How did you go about creating something that works so well? I can't imagine that your first version of this was such a smooth transaction for users. We had a few truths that we believed in where we've designed the product with these as the backdrop from day one. The first one is that we want this to be invisible software - we don't want this to be yet another app, a plugin, an extension, a web service, a Doodle, a something where you have to do additional work. We wanted something where we see software disappear and you hand over the job to that agent and that agent runs away, works on it and comes back once solved. That was the first battle.

We present a method for estimating causal effects in time series data when fine-grained information about the outcome of interest is available. Specifically, we examine what we call the split-door setting, where the outcome variable can be split into two parts: one that is potentially affected by the cause being studied and another that is independent of it, with both parts sharing the same (unobserved) confounders. We show that under these conditions, the problem of identification reduces to that of testing for independence among observed variables, and present a method that uses this approach to automatically find subsets of the data that are causally identified. We demonstrate the method by estimating the causal impact of Amazon's recommender system on traffic to product pages, finding thousands of examples within the dataset that satisfy the split-door criterion. Unlike past studies based on natural experiments that were limited to a single product category, our method applies to a large and representative sample of products viewed on the site. In line with previous work, we find that the widely-used click-through rate (CTR) metric overestimates the causal impact of recommender systems; depending on the product category, we estimate that 50-80\% of the traffic attributed to recommender systems would have happened even without any recommendations. We conclude with guidelines for using the split-door criterion as well as a discussion of other contexts where the method can be applied.

Many of us grew up watching the incredible potential of robots in movies and television. Robots showed us the brightest of the future. However, we haven't really seen the true potential of robotic technology being unleashed. While the popular TV cartoon show, Jetsons from the 60's showed us the infinite possibilities of humans and robots co-existing, movies like the Terminator or Wall-E scared us by showing the prospective dark side of Artificial Intelligence and technology. But the age where robots finally coexist with human beings is not really far.

Cortana might be super helpful at keeping track of your shopping lists, but it turns out it's not so great at keeping your PC secure. Researchers from McAfee have discovered that by activating Cortana on a locked Windows 10 machine, you can trick it into opening up a contextual menu which can then be used for code execution. This could deploy malicious software, or even reset a Windows account password. The vulnerability stems from Cortana's ability to listen for commands even while the PC is locked, combined with regular indexing that makes files accessible in the search function that Cortana uses. Even though potential hackers would need physical access to your PC to do any damage, this could feasibly take place in an office or shared environment.

Voice AI started as a method of convenience. Sending a text message or email through voice apps became a more natural and faster way of communicating than typing. Asking Siri to give you directions to the nearest Starbucks became a more efficient alternative than looking it up yourself. While these tools are still convenient, they have slowly become our "normal" and are no longer seen as "groundbreaking." Hence, we are impatiently awaiting the next wave of voice-powered capabilities, but what will that be?

Over the next five years, we are about to witness the world we live in entirely disrupted by improvements in artificial intelligence (AI) and machine learning. Children today are growing up with AI assistants in their homes (Google Assistant, Siri and Alexa) -- to the point that you might consider their mere presence an extension of co-parenting. As voice and facial recognition continue to evolve, machine learning algorithms are getting smarter. More and more industries are being influenced by AI, and our society as we know it is transforming. The transportation industry looks like it will be the first to be completely disrupted by artificial intelligence.

Moving to a new country can be hard. You don't know the language. Cultural differences create conversational landmines. And you just can't be sure that everyone will like you. As it turns out, that as true for people as it is for Amazon's Alexa voice assistant, which officially sets up residence in France today.

"I'm sorry George, I'm afraid I don't have an answer for that!" Recently I overheard my 6 year old son, George asking Siri if she had a willy. For those of you not familiar with either small children or colloquial English, try it yourselves, and see what happens! We will no doubt all have seen the amazing demo from Google and, whether it was as'real' as Google made out, this tech is coming. Microsoft also have one just in China for now, but again it looks amazing.These two tech giants are investing big and your future workforce i.e.

The FBI is asking for your assistance in finding Sydney Loofe. Sydney was last seen on the evening of November 16th. If you have any information please call 402-493-8688 option 1. Help us bring Sydney home. Two people accused of killing and dismembering a woman one of them met on a Tinder date appeared in a Nebraska court Tuesday on first-degree murder charges. Aubrey Trail, 51, and Bailey Boswell, 23, appeared in Saline County Court to face the charges related to the killing of Sydney Loofe, 24.

Video-game players generate huge amounts of data, as everything they do within a game is recorded. In particular, among all the stored actions and behaviors, there is information on the in-game purchases of virtual products. Such information is of critical importance in modern free-to-play titles, where gamers can select or buy a profusion of items during the game in order to progress and fully enjoy their experience. To try to maximize these kind of purchases, one can use a recommendation system so as to present players with items that might be interesting for them. Such systems can better achieve their goal by employing machine learning algorithms that are able to predict the rating of an item or product by a particular user. In this paper we evaluate and compare two of these algorithms, an ensemble-based model (extremely randomized trees) and a deep neural network, both of which are promising candidates for operational video-game recommender engines. Item recommenders can help developers improve the game. But, more importantly, it should be possible to integrate them into the game, so that users automatically get personalized recommendations while playing. The presented models are not only able to meet this challenge, providing accurate predictions of the items that a particular player will find attractive, but also sufficiently fast and robust to be used in operational settings.