Udacity, the education platform focused on helping workers gain skills they need for great careers in tech, has partnered with IBM Watson, Didi Chuxing and Amazon Alexa to offer a new nanodegree in artificial intelligence, the companies announced today at the IBM World of Watson conference. IBM Watson is co-developing the curriculum of the course with Udacity. Chinese ride-hailing company Didi Chuxing intends to hire students who successfully complete the nanodegree, as does IBM. And Amazon Alexa is serving as an advisor to Udacity in developing the new AI nanodegree. According to Udacity's founder Sebastian Thrun, who previously started Google's innovation shop Google X and its self-driving car initiative, the new AI nanodegree will be for students who already have a level of mastery in software development.

IBM Aims Watson at Embodied Cognition By Darryl K. Taft Posted 2016-11-05 Print Q&A: IBM is focusing its Watson cognitive computing technology on the area of embodied cognition, according to Grady Booch, chief scientist of Watson/M. At the close of IBM's recent World of Watson conference in Las Vegas, eWEEK interviewed Grady Booch, Big Blue's chief scientist of Watson/M about the future of IBM's Watson cognitive computing platform and where IBM is taking the technology to benefit enterprise customers, consumers and developers alike. Among other areas, IBM is applying Watson to embodied cognition or putting artificial intelligence (AI) into the physical world. "This is embodied cognition: By placing the cognitive power of Watson in a robot, in an avatar, an object in your hand or even in the walls of an operating room, conference room or spacecraft, we take Watson's ability to understand and reason and draw it closer to the natural ways in which humans live and work," Booch said in a talk. "In so doing, we augment individual human senses and abilities, giving Watson the ability to see a patient's complete medical condition, feel the flow of a supply chain or drive a factory like a maestro before an orchestra."

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Artificial Intelligence vs. Driverless Cars: Which Tech Trend Has More Opportunity? Self-driving cars are well on their way, and many tech companies are focusing much of their attention on artificial intelligence. Here's how big these two trends could get. IHS Automotive predicts that just 20 years from now, nearly 10% of all new vehicles sold will be fully self-driving. Tesla CEO Elon Musk said last month that all of the company's new cars built from now on will ship with fully autonomous hardware (though the features won't be activated yet).

Please can you give an overview of the current drug discovery paradigm? In what ways do you think it needs to be leaner? With the current drug discovery paradigm, it takes up to 15 years to translate an idea, such as hypothesizing a certain protein is important in a disease and testing this with targeting the protein with a drug, all the way through to proof of concept. The drug has to be filed with the regulatory authorities, having done all the safety and efficacy testing. Estimates vary, but it's currently reckoned to cost over 1 billion dollars per drug.

Patents are given to inventions, which are usually the product of a human mind. But what about inventions that come from not-so-human sources, like artificial intelligence (AI)? Should these patents be awarded to their computer inventors? Well, at least one expert patent attorney thinks so. Ryan Abbott is a professor of law and health sciences at the University of Surrey's School of Law, and he is a patent attorney at the United States Patent and Trademark Office (USPTO). He is also an adjunct assistant professor of medicine at the David Geffen School of Medicine at UCLA--quite the list of credentials, to be sure.

In machine learning, you may often wish to build predictors that allows to classify things into categories based on some set of associated values. For example, it is possible to provide a diagnosis to a patient based on data from previous patients. Many algorithms have been developed for automated classification, and common ones include random forests, support vector machines, Naïve Bayes classifiers, and many types of neural networks. To get a feel for how classification works, we take a simple example of a classification algorithm – k-Nearest Neighbours (kNN) – and build it from scratch in Python 2. You can use a mostly imperative style of coding, rather than a declarative/functional one with lambda functions and list comprehensions to keep things simple if you are starting with Python. Here, we will provide an introduction to the latter approach.

New drugs typically take 12-14 years to make it to market, with a 2014 report finding that the average cost of getting a new drug to market had ballooned to a whopping $2.6 billion. It's a topic I've covered before, with a study published earlier this year highlighting how automation could be used to reduce the cost of drug discovery by approximately 70%. It's an approach that a number of companies are taking to market. For instance, London-based start-up Benevolent.AI utilizes complex AI to look for patterns in the scientific literature. They have already managed to identify two potential drug targets for Alzheimer's that has already attracted the attention of pharmaceutical companies.

Contestants in this beauty contest are judged by robots.

Algorithm selection can be challenging for machine learning newcomers. Often when building classifiers, especially for beginners, an approach is adopted to problem solving which considers single instances of single algorithms. However, in a given scenario, it may prove more useful to chain or group classifiers together, using the techniques of voting, weighting, and combination to pursue the most accurate classifier possible. Ensemble learners are classifiers which provide this functionality in a variety of ways. This post will provide an overview of bagging, boosting, and stacking, arguably the most used and well-known of the basic ensemble methods.