Thought experiment:
We have an AI which controls a robotic arm inside a box. The box also contains various cooking tools and ingredients to make cake, and a big red button that kills a puppy.
We prefer to cake to no cake, and we prefer our cake to be delicious and moist cake, but above all we prefer the companion cu-... puppy to stay alive.
Therefore, we implement in the AI a "puppy safety module" (PSM) which vetoes any course of action proposed by the planning module if it determines that there is any non-negligible probability of the red button being depressed.
When the PSM can't make an accurate prediction it will always err on the safe side: things like throwing tools on the box walls where they can bounce unpredictably and hit the red button don't get approved.
Athena, the Greek goddess of wisdom, justice and math, came down from Mt. Olympus and debugged the code for PSM. She was also so kind to check all the compilers, OS and hardware for bugs and glitches.
Do you agree that with this AI the puppy is most likely safe?
Now, let's assume that the cakes made by this AI aren't very good. We don't really know how to program it to make better cakes, we try our best reinforcement learning algorithms but it doesn't improve much. Then somebody suggest we try to allow the AI to modify it own code:
In addition to sending motor commands to the arm, the planning module can also edit a copy of the AI code and eventually replace the whole program with this successor. Of course, these actions must still be approved by the PSM, but they are otherwise unrestricted: in principle they can modify the PSM, or add a bypass, or get rid of it entirely.
Do you think that this AI is unsafe for the puppy?
Do you think it is safe but unable to improve itself in effective ways?
Since the PSM was designed without self-modification in mind, "safe but unable to improve itself in effective ways".
(Not sure how this thought experiment helps the discussion along.)
Previously: Why Neglect Big Topics.
Why was there no serious philosophical discussion of normative uncertainty until 1989, given that all the necessary ideas and tools were present at the time of Jeremy Bentham?
Why did no professional philosopher analyze I.J. Good’s important “intelligence explosion” thesis (from 19591) until 2010?
Why was reflectively consistent probabilistic metamathematics not described until 2013, given that the ideas it builds on go back at least to the 1940s?
Why did it take until 2003 for professional philosophers to begin updating causal decision theory for the age of causal Bayes nets, and until 2013 to formulate a reliabilist metatheory of rationality?
By analogy to financial market efficiency, I like to say that “theoretical discovery is fairly inefficient.” That is: there are often large, unnecessary delays in theoretical discovery.
This shouldn’t surprise us. For one thing, there aren’t necessarily large personal rewards for making theoretical progress. But it does mean that those who do care about certain kinds of theoretical progress shouldn’t necessarily think that progress will be hard. There is often low-hanging fruit to be plucked by investigators who know where to look.
Where should we look for low-hanging fruit? I’d guess that theoretical progress may be relatively easy where:
These guesses make sense of the abundant low-hanging fruit in much of MIRI’s theoretical research, with the glaring exception of decision theory. Our September decision theory workshop revealed plenty of low-hanging fruit, but why should that be? Decision theory is widely applied in multi-agent systems, and in philosophy it’s clear that visible progress in decision theory is one way to “make a name” for oneself and advance one’s career. Tons of quality-adjusted researcher hours have been devoted to the problem. Yes, new theoretical advances (e.g. causal Bayes nets and program equilibrium) open up promising new angles of attack, but they don’t seem necessary to much of the low-hanging fruit discovered thus far. And progress in decision theory is definitely not valuable only to those with unusual views. What gives?
Anyway, three questions:
1 Good (1959) is the earliest statement of the intelligence explosion: “Once a machine is designed that is good enough… it can be put to work designing an even better machine. At this point an ”explosion“ will clearly occur; all the problems of science and technology will be handed over to machines and it will no longer be necessary for people to work. Whether this will lead to a Utopia or to the extermination of the human race will depend on how the problem is handled by the machines. The important thing will be to give them the aim of serving human beings.” The term itself, “intelligence explosion,” originates with Good (1965). Technically, artist and philosopher Stefan Themerson wrote a "philosophical analysis" of Good's intelligence explosion thesis called Special Branch, published in 1972, but by "philosophical analysis" I have in mind a more analytic, argumentative kind of philosophical analysis than is found in Themerson's literary Special Branch. ↩