this is very timely now that imagenet is “solved” to a first approximation.
We think it’s time to think about how to do something deeper — something more at the level of human understanding of an image
Sapere Aude
Tag: ai
this is very timely now that imagenet is “solved” to a first approximation.
We think it’s time to think about how to do something deeper — something more at the level of human understanding of an image
Towards a much more automated organic chemistry, a series of articles by Derek Lowe.
MIDA complexes have an unusual property: they stick to silica, even when eluted with MeOH/ether. But THF moves them right off. This trick allows something very useful indeed. It’s a universal catch-and-release for organic intermediates. And that, as the paper shows, opens the door to a lot of automated synthesis. The idea, the hope, is that if the field does become modular and mechanized, that it frees us up to do things that we couldn’t do before. Think about biomolecules: if peptides and oligonucleotides still had to be synthesized as if they were huge natural products, by human-wave-attack teams of day-and-night grad students, how far do you think biology would have gotten by now? Synthesizing such things was Nobel-worthy at first, then worth a PhD all by themselves, but now it’s a routine part of everyday work. Organic synthesis is heading down the exact same road
End of synthesis? You must be joking. This is not even close. As I tried (ineffectively) to make clear yesterday, I don’t think that this particular paper is The End. But it’s the first thing I’ve seen that makes me think that there is an end to a lot of traditional organic chemistry.
No software is yet producing “Whoa, look at that” syntheses. But let’s be honest: most humans aren’t, either. The upper reaches of organic synthesis can still produce such things – and the upper stratum of organic chemists can still produce new and starting routes even to less complex molecules. But seeing machine-generated synthesis coming along in its present form just serves to point out that it’s not so much that the machines are encroaching onto human territory, so much as pointing out that some of the human work has gradually become more mechanical.
I remember years ago, donating your spare CPU was all the rage. There was an offshoot called Foldit that allowed to short-circuit this with human spatial sense. I wonder what an updated version that takes these deep learning results into account would look like: Would humans just get asked for the most fiendishly hard problems? Or is this another area where we should concede?
Hours after encountering its first video game, and without any human coaching, the AI has not only become better than any human player but has also discovered a way to win that its creator never imagined. A game like Crazy Climber is a closer analogue to the real world than chess is, and in the real world humans still have the edge. Moreover, whereas Deep Blue was highly specialized, and preprogrammed by human grandmasters with a library of moves and rules, DeepMind is able to use the same all-purpose code for a wide array of games.
we improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error, exceeding the accuracy of human raters
A neural network that simulates the way monkeys recognize faces produces many of the idiosyncratic behaviors found in humans
More evidence that these capabilities evolved before the split, to support social behaviors.
Once trained, a computer can be used to determine what sentiments* a given image is likely to elicit. This information could be useful for things as diverse as measuring economic indicators and predicting elections.
One of the last bastions of human mastery over computers is about to fall to the relentless onslaught of machine learning algorithms.
this very cool. on a related note it triggered the fan on my computer for the first time.
My summer intern (well, okay, late fall intern), Christopher Olah has been investigating various ways of visualizing the behavior of neural networks. As part of this work, he has produced a very nice writeup of various ways of visualizing the 784-dimensional space of MNIST digits, including several nice interactive visualization techniques. I’ve really enjoyed working with Chris as he had developed many of these approaches, and there are more cool visualizations coming down the pike.