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Skynet This Week #4: bots, robots, policy, research questions, and more!
Skynet This Week #4
By Viraat Aryabumi and Andrey Kurenkov
Our latest bi-weekly quick takes on a bunch of the most important recent media stories about AI, right in your email:
AI Advances
A team of AI algorithms just crushed humans in a complex computer game
Will Knight, MIT Tech Review
OpenAI has followed up on its 2017 achievement of beating pros at a 1v1 variation of the popular strategy game DoTA with a far more impressive feat: managing to beat a team of human players at a much more complex 5v5 variation of the game. Interestingly, the achievement was reached without any algorithmic advances, as OpenAI explain:
“OpenAI Five plays 180 years worth of games against itself every day, learning via self-play. It trains using a scaled-up version of Proximal Policy Optimization running on 256 GPUs and 128,000 CPU cores … This indicates that reinforcement learning can yield long-term planning with large but achievable scale — without fundamental advances, contrary to our own expectations upon starting the project.”
Though definitely impressive, it should be remembered that these systems took hundreds of human lifetimes to train just to play one game. So, just as with prior achievements in Go, they represent the success of present day AI at mastering single narrow skills using a ton of computation, and not the ability to match humans at learning many skills with much less experience.
Don’t Just Lecture Robots – Make Them Learn
Matt Simon, WIRED
Normally a robot has to learn everything from scratch, on its own. New research from UC Berkeley changes that - allowing robots to learn from experience and demonstrations. Matt Simon at the WIRED explains how such a systems works and what implications this has for future research.
AI Worries
Inside China’s Dystopian Dreams: A.I., Shame and Lots of Cameras
Paul Mozur, The New York Times
In China, the police are aggressive using AI. Using a network of over 200 million surveillance cameras and even “facial recognition glasses,” local law enforcement is able to sweep crowded areas in the hunt for criminals. Despite the tone of this article, it is worth noting that United States law enforcement uses similar tactics such as license plate readers to perform dragnet surveillance, though such tactics are more troubling in a country of single-party rule.
Facebook’s DensePose Tech Raises Concerns About Potential Misuse
Jeremy Hsu, IEEE Spectrum
Facebook open sourced DensePose, a deep learning based system that can make 3D models of humans from 2D images or videos. Jack Clark from OpenAI raises concerns about how such a system could be used in real-time surveillance. It is up to the researchers to ask how their systems might be used, before releasing them to the world.
The Latest on AI Policy
An Overview of National AI Strategies
Tim Dutton, Medium
The race to become the leading country in AI is on. Russian president Vladimir Putin has said “Whoever becomes the leader in this (AI) sphere will become the ruler of the world”. Various countries have developed a national strategy for AI. Tim Dutton, an AI Policy researcher at CIFAR summarizes the key policies and goals of each national strategy. It makes for an interesting read to look at and compare the various perspectives of different countries regarding AI.
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