Garry Kasparov and DeepMind's CEO Demis Hassabis discuss Garry's new book "Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins ", his chess match with IBM Deep Blue and his thoughts on the future of AI in the world of chess. Event moderated by Demis Hassabis, CEO, DeepMind of Google. In May 1997, the world watched as Garry Kasparov, the greatest chess player in the world, was defeated for the first time by the IBM supercomputer Deep Blue. It was a watershed moment in the history of technology: machine intelligence had arrived at the point where it could best human intellect. It wasn't a coincidence that Kasparov became the symbol of man's fight against the machines.

DUBAI, UNITED ARAB EMIRATES – An unarmed Iranian drone shadowed a U.S. aircraft carrier at night and came close enough to F-18 fighter jets to put the lives of American pilots at risk, the Navy said Tuesday, reporting the second such tense encounter within a week. The Iranian Sadegh drone flew without any warning lights during the encounter Sunday night with the USS Nimitz, said Lt. Ian McConnaughey, a spokesman for the Bahrain-based 5th Fleet. The drone did not respond to repeated calls over the radio and came within 1,000 feet (300 meters) of U.S. fighters, he said. That "created a dangerous situation with the potential for collision and is not in keeping with international maritime customs and laws," McConnaughey said in a statement. The drone was unarmed, the lieutenant said, though that model can carry missiles. Iran's military and state-run media did not immediately report the incident, which came after a similar encounter Aug. 8, in which the Navy said an Iranian drone came within 100 feet (30 meters) of an F-18 preparing to land on the Nimitz.

Financial Technology - or FinTech - Is Now Part of the @CloudExpo Program! Accordingly, attendees at the upcoming 20th Cloud Expo at the Javits Center in New York, June 6-8, 2017, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track. Financial enterprises in New York City, London, Singapore, and other world financial capitals are embracing a new generation of smart, automated FinTech that eliminates many cumbersome, slow, and expensive intermediate processes from their businesses. FinTech brings efficiency as well as the ability to deliver new services and a much improved customer experience throughout the global financial services industry. FinTech is a natural fit with cloud computing, as new services are quickly developed, deployed, and scaled on public, private, and hybrid clouds.

That's right folks, in the words of police chief Martin Brody in the movie Jaws, "we're going to need a bigger boat." AI is happening in law and it's happening RIGHT NOW. There were so many people trying to get into this morning's session on AI and law, "Will Computers Replace Lawyers? The Myths, Realities and Future of Artificial Intelligence and Automation in the Law (Part 1 of 3) #ILTAG2" that we will be doing a short review session tomorrow at…10:00am in Mandalay L on the second floor! Join myself, Martin and Sam for a redux of today's session with some new fun twists for those eager ILTACON-ers who want to do it twice because, why not.

Antshares, the first open-source blockchain platform developed in China, announced a complete rebranding of its blockchain solution into NEO. NEO is China's answer to Ethereum, a currency that has seen a 3,275% increase since the beginning of the year, particularly since the inception of the Ethereum Enterprise Alliance. It has one major difference that give it a competitive edge. Unlike Ethereum, which uses its own programming language: Solidity, NEO will support nearly all programming languages, greatly lowering the difficulty for developers to write smart contracts. This means that NEO is far more open to developers, as they won't need to learn a new coding language.

An artificial intelligence programme from Open AI, a research firm backed by entrepreneur Elon Musk, has beat a hundreds of players to win a world championship video game contest. As reported by CNN, the Open AI team taught a bot to play Dota 2, an online multi-player battle game where teams compete to take down a structure in an opposing team's home base. Explaining how the bot was trained, Greg Brockman, Open AI co-founder and chief technology officer, said that it "self-trained" until its skill level reached that of the world's best Dota 2 players. "Our bot is trained entirely through self-play. It starts out completely random with no knowledge of the world, and simply plays against a copy of itself -- which means it always has an evenly matched opponent," he said.

Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness. Alternatively, state-of-the-art joint modeling techniques can be used for jointly modeling the longitudinal and event data and compute event probabilities conditioned on the longitudinal observations. These approaches, however, make strong parametric assumptions and do not easily scale to multivariate signals with many observations. Our proposed approach consists of several key innovations. First, we develop a flexible and scalable joint model based upon sparse multiple-output Gaussian processes. Unlike state-of-the-art joint models, the proposed model can explain highly challenging structure including non-Gaussian noise while scaling to large data. Second, we derive an optimal policy for predicting events using the distribution of the event occurrence estimated by the joint model. The derived policy trades-off the cost of a delayed detection versus incorrect assessments and abstains from making decisions when the estimated event probability does not satisfy the derived confidence criteria. Experiments on a large dataset show that the proposed framework significantly outperforms state-of-the-art techniques in event prediction.

Making use of a cloud service with massive data collected online, the robot Charpy can respond to learners in natural conversations based on their interests and English skill levels. "We hope that Charpy will help learners to overcome difficulties in conversations," said Mitsunori Fukuchi, president of CAI Media, based in Hamamatsu, Shizuoka Prefecture. According to the company, Charpy's character is set as a little bird that likes chocolate. The robot is programmed with 1,000 conversation phrases. According to Fukuchi, the robot can handle all English skill levels from beginners to advanced levels using the cloud service.

Artificial Intelligence is changing the way we think of technology. It is radically changing the various aspects of our daily life. Companies are now significantly making investments in AI to boost their future businesses. Ericsson, in the sixth edition of its annual trend report, 'The 10 Hot Consumer Trends for 2017 and beyond,' predicts that 35 percent of advanced internet users want an AI advisor at work, and one in four would like an AI as their manager in the next one year. Another study performed by Forrester Research predicted an increase of 300% in investment in AI in the next one year. CXOToday finds out some of the most revolutionary artificial intelligence technologies that will rule in the next 1-2 years.

Content Management today is more than corralling enterprise data into the right systems--it's about enriching that data and making it more valuable for your company. That's why we're announcing that we are bringing OpenText Decisiv Search to OpenText eDOCS as its default search engine. OpenText Decisiv Search takes the reach and depth of AI-enhanced search even further. It uses unsupervised machine-learning technology to develop a deeper understanding of content, and surface more accurate and comprehensive search results, enabling information to be more rapidly pinpointed, extracted, and leveraged into business advantage. This means that users in legal and professional services firms can spend less time looking for the information they need, and more time doing the work that matters.