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.

SpaceX launched JCSAT-16 from Cape Canaveral Air Force Station and landed the first stage on a drone ship in the Atlantic Ocean. A United Launch Alliance Atlas V rocket blasted off from Cape Canaveral Air Force Station on Thursday, July 28, 2016 with a secret payload for the National Reconnaissance Office. SpaceX successfully launched its Falcon 9 rocket from Cape Canaveral and landed it about eight minutes later. United Launch Alliance launched an Atlas V rocket at 10:30 a.m. SpaceX launched a pair of communications satellites from Cape Canaveral on Wednesday, but it was unknown if the first stage landed successfully.

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. How do you say "no" to working on a self-driving car and a robot?

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.

Do machine-learning algorithms processing search engine queries bring on prejudice, discrimination and stereotyping in query results? Search results have been known to highlight these negative attributes in the past. Now researchers at Brazil's Universidade Federal de Minas Gerais suggest it could be true when it comes to female physical attractiveness in images available across the Web. The paper submitted to the International Conference on Social Informatics scheduled for publication analyzes how Google and Bing represent female beauty in their image search results, particularly when it comes to different age and racial groups. They then passed the more than 2,000 images through a program, which estimates subject age, race and gender with an estimated 90% accuracy.

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.

The way in which people engage with brands has changed. Gone are the days when it was a one-way interaction; younger demographics especially love a value-add exchange with brands they can identify with. Chatbots have been heralded as one way of opening up the channels, but as with every new marketing tech innovation, you need to get it right. Brands need access to tech that can do the job well and not make the experience clunky and robotic. One company that recognises this issue is Israel/US based imperson.

Heboi is a Japanese word that loosely means something is technically poor, or crappy. Thus: Hebocon, which, according to the founders, extends to both the robots and the people that make them -- but in an affectionate, pat-on-the-back kind of way. The competition involves several sumo-style matches in which the robots try to push their opponents out of the arena. It's no DARPA challenge, but when you see the robots in action you begin to realize it's almost as difficult a struggle. Most of the robots on show at Hebocon can't be controlled very well.

Seymour Papert with LEGO Mindstorms robotics kits, which were named in recognition of Papert's seminal book, Mindstorms: Children, Computers, and Powerful Ideas. Seymour Papert with LEGO Mindstorms robotics kits, which were named in recognition of Papert's seminal book, Mindstorms: Children, Computers, and Powerful Ideas. The field of educational technology is mourning a visionary whose work was considered 50 years ahead of its time. Seymour Papert, who died July 31 at age 88, was a mathematician and computer scientist who spent decades at MIT. "Seymour was one of the very first people to recognize that new computer technologies could be used by kids to create things in new ways and express themselves," Mitchel Resnick, a professor of learning research at MIT and a longtime colleague and friend, told NPR Ed. "It's amazing that Seymour was thinking these ideas in the 1960s," Resnick adds, "when computers cost hundreds of thousands of dollars, but he foresaw the day that every child would have access to a computer." The great theme of Papert's work and life was the nature of intelligence, or what he called thinking about thinking.