Is your organization using, or planning to adopt, machine learning? If so, please share your experiences and insights in this survey. And even if you have no plans to use machine learning, please take the survey anyway--we'd love to know why.

Here, learn more about how Oracle is using AI in its technology. Plus, find out what workers can do to prepare themselves to ensure job security as technology continues to rapidly advance. "The thing about machine learning, the promise of it, is that it has a huge applicability inside of a company like Oracle. Our job is to find places in Oracle's business where we think machine learning can have an impact." "Those of us making AI tools, and those who use them, need to understand that there are limits. It will be about building human-machine synergy."

I hate my alarm clock, and I bet you hate yours. They're machines that drag you from your comfortable slumber into the cold drudgery of everyday life. I don't think I could ever like an alarm clock. But could I be impressed by one? And Bonjour, by French design house Holi, is a deeply impressive alarm clock in the making.

The short answer is yes. Most economists think the answer is no, because in the past automation hasn't caused lasting unemployment. They call it the Luddite Fallacy because the Luddites, the people who went around smashing up weaving machines during the Industrial Revolution, were wrong about the effect of automation – at least to the extent that they were making a broad economic argument. I think the economists are guilty of the Reverse Luddite Fallacy, which is to say that because automation hasn't caused lasting unemployment in the past it can't do so in the future. It's different this time because in previous rounds of unemployment machines have replaced our muscle jobs while in future rounds they're going to replace our cognitive skills.

Fei-Fei Li is a big deal in the world of AI. As the director of the Artificial Intelligence and Vision labs at Stanford University, she oversaw the creation of ImageNet, a vast database of images designed to accelerate the development of AI that can "see." And, well, it worked, helping to drive the creation of deep learning systems that can recognize objects, animals, people, and even entire scenes in photos--technology that has become commonplace on the world's biggest photo-sharing sites. Now, Fei-Fei will help run a brand new AI group inside Google, a move that reflects just how aggressively the world's biggest tech companies are remaking themselves around this breed of artificial intelligence. Alongside a former Stanford researcher--Jia Li, who more recently ran research for the social networking service Snapchat--the China-born Fei-Fei will lead a team inside Google's cloud computing operation, building online services that any coder or company can use to build their own AI.

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.