The newest artificial intelligence out of San Francisco-based tech firm Aeolus Robotics is a revolutionary robot that can pretty much second guess your family's movements, their identity, and even perform household duties from moping the floor to getting drinks out of the fridge. While the flying cars that were promised to us in The Jetsons are lamentably still on the drawing board, a digital domestic goddess by the likes of Rosie The Robot has actually arrived. The mechanical mate, which is expected to be available by the end of the year, will reportedly cost as much as a car at around $US20,000 ($25,000) and is described by Aeolus as being the height and weight of a 12 year old -- however undoubtedly more house-trained. Just like a creepy scene out of (insert favourite Sci Fi film here) this amazing android can distinguish between family members' faces, recognise where household items are supposed to go (then put them back in place) and can keep a sly eye open for emergencies like a fire or notice a change in posture and possibly prevent a fall. The yet to be officially named "Aeolus Robot" can also move furniture, find lost items and even learn the household schedule via an information sharing network.

Model-based methods for recommender systems have been studied to provide more precise results. In systems with large corpus, the amount of calculation for learnt model to predict all user-item pairs' preferences is tremendous, which makes the model difficult to be directly employed in recommendation candidate generation stage. To overcome the calculation barrier, models like matrix factorization can resort to inner product form (i.e., use the inner product of user and item's latent factors as the preference) and index like hashing to perform efficient approximate k-nearest neighbor search. However, other more expressive interaction forms between user and item features, e.g., interactions through advanced deep neural networks, are still prevented from large corpus recommendation because of the amount of calculation. In this paper, we focus on the problem how arbitrary advanced models can be introduced to generate recommendations from large corpus. We propose a novel tree-based method which can provide logarithmic complexity prediction w.r.t. corpus size with more expressive deep neural networks. The main idea of tree-based model is to predict user interests coarse-to-fine, by traversing tree nodes top-down and making decisions whether to pick up each node to user. Furthermore, we show that the tree structure can also be jointly learnt towards better compatible with user interests' distribution, to facilitate both training and prediction. Experiments in two large-scale real-world datasets indicate that the proposed model significantly outperforms traditional methods. And online A/B test results in Taobao display advertising platform prove the effectiveness of the tree-based deep model in production.

Yes, there's a lot to love about Alexa. The list of her skills grows daily. But one thing she can't do? Now, that might be by design, as some people would it find it creepy -- or just plain irritating --- to have Alexa start talking while they're in the middle of a phone call or TV show. Yet if your voice assistant, be it Amazon Alexa or Google Home, could start talking of its own volition, it would actually be very useful.

The march of Amazon's Alexa into the automotive world continues. At CES in Las Vegas on Tuesday, Toyota announced the voice technology would be incorporated with the carmaker's infotainment systems. Alexa will debut in "select Toyota and Lexus vehicles with Toyota Entune 3.0 App Suite and Lexus Enform App Suite 2.0 in 2018," Toyota said in a statement. Lexus is the company's luxury brand. The statement continued: "Additional models will be available in 2019. This feature will allow Toyota and Lexus customers in the United States to interact with Alexa in the car."

Voice is starting to redefine how consumers buy brands. Brands will increasingly have to navigate a world that doesn't have a traditional user interface -- zero UI. As Google CEO Sundar Pichai put it, "we will move from mobile first to an AI first world." Walker Sands recently reported that one in five (19%) have made a voice purchase through Amazon Echo or similar device and another third (33%) plan to do so in the next year. According to ComScore, 50% of all searches will be voice searches by 2020.

Learn about robotics using a kit to build your own robotic assistant. Futurism's mission is to empower our readers and drive the development of transformative technologies towards maximizing human potential.

Artificial intelligence and machine learning have leapt off the pages of science fiction novels and burst into the real world. These technologies have game-changing implications for businesses of all sizes, whether you're planning to implement them yourself or contemplate the consequences of their adoption on the world at large. With these fields moving so fast, it's hard to stay on top of big changes, let alone smaller advances that can affect IT organizations and you personally. That's where AI and machine learning conferences come in. There's no better way to advance your career, learn new AI and ML skills, make new human connections, and maybe some non-human ones as well.

Artificial intelligence and machine learning technologies are being integrated into many aspects of our everyday lives. If you use Siri or Amazon Echo, you've already been touched by AI to some degree. One area where this so-called "smart" technology has become particularly valuable is in the realm of cybersecurity. But despite the buzz, it's important to understand the real capabilities of intelligent automation in security. Many are surprised to learn that artificial intelligence in cybersecurity isn't a new concept.

The Apple Campus 2 is seen under construction in Cupertino, California in this aerial photo taken January 13, 2017. The case against Big Tech seems to be building by the week. And interestingly, some of the most powerful evidence is being provided by those who really know what they're talking about: tech insiders. Full disclosure: I am a tech insider myself. I run a tech company in Silicon Valley.

We all know that today's state-of-the-art AI can learn all sorts of things. It can learn to recognize kittens in YouTube videos or, perhaps more importantly, how to characterize traffic around an autonomous vehicle. All of us use speech at some point talking to our phones, our Google Home, our TV remote, or our Amazon Alexa. Of course, it does many other things to like stock predictions, robot walking, delivery routing... there are just too many to list. But most of what we read about in the regular press involves vision and occasionally human speech.