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AI to drive GDP gains of $15.7 trillion with productivity, personalisation improvements

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Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.); Personalized marketing and advertising Manufacturing; Enhanced monitoring and auto-correction; Supply chain and production optimisation; On-demand production Energy: Smart metering; More efficient grid operation and storage; Intelligent infrastructure maintenance Transport and logistics; Autonomous trucking and delivery: Traffic control and reduced congestion; Enhanced security Methodology: To estimate AI impact, our team conducted a dual-phased top-down and bottom-up analysis combining a detailed assessment of the current and future use of AI and an exploration of the economic impact in terms of new jobs, new products, and other secondary effects. Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.); Personalized marketing and advertising Manufacturing; Enhanced monitoring and auto-correction; Supply chain and production optimisation; On-demand production Energy: Smart metering; More efficient grid operation and storage; Intelligent infrastructure maintenance Transport and logistics; Autonomous trucking and delivery: Traffic control and reduced congestion; Enhanced security Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.


A Primer on Machine Learning Models for Fraud Detection - Simility

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One area of machine learning that's getting a lot of buzz in recent years is artificial neural networks (ANNs), aka "deep learning" models, which try to simulate how layers of neurons act together in the brain to make a decision. ANN models are highly versatile and can be used to solve highly complex problems like identifying account takeover using the device's sensor data. While other techniques often require limiting the number of features, multi-layer ANNs can train on thousands of features and scale easily. Training such models requires massive amounts of data (typically, millions of labeled transactions), so deep learning models are really only practical for large companies or those that generate a lot of data points.


How AI Is Transforming Drug Creation – The Data Intelligence Connection – Medium

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But samples also were sent to a lab where computers using artificial intelligence are changing the way pharmaceutical companies develop drugs. Biological insights driven by machine learning also could help pharmaceutical companies better identify and recruit patients for clinical trials of therapies most likely to work for them, perhaps boosting the chances of those medications' getting approved by regulatory agencies such as the Food and Drug Administration. Other efforts to leverage AI technology in pharmaceutical research include using it to find new drugs or new uses for already approved medications, as well as speeding up clinical trials by improving patient recruitment and site selection, according to a May 2017 report by analyst Datamonitor Healthcare. Recently, there's been growing interest in leveraging this type of AI for health-care applications, in part due to the vast improvements deep learning has enabled in applications like machine translation and computer vision, which also rely on pattern recognition.


Think weather forecasts are bad? Try forecasting a volcanic eruption.

Popular Science

In a study published Wednesday in Frontiers in Earth Science Mary Grace Bato, a volcanology PhD student at the Institut des Sciences de la Terre (ISTerre) in France, and colleagues detailed how weather forecasting techniques can be applied to volcanic eruptions, potentially setting the stage for volcano forecasts to show up alongside weather reports in places like Alaska or Iceland. In this new study, Bato and her colleagues showed that by combining GPS data from satellites with an existing model of how a volcano functions, and applying applying weather forecasting techniques--also called data assimilation--they could predict what situations would lead to an eruption. That's similar to how meteorologists take real-time data and measurements from around the world and combine them with weather models to predict the track of a storm or a heat wave. Bato hopes that as volcanic eruption models become more robust, volcano forecasting might become more common, eventually helping people and communities living in the shadow of volcanoes be better prepared for the hazards they face.


Kit Cummins awarded the American Chemical Society Pauling Medal

MIT News

Department of Chemistry Professor Christopher (Kit) Cummins has been honored with the 2017 Linus Pauling Medal, in recognition of his unparalleled synthetic and mechanistic studies of early-transition metal complexes, including reaction discovery and exploratory methods of development to improve nitrogen and phosphorous utilization. It is presented annually in recognition of outstanding achievement in chemistry in the spirit of, and in honor of, Linus Pauling, who was awarded the Nobel Prize in chemistry in 1954 and the Nobel Prize for peace in 1962. Cummins joins several current members of the Department of Chemistry in being named a Linus Pauling Medal awardee, including Tim Swager (2016), Stephen Buchwald (2014), and Stephen Lippard (2009), as well as former department members Alexander Rich (1995) and John Waugh (1984). In addition, Cummins Group researchers work to develop new starting materials in phosphate chemistry, including acid forms that provide a starting point for synthesizing new phosphate-based materials with applications in next-generation battery technologies and catalysis.


How artificial intelligence will impact the future of healthcare

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"IBM's Watson read 25 million scientific papers in a week." Quartz Magazine reported on an AI called AtomNet that promises to develop new drug treatments for dangerous diseases like Ebola and multiple sclerosis. Computer-assisted coding isn't exactly a form of artificial intelligence, but some in the industry promised that it would improve coder productivity and efficiency using a spell checker-like system called NLP. By auditing all charts prior to billing, eValuator acts as a highly skilled AI auditor, flagging any charts that are likely to have mistakes.


Tinder 'Gold' offers list of people who already like you

Daily Mail

Tinder users on the free tier have to swipe through a list of profiles without knowing which potential matches have liked them. Unlike Gold members, Tinder users on the free tier have to swipe through a list of profiles without knowing which potential matches have liked them. The dating service is testing a range of price points for the feature, which will begin testing in Australia, Argentina, Mexico, and Canada this week. Researchers found that people's perceptions of potential dates' attractiveness goes up after they have a positive face-to-face interaction - but only for those who were rated mid to low attractiveness based on their photo.


Using the TensorFlow API: An Introductory Tutorial Series

@machinelearnbot

This is the first in a series of seven parts where various aspects and techniques of building Recurrent Neural Networks in TensorFlow are covered. This post is the follow up of the article "How to build a Recurrent Neural Network in TensorFlow", where we built a RNN from scratch, building up the computational graph manually. Now we will go about to build a modification of a RNN that called a "Recurrent Neural Network with Long short-term memory" or RNN-LSTM. In the previous article we learned how to use the TensorFlow API to create a Recurrent neural network with Long short-term memory.


Deep Learning: Artificial Neural Networks with Python How To Learn Online

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This online course is designed to teach you how to create deep learning Algorithms in Python by two expert Machine Learning & Data Science experts. That's what we mean when we say that in this course we teach you the most cutting edge Deep Learning models and techniques. Thanks to Keras we can create powerful and complex Deep Learning models with only a few lines of code. By applying your Deep Learning model the bank may significantly reduce customer churn.


Hey Siri, an ancient algorithm may help you grasp metaphors: Study tracks the cognitive steps humans have taken over centuries to create and comprehend metaphoric language

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Mapping 1,100 years of metaphoric English language, researchers at UC Berkeley and Lehigh University in Pennsylvania have detected patterns in how English speakers have added figurative word meanings to their vocabulary. Using the Metaphor Map of English database, researchers examined more than 5,000 examples from the past millennium in which word meanings from one semantic domain, such as "water," were extended to another semantic domain, such as "mind." Researchers called the original semantic domain the "source domain" and the domain that the metaphorical meaning was extended to, the "target domain." More than 1,400 online participants were recruited to rate semantic domains such as "water" or "mind" according to the degree to which they were related to the external world (light, plants), animate things (humans, animals), or intense emotions (excitement, fear).