A motherboard change can look like a new PC, so you might well have needed to talk to Microsoft even if you had been using a Microsoft account. Either way, you should recognise that Windows 10 is fundamentally different from Windows 7 in at least two important ways. Some major features – including Cortana, Notifications and apps – originated in the smartphone world. As with other mobile operating systems, everything is maintained from the cloud. Second, Windows 10 is just one part of Microsoft's cross-platform ecosystem, which includes smartphones, tablets, the Xbox One range of games consoles, cloud-based services such as OneDrive and Office 365, and dozens of Microsoft apps on Android and Apple iOS devices.

Publishing is no longer about writing just a book. Therefore publishing is not just dependent on itself but on other mediums as well. A best selling book makes it to a Netflix show or a movie and that in turn gives the book sales another fillip. So content in its traditional form - i.e as a book is not dying anytime. In fact, the need for good content is greater than ever as players like Netflix and HBO keep on searching for the next bestselling story to be made into a tv show or a movie.

This year, the Association for Computing Machinery (ACM) celebrates 50 years of the ACM Turing Award, the most prestigious technical award in the computing industry. The Turing Award, generally regarded as the'Nobel Prize of computing', is an annual prize awarded to "an individual selected for contributions of a technical nature made to the computing community". In celebration of the 50 year milestone, renowned computer scientist Melanie Mitchell spoke to CBR's Ellie Burns about artificial intelligence (AI) – the biggest breakthroughs, hurdles and myths surrounding the technology. EB: What are the most important examples of Artificial Intelligence in mainstream society today? MM: There are many important examples of AI in the mainstream; some very visible, others blended in so well with other methods that the AI part is nearly invisible.

In an evaluation of retinal photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy, according to a study published online by JAMA. Among individuals with diabetes, the prevalence of diabetic retinopathy is approximately 29 percent in the United States. Most guidelines recommend annual screening for those with no retinopathy or mild diabetic retinopathy and repeat examination in 6 months for moderate diabetic retinopathy. Retinal photography with manual interpretation is a widely accepted screening tool for diabetic retinopathy. Automated grading of diabetic retinopathy has potential benefits such as increasing efficiency and coverage of screening programs; reducing barriers to access; and improving patient outcomes by providing early detection and treatment.

Click here to view more details about the book. This book is a type of "handbook" on data science and data scientists, and contains information not found in traditional statistical, programming, or computer science textbooks. The author has compiled what he considers some of the most important information you will need for a career in data science, based on his 20 years as a leader in the field. Much of the text was initially published on the Data Science Central website over the last three years, which is read by millions of website visitors. The book shows how data science is different from related fields and the value it brings to organizations using big data.

A doctor's work isn't all done in examination rooms. Many specialists spend lots of time alone with the lights out, examining photographs that reveal their patients' internal workings. A paper by Google published in the Journal of the American Medical Association details an algorithm that can detect when someone has developed blindness as a result of diabetes, trained and tested by board-certified ophthalmologists. A key difference between this research and previous papers on medical imaging by large tech companies is its publication and defense by a respected medical journal like JAMA. Concurrent with Google's paper, JAMA also published an article translating the finding for medical professionals and urging the community that this is a good thing--algorithms can let doctors spend more time with patients, rather than reading scans.

We've already published the top big data presentations on slideshare, as well as great Github list of public data sets, or top machine learning projects, or top R packages. We've asked our readers to share a list of top Data Science videos on YouTube. Here, we share a list of top data science presentations from VideoLectures.net. These presentations received 5 to 20 times fewer page views than those on Slideshare, because they are far more technical, and attract a different, truly technical audience. You can check the entire list here.

The key element which separates today's artificial intelligence (AI) systems and what we consider to be human thought and learning processes could be boiled down to no more than an algorithm. That's according to a recent paper published in the journal Frontiers in Systems Neuroscience, which suggests that despite the complexity of the human brain, an algorithm may be all it takes for our technological creations to mimic our way of thinking. As reported by Business Insider, the idea that human thought can be whittled down to an algorithm lies in the "Theory of Connectivity," which proposes that human intelligence is rooted in "a power-of-two-based permutation logic (N 2i-1)" algorithm, capable of producing perceptions, memories, generalized knowledge and flexible actions, according to the paper. First proposed in 2015, the theory suggests that how we acquire and process knowledge can be explained by how different neurons interact and align in separate areas of the brain. It may also be that our brain power is based on "a relatively simple mathematical logic," according to Dr. Joe Tsien, neuroscientist at the Medical College of Georgia at Augusta University and author of the paper. The logic proposed, N 2i-1, relates to how groups of similar neurons come together to handle tasks such as recognizing food, shelter, and threats.

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.