What do Google Assistant, Siri, Alexa, and Cortana have in common? They tell jokes of varying cleverness, most of which are the work of writing teams operating behind the scenes. They're entertaining, but preliminary research suggests they also play a part in making interactions with assistants engaging. In pursuit of assistants capable of tailoring jokes to individual users' tastes, Amazon researchers investigated joke selection methods that tap either a basic natural language processing model or a machine learning model. They say that when tested against production data, both approaches "positively" impacted user satisfaction and potentially improved joke-telling.

We propose a scalable Bayesian preference learning method for jointly predicting the preferences of individuals as well as the consensus of a crowd from pairwise labels. Peoples' opinions often differ greatly, making it difficult to predict their preferences from small amounts of personal data. Individual biases also make it harder to infer the consensus of a crowd when there are few labels per item. We address these challenges by combining matrix factorisation with Gaussian processes, using a Bayesian approach to account for uncertainty arising from noisy and sparse data. Our method exploits input features, such as text embeddings and user metadata, to predict preferences for new items and users that are not in the training set. As previous solutions based on Gaussian processes do not scale to large numbers of users, items or pairwise labels, we propose a stochastic variational inference approach that limits computational and memory costs. Our experiments on a recommendation task show that our method is competitive with previous approaches despite our scalable inference approximation. We demonstrate the method's scalability on a natural language processing task with thousands of users and items, and show improvements over the state of the art on this task. We make our software publicly available for future work.

Smart home devices are designed to make our lives easier, but they also make it easier for hackers to infiltrate our lives. The FBI has sent out a warning that'hackers can use those innocent devices to do a virtual drive-by of your digital life.' The US intelligence agency urges users to regularly change passwords, check for firmware updates and never have two devices on the same network. Digital assistants, smart watches, fitness trackers, home security devices, thermostats, refrigerators, and even light bulbs are all on the list of devices that can be infiltrated by cybercriminals. And if these devices, among other smart home technology, are not properly protected, they can be used by hackers to'do a virtual drive-by of your digital life.' Samsung are developing an interactive kitchen that includes a fridge, oven and TV.

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"Alexa" was just another female name. Uber hadn't taken anyone for a ride yet. And the buzz around Facebook had more to do with the fact that seemingly everyone you once knew was turning up on "The Social Network," and less about the numerous data and privacy scandals that would tarnish the company's reputation later on. The year was 2010, the dawn of a new decade. And while 10 years is a long time for most every industry, in consumer tech it might as well be a lifetime.

So you've been optimizing your email programme for a while now, you've segmented your database, developed customer personas, and you've implemented a range of triggered campaigns. However, your competitors have also raised their game, and the'business as usual' option just won't cut it anymore. You're looking for something that gives you the edge and helps take email campaign performance to the next level. Artificial Intelligence (AI) is already helping marketers by serving personalized content at scale, reducing campaign production times, and enabling them to boost revenues and engagement. Forrester Research has predicted that businesses who use AI to drive marketing will gain $1.2 trillion per annum from those who don't.

Me: "Alexa, tell me what will happen in 2020." Amazon AI: "Here's what I found on Wikipedia: The 2020 UEFA European Football Championship…[continues to read from Wikipedia]" Me: "Alexa, give me a prediction for 2020." Amazon AI: "The universe has not revealed the answer to me." Well, some slight improvement over last year's responses, when Alexa's answer to the first question was "Do you want to open'this day in history'?" As for the universe, it is an open book for the 120 senior executives featured here, all involved with AI, delivering 2020 predictions for a wide range of topics: Autonomous vehicles, deepfakes, small data, voice and natural language processing, human and augmented intelligence, bias and explainability, edge and IoT processing, and many promising applications of artificial intelligence and machine learning technologies and tools. And there will be even more 2020 AI predictions, in a second installment to be posted here later this month. "Vehicle AI is going to be ...

All the world's tech giants from Alibaba to Amazon are in a race to become the world's leaders in artificial intelligence (AI). These companies are AI trailblazers and embrace AI to provide next-level products and services. Here are 10 of the best examples of how these companies are using artificial intelligence in practice. Chinese company Alibaba is the world's largest e-commerce platform that sells more than Amazon and eBay combined. Artificial intelligence (AI) is integral in Alibaba's daily operations and is used to predict what customers might want to buy.

Latent factor models have been used widely in collaborative filtering based recommender systems. In recent years, deep learning has been successful in solving a wide variety of machine learning problems. Motivated by the success of deep learning, we propose a deeper version of latent factor model. Experiments on benchmark datasets shows that our proposed technique significantly outperforms all state-of-the-art collaborative filtering techniques.

Me: "Alexa, tell me what will happen in 2020." Amazon AI: "Here's what I found on Wikipedia: The 2020 UEFA European Football Championship…[continues to read from Wikipedia]" Me: "Alexa, give me a prediction for 2020." Amazon AI: "The universe has not revealed the answer to me." Well, some slight improvement over last year's responses, when Alexa's answer to the first question was "Do you want to open'this day in history'?" As for the universe, it is an open book for the 120 senior executives featured here, all involved with AI, delivering 2020 predictions for a wide range of topics: Autonomous vehicles, deepfakes, small data, voice and natural language processing, human and augmented intelligence, bias and explainability, edge and IoT processing, and many promising applications of artificial intelligence and machine learning technologies and tools. And there will be even more 2020 AI predictions, in a second installment to be posted here later this month. "Vehicle AI is going to be ...