Existing approaches to combine both additive and multiplicative neural units either use a fixed assignment of operations or require discrete optimization to determine what function a neuron should perform. This leads either to an inefficient distribution of computational resources or an extensive increase in the computational complexity of the training procedure. We present a novel, parameterizable transfer function based on the mathematical concept of non-integer functional iteration that allows the operation each neuron performs to be smoothly and, most importantly, differentiablely adjusted between addition and multiplication. This allows the decision between addition and multiplication to be integrated into the standard backpropagation training procedure.

In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category's attributes. For example, with classifiers for generic attributes like \emph{striped} and \emph{four-legged}, one can construct a classifier for the zebra category by enumerating which properties it possesses---even without providing zebra training images. In practice, however, the standard zero-shot paradigm suffers because attribute predictions in novel images are hard to get right. We propose a novel random forest approach to train zero-shot models that explicitly accounts for the unreliability of attribute predictions. By leveraging statistics about each attribute's error tendencies, our method obtains more robust discriminative models for the unseen classes. We further devise extensions to handle the few-shot scenario and unreliable attribute descriptions. On three datasets, we demonstrate the benefit for visual category learning with zero or few training examples, a critical domain for rare categories or categories defined on the fly.

And the artificial intelligence chatbot that didn't survive a day on the Internet. Plus: Reviewing Santiago Calatrava's latest, how to be unprofessional and the "Grand Theft Auto" modification that may have you watching for hours on end. Time has Russian drone footage that provides an overview of what remains of the old Silk Road crossroads, as well as the contemporary human settlement of Tadmur that sits nearby. About 80% of the artifacts appear to be largely intact. The country's antiquities chief says repairs will take five years.

I have some data that I need to classify into three groups (Q 0,1,2). The Q 0 training data is realatively well seperated from the other training data, but the Q 1, and Q 2 have a good amount of overlaps. See this figure for an example. I'm working with Scikit-Learn, and I've tried Random Forests, Extremely Randomized Forests, and SVM. In the testing step (before I apply it to unknown data) I get pretty good results (The recall and precision are both 60%).

Dire predictions that humans will be replaced by machines in the workplace continue to make headlines. Drones are delivering packages to your doorstep. The manufacturing, automotive and healthcare industries are already highly automated in many countries, and technology companies are racing to create the next "new and improved" version of artificial intelligence (AI). In fact, a much-cited Oxford study that looked at 702 occupations in the U.S. concluded that 47% of U.S. employment is at risk of being lost to computerization. U.S. employee anxiety notwithstanding, this begs the question: What has your company done in the wake of such news?

Julie Sobowale is a freelance journalist and lawyer based in Halifax, Nova Scotia, specializing in legal reporting. She writes about trends in the legal industry including legal technology, innovation, entrepreneurship, diversity and major shifts in legal culture. Her work has appeared in publications from the American Bar Association, the Canadian Bar Association, the Canadian Corporate Council Association, Canadian Lawyer and the Nova Scotia Barristers Society. She's also given presentations on legal trends, alternative careers and legal education. She graduated from the Dalhousie Schulich School of Law in 2012 and was the recipient of the Dalhousie Faculty of Law Leadership Award.

On Thursday, HPE announced the immediate commercial availability of HPE Haven OnDemand, a cloud platform that provides advanced machine-learning APIs, so that developers can build data-rich mobile and web applications. Colin Mahony heads up the HPE Big Data Platform, which includes products like Vertica, Idol, and Haven OnDemand. He said that some people in the enterprise don't embrace machine learning because they view it as requiring extensive understanding of both statistics and coding. And, with Haven OnDemand, they want to make it easier for people to take advantage of machine learning tools. "What we're trying to do is say here's a portfolio, initially these 60 APIs, where you can call in, in very simple protocols through these RESTful APIS, and you can leverage a lot of the really rich machine learning that we've done," Mahony said.

Eric Thayer/GettyHonda Motors demonstrates its Asimo robot during a media preview of the 2014 New York International Auto Show. Bank of America Merrill Lynch recently came out with its "Transforming World Atlas" research note, which examines global economic trends through a series of maps. One notable map showed which countries had the highest number of operational robots. Japan was number one with 310,508 operational robots, according to data from 2012. There's even a hotel staffed almost entirely by robots that opened last year in Nagasaki, Japan, according to BAML.

"The robots are coming" is not something Paul Revere said during the American Revolution, but it is certainly something many people have uttered over the years. So have we finally reached the tipping point where artificial intelligence and robots will begin to take over human jobs en masse? Perhaps not, but we are closer to the time when they will be even more essential assets and presences in the workforce, explains Martin Ford, the author of the book "Rise of the Robots." I caught up with Ford at The Economist magazine's Innovation Forum event, which was held earlier this month. He pointed out that artificial intelligence is making its way into sectors that were once manned by only man, including the legal profession, where computer systems such as Watson could muscle in on human territory to provide legal counsel, and even journalism where stories are being written without direct human input about some articles.