When the Amazon Prime Day deals roll into town in July, it's a no-brainer that the Amazon Echo Dot will get a big discount like it does every year. The thing is, it'll be nowhere near as good as this one - and it ends at 9am tomorrow. This is a UK-only deal, but we've rounded up the latest top deals in the US over on our Memorial Day sales page. UK readers, be sure to check out the Bank Holiday sales this weekend too. We're expecting Prime Day to bring the Echo Dot down to £29.99 as per usual, or £24.99 at the very best if Google aggressively price cuts the Google Home Mini as the search giant can't help but wind Amazon up any chance it gets with its line of rival smart speakers.

Playing with my Lego, as a child, I would build human-like figures. I would create a whole cast of goodies and baddies, who would invariably end up fighting. The goodies always spoke with a North American drawl, while the baddies spoke English with heavy foreign accents. The very few female characters in my games were either shrieking, hyper-feminine princesses who needed saving, or near-voiceless helpers who looked after the base and cared for the wounded heroes. My bedroom carpet was a showground for the stereotypes of the day.

The future of car traffic is self-propelled or at least much more automated than before. Keeping an eye on the many variables and possible situations requires exactly the qualities that a well-designed AI system brings with it. In this way, traffic runs more smoothly and, above all, more safely for all concerned. This is not even about your own vehicle. In China, for example, artificial intelligence is used to dynamically and automatically control traffic light circuits so that ambulances, police or fire brigades can arrive at the scene more quickly and provide assistance.

Just ask the creators of Alexa or Siri and they will confirm how important their dulcet tones are. Imagine the strain of having Amitabh Bachchan rasp out the weather report in the morning and answering sundry other queries throughout the day. For those still sceptical about the significance of a soothing timbre, there are the findings of a recent Unesco report that examines the implications of the charming feminine voice that almost all virtual assistants have been blessed with. The report noted that technology companies justify the use of obliging female voices by citing surveys that show that this is what consumers of both sexes prefer. What is seldom mentioned is that the same surveys show that people like the sound of a male voice when authoritative statements are being made and a female voice when help is being offered.

As a world class tourist destination, Spain is considered to have it all; from a rich vibrant culture, world heritage sights, and a culinary scene fit for any foodie, Spain has something for everyone. On the tech front, Spain is a rising star as its startup scene is becoming the country's most flourishing sector. However, let us rewind approximately 10 years to the global financial crisis that took the world by storm. Spain was heavily hit by the 2008 global financial crisis, when the housing market crashed, leaving half-finished projects scattered from the suburbs of Madrid to the shores of the Mediterranean coastline. The sense of revival in Spain is clearer than the waters off Barcelona's coastline.

Google Assistant turns three this year. Whether you love it or not, it's the AI for everyone. Google's intelligent little helper has been powering almost every other smartphone for the past few years, getting better and smarter each day. Despite its dominance on mobile devices, Google shows no signs of slowing down. Aggressive marketing, widespread device integration and new innovations show where Google is placing its eggs.

Artificial Intelligence (AI) is the new electricity of our times. That's what Chris Duffey, creative technologist says about this incredible technology revolutionizing industries the world over. His new book Superhuman Innovation showcases how AI will supercharge the workforce, the world of work, and can be harnessed to deliver powerful change. It is a practical guide to how AI and Machine Learning are impacting not only how businesses, brands, and agencies innovate, but also what they innovate: products, services and content. In this world of product and pricing parity, the delivery of superior service experience has become the new marketing, and the new real competitive edge. Superhuman Innovation discusses how AI will serve the superstar innovators of tomorrow by enabling them to see deeper insights and set sail for higher goals.

Typical graph embeddings may not capture type-specific bipartite graph features that arise in such areas as recommender systems, data visualization, and drug discovery. Machine learning methods utilized in these applications would be better served with specialized embedding techniques. We propose two embeddings for bipartite graphs that decompose edges into sets of indirect relationships between node neighborhoods. When sampling higher-order relationships, we reinforce similarities through algebraic distance on graphs. We also introduce ensemble embeddings to combine both into a "best of both worlds" embedding. The proposed methods are evaluated on link prediction and recommendation tasks and compared with other state-of-the-art embeddings. Our embeddings are found to perform better on recommendation tasks and equally competitive in link prediction. While being all highly beneficial in applications, we demonstrate that none of the existing state-of-the-art or our embeddings is clearly superior (in contrast to what is claimed in many papers), and discuss the trade offs present among them.

Recommender systems (RS), which have been an essential part in a wide range of applications, can be formulated as a matrix completion (MC) problem. To boost the performance of MC, matrix completion with side information, called inductive matrix completion (IMC), was further proposed. In real applications, the factorized version of IMC is more favored due to its efficiency of optimization and implementation. Regarding the factorized version, traditional IMC method can be interpreted as learning an individual representation for each feature, which is independent from each other. Moreover, representations for the same features are shared across all users/items. However, the independent characteristic for features and shared characteristic for the same features across all users/items may limit the expressiveness of the model. The limitation also exists in variants of IMC, such as deep learning based IMC models. To break the limitation, we generalize recent advances of self-attention mechanism to IMC and propose a context-aware model called collaborative self-attention (CSA), which can jointly learn context-aware representations for features and perform inductive matrix completion process. Extensive experiments on three large-scale datasets from real RS applications demonstrate effectiveness of CSA.

Recommending personalized learning materials for online language learning is challenging because we typically lack data about the student's ability and the relative difficulty of learning materials. This makes it hard to recommend appropriate content that matches the student's prior knowledge. In this paper, we propose a refined hierarchical knowledge structure to model vocabulary knowledge, which enables us to automatically organize the authentic and up-to-date learning materials collected from the internet. Based on this knowledge structure, we then introduce a hybrid approach to recommend learning materials that adapts to a student's language level. We evaluate our work with an online Japanese learning tool and the results suggest adding adaptivity into material recommendation significantly increases student engagement.