Lip-reading 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.

Here is a non-exaustive, work in progress set of resources for data science, machine learning, artificial intelligence, data and text analytics, and data visualization. I've also included links for web and API development, programming languages, DevOps tools, cloud computing, and more. Note that resources are listed in no particular order of preference or relevance.

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

Using a system built in house, Ocado is in the process of eliminating manual review and sorting of the more than 2,000 daily customer service emails it receives. British web grocer Ocado Group Plc. receives about 2,000 customer service emails a day on average. That number can easily double or triple during the holiday season or when other issues come up such as bad weather that may delay a customer's order, says Dan Nelson, head of data for Ocado, No. 23 in the Internet Retailer 2016 Europe 500. Nelson says the problem until recently is that retailer, which sells solely online and was launched in 2000, used to task its customer service staff with going through and sorting each email that came in. "A lot of customer service reps' time was spent filtering emails," Nelson says.

OpenSim is a biomechanical physics environment for musculoskeletal simulations. Biomechanical community designed a range of musculoskeletal models compatible with this environment. These models can be, for example, fit to clinical data to understand underlying causes of injuries using inverse kinematics and inverse dynamics. For many of these models there are controllers designed for forward simulations of movement, however they are often finely tuned for the model and data. Advancements in reinforcement learning may allow building more robust controllers which can in turn provide another tool for validating the models.

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.