With the Apple HomePod, the cotton that has been in our ears since the arrival of the first smart speaker has been removed. The HomePod sounds far better than the popular smart speakers from Amazon, Google--and even Sonos. That's what I've been asking myself during my week testing the HomePod, which goes on sale Friday for $350. In the last three years, Amazon Echo and Google Home have set tens of millions of us at ease with speakers that listen for our commands. Of course, Apple has a long history of crushing incumbents--see MP3 players and smartphones.

Instead, she went on, "we call it GPS." And it's a significant truth because it means the world of AI stays mired in the average person's mind as something of a science fiction-type character -- a Terminator programmed to kill, a Matrix hero designed to liberate, a Star Wars robot set to serve. But AI is not one and the same as a robot. Simply put, AI is everywhere. And people ought to know.

"If ever there was a time for the people creating technologies to keep in mind the impact of their creations, it's now," my colleague Nick Bilton wrote last year, in the wake of the 2016 election, warning that an influx of new technologies was about to make the Internet's misinformation plague 10 times worse. Bilton's fears have been echoed more recently; in an interview with BuzzFeed News earlier this month, Aviv Ovadya, who predicted the fake-news crisis, said that it's likely to accelerate with the advent of more advanced digital tools able to falsify reality and manipulate perception. "We are so screwed it's beyond what most of us can imagine," he said. "And depending how far you look into the future it just gets worse." As modern tech companies struggle to get a handle on extant problems, a new paper authored by 26 technologists, researchers, scientists, and academics, supports the premise that when it comes to new technologies, humankind may be biting off more than it can chew.

Last quarter, the market size of smart home speakers grew by 30 percent. According to a report published by CIRP, the number of installed Amazon Echo speaker devices is 20 million units and Google Home devices is 7 million units. Last quarter, 7 million smart speakers were sold in the USA.

"Fail fast" is a well-known phrase in the startup scene. The spirit of failing fast is getting to market with a minimum viable product and then rapidly iterating toward success. Failing fast acknowledges that entrepreneurs are unlikely to design a successful end-state solution before testing it with real customers and real consequences. This is the "ready, fire, aim" approach. Or, if the blowback is big enough, it's the "ready, fire, pivot" approach.

An important metric of users' satisfaction and engagement within on-line streaming services is the user session length, i.e. the amount of time they spend on a service continuously without interruption. Being able to predict this value directly benefits the recommendation and ad pacing contexts in music and video streaming services. Recent research has shown that predicting the exact amount of time spent is highly nontrivial due to many external factors for which a user can end a session, and the lack of predictive covariates. Most of the other related literature on duration based user engagement has focused on dwell time for websites, for search and display ads, mainly for post-click satisfaction prediction or ad ranking. In this work we present a novel framework inspired by hierarchical Bayesian modeling to predict, at the moment of login, the amount of time a user will spend in the streaming service. The time spent by a user on a platform depends upon user-specific latent variables which are learned via hierarchical shrinkage. Our framework enjoys theoretical guarantees, naturally incorporates flexible parametric/nonparametric models on the covariates and is found to outperform state-of- the-art estimators in terms of efficiency and predictive performance on real world datasets.

Tired of seeing his friends and family sharing questionable content on his Facebook feed, data scientist Zach Estela decided to take action. He built a tool that scans a website's most recent 100 posts and analyzes it to determine whether it's fake news, heavily biased, or a legit news source. "I see my friends post, sometimes, complete garbage or articles recommended to me that are complete garbage," Estela told me over the phone. As fake new purveyors have become more ubiquitous, they've also gotten more sophisticated. It's sometimes hard to tell if a news source is a small local paper, a Russian-backed propaganda forum, or a semi-accurate hype blog that only reports the facts when they align with its agenda. While companies like Google and Facebook have tried to come up with ways to flag shady content, a lot of stuff can still fall through the cracks, especially when we rely on human judgement.

I've first heard of deepfakes a good week ago. You have been warned) consists of people using an app created by user "deepfakes" to create fake celebrity porn. This has caused a shitstorm on the Internet, media discussing the legality of it all, websites taking down the deepfake creations, and people panicking as they realise AI is going to screw us all up (newsflash: it's already been happening in much less obvious ways). And meanwhile, Nicolas Cage is taking over Hollywood. While everyone's debating whether this is good or bad, I just had to find out more.

Artificial intelligence is appearing in a growing range of products, industries and services, so that even the most traditional markets will be impacted, and any growing company can use AI to help boost their performance. Here are some of the cutting edge areas where AI could make a massive impact and will spill over into other industries. Machines watching other machines is a common thread in the 21st century. The Internet of Things could soon see every piece of technology linked together. For now though AI is being used by many IT companies to improve performance.

We began our journey to predicting this year's best picture winner by collecting and cleaning lots of data. From critic ratings to performance at precursors, we looked for any and all publicly available information about the films that had been nominated for Best Picture over the past X years. This data would help inform our algorithm which we would build using SciKit Learn, one of the most popular learning toolkits in the world.