The White House Office of Science and Technology Policy has announced plans to co-host four public workshops to spur public dialogue on artificial intelligence and machine learning, and to learn more about the benefits and risks of artificial intelligence, according to Ed Felten, a Deputy U.S. Chief Technology Officer. These four workshops will be co-hosted by academic and non-profit organizations; two will also be co-hosted by the National Economic Council, with a public report later this year. The Federal Government also is "working to leverage AI for public good and toward a more effective government." A new National Science and Technology Council (NSTC) Subcommittee on Machine Learning and Artificial Intelligence will monitor state-of-the-art advances and technology milestones in artificial intelligence and machine learning within the Federal Government, in the private sector, and internationally; and help coordinate Federal activity in this space. The NSTC group also hopes to increase the use of AI and machine learning to improve the delivery of government services, especially in areas related to urban systems and smart cities, mental and physical health, social welfare, criminal justice, and the environment.

Google is training AI to speak more naturally, and the early results are pretty entertaining. As spotted by Quartz, Google recently presented a paper describing how it's trying to train AI to naturally fill in the gaps between one sentence and another unrelated sentence. To do that, it's using a new neural network model that's been trained by analyzing 12,000 ebooks, primarily fiction -- with a lot of those being romance novels. The technique is working, insofar as it's getting better results than earlier methods did. But its results are still unnatural -- and often, they end up creating what the researchers describe as "rather dramatic" sequences that are certainly inspired by the fiction Google's been feeding it.

Artificial intelligence isn't likely to replace doctors, says a researcher, but it's likely their role will change as more artificial intelligence is developed. A study by Whangarei doctors William Diprose and Nicholas Buist has highlighted rapid progress of machine learning and artificial intelligence (AI) in the health sector. They say a safe and sustainable healthcare system needs to look beyond human potential towards solutions such as AI. Q: Could artificial intelligence spell the end of doctors as we know them? Diprose and Buist are right to highlight the prospects of artificial intelligence in healthcare.

Getting to the root of the problem has never looked quite like this, medically speaking. Thanks to the latest innovation from the minds at MIT, there is now a tiny origami robot capable of performing internal surgery after being swallowed by a patient. As the MIT News Office reported, a collaboration amongst researchers at MIT, the University of Sheffield, and the Tokyo Institute of Technology gave way to this minuscule device, ingested by way of a capsule and steered by external magnetic fields, that can "crawl across the stomach wall to remove a swallowed button battery or patch a wound." "It's really exciting to see our small origami robots doing something with potential important applications to health care," said Daniela Rus, lead researcher on the study and director of MIT's Computer Science and Artificial Intelligence Laboratory. "For applications inside the body, we need a small, controllable, untethered robot system. It's really difficult to control and place a robot inside the body if the robot is attached to a tether."

SK Holdings C&C, the information technology services unit of SK Group, has named a Watson AI service brand as "Aibril" and put it on the market on May 11. Aibril is a compound word of "artificial intelligence (AI)" and "brilliant," meaning our knowledge continues to thrive and become brilliant AI. It shows SK Holdings C&C's will to directly communicate with human, analyze massive data and help make the best decision by providing excellent information and alternatives. Running the Watson cloud platform at SK C&C's cloud center in Pangyo, Gyeonggi Province, the Aibril allows startup businesses and IT developers to develop smartphone applications using the Watson application program interface (API). In a bid to commercialize the Aibril AI service early next year, SK Holdings C&C and IBM are accelerating training IBM's Watson on Korea language.

To reiterate, the space is coloured according to whether the machine learning technique predicts it belongs to the red or the blue class. For example, if a model predicts a high probability that a region is blue, then we shade that area darker blue). The line between coloured regions is called the decision boundary. The first thing you might look for is how many points have been misclassified by being included in an incorrectly coloured region. To perfectly solve this problem, a very complicated decision boundary is required.

No doubt, it has its benefits when it comes to great minds doing great things. However, if the birds and the bees are anything to go by, we have a lot to learn from collective thinking – even if it is used to make some cold, hard cash. Using a "hive mind" artificial intelligence platform, a group of individuals managed to predict the outcome of the top four winners of the Kentucky Derby. This 540-to-1 wager ended up winning a tidy 10,822 for the developers. So was it blind luck?

"They don't have to reinvent the wheel." SyntaxNet is the latest among artificial-intelligence software systems to be released by large tech companies under an open-source license, which allows anyone to access, use and tweak the software. Google released SyntaxNet, its natural language parsing framework, as an open source project on Thursday, adding another option to the growing set of tools for creating applications that utilize artificial intelligence (AI). Amazon has suddenly made a remarkable entrance into the world of open-source software for deep learning, a type of artificial intelligence. SyntaxNet is just one of the numerous artificial intelligence software systems that have been launched by the tech giants under the open source license.

WASHINGTON – Top Republicans suggested on Sunday that Donald Trump needs more public policy schooling, particularly on foreign affairs, to earn the confidence of a fractured party and show he's ready to take on likely Democratic presidential nominee Hillary Clinton. "I think he's going to need to learn. He's going to need to understand really completely … how complex this world is," said Trump's top Senate ally and top foreign policy adviser, Sen. Jeff Sessions, R-Ala. To Rep. Tom Cole, R-Okla., Trump "is a work in progress," more so than most candidates. "Usually you know a lot more about a candidate because they've run for other things. And he does have a shoot-from-the-hip style."

Large-scale classification, where we need to classify hundreds of thousands or millions of items into thousands of classes, is becoming increasingly common in this age of Big Data… So far, however, very little has been published on how large-scale classification has been carried out in practice, even though there are many interesting questions about such cases. Today's paper is a case study on large-scale classification of products at Walmart. The requirement is to classify 10M products into 5000 categories based on fairly minimal product descriptions. Oh, and new products turn up all the time, and the set of categories is continuously evolving. Many learning solutions assume that we can take a random sample from the universe of items, manually label the sample to create training data, then train a classifier.