Artificial Intelligence has been doubted as "beneficiary" since humans realized it can someday turn into a real and serious threat to human race. Can smart machines, objects, humans turned into God-like cyborg transform into dangerous threat towards humans worldwide? Is it even thinkable that human race can come to an end because we ourselves were able to create machines smarter and much more powerful than us that can one day make us become extinct? "It's our responsibility to think about all of the consequences good and bad. We've had the same debate about atomic power and nanotechnology. With any powerful technology there's always the dialogue about how do you use it deliver the most benefit and how it can be used to deliver the most harm," Professor Hawking has recently said for BBC.

Soon, hundreds of billions of devices will be infused with intelligence. AI will revolutionize every industry. READ MORE 7. 7 The global ecosystem for NVIDIA GPU Deep Learning has scaled out rapidly. Breakthrough results triggered a race to adopt AI for consumer internet services: TRANSLATION RECOGNITION SEARCH RECOMMENDATIONS 8. 8 Cloud service providers, from Alibaba and Amazon to IBM and Microsoft, make the NVIDIA GPU deep learning platform available to companies large and small. Pinterest is Changing Online Retail with GPUs 12. 12 AI can solve problems that seemed well beyond our reach just a few years back.

Under, behind and inside many of the apps we use every day, a revolution is underway. It's a revolution that started decades ago but today is empowering companies to deliver better, smarter services with greater ease and on broader scales than ever before. At Singularity University's inaugural Global Summit, Neil Jacobstein, chair of Artificial Intelligence and Robotics, provided a primer showing how artificial intelligence literally transforms everything it touches. First of all, it's critical to define the scope of artificial intelligence (AI), which can be categorized into four areas: techniques in pattern recognition, software agency (that is, software that acts like real users), an exponential technology that is accelerating other exponential technologies, and a vision of a future superhuman intelligence (that fortunately hasn't happened yet). Anyone who has seen a science fiction film is likely familiar with this last area, but it's the other three areas where AI is making huge strides at a revolutionary pace.

This paper describes a computational model, called the Dirichlet process Gaussian mixture model with latent joints (DPGMM-LJ), that can find latent tree structure embedded in data distribution in an unsupervised manner. By combining DPGMM-LJ and a pre-existing body map formation method, we propose a method that enables an agent having multi-link body structure to discover its kinematic structure, i.e., body schema, from tactile information alone. The DPGMM-LJ is a probabilistic model based on Bayesian nonparametrics and an extension of Dirichlet process Gaussian mixture model (DPGMM). In a simulation experiment, we used a simple fetus model that had five body parts and performed structured random movements in a womb-like environment. It was shown that the method could estimate the number of body parts and kinematic structures without any pre-existing knowledge in many cases. Another experiment showed that the degree of motor coordination in random movements affects the result of body schema formation strongly. It is confirmed that the accuracy rate for body schema estimation had the highest value 84.6% when the ratio of motor coordination was 0.9 in our setting. These results suggest that kinematic structure can be estimated from tactile information obtained by a fetus moving randomly in a womb without any visual information even though its accuracy was not so high. They also suggest that a certain degree of motor coordination in random movements and the sufficient dimension of state space that represents the body map are important to estimate body schema correctly.

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks by the average value and generalise Spearman's \rho to extreme ranks. Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's \rho, which measures correlation between a set of ranked lists. Multivariate Spearman's \rho is defined using copulas, and we show that the geometric mean of normalised ranks maximises multivariate correlation. Motivated by this, we propose a weighted geometric mean approach for learning to rank which has a closed form least squares solution. When only the best or worst elements of a ranked list are known, we impute the missing ranks by the average value, allowing us to apply Spearman's \rho. Finally, we demonstrate good performance on the rank aggregation benchmarks MQ2007 and MQ2008.

Humans have been manually extracting patterns from data for centuries, but the increasing volume of data in modern times has called for more automated approaches. Information leads to power and success, and thanks to sophisticated technologies such as computers, satellites, etc., we have been collecting tremendous amounts of information. Initially, with the advent of computers and means for mass digital storage, we started collecting and storing all sorts of data, counting on the power of computers to help sort through this amalgam of information. Unfortunately, these massive collections of data stored on disparate structures very rapidly became overwhelming. A variety of information collected in digital form in databases and in flat files.

Cylance Inc., the company that is revolutionising cybersecurity with the practical application of artificial intelligence to prevent the most advanced cyber threats, today announced that it has selected Arrow Electronics, Inc. as its distributor in Australia and New Zealand (ANZ). This year, Cylance announced its initial launch into Australia to service market growth across Asia Pacific. Arrow will support Cylance in expanding its regional footprint and providing customers with cyberattack prevention technology where traditional anti-virus software has failed. According to Andy Solterbeck, vice president of Cylance Asia Pacific, Arrow was selected because of its extensive security experience and credentials in the local region. "We are excited to work with Arrow as a distributor that is recognised for its proven track record of releasing highly disruptive technologies into the Australia and New Zealand markets. We look forward to leveraging Arrow's infrastructure and reseller programs to scale the business and enable our customers to upskill quickly," said Solterbeck.

Following years of mounting interest in a type of artificial intelligence (AI) called deep learning, the biggest public cloud infrastructure provider, Amazon Web Services (AWS), today announced its first Amazon AI services that make use of deep learning. Deep learning generally involves training artificial neural networks on lots of data, such as photos, and then getting them to make inferences about new data. One of AWS' top competitors, Google Cloud Platform, introduced the Cloud Machine Learning service that can do deep learning earlier this year. In China, the Alibaba public cloud has the DT PAI service available for AI workloads. There is the new Rekognition image recognition service -- presumably drawing on the talent and technology from deep learning startup Orbeus, whose team Amazon hired in the past year.

The'smart' speakers that won't stop talking to each other: Watch Amazon's Echo Dot get stuck in an'infinite loop' chatting to Google's Home Google's $130 Home speaker went on sale earlier this month Amazon's Alexa has been a huge hit with 5.1m sold Both can do everything from control lights to answer questions Google's $130 Home speaker went on sale earlier this month Amazon's Alexa has been a huge hit with 5.1m sold Has YOUR Google account been hacked? Researchers say... Apple goes Red for World AIDS day as firm is revealed to... Britain traded with the Middle East 1,300 years ago: Bitumen... The original human ancestor'Lucy' was a tree climbing... Has YOUR Google account been hacked? Researchers say... Apple goes Red for World AIDS day as firm is revealed to... Britain traded with the Middle East 1,300 years ago: Bitumen... The original human ancestor'Lucy' was a tree climbing... Google Home AI speaker (left) shows the incredible potential of a smart home assistant - but still has a little bit of learning to do before it become indispensable.

Google Explores Use Of Machine Learning To Detect Diabetic Eye Disease By Jaikumar Vijayan Posted 2016-11-29 Print Researchers from the company this week published a paper describing a new deep learning algorithm for detecting signs of diabetic retinopathy. Google is hoping to apply its machine learning expertise to help doctors identify patients at risk of diabetic retinopathy (DR) early enough in the disease cycle to be able to treat them effectively. Researchers from the company this week published a paper in the Journal of the American Medical Association (JAMA) describing a deep learning algorithm for interpreting early signs of DR from retinal photographs. The paper titled "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs" is based on data that Google researchers developed with help from doctors and researchers in various hospitals and universities in the U.S. and India. The goal is to help doctors screen and identify patients in need for DR treatment especially in areas where the specialized ophthalmological skills needed for such diagnosis are in short supply.