Artificial intelligence powers digital medicine

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While this reality has become more tangible in recent years through consumer technology, such as Amazon's Alexa or Apple's Siri, the applications of AI software are already widespread, ranging from credit card fraud detection at VISA to payload scheduling operations at NASA to insider trading surveillance on the NASDAQ. Broadly defined as the imitation of human cognition by a machine, recent interest in AI has been driven by advances in machine learning, in which computer algorithms learn from data without human direction.1 Most sophisticated processes that involve some form of prediction generated from a large data set use this type of AI, including image recognition, web-search, speech-to-text language processing, and e-commerce product recommendations.2 AI is increasingly incorporated into devices that consumers keep with them at all times, such as smartphones, and powers consumer technologies on the horizon, such as self-driving cars. And there is anticipation that these advances will continue to accelerate: a recent survey of leading AI researchers predicted that, within the next 10 years, AI will outperform humans in transcribing speech, translating languages, and driving a truck.3


How science can help us make AI more trustworthy

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Stories about racist Twitter accounts and crashing self-driving cars can make us think that artificial intelligence (AI) is a work in progress. But while these headline-grabbing mistakes reveal the frontiers of AI, versions of this technology are already invisibly embedded in many systems that we use everyday. These everyday uses include everything from fraud detection systems that monitor credit card transactions to email filters that learn not to swamp your inbox with spam. You've probably already interacted with an AI system today without even knowing it and probably enjoyed the experience. One increasingly common form of AI can be found in chatbots, a type of software that lets you interact with it by having a conversation.


How science can help us make AI less creepy and more trustworthy

#artificialintelligence

Stories about racist Twitter accounts and crashing self-driving cars can make us think that artificial intelligence (AI) is a work in progress. But while these headline-grabbing mistakes reveal the frontiers of AI, versions of this technology are already invisibly embedded in many systems that we use everyday. These everyday uses include everything from fraud detection systems that monitor credit card transactions to email filters that learn not to swamp your inbox with spam. You've probably already interacted with an AI system today without even knowing it and probably enjoyed the experience. One increasingly common form of AI can be found in chatbots, a type of software that lets you interact with it by having a conversation.


How science can help us make AI less creepy and more trustworthy

#artificialintelligence

Stories about racist Twitter accounts and crashing self-driving cars can make us think that artificial intelligence (AI) is a work in progress. But while these headline-grabbing mistakes reveal the frontiers of AI, versions of this technology are already invisibly embedded in many systems that we use everyday. These everyday uses include everything from fraud detection systems that monitor credit card transactions to email filters that learn not to swamp your inbox with spam. You've probably already interacted with an AI system today without even knowing it and probably enjoyed the experience. One increasingly common form of AI can be found in chatbots, a type of software that lets you interact with it by having a conversation.


Credit card giants step up AI fraud detection

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The system, however, is scalable, and is able to learn in more personalized ways, as well, Lahrech said. Recently, the company rolled out Eno, a chatbot-type of virtual assistant that can do things like break down a customer's charges or alert a customer to fraud through a conversational user interface. The AI fraud detection tool uses natural language processing (NLP) to accept a wide range of interactive responses from customers if it does report fraud, which Lahrech said can help Capital One build more of a context around the potential fraud case. An example is with cases of "friendly fraud," or when a consumer tried to defraud a credit card company, in which the issues can be complex. With ML technology, those answers from customers can be used to give more personalized responses, and also establish a profile based on pattern recognition.