Portfolio Approach to AI Safety Research

[This article originally appeared on the Deep Safety blog.]

dimensionsLong-term AI safety is an inherently speculative research area, aiming to ensure safety of advanced future systems despite uncertainty about their design or algorithms or objectives. It thus seems particularly important to have different research teams tackle the problems from different perspectives and under different assumptions. While some fraction of the research might not end up being useful, a portfolio approach makes it more likely that at least some of us will be right.

In this post, I look at some dimensions along which assumptions differ, and identify some underexplored reasonable assumptions that might be relevant for prioritizing safety research. In the interest of making this breakdown as comprehensive and useful as possible, please let me know if I got something wrong or missed anything important.

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Death Note: Finally, an Anime about Deep Learning

It’s about time someone developed an anime series about deep learning. In the last several years, I’ve paid close attention to deep learning. And while I’m far from an expert on anime, I’ve watched a nonzero number of anime cartoons. And yet through neither route did I encounter even one single anime about deep learning.

There were some close calls. Ghost in the Shell gives a vague pretense of addressing AI. But the character might as well be a body-jumping alien. Nothing in this story speaks to the reality of machine learning research.

In Knights of Sidonia, if you can muster the superhuman endurance required to follow the series past its only interesting season, you’ll eventually find out that the flying space-ship made out of remnants of Earth on which Tanikaze and friends photosynthesize, while taking breaks from fighting space monsters, while wearing space-faring versions of mecha suits … [breath] contains an artificially intelligent brain-emulating parasitic nematode. But no serious consideration of ML appears.

If you were looking to anime for a critical discourse on artificial intelligence, until recently you’d be disappointed.

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Machine Learning Security at ICLR 2017

(This article originally appeared here. Thanks to Janos Kramar for his feedback on this post.)

The overall theme of the ICLR conference setting this year could be summarized as “finger food and ships”. More importantly, there were a lot of interesting papers, especially on machine learning security, which will be the focus on this post. (Here is a great overview of the topic.)


On the attack side, adversarial perturbations now work in physical form (if you print out the image and then take a picture) and they can also interfere with image segmentation. This has some disturbing implications for fooling vision systems in self-driving cars, such as impeding them from recognizing pedestrians. Adversarial examples are also effective at sabotaging neural network policies in reinforcement learning at test time.

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DeepMind Solves AGI, Summons Demon

In recent years, the rapid advance of artificial intelligence has evoked cries of alarm from billionaire entrepreneur Elon Musk and legendary physicist Stephen Hawking. Others, including the eccentric futurist Ray Kurzweil, have embraced the coming of true machine intelligence, suggesting that we might merge with the computers, gaining superintelligence and immortality in the process. As it turns out, we may not have to wait much longer.

This morning, a group of research scientists at Google DeepMind announced that they had inadvertently solved the riddle of artificial general intelligence (AGI). Their approach relies upon a beguilingly simple technique called symmetrically toroidal asynchronous  bisecting convolutions. By the year’s end, Alphabet executives expect that these neural networks will exhibit fully autonomous self-improvement. What comes next may affect us all.

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