Ben Dickson is a software engineer and the founder of TechTalks. The transportation industry is associated with high maintenance costs, disasters, accidents, injuries and loss of life. Hundreds of thousands of people across the world are losing their lives to car accidents and road disasters every year. According to the National Safety Council, 38,300 people were killed and 4.4 million injured on U.S. roads alone in 2015. The related costs -- including medical expenses, wage and productivity losses and property damage -- were estimated at $152 billion.

Machine learning is playing an increasingly important role in many areas of our businesses and our lives and is being employed in a range of computing tasks where programming explicit algorithms is infeasible. At Amazon, machine learning has been key to many of our business processes, from recommendations to fraud detection, from inventory levels to book classification to abusive review detection. And there are many more application areas where we use machine learning extensively: search, autonomous drones, robotics in fulfillment centers, text and speech recognitions, etc. Among machine learning algorithms, a class of algorithms called deep learning hascome to represent those algorithms that can absorb huge volumes of data and learn elegant and useful patterns within that data: faces inside photos, the meaning of a text, or the intent of a spoken word. A set of programming models has emerged to help developers define and train AI models with deep learning; along with open source frameworks that put deep learning in the hands of mere mortals.

Is there a next phase for cloud computing? During the past few years, cloud computing has become a mainstream element of modern software solutions just as common as websites or databases. The cloud computing market is a race vastly dominated by four companies: Amazon, Microsoft, Google and IBM with a few other platforms with traction in specific regional markets such as AliCloud in China. In such a consolidated market, it's hard to imagine a technology being disruptive enough to alter the existing dynamics. Artificial intelligence (AI) is the type of technology with the potential to not only improve the existing cloud platform incumbents but also power a new generation of cloud computing technologies. The thesis of a new generation of cloud computing platforms might seem ludicrous at first but it also presents a very intriguing argument.

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Microsoft is expanding its real-time Skype translation tool to cover calls made to mobile phones and landlines. The software giant has announced that Windows Insiders -- those who have signed up to access early builds of the Windows operating system -- can now access the new features through the Skype Preview app on Windows 10. Skype Translator was first introduced in English and Spanish back in December2014, and it has since expanded into a number of additional languages, including Mandarin, Italian, Russian, and Arabic. It uses artificial intelligence (AI) techniques such as deep-learning to train artificial neural networks, and it should continuously improve as it listens to more conversations. Essentially, it can convert spoken chats in almost real time across nine languages in total, and more than 50 languages in text-based instant messaging.

Let a thousand virtual worlds rain down from the clouds. That's the call from Google as it gets behind a tiny British startup called Improbable. Founded by two Cambridge graduates and backed by $20 million in funding from the venture capitalists at Andreessen Horowitz, Improbable offers a new way of building virtual worlds, including not just immersive games à la Second Life or World of Warcraft, but also vast digital simulations of real cities, economies, and biological systems. The idea is that these virtual worlds can run in a holistic way across a practically infinite network of computers, so that they can expand to unprecedented sizes and reach new levels of complexity. So far, the startup has shared its technology with just a handful of coders and companies.

Artificial Intelligence (AI) is being used across the financial services industry, becoming more popular in customer-facing digital channels. While the technology is improving, and offering a clear way to improve on customer service offerings while cutting costs, many of the early case studies – featuring chatbots and virtual assistants – don't appear to make the best use of machine learning, or really help banking customers in a meaningful way. So are we entering an age of reliance on artificial, as opposed to actual, intelligence? Or is this just a new – but perhaps more efficient – way to present the same information to customers? Will banks or FinTechs be the first to capture new customers based on a compelling combination of technology and data access?

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I'm fairly new to data mining myself, but I've been reading a book called "Data Mining: Practical Machine Learning Tools and Techniques" by Witten and Frank. The book is a little light on theory, but it has a pretty good overview of many different techniques for data mining, and it has enough detail that you can start applying some of the techniques on your data immediately. I'm using the book in conjunction with a course on decision trees that I'm taking at statistics.com, which I would recommend. "Elements of Statistical Learning" seems to be the closest thing to an authoritative text on data mining that I've found, and it has more theory than the book I'm using for my class, so that may be of use to you. I'm planning on studying it more after I've finished taking my class.