Is it a bird? Is it a plane? No, it’s a MuSHR robocar!
…University of Washington makes DIY robocar…
In the past few years, academics have begun designing all manner of open source robots, ranging from cheap robotic arms (Berkeley BLUE ImportAI #142) to quadruped dogbots (STOCH ImportAI #128) to a menagerie of drones. Now, researchers with the University of Washington have developed MuSHR (Multi-agent System for non-Holonomic Racing).
What MuSHR is: MuSHR is an open source robot car that can be made using a combination of 3D-printed and off-the-shelf parts. Each MuSHR can can costs as little as $610, while a souped-up car equipped with more sensors can cost up to $1000. This compares to prices in the range of thousands to tens of thousands of dollars for other cars. The project ships with a range of inbuilt software utilities to help the cars navigate and move safely around the world.
Why this matters: Hardware – as any roboticist knows – is a difficult, painful, and expensive thing to work on. At the same time, deploying AI systems onto hardware platforms like robot cars and drones is one of the best ways to evaluate the robustness of an AI system. Therefore, projects like MuSHR help more people develop AI systems that can be deployed on hardware, which will inspire research to make more robust, better performing algorithms.
Read more: Allen School releases MuSHR robotic race car platform to drive advances in AI research and education (University of Washington).
Find out more about MuSHR at the official website (mushr.io).
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FAA: Don’t attach weapons to your drone:
…US regulator wants people to not make drones into weapons…
The FAA has published a news release “warning the general public that it is illegal to operate a drone with a dangerous weapon attached”.
Any predictions for when the FAA will issue a similar news release saying something like “it is illegal to operate a drone with a dangerous autonomous policy installed”?
Read more: Drones and Weapons, A Dangerous Mix (FAA).
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DeepFakes are freaking Jordan Peterson out:
…Public intellectual Jordan Peterson worries about how synthesized audio can mess up people’s lives…
Jordan Peterson, the Canadian psychologist and public intellectual and/or provocateur (depending on your personal opinion), is concerned about how synthesized audio may influence society.
Why Peterson is concerned about DeepFakes: “It’s hard to imagine a technology with more power to disrupt,” he says. “I’m already in the position (as many of you soon will be as well) where anyone can produce a believable audio and perhaps video of me saying absolutely anything they want me to say. How can that possibly be fought?”
Why this matters: AI researchers have been aware about the potential for deepfakes for some years, but it was only in the past couple of years that the technology made its way to the mainstream (partially due to pioneering reporting by Samantha Cole at Vice magazine). Now, as celebrities like Peterson become aware of the technology, they’ll help make society aware that our media is about to become increasingly hard to verify.
Read more: Jordan Peterson: The deepfake artists must be stopped before we no longer know what’s real (National Post).
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Google Waymo releases massive self-driving car dataset:
…12 Million 3D bounding boxes across 1,000 recordings of 20 seconds each…
Alphabet Inc subsidiary ‘Waymo’ – otherwise known as Google’s self-driving car project – has released the ‘Waymo Open Dataset’ (WOD) to help other researchers develop self-driving cars.
What’s in the WOD?: The dataset contains 1,000 discrete recordings of different autonomous cars driving on different roads. Each segment is around 20 seconds long and includes sensor data from one mid-range LIDAR, four short-range LIDAR, five cameras, as well as sensor calibrations. The WOD data is also labelled, with each segment annotated with labels for four classes – vehicles, pedestrians, cyclists, and signs. All in all, WOD includes more than 12Million 3D bounding boxes and 1.2Million 2D bounding boxes.
Diverse environments: The WOD contains data from a bunch of different environments, including urban and suburban scenes, as well as scenes recorded at night and in the day.
Why this matters: Datasets like WOD will drive (haha!) progress in self-driving car research. The release of the dataset also seems to indicate that Waymo thinks a slice of its data isn’t sufficiently strategic to keep locked up – my intuition is that’s because the strategic differentiator in self-driving cars is basically how much compute you can throw at the data you’ve gathered, rather than the data itself.
Get the data here (official Waymo website).
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The future of deepfakes: fast, cheap, and out of control:
…Ever wanted to easily morph one face to another? Now you can…
Roboticist Rodney Brooks coined the term Fast, Cheap, and Out of Control when thinking about the future of robots. That prediction hasn’t come to pass for robots (yet), but it’s looking likely to be true for the sorts of AI technology required to generate convincing, synthetic imagery and/or ‘deepfakes’. That’s the intuition you can take from a new system called a Face Swapping GAN (FSGAN), revealed by researchers with Bar-Ilan University and the Open University of Israel.
What is FSGAN? “FSGAN is subject agnostic and can be applied to pairs of faces without requiring training on these faces,” the researchers write. The system is “end-to-end trainable and produces photorealistic, temporally coherent results”. FSGAN was pre-trained on several thousand pictures of people drawn from the IJB-C, LFW, and Figaro datasets.
How convincing is FSGAN? The researchers test their systems in FaceForensics++, a dataset of real videos and synthetic AI-generated videos. They compare the outputs of their system to a classic ‘faceswap’ system, as well as a system called face2face. FSGAN generates significantly more realistic images than the outputs of these systems.
Release strategy: The FSGAN researchers say technologies like this should be published “in order to drive the development of technical counter-measures for detecting such forgeries as well as compel law makers to set clear policies for addressing their implications”. It’s clear the publication can aid research on mitigation, but it’s very unclear that publishing a technology without an associated policy campaign can effect any change at all – and in fact, without a plan to discuss the implications with policymakers, policymakers will likely be surprised by the capabilities of the technology. “We feel strongly that it is of paramount importance to publish such technologies, in order to drive the development of technical counter-measures for detecting such forgeries as well as compel law makers to set clear policies for addressing their implications”, they write.
Why this matters: Technologies that help create synthetic imagery will change how society thinks about ‘truth’ in the media sphere; the rapid evolution of technologies, as exemplified by FSGAN here.
Read more: FSGAN: Subject Agnostic Face Swapping and Reenactment (Arxiv).
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The future? Endless surveillance via drones & ground-robots:
…Towards a fully autonomous surveillance society (FASS)…
One of the problems with today’s drones is their battery life – most sub-military drones just can’t fly for that long, yet around the world businesses and local government services departments (fire, health, police, etc) are starting to use compact, cheap, consumer-based drones – but these drones tend to have quite limited flight times. Now, researchers with the University of Minnesota have published a paper showing how – theoretically – you can pair fleets of drones with ground-based robots to create persistent surveillance over a pre-defined area. “We present a scalable strategy based on optimally partitioning the environment and having uniform teams of a single UGV and multiple UAVs that patrol over a cyclic route of the partitions,” they write.
Why this matters: It won’t be long before we start using machines to automatically sense, analyze, and surveil the world around us. Papers like this show how we’re laying the theoretical foundations for such systems. Next – the easy (haha!) task of designing the ground robots and drones and their software interfaces!
Read more: Persistent Surveillance with Energy-Constrained UAVs and Mobile Charging Stations (Arxiv).
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OpenAI Bits & Pieces:
OpenAI releases ~774 Million parameter GPT-2 model:
As part of our six-month updated on GPT-2, our language model, we’ve released the 774M parameter model, as well as a report documenting our experiences with staged release. In addition, we’ve released an open source legal agreement to help organizations privately share large-scale models with eachother.
Read more: GPT-2: 6-Month Follow-Up (OpenAI Blog).
Get the model here (OpenAI GitHub).
Try out GPT-2 on talktotransformer.com.
Read more in this Medium article from Dave Gershgorn: OpenAI Wants to Move Slow and Not Break Anything (Medium, OneZero).
Want to know if your system is resilient to adversarial examples? Use UAR:
We’ve developed a method to assess whether a neural network classifier can reliably defend against adversarial attacks not seen during training. Our method yields a new metric, UAR (Unforeseen Attack Robustness), which evaluates the robustness of a single model against an unanticipated attack, and highlights the need to measure performance across a more diverse range of unforeseen attacks.
Read more: Testing Robustness Against Unforeseen Adversaries (OpenAI Blog).
Get the code here (Arxiv).
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AI Policy with Matthew van der Merwe:
…Matthew van der Merwe has kindly offered to write some sections about AI & Policy for Import AI. I’m (lightly) editing them. All credit to Matthew, all blame to me, etc. Feedback: jack@jack-clark.net…
Lessons on job displacement from the 19th Century:
AI-driven automation is generally expected to result in significant job displacement in the medium term. The early Industrial Revolution is a familiar historical parallel. This post draws some specific lessons from the period.
Surprising causes: The key driver of change in the textile industry was the popularisation of patterned cotton fabrics from India, in the 17th Century. English weaving technologies were not able to efficiently provide these products, and this spurned the innovation that would drive the radical changes in the textile industry. It’s somewhat surprising that consumer fashion (and not, e.g., basic industry) that prompted this wave of disruption.
Retraining is not a panacea: There were significant efforts to retrain displaced workers throughout the 19th Century, notably in the workhouses. These programs were poorly implemented. They failed to address the mismatches in the labour market, and were unsuccessful at lifting people out of poverty and improving working conditions.
Beware bad evidence: The 1832 Royal Commission was established to address the acute crisis in the labor market. Despite being ostensibly evidence-based, the report had substantial methodological flaws, relying on narrow and biased data, much of which was ignored anyway. It resulted in the establishment of the workhouses, which were generally ineffective and unpopular.
Why it matters: There were more attempts at addressing the problem of labour displacement in Victorian England than I had previously thought, and problems seem to have come more from bad execution than a lack of concern. Addressing technological unemployment seems hard, and efforts can easily backfire. Improving our ability to forecast technological change and the impact of policy decisions might be among the most valuable things we can be doing now.
Read more: The loom and the thresher: Lessons in technological worker displacement (Medium).
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Tech Tales
It was almost midday, and the pigs had begun to cross the road. There were perhaps a hundred of them and they snuffled their way across the asphalt, some of them pausing to smell oil stains and cigarette butts. The car idled until the pigs had made it across, then continued. We turned our heads and watched the pigs as they went into the distance – they were marching in single file.
“Incredible,” I said.
“More like incredible training.” Astrid said.
“How long?”
“A few weeks. It’s getting good.”
As our car approached the complex, a flock of birds took off from a nearby tree and flew toward one of its towers. I used the carscreen to identify them: carrier pigeons.
“Look,” Astrid said, pointing at a couple of bulges on the ankles of some of the birds. “It must be watching us.”
And she was right: some of the birds had little cameras strapped to their ankles, and I was pretty sure that they’d be beaming the data back to the complex as soon as they flew into high-bandwidth range.
“Isn’t it overkill?” I said. “It gets the feeds, why does it need a camera?”
“Practice,” she said.
Maybe it’s practicing on us, I thought.
The car stopped in front of a gate and we got out. The gate started to open for us as we approached it, and four chickens came out. The chickens walked over to us and stood around us in a box formation. We walked through the gate and they followed, maintaining the box. I kept chickens when I was a kid: stupid creatures, though endearing. Borderline untrainable. I’d never have imagined them walking in formation.
At the center of the complex was a courtyard the size of a football field, with its ceiling enclosed in glass. The entire space was perforated with hundreds of tunnels, gates, and iris-opening inlets and outlets; through these portals, the animals travelled. Sometimes lights would flash in the tunnels and they would stop, or sounds would play and birds would change course, or the patterns of wind in the atrium would alter and the routes the animals were taking would change again. The AI that ran the complex was training them and we were here to find out why.
When I turned around, I saw that Astrid was very far away from me – I’d been walking, lost in thought, away from her. She had apparently been doing the same. I called to her but at the moment I spoke her name a couple of loudspeakers blared and a flock of birds flew between us. I could vaguely see her between wingbeats, but when the birds had gone past she was further away from me.
I guess it is training us, now. I’m not sure what for.
Things that inspired this story: Reinforcement learning; zoos; large-scale autonomous wildlife maintenance; Skinner machines.
Ai
via https://AIupNow.com
Jack Clark, Khareem Sudlow
