When 27-year old Mayank Ranka is looking for love, he turns to a chatbot. "It instantly finds me a match which is all I need at the end of the day. The even better part is I can end the conversation anytime I want and not chat ever again," says Mumbai-resident Mayank, who works with an event company in Mumbai. Tech-savvy singles like Mayank don't blink before using a speed-dating chatbot like Neargroup which helps them zoom into the right date. The chatbot's in-built AI helps the user to find the right match based on nearby locations, interests and also conversations.

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed. Machine learning is applied in various fields such as computer vision, speech recognition, NLP, web search, biotech, risk management, cyber security, and many others.

It's almost impossible to escape the impact frontier technologies are having on everyday life. At the core of this impact are the advancements of artificial intelligence, machine learning, and deep learning. These change agents are ushering in a revolution that will fundamentally alter the way we live, work, and communicate akin to the industrial revolution – more specifically, AI is the new industrial revolution. The most exciting and promising of these frontier technologies is the advancements happening in the deep learning space. While still nascent, it's deep learning percolating into your smartphone, driving advancements in healthcare, creating efficiencies in the power grid, improving agricultural yields, and helping us find solutions to climate change.

Aircraft Beach Mountain People marching Police/Security Flower 12. 12 From University-lab to spin-off and your mobile phone • 1000 others * UvA / Euvision / Qualcomm Universities win Start-ups win Snoek et al., TRECVID 2004-2015 13. 13 Latest jump due to deep learning 2006 2009 2015 Meanaverageprecision Progress in video recognition 14. 14 The more features the better Typical shallow learning architecture e.g. That is: given a video, can we find the best matching sentence? That is: given a video, can we find the best matching sentence?

Austin's South by Southwest is historically known as a music showcase, yet the Interactive portion of the conference is now the biggest draw for tens of thousands of attendees who come from across the globe. Alan Melson, KERA's VP of Digital, has been writing about the conference this weekend. I spoke with him about what's on tech folks' minds. Alan, it seems like every year there are several recurring themes that emerge from South By Southwest Interactive. What seems to be bubbling up this year?

The following is paid content sponsored by Veritone. Big Data is everywhere, and it keeps getting bigger. It's estimated that 90 percent of all data on the internet has been generated in the past two years, more than three-quarters of it audio and video clips, and the pace continues to accelerate exponentially. For law enforcement, this creates an information overload of evidence that can't be easily searched or analyzed. This recent explosion of law enforcement data, particularly video and audio that by its nature is unstructured and unsearchable, means police are collecting information without the tools to manage it or turn it into actionable intelligence.

Intermountain Healthcare has approximately 150 protocols built into its electronic health record (EHR) system, alerting clinicians when the patient information they enter indicates certain conditions and then guiding them through further examinations and potential treatments. A 12-member team of doctors, nurses and analytics experts takes upwards of a year to analyze data and build each protocol, said Marc Probst, the chief information officer at the not-for-profit health system based in Salt Lake City. Recently, though, Intermountain teamed with a company that uses artificial intelligence (AI) to do the same work. But AI works faster and more thoroughly, taking just 10 days to develop a protocol that included additional data points not previously identified, Probst said. "The power in that is amazing. We're going to be in the thousands of protocols soon," Probst said, adding that Intermountain is now piloting AI use in building protocols.

Diane Greene, senior vice president of cloud services at Google Inc., speaks during the company's Cloud Next '17 event Photographer: David Paul Morris/Bloomberg I attended Google's annual, enterprise-focused event, Google Cloud Next '17, last week in San Francisco, paying close attention to all the announcements coming out of the three-day event. I attended their industry analyst event as well, held the day before. The conference is Google's chance to showcase their achievements from the past year, as well as announce new products and services within their cloud and collaboration portfolio. Competition for the public cloud and collaboration is intense with giants like Microsoft, Amazon.com Here's my take on some of the key items coming out of Day 1 of the conference--it's a lot, so let's dive in.

Fifty years ago, I published a paper, "Error bounds for convolutional codes and an asymptotically optimum decoding algorithm," on the important class of convolutional codes, which is particularly effective in preventing errors in digital communication over wireless and other transmission media. The algorithm, which became labeled with my name, was a crucial step in establishing the merits as well as evaluating the performance of these codes. The paper was read and understood by only a few specialists. In the next few years, clarity was provided by two papers, the first by a colleague, G.D. Forney Jr., who introduced the trellis model, and the second by myself based on a state diagram, or Markov model. A.A. Markov was a Russian mathematician who proposed and analyzed a statistical concept regarding the relationship between terms of a sequence or, more generally, of successive events; specifically, that each term (or string of terms) or event is statistically dependent only on the previous one.

Artificial intelligence and cognitive computing is being heralded as the brave new frontier of clinical IT, Kim Thomas reports on how it is already beginning to reshape radiology imaging and diagnostics. IBM chose December's annual meeting of the Radiological Society of North America to showcase the ability of its Watson supercomputer to rapidly analyse medical images and suggest a diagnosis. Mark Griffiths, a clinical radiologist at University Hospital Southampton NHS Foundation Trust, who attended RSNA, says he saw some "stunning demonstrations" of the technology, including chest X-rays being "reported in milliseconds." Watson is an example of a technology that IBM refers to as "cognitive computing". Using a form of artificial intelligence known as natural language processing, Watson, a cloud-based system, is able to analyse vast stores of scholarly articles, patient records and medical images.