Scientists don’t know exactly what fast radio bursts (FRBs) are. What they do know is that they come from a long way away. In fact, one that occurs regularly comes from a galaxy 3 billion light years away. They could form from neutron stars or they could be extraterrestrials phoning home. The other thing is — thanks to machine learning — we now know about a lot more of them. You can see a video from Berkeley, below. and find more technical information, raw data, and [Danielle Futselaar’s] killer project graphic seen above from at their site.
The first FRB came to the attention of [Duncan Lorimer] and [David Narkevic] in 2007 while sifting through data from 2001. These broadband bursts are hard to identify since they last a matter of milliseconds. Researchers at Berkeley trained software using previously known FRBs. They then gave the software 5 hours of recordings of activity from one part of the sky and found 72 previously unknown FRBs.
Though most modern phones and cameras have an option to record in slow motion, it often comes at the expense of resolution, and always at the expense of storage space. For really high frame rates you’ll need a specialist camera, and you often don’t know that you should be filming in slow motion until after an event has occurred. Wouldn’t it be nice if we could just convert standard video to slow motion after it was recorded?
That’s just what Nvidia has done, all nicely documented in a paper. At its heart, the algorithm must take two frames, and artificially create one or more frames in between. This is not a manual algorithm that interpolates frames, this is a fully fledged deep-learning system. The Convolutional Neural Network (CNN) was trained on over a thousand videos – roughly 300k individual frames.
Since none of the parameters of the CNN are time-dependent, it’s possible to generate as many intermediate frames as required, something which sets this solution apart from previous approaches. In some of the shots in their demo video, 30fps video is converted to 240fps; this requires the creation of 7 additional frames for every pair of consecutive frames.
The video after the break is seriously impressive, though if you look carefully you can see the odd imperfection, like the hockey player’s skate or dancer’s arm. Deep learning is as much an art as a science, and if you understood all of the research paper then you’re doing pretty darn well. For the rest of us, get up to speed by wrapping your head around neural networks, and trying out the simplest Tensorflow example.
Once you step into the world of controls, you quickly realize that controlling even simple systems isn’t as easy as applying voltage to a servo. Before you start working on your own bipedal robot or scratch-built drone, though, you might want to get some practice with this intricate field of engineering. A classic problem in this area is the inverted pendulum, and [Philip] has created a great model of this which helps illustrate the basics of controls, with some AI mixed in.
Called the ZIPY, the project is a “Cart Pole” design that uses a movable cart on a trolley to balance a pendulum above. The pendulum is attached at one point to the cart. By moving the cart back and forth, the pendulum can be kept in a vertical position. The control uses the OpenAI Gym toolkit which is a way to easily use reinforcement learning algorithms in your own projects. With some Python, some 3D printed parts, and the toolkit, [Philip] was able to get his project to successfully balance the pendulum on the cart.
Of course, the OpenAI Gym toolkit is useful for many more projects where you might want some sort of machine learning to help out. If you want to play around with machine learning without having to build anything, though, you can also explore it in your browser.
Readers of a certain vintage will remember the glee of building your own levels for DOOM. There was something magical about carefully crafting a level and then dialing up your friends for a death match session on the new map. Now computers scientists are getting in on that fun in a new way. Researchers from Politecnico di Milano are using artificial intelligence to create new levels for the classic DOOM shooter (PDF whitepaper).
While procedural level generation has been around for decades, recent advances in machine learning to generate game content (usually levels) are different because they don’t use a human-defined algorithm. Instead, they generate new content by using existing, human-generated levels as a model. In effect they learn from what great game designers have already done and apply those lesson to new level generation. The screenshot shown above is an example of an AI generated level and the gameplay can be seen in the video below.
The idea of an AI generating levels is simple in concept but difficult in execution. The researchers used Generative Adversarial Networks (GANs) to analyze existing DOOM maps and then generate new maps similar to the originals. GANs are a type of neural network which learns from training data and then generates similar data. They considered two types of GANs when generating new levels: one that just used the appearance of the training maps, and another that used both the appearance and metrics such as the number of rooms, perimeter length, etc. If you’d like a better understanding of GANs, [Steven Dufresne] covered it in his guide to the evolving world of neural networks.
While both networks used in this project produce good levels, the one that included other metrics resulted in higher quality levels. However, while the AI-generated levels appeared similar at a high level to human-generated levels, many of the little details that humans tend to include were omitted. This is partially due to a lack of good metrics to describe levels and AI-generated data.
We can only guess that these researcher’s next step is to use similar techniques to create an entire game (levels, characters, and music) via AI. After all, how hard can it be?? Joking aside, we would love to see you take this concept and run with it. We’re dying to play through some gnarly levels whipped up by the AI from Hackaday readers!
Last year, Google released an artificial intelligence kit aimed at makers, with two different flavors: Vision to recognize people and objections, and Voice to create a smart speaker. Now, Google is back with a new version to make it even easier to get started.
The main difference in this year’s (v1.1) kits is that they include some basic hardware, such as a Raspberry Pi and an SD card. While this might not be very useful to most Hackaday readers, who probably have a spare Pi (or 5) lying around, this is invaluable for novice makers or the educational market. These audiences now have access to an all-in-one solution to build projects and learn more about artificial intelligence.
Neural networks are a core area of the artificial intelligence field. They can be trained on abstract data sets and be put to all manner of useful duties, like driving cars while ignoring road hazards or identifying cats in images. Recently, a biologist approached AI researcher [Janelle Shane] with a problem – could she help him name some tomatoes?
It’s a problem with a simple cause – like most people, [Darren] enjoys experimenting with tomato genetics, and thus requires a steady supply of names to designate the various varities produced in this work. It can be taxing on the feeble human brain, so a silicon-based solution is ideal.
[Janelle] decided to use the char-rnn library built by [Andrej Karpathy] to do the heavy lifting. After training it on a list of over 11,000 existing tomato varieties, the neural network was then asked to strike out on its own.
The results are truly fantastic – whether you’re partial to a Speckled Garfech or you prefer the smooth flavor of the Golden Pow, there’s a tomato to suit your tastes. When the network was retrained with additional content in the form of names of metal bands, the results get even better – it’s only a matter of time before Angels of Saucing reach a supermarket shelf near you.
We’ve seen plenty of examples of neural networks listening to speech, reading characters, or identifying images. KickView had a different idea. They wanted to learn to recognize radio signals. Not just any radio signals, but Orthogonal Frequency Division Multiplexing (OFDM) waveforms.
OFDM is a modulation method used by WiFi, cable systems, and many other systems. In particular, they look at an 802.11g signal with a bandwidth of 20 MHz. The question is given a receiver for 802.11g, how can you reliably detect that an 802.11ac signal — up to 160 MHz — is using your channel? To demonstrate the technique they decided to detect 20 MHz signals using a 5 MHz bandwidth.