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.

As the recently launched AI Monthly digest shows, significant improvements, breakthroughs and game-changers in machine learning and AI are months or even weeks away, not years. It is, therefore, worth the challenge to summarize and show the most significant AI trends that are likely to unfold in 2019, as machine learning technology becomes one of the most prominent driving forces in both business and society. According to a recent Deloitte study, 82% of companies that have already invested in AI have gained a financial return on their investment. For companies among all industries, the median return on investment from cognitive technologies is 17%. AI is transforming daily life and business operations in a way seen during previous industrial revolutions.

Within the next decade, Artificial Intelligence (AI) has the power to transform the wine industry forever. Right now, applications of AI for the wine industry are limited – but AI promises to be one of those immersive technologies that are embedded in everything we do, one way or another. According to a growing number of wine experts, AI could impact everything from how we buy wine, to how we grow vines in the vineyard, to how we judge wine. Think of how the Internet has revolutionized the wine industry in just the past two decades. It impacts how we learn about wine, how we order and buy wine online, and how we market wine to end consumers via social media platforms.

Artificial intelligence (AI) has become a part of our everyday conversation and lives. It is used when talking about smart and autonomous cars, in digital personal assistants like Siri and Alexa, and in video games. When it comes to incorporating AI into business technology there are obvious applications like business data analytics or customer service products. According to Brian Burke, chief of research at Gartner, AI is a mega-trend that the industry will continue to talk about for the next 20 years. Speaking at the 2018 Gartner Symposium/ITxpo, he said that AI is going to underlie pretty much everything that we do in technology.

Amazon customers can now adjust the speed of Alexa's voice to make the virtual assistant speak quicker or slower. Beginning today, customers in the US can ask the voice-activated smart speaker to converse slower or faster, depending on their needs. Bosses at the online company have created seven different settings – the standard speaking rate, four faster options and two slower modes. Users can simply simply say'Alexa, speak slower,' or'Alexa, speak faster' to adjust these. They can also reset it by asking it to respond at the default rate.

Home Assistant is a home automation platform running on Python 3. It is able to track and control all devices at home and offer a platform for automating control. Check out home-assistant.io for a demo, installation instructions, tutorials and documentation. The system is built using a modular approach so support for other devices or actions can be implemented easily. See also the section on architecture and the section on creating your own components. If you run into issues while using Home Assistant or during development of a component, check the Home Assistant help section of our website for further help and information.

Hundreds of schoolchildren have been drafted in to make Amazon's Alexa devices in China as part of a controversial and often illegal attempt to meet production targets, documents seen by the Guardian reveal. Interviews with workers and leaked documents from Amazon's supplier Foxconn show that many of the children have been required to work nights and overtime to produce the smart-speaker devices, in breach of Chinese labour laws. According to the documents, the teenagers – drafted in from schools and technical colleges in and around the central southern city of Hengyang – are classified as "interns", and their teachers are paid by the factory to accompany them. Teachers are asked to encourage uncooperative pupils to accept overtime work on top of regular shifts. Some of the pupils making Amazon's Alexa-enabled Echo and Echo Dot devices along with Kindles have been required to work for more than two months to supplement staffing levels at the factory during peak production periods, researchers found.

From the research findings, it seems that most people still believe in the importance of the human touch in their interactions with brands. In our survey, 44% said that they do not think AI will impact them positively in any way and 52% said that it would make dealing with brands more impersonal. You can take a look at this recent blog by my colleague Dave Pattman, Innovation Director at Webhelp, for a deeper dive into the research results, but as I read the results it made me think about some of the complexities of using AI in the customer service environment. When designing any customer service solution the ultimate objective is to deliver a fantastic customer experience (CX), so AI should really just be one more tool or option – why has it recently been under so much scrutiny? It's clear to me that we have been barrelling along what the industry analyst firm Gartner calls'the hype cycle' for the past couple of years with AI.