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.

Apple has bought Turi, a Seattle-based machine-learning startup. The purchase price was "around" 200 million, Geekwire reports, citing a source close to the deal. Turi was founded by a University of Washington professor, and it specialized in providing tools and frameworks for other developers to incorporate machine learning and artificial intelligence into their apps. For example, Turi's tools allow developers to build more advanced fraud detection into their applications. Apple has heavily invested in artificial intelligence in recent years, especially through purchases. Last fall, it bought Perceptio and VocalIQ.

Every startup business faces one crucial initial challenge: Capturing and retaining good employees. Without that key element, success can be very elusive. For Finance, Insurance, and Real Estate, for instance, only 58 % of new companies are still in business after four years. In Retail, only 48% are still on their feet after four years. And with Information startups, only 37% are still running after four years.

Google's DeepMind division has announced a partnership with the NHS's Moorfields Eye Hospital to apply machine learning to spot common eye diseases earlier. The five-year research project will draw on one million anonymous eye scans which are held on Moorfields' patient database, with the aim to speed up the complex and time-consuming process of analysing eye scans. The hope is that this will allow diagnoses of common causes of sight loss, like diabetic retinopathy and age-related macular degeneration, to be spotted more rapidly and hence be treated more effectively. For example, Google says that up to 98 percent of sight loss resulting from diabetes can be prevented by early detection and treatment. Two million people are already living with sight loss in the UK, of whom around 360,000 are registered as blind or partially-sighted.

With the corporate world embracing digital technologies, organisations are increasingly looking for resources with a difference who could enable them to create a distinctive presence in the marketplace. They are keen that the resources they onboard have analytical skills, visioning skills, learning skills, innovation capabilities, social skills and problem solving skills at all levels in the organisation--not just at the top. This is because digital transformation is unleashing a virtuous change at a regular interval to the processes and interactions with various stakeholders at multiple levels within the organisation and within the industry and it is expected of employees to adapt and respond quickly to these changes. Hence in addition to reorienting the employees within the organisation towards the need for the skills mentioned above, the new generation employees organisations wish to employ are expected to have these competencies. The mindset and the capabilities required to be successful in the digital era have to be developed while the next generation is still part of the academic system.

An employee video released in June described how Tronc would use artificial intelligence and machine learning to "allow journalists to do their jobs … Mojtaba Arvin is a computer programmer who's fascinated by artificial intelligence . Right now, he spends his time as an information technology student .

Notable because it uses no programming code. Nice for those procedurally rather than code oriented. I can see some people liking this approach to understand the concept of machine learning. Though you can make the case that ultimately the approach should be understood technically to better detect errors in process. Still I like the alternative.

Before we build stats/machine learning models, it is a good practice to understand which predictors are significant and have an impact on the response variable. In this post we deal with a particular case when both your response and predictor are categorical variables. By the end of this you'd have gained an understanding of what predictive modelling is and what the significance and purpose of chi-square statistic is. We will go through a hypothetical case study to understand the math behind it. We will actually implement a chi-squared test in R and learn to interpret the results.

Logic gates are the fundamental building blocks of electronics. They help in addition, choice, negation and combination thereof to form complex circuits. In this post, I shall walk you through couple of basic logic gates to demonstrate how Neural Networks learn on their own without us having to manually code the if-then-else logic. Let's get started with the "Hello World" of Neural Networks, which is the XOR gate. A XOR gate is a exclusive OR gate with two inputs A and B and an output.

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?