Amazon is a company that has gone from being an innovative online book retailer, to one of the largest ecommerce retailers in the world, and is now the largest cloud computing provider and a major player in IoT.In addition to their core ecommerce website, Amazon has a line of internet connected ebook readers, personal tablets, a smartphone, a smart TV device, and now their latest, an intellig Their new product is the Echo. It's a small, relatively discreet IoT connected speaker that works in much the same way as Apple's Siri, Microsoft's Cortana, and Google's Now services. Are there enough compelling features with the Echo to make it a breakthrough device in IoT mass adoption? More importantly, are there risks with having an always on, always listening, IoT device in the home? Although IoT devices around the world have now exceeded 5 billion, there are still billions of consumers that haven't seen the value, or even recognized the potential of a more connected home.

America's manufacturing heyday is gone, and so are millions of jobs, lost to modernization. Despite what Treasury Secretary Steven Mnuchin might think, the National Bureau of Economic Research and Silicon Valley executives, among many others, know it's already happening. And a new report from PwC estimates that 38 percent of American jobs are at "high risk" of being replaced by technology within the next 15 years. But how soon automation will replace workers is not the real problem. The real threat to American jobs will come if China does it first.

With its latest Galaxy S8 and S8 smartphones, Samsung also launched its very own AI assistant, Bixby. We know what you're thinking -- do I really need yet another Android helper bot like Google Assistant or even Cortana vying for my attention? Actually, Bixby might prove to be useful for Galaxy S8 owners than we expected, thanks to to its tight integration with the smartphones' hardware. Bixby has its own access button on the Galaxy S8 that helps you navigate services, apps and more via voice, touch and text commands. It also comes with a card-centric user interface that looks a lot like Google's former Now app, letting you save cards as reminders for later use.

Natural Language Processing (or NLP) is an area that is a confluence of Artificial Intelligence and linguistics. It involves intelligent analysis of written language. If you have a lot of data written in plain text and you want to automatically get some insights from it, you need to use NLP techniques. These insights could be -- sentiment analysis, information extraction, information retrieval, search etc. to name a few. Machine Learning (or ML) is an area of Artificial Intelligence (AI) that is a set of statistical techniques for problem solving.

I love to write about face recognition, image recognition and all the other cool things you can build with machine learning. Whenever possible, I try to include code examples or even write libraries/APIs to make it as easy as possible for a developer to play around with these fun technologies. But the number one question I get asked is "How in the world do I get all these open source libraries installed and working on my computer?" If you aren't a long-time Linux user, it can be really hard to figure out how to get a system fully configured with all the required machine learning libraries and tools like TensorFlow, Theano, Keras, OpenCV, and dlib. The majority of the issues that get filed on my own open source projects are about how to install these tools.

I am spending some cycles on my algorithmic rotoscope work -- which is basically a stationary exercise bicycle for my learning about what is and what is not Machine Learning. I am using it to help me understand and tell stories about Machine Learning by creating images using Machine Learning that I can use in my Machine Learning storytelling. Picture a bunch of Machine Learning gears all working together to help make sense of what I'm doing, and WTF I am talking about? As I'm writing a story on how image style transfer Machine Learning could be put to use by libraries, museums, and collection curators, I'm reminded of what a con machine learning will be in the future, and how it will be a vehicle for the extraction of value and outright theft. My image style transfer work is just one tiny slice of this pie.

Hedge fund Renaissance Technologies is looked upon by Wall Street with awe and envy in equal measure. Particularly, Medallion Fund, an employees only fund it runs. Bloomberg last year wrote the fund has returned more than $55 billion, making it more profitable than funds run by feted veterans such as George Soros. The Renaissance flagship fund, which will turn 30 next year, has returned more than 25% profits in most of its years of investing. Money doubles in a little more than three years at that rate.

Google is launching its rival to Amazon's Echo, the Google Home, in the UK as the battle for the living room hots up. The smart speaker, which can play music, control Internet of Things devices, and answer questions, will cost British customers £129, some £20 more than Amazon's Echo, when it launches in Britain on 6 April. Google first launched the Home in the US in November, alongside the introduction of the Pixel phone and Google Assistant AI. Google Assistant is a voice-controlled digital assistant that runs on phones, Google Home and even smart TVs, and can provide context-aware assistance, knowing what sort of environment it's in. Since the initial launch, Google Assistant has gradually come to more Android phones, as well as Android Wear-based smartwatches.

Feature hashing is a powerful technique for handling sparse, high-dimensional features in machine learning. It is fast, simple, memory-efficient, and well suited to online learning scenarios. While an approximation, it has surprisingly low accuracy tradeoffs in many machine learning problems. In this post, I will cover the basics of feature hashing and how to use it for flexible, scalable feature encoding and engineering. I'll also mention feature hashing in the context of Apache Spark's MLlib machine learning library.

Driverless vehicles operated by Uber Technologies Inc will be back on the road in Tempe and Pittsburgh on Monday after one of its self-driving cars crashed in Arizona, the ride-hailing company said.