I'm going to try to make the case that note detection in music is essentially image recognition with a few small differences and I'll describe some techniques I used to modify neural networks from computer vision to produce sheet music transcriptions of (polyphonic) music that are actually quite playable. Convolutional neural networks (CNNs) have produced the most accurate results in computer vision for several years. In a typical CNN you start with an image as a 3 dimensional array (width, height, and 3 color channels) and then pass that data through several layers of convolutions, max pooling, and some kind of non-linearity, like a ReLU. Backpropagation is used to iteratively update the convolution parameters from a set of labeled training data (pairs of input and desired output). This process builds up a sophisticated function composed of many simpler functions, primarily convolutions.

In the late 1700s, machinists started making music boxes: intricate little mechanisms that could play harmonies and melodies by themselves. Some incorporated bells, drums, organs, even violins, all coordinated by a rotating cylinder. The more ambitious examples were Lilliputian orchestras, such as the Panharmonicon, invented in Vienna in 1805, or the mass-produced Orchestrion that came along in Dresden in 1851. But the technology had limitations. To make a convincing violin sound, one had to create a little simulacrum of a violin -- quite an engineering feat. How to replicate a trombone? The artisans assumed that an entire instrument had to be copied in order to capture its distinctive tone. The metal, the wood, the reed, the shape, the exact resonance, all of it had to be mimicked. How else were you going to create an orchestral sound?

As we approach what Recode calls "app fatigue," the tech community is focusing its attention and resources on bots -- and chatbots in particular. Chatbots afford a new path for reaching customers via simulated conversation, providing service and in some instances even curating and communicating news. In the near future, bots could well replace the core components of apps -- if not replace them entirely. Facebook, Apple, Microsoft and Google are jumping on the bots bandwagon. Given that these leaders have to date created (and dominated) the app ecosystem -- and are making rapid strides in artificial intelligence -- the recent emphasis on bots is in many ways a natural progression.

"Who was your favorite captain?" When this question was posed by a Comic-Con attendee to the members of the The 50th anniversary "Star Trek" panel -- with Capt. James Tiberius Kirk himself seated at the head of the table onstage in Hall H -- there was only one way for the panelists to answer. One among multiple celebrations around the seminal series, the panel brought together representatives from all five iterations of the television show: William Shatner (Captain Kirk), Brent Spiner (the android Data from "Star Trek: The Next Generation"), Michael Dorn (the Klingon Worf from "The Next Generation," and also holding it down for "Deep Space Nine"), Jeri Ryan (Seven of Nine from "Star Trek: Voyager") and Scott Bakula (Capt. "Is anyone legitimately not going to say Kirk," asked Ryan with a laugh when the favorite captain question emerged.

Now there is an A.I. that can do your job. Customers can direct exactly how their new website should look. This A.I. is still in a limited beta but it is coming. It's called The Grid and it came out of nowhere. It makes you feel like you are interacting with a human counterpart.

As an avid comic book reader, I know that the outside world falls into two categories … people who love comics and those who think that comic book fans don't know how to read a real book. I have had numerous discussions over the years with friends, family and significant others who tell me that comics are for kids or that there is no literary value in graphic novels. My standard response is to hand them a copy of Alan Moore's Watchmen and tell them to read it. If they still don't think that comic books have value, then I quietly point out that most blockbuster movies since 2008 have been based on characters from either the Marvel or DC universes (well, maybe not DC so much). And if they are remain unconvinced, then I make a mental note to not mention comics again.

Back on the judging panel is Professor Noel Sharkey, a robotics expert who featured on the programme for five years. Sharing memories of his time on the show, he told the BBC he was initially hesitant to take part... I was a very reluctant judge for the first series of Robot Wars. I was concerned that being on a popular TV show might damage my reputation as a university professor and as an educator. Even after the first series, I thought that I should opt out - but then something quite remarkable happened.

You can actually enter machine learning with a lot of different states of mathematical knowledge. There are a lot of courses which try to reduce the requirements you need as much as possible and come from a very applied side (most of the MOOCs). Here you need the basic math to calculate some results, but it is more just well if we optimize this function then this will be the solution etc. You will be ready to apply a lot of the methods out there and maybe twist them so that they fit your dataset but it won't help you so much if you want do derive new models yourself or understand where these models came from. If you really want to understand the mathematical framework behind a lot of methods (which is quite beautiful, for example kernels/svms) then you probably will need a lot of high math and should just start with doing some linear algebra or analysis book (to find good starter books go to some good university website, check their linear algebra 1 or analysis 1 courses and see which books the professors recommend).

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Our BratWurst Bot is an autonomous robot that grills sausages all by itself. It is made of off the shelf robotic components: Universal Robots UR-10 arm, Schunk PG-70 gripper, two standard RGB cameras, normal grill tongs and gas grill.

Eyefluence, a company that's rooted in optics, AI, machine learning and mechanical engineering, has built an interface that lets a user communicate with a virtual environment through sight alone. The idea is to convert looking into action. So the software enables you to use your eyes to do anything that you would do with a finger on a smartphone. With a display in front of you, you would be able to navigate a menu, launch applications, pan, zoom and scroll, and even slip in information simply by looking. Beyond the boost in productivity, though, one of the most compelling applications of this eye-machine interaction is in immersive storytelling.