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Finance

AI to Predict the Occurrence of Pump-and-Dump Schemes

Jiahua Xu and Benjamin Livshits at Imperial College London have created an algorithm, that can predict when ‘pump-and-dump’ schemes are about to occur, which offers a promising way to subvert or prevent them. Pump and dump schemes work in a deceptively simple way: a group of malicious actors buy some obscure cryptocurrency quietly, and then generate hype around the coin so that other, unsuspecting traders may buy which, in turn, makes the coin’s price spike. Once the price peaks, the original group sells their hoard and makes a quick profit off anyone who was too slow to sell off. In the case of cryptocurrencies, all of this takes place within a matter of minutes. Xu and Livshits studied a total of 236 pump-and-dump events that took place between July 21st and November 18th. They said that many of them were preceded by unusual buying activity in the target currency. This would be consistent with insiders’ accumulating the currency ahead of the pump, which could be the key to spot target coins before the fraudster scheme begins. Xu and Livshits then used this knowledge and let their machine learning algorithm go live. Between October 30th and November 6th, it recognized six different suspicious activities, five of which turned out to be pump and dump schemes. Read More

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Healthcare

AI System Learns to Diagnose Intracranial Haemorrhage

Researchers have developed a system using Artificial Intelligence (AI) to quickly diagnose and classify brain haemorrhages and to provide the basis of its decisions from relatively small image datasets. To train the system, the research team began with 904 head CT scans, each consisting of around 40 individual images that were labelled by a team of five neuroradiologists as to whether they depicted one of the five haemorrhage subtypes, based on the location within the brain, or no haemorrhage. To improve the accuracy of this deep-learning system, the team built in steps mimicking the way radiologists analyse images, suggested the study published. Once the model system was created, the team tested it on two separate sets of CT scans -- a retrospective set taken before the system was developed, consisting of 100 scans with and 100 without intracranial haemorrhage, and a prospective set of 79 scans with and 117 without haemorrhage, taken after the model was created. In its analysis of the retrospective set, the model system was as accurate in detecting and classifying intracranial haemorrhages as the radiologists that had reviewed the scans had been. Read More

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Sports

Machine Learning is Helping NBA Basketball Players

In the last few years, high-tech cameras have proliferated across the highest levels of basketball. The cameras keep a digitized visual record of every game, collecting far more information than could ever be squeezed into a box score. Not only do they track who scores a bucket, for instance, but they also capture every player's position and speed. With so much data, the challenge is to extract useful knowledge and help players and coaches to gain every competitive edge possible. So, NBA teams and sports companies have turned to artificial intelligence. Combined with an unprecedented flood of data, these techniques are revealing new insights about individual players, their teams and their opponents, promising to change how the pros play basketball and how the fans watch it. The more training data there is, the better the algorithm becomes at recognizing patterns in data it's never seen before. The resulting software can then go through all of the tracking data, identify and label every player and play, and create a database of searchable, annotated and animated diagrams of moving dots that represent each player. Read More

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Miscellaneous

AI Helps to Quickly Remove the Background From Images

If you’ve ever needed to quickly remove the background of an image you know it can be tedious, even with access to software like Photoshop. Remove.bg is a single-purpose website that uses AI to do the hard work for you. Just upload any image and the site will automatically identify any people in it, cut around the foreground, and let you download a PNG of your subject with a transparent background. It’s the latest example of how machine learning techniques that were once cutting-edge are being turned into simple consumer tools Other similar tools include Deepart.io which applies the style of one image (like a painting) to another, and LetsEnhance.io, which uses AI to automatically upscale pictures. But, it’s certainly robust enough to handle a wide range of pictures, and even though the site claims the tool only works with people, it can handle other subjects, as long as they’re clearly foregrounded. Read More

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Renewable Energy

How Artificial Intelligence Spotted Every Solar Panel in the US

Solar panels across the US are converting their energy into electricity, but without a good handle on their locations and capabilities. Solar installations are difficult to track and even more difficult to manage. Some solar installations are privately owned, on the rooftops of family homes. Others are industrial-size and owned by utilities, spanning acres of land. Without thorough data, utility companies can’t plan their energy needs, solar installers don’t know the ideal areas for more panels and lawmakers can’t incentivize adoption of renewables. Stanford University engineers have developed a way to find every solar panel in the contiguous United States. The researcher’s modified network, known as DeepSolar, scanned more than a billion image “tiles” — areas of the US, larger than a neighborhood but smaller than a zip code. Each tile contained 102,400 pixels, and the neural network classified each pixel in each tile, judging whether it was likely part of a solar panel, or not. Once DeepSolar had identified a pixel that it recognized as a solar panel, the network could use that information in concert with the pixels located nearby to calculate the size of solar installations across the country. The team found nearly 50% more solar installations than any previous survey. Read More

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Miscellaneous

Google’s DeepMind AI Outperforms Professional Lip Readers

A team of researchers from the University of Oxford and Google’s DeepMind applied deep learning to a dataset of BBC TV shows to develop a lip-reading system that outperformed even a professional. According to the research paper published this month, the lip-reading AI was fed in total 118,000 sentences from six different TV programs, including BBC Breakfast, Newsnight, and Question Time. After the training, the system was presented with a dataset comprising shows that aired on the network between March and September 2016. It was able to correctly annotate 46.8% of all the words without any mistakes. This outperformed other lip-reading systems and even a human lip-reading professional, who correctly annotated only 12.4% of words without error from 200 randomly selected clips from the same dataset. Read More

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“Quantum Computing, a revolution in technology, has shown great promise towards solving complex computing problems currently outside the capabilities of current computers. Though in its infancy, we at Decimal Point Analytics strongly believe that it is going to grow exponentially in the near future. It holds the potential not only to boost the AI revolution but also transform the way data is synthesized. The articles on Quantum computing will give an insight into the recent developments in this space.”

 
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Quantum Computing

Quantum Messages Can Travel Faster Than Classical Ways of Transmitting Information

A new experiment out of Paris has demonstrated, for the first time, that quantum communication is superior to classical ways of transmitting information. Quantum machines, which exploit quantum properties of matter to encode information — are widely expected to revolutionize computing. The new experiment is a triumph over classical methods. The researchers went into the experiment knowing exactly how much information is needed to be transmitted classically to solve the problem. They then indisputably demonstrated that it could be solved in a far leaner fashion by quantum means. The result also suggests an alternative route for achieving a long-standing goal in computer science: proving that quantum computers reign over classical ones. Read More

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