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Image 23 April 2019
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Finance

AI-Assisted Stock Trading Venue to Help Brokers Undertake Larger Trades

Imperative Execution, a startup electronic stock trading venue operator, plans to use AI to help brokers get larger trades done. The company’s trading platform called IntelligentCross is set to launch a new order type called the Adverse Selection Protection Engine, or ASPEN in May 2019. Adverse selection refers to trading against market participants who have more information than the broker, potentially leaving him on the wrong side of the trade, making larger trade sizes risky. Aspen's AI will queue up displayed orders and match them 150 microseconds to 300 microseconds apart, depending on the order size. Not matching them immediately gives brokers time to adjust their order sizes or cancel them, depending on what the market is doing in the interim. It is designed to allow the trader enough time to make a decision – not a ton of time, but enough with modern technology. If a broker posting a quote is not afraid of getting picked off, it will post larger quote sizes for others to trade against. Read More

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Finance

An AI-Powered Financial Crime Compliance Screening

Accuity, the leading provider of financial crime compliance solutions has launched its AI-driven account screening capability called ‘Firco Automated Alert Reduction’. It increases the level of accuracy in detecting and evaluating screening matches during the KYC process. Existing matching technology compares customer data to the entities listed on regulatory watch lists, but until now, has not been sophisticated enough to eliminate the abundance of ‘false positive’ results produced, with the necessary level of accuracy. Firco Automated Alert Reduction applies AI techniques to the hundreds of thousands of potential matches produced by a financial crime screening system. Its patented scoring methodology calculates the probability that a match is correct and evaluates how material the risk is to the business. The most relevant hits are flagged for immediate attention, based on the organization’s individual risk appetite and compliance policy. Results are fed back into the screening cycle continuously, allowing the statistical model to adapt and become more intelligent as it gathers information. Read More

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Healthcare

AI Can Detect Genetic Defect That Causes Some Cancers

Scientists at Harvard Medical School have developed an AI screening system — SigMA — which they claim can successfully read the molecular signature of homologous recombination (HR) deficiencies with high accuracy and efficiency. PARP inhibitors or substances that block certain cellular enzymes, are commonly given to patients with mutations in their BRCA genes. But not every patient with an HR deficiency has a BRCA mutation, so most standard assays miss them. By comparison, SigMA can identify patterns characteristic of HR defects — patterns that emerge in DNA components scrambled by cancerous malformations — even in clinical tests that analyse only a subset of genes. The researchers culled from thousands of fully sequenced tumour genomes to compile a corpus and train the model, after which they measured its performance against 730 samples analysed by whole-genome sequencing. They report that it correctly identified samples 74.0% of the time and that in subsequent experiments involving 878 breast tumour samples from patients who had previously undergone genetic testing, it detected 23.0% of the samples bearing signs of HR deficiency. Read More

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Finance

Machine Learning Gathers Analyst Emotion for Better Investing

There is increasing demand in the financial sector to utilize text information to guide decision makers in making better investment decisions. Chen Ying, a professor at the National University of Singapore, and PhD student Hitoshi Iwasaki developed a text data analytics method for extracting sentiment indices for specific topics from analyst reports of listed companies. A key feature of this method is that it analyses the reports at the sentence level rather than individual words. The research team performed the sentiment analysis on more than 110,000 analyst reports written in Japanese for stocks listed on the Tokyo Stock Exchange and the Osaka Exchange. They then incorporated the sentiments into a topic sentiment asset pricing model. Compared to other asset pricing models which either do not incorporate sentiment analysis or have overall sentiments (single aggregated value), the researchers showed their model has better predictability on expected returns and improved interpretability. Read More

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Neuroscience

AI Can Accurately Track Biological Neural Networks

Researchers at Duke University in the US have developed an AI-based automated process that can track and map active neurons as accurately as humans can, in a fraction of the time. The technique interprets video images, addressing a critical roadblock in neuron analysis. It allows researchers to rapidly gather and process neuronal signals for real-time behavioural studies. Typically, to measure neural activity, researchers use a process known as two-photon calcium imaging, which is fussy and slow. In contrast, a new open source automated algorithm can accurately identify and segment neurons in minutes. Deep-learning algorithms allow researchers to quickly process large amounts of data by sending it through multiple layers of nonlinear processing units, which can be trained to identify different parts of a complex image. In their framework, the team created an algorithm that could process both spatial and timing information in the input videos. They then trained the algorithm to mimic the segmentation of a human analyst while improving the accuracy. This is a critical step towards allowing neuroscientists to track neural activity in real time. Read More

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Sports

AI Invents a New Sport

AKQA, a design agency, has introduced Speedgate, reportedly the first sport envisioned by an AI. The event has six-player teams competing on a field with three open-ended gates. Once a player kicks the ball through a center gate (which can't be stepped through), the player’s team can score on one of the end gates -- complete with an extra point if the ball bounces through the gate. The player can't stay still, either, as the ball must move every three seconds. AKQA created the game by feeding data on 400 existing sports to a neural network, which then created basic sports concepts and rules. A large chunk of those were completely unrealistic, so the team gradually whittled down the eligible characteristics until there were three remaining sports. Playtesting led to Speedgate winning the prize. The agency even used AI to develop the game's logo and slightly awkward motto. AKQA is talking to the Oregon Sports Authority about Speedgate and there might be an intramural league in the summer. Read More

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Research

A Neural Network to Summarize Scientific Papers

The work of a science writer includes reading journal papers filled with specialized technical terminology and figuring out how to explain their contents in language that readers without a scientific background can understand. Now, a team of scientists at MIT and elsewhere has developed a neural network, a form of artificial intelligence (AI), that can do much the same thing, at least to a limited extent. It can read scientific papers and render a plain-English summary in a sentence or two. While the system still can't match most humans' language abilities, it is a dramatic improvement from current programs, and could help scientists or science writers sift through large numbers of papers for the ones that catch their interest. Such a neural network could be useful for helping editors, writers, and scientists scan a large number of papers to get a preliminary sense of what they’re about. Read More

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Sports

Howzat!! AI to Predict Fall of Wickets in Live Cricket Matches

Fox Sports Australia has rolled out a machine learning model powered by Google Cloud’s AutoML Tables service to predict when wickets might fall in live cricket matches. Dubbed Monty, the model was trained on a year’s worth of historical data on cricket matches licensed from Opta Sports to predict when a wicket could fall five minutes before it did. Monty was designed to parse 83 variables within seconds after a ball is hit. During this prediction window, it also factors in data on recent shots in a game, so that it understands what is happening on the pitch to make its predictions. The insights are then displayed on electronic billboards across major Australian cities and as notifications in the Fox Sports cricket app, which delivers commentary on the fly. This delivers incredible insights to fans on when they should watch and what could happen next. Read More

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Music

AI To Help Violinists Avoid Injuries and Burnout

AI is being used by the Royal College of Music to help the next generation of violinists avoid injuries and burnout. The AI system, named SkyNote, is being tested by students to provide real time feedback on a violinist’s bow position, pressure and speed. A camera records the movement of markers on the bow, while a software highlights where a violinist’s technique deviates from that of professionals. That data can be seen on a computer screen as the musician plays so they can correct their technique. For the 150 violin students at the Royal College of Music, the technology could dramatically cut the hours they spend in practice rooms. The AI can help top musicians push the limits of the instrument without harming their health as well as reduce the high drop-out rate among music students. Researchers hope the system could soon help perfect the technique of musicians who play other string instruments, including the cello and viola. The algorithm helps students learn the technical side of an instrument faster, letting them spend longer focusing on their individual styles. Read More

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Customer Service

AI to Create Customised, Emphatic Chatbots

In order to improve user-experience, researchers at IBM India are using AI to make chatbots more emphatic and personalised, so that they can meet a client's specific needs and provide business value at a much higher scale. The tech giant's IBM Watson Assistant can be trained to represent a company's specific brand voice and values using that firm's customer and business data. This technology empowers more realistic conversational AI, which can handle nuances of the user language and that can handle domain and context understanding. In addition, they can delight their customers with an instantaneous response to their queries. The researchers are using deep learning techniques on conversations between end-users and human agents that provide support in traditional call centers. Their analysis helps identify dominant problems or reasons for which end-users contact call centers as well as gives insights on how expert agents handle those problems. Read More

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