Earlier this year, David Kenny, from IBM Watson, took to the SDC Keynote stage to share how they've been working to help people better understand machine, and help machines better understand us. Their APKs take data patterns and turn them into algorithms allowing developers to take those algorithms and turn them into life-changing experiences. Watson developers have created really great experiences – just look back at the TechCrunch Disrupt hackathon winners. Next week, at Watson Developer Conference, hundreds of developers will join industry leading experts like Uber, Slack, Cyber Code Twins and Omni Earth, to talk about best practices in machine learning and cognitive methodology to create knowledge from both internal and external data. This is a MUST ATTEND event for anyone interested in AI.

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.

This brings the three-year-old company's total funding to over $8 million. The company will use the funds to build out the "Unbabel Language Engine" – its proprietary technology platform that combines Natural Language Processing, artificial intelligence, quality estimation algorithms, and a global network of 40,000 human post-editors. This will enable cost-effective, quality translations for conversational content in emails and chat on a global scale. "With a $38 billion market in translation services, it's clear that the expense is huge for any company looking to do business in a multilingual world. However, this market is comprised of an incredibly long tail of high-cost, low-tech, and massively inefficient solutions where the business model is basically'throw more humans at it,'" explained Vasco Pedro, Co-Founder and CEO of Unbabel.

The former belongs to the realm of explanatory models, the latter to the realm of predictive analytics. Explanatory models, often involving linear regression, are concerned with explaining a given phenomenon and finding causal relationships between an output (dependent) variable, and a host, often very few, input (independent) variables. The objective is to find a good regression model that fits the data very well which meets the underlying assumption of linear regression. The emphasis here is on hypothesis testing, p-values, confidence intervals,…Once a good model is found, one can use it for estimating the value of the output variable for given values of the input variables. It is OK to estimate an output value based on interpolation, but one must use extreme caution in estimating output values based on extrapolation because the regression model is an explanatory model, not a predictive one.

SAN FRANCISCO, CA--(Marketwired - Nov 3, 2016) - While many fans dreamed the Chicago Cubs would make it to the World Series, few fans or pundits actually predicted it. But UNU, the world's first Artificial Swarm Intelligence, made a bold set of baseball predictions back in early July, naming all eight teams to make the playoffs and asserting the Cubs would win their first pennant since 1945 and first World Series since 1908. For good measure, UNU also correctly picked the Cubs' opponent, the Cleveland Indians. It was way back at midseason, during the All-Star break, when the Boston Globe challenged Unanimous A.I. to use their UNU system to do something audacious -- predict the full outcome of the 2016 baseball season. Unanimous delivered a set of picks to the Globe, which they published and have been tracking ever since.

PUNE, India, Nov. 3, 2016 /PRNewswire-iReach/ -- The need to enhance the decision-making capability of machines is driving the Machine Learning as a service (MLaaS) market. MLaaS with the help of pattern recognition, advanced analytical methodologies, and APIs is able to make better decisions. With the use of machine learning algorithm, decision-making abilities improve over time without being explicitly programmed. However, the lack of skilled consultants to deploy machine learning services and government and compliance issues are limiting the growth of MLaaS solutions in the market. Healthcare industry among all the verticals is expected to gain the maximum traction during the forecast period.

This post was written by Ofir Press. Ofir is a graduate student at Tel-Aviv University's Deep Learning Lab. His main focus is on using deep learning for natural language processing. "Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online. If you don't have the relevant math background: There is an incredible amount of free material online that can be used to learn the required math knowledge. Gilbert Strang's course on linear algebra is a great introduction to the field. For the other subjects, edX has courses from MIT on both calculus and probability. If you are interested in learning more about machine learning: Andrew Ng's Coursera class is a popular choice as a first class in machine learning. There are other great options available such as Yaser Abu-Mostafa's machine learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners. Knowledge in machine learning isn't really a prerequisite to learning deep learning, but it does help. In addition, learning classical machine learning and not only deep learning is important because it provides a theoretical background and because deep learning isn't always the correct solution. Geoffrey Hinton's Coursera class "Neural Networks for Machine Learn... covers a lot of different topics, and so does Hugo Larochelle's "Neural Networks Class".

Artificial intelligence has become the pet anxiety of luminaries like Elon Musk, Bill Gates, and Stephen Hawking. They have all expressed concerns about our Promethean quest to develop machine intelligence, and those concerns seem to be spreading every day. But there's another dimension of technological change that ought to worry us every bit as much as AI, if not more so. Bioengineering has already allowed human beings to take control of their own evolution. Whether it's emergent cloning technologies or advanced gene therapy, we're quickly approaching a world in which humans can -- and will -- change the way they live and die. Michael Bess is a historian of science at Vanderbilt University and the author of a fascinating new book, Our Grandchildren Redesigned: Life in a Bioengineered Society. Bess's book offers a sweeping look at our genetically modified future, a future as terrifying as it is promising. "We're going to give ourselves a power that we may not have the wisdom to control very well," he told me.

Finding a wine that pairs perfectly with your unique palate can be difficult, but new technology lets anyone personalize and customize vino with a touch of button. Called Vinfusion, this system lets users craft a glass of wine using terms like'fiery' or'sweet' by pressing options in an accompanied linked to a tabletop tap. The machine houses four red wines that are blended to perfection by a unique flavor algorithm and is then dispensed into a glass. Vinfusion lets users craft their wine using terms like'fiery' or'sweet' by pressing options in an accompanied linked to a tabletop tap. Users choose between light and full-bodied, soft and fiery, and sweetness using an accompanied app on a tablet.

We consider the validation of prognostic diagnostic tests that predict two prognostic subgroups (high-risk vs low-risk) for a given disease or treatment. When comparing survival curves between two prognostic subgroups the possibility of misclassification arises, i.e. a patient predicted as high-risk might be de facto low-risk and vice versa. This is a fundamental difference from comparing survival curves between two populations (e.g. control vs treatment in RCT), where there is not an option of misclassification between members of populations. We show that there is a relationship between prognostic subgroups' survival estimates at a time point and positive and negative predictive values in the classification settings. Consequently, the prevalence needs to be taken into account when validating the survival of prognostic subgroups at a time point. Our findings question current methods of comparing survival curves between prognostic subgroups in the validation set because they do not take into account the survival rates of the population.