If you've spoken to Amazon's Alexa voice assistant through the Alexa app or an Echo device, you may have wondered who the woman is behind the speaker. Do you think that Alexa is voiced by a celebrity or an Amazon employee? You might be surprised to know that the voice of Alexa is not formed from any real person. Rather, Alexa's voice is generated by artificial intelligence. Alexa's voice was developed using special software that evolved from text-to-speech technology.

Alexa can tell you the weather, turn on your lights and even tell you a joke. But if you really want to have a meaningful conversation with a computer, it is probably going to have to be a quantum computer. Cambridge Quantum Computing (CQC), the 2014-founded startup, says it has built "meaning-aware" natural language processing on a quantum computer. The system understands both grammatical structure and the meaning of words, in a way that classical computers cannot. "This is quantum native, it cannot be done with a classical computer with a reasonable amount for resources."

The voice-first community on social media continues to grow almost as quickly as the adoption of voice AI technology itself. In 2018, we identified 15 voice AI influencers to follow on Twitter and heard from a lot of our readers on how much they loved the list--an inspiring mix of engineers, entrepreneurs, academics, linguists and journalists. Two years later, we're updating our list to include 40 experts we recommend you follow to keep up with the latest in voice AI news, trends, predictions, and successes. As we are all in planning mode for 2021, we thought this would be a good time to publish a new list to help inspire you. If you know someone who should be included in our next list, please let us know.)

In many ways, artificial intelligence is the face of the technological future. And yet, there is still so much untapped potential. One area where the AI has been able to run a bit freer, though, is in the fanciful realm of cinema. Movies have been busily employing elaborate machine learning, robotics, and neural networks in various forms of fiction for decades now. Nowhere has this been more heavily on display than in superhero movies -- especially Marvel's ever-expanding cinematic universe.

In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature. However, despite being extensively studied, these sequential methods still suffer from three limitations. First, existing methods mostly utilize attention on the behavior of users, which is not always suitable for CTR prediction, because users often click on new products that are irrelevant to any historical behaviors. Second, in the real scenario, there exist numerous users that have operations a long time ago, but turn relatively inactive in recent times. Thus, it is hard to precisely capture user's current preferences through early behaviors. Third, multiple representations of user's historical behaviors in different feature subspaces are largely ignored. To remedy these issues, we propose a Multi-Interactive Attention Network (MIAN) to comprehensively extract the latent relationship among all kinds of fine-grained features (e.g., gender, age and occupation in user-profile). Specifically, MIAN contains a Multi-Interactive Layer (MIL) that integrates three local interaction modules to capture multiple representations of user preference through sequential behaviors and simultaneously utilize the fine-grained user-specific as well as context information. In addition, we design a Global Interaction Module (GIM) to learn the high-order interactions and balance the different impacts of multiple features. Finally, Offline experiment results from three datasets, together with an Online A/B test in a large-scale recommendation system, demonstrate the effectiveness of our proposed approach.

One of the most popular Machine-Leaning course is Andrew Ng's machine learning course in Coursera offered by Stanford University. I tried a few other machine learning courses before but I thought he is the best to break the concepts into pieces make them very understandable. But I think, there is just only one problem. That is, all the assignments and instructions are in Matlab. I am a Python user and did not want to learn Matlab.

What to buy the tech enthusiast in your life? Here are some ideas – from smart speakers and games consoles to smartwatches and headphones. With Christmas rapidly approaching, time is running out to buy the big-ticket items, so we have also included some instant-delivery gifts for last-minute purchases. Google's cheapest little smart speaker looks like a pincushion and is great for playing the radio, answering questions and controlling smart home devices. It comes in four colours and can even be wall mounted. The best-sounding smart speaker under £100 is often available for far less in sales.

Online dating is rapidly changing as technology progresses in our society. It has become a more popular and more accessible way to meet people and express attraction. While dating apps open up new opportunities, especially during this time of social distancing, the majority of online daters are still struggling with the process of online dating and the reality of harassment. Romantic attraction is difficult to predict. While data on personality traits like the Big Five and attachment types can effectively predict how much individuals want to be in partnerships and how desirable they may be as partners, romantic and sexual compatibility and relationship longevity are difficult to trace and anticipate.

The original SpotCam Eva, which we reviewed in 2016, was a pretty clunky camera, both in terms of its bulky design and its unpolished performance. It was apparent, however, that it contained the seeds of something better, and those have come to fruition in the SpotCam Eva 2. The rebooted camera has a "snowman" shape that's neither particularly attractive nor an eyesore, but it's smaller and easier to hide in plain sight than the original. It also has a wider field of view (130 degrees) and adds a built-in siren and two-way talk capabilities, Amazon Alexa and Google Home integration, and it supports automatic person tracking. Pan-and-tilt is still its core feature, but this feature works much better than it did in the previous iteration. The first evidence that the Eva 2 had improved on its predecessor surfaced during the setup process.

User modeling is critical for developing personalized services in industry. A common way for user modeling is to learn user representations that can be distinguished by their interests or preferences. In this work, we focus on developing universal user representation model. The obtained universal representations are expected to contain rich information, and be applicable to various downstream applications without further modifications (e.g., user preference prediction and user profiling). Accordingly, we can be free from the heavy work of training task-specific models for every downstream task as in previous works. In specific, we propose Self-supervised User Modeling Network (SUMN) to encode behavior data into the universal representation. It includes two key components. The first one is a new learning objective, which guides the model to fully identify and preserve valuable user information under a self-supervised learning framework. The other one is a multi-hop aggregation layer, which benefits the model capacity in aggregating diverse behaviors. Extensive experiments on benchmark datasets show that our approach can outperform state-of-the-art unsupervised representation methods, and even compete with supervised ones.