With its controversial seizure and return of a U.S. underwater drone, Beijing may have inadvertently thrust into the spotlight one of the main motivations behind its ramped-up moves in the South China Sea: the quest to create a safe-haven for its sea-based nuclear deterrent. Submarines, in particular ballistic missile subs, have long figured prominently in China's desire to match the capabilities and prestige of other major nuclear powers. Slowly but surely, experts say, Beijing has made progress on this front, building a formidable program that began very early in the ruling Communist Party's history. But securing the credibility of its overall nuclear deterrent has been a challenge. "In particular, experts worry that growing U.S. missile defense, conventional precision strike, and space-based surveillance capability together allow for sophisticated preemptive attacks that pose a significant threat to China's land-based nuclear forces," Tong Zhao, a fellow at the Carnegie-Tsinghua Center for Global Policy, wrote in a June report on China's sea-based nuclear deterrent.

Microsoft's plan to use machine learning to improve eyecare in India Competition that results in better care for people suffering from visual impairments is the right kind of competition. Following a path similar to that of Google's DeepMind, Microsoft India announced this morning that it's launching a new research group, the Microsoft Intelligent Network for Eyecare, to bring data-driven eyecare services to India. Whereas DeepMind's swing at ophthalmology targeted the UK, Microsoft's ambitions are a considerably more global. The tech company is working alongside researchers from the United States, Brazil, Australia and, of course, India to train machine learning models that can identify conditions that can lead to blindness. Microsoft's key strategic partnership is with the L V Prasad Eye Institute in Hyderabad, India, one of the most prestigious hospitals in the country.

Competition that results in better care for people suffering from visual impairments is the right kind of competition. Following a path similar to that of Google's DeepMind, Microsoft India announced this morning that it's launching a new research group, the Microsoft Intelligent Network for Eyecare, to bring data-driven eyecare services to India. Whereas DeepMind's swing at ophthalmology targeted the UK, Microsoft's ambitions are a considerably more global. The tech company is working alongside researchers from the United States, Brazil, Australia and, of course, India to train machine learning models that can identify conditions that can lead to blindness. Microsoft's key strategic partnership is with the L V Prasad Eye Institute in Hyderabad, India, one of the most prestigious hospitals in the country.

To ramp up its contribution to global fights against money laundering and terrorism financing, South Korea will introduce an artificial intelligence-based system to better filter out financial crimes, said the country's financial intelligence chief. Yoo Kwang-yeol, commissioner of the Korea Financial Intelligence Unit, said his agency is currently working to upgrade the main system that stores and analyzes information regarding hundreds of millions of financial transactions in order to increase accuracy of capturing suspicious transactions out of normal ones. Yoo Kwang-yeol, commissioner of Korea Financial Intelligence Unit speaks during an interview at his office in Gwanghwamun, central Seoul, Dec. 6. For this, a group of KOFIU experts paid a trip to Australia earlier this month to learn from the Australian financial intelligence system. "AI can help improve efficiency of sorting out suspicious financial transactions and accuracy of analyzing related account information," Yoo said.

Hundreds of millions of people around the world suffer from visual impairment. A new program from Microsoft utilizes machine learning techniques and artificial intelligence to help diagnose and treat the condition, according to a report from Mashable. Earlier this year, Microsoft teamed with the not-for-profit LV Prasad Eye Institute (LVPEI) in India, which provided the computing giant with access to more than one million anonymized medical records. Those records were tossed into Microsoft's cloud-based machine learning program and processed. The data gave Microsoft the ability to look through and analyze a wide range of procedures, providing a better understanding as to why a certain operation is chosen and the results of different surgeries for the eye patients.

Though robots and artificial intelligence may not replace our doctors entirely in the foreseeable future, they are already starting to make a difference. Microsoft is now using machine learning and artificial intelligence to help doctors in India to diagnoze and treat eye diseases. Earlier this year, Microsoft began working with the not-for-profit LV Prasad Eye Institute (LVPEI) in India to have its Azure machine learning and Power BI services analyze patterns among cases and predict the surgical outcome of eye surgery patients. The collaboration saw Microsoft going through a trove of data -- anonymized records of 1.1 million people -- and provide doctors with insights into how the blindness spreads in the country, Anil Bhansali, Managing Director of Microsoft India (R&D), explained to Mashable India in a conversation. Microsoft says it utilized Azure machine learning service to crunch the numbers and Power BI service to visualize those numbers to make sense out of them.

Though robots and artificial intelligence may not replace our doctors entirely in the foreseeable future, they are already starting to make a difference. Microsoft is now using machine learning and artificial intelligence to help doctors in India to diagnoze and treat eye diseases. Earlier this year, Microsoft began working with the not-for-profit LV Prasad Eye Institute (LVPEI) in India to have its Azure machine learning and Power BI services analyze patterns among cases and predict the surgical outcome of eye surgery patients. The collaboration saw Microsoft going through a trove of data -- anonymized records of 1.1 million people -- and provide doctors with insights into how the blindness spreads in the country, Anil Bhansali, Managing Director of Microsoft India (R&D), explained to Mashable India in a conversation. Microsoft says it utilized Azure machine learning service to crunch the numbers and Power BI service to visualize those numbers to make sense out of them.

Louis Rosenberg thinks he has found a way to make us all a lot smarter. Rosenberg runs a Silicon Valley startup called Unanimous AI, which has built a tool to support human decision-making by crowdsourcing opinions online. It lets hundreds of participants respond to a question all at once, pooling their collective insight, biases and varying expertise into a single answer. Since launching in June, Unanimous AI has registered around 50,000 users and answered 230,000 questions. Rosenberg thinks this hybrid human-computer decision-making machine – once dubbed an'artificial' artificial intelligence – could help us tackle some of the world's toughest questions.

It's not all about the big boys, however, as NLP, text analysis and text mining technologies are becoming more and more accessible to smaller organizations, innovative startups and even hobbyist programmers. NLP is helping organizations make sense of vast amounts of unstructured data, at scale, giving them a level of insight and analysis that they could have only dreamed about even just a couple of years ago. Today we're going to take a look at 3 industries on the cusp of disruption through the adoption of AI and NLP technologies;

We study the complexity of the problem of searching for a set of patterns that separate two given sets of strings. This problem has applications in a wide variety of areas, most notably in data mining, computational biology, and in understanding the complexity of genetic algorithms. We show that the basic problem of finding a small set of patterns that match one set of strings but do not match any string in a second set is difficult (NP-complete, W[2]-hard when parameterized by the size of the pattern set, and APX-hard). We then perform a detailed parameterized analysis of the problem, separating tractable and intractable variants. In particular we show that parameterizing by the size of pattern set and the number of strings, and the size of the alphabet and the number of strings give FPT results, amongst others.