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% of American jobs are at "high risk" of being replaced by technology within the next 15 years. But how soon automation will replace workers isn't the real problem. The real threat to American jobs will come if China does it first.
Bots are popping up everywhere from Facebook to home personal assistants. Advances in natural language processing, machine learning and other AI technology created the foundation for bots, but the field has a long way to go before it reaches its full potential. The Alexas and Cortanas of the world do an effective job at accomplishing requested tasks as long as people present them one at a time. A multi-threaded version of these digital personal assistants would allow them to remember multiple situations. This use case is closer to how people actually want to engage with the bots.
With great power comes great responsibility--and artificial-intelligence technology is getting much more powerful. Companies in the vanguard of developing and deploying machine learning and AI are now starting to talk openly about ethical challenges raised by their increasingly smart creations. "We're here at an inflection point for AI," said Eric Horvitz, managing director of Microsoft Research, at MIT Technology Review's EmTech conference this week. "We have an ethical imperative to harness AI to protect and preserve over time." Horvitz spoke alongside researchers from IBM and Google pondering similar issues.
Many people wish they could predict what will happen next in the world. Many predictions are assigned to the waste bin of time very quickly. With hindsight, unforeseen factors come into play that changed their'models'. It is because there were so many factors involved to predict. The ability of models to analyse and interpret means technology was not able to process, analyse and predict with a high degree of success.
Facebook's deep-learning artificial intelligence systems have learned to recognize your friends in your photos, and Google's AI has learned to anticipate what you'll be searching for. But there's no need to feel left out, even if your company's computers haven't learned much lately. A growing number of tech giants and startups have begun offering machine learning as a cloud service. That means other companies and startups do not need to develop their own specialized hardware or software to apply deep learning--the high-powered version du jour of machine learning--to their specific business needs. "Deep-learning algorithms dominate other machine-learning methods when data sets are large," says Zachary Chase Lipton, a deep-learning researcher in the Artificial Intelligence Group at the University of California, San Diego, who has examined cloud AI services from companies such as Amazon and IBM.
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.