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jacob_cannell comments on MIRI's Approach - Less Wrong
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No, not really. In fact, I expect that given the right way of modelling, formal verification of learning systems up to epsilon-delta bounds (in the style of PAC-learning, for instance) should be quite doable. Why? Because, as mentioned regarding PAC learning, it's the existing foundation for machine learning.
I do agree that this post reflects an "Old Computer Science" worldview, but to be fair, that's not Nate's personal fault, or MIRI's organizational fault. It's the fault of the entire subfield of AGI that still has not bloody learned the basic lessons of statistical machine learning: that real cognition just is about probably approximately correct statistical modelling.
So as you mention, for instance, there's an immense amount of foundational theory behind modern neural networks. Hell, if I could find the paper showing that deep networks form a "funnel" in the model's free-energy landscape - where local minima are concentrated in that funnel and all yield more-or-less as-good test error, while the global minimum reliably overfits - I'd be posting the link myself.
The problem with deep neural networks is not that they lack theoretical foundations. It's that most of the people going "WOW SO COOL" at deep neural networks can't be bothered to understand the theoretical foundations. The "deep learning cabal" of researchers (out of Toronto, IIRC), and the Switzerland Cabal of Schmidhuber-Hutter-and-Legg fame, all know damn well what they are doing on an analytical level.
(And to cheer for my favorite approach, the probabilistic programming cabal has even more analytical backing, since they can throw Bayesian statistics, traditional machine learning, and programming-languages theory at their problems.)
Sure, it does all require an unusual breadth of background knowledge, but they, this is how real science proceeds, people: shut up and read the textbooks and literature. Sorry, but if we (as in, this community) go around claiming that important problems can be tackled without background knowledge and active literature, or with as little as the "AGI" field seems to generate, then we are not being instrumentally rational. Period. Shut up and PhD.
Because that requires a way to state and demonstrate safety properties such that safety guarantees obtained with small amounts of resources remain strong when the system gets more resources. More on that below.
You're not really being fair to Nate here, but let's be charitable to you: this is fundamentally a dispute between the heuristics-and-biases school of thought about cognition and the bounded/resource-rational school of thought.
In the heuristics-and-biases school of thought, the human mind uses heuristics or biases when it believes it doesn't have the computing power on hand to use generally intelligent inference, or sometimes the general intelligence is even construed as an emergent computational behavior of an array of heuristics and biases that happened to get thrown together by evolution in the right way. Computationally, this is saying, "When we have enough resources that only asymptotic complexity matters, we use the Old Computer Science way of just running the damn algorithm that implements optimal behavior and optimal asymptotic complexity." Trying to extend this approach into statistical inference gets you basic Bayesianism and AIXI, which appear to have nice "optimality" guarantees, but are computationally intractable and are only optimal up to the training data you give them.
In the bounded-rationality school of thought, computing power is considered a strictly (not asymptotically) finite resource, which must be exploited in an optimal way. I've seen a very nice paper on how thermodynamics actually yields a formal theory for how to do this. Cognition is then analyzed as a algorithmic ways to tractably build and evaluate models that deal well with the data. This approach yields increasingly fruitful analyses of such cognitive activities as causal learning, concept learning, and planning in arbitrary environments as probabilistic inference enriched with causal/logical structure.
In terms of LW posts, the former alternative is embodied in Eliezer's Sequences, and the latter in jacob_cannell's post on The Brain as a Universal Learning Machine and my book review of Plato's Camera.
The kinds of steps needed to get both "AI" as such, and "Friendliness" as such, are substantively different in the "possible worlds" where the two different schools of thought apply. Or, perhaps, both are true in certain ways, and what we're really talking about is just two different ways of building minds. Personally, I think the one true distinction is that Calude's work on measuring nonhalting computations gives us a definitive way to deal with the kinds of self-reference scenarios that Old AGI's "any finite computation" approach generates paradoxes in.
But time will tell and I am not a PhD, so everything I say should be taken with substantial sprinklings of salt. On the other hand, to wit, while you shouldn't think for a second that I am one of them, I am certainly on the side of the PhDs.
(Nate: sorry for squabbling on your post. All these sorts of qualms with the research program were things I was going to bring up in person, in a much more constructive way. Still looking forward to meeting you in September!)
This isn't really a problem, because - as you point out - the formidable researchers all "know damn well what they are doing on an analytical level".
Thus the argument that there are people using DL without understanding it - and moreover that this is dangerous - is specious and weak because these people are not the ones actually likely to develop AGI let alone superintelligence.
Ah - the use of guarantees belies the viewpoint problem. Instead of thinking of 'safety' or 'alignment' as some absolute binary property we can guarantee, it is more profitable to think of a complex distribution over the relative amounts of 'safety' or 'alignment' in an AI population (and any realistic AI project will necessarily involve a population due to scaling constraints). Strong guarantees may be impossible, but we can at least influence or steer the distribution by selecting for agent types that are more safe/altruistic. We can develop a scaling theory of if, how, and when these desirable properties change as agents grow in capability.
In other words - these issues are so incredibly complex that we can't really develop any good kind of theory without alot of experimental data to back it up.
Also - I should point out that one potential likely result of ANN based AGI is the creation of partial uploads through imitation and reverse reinforcement learning - agents which are intentionally close in mindspace to their human 'parent' or 'model'.
Yes, but I don't think that's an argument anyone has actually made. Nobody, to my knowledge, sincerely believes that we are right around the corner from superintelligent, self-improving AGI built out of deep neural networks, such that any old machine-learning professor experimenting with how to get a lower error rate in classification tasks is going to suddenly get the Earth covered in paper-clips.
Actually, no, I can think of one person who believed that: a radically underinformed layperson on reddit who, for some strange reason, believed that LessWrong is the only site with people doing "real AI" and that "[machine-learning researchers] build optimizers! They'll destroy us all!"
Hopefully he was messing with me. Nobody else has ever made such ridiculous claims.
Sorry, wait, I'm forgetting to count sensationalistic journalists as people again. But that's normal.
No, "guarantees" in this context meant PAC-style guarantees: "We guarantee that with probability 1-\delta, the system will only 'go wrong' from what its sample data taught it 1-\epsilon fraction of the time." You then need to plug in the epsilons and deltas you want and solve for how much sample data you need to feed the learner. The links for intro PAC lectures in the other comment given to you were quite good, by the way, although I do recommend taking a rigorous introductory machine learning class (new grad-student level should be enough to inflict the PAC foundations on you).
"Altruistic" is already a social behavior, requiring the agent to have a theory of mind and care about the minds it believes it observes in its environment. It also assumes that we can build in some way to learn what the hypothesized minds want, learn how they (ie: human beings) think, and separate the map (of other minds) from the territory (of actual people).
Note that "don't disturb this system over there (eg: a human being) because you need to receive data from it untainted by your own causal intervention in any way" is a constraint that at least I, personally, do not know how to state in computational terms.
I think you are overhyping the PAC model. It surely is an important foundation for probabilistic guarantees in machine learning, but there are some serious limitations when you want to use it to constrain something like an AGI:
It only deals with supervised learning
Simple things like finite automata are not learnable, but in practice it seems like humans pick them up fairly easily.
It doesn't deal with temporal aspects of learning.
However, there are some modification of the PAC model that can ameliorate these problems, like learning with membership queries (item 2).
It's also perhaps a bit optimistic to say that PAC-style bounds on a possibly very complex system like an AGI would be "quite doable". We don't even know, for example, whether DNF is learnable in polynomial time under the distribution free assumption.
I would definitely call it an open research problem to provide PAC-style bounds for more complicated hypothesis spaces and learning settings. But that doesn't mean it's impossible or un-doable, just that it's an open research problem. I want a limitary theorem proved before I go calling things impossible.