The quest to give machines human-level intelligence has been around for decades, and it has captured imaginations for far longer -- think of Mary Shelley's Frankenstein in the 19th century. Artificial intelligence, or AI, was born in the 1950s, with boom cycles leading to busts as scientists failed time and again to make machines act and think like the human brain. But this time could be different because of a major breakthrough -- deep learning, where data structures are set up like the brain's neural network to let computers learn on their own. Together with advances in computing power and scale, AI is making big strides today like never before. Frank Chen, a partner specializing in AI at top venture capital firm Andreessen Horowitz, makes a case that AI could be entering a golden age.
A deep learning algorithm can detect metastases in sections of lymph nodes from women with breast cancer; and a deep learning system (DLS) has high sensitivity and specificity for identifying diabetic retinopathy, according to two studies published online Dec. 12 in the Journal of the American Medical Association. Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, M.D., Ph.D., from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images.
There's an awful lot of text data available today, and enormous amounts of it are being created on a daily basis, ranging from structured to semi-structured to fully unstructured. What can we do with it? Well, quite a bit, actually; it depends on what your objectives are, but there are 2 intricately related yet differentiated umbrellas of tasks which can be exploited in order to leverage the availability of all of this data. NLP is a major aspect of computational linguistics, and also falls within the realms of computer science and artificial intelligence. Text mining exists in a similar realm as NLP, in that it is concerned with identifying interesting, non-trivial patterns in textual data.
Data can change over time. This can result in poor and degrading predictive performance in predictive models that assume a static relationship between input and output variables. This problem of the changing underlying relationships in the data is called concept drift in the field of machine learning. In this post, you will discover the problem of concept drift and ways to you may be able to address it in your own predictive modeling problems. A Gentle Introduction to Concept Drift in Machine Learning Photo by Joe Cleere, some rights reserved.
The Robot Launch global startup competition is over for 2017. We've seen startups from all over the world and all sorts of application areas – and we'd like to congratulate the overall winner Semio, and runners up Apellix and Mothership Aeronautics. All three startups met the judges criteria; to be an early stage platform technology in robotics or AI with great impact, large market potential and near term customer pipeline. Semio from Southern California is a software platform for developing and deploying social robot skills. Ross Mead, founder and CEO of Semio said that "he was greatly looking forward to spending more time with The Robotics Hub, and is excited about the potential for Semio moving forward."
You would be forgiven for thinking that your private conversations were just that, but two leading voice assistants are listening to everything you say, a new report claims. Patent applications from Amazon and Google revealed how their Alexa and Voice Assistant powered smart speakers are'spying' on you. The study warns of an Orwellian future in which the gadgets eavesdrop on everything from confidential conversations to your toilet flushing habits. Future versions of gadgets like the Echo and Home will use this data to try and sell you products, it says. You would be forgiven for thinking that your private conversations were just that, but two leading voice assistants are listening to everything you say, a new report claims.
The FDA has been championing digital health of late with wide-ranging guidance that derives from the 21st Century Cures Act. This legislation acknowledges the potential that digital health has to make a difference in patient care, potentially leading to more precise therapies. Several developments this week show that the regulator is right to be excited about its potential. Some of the most exciting advances have come in the field of cancer – medical devices firm Angle has produced a new analysis showing that its liquid biopsy device Parsortix could be used instead of conventional tissue biopsies. Parsortix works by monitoring a patient's bloodstream for circulating cancer cells and the University of Southern California research adds to the body of evidence showing that liquid biopsies could replace invasive and unpleasant tissue biopsies in the future.
MIT Institute Professor John Deutch, who has been on the MIT faculty since 1970, has served as a department head, dean of the School of Science, and provost, and has published over 160 technical publications as well as numerous publications on technology, energy, international security, and public policy issues. He served in the U.S. government as director of central intelligence from 1995 to 1996, as deputy secretary of defense from 1994 to 1995, and in other posts in the departments of Defense and Energy. He is a member of the nonpartisan Aspen Strategy Group, which is composed of current and former policymakers, academics, journalists, and business leaders whose aim is to explore foreign policy and national security challenges facing the United States. The group has just released its annual report, and it includes a chapter co-written by Deutch and former U.S. Secretary of State Condoleezza Rice, about how the U.S. should deal with the risk of losing important intellectual property rights regarding technological innovations, in the face of efforts by China to acquire such technology through underhanded means. MIT News asked Deutch to describe the potential risks and remedies for such actions that he and Rice outlined in their report.
Cognovi Labs, developer of the SaaS platform for emotion-based artificial intelligence, saw their Oct. 22 prediction of a Doug Jones victory realized in Tuesday's Alabama Senate election. Acting well ahead of poll data, Cognovi made the prediction almost three weeks before the sexual harassment claims surfaced against Moore. The prediction was based around Cognovi Emotion AI findings that showed Jones' superior ability to trigger an intense emotional bond with the electorate, which allowed him to generate the required turnout to win the election. This result follows a string of successful Cognovi predictions for several high-profile outcomes, including predicting the Brexit referendum hours before the polls closed; and foretelling the results of the 2016 U.S. presidential election. In addition to political events, Cognovi utilizes its AI platform to make predictions for businesses, investors, corporations, ad agencies and public figures.
The Fourth Industrial Revolution is fundamentally changing the ways that people work and live in three main ways. First, it is untethering some types of work from a physical location, making it easier to remotely connect workers in one region or country to jobs in another – but also making it less clear which set of employment laws and taxes apply, creating greater global competition for workers, potentially weakening employment protections and draining public social protection coffers. Second, human labour is being displaced by automation, robotics and artificial intelligence. Opinions differ on the extent of what is possible: Frey and Osborne's (2013) study found that 47% of US employment is at high risk of being automated over the next two decades, while a 2016 study of 21 Organisation for Economic Co-operation and Development (OECD) countries, using a different methodology, concluded that only 9% of jobs are automatable. In general, lower-skilled workers are more likely to see their jobs disappear to automation, increasing their vulnerability and exacerbating societal inequality.