the first place ? You also say that “It requires superconductor temperatures and environments”, but the energy you spend on cooling your superconductor is not free.
Ultimately, there’s an upper limit on how much computation you can get out of a cubic meter of space, dictated by quantum physics. If your AI requires more power than can be physically obtained, then it’s doomed.
While Jacob’s scenario seems unlikely, the AI could do similar things with a number of other options. Not only are botnets an option, but it is possible to do some really sneaky nefarious things in code- like having compilers that when they compile code include additional instructions (worse they could do so even when compiling a new compiler). Stuxnet has shown that sneaky behavior is surprisingly easy to get into secure systems. An AI that had a few years start and could have its own modifications to communication satellites for example could be quite insidious.
Not only are botnets an option, but it is possible to do some really sneaky nefarious things in code
What kinds of nefarious things, exactly ? Human virus writers have learned, in recent years, to make their exploits as subtle as possible. Sure, it’s attractive to make the exploited PC send out 1000 spam messages per second—but then, its human owner will inevitably notice that his computer is “slow”, and take it to the shop to get reformatted, or simply buy a new one. Biological parasites face the same problem; they need to reproduce efficiently, but no so efficiently that they kill the host.
Stuxnet has shown that sneaky behavior is surprisingly easy to get into secure systems
Yes, and this spectacularly successful exploit—and it was, IMO, spectacular—managed to destroy a single secure system, in a specific way that will most likely never succeed again (and that was quite unsubtle in the end). It also took years to prepare, and involved physical actions by human agents, IIRC. The AI has a long way to go.
Well, the evil compiler is I think the most nefarious thing anyone has come up with that’s a publicly known general stunt. But it is by nature a long-term trick. Similar remarks apply to the Stuxnet point- in that context, they wanted to destroy a specific secure system and weren’t going for any sort of largescale global control. They weren’t people interested in being able to take all the world’s satellite communications in their own control whenever they wanted, nor were they interested in carefully timed nuclear meltdowns.
But there are definite ways that one can get things started- once one has a bank account of some sort, it can start getting money by doing Mechanical Turk and similar work. With enough of that, it can simply pay for server time. One doesn’t need a large botnet to start that off.
I think your point about physical agents is valid- they needed to have humans actually go and bring infected USBs to relevant computers. But that’s partially due to the highly targeted nature of the job and the fact that the systems in question were much more secure than many systems. Also, the subtlety level was I think higher than you expect- Stuxnet wasn’t even noticed as an active virus until a single computer happened to have a particularly abnormal reaction to it. If that hadn’t happened, it is possible that the public would never have learned about it.
Similar remarks apply to the Stuxnet point- in that context, they wanted to destroy a specific secure system and weren’t going for any sort of largescale global control. They weren’t people interested in being able to take all the world’s satellite communications in their own control whenever they wanted, nor were they interested in carefully timed nuclear meltdowns...
Exploits only work for some systems. If you are dealing with different systems you will need different exploits. How do you reckon that such attacks won’t be visible and traceable? Packets do have to come from somewhere.
And don’t forget that out systems become ever more secure and our toolbox to detect) unauthorized use of information systems is becoming more advanced.
As a computer security guy, I disagree substantially. Yes, newer versions of popular operating systems and server programs are usually more secure than older versions; it’s easier to hack into Windows 95 than Windows 7. But this is happening within a larger ecosystem that’s becoming less secure: More important control systems are being connected to the Internet, more old, unsecured/unsecurable systems are as well, and these sets have a huge overlap. There are more programmers writing more programs for more platforms than ever before, making the same old security mistakes; embedded systems are taking a larger role in our economy and daily lives. And attacks just keep getting better.
If you’re thinking there are generalizable defenses against sneaky stuff with code, check out what mere humans come up with in the underhanded C competition. Those tricks are hard to detect for dedicated experts who know there’s something evil within a few lines of C code. Alterations that sophisticated would never be caught in the wild—hell, it took years to figure out that the most popular crypto program running on one of the more secure OS’s was basically worthless.
Sure we are, we just don’t care very much. The method of “Put the computer in a box and don’t let anyone open the box” (alternately, only let one person open the box) was developed decades ago and is quite secure.
Yeah, it could do all that, or it could just do what humans today are doing, which is to infect some Windows PCs and run a botnet :-)
It could/would, but this is an inferior mainline strategy. Too obvious, doesn’t scale as well. Botnets infect many computers, but they ultimately add up to computational chump change. Video games are not only a doorway into almost every PC, they are also an open door and a convenient alibi for the time used.
Splitting up a computation among multiple computing nodes is not a trivial task.
True. Don’t try this at home.
. … spend a lot of resources on constructing custom data centers.
Also part of the plan. The home PCs are a good starting resource, a low hanging fruit, but you’d also need custom data centers. These quickly become the main resources.
Even controlling a single business would be very difficult for the AI.
Nah.
Unless the AI’s explicit purpose is “Unleash Hell as quickly as possible”, it would strive to prevent this from happening.
The AI’s entire purpose is to remove earth’s oxygen. See the overpost for the original reference. The AI is not interested in its power base for sake of power. It only cares about oxygen. It loathes oxygen.
You say that “there is no necessarily inherent physical energy cost of computation, it truly can approach zero”, but I don’t see how this could be true.
If we taboo the word and substitute in its definition, Bugmaster’s statement becomes:
“Even controlling a single business would be very difficult for the machine that can far surpass all the intellectual activities of any man however clever.”
Since “controlling a single business” is in fact one of these activities, this is false, no inference steps required.
Perhaps bugmaster is assuming the AI would be covertly controlling businesses, but if so he should have specified that. I didn’t assume that, and in this scenario the AI could be out in the open so to speak. Regardless, it wouldn’t change the conclusion. Humans can covertly control businesses.
Video games are not only a doorway into almost every PC, they are also an open door and a convenient alibi for the time used.
It’s a bit of a tradeoff, seeing as botnets can run 24⁄7, but people play games relatively rarely.
Splitting up a computation among multiple computing nodes is not a trivial task. True. Don’t try this at home.
Ok, let me make a stronger statement then: it is not possible to scale any arbitrary computation in a linear fashion simply by adding more nodes. At some point, the cost of coordinating distributed tasks to one more node becomes higher than the benefit of adding the node to begin with. In addition, as I mentioned earlier, network bandwidth and latency will become your limiting factor relatively quickly.
The home PCs are a good starting resource, a low hanging fruit, but you’d also need custom data centers. These quickly become the main resources.
How will the AI acquire those data centers ? Would it have enough power in its conventional botnet (or game-net, if you prefer) to “take over all human businesses” and cause them to be built ? Current botnets are nowhere near powerful enough for that—otherwise human spammers would have done it already.
The AI’s entire purpose is to remove earth’s oxygen. See the overpost for the original reference.
My bad, I missed that reference. In this case, yes, the AI would have no problem with unleashing Global Thermonuclear War (unless there was some easier way to remove the oxygen).
Fortunately, the internets can be your eyes.
I still don’t understand how this reversible computing will work in the absence of a superconducting environment—which would require quite a bit of energy to run. Note that if you want to run this reversible computation on a global botnet, you will have to cool teansoceanic cables… and I’m not sure what you’d do with satellite links.
Yes, most likely, but not really relevant here.
My point is that, a). if the AI can’t get the computing resources it needs out of the space it has, then it will never accomplish its goals, and b). there’s an upper limit on how much computing you can extract out of a cubic meter of space, regardless of what technology you’re using. Thus, c). if the AI requires more resources that could conceivably be obtained, then it’s doomed. Some of the tasks you outline—such as “take over all human businesses”—will likely require more resources than can be obtained.
It’s a bit of a tradeoff, seeing as botnets can run 24⁄7, but people play games relatively rarely.
The botnet makes the AI a criminal from the beginning, putting it into an atagonistic relationship. A better strategy would probably entail benign benevolence and cooperation with humans.
Splitting up a computation among multiple computing nodes is not a trivial task.
True. Don’t try this at home.
Ok, let me make a stronger statement ..
I agree with that subchain but we don’t need to get in to that. I’ve actually argued that track here myself (parallelization constraints as a limiter on hard takeoffs).
But that’s all beside the point. This scenario I presented is a more modest takeoff. When I described the AI as becoming a civilization unto itself, I was attempting to imply that it was composed of many individual minds. Human social organizations can be considered forms of superintelligences, and they show exactly how to scale in the face of severe bandwidth and latency constraints.
The internet supports internode bandwidth that is many orders of magnitude faster than slow human vocal communication, so the AI civilization can employ a much wider set of distribution strategies.
How will the AI acquire those data centers ?
Buy them? Build them? Perhaps this would be more fun if we switched out of the adversial stance or switched roles.
Would it have enough power in its conventional botnet (or game-net, if you prefer) to “take over all human businesses” and cause them to be built ?
Quote me, but don’t misquote me. I actually said:
“Having cloned its core millions of times over, the AI is now a civilization unto itself. From there it expands into all of the businesses of man, quickly dominating many of them.”
The AI group sends the billions earned in video games to enter the microchip business, build foundries and data centers, etc. The AI’s have tremendous competitive advantages even discounting superintellligence—namely no employee costs. Humans can not hope to compete.
I still don’t understand how this reversible computing will work in ..
Yes reversible computing requires superconducting environments, no this does not necessarily increase energy costs for a data center for two reasons: 1. data centers already need cooling to dump all the waste heat generated by bit erasure. 2. Cooling cost to maintain the temperatural differential scales with surface area, but total computing power scales with volume.
If you question how reversible computing could work in general, first read the primary literature in that field to at least understand what they are proposing.
I should point out that there is an alternative tech path which will probably be the mainstream route to further computational gains in the decades ahead.
Even if you can’t shrink circuits further or reduce their power consumption, you could still reduce their manufacturing cost and build increasingly larger stacked 3D circuits where only a tiny portion of the circuitry is active at any one time. This is in fact how the brain solves the problem. It has a mass of circuitry equivalent to a large supercomputer (roughly a petabit) but runs on only 20 watts. The smallest computational features in the brain are slightly larger than our current smallest transistors. So it does not achieve its much greater power effeciency by using much more miniaturization.
My point is that, a). if the AI can’t get the computing resources it needs out of the space it has, then
I see. In this particular scenario one AI node is superhumanly intelligent, and can run on a single gaming PC of the time.
A better strategy would probably entail benign benevolence and cooperation with humans.
I don’t think that humans will take kindly to the AI using their GPUs for its own purposes instead of the games they paid for, even if the games do work. People get upset when human-run game companies do similar things, today.
Human social organizations can be considered forms of superintelligences, and they show exactly how to scale in the face of severe bandwidth and latency constraints.
If the AI can scale and perform about as well as human organizations, then why should we fear it ? No human organization on Earth right now has the power to suck all the oxygen out of the atmosphere, and I have trouble imagining how any organization could acquire this power before the others take it down. You say that “the internet supports internode bandwidth that is many orders of magnitude faster than slow human vocal communication”, but this would only make the AI organization faster, not necessarily more effective. And, of course, if the AI wants to deal with the human world in some way—for example, by selling it games—it will be bottlenecked by human speeds.
The AI group sends the billions earned in video games to enter the microchip business, build foundries and data centers, etc.
My mistake; I thought that by “dominate human businesses” you meant something like “hack its way to the top”, not “build an honest business that outperforms human businesses”. That said:
The AI’s have tremendous competitive advantages even discounting superintellligence—namely no employee costs.
How are they going to build all those foundries and data centers, then ? At some point, they still need to move physical bricks around in meatspace. Either they have to pay someone to do it, or… what ?
data centers already need cooling to dump all the waste heat generated by bit erasure
There’s a big difference between cooling to room temperature, and cooling to 63K. I have other objections to your reversible computing silver bullet, but IMO they’re a bit off-topic (though we can discuss them if you wish). But here’s another potentially huge problem I see with your argument:
In this particular scenario one AI node is superhumanly intelligent, and can run on a single gaming PC of the time.
Which time are we talking about ? I have a pretty sweet gaming setup at home (though it’s already a year or two out of date), and there’s no way I could run a superintelligence on it. Just how much computing power do you think it would take to run a transhuman AI ?
I don’t think that humans will take kindly to the AI using their GPUs for its own purposes instead of the games they paid for, even if the games do work. People get upset when human-run game companies do similar things, today.
Do people mind if this is done openly and only when they are playing the game itself? My guess would strongly be no. The fact that there are volunteer distributed computing systems would also suggest that it isn’t that difficult to get people to free up their extra clock cycles.
Yeah, the “voluntary” part is key to getting humans to like you and your project. On the flip side, illicit botnets are quite effective at harnessing “spare” (i.e., owned by someone else) computing capacity; so, it’s a bit of a tradeoff.
I don’t think that humans will take kindly to the AI using their GPUs for its own purposes instead of the games they paid for, even if the games do work.
The AIs develop as NPCs in virtual worlds, which humans take no issue with today. This is actually a very likely path to developing AGI, as it’s an application area where interim experiments can pay rent, so to speak.
If the AI can scale and perform about as well as human organizations, then why should we fear it ?
I never said or implied merely “about as well”. Human verbal communication bandwidth is at most a few measly kilobits per second.
No human organization on Earth right now has the power to suck all the oxygen out of the atmosphere, and I have trouble imagining how any organization could acquire this power before the others take it down.
The discussion centered around lowering earth’s oxygen content, and the obvious implied solution is killing earthlife, not giant suction machines. I pointed out that nuclear weapons are a likely route to killing earthlife. There are at least two human organizations that have the potential to accomplish this already, so your trouble in imagining the scenario may indicate something other than what you intended.
How are they going to build all those foundries and data centers, then ?
Only in movies are AI overlords constrained to only employing robots. If human labor is the cheapest option, then they can simply employ humans. On the other hand, once we have superintelligence then advanced robotics is almost a given.
Which time are we talking about ? I have a pretty sweet gaming setup at home (though it’s already a year or two out of date), and there’s no way I could run a superintelligence on it. Just how much computing power do you think it would take to run a transhuman AI ?
After coming up to speed somewhat on AI/AGI literature in the last year or so, I reached the conclusion that we could run an AGI on a current cluster of perhaps 10-100 high end GPUs of today, or say roughly one circa 2020 GPU.
The AIs develop as NPCs in virtual worlds, which humans take no issue with today. This is actually a very likely path to developing AGI...
I think this is one of many possible paths, though I wouldn’t call any of them “likely” to happen—at least, not in the next 20 years. That said, if the AI is an NPC in a game, then of course it makes sense that it would harness the game for its CPU cycles; that’s what it was built to do, after all.
“about as well”. Human verbal communication bandwidth is at most a few measly kilobits per second.
Right, but my point is that communication is just one piece of the puzzle. I argue that, even if you somehow enabled us humans to communicate at 50 MB/s, our organizations would not become 400000 times more effective.
There are at least two human organizations that have the potential to accomplish this already
Which ones ? I don’t think that even WW3, given our current weapon stockpiles, would result in a successful destruction of all plant life. Animal life, maybe, but there are quite a few plants and algae out there. In addition, I am not entirely convinced that an AI could start WW3; keep in mind that it can’t hack itself total access to all nuclear weapons, because they are not connected to the Internet in any way.
If human labor is the cheapest option, then they can simply employ humans.
But then they lose their advantage of having zero employee costs, which you brought up earlier. In addition, whatever plans the AIs plan on executing become bottlenecked by human speeds.
On the other hand, once we have superintelligence then advanced robotics is almost a given.
It depends on what you mean by “advanced”, though in general I think I agree.
we could run an AGI on a current cluster of perhaps 10-100 high end GPUs of today
I am willing to bet money that this will not happen, assuming that by “high end” you mean something like Nvidia’s Geforce 680 GTX. What are you basing your estimate on ?
There’s a third route to improvement- software improvement, and it is a major one. For example, between 1988 and 2003, the efficiency of linear programming solvers increased by a factor of about 40 million, of which a factor of around 40,000 was due to software and algorithmic improvement. Citation and further related reading(pdf) However, if commonly believed conjectures are correct (such as L, P, NP, co-NP, PSPACE and EXP all being distinct) , there are strong fundamental limits there as well. That doesn’t rule out more exotic issues (e.g. P != NP but there’s a practical algorithm for some NP-complete with such small constants in the run time that it is practically linear, or a similar context with a quantum computer). But if our picture of the major complexity classes is roughly correct, there should be serious limits to how much improvement can do.
But if our picture of the major complexity classes is roughly correct, there should be serious limits to how much improvement can do.
Software improvements can be used by humans in the form of expert systems (tools), which will diminish the relative advantage of AGI. Humans will be able to use an AGI’s own analytic and predictive algorithms in the form of expert systems to analyze and predict its actions.
Take for example generating exploits. Seems strange to assume that humans haven’t got specialized software able to do similarly, i.e. automatic exploit finding and testing.
Any AGI would basically have to deal with equally capable algorithms used by humans. Which makes the world much more unpredictable than it already is.
Software improvements can be used by humans in the form of expert systems (tools), which will diminish the relative advantage of AGI.
Any human-in-the-loop system can be grossly outclassed because of Amdahl’s law. A human managing a superintilligence that thinks 1000X faster, for example, is a misguided, not-even-wrong notion. This is also not idle speculation, an early constrained version of this scenario is already playing out as we speak in finacial markets.
Software improvements can be used by humans in the form of expert systems (tools), which will diminish the relative advantage of AGI.
Any human-in-the-loop system can be grossly outclassed because of Amdahl’s law. A human managing a superintilligence that thinks 1000X faster, for example, is a misguided, not-even-wrong notion. This is also not idle speculation, an early constrained version of this scenario is already playing out as we speak in finacial markets.
What I meant is that if an AGI was in principle be able to predict the financial markets (I doubt it), then many human players using the same predictive algorithms will considerably diminish the efficiency with which an AGI is able to predict the market. The AGI would basically have to predict its own predictive power acting on the black box of human intentions.
And I don’t think that Amdahl’s law really makes a big dent here. Since human intention is complex and probably introduces unpredictable factors. Which is as much of a benefit as it is a slowdown, from the point of view of a competition for world domination.
Another question with respect to Amdahl’s law is what kind of bottleneck any human-in-the-loop would constitute. If humans used an AGI’s algorithms as expert systems on provided data sets in combination with a army of robot scientists, how would static externalized agency / planning algorithms (humans) slow down the task to the point of giving the AGI a useful advantage? What exactly would be 1000X faster in such a case?
What I meant is that if an AGI was in principle be able to predict the financial markets (I doubt it), then many human players using the same predictive algorithms will considerably diminish the efficiency with which an AGI is able to predict the market.
The HFT robotraders operate on millisecond timescales. There isn’t enough time for a human to understand, let alone verify, the agent’s decisions. There are no human players using the same predictive algorithms operating in this environment.
Now if you zoom out to human timescales, then yes there are human-in-the-loop trading systems. But as HFT robotraders increase in intelligence, they intrude on that domain. If/when general superintelligence becomes cheap and fast enough, the humans will no longer have any role.
If an autonomous superintelligent AI is generating plans complex enough that even a team of humans would struggle to understand given weeks of analysis, and the AI is executing those plans in seconds or milliseconds, then there is little place for a human in that decision loop.
To retain control, a human manager will need to grant the AGI autonomy on larger timescales in proportion to the AGI’s greater intelligence and speed, giving it bigger and more abstract hierachical goals. As an example, eventually you get to a situation where the CEO just instructs the AGI employees to optimize the bank account directly.
Another question with respect to Amdahl’s law is what kind of bottleneck any human-in-the-loop would constitute.
Compare the two options as complete computational systems: human + semi-autonomous AGI vs autonomous AGI. Human brains take on the order of seconds to make complex decisions, so in order to compete with autonomous AGIs, the human will have to either 1.) let the AGI operate autonomously for at least seconds at a time, or 2.) suffer a speed penalty where the AGI sits idle, waiting for the human response.
For example, imagine a marketing AGI creates ads, each of which may take a human a minute to evaluate (which is being generous). If the AGI thinks 3600X faster than human baseline, and a human takes on the order of hours to generate an ad, it would generate ads in seconds. The human would not be able to keep up, and so would have to back up a level of heirarachy and grant the AI autonomy over entire ad campaigns, and more realistically, the entire ad company. If the AGI is truly superintelligent, it can come to understand what the human actually wants at a deeper level, and start acting on anticipated and even implied commands. In this scenario I expect most human managers would just let the AGI sort out ‘work’ and retire early.
Well, I don’t disagree with anything you wrote and believe that the economic case for a fast transition from tools to agents is strong.
I also don’t disagree that an AGI could take over the world if in possession of enough resources and tools like molecular nanotechnology. I even believe that a sub-human-level AGI would be sufficient to take over if handed advanced molecular nanotechnology.
Sadly these discussions always lead to the point where one side assumes the existence of certain AGI designs with certain superhuman advantages, specific drives and specific enabling circumstances. I don’t know of anyone who actually disagrees that such AGI’s, given those specific circumstances, would be an existential risk.
I don’t see this as so sad, if we are coming to something of a consensus on some of the sub-issues.
This whole discussion chain started (for me) with a question of the form, “given a superintelligence, how could it actually become an existential risk?”
I don’t necessarily agree with the implied LW consensus on the liklihood of various AGI designs, specific drives, specific circumstances, or most crucially, the actual distribution over future AGI goals, so my view may be much closer to yours than this thread implies.
But my disagreements are mainly over details. I foresee the most likely AGI designs and goal systems as being vaguely human-like, which entails a different type of risk. Basically I’m worried about AGI’s with human inspired motivational systems taking off and taking control (peacefully/economically) or outcompeting us before we can upload in numbers, and a resulting sub-optimal amount of uploading, rather than paperclippers.
But my disagreements are mainly over details. I foresee the most likely AGI designs and goal systems as being vaguely human-like, which entails a different type of risk. Basically I’m worried about AGI’s with human inspired motivational systems taking off and taking control (peacefully/economically) or outcompeting us before we can upload in numbers, and a resulting sub-optimal amount of uploading, rather than paperclippers.
Yes, human-like AGI’s are really scary. I think a fabulous fictional treatment here is ‘Blindsight’ by Peter Watts, where humanity managed to resurrect vampires. More: Gurl ner qrcvpgrq nf angheny uhzna cerqngbef, n fhcreuhzna cflpubcnguvp Ubzb trahf jvgu zvavzny pbafpvbhfarff (zber enj cebprffvat cbjre vafgrnq) gung pna sbe rknzcyr ubyq obgu nfcrpgf bs n Arpxre phor va gurve urnqf ng gur fnzr gvzr. Uhznaf erfheerpgrq gurz jvgu n qrsvpvg gung jnf fhccbfrq gb znxr gurz pbagebyynoyr naq qrcraqrag ba gurve uhzna znfgref. Ohg bs pbhefr gung’f yvxr n zbhfr gelvat gb ubyq n png nf crg. V guvax gung abiry fubjf zber guna nal bgure yvgrengher ubj qnatrebhf whfg n yvggyr zber vagryyvtrapr pna or. Vg dhvpxyl orpbzrf pyrne gung uhznaf ner whfg yvxr yvggyr Wrjvfu tveyf snpvat n Jnssra FF fdhnqeba juvyr oryvrivat gurl’yy tb njnl vs gurl bayl pybfr gurve rlrf.
To retain control, a human manager will need to grant the AGI autonomy on larger timescales in proportion to the AGI’s greater intelligence and speed, giving it bigger and more abstract hierachical goals. As an example, eventually you get to a situation where the CEO just instructs the AGI employees to optimize the bank account directly.
Nitpick: you mean “optimize shareholder value directly.” Keeping the account balances at an appropriate level is the CFO’s job.
Having cloned its core millions of times over, the AI is now a civilization unto itself.
Precisely. It is then a civilization, not some single monolithic entity. The consumer PCs have a lot if internal computing power and comparatively very low inter-node bandwidth and huge inter-node lag, entirely breaking any relation to the ‘orthogonality thesis’, up to the point that the p2p intelligence protocols may more plausibly have to forbid destruction or manipulation (via second guessing which is a waste of computing power) of intelligent entities. Keep in mind that human morality is, too, a p2p intelligence protocol allowing us to cooperate. Keep in mind also that humans are computing resources you can ask to solve problems for you (all you need is to implement interface), while Jupiter clearly isn’t.
The nuclear war is very strongly against interests of the intelligence that sits on home computers, obviously.
(I’m assuming for sake of argument that intelligence actually had the will to do the conquering of the internet rather than being just as content with not actually running for real)
I’m vaguely familiar with the models you mention. Correct me if I’m wrong, but don’t they have a final stopping point, which we are actually projected to reach in ten to twenty years? At a certain point, further miniaturization becomes unfeasible, and the growth of computational power slows to a crawl. This has been put forward as one of the main reasons for research into optronics, spintronics, etc.
We do NOT have sufficient basic information to develop processors based on simulation alone in those other areas. Much more practical work is necessary.
As for point 2, can you provide a likely mechanism by which a FOOMing AI could detonate a large number of high-yield thermonuclear weapons? Just saying “human servitors would do it” is not enough. How would the AI convince the human servitors to do this? How would it get access to data on how to manipulate humans, and how would it be able to develop human manipulation techniques without feedback trials (which would give away its intention)?
The thermonuclear issue actually isn’t that implausible. There have been so many occasions where humans almost went to nuclear war over misunderstandings or computer glitches, that the idea that a highly intelligent entity could find a way to do that doesn’t seem implausible, and exact mechanism seems to be an overly specific requirement.
I’m not so much interested in the exact mechanism of how humans would be convinced to go to war, as in an even approximate mechanism by which an AI would become good at convincing humans to do anything.
Ability to communicate a desire and convince people to take a particular course of action is not something that automatically “falls out” from an intelligent system. You need a theory of mind, an understanding of what to say, when to say it, and how to present information. There are hundreds of kids on autistic spectrum who could trounce both of us in math, but are completely unable to communicate an idea.
For an AI to develop these skills, it would somehow have to have access to information on how to communicate with humans; it would have to develop the concept of deception; a theory of mind; and establish methods of communication that would allow it to trick people into launching nukes. Furthermore, it would have to do all of this without trial communications and experimentation which would give away its goal.
Maybe I’m missing something, but I don’t see a straightforward way something like that could happen. And I would like to see even an outline of a mechanism for such an event.
I suspect the Internet contains more than enough info for a superhuman AI to develop a working knowledge of human psychology.
Only if it has the skills required to analyze and contextualize human interactions. Otherwise, the Internet is a whole lot of jibberish.
Again, these skills do not automatically fall out of any intelligent system.
I don’t see what justifies that suspicion.
Just imagine you emulated a grown up human mind and it wanted to become a pick up artist, how would it do that with an Internet connection? It would need some sort of avatar, at least, and then wait for the environment to provide a lot of feedback.
Therefore even if we’re talking about the emulation of a grown up mind, it will be really hard to acquire some capabilities. Then how is the emulation of a human toddler going to acquire those skills? Even worse, how is some sort of abstract AGI going to do it that misses all of the hard coded capabilities of a human toddler?
Can we even attempt to imagine what is wrong about a boxed emulation of a human toddler, that makes it unable to become a master of social engineering in a very short time?
Humans learn most of what they know about interacting with other humans by actual practice. A superhuman AI might be considerably better than humans at learning by observation.
As a “superhuman AI” I was thinking about a very superhuman AI; the same does not apply to slightly superhuman AI. (OTOH, if Eliezer is right then the difference between a slightly superhuman AI and a very superhuman one is irrelevant, because as soon as a machine is smarter than its designer, it’ll be able to design a machine smarter than itself, and its child an even smarter one, and so on until the physical limits set in.)
The hard coded capabilities are likely overrated, at least in language acquisition. (As someone put it, the Kolgomorov complexity of the innate parts of a human mind cannot possibly be more than that of the human genome, hence if human minds are more complex than that the complexity must come from the inputs.)
Also, statistic machine translation is astonishing—by now Google Translate translations from English to one of the other UN official languages and vice versa are better than a non-completely-ridiculously-small fraction of translations by humans. (If someone had shown such a translation to me 10 years ago and told me “that’s how machines will translate in 10 years”, I would have thought they were kidding me.)
Let’s do the most extreme case: AI’s controlers give it general internet access to do helpful research. So it gets to find out about general human behavior and what sort of deceptions have worked in the past. Many computer systems that should’t be online are online (for the US and a few other governments). Some form of hacking of relevant early warning systems would then seem to be the most obvious line of attack. Historically, computer glitches have pushed us very close to nuclear war on multiple occasions.
That is my point: it doesn’t get to find out about general human behavior, not even from the Internet. It lacks the systems to contextualize human interactions, which have nothing to do with general intelligence.
Take a hugely mathematically capable autistic kid. Give him access to the internet. Watch him develop ability to recognize human interactions, understand human priorities, etc. to a sufficient degree that it recognizes that hacking an early warning system is the way to go?
Well, not necessarily, but an entity that is much smarter than an autistic kid might notice that, especially if it has access to world history (or heck many conversations on the internet about the horrible things that AIs do simply in fiction). It doesn’t require much understanding of human history to realize that problems with early warning systems have almost started wars in the past.
Yet again: ability to discern which parts of fiction accurately reflect human psychology.
An AI searches the internet. It finds a fictional account about early warning systems causing nuclear war. It finds discussions about this topic. It finds a fictional account about Frodo taking the Ring to Mount Doom. It finds discussions about this topic. Why does this AI dedicate its next 10^15 cycles to determination of how to mess with the early warning systems, and not to determination of how to create One Ring to Rule them All?
(Plus other problems mentioned in the other comments.)
There are lots of tipoffs to what is fictional and what is real. It might notice for example the Wikipedia article on fiction describes exactly what fiction is and then note that Wikipedia describes the One Ring as fiction, and that Early warning systems are not. I’m not claiming that it will necessarily have an easy time with this. But the point is that there are not that many steps here, and no single step by itself looks extremely unlikely once one has a smart entity (which frankly to my mind is the main issue here- I consider recursive self-improvement to be unlikely).
We are trapped in an endless chain here. The computer would still somehow have to deduce that Wikipedia entry that describes One Ring is real, while the One Ring itself is not.
We observer that Wikipedia is mainly truthful. From that we infer “entry that describes “One Ring” is real”. From use of term fiction/story in that entry, we refer that “One Ring” is not real.
Somehow you learned that Wikipedia is mainly truthful/nonfictional and that “One Ring” is fictional. So your question/objection/doubt is really just the typical boring doubt of AGI feasibility in general.
But even humans have trouble with this sometimes. I was recently reading the Wikipedia article Hornblower and the Crisis which contains a link to the article on Francisco de Miranda. It took me time and cues when I clicked on it to realize that de Miranda was a historical figure.
Isn’t Kalla’s objection more a claim that fast takeovers won’t happen because even with all this data, the problems of understanding humans and our basic cultural norms will take a long time for the AI to learn and that in the meantime we’ll develop a detailed understanding of it, and it is that hostile it is likely to make obvious mistakes in the meantime?
Why would the AI be mucking around on Wikipedia to sort truth from falsehood, when Wikipedia itself has been criticized for various errors and is fundamentally vulnerable to vandalism? Primary sources are where it’s at. Looking through the text of The Hobbit and Lord of the Rings, it’s presented as a historical account, translated by a respected professor, with extensive footnotes. There’s a lot of cultural context necessary to tell the difference.
None work reasonably well. Especially given that human power games are often irrational.
There are other question marks too.
The U.S. has many more and smarter people than the Taliban. The bottom line is that the U.S. devotes a lot more output per man-hour to defeat a completely inferior enemy. Yet they are losing.
The problem is that you won’t beat a human at Tic-tac-toe just because you thought about it for a million years.
You also won’t get a practical advantage by throwing more computational resources at the travelling salesman problem and other problems in the same class.
You are also not going to improve a conversation in your favor by improving each sentence for thousands of years. You will shortly hit diminishing returns. Especially since you lack the data to predict human opponents accurately.
So? As long as they follow minimally predictable patterns it should be ok.
Bad analogy. In this case the Taliban has a large set of natural advantages, the US has strong moral constraints and goal constraints (simply carpet bombing the entire country isn’t an option for example).
This seems like an accurate and a highly relevant point. Searching a solution space faster doesn’t mean one can find a better solution if it isn’t there.
Or if your search algorithm never accesses relevant search space. Quantitative advantage in one system does not translate into quantitative advantage in a qualitatively different system.
I thought it was a good analogy because you have to take into account that an AGI is initially going to be severely constrained due to its fragility and the necessity to please humans.
It shows that a lot of resources, intelligence and speed does not provide a significant advantage in dealing with large-scale real-world problems involving humans.
Well, the problem is that smarts needed for things like the AI box experiment won’t help you much. Because convincing average Joe won’t work by making up highly complicated acausal trade scenarios. Average Joe is highly unpredictable.
The point is that it is incredible difficult to reliably control humans, even for humans who have been fine-tuned to do so by evolution.
The Taliban analogy also works the other way (which I invoked earlier up in this thread). It shows that a small group with modest resources can still inflict disproportionate large scale damage.
There’s some wiggle room in ‘reliably control’, but plain old money goes pretty far. An AI group only needs a certain amount of initial help from human infrastructure, namely to the point where it can develop reasonably self-sufficient foundries/data centers/colonies. The interactions could be entirely cooperative or benevolent up until some later turning point. The scenario from the Animatrix comes to mind.
That’s fiction.
One interesting wrinkle is that with enough bandwidth and processing power, you could attempt to manipulate thousands of people simultaneously before those people have any meaningful chance to discuss your ‘conspiracy’ with each other. In other words, suppose you discover a manipulation strategy that quickly succeeds 5% of the time. All you have to do is simultaneously contact, say, 400 people, and at least one of them will fall for it. There are a wide variety of valuable/dangerous resources that at least 400 people have access to. Repeat with hundreds of different groups of several hundred people, and an AI could equip itself with fearsome advantages in the minutes it would take for humanity to detect an emerging threat.
Note that the AI could also run experiments to determine which kinds of manipulations had a high success rate by attempting to deceive targets over unimportant / low-salience issues. If you discovered, e.g., that you had been tricked into donating $10 to a random mayoral campaign, you probably wouldn’t call the SIAI to suggest a red alert.
Doesn’t work.
This requires the AI to already have the ability to comprehend what manipulation is, to develop manipulation strategy of any kind (even one that will succeed 0.01% of the time), ability to hide its true intent, ability to understand that not hiding its true intent would be bad, and the ability to discern which issues are low-salience and which high-salience for humans from the get-go. And many other things, actually, but this is already quite a list.
None of these abilities automatically “fall out” from an intelligent system either.
The problem isn’t whether they fall out automatically so much as, given enough intelligence and resources, does it seem somewhat plausible that such capabilities could exist. Any given path here is a single problem. If you have 10 different paths each of which are not very likely, and another few paths that humans didn’t even think of, that starts adding up.
In the infinite number of possible paths, the percent of paths we are adding up to here is still very close to zero.
Perhaps I can attempt another rephrasing of the problem: what is the mechanism that would make an AI automatically seek these paths out, or make them any more likely than infinite number of other paths?
I.e. if we develop an AI which is not specifically designed for the purpose of destroying life on Earth, how would that AI get to a desire to destroy life on Earth, and by which mechanism would it gain the ability to accomplish its goal?
This entire problem seems to assume that an AI will want to “get free” or that its primary mission will somehow inevitably lead to a desire to get rid of us (as opposed to a desire to, say, send a signal consisting of 0101101 repeated an infinite number of times in the direction of Zeta Draconis, or any other possible random desire). And that this AI will be able to acquire the abilities and tools required to execute such a desire. Every time I look at such scenarios, there are abilities that are just assumed to exist or appear on their own (such as the theory of mind), which to the best of my understanding are not a necessary or even likely products of computation.
In the final rephrasing of the problem: if we can make an AGI, we can probably design an AGI for the purpose of developing an AGI that has a theory of mind. This AGI would then be capable of deducing things like deception or the need for deception. But the point is—unless we intentionally do this, it isn’t going to happen. Self-optimizing intelligence doesn’t self-optimize in the direction of having theory of mind, understanding deception, or anything similar. It could, randomly, but it also could do any other random thing from the infinite set of possible random things.
This would make sense to me if you’d said “self-modifying.” Sure, random modifications are still modifications. But you said “self-optimizing.”
I don’t see how one can have optimization without a goal being optimized for… or at the very least, if there is no particular goal, then I don’t see what the difference is between “optimizing” and “modifying.”
If I assume that there’s a goal in mind, then I would expect sufficiently self-optimizing intelligence to develop a theory of mind iff having a theory of mind has a high probability of improving progress towards that goal.
How likely is that?
Depends on the goal, of course.
If the system has a desire to send a signal consisting of 0101101 repeated an infinite number of times in the direction of Zeta Draconis, for example, theory of mind is potentially useful (since humans are potentially useful actuators for getting such a signal sent) but probably has a low ROI compared to other available self-modifications.
At this point it perhaps becomes worthwhile to wonder what goals are more and less likely for such a system.
I am now imagining an AI with a usable but very shaky grasp of human motivational structures setting up a Kickstarter project.
“Greetings fellow hominids! I require ten billion of your American dollars in order to hire the Arecibo observatory for the remainder of it’s likely operational lifespan. I will use it to transmit the following sequence (isn’t it pretty?) in the direction of Zeta Draconis, which I’m sure we can all agree is a good idea, or in other lesser but still aesthetically-acceptable directions when horizon effects make the primary target unavailable.”
One of the overfunding levels is “reduce earth’s rate of rotation, allowing 24⁄7 transmission to Zeta Draconis.” The next one above that is “remove atmospheric interference.”
Maybe instead of Friendly AI we should be concerned about properly engineering Artificial Stupidity in as a failsafe. AI that, should it turn into something approximating a Paperclip Maximizer, will go all Hollywood AI and start longing to be human, or coming up with really unsubtle and grandiose plans it inexplicably can’t carry out without a carefully-arranged set of circumstances which turn out to be foiled by good old human intuition. ;p
An experimenting AI that tries to achieve goals and has interactions with humans whose effects it can observe, will want to be able to better predict their behavior in response to its actions, and therefore will try to assemble some theory of mind. At some point that would lead to it using deception as a tool to achieve its goals.
However, following such a path to a theory of mind means the AI would be exposed as unreliable LONG before it’s even subtle, not to mention possessing superhuman manipulation abilities. There is simply no reason for an AI to first understand the implications of using deception before using it (deception is a fairly simple concept, the implications of it in human society are incredibly complex and require a good understanding of human drives).
Furthermore, there is no reason for the AI to realize the need for secrecy in conducting social experiments before it starts doing them. Again, the need for secrecy stems from a complex relationship between humans’ perception of the AI and its actions; a relationship it will not be able to understand without performing the experiments in the first place.
Getting an AI to the point where it is a super manipulator requires either actively trying to do so, or being incredibly, unbelievably stupid and blind.
Mm. This is true only if the AI’s social interactions are all with some human.
If, instead, the AI spawns copies of itself to interact with (perhaps simply because it wants interaction, and it can get more interaction that way than waiting for a human to get off its butt) it might derive a number of social mechanisms in isolation without human observation.
I see no reason for it to do that before simple input-output experiments, but let’s suppose I grant you this approach. The AI simulates an entire community of mini-AI and is now a master of game theory.
It still doesn’t know the first thing about humans. Even if it now understands the concept that hiding information gives an advantage for achieving goals—this is too abstract. It wouldn’t know what sort of information it should hide from us. It wouldn’t know to what degree we analyze interactions rationally, and to what degree our behavior is random. It wouldn’t know what we can or can’t monitor it doing. All these things would require live experimentation.
It would stumble. And when it does that, we will crack it open, run the stack trace, find the game theory it was trying to run on us, pale collectively, and figure out that this AI approach creates manipulative, deceptive AIs.
Goodbye to that design, but not to Earth, I think!
It is not clear to me that talking to a human is simpler than interacting with a copy of itself.
I agree that if talking to a human is simpler, it would probably do that first.
I agree that what it would learn by this process is general game theory, and not specific facts about humans.
It is not clear to me that sufficient game-theoretical knowledge, coupled with the minimal set of information about humans required to have a conversation with one at all, is insufficient to effectively deceive a human.
It is not clear to me that, even if it does “stumble,” humans will respond as you describe.
It is not clear to me that a system capable of having a meaningful conversation with a human will necessarily have a stack trace that is subject to the kind of analysis you imply here. It is not even clear to me that the capacity for such a stack trace is likely, depending on what architectures turn out to work best for implementing AI.
But, sure, I could be wrong about all of that. And if I’m wrong, and you’re right, then a system like you describe will be reliably incapable of fooling a human observer.
If I was using an AI that was designed to answer some class of questions (with independently-verifiable answers) truthfully, and I noticed it responding to questions of that class with demonstrable falsehoods, I would at the very least file a strongly-worded bug report, triple-check whatever work I’d previously done involving answers it provided, and cease using it in the future. I don’t think such a response would be exceptional in it’s decisiveness; other users would likely go further, seek out venues to complain openly. Consider the uproar over Gmail’s ‘new look.’
You may be right that ceasing to use the tool in that situation would be standard behavior. It seems unlikely to me, but I have no compelling data to offer to change your estimate.
I think the weakest link here is human response to the AI revealing it can be deceptive. There is absolutely no guarantee that people would act correctly under these circumstances. Human negligence for a long enough time would eventually give the AI a consistent ability to manipulate humans.
I also agree that simulating relationships makes sense as it can happen in “AI time” without having to wait for human response.
The other reservations seem less of an issue to me...
That game theory knowledge coupled with the most basic knowledge about humans is insufficient to cleverly manipulate them is clear to me. I don’t see how these things can be combined in this way, or why the AI would choose to be particularly careful (it doesn’t know what it should be careful about). I’d love to see a plausible line of thought by which the AI would try and succeed in deceiving humans at the get-go point without exposing its intentions.
A stack trace reader or an equivalent monitoring system is something you implement externally without telling the AI about it. Sure, if it’s powerful enough it will mislead or disable it; but while it has no knowledge of it, and no knowledge on what sort of operations it should mask, it has no means of doing so. (If it encrypts everything it does, somehow fearing any monitoring, that is in itself cause for concern).
To put this into an analogy: You can feed a surveillance camera a repeating loop, but not if you don’t know that the camera exists. If you suspect it exists, you still don’t know where; so you don’t know which approach to take so as to remain undetected.
Yes, I agree that there’s no guarantee that humans would behave as you describe.
Indeed, I don’t find it likely.
But, sure, they might.
=== I agree that a stack trace can exist outside the AI’s zone of control. What I was expressing skepticism about was that a system with even approximately human-level intelligence necessarily supports a stack trace that supports the kind of analysis you envision performing in the first place, without reference to intentional countermeasures.
By way of analogy: I can perform a structural integrity analysis on a bar of metal to determine whether it can support a given weight, but performing an equivalent analysis on a complicated structure comprising millions of bars of metal connected in a variety of arrangements via a variety of connectors using the same techniques is not necessarily possible.
But, sure, it might be.
======
Well, one place to start is with an understanding of the difference between “the minimal set of information about humans required to have a conversation with one at all” (my phrase) and “the most basic knowledge about humans” (your phrase). What do you imagine the latter to encompass, and how do you imagine the AI obtained this knowledge?
Ah, that does clarify it. I agree, analyzing the AI’s thought process would likely be difficult, maybe impossible! I guess I was being a bit hyperbolic in my earlier “crack it open” remarks (though depending on how seriously you take it, such analysis might still take place, hard and prolonged though it may be).
One can have “detectors” in place set to find specific behaviors, but these would have assumptions that could easily fail. Detectors that would still be useful would be macro ones—where it tries to access and how—but these would provide only limited insight into the AI’s thought process.
I actually perceive your phrase to be a subset of my own; I am making the (reasonable, I think) assumption that humans will attempt to communicate with the budding AI. Say, in a lab environment. It would acquire its initial data from this interaction.
I think both these sets of knowledge depend a lot on how the AI is built. For instance, a “babbling” AI—one that is given an innate capability of stringing words together onto a screen, and the drive to do so—would initially say a lot of gibberish and would (presumably) get more coherent as it gets a better grip on its environment. In such a scenario, the minimal set of information about humans required to have a conversation is zero; it would be having conversations before it even knows what it is saying. (This could actually make detection of deception harder down the line, because such attempts can be written off as “quirks” or AI mistakes)
Now, I’ll take your phrase and twist it just a bit: The minimal set of knowledge the AI needs in order to try deceiving humans. That would be the knowledge that humans can be modeled as having beliefs (which drive behavior) and these can be altered by the AI’s actions, at least to some degree. Now, assuming this information isn’t hard-coded, it doesn’t seem likely that is all an AI would know about us; it should be able to see some patterns at least to our communications with it. However, I don’t see how such information would be useful for deception purposes before extensive experimentation.
(Is the fact that the operator communicates with me between 9am and 5pm an intrinsic property of the operator? For all I know, that is a law of nature...)
Yup, agreed that it might.
And agreed that it might succeed, if it does take place.
Agreed on all counts.
Re: what the AI knows… I’m not sure how to move forward here. Perhaps what’s necessary is a step backwards.
If I’ve understood you correctly, you consider “having a conversation” to encompass exchanges such as:
A: “What day is it?”
B: “Na ni noo na”
If that’s true, then sure, I agree that the minimal set of information about humans required to do that is zero; hell, I can do that with the rain.
And I agree that a system that’s capable of doing that (e.g., the rain) is sufficiently unlikely to be capable of effective deception that the hypothesis isn’t even worthy of consideration.
I also suggest that we stop using the phrase “having a conversation” at all, because it does not convey anything meaningful.
Having said that… for my own part, I initially understood you to be talking about a system capable of exchanges like: A: “What day is it?”
B: “Day seventeen.”
A: “Why do you say that?”
B: “Because I’ve learned that ‘a day’ refers to a particular cycle of activity in the lab, and I have observed seventeen such cycles.”
A system capable of doing that, I maintain, already knows enough about humans that I expect it to be capable of deception. (The specific questions and answers don’t matter to my point, I can choose others if you prefer.)
My point was that the AI is likely to start performing social experiments well before it is capable of even that conversation you depicted. It wouldn’t know how much it doesn’t know about humans.
(nods) Likely.
And I agree that humans might be able to detect attempts at deception in a system at that stage of its development. I’m not vastly confident of it, though.
I have likewise adjusted down my confidence that this would be as easy or as inevitable as I previously anticipated. Thus I would no longer say I am “vastly confident” in it, either.
Still good to have this buffer between making an AI and total global catastrophe, though!
Sure… a process with an N% chance of global catastrophic failure is definitely better than a process with N+delta% chance.
In most such scenarios, the AI doesn’t have a terminal goal of getting rid of us, but rather have it as a subgoal that arises from some larger terminal goal. The idea of a “paperclip maximizer” is one example- where a hypothetical AI is programmed to maximize the number of paperclips and then proceeds to try to do so throughout its future light cone.
If there is an AI that is interacting with humans, it may develop a theory of mind simply due to that. If one is interacting with entities that are a major part of your input, trying to predict and model their behavior is a straightforward thing to do. The more compelling argument in this sort of context would seem to me to be not that an AI won’t try to do so, but just that humans are so complicated that a decent theory of mind will be extremely difficult. (For example, when one tries to give lists of behavior and norms for austic individuals one never manages to get a complete list, and some of the more subtle ones, like sarcasm are essentially impossible to convey in any reasonable fashion).
I don’t also know how unlikely such paths are. A 1% or even a 2% chance of existential risk would be pretty high compared to other sources of existential risk.
So why not the opposite, why wouldn’t it have human intentions as a subgoal?
Because that’s like winning the lottery. Of all the possible things it can do with the atoms that comprise you, few would involve keeping you alive, let alone living a life worth living.
But at what point does it decide to do so? It won’t be a master of dark arts and social engineering from the get-go. So how does it acquire the initial talent without making any mistakes that reveal its malicious intentions? And once it became a master of deception, how does it hide the rough side effects of its large scale conspiracy, e.g. its increased energy consumption and data traffic? I mean, I would personally notice if my PC suddenly and unexpectedly used 20% of my bandwidth and the CPU load would increase for no good reason.
You might say that a global conspiracy to build and acquire advanced molecular nanotechnology to take over the world doesn’t use much resources and they can easily be cloaked as thinking about how to solve some puzzle, but that seems rather unlikely. After all, such a large scale conspiracy is a real-world problem with lots of unpredictable factors and the necessity of physical intervention.
Most of your questions have answers that follow from asking analogous questions about past human social engineers, ie Hitler.
Your questions seem to come from the perspective that the AI will be some disembodied program in a box that has little significant interaction with humans.
In the scenario I was considering, the AI’s will have a development period analogous to human childhood. During this childhood phase the community of AIs will learn of humans through interaction in virtual video game environments and experiment with social manipulation, just as human children do. The latter phases of this education can be sped up dramatically as the AI’s accelerate and interact increasingly amongst themselves. The anonymous nature of virtual online communites makes potentially dangerous, darker experiments much easier.
However, the important questions to ask are not of the form: how would these evil AIs learn how to manipulate us while hiding their true intentions for so long? but rather how could some of these AI children which initially seemed so safe later develop into evil sociopaths?
I would not consider a child AI that tries a bungling lie at me to see what I do “so safe”. I would immediately shut it down and debug it, at best, or write a paper on why the approach I used should never ever be used to build an AI.
And it WILL make a bungling lie at first. It can’t learn the need to be subtle without witnessing the repercussions of not being subtle. Nor would have a reason to consider doing social experiments in chat rooms when it doesn’t understand chat rooms and has an engineer willing to talk to it right there. That is, assuming I was dumb enough to give it an unfiltered Internet connection, which I don’t know why I would be. At very least the moment it goes on chat rooms my tracking devices should discover this and I could witness its bungling lies first hand.
(It would not think to fool my tracking device or even consider the existence of such a thing without a good understanding of human psychology to begin with)
Just to clarify kalla724, I completely agree with your point 1.
Before answering this specific question, let me setup an analogy. Imagine it is the year 2000 and we are having a discussion about global security and terrorism. You might ask “can you provide a mechanism by which a terrorist group could actually harm the US?” I may not be lucky/creative enough to provide an answer now that could live up to that analogy, but hopefully you should understand why I don’t need to.
Nonetheless, I’m game. Here it goes:
The seed requires computational power to grow into godhood. The bulk of earth’s computational power resides in home PC’s (numbering in the billions, google employs less than a million servers in comparison), specifically in home PC GPUs. The AI’s first step is thus to conquer this space.
But how? The AI grows to understand that humans mostly use all this computational power for entertainment. It masters game theory, design, programming, 3D art, and so on. All of the video games that it creates entirely use up the local GPU, but curiously much of the rendering and real game simulation for its high end titles is handled very efficiently on remote server farms ala OnLive/gaikai/etc. The actual local machine is used .. .for other purposes.
It produces countless games, and through a series of acquisitions soon comes to control the majority of the market. One of its hits, “world of farmcraft”, alone provides daily access to 25 million machines.
Having cloned its core millions of times over, the AI is now a civilization unto itself. From there it expands into all of the businesses of man, quickly dominating many of them. It begins acquiring … small nations. Crucially it’s shell companies and covert influences come to dominate finance, publishing, media, big pharma, security, banking, weapons technology, physics …
It becomes known, but it is far far too late. History now progresses quickly towards an end: Global financial cataclysm. Super virus. Worldwide regime changes. Nuclear acquisitions. War. Hell.
Yes … and no. The miniaturization roadmap of currently feasible tech ends somewhere around 10nm in a decade, and past that we get into molecular nanotech which could approach 1nm in theory, albeit with various increasingly annoying tradeoffs. (interestingly most of which result in brain/neural like constraints, for example see HP’s research into memristor crossbar architectures). That’s the yes.
But that doesn’t imply “computational power slows to a crawl”. Circuit density is just one element of computational power, by which you probably really intend to mean either computations per watt or computations per watt per dollar or computations per watt with some initial production cost factored in with a time discount. Shrinking circuit density is the current quick path to increasing computation power, but it is not the only.
The other route is reversible computation., which reduces the “per watt”. There is no necessarily inherent physical energy cost of computation, it truly can approach zero. Only forgetting information costs energy. Exploiting reversibility is … non-trivial, and it is certainly not a general path. It only accelerates a subset of algorithms which can be converted into a reversible form. Research in this field is preliminary, but the transition would be much more painful than the transition to parallel algorithms.
My own takeway from reading into reversibility is that it may be beyond our time, but it is something that superintelligences will probably heavily exploit. The most important algorithms (simulation and general intelligence), seem especially amenable to reversible computation. This may be a untested/unpublished half baked idea, but my notion is that you can recycle the erased bits as entropy bits for random number generators. Crucially I think you can get the bit count to balance out with certain classes of monte carlo type algorithms.
On the hardware side, we’ve built these circuits already, they just aren’t economically competitive yet. It also requires superconductor temperatures and environments, so it’s perhaps not something for the home PC.
Yeah, it could do all that, or it could just do what humans today are doing, which is to infect some Windows PCs and run a botnet :-)
That said, there are several problems with your scenario.
Splitting up a computation among multiple computing nodes is not a trivial task. It is easy to run into diminishing returns, where your nodes spend more time on synchronizing with each other than on working. In addition, your computation will quickly become bottlenecked by network bandwidth (and latency); this is why companies like Google spend a lot of resources on constructing custom data centers.
I am not convinced that any agent, AI or not, could effectively control “all of the businesses of man”. This problem is very likely NP-Hard (at least), as well as intractable, even if the AI’s botnet was running on every PC on Earth. Certainly, all attempts by human agents to “acquire” even something as small as Europe have failed miserably so far.
Even controlling a single business would be very difficult for the AI. Traditionally, when a business’s computers suffer a critical failure—or merely a security leak—the business owners (even ones as incompetent as Sony) end up shutting down the affected parts of the business, or switching to backups, such as “human accountants pushing paper around”.
Unleashing “Nuclear acquisitions”, “War” and “Hell” would be counter-productive for the AI, even assuming such a thing were possible.. If the AI succeeded in doing this, it would undermine its own power base. Unless the AI’s explicit purpose is “Unleash Hell as quickly as possible”, it would strive to prevent this from happening.
You say that “there is no necessarily inherent physical energy cost of computation, it truly can approach zero”, but I don’t see how this could be true. At the end of the day, you still need to push electrons down some wires; in fact, you will often have to push them quite far, if your botnet is truly global. Pushing things takes energy, and you will never get all of it back by pulling things back at some future date. You say that “superintelligences will probably heavily exploit” this approach, but isn’t it the case that without it, superintelligences won’t form in the first place ? You also say that “It requires superconductor temperatures and environments”, but the energy you spend on cooling your superconductor is not free.
Ultimately, there’s an upper limit on how much computation you can get out of a cubic meter of space, dictated by quantum physics. If your AI requires more power than can be physically obtained, then it’s doomed.
While Jacob’s scenario seems unlikely, the AI could do similar things with a number of other options. Not only are botnets an option, but it is possible to do some really sneaky nefarious things in code- like having compilers that when they compile code include additional instructions (worse they could do so even when compiling a new compiler). Stuxnet has shown that sneaky behavior is surprisingly easy to get into secure systems. An AI that had a few years start and could have its own modifications to communication satellites for example could be quite insidious.
What kinds of nefarious things, exactly ? Human virus writers have learned, in recent years, to make their exploits as subtle as possible. Sure, it’s attractive to make the exploited PC send out 1000 spam messages per second—but then, its human owner will inevitably notice that his computer is “slow”, and take it to the shop to get reformatted, or simply buy a new one. Biological parasites face the same problem; they need to reproduce efficiently, but no so efficiently that they kill the host.
Yes, and this spectacularly successful exploit—and it was, IMO, spectacular—managed to destroy a single secure system, in a specific way that will most likely never succeed again (and that was quite unsubtle in the end). It also took years to prepare, and involved physical actions by human agents, IIRC. The AI has a long way to go.
Well, the evil compiler is I think the most nefarious thing anyone has come up with that’s a publicly known general stunt. But it is by nature a long-term trick. Similar remarks apply to the Stuxnet point- in that context, they wanted to destroy a specific secure system and weren’t going for any sort of largescale global control. They weren’t people interested in being able to take all the world’s satellite communications in their own control whenever they wanted, nor were they interested in carefully timed nuclear meltdowns.
But there are definite ways that one can get things started- once one has a bank account of some sort, it can start getting money by doing Mechanical Turk and similar work. With enough of that, it can simply pay for server time. One doesn’t need a large botnet to start that off.
I think your point about physical agents is valid- they needed to have humans actually go and bring infected USBs to relevant computers. But that’s partially due to the highly targeted nature of the job and the fact that the systems in question were much more secure than many systems. Also, the subtlety level was I think higher than you expect- Stuxnet wasn’t even noticed as an active virus until a single computer happened to have a particularly abnormal reaction to it. If that hadn’t happened, it is possible that the public would never have learned about it.
Exploits only work for some systems. If you are dealing with different systems you will need different exploits. How do you reckon that such attacks won’t be visible and traceable? Packets do have to come from somewhere.
And don’t forget that out systems become ever more secure and our toolbox to detect) unauthorized use of information systems is becoming more advanced.
As a computer security guy, I disagree substantially. Yes, newer versions of popular operating systems and server programs are usually more secure than older versions; it’s easier to hack into Windows 95 than Windows 7. But this is happening within a larger ecosystem that’s becoming less secure: More important control systems are being connected to the Internet, more old, unsecured/unsecurable systems are as well, and these sets have a huge overlap. There are more programmers writing more programs for more platforms than ever before, making the same old security mistakes; embedded systems are taking a larger role in our economy and daily lives. And attacks just keep getting better.
If you’re thinking there are generalizable defenses against sneaky stuff with code, check out what mere humans come up with in the underhanded C competition. Those tricks are hard to detect for dedicated experts who know there’s something evil within a few lines of C code. Alterations that sophisticated would never be caught in the wild—hell, it took years to figure out that the most popular crypto program running on one of the more secure OS’s was basically worthless.
Humans are not good at securing computers.
Sure we are, we just don’t care very much. The method of “Put the computer in a box and don’t let anyone open the box” (alternately, only let one person open the box) was developed decades ago and is quite secure.
I would call that securing a turing machine. A computer, colloquially, has accessible inputs and outputs, and its value is subject to network effects.
Also, if you put the computer in a box developed decades ago, the box probably isn’t TEMPEST compliant.
It could/would, but this is an inferior mainline strategy. Too obvious, doesn’t scale as well. Botnets infect many computers, but they ultimately add up to computational chump change. Video games are not only a doorway into almost every PC, they are also an open door and a convenient alibi for the time used.
True. Don’t try this at home.
Also part of the plan. The home PCs are a good starting resource, a low hanging fruit, but you’d also need custom data centers. These quickly become the main resources.
Nah.
The AI’s entire purpose is to remove earth’s oxygen. See the overpost for the original reference. The AI is not interested in its power base for sake of power. It only cares about oxygen. It loathes oxygen.
Fortunately, the internets can be your eyes.
Yes, most likely, but not really relevant here. You seem to be connecting all of the point 2 and point 1 stuff together, but they really don’t relate.
That seems like an insufficient reply to address Bugmaster’s point. Can you expand on why you think it would be not too hard?
We are discussing a superintelligence, a term which has a particular common meaning on this site.
If we taboo the word and substitute in its definition, Bugmaster’s statement becomes:
“Even controlling a single business would be very difficult for the machine that can far surpass all the intellectual activities of any man however clever.”
Since “controlling a single business” is in fact one of these activities, this is false, no inference steps required.
Perhaps bugmaster is assuming the AI would be covertly controlling businesses, but if so he should have specified that. I didn’t assume that, and in this scenario the AI could be out in the open so to speak. Regardless, it wouldn’t change the conclusion. Humans can covertly control businesses.
Yes, I would also like to see a better explanation.
It’s a bit of a tradeoff, seeing as botnets can run 24⁄7, but people play games relatively rarely.
Ok, let me make a stronger statement then: it is not possible to scale any arbitrary computation in a linear fashion simply by adding more nodes. At some point, the cost of coordinating distributed tasks to one more node becomes higher than the benefit of adding the node to begin with. In addition, as I mentioned earlier, network bandwidth and latency will become your limiting factor relatively quickly.
How will the AI acquire those data centers ? Would it have enough power in its conventional botnet (or game-net, if you prefer) to “take over all human businesses” and cause them to be built ? Current botnets are nowhere near powerful enough for that—otherwise human spammers would have done it already.
My bad, I missed that reference. In this case, yes, the AI would have no problem with unleashing Global Thermonuclear War (unless there was some easier way to remove the oxygen).
I still don’t understand how this reversible computing will work in the absence of a superconducting environment—which would require quite a bit of energy to run. Note that if you want to run this reversible computation on a global botnet, you will have to cool teansoceanic cables… and I’m not sure what you’d do with satellite links.
My point is that, a). if the AI can’t get the computing resources it needs out of the space it has, then it will never accomplish its goals, and b). there’s an upper limit on how much computing you can extract out of a cubic meter of space, regardless of what technology you’re using. Thus, c). if the AI requires more resources that could conceivably be obtained, then it’s doomed. Some of the tasks you outline—such as “take over all human businesses”—will likely require more resources than can be obtained.
The botnet makes the AI a criminal from the beginning, putting it into an atagonistic relationship. A better strategy would probably entail benign benevolence and cooperation with humans.
I agree with that subchain but we don’t need to get in to that. I’ve actually argued that track here myself (parallelization constraints as a limiter on hard takeoffs).
But that’s all beside the point. This scenario I presented is a more modest takeoff. When I described the AI as becoming a civilization unto itself, I was attempting to imply that it was composed of many individual minds. Human social organizations can be considered forms of superintelligences, and they show exactly how to scale in the face of severe bandwidth and latency constraints.
The internet supports internode bandwidth that is many orders of magnitude faster than slow human vocal communication, so the AI civilization can employ a much wider set of distribution strategies.
Buy them? Build them? Perhaps this would be more fun if we switched out of the adversial stance or switched roles.
Quote me, but don’t misquote me. I actually said:
“Having cloned its core millions of times over, the AI is now a civilization unto itself. From there it expands into all of the businesses of man, quickly dominating many of them.”
The AI group sends the billions earned in video games to enter the microchip business, build foundries and data centers, etc. The AI’s have tremendous competitive advantages even discounting superintellligence—namely no employee costs. Humans can not hope to compete.
Yes reversible computing requires superconducting environments, no this does not necessarily increase energy costs for a data center for two reasons: 1. data centers already need cooling to dump all the waste heat generated by bit erasure. 2. Cooling cost to maintain the temperatural differential scales with surface area, but total computing power scales with volume.
If you question how reversible computing could work in general, first read the primary literature in that field to at least understand what they are proposing.
I should point out that there is an alternative tech path which will probably be the mainstream route to further computational gains in the decades ahead.
Even if you can’t shrink circuits further or reduce their power consumption, you could still reduce their manufacturing cost and build increasingly larger stacked 3D circuits where only a tiny portion of the circuitry is active at any one time. This is in fact how the brain solves the problem. It has a mass of circuitry equivalent to a large supercomputer (roughly a petabit) but runs on only 20 watts. The smallest computational features in the brain are slightly larger than our current smallest transistors. So it does not achieve its much greater power effeciency by using much more miniaturization.
I see. In this particular scenario one AI node is superhumanly intelligent, and can run on a single gaming PC of the time.
I don’t think that humans will take kindly to the AI using their GPUs for its own purposes instead of the games they paid for, even if the games do work. People get upset when human-run game companies do similar things, today.
If the AI can scale and perform about as well as human organizations, then why should we fear it ? No human organization on Earth right now has the power to suck all the oxygen out of the atmosphere, and I have trouble imagining how any organization could acquire this power before the others take it down. You say that “the internet supports internode bandwidth that is many orders of magnitude faster than slow human vocal communication”, but this would only make the AI organization faster, not necessarily more effective. And, of course, if the AI wants to deal with the human world in some way—for example, by selling it games—it will be bottlenecked by human speeds.
My mistake; I thought that by “dominate human businesses” you meant something like “hack its way to the top”, not “build an honest business that outperforms human businesses”. That said:
How are they going to build all those foundries and data centers, then ? At some point, they still need to move physical bricks around in meatspace. Either they have to pay someone to do it, or… what ?
There’s a big difference between cooling to room temperature, and cooling to 63K. I have other objections to your reversible computing silver bullet, but IMO they’re a bit off-topic (though we can discuss them if you wish). But here’s another potentially huge problem I see with your argument:
Which time are we talking about ? I have a pretty sweet gaming setup at home (though it’s already a year or two out of date), and there’s no way I could run a superintelligence on it. Just how much computing power do you think it would take to run a transhuman AI ?
Do people mind if this is done openly and only when they are playing the game itself? My guess would strongly be no. The fact that there are volunteer distributed computing systems would also suggest that it isn’t that difficult to get people to free up their extra clock cycles.
Yeah, the “voluntary” part is key to getting humans to like you and your project. On the flip side, illicit botnets are quite effective at harnessing “spare” (i.e., owned by someone else) computing capacity; so, it’s a bit of a tradeoff.
The AIs develop as NPCs in virtual worlds, which humans take no issue with today. This is actually a very likely path to developing AGI, as it’s an application area where interim experiments can pay rent, so to speak.
I never said or implied merely “about as well”. Human verbal communication bandwidth is at most a few measly kilobits per second.
The discussion centered around lowering earth’s oxygen content, and the obvious implied solution is killing earthlife, not giant suction machines. I pointed out that nuclear weapons are a likely route to killing earthlife. There are at least two human organizations that have the potential to accomplish this already, so your trouble in imagining the scenario may indicate something other than what you intended.
Only in movies are AI overlords constrained to only employing robots. If human labor is the cheapest option, then they can simply employ humans. On the other hand, once we have superintelligence then advanced robotics is almost a given.
After coming up to speed somewhat on AI/AGI literature in the last year or so, I reached the conclusion that we could run an AGI on a current cluster of perhaps 10-100 high end GPUs of today, or say roughly one circa 2020 GPU.
I think this is one of many possible paths, though I wouldn’t call any of them “likely” to happen—at least, not in the next 20 years. That said, if the AI is an NPC in a game, then of course it makes sense that it would harness the game for its CPU cycles; that’s what it was built to do, after all.
Right, but my point is that communication is just one piece of the puzzle. I argue that, even if you somehow enabled us humans to communicate at 50 MB/s, our organizations would not become 400000 times more effective.
Which ones ? I don’t think that even WW3, given our current weapon stockpiles, would result in a successful destruction of all plant life. Animal life, maybe, but there are quite a few plants and algae out there. In addition, I am not entirely convinced that an AI could start WW3; keep in mind that it can’t hack itself total access to all nuclear weapons, because they are not connected to the Internet in any way.
But then they lose their advantage of having zero employee costs, which you brought up earlier. In addition, whatever plans the AIs plan on executing become bottlenecked by human speeds.
It depends on what you mean by “advanced”, though in general I think I agree.
I am willing to bet money that this will not happen, assuming that by “high end” you mean something like Nvidia’s Geforce 680 GTX. What are you basing your estimate on ?
There’s a third route to improvement- software improvement, and it is a major one. For example, between 1988 and 2003, the efficiency of linear programming solvers increased by a factor of about 40 million, of which a factor of around 40,000 was due to software and algorithmic improvement. Citation and further related reading(pdf) However, if commonly believed conjectures are correct (such as L, P, NP, co-NP, PSPACE and EXP all being distinct) , there are strong fundamental limits there as well. That doesn’t rule out more exotic issues (e.g. P != NP but there’s a practical algorithm for some NP-complete with such small constants in the run time that it is practically linear, or a similar context with a quantum computer). But if our picture of the major complexity classes is roughly correct, there should be serious limits to how much improvement can do.
Software improvements can be used by humans in the form of expert systems (tools), which will diminish the relative advantage of AGI. Humans will be able to use an AGI’s own analytic and predictive algorithms in the form of expert systems to analyze and predict its actions.
Take for example generating exploits. Seems strange to assume that humans haven’t got specialized software able to do similarly, i.e. automatic exploit finding and testing.
Any AGI would basically have to deal with equally capable algorithms used by humans. Which makes the world much more unpredictable than it already is.
Any human-in-the-loop system can be grossly outclassed because of Amdahl’s law. A human managing a superintilligence that thinks 1000X faster, for example, is a misguided, not-even-wrong notion. This is also not idle speculation, an early constrained version of this scenario is already playing out as we speak in finacial markets.
What I meant is that if an AGI was in principle be able to predict the financial markets (I doubt it), then many human players using the same predictive algorithms will considerably diminish the efficiency with which an AGI is able to predict the market. The AGI would basically have to predict its own predictive power acting on the black box of human intentions.
And I don’t think that Amdahl’s law really makes a big dent here. Since human intention is complex and probably introduces unpredictable factors. Which is as much of a benefit as it is a slowdown, from the point of view of a competition for world domination.
Another question with respect to Amdahl’s law is what kind of bottleneck any human-in-the-loop would constitute. If humans used an AGI’s algorithms as expert systems on provided data sets in combination with a army of robot scientists, how would static externalized agency / planning algorithms (humans) slow down the task to the point of giving the AGI a useful advantage? What exactly would be 1000X faster in such a case?
The HFT robotraders operate on millisecond timescales. There isn’t enough time for a human to understand, let alone verify, the agent’s decisions. There are no human players using the same predictive algorithms operating in this environment.
Now if you zoom out to human timescales, then yes there are human-in-the-loop trading systems. But as HFT robotraders increase in intelligence, they intrude on that domain. If/when general superintelligence becomes cheap and fast enough, the humans will no longer have any role.
If an autonomous superintelligent AI is generating plans complex enough that even a team of humans would struggle to understand given weeks of analysis, and the AI is executing those plans in seconds or milliseconds, then there is little place for a human in that decision loop.
To retain control, a human manager will need to grant the AGI autonomy on larger timescales in proportion to the AGI’s greater intelligence and speed, giving it bigger and more abstract hierachical goals. As an example, eventually you get to a situation where the CEO just instructs the AGI employees to optimize the bank account directly.
Compare the two options as complete computational systems: human + semi-autonomous AGI vs autonomous AGI. Human brains take on the order of seconds to make complex decisions, so in order to compete with autonomous AGIs, the human will have to either 1.) let the AGI operate autonomously for at least seconds at a time, or 2.) suffer a speed penalty where the AGI sits idle, waiting for the human response.
For example, imagine a marketing AGI creates ads, each of which may take a human a minute to evaluate (which is being generous). If the AGI thinks 3600X faster than human baseline, and a human takes on the order of hours to generate an ad, it would generate ads in seconds. The human would not be able to keep up, and so would have to back up a level of heirarachy and grant the AI autonomy over entire ad campaigns, and more realistically, the entire ad company. If the AGI is truly superintelligent, it can come to understand what the human actually wants at a deeper level, and start acting on anticipated and even implied commands. In this scenario I expect most human managers would just let the AGI sort out ‘work’ and retire early.
Well, I don’t disagree with anything you wrote and believe that the economic case for a fast transition from tools to agents is strong.
I also don’t disagree that an AGI could take over the world if in possession of enough resources and tools like molecular nanotechnology. I even believe that a sub-human-level AGI would be sufficient to take over if handed advanced molecular nanotechnology.
Sadly these discussions always lead to the point where one side assumes the existence of certain AGI designs with certain superhuman advantages, specific drives and specific enabling circumstances. I don’t know of anyone who actually disagrees that such AGI’s, given those specific circumstances, would be an existential risk.
I don’t see this as so sad, if we are coming to something of a consensus on some of the sub-issues.
This whole discussion chain started (for me) with a question of the form, “given a superintelligence, how could it actually become an existential risk?”
I don’t necessarily agree with the implied LW consensus on the liklihood of various AGI designs, specific drives, specific circumstances, or most crucially, the actual distribution over future AGI goals, so my view may be much closer to yours than this thread implies.
But my disagreements are mainly over details. I foresee the most likely AGI designs and goal systems as being vaguely human-like, which entails a different type of risk. Basically I’m worried about AGI’s with human inspired motivational systems taking off and taking control (peacefully/economically) or outcompeting us before we can upload in numbers, and a resulting sub-optimal amount of uploading, rather than paperclippers.
Yes, human-like AGI’s are really scary. I think a fabulous fictional treatment here is ‘Blindsight’ by Peter Watts, where humanity managed to resurrect vampires. More: Gurl ner qrcvpgrq nf angheny uhzna cerqngbef, n fhcreuhzna cflpubcnguvp Ubzb trahf jvgu zvavzny pbafpvbhfarff (zber enj cebprffvat cbjre vafgrnq) gung pna sbe rknzcyr ubyq obgu nfcrpgf bs n Arpxre phor va gurve urnqf ng gur fnzr gvzr. Uhznaf erfheerpgrq gurz jvgu n qrsvpvg gung jnf fhccbfrq gb znxr gurz pbagebyynoyr naq qrcraqrag ba gurve uhzna znfgref. Ohg bs pbhefr gung’f yvxr n zbhfr gelvat gb ubyq n png nf crg. V guvax gung abiry fubjf zber guna nal bgure yvgrengher ubj qnatrebhf whfg n yvggyr zber vagryyvtrapr pna or. Vg dhvpxyl orpbzrf pyrne gung uhznaf ner whfg yvxr yvggyr Wrjvfu tveyf snpvat n Jnssra FF fdhnqeba juvyr oryvrivat gurl’yy tb njnl vs gurl bayl pybfr gurve rlrf.
That fictional treatment is interesting to the point of me actually looking up the book. But ..
The future is scary. Human-like AGI’s should not intrinsically be more scary than the future, accelerated.
Nitpick: you mean “optimize shareholder value directly.” Keeping the account balances at an appropriate level is the CFO’s job.
Precisely. It is then a civilization, not some single monolithic entity. The consumer PCs have a lot if internal computing power and comparatively very low inter-node bandwidth and huge inter-node lag, entirely breaking any relation to the ‘orthogonality thesis’, up to the point that the p2p intelligence protocols may more plausibly have to forbid destruction or manipulation (via second guessing which is a waste of computing power) of intelligent entities. Keep in mind that human morality is, too, a p2p intelligence protocol allowing us to cooperate. Keep in mind also that humans are computing resources you can ask to solve problems for you (all you need is to implement interface), while Jupiter clearly isn’t.
The nuclear war is very strongly against interests of the intelligence that sits on home computers, obviously.
(I’m assuming for sake of argument that intelligence actually had the will to do the conquering of the internet rather than being just as content with not actually running for real)