If CES is the tech industry's Super Bowl, IFA is like the NCAA Football Bowl. Not quite the trendsetting tech event of the year, but still worth getting excited for. Held annually in Berlin, Germany, IFA is essentially Europe's version of CES with one big key difference: It's open to the public and not just industry folks. We'll be bringing you all the major tech news from the proceedings all week. It's been a quiet year for smartwatches.
Areas like virtual reality, self-driving cars, and artificial intelligence matured from seemingly distant concepts into tangible products that will eventually upend the ways people live and work. Amid all of this excitement, shares of artificial intelligence companies like Apple, Microsoft, and Facebook each outperformed the Nasdaq Composite benchmark in 2016. Not all AI stocks performed as swimmingly, though. And more importantly, what does this mean for each of these artificial intelligence stocks heading in 2017? Baidu is in the midst of conforming to new, tougher standards for its search results that the Chinese government mandated earlier this year after the death of a Chinese student sparked a national uproar surrounding shady online advertising practices among pseudo-healthcare companies.
The branch of artificial intelligence called deep learning has given us new wonders such as self-driving cars and instant language translation on our phones. Now it's about to injects smarts into every other object imaginable. That's because makers of silicon processors from giants such as Intel Corp. and Qualcomm Technologies Inc. as well as a raft of smaller companies are starting to embed deep learning software into their chips, particularly for mobile vision applications. In fairly short order, that's likely to lead to much smarter phones, drones, robots, cameras, wearables and more. "Consumers will be genuinely amazed at the capabilities of these devices," said Cormac Brick, vice president of machine learning for Movidius Ltd., a maker of vision processor chips in San Mateo, Calif.
Google Pixel Tomorrow, Oct 4, is Google's day with the tech giant expected to launch products from new Pixel smartphones to showcase what Android can do ( and already leaked as below), to the much hyped about smart home hub, the Google Home speaker. This product, which will make use of its voice-enabled AI Google Assistant, is meant to be a direct rival to Amazon's Alexa. But as voice-control heats up, and consumers grow more comfortable running their homes by just saying what they want, tech companies are eager to be the app used to run people's lives. Images of Google Pixel phones were leaked late last night by a retailer Carphone Warehouse, before images were taken down. California has just given the greenlight to self-driving cars--without drivers.
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?