nhaliday + miri-cfar   58






















april 2018 by nhaliday
[1410.0369] The Universe of Minds
kinda dumb, don't think this guy is anywhere close to legit (e.g., he claims set of mind designs is countable, but gives no actual reason to believe that)
papers  preprint  org:mat  ratty  miri-cfar  ai  intelligence  philosophy  logic  software  cs  computation  the-self 
march 2018 by nhaliday
Existential Risks: Analyzing Human Extinction Scenarios





march 2018 by nhaliday





Deciphering China’s AI Dream
The context, components, capabilities, and consequences of
China’s strategy to lead the world in AI

Brussels is failing to grasp threats and opportunities of artificial intelligence.















US AI researchers: "No."


US AI researchers: "NO."

AI-risk was a mistake.
february 2018 by nhaliday
What Peter Thiel thinks about AI risk - Less Wrong



other stuff:
"learning from failure overrated"
cleantech a huge market, hard to compete
ethical behavior is a luxury of monopoly
february 2018 by nhaliday
Charity Cost-Effectiveness in an Uncertain World – Foundational Research Institute
Evaluating the effectiveness of our actions, or even just whether they're positive or negative by our values, is very difficult. One approach is to focus on clear, quantifiable metrics and assume that the larger, indirect considerations just kind of work out. Another way to deal with uncertainty is to focus on actions that seem likely to have generally positive effects across many scenarios, and often this approach amounts to meta-level activities like encouraging positive-sum institutions, philosophical inquiry, and effective altruism in general. When we consider flow-through effects of our actions, the seemingly vast gaps in cost-effectiveness among charities are humbled to more modest differences, and we begin to find more worth in the diversity of activities that different people are pursuing.
ratty  effective-altruism  subculture  article  decision-making  miri-cfar  charity  uncertainty  moments  reflection  regularizer  wire-guided  robust  outcome-risk  flexibility  🤖  spock  info-dynamics  efficiency  arbitrage 
august 2017 by nhaliday
Superintelligence Risk Project Update II

For example, I asked him what he thought of the idea that to we could get AGI with current techniques, primarily deep neural nets and reinforcement learning, without learning anything new about how intelligence works or how to implement it ("Prosaic AGI" [1]). He didn't think this was possible, and believes there are deep conceptual issues we still need to get a handle on. He's also less impressed with deep learning than he was before he started working in it: in his experience it's a much more brittle technology than he had been expecting. Specifically, when trying to replicate results, he's often found that they depend on a bunch of parameters being in just the right range, and without that the systems don't perform nearly as well.

The bottom line, to him, was that since we are still many breakthroughs away from getting to AGI, we can't productively work on reducing superintelligence risk now.

He told me that he worries that the AI risk community is not solving real problems: they're making deductions and inferences that are self-consistent but not being tested or verified in the world. Since we can't tell if that's progress, it probably isn't. I asked if he was referring to MIRI's work here, and he said their work was an example of the kind of approach he's skeptical about, though he wasn't trying to single them out. [2]

Earlier this week I had a conversation with an AI researcher [1] at one of the main industry labs as part of my project of assessing superintelligence risk. Here's what I got from them:

They see progress in ML as almost entirely constrained by hardware and data, to the point that if today's hardware and data had existed in the mid 1950s researchers would have gotten to approximately our current state within ten to twenty years. They gave the example of backprop: we saw how to train multi-layer neural nets decades before we had the computing power to actually train these nets to do useful things.

Similarly, people talk about AlphaGo as a big jump, where Go went from being "ten years away" to "done" within a couple years, but they said it wasn't like that. If Go work had stayed in academia, with academia-level budgets and resources, it probably would have taken nearly that long. What changed was a company seeing promising results, realizing what could be done, and putting way more engineers and hardware on the project than anyone had previously done. AlphaGo couldn't have happened earlier because the hardware wasn't there yet, and was only able to be brought forward by massive application of resources.

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july 2017 by nhaliday
Discovering Limits to Growth | Do the Math
march 2017 by nhaliday
Intelligent Agent Foundations Forum | Online Learning 1: Bias-detecting online learners
apparently can maybe be used to shave exponent from Christiano's manipulation-resistant reputation system paper
ratty  clever-rats  online-learning  acm  research  ai-control  miri-cfar 
november 2016 by nhaliday

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