Singularity Pulse  
     
  Finance

Corporate Finance to Benefit from AI

Many Corporate finance teams spend about 80% of their time manually gathering, verifying and consolidating data, leaving only about 20% for higher-level tasks like analysis and decision making. Robotic process automation (RPA) is expected to support corporate finance professionals to perform certain tasks automatically. RPA uses artificial intelligence (AI) to train and teach software robots to process transactions, monitor compliance and audit processes automatically. CFOs can optimize their accounts-payable and accounts-receivable processes through AI. Machine learning could support finance teams by automatically tracking down the relevant information, finding the source of a GL problem and suggesting which payments to match to which invoices. AI can also mitigate financial risks by stopping erroneous payments to vendors in near real time.

 
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Machine Learning to Aid Pharmaceutical Discovery

MIT researchers and pharma industry have formed a new consortium, ‘Machine Learning for Pharmaceutical Discovery and Synthesis’, to aid the drug discovery process. All major players in the pharmaceutical field, including Amgen, BASF, Bayer, Lilly, Novartis, Pfizer, Sunovion, and WuXi are a part of this consortium. The drug discovery process can often be exceedingly expensive and time-consuming, but machine learning offers tremendous opportunities to more efficiently access and understand vast amounts of chemical data — with great potential to improve both processes and outcomes. The consortium aims to break down the divide between machine learning research at MIT and drug discovery research. According to professor Tommi Jaakkola “By marrying chemical insights with modern machine learning concepts and methods, we are opening new avenues for designing, understanding, optimizing, and synthesizing drugs”.

Pharma  
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  Healthcare

Quantum Computing to Improve Medical Imaging

Microsoft has stated that it is using its technology to advance medical imaging beyond the capabilities of today’s computers. It will bring HoloLens technology to a new form of MRI called Magnetic Resonance Fingerprinting (MRF). In order to do so, the company’s quantum computing division will develop quantum-inspired algorithms capable of processing greater amounts of data than is typically feasible with traditional computers. Medical professionals will use the Hololens, an augmented reality headset, to view 3D MRF images created by the quantum-inspired algorithms. the MRF scans contain more information for doctors and analysts to diagnose with, and they’re quicker and more comfortable for patients. The project combines mixed-reality, machine learning, and quantum computing to solve a problem which would otherwise remain out of reach.

 
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Detecting Spear Phishing with AI

Spear phishing attacks are becoming more sophisticated and harder to spot, but machine-learning algorithms are helping businesses detect and stop these malicious attacks. However, these algorithms need usable data in order to draw valid conclusions. There are presently three methods by which machine learning and anomaly detection algorithms can be applied to the detection of spear phishing emails. The three methods are, Social graph analysis- observing the connections used by each email and comparing them to the model, User communication profiling and Email structural analysis.

Information Technology  
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  Digital Imaging

Machine Learning is Reshaping the Way the Pictures are Clicked

New capabilities in software, new hardware formats and imaging technologies, and emerging user behaviors around image creation are radically reshaping the object we know of as the "camera" into new categories. Software trained on vast datasets of labeled images can recognize things like vehicles, dogs, cats, and people, along with facial features and emotions. Cameras enabled with machine learning therefore have the potential to both automate existing functions of the camera as a tool for human use and extend its creative possibilities far beyond image capture. The camera will now be able to choose an appropriate moment to capture the photograph. Decisions about the moment of capture will increasingly be shifted to the device.

 
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AI to Help in House Hunting

AI is now being applied to Real Estate. Zillow, is now using AI to transform it from a simple real-estate search engine to a tool that understands the user. The Company is building AI into the search engine, in the form of a new feature called Personalized Sort, expected to roll out later this year. Details are few right now, but the idea is Personalized Sort will learn the types of criteria you're looking for and recommend homes based on that. The AI will take into account the consumer’s taste in decor as well. the AI into account the interior photos of homes you're looking at and which ones you seem to like. Then it'll take into account what the kitchens, living rooms, and bedrooms look like when making recommendations. AI will also make an appearance in the upcoming rollout of Zillow's 3D Home app, a Google Street View-like service for viewing the inside of a house.

Real Estate  
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  General

AI to Determine if Travelers are Lying

According to a recent report, the United States, Canada, and the European Union are all said to be testing out new technology involving lie-detecting computer kiosks, which uses the latest AI technology to determine whether a person is trying to deceive officials. While facial-recognition technology has been used as a security system in airports for at least the past decade, this proposed AI kiosk — has the power to flag you as a possible cause for concern — and goes further than simple face-matching. To make its judgments, the lie-detecting technology incorporates smart image recognition to spot signs of potential shiftiness. This includes giveaway eye movements, vocal changes, odd posture, or facial movements. According to a CNBC report, the technology is up 80 % accurate when it comes to spotting potential deceit, which it a better hit ratio than that of human agents employed to carry out this task.

 
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Robo-Bees to Stop Declining Bee Numbers from Going Extinct

According to Greenpeace, some 40% of commercial honeybees have been lost across the US since 2006. The decline is attributed to a rise in colony collapse disorder (CCD), a phenomenon that occurs when the majority of a colony’s worker bees suddenly disappear. Insect pollination directly contributes to the production of a huge portion of the world’s food supply, so the dramatic decline of the honeybee should be a warning: either we get to the root of the problem or develop a technological alternative capable of performing the same function. A manually controlled drone, a device of around four centimeters wide and is covered in horse hair treated with a gel has been designed to collect pollen. Engineers from the University of Graz used AI to evolve the robots’ behavior to be more like that of real-life bees as the drones themselves became more sophisticated. They also got two robotic swarms to interact with each other, flying around as cohesive units without individuals flying off on their own, or crashing into one another.

General  
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  Healthcare

AI Software to Identify Cause of Stroke and Dementia

The researchers at Imperial College London have developed a new AI backed software that can identify and measure the severity of small vessel disease (SVD), which is one of the most common causes of stroke and dementia, using brain scans with AI can more accurately than all current method. During multiple tests, this AI-backed software was able to identify and measure the SVD and then provided the researchers with a score that indicated the severity of the disease. It provided 85% accurate results, which may not seem significant at first but is better than what other modern methods such as CT or MRI scans. The study used 1082 CT scans of stroke patients across 70 hospitals in the UK between 2000-2014. The team of researchers is now using similar methods to train the AI software to measure the amount of brain shrinkage and other types of conditions that doctors could commonly be diagnosed using a brain CT scan. It can also be used to detect the risk of hemorrhage in patients in the future as the AI learns more from the brain scans.

 
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