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.

Starting today, Google will rely more heavily on artificial intelligence when it translates language. The new method, called Google Machine Neural Translation, cuts down errors by 80 percent compared to its current algorithm, and is nearly indistinguishable from human translation on standardized tests, the company said. It's a radical change from how Google translates now, which is called phrase-based translation, and is standard for the industry. Under this method, an algorithm cuts up a sentence, like one entered by a high-schooler trying to game their homework, and attempts to match words or phrases to a large dictionary. The new method takes that same large dictionary and uses it to train two neural networks.

The deep learning software driving the modern artificial intelligence revolution has mostly run on fairly standard computer hardware. Some tech giants such as Google and Intel have focused some of their considerable resources on creating more specialized computer chips designed for deep learning. But IBM has taken a more unusual approach: It is testing its brain-inspired TrueNorth computer chip as a hardware platform for deep learning. Deep learning's powerful capabilities rely on algorithms called convolutional neural networks that consist of layers of nodes (also known as neurons). Such neural networks can filter huge amounts of data through their "deep" layers to become better at, say, automatically recognizing individual human faces or understanding different languages. These are the types of capabilities that already empower online services offered by the likes of Google, Facebook, Amazon and Microsoft.

Most people think Photoshop is a magical tool that can change reality, but it does require a skilled artist for decent results. Using neural networks, however, University of Edinburgh researcher Andrew Brock has built an uncanny image editing app that can transform someone's entire hairstyle with just the stroke of a brush. To use the Neural Photo Editor, you select the "contextual paintbrush," choose a color and start mousing the part you want to change. The system can recognize if you're on the model's hair, for instance, and intelligently fill in the area, changing the color to match your brush. In another example, the user paints over the subject's mouth with a white brush to make their smile bigger.

Big data is in many ways still a wild frontier, requiring wily smarts and road-tested persistence on the part of those hoping to find insight in all the petabytes. On Tuesday, IBM announced a new platform it hopes will make things easier. Dubbed Project DataWorks, the new cloud-based platform is the first to integrate all types of data and bring AI to the table for analytics, IBM said. Project DataWorks is available on IBM's Bluemix cloud platform and aims to foster collaboration among the many types of people who need to work with data. Tapping technologies including Apache Spark, IBM Watson Analytics and the IBM Data Science Experience launched in June, the new offering is designed to give users self-service access to data and models while ensuring governance and rapid-iteration capabilities. Project DataWorks can ingest data faster than any other data platform, from 50 to hundreds of Gbps, deriving from sources including enterprise databases, the internet of things (IoT) and social media, according to IBM.

But I am even more excited about the train your own model feature. We believe that the same AI technology that gives big tech companies a competitive edge should be available to developers or businesses of any size or budget. That's why we built our new Custom Training and Visual Search products – to make it easy, quick, and inexpensive for developers and businesses to innovate with AI, go to market faster, and build better user experiences. Machine learning requires large data sets and skilled engineers to build the technology that can derive "intelligence" from data. And so without machine learning as a service from companies like Clarifai, the largest tech companies will have a structural advantage over small developers.

Neural Photo Editor is an experimental piece of retouching software from researchers at the University of Edinburgh that uses neural networks to act like Photoshop on steroids. Thanks to machine learning, it can intuitively interpret how a user intends to retouch a photo based on a "contextual paintbrush." A single brush can change hair color, fill in bald spots, or add a toothy grin. The process couldn't be simpler: Users select a color for their paintbrush and the system analyzes that color in context with the image in order to produce an intelligent output. Painting over a subject's mouth with a white brush, for example, can make a smile larger, while painting with a dark color on a forehead can add bangs.

The question of which model type to apply to a Machine Learning task can be a daunting one given the immense number of algorithms available in the literature. It can be difficult to compare the relative merits of two methods, as one can outperform the other in a certain class of problems while consistently coming in behind for another class. In this post, the last one of our series of posts about Logistic Regression, we'll explore the differences between Decision Trees and Logistic Regression for classification problems, and try to highlight scenarios where one might be recommended over the other. Logistic Regression and trees differ in the way that they generate decision boundaries i.e. the lines that are drawn to separate different classes. To illustrate this difference, let's look at the results of the two model types on the following 2-class problem: Decision Trees bisect the space into smaller and smaller regions, whereas Logistic Regression fits a single line to divide the space exactly into two.