But I am stuck with section 4(Convnet Visualization) and having a hard time understanding the visualizations shown in Figure 2. Can anyone explain these portions to me?

It used to be you could spend an afternoon drumming up a home movie with your little sister, soundtrack it with your favorite mixtape cuts, and upload it to the Internet for sharing without a care. What's an amateur-at-best musician to do? I may have marched on a collegiate snare line (and therefore understand rhythm, phrasings, and tempo), but my ability to create melody probably stopped with middle school recorder lessons. Luckily, we musically challenged filmmakers and podcasters now have robots. A number of high-profile AI composition initiatives have surfaced in recent years--perhaps most notably, Sony's Flow Machine released its debut album in January--and slowly but surely these tools are moving from the research labs and professional production studios into publicly available spaces.

Before any build log, here's a video: That was a Rubik's cube being solved in 0.38 seconds. The time is from the moment the keypress is registered on the computer, to when the last face is flipped. It includes image capture and computation time, as well as actually moving the cube. The motion time is 335 ms, and the remaining time image acquisition and computation. For reference, the current world record is/was 0.637 seconds.

Your "speed things up by compressing them" idea (and, yes, it makes sense and is the basis of a lot of techniques in information retrieval) can also work without the autoencoder. Siamese networks process separate inputs through separate paths where each of the distinct paths share their parameters. What's stopping you from, for all of your repository entries, precomputing the forward pass up to the point where the paths of the network merge and storing the result instead of the raw input? This would mean that at lookup time, you'd only have to do a full forward pass for the query image and then substitute in the precomputed representation for the candidates. All this can be done no matter how the network was trained -- it doesn't rely on training a separate autoencoder as a preprocessor.

I hope you all had a wonderful Christmas and a spectacular start to the New Year! Yes, 2017 should have received a speeding ticket, since it went by too fast! And, I'm sure, 2018 will similarly speed along at an unsuitable pace – apparently time flies when you're having fun, or is it just old age? So, it's 2018, and I thought in this month's column I would steer away from the traditional diet of "Here are my predictions for 2018." To be honest, I simply couldn't swallow writing a piece of this ilk, as I'm sure plenty of others will have their own unique perspective.

We better hope that when the artificial intelligence apocalypse finally descends on us, it'll be something like "Westworld." That's the word from show co-creator Jonathan Nolan, who spoke Saturday at the South by Southwest Conference during a panel about the hit series. "I think we'd be lucky if this was the AI apocalypse, if it was this attractive and charming," Nolan said, noting that while folks tend to think it'll take a super AI to overthrow humans, bots are already manipulating social media users. The implications of AI comprised just some of the discussion during the panel, which in addition to Nolan featured co-creator Lisa Joy, a cast member or two, and a rocket-fast cameo by tech-minded Renaissance man Elon Musk, founder of SpaceX, Tesla and the Boring Company. After a preamble from Nolan on the aspirational qualities of going to space, Musk walked on stage in the last few minutes to show off a short video by Nolan and Joy. The clip focused on the recent launch of SpaceX's Falcon Heavy rocket, and on Starman, the dummy at the wheel of a Tesla SpaceX Roadster carried into space by the rocket.

As a little project, I'm working on building out a neural network that learns to play a snake game. The game is composed of a max 20x20 board where each cell is either an apple, a snake, or a wall. One way I thought of encoding that data in the input layer would be to have a 20x20 400 neuron input layer where each neuron has one of 3 different states signaled by neuron value. Say, 0 apple, 0.5 snake, 1 wall. Is encoding multiple states in a single neuron like this a good idea?

It all depends on your skills as a developer. If you do know how to work with many threads on many cores, I'd go for a cheap xeon/amd server with 2 or 4 cpu sockets to get up to 64 cores, a min of 1 gigabyte of ram per core, A BOOTABLE RAM DISK ON PCI-EXPRESS WITH AUTOMATIC BACKUP ( SSDs are ridiculous and overrated, they burn out so easily and they're not worth the risk for long-term storage purposes) and a fast HD (10k rpm minimum) as storage. For the GPU, honestly, unless you plan on working with CUDA/ opencl, anything is fine because you'd rarely compute on it. But if you will develop GPU-"powered" neural networks and if wattage isn't of a concern for you, given a proper thermal dissipation, there are many AMDs that can pack a punch for little money both in single and double precision. But if you don't know how to take advantage of multithreading and if frameworks are what you have in mind, whatever you buy, as long as it is fast, "it's gonna be fine".

Flippy, the burger flipping robot only lasted two days before the plug was pulled, temporarily, suggesting that the future we dream about is going to take a whole lot longer to arrive. But it hit with a thud as reality quickly sunk in. The growing robotic list goes way beyond factory production lines to include self-driving cars, delivery drones and especially the one we encountered this past week, the burger-flipper. On its surface, the idea sounds like a no-brainer: Replace young men and women who can't take the heat and monotony of standing over a hot griddle -- and end up quitting in weeks, if not months -- with a robot. A $100,000 robot can grill hamburgers to perfection all day long.

Dulce Sloan finds out that the downside (and upside) of facial recognition software is that people of color often go undetected.