Earlier this year, Christopher Manning, a Stanford professor of computer science and of linguistics, was named the Thomas M. Siebel Professor in Machine Learning, thanks to a gift from the Thomas and Stacey Siebel Foundation. Manning specializes in natural language processing – designing computer algorithms that can understand meaning and sentiment in written and spoken language and respond intelligently. His work is closely tied to the sort of voice-activated systems found in smartphones and in online applications that translate text between human languages. He relies on an offshoot of artificial intelligence known as deep learning to design algorithms that can teach themselves to understand meaning and adapt to new or evolving uses of language. Siebel, a pioneer in numerous areas of information technology and known for his ability to see and understand emerging trends in computer science and beyond, has long held an interest in precisely this kind of work.

The New York Times published a provocative article on Friday, asking in the headline "Is China outsmarting America in A.I.?". But depending on how you define "outsmarting," and the context in which the question is asked, the answer might not even matter much. The answer might matter very much in terms of geopolitics and national security. Just like with supercomputing, quantum computing and other areas of deep computer science research, having better capabilities in artificial intelligence can arguably lead to an edge in areas like military, energy and climate science that can shift the world-power balance. But if we're talking about consumer or enterprise AI, then comparing China and the United States is kind of like comparing apples and oranges.

Among the list of technologies that have radically changed our economy in the last year is a handful that did not receive the same level of attention as artificial intelligence or self-driving cars. One, in particular, is called Neural Machine Translation (NMT), a major breakthrough in language technology that some believe is a turning point in how business gets done. The Internet and the connectivity it facilitates is primarily responsible for what we now call the global economy. Emails, web pages, and mobile applications have created a marketplace for ideas and products, as well as empowered organizations to collaborate instantly from thousands of miles away. But for as small as the world is today, it can get smaller, and language is a major part of that.

Data is the fuel for AI, and Google owns some of the largest data sets in the world. The company operates seven services with over a billion monthly active users: Android, Chrome, YouTube, Gmail, Google Maps, Google Search, and Google Play. In addition, Google Translate and Google Photos are used by over 500 million people each. By operating such a diverse range of services, Google collects data of various types: text, images, video, maps, and webpages--helping the company master not just one kind of AI, but AI across various use cases. Just as important as the data are the apps, which Google also owns.

As seen at Google DeepMind's conference this week, machine learning with AI has seeped into a number of industries in recent years. Whereas in the past it was more a topic of discussion on theoretical applications, we now see machine learning being applied in smart cars, video games, digital marketing, virtual personal assistants, chatbots, and other areas of daily life. As AI moves to disrupt and improve more sectors, there are still barriers to overcome before we need to fear for our jobs. In a recent translation competition, human beings beat AI, but it's only a matter of time before machines become digital babel fish. It's worth recapping how machine learning and AI have already surpassed human abilities. In 1996, IBM's Deep Blue computer first challenged world-leading chess player Garry Kasparov.

Over the last few years, data-intensive machine-learning techniques have made dramatic strides in speech recognition and image analysis. Now these methods are making significant advances on another long-standing challenge: translation of written text between languages. Until a couple of years ago, the steady progress in machine translation had always been dominated by Google, with its well-supported phrase-based statistical analysis, said Kyunghyun Cho, an assistant professor of computer science and data science at New York University (NYU). However, in 2015, Cho (then a post-doc in Yoshua Bengio's group at the University of Montreal) and others brought neural-network-based statistical approaches to the annual Workshop on Machine Translation (WMT 15), and for the first time, the "Google translation was not doing better than any of those academic systems." Since then, "Google has been really quick in adapting this (neural network) technology" for translation, Cho observed.

Google Translate's knowledge of more than 100 languages can help you in your daily workflow as much as it can help you on your next trip. The features below show how it can help you with entire documents or websites, or even your native tongue. Google Translate can parse individual words and phrases, of course, but you can also translate entire websites into a chosen language. You can translate foreign websites like this Italian news publication into another language with Google Translate. Just type the entire URL of the website you want translated in the text box on the left side of Google Translate's home page.

While EMMA is a relatively simple application, developers are thinking bigger as well: Today's cognitive technologies can track the course, speed, and destination of nearly 2,000 airliners at a time, allowing them to fly safely.4 Over time, AI will spawn massive changes in the public sector, transforming how government employees get work done. It's likely to eliminate some jobs, lead to the redesign of countless others, and create entirely new professions.5 In the near term, our analysis suggests, large government job losses are unlikely. But cognitive technologies will change the nature of many jobs--both what gets done and how workers go about doing it--freeing up to one quarter of many workers' time to focus on other activities.

So how do we program a computer to translate human language? The simplest approach is to replace every word in a sentence with the translated word in the target language. This is easy to implement because all you need is a dictionary to look up each word's translation. But the results are bad because it ignores grammar and context. So the next thing you might do is start adding language-specific rules to improve the results.

Today we are into digital age, every business is using big data and machine learning to effectively target users with messaging in a language they really understand and push offers, deals and ads that appeal to them across a range of channels. With exponential growth in data from people and & internet of things, a key to survival is to use machine learning & make that data more meaningful, more relevant to enrich customer experience. Machine Learning can also wreak havoc on a business if improperly implemented. Before embracing this technology, enterprises should be aware of the ways machine learning can fall flat.Data scientists have to take extreme care while developing these machine learning models so that it generate right insights to be consumed by business. Here are 5 ways to improve the accuracy & predictive ability of machine learning model and ensure it produces better results.