When Google launched Google Translate 10 years ago, the key algorithm behind the service was Phrase-Based Machine Translation. The translations provided by the service have since vastly improved due to the developments in machine intelligence, but the recent addition of neural networks has provided Google Translate with the biggest boost that it has ever received. Language is naturally phrase-based, which is why translating between languages is not as simple as plugging in the translation of words in sentences. While computers have been developed to handle phrase-based translation, there are still nuances in languages that the machines are not able to understand. Google has now deployed the Google Neural Machine Translation system, which utilizes machine learning and neural networks to provide a massive boost in translation accuracy.

In the previous parts of this series, we looked at an overview of some popular tricks for feature engineering, and examined those tricks in greater detail. In this part, we continue our closer examination of these approaches with a deeper dive into the final techniques described in Part 1. The examples discussed in this article can be reproduced with the source code and datasets available here. As an analyst, you savor the scenario in which you have a lot of data. But, with a lot of data comes the added complexity of analyzing and making better sense of that data.

One of the many questions in the design and use of Clinical Decision Support software is whether or not the user can recreate the logic used by the system in reaching its conclusions and recommendations–or alerts, or suggestions. If the CDS is based on sound medical logic, perhaps supported by specific reference material, then the user could in principle reach the same conclusions by reading the same literature, or perhaps reach a different conclusion. This transparency was part of the proposed criteria for some CDS systems not falling under FDA regulation in 2015 federal draft legislation--which didn't pass. The FDA has otherwise not been forthcoming on the general subject of CDS despite many pleas for guidance, and a draft guidance in this domain is an as yet unfulfilled part of the 2015 strategic plan. However underlying logic and science is not the only way to build "artificial intelligence" (AI), which might in some instances turn out to be artificial mediocrity if not artificial stupidity.

"Is data the new oil?" asked proponents of big data back in 2012 in Forbes magazine. By 2016, and the rise of big data's turbo-powered cousin deep learning, we had become more certain: "Data is the new oil," stated Fortune. Amazon's Neil Lawrence has a slightly different analogy: Data, he says, is coal. Not coal today, though, but coal in the early days of the 18th century, when Thomas Newcomen invented the steam engine. A Devonian ironmonger, Newcomen built his device to pump water out of the south west's prolific tin mines. The problem, as Lawrence told the Re-Work conference on Deep Learning in London, was that the pump was rather more useful to those who had a lot of coal than those who didn't: it was good, but not good enough to buy coal in to run it.

Microsoft is well positioned to give Amazon a run for its money in the cloud market, but it needs to break away from its Microsoft-centric approach. Seeing as how the cloud has been tied to digital transformation, and seeing as how more businesses are embarking on digital transformation projects, it makes perfect sense to me that cloud has been one of the hot topics at Microsoft's Ignite conference for enterprise IT, taking place this week in Atlanta. Microsoft has an interesting position in cloud, in that it was simultaneously early and late to the market. Almost 20 years ago, Microsoft launched Bing, and to support it, the company had to build out a massively scalable, global cloud network. Google had done this with its search platform, and Amazon had done similar to support its e-commerce business.

Imagine you're sitting in a self-driving car that's about to make a left turn into on-coming traffic. One small AI system in the car will be responsible for making the vehicle turn, one system might speed it up or hit the brakes, other systems will have sensors that detect obstacles, and yet another system may be in communication with other vehicles on the road. Each system has its own goals -- starting or stopping, turning or traveling straight, recognizing potential problems, etc. -- but they also have to all work together toward one common goal: turning into traffic without causing an accident. Harvard professor and Future of Life researcher, David Parkes, is trying to solve just this type of problem. Parkes told FLI, "The particular question I'm asking is: If we have a system of AIs, how can we construct rewards for individual AIs, such that the combined system is well behaved?"

Over the past few years, Artificial Intelligence (AI) as a technology has matured and come into its own. With each passing day, experts across industries identify yet another AI application that has the potential to change millions of human lives. California-based University of Southern California's Viterbi School of Engineering and its School of Social Work recently announced that they had joined forces to launch the Center on Artificial Intelligence for Social Solutions. Also in California, University of California Berkeley unveiled its Center for Human-Compatible Artificial Intelligence. Google's DeepMind is learning how to better apply radiotherapy to cancer patients to reduce the impact of dangerous doses of radiation on areas surrounding a tumor.

Artificial intelligence for education is a fast growing market the coming years a new report from Technavio shows. Two areas where AI will have a considerable impact is with its adaptive sequencing capabilities as well as its ability to transform pedagogical models. The first means artificial intelligence that is embedded in content that help students learn in a self-directed manner, which improves the learning-process. Both students and teachers will have access to all the data captured, where the artificial intelligence-backed learner solutions personalizing learning pathways. The secondly, transforming and improving traditional pedagogical models, that with AI basically develops online models.

Retail investors do not always have adequate time to research opportunities for making money. Fortunately, the rise of big data and artificial intelligence (AI) is helping individual investors make more informed investment choices. Due to the increase in data mining becoming available for the public, investors can gain information and insights in the marketplace that were formerly available only to institutional investors. Many investors believe that Wall Street was hesitant to accept making big data available to the public because it posed a potential threat to the ways many investment firms make money. If retail investors are able to gain the same facts and financial forecasts as institutional investors, individuals may perform more of the work themselves rather than utilizing the firms' services, resulting in substantial losses.

Spend half an hour checking out what people are writing online (especially in the business niche) and you will inevitably stumble across an article on how artificial intelligence (AI) is the future of this and that. For one reason or another, AI has once again caught the attention of people who, in most cases, know little to nothing about artificial intelligence. This can also be seen in many an HR-oriented article where AI is used in broad strokes that feel more like a plot of an 80s B-movie with Rutger Hauer, than a serious piece of writing on this potentially exciting proposition. If we were to answer this question in this article, we would probably all receive some kind of a prize for solving one of the most hotly debated questions of the last 70-odd years. Namely, when discussing AI and what it should encapsulate, it is only a matter of time before the debate grows extremely philosophical in nature and various theories, limits and questions of ethics arise.