Artificial intelligence (AI) is in the midst of a renaissance. New techniques are producing results, such as the defeat of a Go world champion by an AI system developed by a subsidiary of Google, which would have been unthinkable five years ago. These techniques are not just being used to play games. Today, AI is being applied to one of humanity's most daunting challenges: the hunt for a cure for cancer. AI has huge potential for helping scientists manage the mind-boggling complexities of research and other data, to expedite cancer drug discovery and translate scientific findings into real benefits for patients.

Want to watch this again later? Need to report the video? This feature is not available right now. It is an algorithm-based piece of artificial intelligence, or AI, created by a team of researchers at CSIRO and the Massachusetts Institute of Technology (MIT) that spontaneously generates zombie faces out of human ones and transforms images of places into visions of the inferno. Audio: At the Foot of the Sphinx 04:29 At the Foot of the Sphinx by Twin Musicom is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/...) Artist: http://www.twinmusicom.org/

Add Adobe Systems to the list of software companies proclaiming themselves to be artificial intelligence powers. It's in august company: IBM ibm, Microsoft, Salesforce crm are all wrapping themselves in the artificial intelligence (aka AI) mantle. On Wednesday, Adobe adbe will show off Sensei, an array of AI technology that it is already adding to software applications for creating and publishing documents, and for analyzing and tracking web and mobile application performance. "Sensei" is the Japanese term for a respected teacher or leader. Adobe founded in 1982, is a venerable force in the creative software world--it's behind familiar tools including the Portable Document Format (PDF) used to format and distribute professional-looking documents; Photoshop for editing photographs; Illustrator for creating graphics; and InDesign for creating professional-looking documents for print or online.

A law professor at the University of Surrey is arguing that it should be possible for computer-based artificial intelligence (AI) systems to be formally considered as inventors for any invention they contribute to, much in the same way a person would. The argument forms part of a paper, which has been published in the Boston College Law Review, entitled I Think, Therefore I Invent: Creative Computers and the Future of Patent Law. In its introduction the report makes the point that while inventions by computers have been granted patents previously, the concept of computer inventorship has never actually been considered by the courts. The concept of giving creative computers the credit for their own inventions may sound surreal but, in reality, they have been generating potentially patentable ideas for decades without acknowledgment. As Professor Ryan Abbott points out in his paper, 'machines have been autonomously generating patentable results for at least twenty years and the pace of such invention is likely increasing.'

The Guardian recently reported on a new AI software capable of predicting the outcome of trials developed by a group of British scientists at University College London.[1] After examining English language data sets for 584 cases relating to torture and degrading treatment, fair trials, and privacy, the AI verdict was the same as the one delivered by the court in 79% of the cases. What's the point, you may ask? Not to replace judges and juries by artificial intelligence, if that's what you fear. As the lead researcher on this project, Dr. Nikolaos Aletras, explains: "We don't see AI replacing judges or lawyers, but we think they'd find it useful for rapidly identifying patterns in cases that lead to certain outcomes.

As a part of Twitter Data Analysis, So far I have completed Movie review using R& Document Classification using R. Today we will be dealing with discovering topics in Tweets, i.e. to mine the tweets data to discover underlying topics– approach known as Topic Modeling. A statistical approach for discovering "abstracts/topics" from a collection of text documents based on statistics of each word. In simple terms, the process of looking into a large collection of documents, identifying clusters of words and grouping them together based on similarity and identifying patterns in the clusters appearing in multitude. When we apply Topic Modeling to the above statements, we will be able to group statement 1&2 as Topic-1 (later we can identify that the topic is Sport),statement 3 as Topic-2 (topic is Movies), statement 4&5 as Topic-3 (topic isdata Analytics). Topic Modeling can be achieved by using Latent Dirichlet Allocation algorithm.

Can your financial institution's fraud detection system learn, reason and adapt to new and emerging cyberthreats? Can it identify fraudulent behavior within your account simply by analyzing interactions and patterns? In this day and age, people can access their bank accounts anywhere, anytime. We need strong, agile and efficient fraud detection systems to keep financial institutions and their customers safe. Mobile functionality and safety are among customers' top concerns when it comes to online banking -- so IBM Security Trusteer is releasing new cognitive fraud detection and behavioral biometric functionality that accomplishes just that. This enhanced functionality adds even more strength to an already robust security platform without impacting user experience.

Classification Learner lets you perform common supervised learning tasks such as interactively exploring your data, selecting features, specifying validation schemes, training models, and assessing results. You can export classification models to the MATLAB workspace, or generate MATLAB code to integrate models into applications. Choose your country to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .

Real-time machine learning has access to a continuous flow of transactional data, but what it really needs in order to be effective is a continuous flow of labeled transactional data, and accurate labeling introduces latency. During this year, I heard and read a lot about real-time machine learning. People usually provide this appealing business scenario when discussing credit card fraud detection systems. They say that they can continuously update credit card fraud detection model in real-time (See "What is Apache Spark?", "…real-time use cases…" and "Real time machine learning"). It looks fantastic but not realistic to me.

NEC Corporation, one of Japan's biggest IT providers, says it has built an AI that can rapidly search CCTV footage and spot a specific person out of a million or more faces. The application – snappily titled NeoFace Image data mining – can find wanted criminals, missing kids, and so on, all from video surveillance. We're told "when searching video where roughly one million individual instances of facial data appear, the software is capable of conducting searches within approximately 10 seconds." In other words, you can feed 24 hours of CCTV into NeoFace, and it could identify, say, a million faces in the video frames. Then when you need to find a sought-after person, the software will take just seconds to scan the database and locate them.