Most of the talks will be given by well-known invited speakers. However, we would also like to give students and startups an opportunity to present their work and research. It will be realized in the form of short lightning talks. Each speaker will get a free ticket to the conference. If you are interested in becoming a lightning speaker, please send us a short paper (less than 1000 words) describing your machine learning project.

"Algorithm" might be one of the most popular terms that almost no one understands. Not many people have PhDs in data science, and even those experts don't always know what's happening. "It's not clear even from a technical perspective that every aspect of AI algorithms can be understood by humans," says Guruduth Banavar, IBM's chief science officer for cognitive computing, which is what IBM calls AI. Artificial intelligence is making decisions by reviewing people's medical tests in hospitals, credit histories in banking, job applications in some HR systems, even criminal risk factors in the justice system. Yet it's not always clear how the computers are thinking.

My name is Peter Chen and I am the instructor for this course. I want to introduce you to the wonderful world of Machine Learning through practical examples and code. The course will cover Supervised Learning algorithms and models in machine learning. More importantly, it will get you up and running quickly with a practical and at times funny applications of Supervised Learning algorithms. The course has code & sample data for you to run and learn from.

'Siri, catch market cheats': Wall Street watchdogs turn to A.I. NEW YORK (Reuters) - Artificial intelligence programs have beaten chess masters and TV quiz show champions. Two exchange operators have announced plans to launch artificial intelligence tools for market surveillance in the coming months and officials at a Wall Street regulator tell Reuters they are not far behind. Executives are hoping computers with humanoid wit can help mere mortals catch misbehavior more quickly. The software could, for instance, scrub chat-room messages to detect dubious bragging or back slapping around the time of a big trade. It could also more quickly unravel complex issues, like "layering," where orders are rapidly sent to exchanges and then canceled to artificially move a stock price.

H2O.ai Melds Machine Learning with Spark, Via Sparkling Water 2.0 by - Jul. 01, 2016 The Renaissance Continues for Open Source Artificial Intelligence Baidu Delivers a Hardened Open Source Deep Learning Tool Google Launches a Slew of Open Source Parsers, to Work with 40 Languages IBM's Massive Spark Initiatives Include an Offering for Data Scientists Google's Custom Chip Can Accelerate Machine Learning Jobs In recent interviews here on OStatic, found here and here, we have explored the efforts of H2O.ai, formerly known as Oxdata, which has steadily been carving out a niche with its open source software for big data analysis and machine learning. You can get the main H2O platform and Sparkling Water, a package that works with Apache Spark, by simply downloading them. You can run them on clusters powered by Amazon Web Services (AWS) and others for just a few hundred dollars, putting powerful artificial intelligence muscle in reach of everyone. Now, H2O.ai has announced the availability of Sparkling Water 2.0. Sparkling Water 2.0 builds off the popularity of Sparkling Water, H2O.ai's API for Apache Spark, with additional features and functionality.

Coenraets: "I've been building apps for many, many years using all the different technologies that came and went and looking at the different ways people access information," Coenraets says. "Bots caught my attention as a really new disruptive way that people were going to use to access and apply information. Basically, AI makes the information available to customers in new ways. "The way I started my presentation at TrailheaDX was to ask people, OK, tell me the outside temperature.' And then I asked people, 'What app did you use to get me that information?'

When Watson answered the final question to win "Jeopardy!" in 2011, voice recognition and artificial intelligence software were just making their large consumer debut on mobile devices. At the start, these capabilities were engaging, interesting and even exciting, but sometimes more as a parlor game than a deeply functional application. Yet they've continued to improve at an accelerating rate and now demand our serious attention as productivity tools. We are now half a decade on, and cognitive computing is making its presence felt at a deeply functional business level -- not just at the gateway of the journey on our devices, but deep within the industry-process level. For instance, cognitive computing is having a real impact at the clinical-process level for healthcare, which was featured specifically in a segment on artificial intelligence on "60 Minutes."

Microsoft Corp. struck a partnership with Elon Musk's artificial intelligence research group, OpenAI, and said the organization will use the company's Azure cloud system for most of its large-scale experiments. OpenAI has been an early customer for Microsoft's Azure N-Series Virtual Machines, a powerful cloud-computing service that relies on Nvidia Corp. graphical processing units. The two will also collaborate on ways to advance AI research and its use, Microsoft and Open AI said Tuesday in blog posts. "In the coming months we will use thousands to tens of thousands of these machines to increase both the number of experiments we run and the size of the models we train," OpenAI said in its post. Everything you need to know about what's moving markets, in your inbox daily.

As artificial intelligence (AI) has progressed at quite a pace in recent years, it is inevitable that governments have tried to get a handle on things and better understand how it might influence society. For instance, I wrote recently about a report by the British government's Science & Technology Select Committee into AI, which looked at a number of topics, from ethics to employment. Hot on the heels of this is another British government report, this time from the office of the Chief Scientific Advisor. It examines the increasingly blurred lines between big data and AI, and particularly the benefits of this for the state. For instance, it highlights that governments could secure enormous savings by utilizing both to reduce tax fraud and minimize errors in tax collection.

Video games where people matter? If you're a video game fan of a certain age, you may remember Edge magazine's controversial review of the bloody sci-fi shooting game, Doom. Perhaps you enjoyed a good laugh, as many first-person shooter fans have, at the writer's much-mocked assertion: "if only you could talk to these creatures, then perhaps you could try and make friends with them, form alliances … Now that would be interesting." Of course, we all know what happened. There would be no room in the Doom series, nor any subsequent first-person blast-'em-up, for such socio-psychological niceties. Instead, we enjoyed 20 years of shooting, bludgeoning and stabbing, the ludicrous idea of diplomacy cast roughly aside. But during this era, something else was happening in game design, and in academic thinking around video games and artificial intelligence.