General Motors and IBM have partnered to bring personalized content to drivers. GM's new OnStar system, which is called OnStar Go, will incorporate IBM's Watson artificial intelligence technology in an attempt to optimize the driver's time in the vehicle. But there's a catch – targeted offers and services. Thanks to IBM, OnStar Go will learn from drivers' behaviors and provide customized offers from GM's partners, which of right now include Exxon Mobil, iHeartRadio, Glympse, Parkopedia, and Mastercard. If your GM vehicle needs fuel, for instance, OnStar Go would point you towards an Exxon Mobil gas station.

Shimon Whiteson says we will all become cyborgs. I really think in the future we are all going to be cyborgs. I think this is something that people really underestimate about AI. They have a tendency to think, there's us and then there's computers. Maybe the computers will be our friends and maybe they'll be our enemies, but we'll be separate from them.

General Motors gm and International Business Machines ibm on Tuesday said they would combine IBM's artificial intelligence software Watson with the carmaker's OnStar system in order to market services to drivers in their vehicles. The feature, called OnStar Go, is set to debut early next year in more than 2 million GM vehicles with 4G service, IBM and GM said in a joint statement. IBM's Watson, which beat two previous winners of the quiz show "Jeopardy!" in 2011, will sift through data in order to recognize a driver's habits, allowing third-party marketers to deliver targeted offers, whether nearby coffee shops, reminders about shopping-list items, or paying for fuel from their dashboards. Carmakers have been adding connected services into their vehicles to duplicate the convenience of smartphones, which can suggest nearby restaurant offers, or point the way to a gas station. Data generated from connected vehicles is valuable to automakers, although some consumers have been wary of privacy and data security issues.

Neural networks designed for sequence predictions have recently gained renewed interested by achieving state-of-the-art performance across areas such as speech recognition, machine translation or language modeling. However, these models are quite computationally demanding, which in turn can limit their application. In the area of language modeling, recent advances have been made leveraging massively large models that could only be trained on a large GPU cluster for weeks at a time. While impressive, these processing-intensive practices favor exploring on large computational infrastructures that are typically too expensive for academic environments and impractical in a production setting, limiting the speed of research, reproducibility, and usability of the results. Recognizing this computational bottleneck, Facebook AI Research (FAIR) designed a novel softmax function approximation tailored for GPUs to efficiently train neural network based language models over very large vocabularies.

One of the most popular ways artificial intelligence has found use on the internet is via its ability to intelligently target visitors based on their behavioral patterns and use the data thus collected to supply them with content recommendations. Rankbrain, the revolutionary new algorithm from Google, makes use of artificial intelligence to process unique search engine queries and supply users with customized results. AdWords, Google's advertisement counterpart, makes heavy use of artificial intelligence to target visitors on the web and supply them with tethered advertisements customized according to their behavioral patterns. Apart from these, several content developers such as Netflix and Amazon Cloud have adapted similar artificial intelligence technologies to target users and provide them with a selective assortment of relevant content based on their browsing history.

Suffice to say, the technology will only get better as more data is made available for them to use to fine tune their algorithms. Traditional healthcare settings offer scant optimism, but with areas such as telehealth becoming more popular then it seems inevitable that data will not be an issue in future.

Apple has posted its first decline in annual revenue and profit since 2001, as the company looks for a way to offset falling sales of its flaghship iPhone. The tech giant has never posted a decline in annual revenues since the release of the iPod - until this week, when Apple revealed an 8 per cent drop in sales to $215 billion (£176 million) for 2016. The decline in sales dragged the company's profits down by 14 per cent to $45.7billion. The drop in sales was mostly down to declining sales of the iPhone, which accounts for two-thirds of Apple sales. Apple sold 45.5 million iPhones in the quarter, which while better than expected, compares to 48 million sold this time last year.

As our work and investment in Data Science and AI continue to grow, we've added to our recent good news on the hiring front here at Zalando Tech. Now that he's fully onboarded, we'd like to introduce Alex Rahin, our new VP of Data and Machine Learning Platforms. Alex joins us with extensive product experience from Amazon, Microsoft, Intel, and several technology startups. He is responsible for Zalando Tech's Core Data Platforms and Applications, such as Data Infrastructure, Machine Learning, Business Intelligence, and Web Analytics. We wanted to share more about his role and what his future endeavours will entail for Zalando in the world of Data Science.

This is an attempt to showcase some worked out examples of Machine Learning (ML) use German Credit Data. Though we have selected credit scoring problem as a case study in this article, the same process will be applicable for wide range of classification or regression problems "Response modeling", "Risk Management", "Attrition/Churn management", "Cross-Sell/Up-Sell", "usage Patterns", "Net Present Value", "Life Time Value", "Predictive Maintenance and condition based monitoring", "Warranty", "Reliability", "Failure Prediction", "Image/Video Processing", "Crime", "Medical Experiments", "Hidden pattern recognition" . The basic difference of traditional modeling and machine learning is that "in traditional modeling we intend to set up a modeling framework and try to establish relationships while in machine learning we allow the model to learn from the data by understanding the hidden patterns". Hence the first one requires analyst to have solid understanding of statistical techniques and business knowledge while the later one is more complex in nature and computational intensive, hence requires higher computation power of the systems and analyst needs to be tech savvy. Kindly note that while traditional techniques perform well on small to large amount of data, machine learning will certainly learn better on high-dimensional and complex data such as Big Data set up.

Speech is a fundamental aspect of human behavior, yet it remains something that many of us struggle with. It's believed that around 1 in 14 adults in the United States have some kind of voice disorder, and our understanding of such disorders makes it difficult to both diagnose and treat. A team from MIT and the Massachusetts General Hospital believe that machine learning can play a part in better understanding speech disorders. In a recent paper, they describe using a wearable device to collect accelerometer data to detect differences in people with Muscle Tension Dysphonia (MTD) and a control group. After such individuals with MTD had received therapy for the condition, their behaviors appeared to converge with that of the control group.