After one week with a new chief executive, Mattel Inc. is already stepping into new territory -- partnering with Chinese e-commerce giant Alibaba to design products for China and announcing its first male American Girl doll. The El Segundo toy maker said Tuesday it will sell items through Tmall.com, Mattel will also work with the Chinese tech giant's artificial intelligence lab to develop "innovative products" to nurture childhood development. It's the first public move by Margo Georgiadis, a former Google executive who took over as CEO last Wednesday. In another splash, Mattel announced it will add a boy character for the first time to its American Girl line of dolls and accessories. In a Tuesday statement, she said China presents a big opportunity for Mattel.

This week, authorities signed agreements with two truck makers with strong track records in self-driving technology-- Sweden's Scania and Japan's Toyota Tsusho--to work on the project. In the first phase, lasting about a year and starting this month, each company will design, develop, and test a truck platooning system in their respective countries. In the second, one company will be chosen for local trials on a 10 km (6.2 miles) stretch of Singapore's West Coast Highway, hauling cargo between the Brani and Pasir Panjang terminals.

The 1979 film "Apocalypse Now" is getting the video game treatment, but the people behind the adaptation really need financial help to realize the project. Surprisingly, the Kickstarter project for first-person perspective survival and horror RPG was canceled this Tuesday. Game director Montgomery Markland of Erebus LLC -- the studio working on the game alongside film director Francis Ford Coppola's American Zoetrope -- broke the news on the Kickstarter page for the game. The cancellation of the Kickstarter shouldn't come as a surprise to backers. Despite managing to accumulate $172,514 from the 2,718 backers who pledged for the game's completion, the project was just too far from its $900,000 initial goal.

Dropout, a simple and effective way to train deep neural networks, has led to a number of impressive empirical successes and spawned many recent theoretical investigations. However, the gap between dropout's training and inference phases, introduced due to tractability considerations, has largely remained under-appreciated. In this work, we first formulate dropout as a tractable approximation of some latent variable model, leading to a clean view of parameter sharing and enabling further theoretical analysis. Then, we introduce (approximate) expectation-linear dropout neural networks, whose inference gap we are able to formally characterize. Algorithmically, we show that our proposed measure of the inference gap can be used to regularize the standard dropout training objective, resulting in an \emph{explicit} control of the gap. Our method is as simple and efficient as standard dropout. We further prove the upper bounds on the loss in accuracy due to expectation-linearization, describe classes of input distributions that expectation-linearize easily. Experiments on three image classification benchmark datasets demonstrate that reducing the inference gap can indeed improve the performance consistently.

Language students are most engaged while reading texts at an appropriate difficulty level. However, existing methods of evaluating text difficulty focus mainly on vocabulary and do not prioritize grammatical features, hence they do not work well for language learners with limited knowledge of grammar. In this paper, we introduce grammatical templates, the expert-identified units of grammar that students learn from class, as an important feature of text difficulty evaluation. Experimental classification results show that grammatical template features significantly improve text difficulty prediction accuracy over baseline readability features by 7.4%. Moreover, we build a simple and human-understandable text difficulty evaluation approach with 87.7% accuracy, using only 5 grammatical template features.

Data and analytics leaders face a dilemma. Without data scientists, venturing into machine learning and data science is difficult. Without any successful pilots, convincing the business to hire data scientists is equally challenging. Enterprises don't have to have a large data science lab in order to take advantage of machine learning. "Many organizations are still in the early phases of their data science journey and struggle to understand what machine learning and data science can do for them," says Cindi Howson, research vice president at Gartner.

Successful Student has compiled the 25 Best Artificial Intelligence Colleges in the United States. Artificial Intelligence (AI), also known as machine learning, is a discipline within computer science. Artificial Intelligence is usually conceived of as doing more than just computing numbers (such as a calculator), but is more conceptual in nature (such as describing subjective qualities, or giving meanings to different contexts). An example of AI would be speech recognition and communicating, such as Apple's Siri, or Amazon's Alexa. Amazon has announced three new AI tools for anyone wanting to build apps on Amazon Web Services: Amazon Lex, Amazon Polly, and Amazon Rekognition. According to Amazon "This frees developers to focus on defining and building an entirely new generation of apps that can see, hear, speak, understand, and interact with the world around them." For those interested in developing apps, see our 20 Best App Development Colleges article. Google, Facebook, Amazon, Apple and Microsoft are all working on AI. Facebook's FAIR (Facebook Artificial Intelligence Research) program engages with academia to assist in solving long term problems in AI. Facebook is hiring AI experts around the world to assist in their project.

Let's be clear here: What Musk is proposing may be far-fetched, but it's a response to a very real problem that's going to affect a lot of people in the near future. Jobs that involve predictable manual labor are in danger of becoming obsolete. McKinsey & Company estimates that about 78 percent of those types of jobs (along with 69 percent of data processing and 64 percent of data collecting) could become completely automated. Driving-related jobs are likely to become increasingly automated as self-driving technology improves, and given that those were the most common jobs in 29 states as of 2014, we should absolutely be focused on finding a solution. But Musk has also floated a much simpler--and more realistic--solution.

Overwrought love letters began turning up on the notice board at the University of Manchester's computer lab in August, 1953. Dripping with lustful vocabulary, they were all variations on a basic syntactic template: "YOU ARE MY [adjective] [noun]. And the signatory was always the same: "M.U.C.," for the Manchester University computer, a Ferranti Mark 1, the world's first general-purpose and commercially available machine of its kind. But the real author of the letters (in the first instance, anyway) was Christopher Strachey, a pioneering programmer. As he confessed in an article the following year, "There are many obvious imperfections in this scheme (indeed very little thought went into its devising), and the fact that the vocabulary was largely based on Roget's Thesaurus lends a very peculiar flavor to the results." For Strachey, though, the interesting thing was how a simple setup, using only about seventy base words, could produce a combinatorial explosion of results--on the order of three hundred billion different letters. The lovelorn user could run the program over and over until his fingers seized up, and never see the same letter twice. Strachey was something of an outlier, according to Martin Campbell-Kelly, a historian of computing at the University of Warwick. While scientists and mathematicians of the day typically used computers strictly for numerical calculations, like analyzing weapons trajectories or seeking prime factors of huge numbers, his fascination was with non-numerical computations--what soon became known as artificial intelligence. "Strachey grabbed hold of that much more than anybody else," Campbell-Kelly told me. The results were not always lovey-dovey. Besides training the Mark 1 to churn out billets-doux, he also taught it to play checkers ("draughts," in British parlance). If M.U.C.'s opponent made too many mistakes, it would get crotchety and print out a reprimand: "I refuse to waste any more time.

Alexa, the talking lady who sits inside the Amazon Echo waiting for you to say her name, is having a personality makeover. The talking AI's latest update comes with a range of "speechcons" – little expressions or verbal tics. Alexa will say 100 new words, including "bazinga" and "woohoo". In a nod to nerd culture, she will range from "Kapow!" (Batman) to "Great Scott!" (Superman). She will even quote the Teenage Mutant Ninja Turtles ("Cowabunga") and The Godfather ("Bada bing!") if she needs to give deeper vent to her feelings.