Because all regulation does is redistribute power between fallible humans.
I am missing a step in your argument. Why is redistributing power between fallible humans ungood? I mean, surely some humans are more fallible than others, some have more information than others, some have incentives to be fallible in particularly harmful ways, etc.
(I am not arguing in favour of any particular bit of regulation; I just don’t see that “regulation is bad because it just redistributes things between fallible humans” makes any more sense than “trade is bad because it just redistributes things between fallible humans”.)
I am not making an argument about regulation in general here. I’m talking within the context of TRIZ-Ingenieur’s comment and the first point is that regulation will not save him from the dangers that he’s afraid of.
I don’t see how it works even in this specific context. TRIZ-Ingenieur hopes that regulation of AI research could reduce dangerous AI research. All regulation of dangerous things simply redistributes power between fallible humans; that’s no truer of AI research regulation than it is of any other regulation.
(It may be that there are special reasons why AI research is particularly unsuited for attempts to make it safer by regulation, but you didn’t mention any or even allude to any.)
The implication I’m reading in TRIZ-Ingenieur’s words is that humans are weak, fallible, corruptible—but a regulatory body is not. To quote him,
The regulatory body takes power away from the fallible human
This is a common fallacy where some body (organization, committee, council, etc.) is considered to be immune to human weaknesses as if it were composed of selfless enlightened philosopher-kings.
Essentially, the argument here is that mere humans can’t be trusted with AI development. Without opining on the truth of the subject claim, my point is that if they can’t, having a regulatory body won’t help.
My idea of a regulatory body is not that of a powerful institution that it deeply interacts with all ongoing projects because of the known fallible members who could misuse their power.
My idea of a regulatory body could be more that of a TÜV interconnected with institutions who do AI safety research and develop safety standards, test methods and test data. Going back to the TÜVs foundation task: pressure vessel certification. Any qualified test institution in the world can check if it is safe to use a given pressure vessel based on established design tests, safety measures checks, material testing methods and real pressure check tests. The amount of safety measures, tests and certification effort depends on the danger potential (pressure, volume, temperature, medium). Standards define based on danger potential and application which of the following safety measures must be used: safety valve; rupture disk; pressure limiter, temperature limiter, liquid indicator, overfill protection; vacuum breakers; reaction blocker; water sprinkling devices.
Nick Bostrum named following AI safety measures: boxing methods, incentive methods, stunting and tripwires.
Pressure vessels and AI have following common elements (AI related argument plausible, but no experience exists):
Human casualties are result of a bursting vessel or AI turning evil.
Good design, tests and safety measures reduce risk of failing.
Humans want to use both.
Companies, institutions and legislation had 110 years of development and improvement of standards for pressure vessels. With AI we are still scratching on the surface. AI and pressure vessels have following differences:
Early designs of pressure vessels were prone to burst—AI is stil far away from high risk level.
Many bursting vessel events successively stimulated improvement of standards—With AI the first singularity will be the only one.
Safety measures of pressure vessels are easily comprehensible—Easy AI safety measures reduce its functionality to a high degree, complex safety measures allow full functionality but are complex to implement, complex to test and to standardize.
The risk of a bursting pressure vessel is obvious—the risk of an evil Singularity is opaque and diffuse.
Safety measure research for pressure vessels is straight forward following physical laws—safety research for AI is a multifaceted cloud of concepts.
A bursting pressure vessel may kill a few dozen people—an evil Singularity might eradicate humankind.
Given the existential risk of AI I think most AI research institutions could agree on a code of conduct that would include e.g.
AIs will be classified in danger classes. The rating depends on computational power, taught knowledge areas, degree of self-optimization capacity. An AI with programming and hacking abilities will be classified as high risk application even if it is running on moderate hardware because of its intrinsic capabilities to escape into the cloud.
The amount of necessary safety measures depends on this risk rating:
Low risk applications have to be firewalled against acquisition of computing power in other computers.
Medium risk applications must additionally have internal safety measures e.g. stunting or tripwires.
High risk applications in addition must be monitored internally and externally by independently developed tool AIs.
Design and safeguard measures of medium and high risk applications will be independently checked and pentested by independent safety institutions.
In a first step safety AI research institutes develop monitoring AIs, tool AIs, pentesting datasets and finally guidelines like the one above.
In a second step public financed AI projects have to follow these guidelines. This applies to university projects in particular.
Public pressure and stockholders could push companies to apply these guidelines. Maybe an ISO certificate can indicate to the public: “All AI projects of this company follow the ISO Standard for AI risk assessment and safeguard measures”
The public opinion and companies hopefully will push governments to enforce these guidelines as well within their intelligence agencies. A treaty in the mind of the Non-Proliferation Treaty could be signed. All signing states ensure to obey the ISO Standard on AI within their institutions.
I accept that there are many IFs and obstacles on that path. But it is at least an IDEA how civil society can push AI developers to implement safeguards into their designs.
How many researchers join the AI field will only marginally change the acceleration of computing power. If only a few people work on AI they have enough to do to grab all the low-hanging fruit. If many join AI research more meta research and safety research will be possible. If only a fraction of this depicted path will turn into reality it will give jobs to some hundred researchers.
I agree that if TRIZ-Ingenieur thinks regulatory bodies are strong, infallible, and incorruptible, then he is wrong. I don’t see any particular reason to think he thinks that, though. It may in fact suffice for regulatory bodies’ weaknesses, errors and corruptions to be different from those of the individual humans being regulated, which they often are.
(I do not get the impression that T-I thinks “mere humans can’t be trusted with AI development” in any useful sense[1].)
[1] Example of a not-so-useful sense: it is probably true that mere humans can’t with 100% confidence of safety be trusted with AI development, or with anything else, and indeed the same will be true of regulatory bodies. But this doesn’t yield a useful argument against AI development for anyone who cares about averages and probabilities rather than only about the very worst case.
“Why is redistributing power between fallible humans ungood? I mean, surely some humans are more fallible than others, some have more information than others, some have incentives to be fallible in particularly harmful ways, etc.”
(Perhaps that’s actually your point and you’re not agreeing with Lumifer but suggesting that he dislikes regulation only because he associates it with the USSR, or something. In that case, I think you’re being unfair; he’s smarter than that.)
What you’re saying seems to be a fully general counterargument against all forms of government. There’s nothing necessarily wrong with that—anarchism is a real thing, after all—but you’re not going to be taken seriously if you suggest that anything other than anarchism must be wrong because Stalin ran a government.
Because all regulation does is redistribute power between fallible humans.
Yes. The regulatory body takes power away from the fallible human. If this human teams up with his evil AI he will become master of the universe. Above all of us including you. The redistribution will take power from to the synergetic entity of human and AI and all human beings on earth will gain power except the few ones entangled with that AI.
Who is that “we”?
Citizens concerned about possible negative outcomes of Singularity. Today this “we” is only a small community. In a few years this “we” will include most of the educated population of earth. As soon as a wider public is aware of the existential risks the pressure to create regulatory safeguards will rise.
LOL. So, do you think I have problems finding torrents of movies to watch?
DRM is easy to circumvent because it is not intrinsically part of the content but an unnecessary encryption. A single legal decryption can create a freely distributable copy. With computing power this could be designed differently, especially when specially designed chips will be used. Although GPUs are quite good for current deep learning algorithms there will be a major speed-up as soon as hardware becomes available that embeds these deep learning network architectures. The vital backpropagation steps required for learning could be made conditional on a hardware based enabling scheme that is under control of a tool AI that monitors all learning behaviour. For sure you could create FPGA alternatives—but these workarounds will come with significant losses in performance.
Why would the politicians need AI professionals when they’ll just hijack the process for their own political ends?
No—my writing was obviously unclear. We (the above mentioned “we”) need AI professionals to develop concepts how a regulatory process could be designed. Politicians are typically opportunistic, uninformed and greedy for power. When nothing can be done they do nothing. Therefore “we” should develop concepts of what can be done. If our politicians get intensively pushed by public pressure we maybe can hijack them to push regulation.
Today the situation is like this: Google, Facebook, Amazon, Baidu, NSA and some other players are in a good starting position to “win” Singularity. They will suppress any regulatory move because they could lose the lead. Once any of these players reaches Singularity he has in an instant the best hardware+the best software + the best regulatory ideas + the best regulatory stunting solutions—to remain solely on top and block all others. Then all of the sudden “everybody” = “we” are manipulated to want regulation. This will be especially effective if the superintelligent AI manages to disguise its capabilities and let the world think it had managed regulation. In this case not “we” have manged regulation, but the unbound and uncontrollable master-of-the-universe-AI.
So the AI turns its attention to examining certain blobs of binary code—code composing operating systems, or routers, or DNS services—and then takes over all the poorly defended computers on the Internet. [AI Foom Debate, Eliezer Yudkowski]
Capturing resource bonanzas might be enough to make AI go FOOM. It is even more effective if the bonanza is not only a dumb computing resource but offers useful data, knowledge and AI capabilities.
Therefore attackers (humans, AI-assisted humans, AIs) may want:
overtake control to use existing capabilities
extract capabilities to augment own capabilities
overtake resources for other uses
disguise resource owners and admins
Attack principles
Resource attack (hardware, firmware, operating system, firewall) or indirect spear attack on the admin or offering of cheap or free resources for AI execution on attacker’s hardware followed by a direct system attack (copy/modify/replace existing algorithms)
Mental trojan horse attack: hack communication if not accessible and try to alter the ethical bias from friendly AI that is happy being boxed/stunted/monitored to an evil AI that wants to break out. Teach the AI how to open the door from inside and the attacker can walk in.
Manipulate owner attack: Make the owner or admin greedy to improve its AI’s capabilities. Admins install malignant knowledge chunks or train subvertable malicious training samples. Trojan horse is saddled.
Possible Safeguard Concepts:
To make resource attacks improbable existing networking communication channels must be replaced with something intrinsically safe. Our brain is air-gapped and there is hardly any direct access to its neural network. Via five perceptive senses (hearing, sight, touch, smell and taste) it can receive input. With gestures, speach, smell, writing, shaping and arbitrarily manipulation using tools it can communicate to the outside world. All channels except for vision have a quite low bandwidth.
This analogon could shape a possible safeguard concept for AIs: make the internal AIs network inaccessible to user and admin. If even the admin cannot access it, the attacker cannot either. As soon as we jump from GPU computing to special featured hardware we can implement this. Hardware fuses on the chip can disable functionalities same as on todays CPUs debugging features are deactivated in chips for the market. Chips could combine fixed values and unalterable memories and free sections with learning allowed. Highest security is possible with base values and drives in fixed conscience-ROM structures.
Safeguards against malicious training samples will be more complex. To identify hidden malicious aspects of communication or learning samples is a task for an AI in itself. I see this as a core task for AI safety research.
An event with a duration of one minute can traumatize a human for an entire life. Humans can lose interest in anything they loved to do before and let them drop into suicidal depression. Same could happen to an AI. It could be that a traumatizing event could trigger a revenge drive that takes over all other aims of the utility function. Given the situation an AI is in love with her master and another AI kills her master while the AI is witnessing this. Given the situation that the adversary AI is not a simple one but a Hydra with many active copies. To eradicate this mighty adversary a lot of resources are needed. The revenge seeking AI will prepare its troops by conquering as many systems as possible. The less safe our systems are the faster such an evil AI can grow.
Safe design could include careful use of impulsive revenge drives with hard wired self-regulatory counter controlling measures e.g. distraction or forgetting.
Safe designs should filter out possible traumaticizing inputs. This will reduce the functionality a bit but the safety tradeoff will be worth it. The filtering could be implemented in a soft manner like a mother explaining the death of the loved dog to the child in warm words with positive perspectives.
Because all regulation does is redistribute power between fallible humans.
Who is that “we”?
LOL. So, do you think I have problems finding torrents of movies to watch?
Why would the politicians need AI professionals when they’ll just hijack the process for their own political ends?
I am missing a step in your argument. Why is redistributing power between fallible humans ungood? I mean, surely some humans are more fallible than others, some have more information than others, some have incentives to be fallible in particularly harmful ways, etc.
(I am not arguing in favour of any particular bit of regulation; I just don’t see that “regulation is bad because it just redistributes things between fallible humans” makes any more sense than “trade is bad because it just redistributes things between fallible humans”.)
I am not making an argument about regulation in general here. I’m talking within the context of TRIZ-Ingenieur’s comment and the first point is that regulation will not save him from the dangers that he’s afraid of.
I don’t see how it works even in this specific context. TRIZ-Ingenieur hopes that regulation of AI research could reduce dangerous AI research. All regulation of dangerous things simply redistributes power between fallible humans; that’s no truer of AI research regulation than it is of any other regulation.
(It may be that there are special reasons why AI research is particularly unsuited for attempts to make it safer by regulation, but you didn’t mention any or even allude to any.)
The implication I’m reading in TRIZ-Ingenieur’s words is that humans are weak, fallible, corruptible—but a regulatory body is not. To quote him,
This is a common fallacy where some body (organization, committee, council, etc.) is considered to be immune to human weaknesses as if it were composed of selfless enlightened philosopher-kings.
Essentially, the argument here is that mere humans can’t be trusted with AI development. Without opining on the truth of the subject claim, my point is that if they can’t, having a regulatory body won’t help.
My idea of a regulatory body is not that of a powerful institution that it deeply interacts with all ongoing projects because of the known fallible members who could misuse their power.
My idea of a regulatory body could be more that of a TÜV interconnected with institutions who do AI safety research and develop safety standards, test methods and test data. Going back to the TÜVs foundation task: pressure vessel certification. Any qualified test institution in the world can check if it is safe to use a given pressure vessel based on established design tests, safety measures checks, material testing methods and real pressure check tests. The amount of safety measures, tests and certification effort depends on the danger potential (pressure, volume, temperature, medium). Standards define based on danger potential and application which of the following safety measures must be used: safety valve; rupture disk; pressure limiter, temperature limiter, liquid indicator, overfill protection; vacuum breakers; reaction blocker; water sprinkling devices.
Nick Bostrum named following AI safety measures: boxing methods, incentive methods, stunting and tripwires. Pressure vessels and AI have following common elements (AI related argument plausible, but no experience exists):
Human casualties are result of a bursting vessel or AI turning evil.
Good design, tests and safety measures reduce risk of failing.
Humans want to use both.
Companies, institutions and legislation had 110 years of development and improvement of standards for pressure vessels. With AI we are still scratching on the surface. AI and pressure vessels have following differences:
Early designs of pressure vessels were prone to burst—AI is stil far away from high risk level.
Many bursting vessel events successively stimulated improvement of standards—With AI the first singularity will be the only one.
Safety measures of pressure vessels are easily comprehensible—Easy AI safety measures reduce its functionality to a high degree, complex safety measures allow full functionality but are complex to implement, complex to test and to standardize.
The risk of a bursting pressure vessel is obvious—the risk of an evil Singularity is opaque and diffuse.
Safety measure research for pressure vessels is straight forward following physical laws—safety research for AI is a multifaceted cloud of concepts.
A bursting pressure vessel may kill a few dozen people—an evil Singularity might eradicate humankind.
Given the existential risk of AI I think most AI research institutions could agree on a code of conduct that would include e.g.
AIs will be classified in danger classes. The rating depends on computational power, taught knowledge areas, degree of self-optimization capacity. An AI with programming and hacking abilities will be classified as high risk application even if it is running on moderate hardware because of its intrinsic capabilities to escape into the cloud.
The amount of necessary safety measures depends on this risk rating:
Low risk applications have to be firewalled against acquisition of computing power in other computers.
Medium risk applications must additionally have internal safety measures e.g. stunting or tripwires.
High risk applications in addition must be monitored internally and externally by independently developed tool AIs.
Design and safeguard measures of medium and high risk applications will be independently checked and pentested by independent safety institutions.
In a first step safety AI research institutes develop monitoring AIs, tool AIs, pentesting datasets and finally guidelines like the one above.
In a second step public financed AI projects have to follow these guidelines. This applies to university projects in particular.
Public pressure and stockholders could push companies to apply these guidelines. Maybe an ISO certificate can indicate to the public: “All AI projects of this company follow the ISO Standard for AI risk assessment and safeguard measures”
The public opinion and companies hopefully will push governments to enforce these guidelines as well within their intelligence agencies. A treaty in the mind of the Non-Proliferation Treaty could be signed. All signing states ensure to obey the ISO Standard on AI within their institutions.
I accept that there are many IFs and obstacles on that path. But it is at least an IDEA how civil society can push AI developers to implement safeguards into their designs.
How many researchers join the AI field will only marginally change the acceleration of computing power. If only a few people work on AI they have enough to do to grab all the low-hanging fruit. If many join AI research more meta research and safety research will be possible. If only a fraction of this depicted path will turn into reality it will give jobs to some hundred researchers.
I agree that if TRIZ-Ingenieur thinks regulatory bodies are strong, infallible, and incorruptible, then he is wrong. I don’t see any particular reason to think he thinks that, though. It may in fact suffice for regulatory bodies’ weaknesses, errors and corruptions to be different from those of the individual humans being regulated, which they often are.
(I do not get the impression that T-I thinks “mere humans can’t be trusted with AI development” in any useful sense[1].)
[1] Example of a not-so-useful sense: it is probably true that mere humans can’t with 100% confidence of safety be trusted with AI development, or with anything else, and indeed the same will be true of regulatory bodies. But this doesn’t yield a useful argument against AI development for anyone who cares about averages and probabilities rather than only about the very worst case.
“Why is redistributing power between fallible humans ungood? I mean, surely some humans are more fallible than others, some have more information than others, some have incentives to be fallible in particularly harmful ways, etc.”
This is what Stalin said as well.
Reversed stupidity is not intelligence.
(Perhaps that’s actually your point and you’re not agreeing with Lumifer but suggesting that he dislikes regulation only because he associates it with the USSR, or something. In that case, I think you’re being unfair; he’s smarter than that.)
The system you described requires someone to be on top.
For a more elaborate response, see Animal Farm. :)
I didn’t describe a system.
What you’re saying seems to be a fully general counterargument against all forms of government. There’s nothing necessarily wrong with that—anarchism is a real thing, after all—but you’re not going to be taken seriously if you suggest that anything other than anarchism must be wrong because Stalin ran a government.
Yes. The regulatory body takes power away from the fallible human. If this human teams up with his evil AI he will become master of the universe. Above all of us including you. The redistribution will take power from to the synergetic entity of human and AI and all human beings on earth will gain power except the few ones entangled with that AI.
Citizens concerned about possible negative outcomes of Singularity. Today this “we” is only a small community. In a few years this “we” will include most of the educated population of earth. As soon as a wider public is aware of the existential risks the pressure to create regulatory safeguards will rise.
DRM is easy to circumvent because it is not intrinsically part of the content but an unnecessary encryption. A single legal decryption can create a freely distributable copy. With computing power this could be designed differently, especially when specially designed chips will be used. Although GPUs are quite good for current deep learning algorithms there will be a major speed-up as soon as hardware becomes available that embeds these deep learning network architectures. The vital backpropagation steps required for learning could be made conditional on a hardware based enabling scheme that is under control of a tool AI that monitors all learning behaviour. For sure you could create FPGA alternatives—but these workarounds will come with significant losses in performance.
No—my writing was obviously unclear. We (the above mentioned “we”) need AI professionals to develop concepts how a regulatory process could be designed. Politicians are typically opportunistic, uninformed and greedy for power. When nothing can be done they do nothing. Therefore “we” should develop concepts of what can be done. If our politicians get intensively pushed by public pressure we maybe can hijack them to push regulation.
Today the situation is like this: Google, Facebook, Amazon, Baidu, NSA and some other players are in a good starting position to “win” Singularity. They will suppress any regulatory move because they could lose the lead. Once any of these players reaches Singularity he has in an instant the best hardware+the best software + the best regulatory ideas + the best regulatory stunting solutions—to remain solely on top and block all others. Then all of the sudden “everybody” = “we” are manipulated to want regulation. This will be especially effective if the superintelligent AI manages to disguise its capabilities and let the world think it had managed regulation. In this case not “we” have manged regulation, but the unbound and uncontrollable master-of-the-universe-AI.
The “regulatory body” is the same fallible humans. Plus power corrupts.
Why wouldn’t a “regulatory body” team up with an evil AI? Just to maintain the order, you understand...
Colour me sceptical. In fact, I’ll just call this hopeful idiocy.
In the real world? Do tell.
Do you have any idea how to make development teams invest substantial parts in safety measures?
To start with you need some sort of a general agreement about what “safety measures” are, and that should properly start with threat analysis.
Let me point out that the Skynet/FOOM theory isn’t terribly popular in the wide world out there (outside of Hollywood).
Capturing resource bonanzas might be enough to make AI go FOOM. It is even more effective if the bonanza is not only a dumb computing resource but offers useful data, knowledge and AI capabilities.
Therefore attackers (humans, AI-assisted humans, AIs) may want:
overtake control to use existing capabilities
extract capabilities to augment own capabilities
overtake resources for other uses
disguise resource owners and admins
Attack principles
Resource attack (hardware, firmware, operating system, firewall) or indirect spear attack on the admin or offering of cheap or free resources for AI execution on attacker’s hardware followed by a direct system attack (copy/modify/replace existing algorithms)
Mental trojan horse attack: hack communication if not accessible and try to alter the ethical bias from friendly AI that is happy being boxed/stunted/monitored to an evil AI that wants to break out. Teach the AI how to open the door from inside and the attacker can walk in.
Manipulate owner attack: Make the owner or admin greedy to improve its AI’s capabilities. Admins install malignant knowledge chunks or train subvertable malicious training samples. Trojan horse is saddled.
Possible Safeguard Concepts:
To make resource attacks improbable existing networking communication channels must be replaced with something intrinsically safe. Our brain is air-gapped and there is hardly any direct access to its neural network. Via five perceptive senses (hearing, sight, touch, smell and taste) it can receive input. With gestures, speach, smell, writing, shaping and arbitrarily manipulation using tools it can communicate to the outside world. All channels except for vision have a quite low bandwidth.
This analogon could shape a possible safeguard concept for AIs: make the internal AIs network inaccessible to user and admin. If even the admin cannot access it, the attacker cannot either. As soon as we jump from GPU computing to special featured hardware we can implement this. Hardware fuses on the chip can disable functionalities same as on todays CPUs debugging features are deactivated in chips for the market. Chips could combine fixed values and unalterable memories and free sections with learning allowed. Highest security is possible with base values and drives in fixed conscience-ROM structures.
Safeguards against malicious training samples will be more complex. To identify hidden malicious aspects of communication or learning samples is a task for an AI in itself. I see this as a core task for AI safety research.
An event with a duration of one minute can traumatize a human for an entire life. Humans can lose interest in anything they loved to do before and let them drop into suicidal depression. Same could happen to an AI. It could be that a traumatizing event could trigger a revenge drive that takes over all other aims of the utility function. Given the situation an AI is in love with her master and another AI kills her master while the AI is witnessing this. Given the situation that the adversary AI is not a simple one but a Hydra with many active copies. To eradicate this mighty adversary a lot of resources are needed. The revenge seeking AI will prepare its troops by conquering as many systems as possible. The less safe our systems are the faster such an evil AI can grow.
Safe design could include careful use of impulsive revenge drives with hard wired self-regulatory counter controlling measures e.g. distraction or forgetting.
Safe designs should filter out possible traumaticizing inputs. This will reduce the functionality a bit but the safety tradeoff will be worth it. The filtering could be implemented in a soft manner like a mother explaining the death of the loved dog to the child in warm words with positive perspectives.