Google Builds A Synthesizer With Neural Nets And Raspberry Pis.

March 16, 2018 by Brian Benchoff 16 Comments

AI is the new hotness! It’s 1965 or 1985 all over again! We’re in the AI Rennisance Mk. 2, and Google, in an attempt to showcase how AI can allow creators to be more… creative has released a synthesizer built around neural networks.

The NSynth Super is an experimental physical interface from Magenta, a research group within the Big G that explores how machine learning tools can create art and music in new ways. The NSynth Super does this by mashing together a Kaoss Pad, samples that sound like General MIDI patches, and a neural network.

Here’s how the NSynth works: The NSynth hardware accepts MIDI signals from a keyboard, DAW, or whatever. These MIDI commands are fed into an openFrameworks app that uses pre-compiled (with Machine Learning™!) samples from various instruments. This openFrameworks app combines and mixes these samples in relation to whatever the user inputs via the NSynth controller. If you’ve ever wanted to hear what the combination of a snare drum and a bassoon sounds like, this does it. Basically, you’re looking at a Kaoss pad controlling rompler that takes four samples and combines them, with the power of Neural Networks. The project comes with a set of pre-compiled and neural networked samples, but you can use this interface to mix your own samples, provided you have a beefy computer with an expensive GPU.

Not to undermine the work that went into this project, but thousands of synth heads will be disappointed by this project. The creation of new audio samples requires training with a GPU; the hardest and most computationally expensive part of neural networks is the training, not the performance. Without a nice graphics card, you’re limited to whatever samples Google has provided here.

Since this is Open Source, all the files are available, and it’s a project that uses a Raspberry Pi with a laser-cut enclosure, there is a huge demand for this machine learning Kaoss pad. The good news is that there’s a group buy on Hackaday.io, and there’s already a seller on Tindie should you want a bare PCB. You can, of course, roll your own, and the Digikey cart for all the SMD parts comes to about $40 USD. This doesn’t include the OLED ($2 from China), the Raspberry Pi, or the laser cut enclosure, but it’s a start. Of course, for those of you who haven’t passed the 0805 SMD solder test, it looks like a few people will be selling assembled versions (less Pi) for $50-$60.

Is it cool? Yes, but a basement-bound producer that wants to add this to a track will quickly learn that training machine learning algorithms cost far more than playing with machine algorithms. The hardware is neat, but brace yourself for disappointment. Just like AI suffered in the late 60s and the late 80s. We’re in the AI Renaissance Mk. 2, after all.

Continue reading “Google Builds A Synthesizer With Neural Nets And Raspberry Pis.” →

Up AlphaGoer Five

March 13, 2018 by Al Williams 9 Comments

AlphaGo is the deep learning program that can beat humans at the game Go. You can read Google’s highly technical paper on it, but you’ll have to wade through some very academic language. [Aman Agarwal] has done us a favor. He took the original paper and dissected the important parts of in in plain English. If the title doesn’t make sense to you, you need to read more XKCD.

[Aman] says his treatment will be useful for anyone who doesn’t want to become an expert on neural networks but still wants to understand this important breakthrough. He also thinks people who don’t have English as a first language may find his analysis useful. By the way, the actual Go matches where AlphaGo beat [Sedol] were streamed and you can watch all the replays on YouTube (the first match appears below).

Continue reading “Up AlphaGoer Five” →

Google Ups The Ante In Quantum Computing

March 9, 2018 by Al Williams 36 Comments

At the American Physical Society conference in early March, Google announced their Bristlecone chip was in testing. This is their latest quantum computer chip which ups the game from 9 qubits in their previous test chip to 72 — quite the leap. This also trounces IBM and Intel who have 50- and 49-qubit devices. You can read more technical details on the Google Research Blog.

It turns out that just the number of qubits isn’t the entire problem, though. Having qubits that last longer is important and low-noise qubits help because the higher the noise figure, the more likely you will need redundant qubits to get a reliable answer. That’s fine, but it does leave fewer qubits for working your problem.

Continue reading “Google Ups The Ante In Quantum Computing” →

Google’s AIY Vision Kit Augments Pi With Vision Processor

December 17, 2017 by Steven Dufresne 29 Comments

Google has announced their soon to be available Vision Kit, their next easy to assemble Artificial Intelligence Yourself (AIY) product. You’ll have to provide your own Raspberry Pi Zero W but that’s okay since what makes this special is Google’s VisionBonnet board that they do provide, basically a low power neural network accelerator board running TensorFlow.

AIY VisionBonnet with Myriad 2 (MA2450) chip

The VisionBonnet is built around the Intel® Movidius™ Myriad 2 (aka MA2450) vision processing unit (VPU) chip. See the video below for an overview of this chip, but what it allows is the rapid processing of compute-intensive neural networks. We don’t think you’d use it for training the neural nets, just for doing the inference, or in human terms, for making use of the trained neural nets. It may be worth getting the kit for this board alone to use in your own hacks. An alternative is to get Modivius’s Neural Compute Stick, which has the same chip on a USB stick for around $80, not quite double the Vision Kit’s $45 price tag.

The Vision Kit isn’t out yet so we can’t be certain of the details, but based on the hardware it looks like you’ll point the camera at something, press a button and it will speak. We’ve seen this before with this talking object recognizer on a Pi 3 (full disclosure, it was made by yours truly) but without the hardware acceleration, a single object recognition took around 10 seconds. In the vision kit we expect the recognition will be in real-time. So the Vision Kit may be much more dynamic than that. And in case it wasn’t clear, a key feature is that nothing is done on the cloud here, all processing is local.

The kit comes with three different applications: an object recognition one that can recognize up to 1000 different classes of objects, another that recognizes faces and their expressions, and a third that detects people, cats, and dogs. While you can get up to a lot of mischief with just that, you can run your own neural networks too. If you need a refresher on TensorFlow then check out our introduction. And be sure to check out the Myriad 2 VPU video below the break.

Continue reading “Google’s AIY Vision Kit Augments Pi With Vision Processor” →

High-Speed Drones Use AI To Spoil The Fun

November 29, 2017 by Dan Maloney 17 Comments

Some people look forward to the day when robots have taken over all our jobs and given us an economy where we can while our days away on leisure activities. But if your idea of play is drone racing, you may be out of luck if this AI pilot for high-speed racing drones has anything to say about it.

NASA’s Jet Propulsion Lab has been working for the past two years to develop the algorithms needed to let high-performance UAVs navigate typical drone racing obstacles, and from the look of the tests in the video below, they’ve made a lot of progress. The system is vision based, with the AI drones equipped with wide-field cameras looking both forward and down. The indoor test course has seemingly random floor tiles scattered around, which we guess provide some kind of waypoints for the drones. A previous video details a little about the architecture, and it seems the drones are doing the computer vision on-board, which we find pretty impressive.

Despite the program being bankrolled by Google, we’re sure no evil will come of this, and that we’ll be in no danger of being chased down by swarms of high-speed flying killbots anytime soon. For now we can take solace in the fact that JPL’s algorithms still can’t beat an elite human pilot like [Ken Loo], who bested the bots overall. But alarmingly, the human did no better than the bots on his first lap, which suggests that once the AI gets a little creativity and intuition like that needed to best a Go champion, [Ken] might need to find another line of work.

Continue reading “High-Speed Drones Use AI To Spoil The Fun” →

Make Cars Safer By Making Them Softer

September 3, 2017 by James Hobson 78 Comments

Would making autonomous vehicles softer make them safer?

Alphabet’s self-driving car offshoot, Waymo, feels that may be the case as they were recently granted a patent for vehicles that soften on impact. Sensors would identify an impending collision and adjust ‘tension members’ on the vehicle’s exterior to cushion the blow. These ‘members’ would be corrugated sections or moving panels that absorb the impact alongside the crumpling effect of the vehicle, making adjustments based on the type of obstacle the vehicle is about to strike.

Continue reading “Make Cars Safer By Making Them Softer” →

Ok Google. Navigate To The International Space Station

July 23, 2017 by Al Williams 18 Comments

If you’d have asked most people a few decades ago if they wanted a picture of every street address in the world, they would have probably looked at you like you were crazy. But turns out that Google Street View is handy for several reasons. Sure, it is easy to check out the neighborhood around that cheap hotel before you book. But it is also a great way to visit places virtually. Now one of those places is the International Space Station (ISS).

[Thomas Pesquet] in a true hack used bungee cords and existing cameras to take panoramas of all 15 ISS modules. Google did their magic, and you can enjoy the results. You can also see a video on how it was all done, below.

Continue reading “Ok Google. Navigate To The International Space Station” →