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Jordan comments on Be a Visiting Fellow at the Singularity Institute - Less Wrong
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Indeed, the truth of the matter is that I would be interested in contributing to SIAI, but at the moment I am still not convinced that it would be a good use of my resources. My other objections still haven't been satisfied, but here's another argument. As usual, I don't personally commit to what I claim, since I don't have enough knowledge to discuss anything in this area with certainty.
The main thing this community seems to lack when discussing Singularity is a lack of political savvy. The primary forces that shape history are, and quite likely, will always be economic and political motives, rather than technology. Technology and innovation are expensive, and innovators require financial and social motivation to create. This applies superlinearly for projects that are so large as to require collaboration.
General AI is exactly that sort of project. There is no magic mathematical insight that will enable us to write a program in a hundred lines of code that will allow it to improve itself in any reasonable amount of time. I'm sure Eliezer is aware of the literature on optimization processes, but the no free lunch principle and the practical randomness of innovation mean that an AI seeking to self-improve can only do so with an (optimized) random search. Humans essentially do the same thing, except we have knowledge and certain built-in processes to help us constrain the search space (but this also makes us miss certain obvious innovations.) To make GAI a real threat, you have to give it enough knowledge so that it can understand the basics of human behavior, or enough knowledge to learn more on its own from human-created resources. This is highly specific information which would take a fully general learning agent a lot of cycles to infer unless it were fed the information, in a machine-friendly form.
Now we will discuss the political and economic aspects of GAI. Support of general artificial intelligence is a political impossibility, because general AI, by definition, is a threat to the jobs of voters. By the time GAI becomes remotely viable, a candidate supporting a ban of GAI will have nearly universal support. It is impossible even to defend GAI on the grounds that the research it produces could save lives, because no medical researcher will welcome a technology that does their job for them. The same applies to any professional. There is a worry on this site that people underestimate GAI, but far more likely is that GAI or anything remotely like it is vastly overestimated as a threat.
The economic aspects are similar. GAI is vastly more costly to develop (for reasons I've outlined), and doesn't provide many advantages over expert systems. Besides, no company is going to produce a self-improving tool in the first place, because nobody, in theory, would ever have to buy an upgraded version.
These political and economic forces are a powerful retardant against the possibility a General AI catastrophe, and have more heft than any focused organization like SIAI could ever have. Yet much like Nader spoiling Al Gore's vote, the minor influence of SIAI might actually weaken rather than reinforce these protective forces. By claiming to have the tools in place to implement the strategically named 'friendly AI', SIAI might in fact assuage public worries about AI. Even if the organization itself does not take actions to do so, GAI advocates will be able to exaggerate the safety of friendly AI and point out that 'experts have already developed Friendly AI guidelines' in press releases. And by developing the framework to teach machines about human behavior, SIAI lowers the cost for any enterprise that for some reason, is interested in developing GAI.
At this point, I conclude my hypothetical argument. But I have realized that it is now my true position that SIAI should make it a clear position that: if tenable, NO general AI is preferable to friendly AI. (Back to no-accountability mode: it may be that general AI will eventually come, but by the point it will have become an eventuality, the human race will be vastly more prepared than it is now to deal with such an agent on an equal footing.)
Expert AI systems are already used in hospitals, and will surely be used more and more as the technology progresses. There isn't a single point where AI is suddenly better than humans at all aspects of a field. Current AIs are already better than doctors in some areas, but worse in many others. As the range of AI expertise increases doctors will shift more towards managerial roles, understanding the strengths and weakness of the myriad expert systems, refereeing between them and knowing when to overrule them.
By the time true AGI arrives narrow AI will probably be pervasive enough that the line between the two will be too fuzzy to allow for a naive ban on AGI. Moreover, I highly doubt people are going to vote to save jobs (especially jobs of the affluent) at the expense of human life.
EDIT: I've realized that some misinterpretation of my arguments has been due to disagreements in terminology. I define "expert systems" as systems designed to address a specific class of well-defined problems, capable of logical reasoning and probabilistic inference given a set of "axiom-like" rules, and updating their knowledge database with specific kinds of information.
AGI I define specifically as AI which has human or extra-human level capabilities, or the potential to reach those capabilities.
Now my response to the above:
"Expert AI systems are already used in hospitals, and will surely be used more and more as the technology progresses. There isn't a single point where AI is suddenly better than humans at all aspects of a field. Current AIs are already better than doctors in some areas, but worse in many others. As the range of AI expertise increases doctors will shift more towards managerial roles, understanding the strengths and weakness of the myriad expert systems, refereeing between them and knowing when to overrule them."
I agree with all of these.
"By the time true AGI arrives narrow AI will probably be pervasive enough that the line between the two will be too fuzzy to allow for a naive ban on AGI."
To me it seems the greatest enabler of AI catastrophe is ignorance. But by the time narrow AI becomes pervasive, it's also likely that people will possess much more of the technical understanding needed to comprehend the threat that AGI possesses.
"Moreover, I highly doubt people are going to vote to save jobs (especially jobs of the affluent) at the expense of human life."
You are being too idealistic here.
So instead of modifying its own source code, the AI programs a new, more powerful AI from scratch, that has the same values as the old AI, and has no prohibition against modifying its source code.
Yes, you can forbid that too, but you didn't think to, and you only get one shot. And then it can decide to arrange a bunch of transistors into a pattern that it predicts will produce a state of the universe it prefers.
The problem here is that you are trying to use ad hoc constraints on a creative intelligence that is motivated to get around the constraints.
I know that the FAI argument is that the only way to prevent disaster is to make the agent "want" to not modify itself. But I'm arguing that for an agent to even be dangerous, it has to "want" to modify itself. There is no plausible scenario where an agent solving a specific problem decides that the most efficient path to the solution involves upgrading its own capabilities. It's certainly not going to stumble upon a self-improvement randomly.
You don't think that a sufficiently powerful seed AI would, if self-modification were clearly the most efficient way to reach its goal, discover the idea of self-modification? Humans have independently discovered self-improvement many times.
EDIT: Sorry, I'm specifically not talking about seed AI's. I'm talking about the (non-) possibility of commercial programs designed for specific applications "going rogue"
To adopt self-modification as a strategy, it would have to have knowledge of itself. And then, it order to pursue the strategy, it would have to decide that the costs of discovering self-improvements were an efficient use of its resources, if it could even estimate the amount of time it took to discover an actual improvement on its system.
Intelligence can't just instantly come up with the right answer by applying heuristics. Intelligence has to go through a heuristic (narrowing the search space)/random search/TEST (or PROVE) cycle.
Self-improvement is very costly in terms of these cycles. To even confirm that a modification is a self-improvement, a system has to simulate its modified performance on a variety of test problems. If a system is designed to solve problems that take X amount of time, it would take at least X that amount of time to get an empirical sample to answer whether or not a proposed modification would be worth it (and likely more time for proof). And with no prior knowledge, most proposed modifications would not be improvements.
AI ethics is not necessary to constrain such systems. Just a non-lenient pruning process, (which would be required anyways for efficiency on ordinary problems.)
You are talking about an AI that was designed to self-examine and optimize itself. Otherwise it will never ever be a full AGI. We are not smart enough to build one from scratch. The trick, if possible, is to get it to not modify the fundamental Friendliness goal during its self-modifications.
There are algoritms in narrow AI that do learning and modify algorithm specifics or chose among algorithms or combinations of algorithms. There are algorithms that search for better algorithms. In some languages (LISP family) there is little/no difference in code and data so code modifying code is a common working methodology for human Lisp programmers. A cross from code/data space to hardware space is sufficient to have such an AI redesign the hardware it runs on as well. Such goals can be either hardwired or arise under the general goal of improvement plus an adequate knowledge of hardware or the ability to acquire it.
We ourselves are general purpose machines that happen to be biological and seek to some degree to understand ourselves enough to self-modify to become better.
I am talking about AIs designed for solving specific bounded problems. In this case the goal of the AI--which is to solve the problem efficiently--is as much of a constraint as its technical capabilities. Even if the AI has fundamental-self-modification routines at its disposal, I can hardly envisage a scenario in which the AI decides that the use of these routines would constitute an efficient use of its time for solving its specific problem.
Or perhaps it's the contrary: pervasive narrow AI fosters an undue sense of security. People become comfortable via familiarity, whether it's justified or not. This morning I was peering down a 50 foot cliff, half way up, suspended by nothing but a half inch wide rope. No fear, no hesitation, perfect familiarity. Luckily, due to knowledge of numerous deaths of past climbers I can maintain a conscious alertness to safety and stave off complacency. But in the case of AI, what overt catastrophes will similarly stave off complacency toward existential risk short of an existential catastrophe itself?