What do you do when you're reading an article or paper, one that it's very important you understand, and get stumped by a particular passage? More often than not, you'll head over to Google--or whatever your favorite search engine is--start surfing the Web, and won't stop until you find a satisfactory answer to the puzzle. Researchers at MIT have developed a machine learning system that behaves much the same way in the course of performing information extraction, the process of creating structured data from unstructured formats such as plain text. Here are the key details from MIT's newsroom: Most machine-learning systems work by combing through training examples and looking for patterns that correspond to classifications provided by human annotators. For instance, humans might label parts of speech in a set of texts, and the machine-learning system will try to identify patterns that resolve ambiguities -- for instance, when "her" is a direct object and when it's an adjective.

Over the last few months, I've been focused on product design for chatbots--both text and voice. As a UX designer with a background in graphic design, it's been refreshing to shift focus towards non-visual user experiences. Culled from my research on conversational chatbot interfaces, user journeys, personas and bots I've created with talented engineers at Wizeline, here are my tips for designing the best chatbot experiences. The most basic chatbot user experience is to inform the user of your bot's capabilities, so they know what to expect from it in the future. It is just as crucial to offer some automated content to push regularly, to re-engage the user rather than wait for them to interact with the bot.

Eyewire Executive Director Amy Robinson Sterling will lead us through unprecedented scientific landscapes on humankind's neuroscientific journey of self-discovery, exploring the exciting prospects in blending machine intelligence and crowdsourced human intellect for the benefit of all. As a leading catalyst of neuroscience visualization spanning interactive web to virtual reality, Amy has advised the White House OSTP and the US Senate on crowdsourcing and open innovation. Fast Company credits her with "making neuroscience into a playground for today's hot tech du jour." Amy founded the TEDx Music Project (a collection of the best live music from TEDx events around the world), and was named one of Forbes' 30 Under 30 in 2015. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.

I, for one, can't wait to fill my plate up with some stuffing. Also, whether you're ready for it or not, the holiday shopping season is almost upon us. In honor of Black Friday this week, let's take a look at why shoppers choose the stores they do. The world of retail is fast-paced and constantly changing, and it can be a real struggle for companies to feel that they're succeeding in this environment. While the key to attracting and retaining customers can change from retailer to retailer, there are some fundamental drivers of customer satisfaction – and dissatisfaction – that hold true for most companies.

Lipreading is notoriously difficult, depending as much on context and knowledge of language as it does on visual clues. But researchers are showing that machine learning can be used to discern speech from silent video clips more effectively than professional lip-readers can. In one project, a team from the University of Oxford's Department of Computer Science has developed a new artificial-intelligence system called LipNet. As Quartz reported, its system was built on a data set known as GRID, which is made up of well-lit, face-forward clips of people reading three-second sentences. Each sentence is based on a string of words that follow the same pattern.

The news was revealed by two exchange operators who are planning to apply artificial intelligence tools to perform market surveillance in the coming months, and according to a Wall Street regulator, they are not far behind, Reuters reports. The intentions of the use of the software is to for instance search through chat-room messages to detect any "bragging or back slapping" around the time of the launch of a big trade. It will also be able to unravel complex issues more efficiently, such as incidents called "layering" where orders are rapidly sent to exchanges to then get cancelled in order to artificially move a stock price. Tom Gira, executive vice president for market regulation at the Financial Industry Regulatory Authority (FINRA) believes that AI might even have the ability to detect new types of cicanery. "The biggest concern we have is that there is some manipulative scheme that we are not even aware of," he told Reuters.

Two captive harbour porpoises called Freja and Sif have helped to reveal that porpoises --and probably all cetaceans -- consciously adjust their heart rate to suit the length of a planned dive. By doing this, the animals optimise the rate at which they consume oxygen beforehand to match the intended depth and length of their dive. "Until now, we knew that the heart rates of porpoises and cetaceans in general correlate with different dive factors, such as dive duration, depth and exercise," says Siri Elmegaard of Aarhus University in Denmark, who led the research. "Now we can conclude that harbour porpoises have cognitive control of their heart rate." The discovery might also provide another explanation for how exposure to loud noise from shipping, sonar or subsea exploration harms cetaceans and possibly triggers strandings.

A few months ago I demonstrated how to install the Keras deep learning library with a Theano backend. In today's blog post I provide detailed, step-by-step instructions to install Keras using a TensorFlow backend, originally developed by the researchers and engineers on the Google Brain Team. I'll also (optionally) demonstrate how you can integrate OpenCV into this setup for a full-fledged computer vision deep learning development environment. The first part of this blog post provides a short discussion of Keras backends and why we should (or should not) care which one we are using. From there I provide detailed instructions that you can use to install Keras with a TensorFlow backend for machine learning on your own system.

Artificial intelligence and its machine learning subset are all the rage these days. That was evident when I spoke this week at the AI World event, which was packed with vendors and users seeking to understand what the hell AI and machine learning are--and wanting to know how they could use this old but revitalized technology effectively. Amazon Web Services, Google, IBM, Microsoft, and the other major cloud providers all have machine learning services in their clouds now. But most enterprises have no clue on what the heck to do with machine learning systems, whether cloud or on-premises. It is critical to find the right uses for machine learning.

Pop culture is filled with images of crooked robots defying and defeating humans. Dystopian universes depicting raging wars between machines and mankind are legion: Asimov's writings, Hollywoodian blockbusters such as The Matrix, countless video games, etc. As evolved as we are, all of this -- thankfully -- remains fantasy. The funny thing however is that all those universes are offsprings of a human activity that is soon going to be covered by machines: Art. I believe that robots already are able to produce Art, and will be increasingly able to do so when powered by Artificial Intelligence.