2016 news

Overview

Continuing the 2015 trends, 2016 was a banner year for AI & genetics.

In AI, demonstrating the potential for rapid advance, AlphaGo went from low professional level as of October 2015 to world champion level, crushing Lee Sedol 4-1 with substantial margin, and just when everyone had forgotten, then a refined (presumably pure self-play) version of AlphaGo went 60-0 in blitz matches online with many of the top Go players (including Ke Jie). The translation RNNs finally made their long-awaited appearance in commercial production with Google Translate, making for the largest jump in translation quality in decades, bringing many translation pairs up to surprisingly high quality (even Japanese⟺English translations are now semi-comprehensible, as opposed to the status quo total gibberish); combined with the rapid progress in voice transcription and the surprising results of human-level lipreading, one can now imagine a NN-powered Babelfish (which, combined with HUDs, could be revolutionary for the deaf & hearing-impaired). Generative adversarial networks (GANs) remained a central topic of AI research, with better theoretical understanding (linking them to reinforcement learning), and many tweaks and incremental refinements increasing the size of feasible generated images (eg StackGAN’s large bird/flower image generation capability); however, GANs currently have not delivered any meaningful increases on any applied tasks & remain a solution in search of a problem, so that is something to hope for in 2017—demonstration that the unsupervised or generative aspects of GANs can be usefully employed for planning or something. Perhaps the most exciting work in 2016 was the long-term work on architecture in providing large-scale memory mechanisms (in the form of efficient external memory or encoded into the weights of large expanding or sharded NNs), in learning to train large-scale NNs (“synthetic gradients”), and in a particularly surprising set of papers, demonstrating that NNs+reinforcement-learning can efficiently learn how to design NN architectures & units. (This was not something anyone doubted could be done, but previous RL work suggested that it was years away & no one could manage it without whole GPU farms; but as far as Google was concerned… “You see, I told you it couldn’t be done without turning the whole country into a factory. You have done just that.”) Since NNs do not decay like biological neurons, and are not hard-limited by skull volumes or calories, and since all tasks share mutual information & form informative priors for each other critical to sample-efficient learning, there is a lot of inherent pressure towards large growing multi-task NNs which do transfer learning & can optimize at multiple levels end-to-end; as GPU RAM limits lift, we’ll see more of these. Aside from the important work in “NNs all the way down” vein, reinforcement learning grew in importance and it is increasingly common to use RL methods to control memory or network components, interact with an environment (often broadly interpreted, as anything which can be turned into a tree, which goes far beyond games like Go or chess & includes theorem proving or program optimization), or learn to optimize a non-differentiable reward/loss function, and I am excited to see planning re-emerge as a theme after the dominance of model-free methods over the past 3 years; we will see more of that in 2017, doubtless, especially as some of the architectural tweaks from 2016 (some of which claim as much as an order of magnitude improvement on ALE sample-efficiency) get tried out & reused.

In genetics, the growth of UK Biobank and the introduction of LD score regression & other summary-statistic-only methods continued driving large-scale results; the study of human genetic correlations made an absurd amount of progress in 2016, demonstrating shared genetic influences on countless phenotypic traits and pervasive intercorrelations of good traits and disease traits, respectively. Detecting recent human evolution has been difficult due to lack of ancient DNA to compare with, but the supply of that has grown steadily, permitting some specific examples to be nailed down, and a new method based on contemporary whole genomes may blow the area wide open as whole genomes have recently crossed the $1000 mark and in coming years, scientific projects & medical biobanks will shift over to whole genomes. Another possible field explosion is “genome synthesis”—I was astonished to learn that it is now feasible to synthesize from scratch entire chromosomes of arbitrary design, and that a human genome could potentially be synthesized for ~$1 billion, which would render totally obsolete any considerations of embryo selection/CRISPR/iterated embryo selection, with an active advocacy effort for a genome synthesis project to be launched. 2017 will bring further discoveries of how humans have adapted to local environments and their societies over the past centuries & millennia. Honorable mentions should also go to the steady (and disquieting) progress towards iterated embryo selection, and a scattering of results from the continuously-growing-sample-sizes GWASes: as predicted, the education/intelligence hits have increased drastically as sample size increased, and the historically difficult targets of personality & depression have finally yielded some more hits. One particularly intriguing GWAS focused on violence & criminal behavior with good results, so that trait will yield as well to further study. Past GWASes continued to be applied; the results of Belsky et al 2016 will come as no surprise, but will frustrate the critics who insist that all non-disease results are methodological artifacts or merely reflect population structure. CRISPR progress continues as expected, with the first uses in humans in 2016 by Chinese & American scientists.

Less cosmically, one of the big tech stories of 2016 was the rollout of consumer VR—successful but not epochal, clearly the (or at least, a) future of gaming but no killer app. Oculus had a rocky launch caused by its decision to launch prematurely, without motion controls, which the launch of HTC/Valve’s Vive made clear is not an optional feature for truly compelling VR (and my own brief experience with an Oculus Rift at a Best Buy demo left me longing, after just 20 seconds in The Climb, for hand tracking), but the lack of motion controls & compelling content made for a slow launch. The Vive had a better launch with excellent motion controls & tracking, the comparable Oculus Touch controls only really shipping half a year later in December, demonstrating why Oculus launched when it did—it was either bite the bullet of a bad launch, or let Vive rule unopposed. Somewhat to my surprise, Sony’s quiet Project Morpheus launched successfully as PlayStation VR, making for 3 high-quality competing VR headsets/ecosystems. (Sony had not seemed serious about the whole VR thing so I doubted it would launch in 2016 or at all.) While most gamers, much less people, do not feel a burning need for getting into VR at the moment (myself included, as I think the screen resolutions need improvement), what is notable is what didn’t happen: we did not see widespread reports of vomiting, of people swearing off VR forever, of VR being discarded as a 3D-TV-like gimmick, of developers flooding in & getting burned, of sales plummeting and being well below the million-mark, of the initial trickle of games sputtering out… In short, of any of the things that the naysayers predicted would doom consumer VR. The worst that the early adopters, critics, and regular people have to say is that there are not enough good games (decreasingly true by the end of 2016), that the headsets and GPUs cost too much (true but will predictably be fixed as time passes), that the Oculus Rift lacked motion controls (fixed as of December 2016), and the resolution is too low / devices are wired / require external tracking (likely improved substantially in the second generation, possibly fixed entirely by the third or fourth)—nothing fatal or important, in other words. So it looks like VR is here to stay! It’s nice that at least one part of my childhood’s future has finally happened.