Summary:
Regardless of whether one adopts a pessimistic or optimistic view of artificial intelligence, policy will shape how it affects society. This column looks at both the policies that will influence the diffusion of AI and policies that will address its consequences. One of the most significant long-run policy issues relates to the potential for artificial intelligence to increase inequality.
The author is Selmer Bringsjord.
Academic: https://homepages.rpi.edu/~brings/
Wikipedia: https://en.wikipedia.org/wiki/Selmer_Bringsjord
Author:
Website: https://www.joshuagans.com
Summary:
Philosophers have speculated that an AI tasked with a task such as creating paperclips might cause an apocalypse by learning to divert ever-increasing resources to the task, and then learning how to resist our attempts to turn it off. But this column argues that, to do this, the paperclip-making AI would need to create another AI that could acquire power both over humans and over itself, and so it would self-regulate to prevent this outcome. Humans who create AIs with the goal of acquiring power may be a greater existential threat.
Key paragraph:
The insight from economics is that while it may be hard, or even impossible, for a human to control a super-intelligent AI, it is equally hard for a super-intelligent AI to control another AI. Our modest super-intelligent paperclip maximiser, by switching on an AI devoted to obtaining power, unleashes a beast that will have power over it. Our control problem is the AI’s control problem too. If the AI is seeking power to protect itself from humans, doing this by creating a super-intelligent AI with more power than its parent would surely seem too risky.
Link to actual paper: https://arxiv.org/abs/1711.04309
Abstract:
Here we examine the paperclip apocalypse concern for artificial general intelligence (or AGI) whereby a superintelligent AI with a simple goal (ie., producing paperclips) accumulates power so that all resources are devoted towards that simple goal and are unavailable for any other use. We provide conditions under which a paper apocalypse can arise but also show that, under certain architectures for recursive self-improvement of AIs, that a paperclip AI may refrain from allowing power capabilities to be developed. The reason is that such developments pose the same control problem for the AI as they do for humans (over AIs) and hence, threaten to deprive it of resources for its primary goal.
Well put.
Thanks to Alex Tabarrok at Marginal Revolution: https://marginalrevolution.com/marginalrevolution/2018/05/one-parameter-equation-can-exactly-fit-scatter-plot.html
Title: “One parameter is always enough”
Author: Steven T. Piantadosi, ( University of Rochester)
Abstract:
We construct an elementary equation with a single real valued parameter that is capable of fitting any “scatter plot” on any number of points to within a fixed precision. Specifically, given given a fixed > 0, we may construct fθ so that for any collection of ordered pairs {(xj , yj )} n j=0 with n, xj ∈ N and yj ∈ (0, 1), there exists a θ ∈ [0, 1] giving |fθ(xj ) − yj | < for all j simultaneously. To achieve this, we apply prior results about the logistic map, an iterated map in dynamical systems theory that can be solved exactly. The existence of an equation fθ with this property highlights that “parameter counting” fails as a measure of model complexity when the class of models under consideration is only slightly broad.
After highlighting the two examples in the paper, Tabarrok provocatively writes:
Aside from the wonderment at the result, the paper also tells us that Occam’s Razor is wrong. Overfitting is possible with just one parameter and so models with fewer parameters are not necessarily preferable even if they fit the data as well or better than models with more parameters.
Occam’s Razor in its narrow form—the insight that simplicity is renders a claim more probable—is a consequence of the interaction between Kolmogorov complexity and Bayes’ theorem. I don’t see how this result affects this idea per se. But perhaps it shows the flaws of conceptualizing complexity as “number of parameters.”
Ah, of course. Thanks.
The term “affordance width” makes sense, but perhaps there’s no need to coin a new term when “tolerance” exists already.
A ∨ B ⟷ ¬A ⟶ B
But this is not true, because ¬(¬A ⟶ B) ⟶ A ∨ B. With what you’ve written you can get from the left side to the right side, but you can’t get from the right side to the left side.
What you need is: “Either Alice did it or Bob did it. If it wasn’t Alice, then it was Bob; and if it wasn’t Bob, then it was Alice.”
Thus: A ∨ B ⟷ (¬A ⟶ B ∧ ¬B ⟶ A)
Interesting post, and I’m sure “not having thought of it” helps explain the recency of vehicular attacks (though see the comment from /r/CronoDAS questioning the premise that they are as recent as they may seem).
Another factor: Other attractive methods, previously easy, are now harder—lowering the opportunity cost of a vehicular attack. For example, increased surveillance has made carefully coordinated attacks harder. And perhaps stricter regulations have made it harder to obtain bomb-making materials or disease agents.
This also helps to explain apparent geographical distribution of vehicle attacks: more common in Europe and Canada than the United States, especially per capita. Alternative ways to kill many people, like with a gun, are much easier in the US.
Yet another explanation: Perhaps terrorist behavior doesn’t appear to maximize damage or terror is that much terrorism is not intended to do so. My favorite piece arguing this is from Gwern:
How much support is there for promotion of prediction markets? I see three levels:
1. Legalization of real-money markets (they are legal in some places, but their illegality or legal dubiousness in the US—combined with the centrality of the US companies in global finance—makes it hard to run a big one without US permission)
2. Subsidies for real-money markets in policy-relevant issues, as advocated by Robin Hanson
3. Use of prediction markets to determine policy (futarchy), as envisioned by Robin Hanson
1. We want public policy that’s backed up by empiric evidence. We want a government that runs controlled trials to find out what policies work.
This seems either empty (because no policy has zero empirical backing), throttling (because you can’t possibly have an adequate controlled trial on every proposal), or pointless (because most political disputes are not good-faith disagreements over empirical support).
Second, as this list seems specific to one country, I wonder how rationalists who don’t follow its politics can inform this consensus.
Third, did you choose eight demands only to mimic the Fabians? Does that mean you omitted some other plausible demands, or that you stretched a few that perhaps should not have made the cut?
Upvoted for the suggestion to reword the euthanasia point.
Useful distinction: “rationalist” vs. “rational person.” By the former I mean someone who deliberately strives to be the latter. By the latter I mean someone who wins systematically in their life.
It’s possible that rationalists tend to be geeks, especially if the most heavily promoted methods for deliberately improving rationality are mathy things like explicit Bayesian reasoning, or if most of the material advocating rationality is heavily dependent on tech metaphors.
Rational people need not fit the stereotypes you’ve listed. Most people I know who seem to be good at living have excellent social skills and are physically fit. Some well-known rationalists, or fellow travelers, also do not fit. An example is Tim Ferriss.
Hey, I just saw this post. I like it. The coin example is a good way to lead in, and the non-quant teacher example is helpful too. But here’s a quibble:
If we follow Bayes’ Theorem, then nothing is just true. Thing are instead only probable because they are backed up by evidence.
The map is not the territory; things are still true or false. Bayes’ theorem doesn’t say anything about the nature of truth itself; whatever your theory of truth, that should not be affected by the acknowledgement of Bayes’ theorem. Rather, it’s our beliefs (or at least the beliefs of an ideal Bayesian agent) that are on a spectrum of confidence.
Introduction:
Artificial intelligence (AI) is useful for optimally controlling an existing system, one with clearly understood risks. It excels at pattern matching and control mechanisms. Given enough observations and a strong signal, it can identify deep dynamic structures much more robustly than any human can and is far superior in areas that require the statistical evaluation of large quantities of data. It can do so without human intervention.
We can leave an AI machine in the day-to-day charge of such a system, automatically self-correcting and learning from mistakes and meeting the objectives of its human masters.
This means that risk management and micro-prudential supervision are well suited for AI. The underlying technical issues are clearly defined, as are both the high- and low-level objectives.
However, the very same qualities that make AI so useful for the micro-prudential authorities are also why it could destabilise the financial system and increase systemic risk, as discussed in Danielsson et al. (2017).
Conclusion:
Artificial intelligence is useful in preventing historical failures from repeating and will increasingly take over financial supervision and risk management functions. We get more coherent rules and automatic compliance, all with much lower costs than current arrangements. The main obstacle is political and social, not technological.
From the point of view of financial stability, the opposite conclusion holds.
We may miss out on the most dangerous type of risk-taking. Even worse, AI can make it easier to game the system. There may be no solutions to this, whatever the future trajectory of technology. The computational problem facing an AI engine will always be much higher than that of those who seek to undermine it, not the least because of endogenous complexity.
Meanwhile, the very formality and efficiency of the risk management/supervisory machine also increases homogeneity in belief and response, further amplifying pro-cyclicality and systemic risk.
The end result of the use of AI for managing financial risk and supervision is likely to be lower volatility but fatter tails; that is, lower day-to-day risk but more systemic risk.
I agree with most of what you’ve said, but here’s a quibble:
If you are an evil pharma-corp, vaccines are a terrible way to be evil.
Unless you’re one of the sellers of vaccines, right?
That’s too bad; it probably doesn’t have to be that way. If you can articulate what infrastructural features of 1.0 are missing from 2.0, perhaps the folks at 2.0 can accommodate them in some way.
Done.
Two points:
First, can you clarify what you mean by rational persuasion, if you are distinguishing it from logical proof? Do you mean that we can skip arguing for some premises because we can rely on our intuition to identify them as already shared? Or do you mean that we need not aim for deductive certainty—a lower confidence level is acceptable? Or something else?
Second, I appreciate this post because what Harris’s disagreements with others so often need is exactly dissolution. And you’ve accurately described Harris’s project: He is trying to persuade an ideal listener of moral claims (e.g., it’s good to help people live happy and fulfilling lives), rather than trying to prove the truth of these claims from non-moral axioms.
Some elaboration on what Harris is doing, in my view:
Construct a hellish state of affairs (e.g., everyone suffering for all eternity to no redeeming purpose), call on the interlocutor to admit that such a situation is bad.
Construct a second state of affairs that is not so hellish (e.g., everyone happy and virtuous).
Call on the interlocutor to admit that the first situation is bad, and that the second situation is better.
Conclude that the interlocutor has admitted the truth of moral claims, even though Harris himself never explicitly said anything moral.
But by adding notions like “to no redeeming purpose” and “virtuous,” Harris is smuggling oughts into the universes he describes. (He has to do this in order to block the interlocutor from saying “I don’t admit the first situation is bad because the suffering could be for a good reason, and the second situation might not be good because maybe everyone is happy in a trivial sense because they’ve just wireheaded.)
In other words, Harris has not bridged the gap because he has begun on the “ought” side.
Rhetorically, Harris might omit the bits about purpose or virtue, and the interlocutor might still admit that the first state is bad and the second better, because the interlocutor has cooperatively embedded these additional moral premises.
In this case, to bridge the gap Harris counts on the listener supplying the first “ought.”