Exploring Animal Intelligence Hack Chat

October 19, 2020 by Dan Maloney 4 Comments

Join us on Wednesday, October 21st at noon Pacific for the Exploring Animal Intelligence Hack Chat with Hans Forsberg!

From our lofty perch atop the food chain it’s easy to make the assumption that we humans are the last word in intelligence. A quick glance at social media or a chat with a random stranger at the store should be enough to convince you that human intelligence isn’t all it’s cracked up to be, or at least that it’s not evenly distributed. But regardless, we are pretty smart, thanks to those big, powerful brains stuffed into our skulls.

We’re far from the only smart species on the planet, though. Fellow primates and other mammals clearly have intelligence, and we’ve seen amazingly complex behaviors from animals in just about every taxonomic rank. But it’s the birds who probably stuff the most functionality into their limited neural hardware, with tool use, including the ability to make new tools, being common, along with long-distance navigation, superb binocular vision, and of course the ability to rapidly maneuver in three-dimensions while flying.

Hans Forsberg has taken an interest in avian intelligence lately, and to explore just what’s possible he devised a fiendishly clever system to train his local magpie flock to clean up his yard, which he calls “BirdBox”. We recently wrote up his initial training attempts, which honestly bear a strong resemblance to training a machine learning algorithm, which is probably no small coincidence since his professional background is with neural networks. He has several years of work into his birds, and he’ll stop by the Hack Chat to talk about what goes into leveraging animal intelligence, what we can learn about our systems from it, and where BirdBox goes next.

Our Hack Chats are live community events in the Hackaday.io Hack Chat group messaging. This week we’ll be sitting down on Wednesday, October 21 at 12:00 PM Pacific time. If time zones baffle you as much as us, we have a handy time zone converter.

Click that speech bubble to the right, and you’ll be taken directly to the Hack Chat group on Hackaday.io. You don’t have to wait until Wednesday; join whenever you want and you can see what the community is talking about.

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Tube Amp Is Modeled With The Power Of AI

October 11, 2020 by Matthew Carlson 4 Comments

There is a certain magic and uniqueness to hardware, particularly when it comes to audio. Tube amplifiers are well-known and well-loved by audio enthusiasts and musicians alike. However, that uniqueness also comes with the price of the fact that gear takes up space and cannot be configured outside the bounds of what it was designed to do. [keyth72] has decided to take it upon themselves to recreate the smooth sound of the Fenders Blues Jr. small tube guitar amp. But rather than using hardware or standard audio software, the magic of AI was thrown at it.

In some ways, recreating a transformation is exactly what AI is designed for. There’s a clear and recordable input with a similar output. In this case, [keyth72] recorded several guitar sessions with the guitar audio sent through the device they wanted to recreate. Using WaveNet, they created a model that applies the transform to input audio in real-time. The Gain and EQ knobs were handled outside the model itself to keep things simple. Instructions on how to train your own model are included on the GitHub page.

While the model is simply approximating the real hardware, it still sounds quite impressive, and perhaps the next time you need a particular sound of your home-built amp or guitar pedal, you might reach for your computer instead.
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NVIDIA Announces $59 Jetson Nano 2GB, A Single Board Computer With Makers In Mind

October 5, 2020 by Tom Nardi 47 Comments

NVIDIA kicked off their line of GPU-accelerated single board computers back in 2014 with the Jetson TK1, a $200 USD development system for those looking to get involved with the burgeoning world of so-called “edge computing”. It was designed to put high performance computing in a small and energy efficient enough package that it could be integrated directly into products, rather than connecting to a data center half-way across the world.

The TK1 was an impressive piece of hardware, but not something the hacker and maker community was necessarily interested in. For one thing, it was fairly expensive. But perhaps more importantly, it was clearly geared more towards industry types than consumers. We did see the occasional project using the TK1 and the subsequent TX1 and TX2 boards, but they were few and far between.

Then came the Jetson Nano. Its 128 core Maxwell CPU still packed plenty of power and was fully compatible with NVIDIA’s CUDA architecture, but its smaller size and $99 price tag made it far more attractive for hobbyists. According to the company’s own figures, the number of active Jetson developers has more than tripled since the Nano’s introduction in March of 2019. With the platform accessible to a larger and more diverse group of users, new and innovative applications for machine learning started pouring in.

Cutting the price of the entry level Jetson hardware in half was clearly a step in the right direction, but NVIDIA wanted to bring even more developers into the fray. So why not see if lightning can strike twice? Today they’ve officially announced that the new Jetson Nano 2GB will go on sale later this month for just $59. Let’s take a close look at this new iteration of the Nano to see what’s changed (and what hasn’t) from last year’s model.

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Hackaday Links: October 4, 2020

October 4, 2020 by Dan Maloney 9 Comments

In case you hadn’t noticed, it was a bad week for system admins. Pennsylvania-based United Health Services, a company that owns and operates hospitals across the US and UK, was hit by a ransomware attack early in the week. The attack, which appears to be the Ryuk ransomware, shut down systems used by hospitals and health care providers to schedule patient visits, report lab results, and do the important job of charting. It’s not clear how much the ransomers want, but given that UHS is a Fortune 500 company, it’s likely a tidy sum.

And as if an entire hospital corporation’s IT infrastructure being taken down isn’t bad enough, how about the multi-state 911 outage that occurred around the same time? Most news reports seemed to blame the outage on an Office 365 outage happening at the same time, but Krebs on Security dug a little deeper and traced the issue back to two companies that provide 911 call routing services. Each of the companies is blaming the other, so nobody is talking about the root cause of the issue. There’s no indication that it was malware or ransomware, though, and the outage was mercifully brief. But it just goes to show how vulnerable our systems have become.

Our final “really bad day at work” story comes from Japan, where a single piece of failed hardware shut down a $6-trillion stock market. The Tokyo Stock Exchange, third-largest bourse in the world, had to be completely shut down early in the trading day Thursday when a shared disk array failed. The device was supposed to automatically failover to a backup unit, but apparently the handoff process failed. This led to cascading failures and blank terminals on the desks of thousands of traders. Exchange officials made the call to shut everything down for the day and bring everything back up carefully. We imagine there are some systems people sweating it out this weekend to figure out what went wrong and how to keep it from happening again.

With our systems apparently becoming increasingly brittle, it might be a good time to take a look at what goes into space-rated operating systems. Ars Technica has a fascinating overview of the real-time OSes used for space probes, where failure is not an option and a few milliseconds error can destroy billions of dollars of hardware. The article focuses on the RTOS VxWorks and goes into detail on the mysterious rebooting error that affected the Mars Pathfinder mission in 1997. Space travel isn’t the same as running a hospital or stock exchange, of course, but there are probably lessons to be learned here.

As if 2020 hasn’t dealt enough previews of various apocalyptic scenarios, here’s what surely must be a sign that the end is nigh: AI-generated PowerPoint slides. For anyone who has ever had to sit through an endless slide deck and wondered who the hell came up with such drivel, the answer may soon be: no one. DeckRobot, a startup company, is building an AI-powered extension to Microsoft Office to automate the production of “company compliant and visually appealing” slide decks. The extension will apparently be trained using “thousands and thousands of real PowerPoint slides”. So, great — AI no longer has to have the keys to the nukes to do us in. It’ll just bore us all to death.

And finally, if you need a bit of a palate-cleanser after all that, please do check out robotic curling. Yes, the sport that everyone loves to make fun of is actually way more complicated than it seems, and getting a robot to launch the stones on the icy playing field is a really complex and interesting problem. The robot — dubbed “Curly”, of course — looks like a souped-up Roomba. After sizing up the playing field with a camera on an extendable boom, it pushes the stone while giving it a gentle spin to ease it into exactly the right spot. Sadly, the wickedly energetic work of the sweepers and their trajectory-altering brooms has not yet been automated, but it’s still pretty cool to watch. But fair warning: you might soon find yourself with a curling habit to support.

Community Testing Suggests Bias In Twitter’s Cropping Algorithm

September 23, 2020 by Lewin Day 77 Comments

With social media and online services are now huge parts of daily life to the point that our entire world is being shaped by algorithms. Arcane in their workings, they are responsible for the content we see and the adverts we’re shown. Just as importantly, they decide what is hidden from view as well.

Important: Much of this post discusses the performance of a live website algorithm. Some of the links in this post may not perform as reported if viewed at a later date.

The initial Zoom problem that brought Twitter’s issues to light.

Recently, [Colin Madland] posted some screenshots of a Zoom meeting to Twitter, pointing out how Zoom’s background detection algorithm had improperly erased the head of a colleague with darker skin. In doing so, [Colin] noticed a strange effect — although the screenshot he submitted shows both of their faces, Twitter would always crop the image to show just his light-skinned face, no matter the image orientation. The Twitter community raced to explore the problem, and the fallout was swift.

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Into The Belly Of The Beast With Placemon

September 17, 2020 by Brian McEvoy 11 Comments

No, no, at first we thought it was a Pokemon too, but Placemon monitors your place, your home, your domicile. Instead of a purpose-built device, like a CO detector or a burglar alarm, this is a generalized monitor that streams data to a central processor where machine learning algorithms notify you if something is awry. In a way, it is like a guard dog who texts you if your place is unusually cold, on fire, unlawfully occupied, or underwater.

[anfractuosity] is trying to make a hacker-friendly version based on inspiration from a scientific paper about general-purpose sensing, which will have less expensive components but will lose accuracy. For example, the article suggests thermopile arrays, like low-resolution heat-vision, but Placemon will have a thermometer, which seems like a prudent starting place.

The PCB is ready to start collecting sound, temperature, humidity, barometric pressure, illumination, and passive IR then report that telemetry via an onboard ESP32 using Wifi. A box utilizing Tensorflow receives the data from any number of locations and is training to recognize a few everyday household events’ sensor signatures. Training starts with events that are easy to repeat, like kitchen sounds and appliance operations. From there, [anfractuosity] hopes that he will be versed enough to teach it new sounds, so if a pet gets added to the mix, it doesn’t assume there is an avalanche every time Fluffy needs to go to the bathroom.

We have another outstanding example of sensing household events without directly interfacing with an appliance, and bringing a sensor suite to your car might be up your alley.

Garbage Can Takes Itself Out

September 10, 2020 by Bryan Cockfield 12 Comments

Home automation is a fine goal but typically remains confined to lights, blinds, and other things that are relatively stationary and/or electrical in nature. There is a challenge there to be certain, but to really step up your home automation game you’ll need to think outside the box. This automated garbage can that can take itself out, for example, has all the home automation street cred you’d ever need.

The garbage can moves itself by means of a scooter wheel which has a hub motor inside and is powered by a lithium battery, but the real genius of this project is the electronics controlling everything. A Raspberry Pi Zero W is at the center of the build which controls the motor via a driver board and also receives instructions on when to wheel the garbage can out to the curb from an Nvidia Jetson board. That board is needed because the creator, [Ahad Cove], didn’t want to be bothered to tell his garbage can to take itself out or even schedule it. He instead used machine learning to detect when the garbage truck was headed down the street and instruct the garbage can to roll itself out then.

The only other thing to tie this build together was to get the garage door to open automatically for the garbage can. Luckily, [Ahad]’s garage door opener was already equipped with WiFi and had an available app, unbeknownst to him, which made this a surprisingly easy part of the build. If you have a more rudimentary garage door opener, though, there are plenty of options available to get it on the internets.

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