Month: October 2017

In September, Honda stole the Frankfurt Auto Show with its totally adorable Urban EV. On Wednesday, the company showed there's more where that came from, using the Tokyo Motor Show to debut the Sports EV. If the Urban EV channeled designs from the past like the Z600, the Sports EV looks heavily influenced by Toyota's drop-dead gorgeous 2000GT. Unfortunately, we know almost nothing about this wonderful little electric vehicle. Honda says that it "designed the sporty electric car to deliver a feeling of joy and emotional unity to the drive, achieved by efficiently combining the EV powerunit and AI technology in a compact bodyshape."

As with the Urban EV, we have just one message for Honda: build it, in volume, and sell it here in the US. Please.

Enlarge / See a Higgs there? A quantum AI might. (credit: Los Alamos National Lab)

Machine learning has returned with a vengeance. I still remember the dark days of the late '80s and '90s, when it was pretty clear that the current generation of machine-learning algorithms didn't seem to actually learn much of anything. Then big data arrived, computers became chess geniuses, conquered Go (twice), and started recommending sentences to judges. In most of these cases, the computer had sucked up vast reams of data and created models based on the correlations in the data.

But this won't work when there aren't vast amounts of data available. It seems that quantum machine learning might provide an advantage here, as a recent paper on searching for Higgs bosons in particle physics data seems to hint.

Learning from big data

In the case of chess, and the first edition of the Go-conquering algorithm, the computer wasn't just presented with the rules of the game. Instead, it was given the rules and all the data that the researchers could find. I'll annoy every expert in the field by saying that the computer essentially correlated board arrangements and moves with future success. Of course, it isn't nearly that simple, but the key was in having a lot of examples to build a model and a decision tree that would let the computer decide on a move.

Enlarge / Neutron star mergers, the slayers of zombies. (credit: NASA)

Theoreticians claim to love data. Data is the thing that allows them to test their theories and prove that they are right. Unfortunately for them, the data often doesn't support the theory. In those cases, the data has just stabbed your labor of love right in the heart, and you are expected to say "thank you, sir. May I have another?"

The recent observation of the in-spiral and merger of two neutron stars has done just that to a room full of theorists working on gravitational theories that try to get rid of dark matter.

Illuminating dark matter

Dark matter is a particle that is posited to exist in large quantities in the Universe. Physicists did not dream it up because they were bored, but because the internal gravitational structure of galaxies could not be explained by the distribution of visible matter. After the existence of dark matter was first proposed, it got some critical supporting evidence. The cosmic microwave background—the radiation emitted during the Big Bang that permeates the Universe—has features that, at the moment, we can only explain with dark matter.