USA TODAY's Jefferson Graham reviews the 2018 year in tech, which includes the most popular apps, binge-worthy TV shows and much more USA TODAY We didn't see every product released -- but we did see, touch and feel a lot of them. Several of the ones mentioned here I went out and bought after I reviewed them -- others I lusted after. Let's work our way from 10 to 1. Unlike smartphones, which hit ridiculous prices in 2018, many topping $1,000, you've got to love what LaCie is selling you for a portable, rugged hard drive. I picked up a 4 terabyte external drive for $150. Two years ago, these drives were 1 TB at best for the same price, and now I've got four times as much room.

There is no shortage of articles attempting to lay out a step-by-step process of how to become a data scientist. Are you a recent graduate? Do this… Are you changing careers? Do that… And make sure you're focusing on the top skills: coding, statistics, machine learning, storytelling, databases, big data… Need resources? Check out Andrew Ng's Coursera ML course, …". Although these are important things to consider once you have made up your mind to pursue a career in data science, I hope to answer the question that should come before all of this. It's the question that should be on every aspiring data scientist's mind: "should I become a data scientist?" This question addresses the why before you try to answer the how. What is it about the field that draws you in and will keep you in it and excited for years to come? In order to answer this question, it's important to understand how we got here and where we are headed. Because by having a full picture of the data science landscape, you can determine whether data science makes sense for you. Before the convergence of computer science, data technology, visualization, mathematics, and statistics into what we call data science today, these fields existed in siloes -- independently laying the groundwork for the tools and products we are now able to develop, things like: Oculus, Google Home, Amazon Alexa, self-driving cars, recommendation engines, etc. The foundational ideas have been around for decades... early scientists dating back to the pre-1800s, coming from wide range of backgrounds, worked on developing our first computers, calculus, probability theory, and algorithms like: CNNs, reinforcement learning, least squares regression. With the explosion in data and computational power, we are able to resurrect these decade old ideas and apply them to real-world problems. In 2009 and 2012, articles were published by McKinsey and the Harvard Business Review, hyping up the role of the data scientist, showing how they were revolutionizing the way businesses are operating and how they would be critical to future business success. They not only saw the advantage of a data-driven approach, but also the importance of utilizing predictive analytics into the future in order to remain competitive and relevant. Around the same time in 2011, Andrew Ng came out with a free online course on machine learning, and the curse of AI FOMO (fear of missing out) kicked in. Companies began the search for highly skilled individuals to help them collect, store, visualize and make sense of all their data. "You want the title and the high pay?

Voice powered assistants like Cortana, Siri and the Google Assistant have surpassed their initial developmental stages to become helpful tools rather than a source of amusement. It's possible that, one day, all of our technological commands could be issued via speech. Here are five sector wide organisations already making the most of voice technology, starting, of course, with Amazon. Having developed the hugely popular Alexa platform, Amazon remains one of the pioneers of voice technology. Alexa powers Amazon's smart home Echo devices, which gradually learn about owner preferences and deliver relevant suggestions.

Earlier this year, Google redesigned Maps to add a bunch of features, including personalized suggestions based on your tastes and interests. Now, that feature will be more broadly available as Google is bringing the For You tab to iOS in more than 40 countries, and more than 130 additional nations on Android. The feature can provide you with news and updates on events, activities, openings, pop-ups and even menu changes in neighborhoods, restaurants and stores you follow. It also suggests new places for you to check out. The update is rolling out today, and it follows the arrival of the Group Planning feature in September, which helps you and your friends decide where to have dinner or hang out using a simple voting system.

Thanks to an update from Amazon, Alexa devices can perform time- and location-based routines and reminders, triggered at a given time or when you enter for leave a specific area. The update, which includes some features first introduced earlier this year, also adds the ability to place calls to businesses in your city and provide a summarized version of your emails. To make the new location-based features work, you'll need to update the Alexa app on your phone and give it access to your location. Once you've done that, you can set up routines or reminders that will kick in when you cross over a geofenced location -- like turning off your internet-connected lights when you walk out of the door. Similarly, if you set up a reminder based on location ("Alexa, remind me to take out the trash when I get home), Alexa will provide it to you on cue.

A north Phoenix resident said he wants to warn others that these devices aren't as secure as you may think. Andy Gregg, a real-estate agent in north Phoenix, says a hacker spoke to him through his Nest security camera. Andy Gregg was in his back yard a few weeks ago when he heard a voice he didn't recognize inside his house. It was dark, and Gregg, who lives in the north Phoenix, said his first thought was somebody had broken into his home. The source of the voice surprised him: It was coming from a Nest Cam IQ security camera in his front window.

Industry-scale recommendation systems have become a cornerstone of the e-commerce shopping experience. For Etsy, an online marketplace with over 50 million handmade and vintage items, users come to rely on personalized recommendations to surface relevant items from its massive inventory. One hallmark of Etsy's shopping experience is the multitude of ways in which a user can interact with an item they are interested in: they can view it, favorite it, add it to a collection, add it to cart, purchase it, etc. We hypothesize that the different ways in which a user interacts with an item indicates different kinds of intent. Consequently, a user's recommendations should be based not only on the item from their past activity, but also the way in which they interacted with that item. In this paper, we propose a novel method for learning interaction-based item embeddings that encode the co-occurrence patterns of not only the item itself, but also the interaction type. The learned embeddings give us a convenient way of approximating the likelihood that one item-interaction pair would co-occur with another by way of a simple inner product. Because of its computational efficiency, our model lends itself naturally as a candidate set selection method, and we evaluate it as such in an industry-scale recommendation system that serves live traffic on Etsy.com. Our experiments reveal that taking interaction type into account shows promising results in improving the accuracy of modeling user shopping behavior.

Budget-priced robot vacuums with advanced features remain something of a novelty. Some earlier Ecovacs models have defied convention with a smattering premium perks, such as app control Google Home and Amazon Alexa integration. The company did this exceptionally well with its Deebot N79S, our current top pick in the budget category. The Deebot 601, which includes all those aforementioned perks, isn't quite as successful. The 601 measures 13.9 by 13.9 by 3.27 inches, with a low enough profile to get beneath most low-clearance furniture and edge its way under cabinets.

Consultants Deloitte in their predictions for 2018 expect more store closures but point to three strategies that retailers should adopt if they are to be successful. Research, however, reveals that so far, experience of these applications has not met expectations. More than half of 1,000 consumers surveyed across the UK said the AR solutions they had used had not given them a great experience. For the time being, this is not too damaging to business, since only 27 per cent of respondents expect to see the technology in a store within 12 months. This gives retailers a breathing space in which to get it right.

Me: "Alexa, tell me what will happen in 2019." Amazon AI: "Do you want to open'this day in history'?" Me: "Alexa, give me a prediction for 2019." Amazon AI: "The crystal ball is clouded, I can't tell." My conversation with Amazon's "smart speaker" or "intelligent voice assistant" just about sums up the present state of "artificial intelligence" (AI) at home, the office, and the factory: Try a few times and sooner or later you will probably get the correct action the human intelligence behind it programmed it to perform. What will be the state of AI in 2019? The following list features 120 senior executives involved with AI, all peering into their not-so-clouded crystal ball, and promising less hype and more practical, precise, and narrow AI. "Self-Driving Finance is a practical implementation of AI that is already used in one form or another by millions of bank customers around the globe and will only get better in the coming years. Based on projects that are currently underway with ...