Microsoft India today announced the winners of the 24 hour hackathon, held as part of the first Machine Learning & Data Sciences (ML&DS) Conference. Team Builders won the grand prize, while Eavesdroppers and NHacks emerged as the first and second runners up respectively by creating applications to address challenges in the field of agriculture, call center management and human behavior. The hackathon invited students and early-stage developers to work in teams to build intelligent, working applications with the help of Microsoft Cognitive Services, Microsoft Bot Framework and Microsoft R Services. The top three teams won cash prizes worth INR 50,000, INR 40,000 and INR 30,000 for developing solutions fulfilling parameters on design, innovation, foreseeable impact and marketability. In addition to the cash awards, the student participants from the winning teams will also be offered interviews for internships and full time positions at Microsoft India (R&D) Pvt. Ltd. "Microsoft has the vision, strategy and talent to democratize data and machine learning and use it to realize our mission to empower every individual and organization on the planet to achieve more.

Global software major Microsoft on Monday invited developers to use its machine learning and data platforms for building digital products and solutions to transform businesses and drive inclusive growth. "Our ambition is to democratise access to new technologies so that software developers can build, innovate and transform the world with them," said Microsoft data group Vice President Joseph Sirosh at a conference on machine learning and data sciences in India. Noting that cloud-based services for machine learning and big data, coupled with the Internet of Things (IoT) have the potential to revolutionise every aspect of life, including sports, healthcare, education and even government, he said the company's unique platforms such as the Cortana Intelligence suite were helping customers to harness the power of artificial intelligence. The two-day conference is aimed at exploring the possibilities with big data, machine learning, artificial intelligence and open source technologies in enabling platforms, intelligent apps, services and experiences to accelerate economic growth, empower people and drive real impact. Demonstrating the power of data analytics, Microsoft unveiled an Azure machine learning-based approach for calculating target scores in weather-interrupted T-20 cricket matches.

For more on this, we are joined in the studio by CCTV's Jin Yingqiao. Q1, Tell us more about some of the new trends in the industry in China. A1, It seems like everyone is betting on AI, or Artificial Intelligence, for the future. Be it Google, Facebook, IBM, Amazon or Apple racing against each other globally. And in China, tech giants the BAT, or Baidu, Alibaba and Tencent, and Huawei, and home appliance makers Midea and Changhong all have weighed in.

The Global Artificial Intelligence & Robotics Summit has opened in the southern Chinese city of Shenzhen. Our reporter Ge Yunfei went to China's southern technology center to find out more about the potential of the budding industry. The success of Alpha Go in defeating the human mind has aroused the world of artificial intelligence. But at the Global Artificial Intelligence & Robotics Summit in Shenzhen, it's limitations have been projected. Michael Wooldrige -- who is leading a cooperation initiative between Oxford University and Google on artificial intelligence -- said although Alpha Go is unique in its own right, it's not THAT smart.

ISLAMABAD – The U.S. drone strike that killed the Islamic State group's commander for Afghanistan and Pakistan was the latest blow to the Middle East-led movement's ambitions to expand into a region where the long-established Taliban remain the dominant Islamist force. The Islamic State group has enticed hundreds, perhaps thousands, of jihadi fighters in Afghanistan and Pakistan to switch loyalty and has held a small swath of territory in the eastern Afghan province of Nangarhar, where leader Hafiz Saeed Khan was killed on July 26 by a U.S. drone, Washington confirmed late Friday. But outside that pocket of territory, security officials and analysts say that the group remains -- for now -- more of a "brand name" than a cohesive militant force in much of the region. "Groups around the world want to jump on that bandwagon and cash in on their popularity and the fear they command," said a Pakistani police official based in Islamabad, on condition of anonymity because he was not authorized to speak to media. Anxiety over the Islamic State group -- also known as ISIS or "Daesh" -- in Afghanistan and Pakistan has been building since the al-Qaida breakaway movement seized portions of territory in Iraq and Syria in 2014 and began promoting itself worldwide.

Artificial intelligence could scale new heights on its growing application in Southeast Asia, thanks to the region's relative openness to data collection. Bumrungrad International Hospital in Bangkok began using IBM's Watson AI platform to assist in cancer treatment late last year. The system automatically collects and learns from clinical studies and new papers, suggesting treatment options and giving expected success rates for each patient. Watson lets doctors make unbiased decisions on the best options, according to James Miser, Bumrungrad's chief medical information officer. The Thai hospital has consulted with Watson in treating breast, colon, lung and prostate cancer in about 50 patients so far and intends to expand its use.

Toyota plans to have its self-driving car out by 2020. It's been testing a modified Lexus GS as well as their own hybrid self-driving vehicles on the road. In January 2016, Toyota announced the creation of the Toyota Research Institute (TRI), a 1 billion investment in AI to develop autonomous driving capabilities as well as home-care robots. Jim Adler, the first head of data at TRI, has been on the job for just two months. Before that, he was an executive at Metanautix, a data analytics platform that sold to Microsoft last year. Adler talked to TechRepublic about how Toyota is using data and simulation to teach cars to drive themselves. It sounded like so much fun and interesting, and leveraged quite a bit of my experience.

If you've watch TV shows like House, you probably know that there are some conditions that people suffer from that are rare and aren't as easy to diagnose. However it seems that in the future, such conditions could be diagnosed much quicker thanks to the help of AI, which is what doctors in Japan did. In what could be described as a world's first, doctors in Japan relied on artificial intelligence to help diagnose a woman who was suffering from a rare form of leukemia. The patient was initially treated for acute myeloid leukemia, but her recovery from post-remission therapy was slow, which is when doctors decided that the initial diagnosis could have been wrong. This is when they turned to IBM's Watson to help them with their case.

After conventional methods of detection failed, a team of Japanese researchers from the University of Tokyo's Institute of Medical Science used IBM Watson to successfully diagnose a 60 year-old woman where physicians were unable to, according to NDTV. The patient was initially diagnosed with acute myeloid leukemia, but treatments for that condition proved ineffective. Watson was able to identify the more rare form of leukemia she suffered from and ultimately provide a different, more successful form of treatment, according to the report. Artificial intelligence systems like IBM Watson may still be a ways off from being regularly used in hospitals, as they require large amounts of comparative data, according to Engadget. However, when given access to that type of information, AI systems can work quickly -- Watson produced the accurate diagnosis for the Japanese patient after comparing her genetic data against a database of 20 million researcher papers in just ten minutes.

The estimation of normalizing constants is a fundamental step in probabilistic model comparison. Sequential Monte Carlo methods may be used for this task and have the advantage of being inherently parallelizable. However, the standard choice of using a fixed number of particles at each iteration is suboptimal because some steps will contribute disproportionately to the variance of the estimate. We introduce an adaptive version of the Resample-Move algorithm, in which the particle set is adaptively expanded whenever a better approximation of an intermediate distribution is needed. The algorithm builds on the expression for the optimal number of particles and the corresponding minimum variance found under ideal conditions. Benchmark results on challenging Gaussian Process Classification and Restricted Boltzmann Machine applications show that Adaptive Resample-Move (ARM) estimates the normalizing constant with a smaller variance, using less computational resources, than either Resample-Move with a fixed number of particles or Annealed Importance Sampling. A further advantage over Annealed Importance Sampling is that ARM is easier to tune.