Summer is winding down and that means the return to the classroom just around the corner, however, the school supply list isn't the only way to prep for the upcoming academic year. Outfitting your residence with smart home gadgets can make the busy back-to-school-season easier for the whole family. Does your student walk home from school? A digital smart lock can be a lifesaver if your child ever loses his or her house keys. Don't want your child fiddling with the air conditioner after coming home from soccer practice?

Game development is a complex task involving multiple disciplines and technologies. Developers and researchers alike have suggested that AI-driven game design assistants may improve developer workflow. We present a recommender system for assisting humans in game design as well as a rigorous human subjects study to validate it. The AI-driven game design assistance system suggests game mechanics to designers based on characteristics of the game being developed. We believe this method can bring creative insights and increase users' productivity. We conducted quantitative studies that showed the recommender system increases users' levels of accuracy and computational affect, and decreases their levels of workload.

User attributes provide rich and useful information for user understanding, yet structured and easy-to-use attributes are often sparsely populated. In this paper, we leverage dialogues with conversational agents, which contain strong suggestions of user information, to automatically extract user attributes. Since no existing dataset is available for this purpose, we apply distant supervision to train our proposed two-stage attribute extractor, which surpasses several retrieval and generation baselines on human evaluation. Meanwhile, we discuss potential applications (e.g., personalized recommendation and dialogue systems) of such extracted user attributes, and point out current limitations to cast light on future work.

Communication has been an essential part of human evolution. Communication enabled us to form groups, make tactics, plan ahead and essentially survive the wild. So, the importance of communication cannot be ruled out. It was only natural that humans would want to introduce such communicating abilities into the machines they made. With the advancements in A.I., each next generation of the system could hold up a longer conversation with humans than the previous system.

Check out the "Text, Language, and Speech" sessions at the O'Reilly Artificial Intelligence conference in London, 14-17 October 2019. Early price ends 6 September. As companies begin to explore AI technologies, three areas in particular are garnering a lot of attention: computer vision, natural language applications, and speech technologies. A recent report from the World Intellectual Patent Office (WIPO) found that together these three areas accounted for a majority of patents related to AI: computer vision (49% of all patents), natural language processing (NLP) (14%), and speech (13%). Companies are awash with unstructured and semi-structured text, and many organizations already have some experience with NLP and text analytics.

Get ready to say: "Hey Google. How much do I have in my bank account?" NatWest is to begin voice-only banking that will give customers direct access to their accounts by talking to the Google Home smart speakers now in millions of British homes. The trial – the first by a UK high street bank – will let customers ask Google "What's my balance?", "What's my latest transactions?" Google devices will answer verbally, and also flash the answers up on the customer's smartphone.

On paper, it's a great time to be on a dating app. In the seven years since Tinder's entrance on to the dating scene in 2012, it has gone from fringe novelty to romantic ubiquity; within two years of launching, it was seeing 1bn swipes a day. Other apps have similarly impressive stats: in 2018, Bumble's global brand director revealed it had more than 26 million users and a confirmed 20,000 marriages. It's a far cry from the considerably less optimistic response Tinder received when it launched. Many hailed it as the end of romance itself.

It's only been a few years since Amazon unveiled the Alexa-powered Echo, but since then, smart speakers have become a major consumer-electronics category. Key to its success is the notion of the always-on virtual assistant, which other companies like Apple and Google have adopted as well. In fact, not only has Google made Assistant the driving force behind its Android smartphones, it has launched its own line of Echo rivals. But underneath all of this technology is the potential risk to your privacy. In just the past few months, news reports have uncovered a series of alarming revelations that companies like Amazon, Google and even Apple have been listening in on conversations without permission.

Imagine if you had a version of Amazon's Alexa or Google Assistant inside your head, capable of feeding you external information whenever you required it, without you needing to say a single word and without anyone else hearing what it had to say back to you. An advanced version of this idea is the basis for future tech-utopian dreams like Elon Musk's Neuralink, a kind of connected digital layer above the cortex that will let our brains tap into hitherto unimaginable machine intelligence. Arnav Kapur, a postdoctoral student with the MIT Media Lab, has a similar idea. And he's already shown it off. The current AlterEgo device prototype looks a bit like one of those popstar Britney mics, as imagined by the designers of the Star Trek: The Next Generation TV show.

With the proliferation of social media platforms and e-commerce sites, several cross-domain collaborative filtering strategies have been recently introduced to transfer the knowledge of user preferences across domains. The main challenge of cross-domain recommendation is to weigh and learn users' different behaviors in multiple domains. In this paper, we propose a Cross-Domain collaborative filtering model following a Translation-based strategy, namely CDT. In our model, we learn the embedding space with translation vectors and capture high-order feature interactions in users' multiple preferences across domains. In doing so, we efficiently compute the transitivity between feature latent embeddings, that is if feature pairs have high interaction weights in the latent space, then feature embeddings with no observed interactions across the domains will be closely related as well. We formulate our objective function as a ranking problem in factorization machines and learn the model's parameters via gradient descent. In addition, to better capture the non-linearity in user preferences across domains we extend the proposed CDT model by using a deep learning strategy, namely DeepCDT. Our experiments on six publicly available cross-domain tasks demonstrate the effectiveness of the proposed models, outperforming other state-of-the-art cross-domain strategies.