Machine Learning IDE in Alpha

Machine is an IDE for building machine learning systems using TensorFlow. You can sign up for the alpha, but first, have a look at the video below to see what it is all about.

You’ll see in the video, that you can import data for a model and then do training (in this case, to find a mustache in an image). You’ll see the IDE invites an iterative approach to development since you can alter parameters, run experiments, and see the results.

The IDE syncs with “the cloud” so you can work on it from multiple computers and roll back to previous results easily. We don’t know when the IDE will leave alpha status (or beta, for that matter), but the team’s goal is to release a free version of Machine to encourage widespread adoption.

If you want to learn more about TensorFlow, you are in the right place. We’ve also covered a bare-bones project if you’d rather get started that way. You can also find some good background material going all the way back to the early perceptron-based neural networks.

Simple Wave Generation in Python (and SciPy)

[153Armstrong] did a short post on how easy it is to generate waveforms using Python. We agree it is simple, but actually, it isn’t so much Python per se, it is some pretty cool libraries (SciPy, in particular) that do all the hard work. That may be splitting hairs, but it is worth nothing that SciPy (pronounced “Sigh Pie”) also does other handy tricks like Fourier transforms, too. You can see a video of his results, below.

The code is simple and one of the commenters pointed out an even more efficient way to write the data to a WAV file. The basic idea is to create an array of samples in a buffer using some features of SciPy’s NumPy component.

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Catastrophic Forgetting: Learning’s Effect on Machine Minds

What if every time you learned something new, you forgot a little of what you knew before? That sort of overwriting doesn’t happen in the human brain, but it does in artificial neural networks. It’s appropriately called catastrophic forgetting. So why are neural networks so successful despite this? How does this affect the future of things like self-driving cars? Just what limit does this put on what neural networks will be able to do, and what’s being done about it?

The way a neural network stores knowledge is by setting the values of weights (the lines in between the neurons in the diagram). That’s what those lines literally are, just numbers assigned to pairs of neurons. They’re analogous to the axons in our brain, the long tendrils that reach out from one neuron to the dendrites of another neuron, where they meet at microscopic gaps called synapses. The value of the weight between two artificial neurons is roughly like the number of axons between biological neurons in the brain.

To understand the problem, and the solutions below, you need to know a little more detail.

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From 50s Perceptrons To The Freaky Stuff We’re Doing Today

Things have gotten freaky. A few years ago, Google showed us that neural networks’ dreams are the stuff of nightmares, but more recently we’ve seen them used for giving game character movements that are indistinguishable from that of humans, for creating photorealistic images given only textual descriptions, for providing vision for self-driving cars, and for much more.

Being able to do all this well, and in some cases better than humans, is a recent development. Creating photorealistic images is only a few months old. So how did all this come about?

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The GNU GPL Is An Enforceable Contract At Last

It would be difficult to imagine the technological enhancements to the world we live in today without open-source software. You will find it somewhere in most of your consumer electronics, in the unseen data centres of the cloud, in machines, gadgets, and tools, in fact almost anywhere a microcomputer is used in a product. The willingness of software developers to share their work freely under licences that guarantee its continued free propagation has been as large a contributor to the success of our tech economy as any hardware innovation.

Though open-source licences have been with us for decades now, there have been relatively few moments in which they have been truly tested in a court. There have been frequent licence violations in which closed-source products have been found to contain open-source software, but they have more often resulted in out-of-court settlement than lengthy public legal fights. Sometimes the open-source community has gained previously closed-source projects, as their licence violations have involved software whose licence terms included a requirement for a whole project in which it is included to have the same licence. These terms are sometimes referred to as viral clauses by open-source detractors, and the most famous such licence is the GNU GPL, or General Public Licence. If you have ever installed OpenWRT on a router you will have been a beneficiary of this: the project has its roots in the closed-source firmware for a Linksys router that was found to contain GPL code.

Now we have news of an interesting milestone for the legal enforceability of open-source licences, a judge in California has ruled that the GPL is an enforceable contract. Previous case-law had only gone as far as treating GPL violations as a copyright matter, while this case extends its protection to another level.

The case in question involves a Korean developer of productivity software, Hancom Office, who were found to have incorporated the open-source Postscript and PDF encoder Ghostscript into their products without paying its developer a licence fee. Thus their use of Ghostscript falls under the GPL licencing of its open-source public version, and it was  on this basis that Artifex, the developer of Ghostscript, brought the action.

It’s important to understand that this is not a win for Artifex, it is merely a decision on how the game can be played. They must now go forth and fight the case, but that they can do so on the basis of a contract breach rather than a copyright violation should help them as well as all future GPL-licenced developers who find themselves in the same position.

We’re not lawyers here at Hackaday, but if we were to venture an opinion based on gut feeling it would be that we’d expect this case to end in the same way as so many others, with a quiet out-of-court settlement and a lucrative commercial licencing deal for Artifex. But whichever way it ends the important precedent will have been set, the GNU GPL is now an enforceable contract in the eyes of the law. And that can only be a good thing.

Via Hacker News.

GNU logo, CC-BY-SA 2.0.

Industrial Robots, Hacking and Sabotage

Everything is online these days creating the perfect storm for cyber shenanigans. Sadly, even industrial robotic equipment is easily compromised because of our ever increasingly connected world. A new report by Trend Micro shows a set of attacks on robot arms and other industrial automation hardware.

This may not seem like a big deal but image a scenario where an attacker intentionally builds invisible defects into thousands of cars without the manufacturer even knowing. Just about everything in a car these days is built using robotic arms. The Chassis could be built too weak, the engine could be built with weaknesses that will fail far before the expected lifespan. Even your brake disks could have manufacturing defects introduced by a computer hacker causing them to shatter under heavy braking. The Forward-looking Threat Research (FTR) team decided to check the feasibility of such attacks and what they found was shocking. Tests were performed in a laboratory with a real in work robot. They managed to come up with five different attack methods.


Attack 1: Altering the Controller’s Parameters
The attacker alters the control system so the robot moves unexpectedly or inaccurately, at the attacker’s will.

  • Concrete Effects: Defective or modified products
  • Requirements Violated: Safety, Integrity, Accuracy

Attack 2: Tampering with Calibration Parameters
The attacker changes the calibration to make the robot move unexpectedly or inaccurately, at the attacker’s will.

  • Concrete Effects: Damage to the robot
  • Requirements Violated: Safety, Integrity, Accuracy

Why are these robots even connected? As automated factories become more complex it becomes a much larger task to maintain all of the systems. The industry is moving toward more connectivity to monitor the performance of all machines on the factory floor, tracking their service lifetime and alerting when preventive maintenance is necessary. This sounds great for its intended use, but as with all connected devices there are vulnerabilities introduced because of this connectivity. This becomes especially concerning when you consider the reality that often equipment that goes into service simply doesn’t get crucial security updates for any number of reasons (ignorance, constant use, etc.).

For the rest of the attack vectors and more detailed info you should refer to the report (PDF) which is quite an interesting read. The video below also shows insight into how these type of attacks might affect the manufacturing process.

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Hack Your Own Samsung TV With The CIA’s Weeping Angel Exploit

[Wikileaks] has just published the CIA’s engineering notes for Weeping Angel Samsung TV Exploit. This dump includes information for field agents on how to exploit the Samsung’s F-series TVs, turning them into remotely controlled spy microphones that can send audio back to their HQ.

An attacker needs physical access to exploit the Smart TV, because they need to insert a USB drive and press keys on the remote to update the firmware, so this isn’t something that you’re likely to suffer personally. The exploit works by pretending to turn off the TV when the user puts the TV into standby. In reality, it’s sitting there recording all the audio it can, and then sending it back to the attacker once it comes out of “fake off mode”.

It is still unclear if this type of vulnerability could be fully patched without a product recall, although firmware version 1118+ eliminates the USB installation method.

The hack comes along with a few bugs that most people probably wouldn’t notice, but we are willing to bet that your average Hackaday reader would. For instance, a blue LED stays on during “fake off mode” and the Samsung and SmartHub logos don’t appear when you turn the TV back on. The leaked document is from 2014, though, so maybe they’ve “fixed” them by now.

Do you own a Samsung F-series TV? If you do, we wouldn’t worry too much about it unless you are tailed by spies on a regular basis. Don’t trust the TV repairman!