freedryk

Forget video games. This is a huge step forward for AGI and Robotics. There's a lot of evidence from Neurobiology that we must be running something like this in our brains--things like optical illusions, the editing out of our visual blind spot, the relatively low bandwidth measured in neural signals from our senses to our brain, hallucinations, our ability to visualize 3d shapes, to dream. This is the start of adding all those abilities to our machines. Low bandwidth telepresence rigs. Subatomic VR environments synthesized from particle accelerator data. Glasses that make the world 20% more pleasant to look at. Schizophrenic automobiles. One day a power surge is going to fry your doorbell camera and it'll start tripping balls.

The biggest question I see with this study is it doesn't seem like subjects had access to tools outside of the AI. Were the subjects without AI able to do google searches? If not, then what is the performance gain of the AI users over people who can google stuff?

We have reasonably accurate sea surface temperature data from 1945 to present, about 78 years. We have less accurate measurements back to the 1880s. Finally, we have proxy records going back thousands of years, based on isotope fractionation ratios and other paleoclimate data.

What's really astonishing about this is that to increase sea surface temperature by 1.5 degrees C, you need to heat the whole ocean mixed layer, which in summer is around 50 meters deep. E = c_p * rho * h * dT ~ (4e3 J / kg K) * (10e3 kg / m^3) * (50 m) * (1.5 K) = 3e8 J / m^2. So over the whole North Atlantic, this is about a (4e14 m^2) * (3e8 J / m^2) = 1.2e23 Joules of energy that have been added to the North Atlantic. That's about the same amount of energy that the whole Earth absorbs from the sun in a month.

This is a totally uncontrolled test. How do they know leads wouldn't have dropped anyways? Without an A/B test you can't draw any firm conclusions from this.

Vancouver, BC has been operating a driverless train system for its rapid transit since 1986.

This is so true. It's even worse than spelling bees though. Dyslexia is less of a problem for non-english speakers because our writing system is so messed up. We we have letters that not only represent multiple sounds, but redundantly duplicate the sound of other letter combos (c->k,s; x->z,ks; q->kw). We have multiple letter combos for single phonemes (ch, sh, th). "th" actually represents two phonemes--compare "there" to "through" and you'll see they are two different sounds (try to say through like you would say there). English is a mess.

To elaborate on Fede_V's comment, Deep Neural Networks seem to work well on classification problems because they can automatically build abstract features from a dataset and combine them in different ways. Like, in image data they will automatically identify common shapes and patterns of light and dark, and combine these simple patterns together to identify faces or whatever (like by saying a face is a circle with two dots for eyes and a line for a mouth). Random Forests, on the other hand, are really good at classifying things if you give them meaningful dataset features to learn on, but aren't as good at building these features in the first place. By combining them together, they get a system with the classification abilities of random forests and the automated feature discovery of deep neural nets, and it seems to work a bit better than either.

For me it all changed as soon as the PhD ended. Suddenly the cost to employ you skyrockets, and it becomes apparent how few permanent positions there are relative to the applicant pool. My PhD was pretty great, but my experience afterwards was pretty depressing.