US support was probably critical to IPCC’s establishment. And why did the US government support it? Assistant Undersecretary of State Bill Nitze wrote to me a few years later saying that our group’s activities played a significant role. Among other motivations, the US government saw the creation of the IPCC as a way to prevent the activism stimulated by my colleagues and me from controlling the policy agenda.
I suspect that the Reagan Administration believed that, in contrast to our group, most scientists were not activists, and would take years to reach any conclusion on the magnitude of the threat. Even if they did, they probably would fail to express it in plain English. The US government must have been quite surprised when IPCC issued its first assessment at the end of 1990, stating clearly that human activity was likely to produce an unprecedented warming.
The IPCC’s first assessment laid the groundwork for negotiation of the UN Framework Convention on Climate Change (UNFCCC), signed at the Earth Summit in 1992. In a sense, the UNFCCC and its progeny, the Kyoto Protocol, were unintended consequences of the US support for establishment of IPCC – not what the Reagan Administration had in mind!
From Burkholder et al.’s History of Western Music, 9th edition (pp. 938-939):
The demands on performers by composers like Berio and Carter [and] Babbitt, Stockhausen, and Boulez, were matched by their demands on listeners. Each piece was difficult to understand in its own right, using a novel musical language even more distant from the staples of the concert repertoire than earlier modernist music had been. Compounding listeners’ difficulties was that each composer and often each piece used a unique approach, so that even after getting to know one such work, encountering the next one could be like starting from scratch.
It’ll never work! A collection of experts being wrong about what is technologically feasible.
GiveWell update on its investigations into global catastrophic risks. My biggest disagreement is that I think nano-risk deserves more attention, if someone competent can be found to analyze the risks in more detail. GiveWell’s prioritization of biosecurity makes complete sense given their criteria.
Online calibration test with database of 150,000+ questions.
An ambitious Fermi estimate exercise: Estimating the energy cost of artificial evolution.
Nautilus publishes an excellent and wide-ranging interview with Scott Aaronson.
Video: robot autonomously folds pile of 5 previously unseen towels.
Somehow I had previously missed the Dietterich-Horvitz letter on Benefits and Risks of AI.
Robin Hanson reviews Martin Ford’s new book on tech unemployment.
Heh. That “stop the robots” campaign at SXSW was a marketing stunt for a dating app.
Winfield, Towards an Ethical Robot. They actually bothered to build simple consequentialist robots that obey a kind-of Asimovian rule.
…consider assigning a robot with superhuman intelligence the task of making paper clips. The robot has a great deal of computational power and general intelligence at its disposal, so it ought to have an easy time figuring out how to fulfill its purpose, right?
Not really. Human reasoning is based on an understanding derived from a combination of personal experience and collective knowledge derived over generations, explains MIRI researcher Nate Soares, who trained in computer science in college. For example, you don’t have to tell managers not to risk their employees’ lives or strip mine the planet to make more paper clips. But AI paper-clip makers are vulnerable to making such mistakes because they do not share our wealth of knowledge. Even if they did, there’s no guarantee that human-engineered intelligent systems would process that knowledge the same way we would.
MIRI’s worry is not that a superhuman AI will find it difficult to fulfill its programmed goal of — to use a silly, arbitrary example — making paperclips. Our worry is that a superhuman AI will be very, very good at achieving its programmed goals, and that unfortunately, the best way to make lots of paperclips (or achieve just about any other goal) involves killing all humans, so that we can’t interfere with the AI’s paperclip making, and so that the AI can use the resources on which our lives depend to make more paperclips. See Bostrom’s “The Superintelligent Will” for a primer on this.
Moreover, a superhuman AI may very well share “our wealth of knowledge.” It will likely be able to read and understand all of Wikipedia, and every history book on Google Books, and the Facebook timeline of more than a billion humans, and so on. It may very well realize that when we programmed it with the goal to make paperclips (or whatever), we didn’t intend for it to kill us all as a side effect.
But that doesn’t matter. In this scenario, we didn’t program the AI to do as we intended. We programmed it to make paperclips. The AI knows we don’t want it to use up all our resources, but it doesn’t care, because we didn’t program it to care about what we intended. We only programmed it to make paperclips, so that’s what it does — very effectively.
“Okay, so then just make sure we program the superhuman AI to do what we intend!”
Yes, exactly. That is the entire point of MIRI’s research program. The problem is that the instruction “do what we intend, in every situation including ones we couldn’t have anticipated, and even as you reprogram yourself to improve your ability to achieve your goals” is incredibly difficult to specify in computer code.
Nobody on Earth knows how to do that, not even close. So our attitude is: we’d better get crackin’.
Cotton-Barratt, Allocating risk mitigation across time.
The new Ian Morris book sounds very Hansonian, which probably means it’ll end up being one of my favorite books of 2015 when I have a chance to read it.
Why do we pay pure mathematicians? A dialogue.
Grace, The economy of weirdness.
On March 14th, there will be wrap parties for Harry Potter and the Methods of Rationality in at least 15 different countries. I’m assuming this is another first for a fanfic.
YC President Sam Altman on superhuman AI: part 1, part 2. I agree with most of what he writes, the biggest exceptions being that I think (1) AGI probably isn’t the Great Filter, (2) AI progress isn’t a double exponential, and (3) I don’t have much of an opinion on the role of regulation, as it’s not something I’ve tried hard to figure out.
Stuart Russell and Rodney Brooks debated the value alignment problem at Davos 2015. (Watch at 2x speed.)
Pretty good coverage of MIRI’s value learning paper at Nautilus.