A new piece of software has been trained to use wifi signals — which pass through walls, but bounce off living tissue — to monitor the movements, breathing, and heartbeats of humans on the other side of those walls. The researchers say this new tech’s promise lies in areas like remote healthcare, particularly elder care, but it’s hard to ignore slightly more dystopian applications.

[…]

“We actually are tracking 14 different joints on the body … the head, the neck, the shoulders, the elbows, the wrists, the hips, the knees, and the feet,” Katabi said. “So you can get the full stick-figure that is dynamically moving with the individuals that are obstructed from you — and that’s something new that was not possible before.”

The technology works a little bit like radar, but to teach their neural network how to interpret these granular bits of human activity, the team at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) had to create two separate A.I.s: a student and a teacher.

[…]

the team developed one A.I. program that monitored human movements with a camera, on one side of a wall, and fed that information to their wifi X-ray A.I., called RF-Pose, as it struggled to make sense of the radio waves passing through that wall on the other side.

 

Source: A.I. Can Track Human Bodies Through Walls Now, With Just a Wifi Signal | Inverse

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In these scenarios, a deep-learning machine is given the rules of the game and then plays against itself. Crucially, it is rewarded at each step according to how it performs. This reward process is hugely important because it helps the machine to distinguish good play from bad play. In other words, it helps the machine learn.

But this doesn’t work in many real-world situations, because rewards are often rare or hard to determine.

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For example, random turns of a Rubik’s Cube cannot easily be rewarded, since it is hard to judge whether the new configuration is any closer to a solution. And a sequence of random turns can go on for a long time without reaching a solution, so the end-state reward can only be offered rarely.

In chess, by contrast, there is a relatively large search space but each move can be evaluated and rewarded accordingly. That just isn’t the case for the Rubik’s Cube.

Enter Stephen McAleer and colleagues from the University of California, Irvine. These guys have pioneered a new kind of deep-learning technique, called “autodidactic iteration,” that can teach itself to solve a Rubik’s Cube with no human assistance. The trick that McAleer and co have mastered is to find a way for the machine to create its own system of rewards.

Here’s how it works. Given an unsolved cube, the machine must decide whether a specific move is an improvement on the existing configuration. To do this, it must be able to evaluate the move.

Autodidactic iteration does this by starting with the finished cube and working backwards to find a configuration that is similar to the proposed move. This process is not perfect, but deep learning helps the system figure out which moves are generally better than others.

Having been trained, the network then uses a standard search tree to hunt for suggested moves for each configuration.

The result is an algorithm that performs remarkably well. “Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves—less than or equal to solvers that employ human domain knowledge,” say McAleer and co.

That’s interesting because it has implications for a variety of other tasks that deep learning has struggled with, including puzzles like Sokoban, games like Montezuma’s Revenge, and problems like prime number factorization.

Indeed, McAleer and co have other goals in their sights: “We are working on extending this method to find approximate solutions to other combinatorial optimization problems such as prediction of protein tertiary structure.”

Source: A machine has figured out Rubik’s Cube all by itself – MIT Technology Review

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Video Machine-learning experts have built a neural network that can manipulate facial movements in videos to create fake footage – in which people appear to say something they never actually said.

It could be used to create convincing yet faked announcements and confessions seemingly uttered by the rich and powerful as well as the average and mediocre, producing a new class of fake news and further separating us all from reality… if it works well enough, naturally.

It’s not quite like Deepfakes, which perversely superimposed the faces of famous actresses and models onto the bodies of raunchy X-rated movie stars.

Instead of mapping faces onto different bodies, though, this latest AI technology controls the target’s face, and manipulates it into copying the head movements and facial expressions of a source. In one of the examples, Barack Obama acts as the source and Vladimir Putin as the target. So it looks as though a speech given by Obama was instead given by Putin.

Obama’s facial expressions are mapped onto Putin’s face using this latest AI technique … Image credit: Hyeongwoo Kim et al

A paper describing the technique, which popped up online at the end of last month, claims to produce realistic results. The method was developed by Hyeongwoo Kim, Pablo Garrido, Ayush Tewari, Weipeng Xu, Justus Thies, Matthias Nießner, Patrick Pérez, Christian Richardt, Michael Zollhöfer, and Christian Theobalt.

The Deepfakes Reddit forum, which has since been shut down, was flooded with people posting tragically bad computer-generated videos of celebs’ blurry and twitchy faces pasted onto porno babes using machine-learning software, with mismatched eyebrows and skittish movements. You could, after a few seconds, tell they were bogus, basically.

A previous similar project created a video of someone pretending to say something he or she hadn’t through lip-synching and an audio clip. Again, the researchers used Barack Obama as an example. But the results weren’t completely convincing since the lip movements didn’t always align properly.

That’s less of a problem with this new approach, however. It’s, supposedly, the first model that can transfer the full three-dimensional head position, head rotation, face expression, eye gaze and blinking from a source onto a portrait video of a target, according to the paper.

Controlling the target head

It uses a series of landmarks to reconstruct a face so it can track the head and facial movements to capture facial expressions for the input source video and output target video for every frame. A facial representation method computes the parameters of the face for both videos.

Next, these parameters are slightly modified and copied from the source to the target face for a realistic mapping. Synthetic images of the target’s face are rendered using an Nvidia GeForce GTX Titan X GPU.

The rendering part is where the generative adversarial network comes in. The training data comes from the tracked video frames of the target video sequence. The goal is to generate fake images that are as good as enough as the ones in the target video frames to trick a discriminator network.

Only about two thousand frames – which amounts to a minute of footage – is enough to train the network. At the moment, it’s only the facial expressions that can be modified realistically. It doesn’t copy the upper body, and cannot deal with backgrounds that change too much.

Source: Uh oh! Here’s yet more AI that creates creepy fake talking heads • The Register

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Deep reinforcement learning methods traditionally struggle with tasks where environment rewards are particularly sparse. One successful method of guiding exploration in these domains is to imitate trajectories provided by a human demonstrator. However, these demonstrations are typically collected under artificial conditions, i.e. with access to the agent’s exact environment setup and the demonstrator’s action and reward trajectories. Here we propose a two-stage method that overcomes these limitations by relying on noisy, unaligned footage without access to such data. First, we learn to map unaligned videos from multiple sources to a common representation using self-supervised objectives constructed over both time and modality (i.e. vision and sound). Second, we embed a single YouTube video in this representation to construct a reward function that encourages an agent to imitate human gameplay, this has been a boom in society, and there are more and more games to be improved with this,and it’s more popular now between adults than kids, you can look here to see how to get gaming services. This method of one-shot imitation allows our agent to convincingly exceed human-level performance on the infamously hard exploration games Montezuma’s Revenge, Pitfall! and Private Eye for the first time, even if the agent is not presented with any environment rewards.

Source: [1805.11592] Playing hard exploration games by watching YouTube

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or the first time, new research suggests artificial intelligence may be better than highly-trained humans at detecting skin cancer. A study conducted by an international team of researchers pitted experienced dermatologists against a machine learning system, known as a deep learning convolutional neural network, or CNN, to see which was more effective at detecting malignant melanomas.

[…]

Fifty-eight dermatologists from 17 countries around the world participated in the study. More than half of the doctors were considered expert level with more than five years’ experience. Nineteen percent said they had between two to five years’ experience, and 29 percent had less than two years’ experience.

[…]

At first look, dermatologists correctly detected an average of 87 percent of melanomas, and accurately identified an average of 73 percent of lesions that were not malignant. Conversely, the CNN correctly detected 95 percent of melanomas.

Things improved a bit for the dermatologists when they were given additional information about the patients along with the photos; then they accurately diagnosed 89 percent of malignant melanomas and 76 percent of benign moles. Still, they were outperformed by the artificial intelligence system, which was working solely from the images.

“The CNN missed fewer melanomas, meaning it had a higher sensitivity than the dermatologists, and it misdiagnosed fewer benign moles as malignant melanoma, which means it had a higher specificity; this would result in less unnecessary surgery,” study author Professor Holger Haenssle, senior managing physician in the Department of Dermatology at the University of Heidelberg in Germany, said in a statement.

The expert dermatologists performed better in the initial round of diagnoses than the less-experienced doctors at identifying malignant melanomas. But their average of correct diagnoses was still worse than the AI system’s.

Source: AI better than dermatologists at detecting skin cancer, study finds – CBS News

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Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate of 0.7% (equal error rate), an improvement ratio of 371% from previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of client’s footstep data and provide insights of the feature learning process.

Source: Analysis of Spatio-temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks – IEEE Journals & Magazine

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In a field of sugar beet in Switzerland, a solar-powered robot that looks like a table on wheels scans the rows of crops with its camera, identifies weeds and zaps them with jets of blue liquid from its mechanical tentacles.

Undergoing final tests before the liquid is replaced with weedkiller, the Swiss robot is one of new breed of AI weeders that investors say could disrupt the $100 billion pesticides and seeds industry by reducing the need for universal herbicides and the genetically modified (GM) crops that tolerate them.

[…]

While still in its infancy, the plant-by-plant approach heralds a marked shift from standard methods of crop production.

Now, non-selective weedkillers such as Monsanto’s Roundup are sprayed on vast tracts of land planted with tolerant GM seeds, driving one of the most lucrative business models in the industry.

‘SEE AND SPRAY’

But ecoRobotix www.ecorobotix.com/en, developer of the Swiss weeder, believes its design could reduce the amount of herbicide farmers use by 20 times. The company said it is close to signing a financing round with investors and is due to go on the market by early 2019.

Blue River, a Silicon Valley startup bought by U.S. tractor company Deere & Co. for $305 million last year, has also developed a machine using on-board cameras to distinguish weeds from crops and only squirt herbicides where necessary.

Its “See and Spray” weed control machine, which has been tested in U.S. cotton fields, is towed by a tractor and the developers estimate it could cut herbicide use by 90 percent once crops have started growing.

German engineering company Robert Bosch here is also working on similar precision spraying kits as are other startups such as Denmark’s Agrointelli here

ROBO Global www.roboglobal.com/about-us, an advisory firm that runs a robotics and automation investment index tracked by funds worth a combined $4 billion, believes plant-by-plant precision spraying will only gain in importance.

“A lot of the technology is already available. It’s just a question of packaging it together at the right cost for the farmers,” said Richard Lightbound, Robo’s CEO for Europe, the Middle East and Africa.

“If you can reduce herbicides by the factor of 10 it becomes very compelling for the farmer in terms of productivity. It’s also eco friendly and that’s clearly going to be very popular, if not compulsory, at some stage,” he said.

 

Source: Robots fight weeds in challenge to agrochemical giants | Reuters

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Computer vision has advanced significantly that many discriminative approaches such as object recognition are now widely used in real applications. We present another exciting development that utilizes generative models for the mass customization of medical products such as dental crowns. In the dental industry, it takes a technician years of training to design synthetic crowns that restore the function and integrity of missing teeth. Each crown must be customized to individual patients, and it requires human expertise in a time-consuming and labor-intensive process, even with computer-assisted design software. We develop a fully automatic approach that learns not only from human designs of dental crowns, but also from natural spatial profiles between opposing teeth. The latter is hard to account for by technicians but important for proper biting and chewing functions. Built upon a Generative Adversar-ial Network architecture (GAN), our deep learning model predicts the customized crown-filled depth scan from the crown-missing depth scan and opposing depth scan. We propose to incorporate additional space constraints and statistical compatibility into learning. Our automatic designs exceed human technicians’ standards for good morphology and functionality, and our algorithm is being tested for production use.

Source: [1804.00064] Learning Beyond Human Expertise with Generative Models for Dental Restorations

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