Singularity Pulse  
     
  Finance

Robots to Help Corporations Collect Payments from Customers

Citigroup is deploying robots to help massive corporations collect payments from customers. Citi is partnering with fintech company HighRadius to launch Citi Smart Match, which automates a key money-collection process for businesses. The new feature applies artificial intelligence and machine learning to the process of matching open invoices to received payments - automating and streamlining the otherwise painstaking and costly task. The new technology can learn where to look for information and how to identify it. In addition, it can also identify emails containing remittance information by examining keywords and attachments, build custom rules for cleaning up and formatting data so invoices are recognized and matched automatically.

 
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Machine-Learning to Better Select Lead Molecule Candidates

Designing new molecules for pharmaceuticals is primarily a manual, time-consuming process that is prone to error. Drug discovery relies on lead optimization. In this process, chemists select a target (“lead”) molecule with known potential to interact with a specific biological target, then tweak its chemical properties for higher potency and other factors. Researchers from MIT have developed a model that better selects lead molecule candidates based on desired properties. It also modifies the molecular structure needed to achieve a higher potency, while ensuring the molecule is still chemically valid. The researchers trained their model on 250,000 molecular graphs from the ZINC database, a collection of 3-D molecular structures available for public use. They tested the model on tasks to generate valid molecules, find the best lead molecules, and design novel molecules with increase potencies.

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  Miscellaneous

AI-Powered App That Converts Voice to Text in Real Time

Otter is a new-age, AI-powered app that generates text transcripts of voice conversations. Its speech recognition technology can identify multiple voices in a conversation. The app records and transcribes voices and spits out text transcripts in near real-time (only a 2-3 seconds delay is experienced), automatically adds keywords that reflect what the meeting was all about and makes any part of the conversation searchable and shareable, instantly.

 
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Artificial Intelligence Helps Design an Ultra-Aerodynamic Bike

A new bike designed by researchers at IUT Annecy, with the help of computer scientists at Neural Concept, a Subsidiary of the Swiss Federal Institute of Technology in Lausanne (EPFL) can quickly calculate the most aerodynamic shape for a bike. This AI has a neural net capable of evaluating the aerodynamic properties of various shapes, which are internally represented by polygon meshes, or points that are used to create 3D shapes. The system starts with the most basic shapes and works toward increasing complexity until it finally settles on an optimal, super-aerodynamic shape. To get the AI started on a project, the researchers feed the system with the basics about the bike, like the maximum length and width required to squeeze in the cyclist, the drivetrain, and its four wheels. Armed with these constraints, the AI sorts through an assembly of possible shapes, looking for the best configuration.

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  Miscellaneous

Amazon’s Alexa to Respond to Sign Language

Amazon’s virtual assistant, Alexa, will now respond to sign language. Using a JavaScript machine learning package called TensorFlow.js, a program is built to translate sign language into verbal speech that Amazon’s Alexa can understand. The camera interprets signs from the user and converts the signs into text. This could be a big support to people who cannot hear or speak.

 
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Artificial Intelligence to Predict Drug Side Effects

Artificial intelligence is helping researchers to predict medicinal drug combinations' side effects. This information will help to enhance patient safety. Computer scientists from Stanford University have worked out on how to predict side effects of combination medicines using artificial intelligence. The new system is called Decagon, and it is designed to aid doctors with making more informed decisions about which drugs to prescribe. Decagon was trained using deep learning, testing out the platform on those drug combinations that were known. Currently the AI can only assess pairs of drugs; the aim in the future is to use the system for three or more drugs in combination.

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  Healthcare

Machine Learning to Predict the Treatment Outcomes of Schizophrenia

Researchers from the University of Alberta in collaboration with University of Texas Health Science Center, used a machine-learning algorithm to examine functional magnetic resonance imaging (MRI) images of newly diagnosed, previously untreated schizophrenia patients and healthy subjects. By measuring the connections of a brain region called the superior temporal cortex to other regions of the brain, the algorithm successfully identified patients with schizophrenia at 78% accuracy. It also predicted with 82% accuracy whether a patient would respond positively to a specific antipsychotic treatment named risperidone. Current treatment of schizophrenia is still often determined by a trial-and-error style. If a drug is not working properly, the patient may suffer prolonged symptoms and side effects, and miss the best time window to get the disease controlled and treated.

 
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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

DARPA Plans to Apply Quantum Computing to Machine Learning

The Defence Advanced Research Projects Agency (DARPA), is planning to leverage quantum computing’s increased computing power for applications like optimizing AI and ML and addressing scientific modelling problems. DARPA is asking how quantum computing can help understand complex physical systems, optimizing AI and ML and enhancing distributed sensing. DARPA has raised few questions like, how speed gains are affected by the size of the quantum machine and whether any new technology needs to be developed to interface quantum and classical resources, how to best interface quantum computers with quantum sensors, detailing the fundamental limits of quantum computing including “near-term wins” for modelling hard science problems as well as addressing scaling issues and possible integration with classical computers.

 
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Quantum Computing Could Put A Stop to Traffic Jams

Traffic optimization using quantum computers, while still unproven can aid in reducing traffic jams. For example, instead of having to travel down each traffic route one by one, quantum algorithms could take advantage of the qubits’ computational properties to determine the best route. The premise of quantum computer managing traffic flow is that, with the right algorithms, it could approximate the most-efficient route during rush hour and orchestrate routes that not just redirect cars and buses around a traffic jam but also steer them home on routes that prevent the traffic jam from happening in the first place.

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  Quantum Computing

Quantum Computing Could be a Threat to Bitcoins

Bitcoin transactions are essentially a series of puzzles stored in public on the blockchain. The puzzles used to protect bitcoin are so complex that current computer technology isn’t powerful enough to crack them. However, quantum computers are expected to crack these puzzles in coming decades. Bitcoin transactions are electronically signed using complicated algorithms based on what mathematicians call elliptic curves. The idea is that creating such a signature is prohibitively difficult for any computer unless one holds the secret key. However, Quantum computers are not restricted to processing digital information, but instead perform calculations directly using the quantum mechanical interactions that dominate physics at a microscopic scale.

 
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Pentagon Plans to Deploy Quantum Computing to Secure Communications

The Pentagon plans to foster quantum computing to develop secure communications and inertial navigation in GPS denied and contested environments. The Air Force Office of Scientific Research is studying the use of super-fast computers that promise improved security for data storage and transmission on Air Force systems. Artificial intelligence algorithms, highly secure encryption for communications satellites and accurate navigation that does not require GPS signals are some of the most coveted capabilities that would be aided by quantum computing. The Pentagon has announced major investments in the field of quantum information science.

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