Artificial intelligence (AI) will play a major role in healthcare digital transformation, according to new research. The study, "Human Amplification in the Enterprise," surveyed more than 1,000 business leaders from organizations of more than 1,000 employees, with $500 million or more annual revenue and from a range of sectors, all in the U.S. Survey respondents from the healthcare sector indicated that the following AI-supported activities will play a significant role in their transformations: Machine learning (77%), robotic automation (61%), institutionalization of enterprise knowledge using AI (59%), cognitive AI-led processes or tasks (50%) and automated predictive analytics (47%). The research also found that almost half of the respondents in healthcare indicate their organizations' priorities for automation initiatives is to automate processes to: "This suggests that many processes in the healthcare sector are still manual-driven and produce a high volume of errors as a result," says Sanjay Dalwani, vice president and head of hospital and healthcare at Infosys. The survey found that 73% of respondents want AI to process complete structured and unstructured data and to automate insights-led decisions. It also found that 72% want AI to provide human-like recommendations for automated customer support/advice.

The selection process of data scientists at Google gives higher priority to candidates with strong background in statistics and mathematics. Not just Google, other top companies (Amazon, Airbnb, Uber etc) in the world also prefer candidates with strong fundamentals rather than mere know-how in data science. If you too aspire to work for such top companies in future, it is essential for you to develop a mathematical understanding of data science. Data science is simply the evolved version of statistics and mathematics, combined with programming and business logic. I've met many data scientists who struggle to explain predictive models statistically. More than just deriving accuracy, understanding & interpreting every metric, calculation behind that accuracy is important.

This past week, we had our 2Q Kinvey internal hackathon (#kinveyhack). The week was filled with hacking, innovation and teamwork. Our engineers presented the results and boy were there some amazing presentations! We had teams working on Alexa Skills, cognitive computing, machine learning and many more amazing things. Here's a roundup of some of the great things presented.

Recently, I had the honor of speaking with a number of the world's most influential thought-leaders in the fields of data science, data analytics, machine learning and digital transformation. This group of prominent data technologists was more than happy to answer a wide variety of question on topics ranging from the fast-evolving area of unified governance and preparing for General Data Protection Regulations (GDPR) to transformative hybrid data management technologies, and of course, data science and machine learning. You'll learn more about all of the topics discussed here during the main event, breakout sessions and demos, plus have the opportunity to join in the conversation and connect with these renowned data pioneers who can help you understand how to build a data-driven strategy to outsmart your competition. There will be an additional opportunity to chat with a number of these thought-leaders, as well as fellow data enthusiasts, at the Fast Track Your Data CrowdChat on Tuesday, June 20th, 2017 at 1:00 PM (EDT). Now let's meet our panel of experts: His latest book is Leading Innovation: Building a Scalable, Innovative Organization. She is also on the faculty of the Data Science Graduate Program at UC Berkeley, the Data Analytics MS Advisory Board at CUNY SPS, and the Data Science Committee for the Grace Hopper Conference. Ronald Van Loon, Director Adversitement, where he is helping data-driven companies generate business value as a globally recognized Top 10 Big Data, Data Science, IoT, and BI Influencer. Aylee Nielsen: Thank you all so much for joining me today, I'd like to start off with questions on a subject matter that I know you are all very familiar with – Data Science and Machine Learning.

This week's news reminds me how much fun it is to be surprised by technology. Yesterday, the Paris-based AI startup raised $13 million, on top of an earlier $8 million investment, for technology that let developers put a voice assistant on nearly any device. Add this to recent advances from Amazon Alexa, Google Assistant, and Apple Siri, and it's obvious that voice is becoming the new interface much sooner than many people, including yours truly, ever anticipated. These are exponential leaps forward in the steady progress from command-based interfaces to conversational ones. It's as if the machines themselves are disappearing -- the "thing" we're conversing with is some crazy fantastic blend of artificial intelligence, super computer, bandwidth, and what have you, that we never see.

R language is the world's most widely used programming language for statistical analysis, predictive modeling and data science. It's popularity is claimed in many recent surveys and studies. R programming language is getting powerful day by day as number of supported packages grows. Some of big IT companies such as Microsoft and IBM have also started developing packages on R and offering enterprise version of R. What is R? R is a free language and environment for statistical computing and graphics. You can perform a variety of tasks using R language.

We'll be discussing Watson, of course, but also artificial intelligence, machine learning, and--most importantly--how businesses use these tools to better understand their customers, partners, and how they operate their businesses. Read or watch our full discussion below. Dan Costa: A lot of people have seen the IBM Watson commercials on TV, and they know that Bob Dylan had something to do with this in some vague way, but how would you define Watson as a product? Mark Simpson: Watson's a cognitive computing product, which can learn like humans. It learns, it understands, it reasons in the same way that humans do and can be taught over time. So that computing can be applied into many different areas ... Essentially it is a trusted advisor that we can give to humans that can take in masses of data and help them in the decisions that they make, augmenting their intelligence. I think that's the phrase that has stuck with a variety of people.

Picture this: you're sitting in a police interrogation room, struggling to describe the face of a criminal to a sketch artist. You pause, wrinkling your brow, trying to remember the distance between his eyes and the shape of his nose. Suddenly, the detective offers you an easier way: would you like to have your brain scanned instead, so that machines can automatically reconstruct the face in your mind's eye from reading your brain waves? After decades of work, scientists at Caltech may have finally cracked our brain's facial recognition code. Using brain scans and direct neuron recording from macaque monkeys, the team found specialized "face patches" that respond to specific combinations of facial features.

This paper introduces DeepBach, a graphical model aimed at modeling polyphonic music and specifically hymn-like pieces. We claim that, after being trained on the chorale harmonizations by Johann Sebastian Bach, our model is capable of generating highly convincing chorales in the style of Bach. DeepBach's strength comes from the use of pseudo-Gibbs sampling coupled with an adapted representation of musical data. This is in contrast with many automatic music composition approaches which tend to compose music sequentially. Our model is also steerable in the sense that a user can constrain the generation by imposing positional constraints such as notes, rhythms or cadences in the generated score. We also provide a plugin on top of the MuseScore music editor making the interaction with Deep-Bach easy to use.

Non-convex optimization with local search heuristics has been widely used in machine learning, achieving many state-of-art results. It becomes increasingly important to understand why they can work for these NP-hard problems on typical data. The landscape of many objective functions in learning has been conjectured to have the geometric property that "all local optima are (approximately) global optima", and thus they can be solved efficiently by local search algorithms. However, establishing such property can be very difficult. In this paper, we analyze the optimization landscape of the random over-complete tensor decomposition problem, which has many applications in unsupervised learning, especially in learning latent variable models. In practice, it can be efficiently solved by gradient ascent on a non-convex objective. We show that for any small constant $\epsilon > 0$, among the set of points with function values $(1+\epsilon)$-factor larger than the expectation of the function, all the local maxima are approximate global maxima. Previously, the best-known result only characterizes the geometry in small neighborhoods around the true components. Our result implies that even with an initialization that is barely better than the random guess, the gradient ascent algorithm is guaranteed to solve this problem. Our main technique uses Kac-Rice formula and random matrix theory. To our best knowledge, this is the first time when Kac-Rice formula is successfully applied to counting the number of local minima of a highly-structured random polynomial with dependent coefficients.