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Is IBM Watson A 'Joke'?

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On the May 8th edition of Closing Bell on CNBC, venture capitalist Chamath Palihapitiya, founder and CEO of Social Capital, created quite a stir in enterprise artificial intelligence (AI) circles, when he took on Watson, Big Blue's AI platform. "Human intelligence outperforms machine-learning applications in complex decision making routinely required during the course of care, because machines do not yet possess mature capabilities for perceiving, reasoning, or explaining," explained Ernest Sohn, a chief data scientist in Booz Allen's Data Solutions and Machine Intelligence group; Joachim Roski, a principal at Booz Allen Hamilton; Steven Escaravage, vice president in Booz Allen's Strategic Innovation Group; and Kevin Maloy, MD, assistant professor of emergency medicine at Georgetown University School of Medicine. "A health care organization that relies on a single EHR [Electronic Health Record] vendor's analytic solutions, as well as its own legacy analytics infrastructure created before the era of big data, may see limited progress," they continued. "While many machine-learning solutions are not yet mature and sophisticated enough to support complex clinical decisions, machine learning can be effectively deployed today to reduce more routine, time-consuming, and resource-intensive tasks, allowing freed-up personnel to be redeployed to support higher-end work."


How AI Is Transforming Drug Creation

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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. AI systems trained on various data sources, including preclinical data sets, have helped make "significant performance improvements" by enabling "better selections of which compounds to…make and test" in the lab and by "flagging" whether compounds might have "toxic" effects or "unexpected favorable" ones, he says. German pharmaceutical company Merck KGaA has developed two drugs using computer-vision software, which analyzes images of cells and tissues, and other AI systems capable of drawing insights from public databases of genetic and chemical information, says Joern-Peter Halle, Merck KGaA's head of external innovation.


The Future of Artificial Intelligence and Cybernetics

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For example, if the robot brain has roughly the same number of human neurons as a typical human brain, then could it, or should it, have rights similar to those of a person? Also, if such robots have far more human neurons than in a typical human brain--for example, a million times more neurons--would they, rather than humans, make all future decisions? With those cases, the situation isn't straightforward, as patients receive abilities that normal humans don't have--for example, the ability to move a cursor on a computer screen using nothing but neural signals. It's clear that connecting a human brain with a computer network via an implant could, in the long term, open up the distinct advantages of machine intelligence, communication, and sensing abilities to the individual receiving the implant.


The Future of Artificial Intelligence and Cybernetics

#artificialintelligence

For example, if the robot brain has roughly the same number of human neurons as a typical human brain, then could it, or should it, have rights similar to those of a person? Also, if such robots have far more human neurons than in a typical human brain--for example, a million times more neurons--would they, rather than humans, make all future decisions? With those cases, the situation isn't straightforward, as patients receive abilities that normal humans don't have--for example, the ability to move a cursor on a computer screen using nothing but neural signals. It's clear that connecting a human brain with a computer network via an implant could, in the long term, open up the distinct advantages of machine intelligence, communication, and sensing abilities to the individual receiving the implant.


Meet These Incredible Women Advancing A.I. Research

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"Through making our large datasets and systems publicly available, we've enabled research groups around the world to make significant progress on building machines that can automatically answer questions about visual content," she highlights. When Thomas first started researching deep neural networks a few years ago, virtually no educational resources existed online. Suchi Saria, Assistant Professor at Johns Hopkins University, believes computational modeling of data from sensor platforms and electronic medical records presents "a tremendous opportunity for high impact work." Since receiving her PhD in Computer Science from Stanford, Shubha Nabar has built data products and data science teams at Microsoft, LinkedIn, and now Salesforce.


This chart illustrates how AI is exploding at Google

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These are some the most elite academic journals in the world. And last year, one tech company, Alphabet's Google, published papers in all of them. The unprecedented run of scientific results by the Mountain View search giant touched on everything from ophthalmology to computer games to neuroscience and climate models. For Google, 2016 was an annus mirabilis during which its researchers cracked the top journals and set records for sheer volume. Behind the surge is Google's growing investment in artificial intelligence, particularly "deep learning," a technique whose ability to make sense of images and other data is enhancing services like search and translation (see "10 Breakthrough Technologies 2013: Deep Learning").


This chart illustrates how AI is exploding at Google

#artificialintelligence

These are some the most elite academic journals in the world. And last year, one tech company, Alphabet's Google, published papers in all of them. The unprecedented run of scientific results by the Mountain View search giant touched on everything from ophthalmology to computer games to neuroscience and climate models. For Google, 2016 was an annus mirabilis during which its researchers cracked the top journals and set records for sheer volume. Behind the surge is Google's growing investment in artificial intelligence, particularly "deep learning," a technique whose ability to make sense of images and other data is enhancing services like search and translation (see "10 Breakthrough Technologies 2013: Deep Learning").


This chart illustrates how AI is exploding at Google

#artificialintelligence

And last year, one tech company, Alphabet's Google, published papers in all of them. According to the tally Google provided to MIT Technology Review, it published 218 journal or conference papers on machine learning in 2016, nearly twice as many as it did two years ago. "The top people care about advancing the world, and that means writing papers the world can use, and writing code the world can use." So when Apple hired computer scientist Russ Salakhutdinov from Carnegie Mellon last year as its new head of AI, he was immediately allowed to break Apple's code of secrecy by blogging and giving talks.


This chart illustrates how AI is exploding at Google

#artificialintelligence

And last year, one tech company, Alphabet's Google, published papers in all of them. According to the tally Google provided to MIT Technology Review, it published 218 journal or conference papers on machine learning in 2016, nearly twice as many as it did two years ago. "The top people care about advancing the world, and that means writing papers the world can use, and write code the world can use." So when Apple hired computer scientist Russ Salakhutdinov from Carnegie Mellon last year as its new head of AI, he was immediately allowed to break Apple's code of secrecy by blogging and giving talks.


This chart illustrates how AI is exploding at Google

#artificialintelligence

And last year, one tech company, Alphabet's Google, published papers in all of them. According to the tally Google provided to MIT Technology Review, it published 218 journal or conference papers on machine learning in 2016, nearly twice as many as it did two years ago. "The top people care about advancing the world, and that means writing papers the world can use, and write code the world can use." So when Apple hired computer scientist Russ Salakhutdinov from Carnegie Mellon last year as its new head of AI, he was immediately allowed to break Apple's code of secrecy by blogging and giving talks.