Machine learning is the name of the game for Amazon Web Services Inc. this week, as it spills out a couple of major announcements ahead of its big re:Invent conference next week in Las Vegas. The public cloud computing giant said Wednesday it's planning to open a new machine learning laboratory, called the ML Solutions Lab, that will see its experts work alongside customers looking to build new artificial intelligence-based technologies. In addition, Amazon said it's adding new features to Amazon Rekognition, a deep-learning-powered image recognition platform, including real-time face recognition and the ability to spot text in images. The announcements underline the importance Amazon is placing on its AI efforts, which can benefit both its cloud computing arm and also its main retail business. The company has made a number of announcements in the field in recent months.

A new partnership between Rolls-Royce and Google will see ships become smarter and self-learning thanks to advanced machine learning algorithms. It will also bring the company's vision of a fully autonomous ship setting sail by 2020 a step closer to reality. Rolls Royce announced this month that it will use Google's Cloud Machine Learning Engine across a range of applications, designed to both make today's ships safer and more efficient, and to launch the ships of tomorrow. Initially the machine learning engine will be used to further train existing AI algorithms designed to power the image recognition systems of vessels. These identify and track objects that can be encountered while a ship is at sea and classify them according to the hazards they may pose.

The EnhanceNet-PAT algorithm could help with everything from restoring old photos to improving image recognition for self-driving cars. Anyone who has ever worked with image files knows that, unlike the fictional world of shows like CSI, there's no easy way to take a low-resolution image and magically transform it into a high-resolution picture using some fancy "enhance" tool. Fortunately, some brilliant computer scientists at the Max Planck Institute for Intelligent Systems in Germany are working on the problem -- and they've come up with a pretty nifty algorithm to address it. What they have developed is a tool called EnhanceNet-PAT, which uses artificial intelligence to create high-definition versions of low-res images. While the solution is not a miracle fix, it does produce a noticeably better result than previous attempts, thanks to some smart machine-learning algorithms.

But it does seem that way. A newly viral post is encouraging people to find out the "folder", and look at what is contained in there. And while some of the reports are true, they aren't all – or as intimate – they seem. The tweet – since reposted more than 10,000 times – instructs all women to go and search "brassiere" in their pictures. Many reported that it revealed some of their most intimate pictures, including some of them entirely naked or even having sex.

Can a computer automatically detect pictures of shirts, pants, dresses, and sneakers? It turns out that accurately classifying images of fashion items is surprisingly straight-forward to do, given quality training data to start from. Supervised learning, in particular for classification, is a popular topic amongst artificial intelligence and machine learning enthusiasts. It's common for developers to utilize a well known and easy to process dataset for their first attempts at using supervised learning. The MNIST dataset is an example of such a source, providing thousands of examples of handwritten digits that can be used for supervised learning with your machine learning algorithms. I've previously written about classifying handwritten digits with the MNIST data-set, achieving accuracies of 99% on the training set and 97% on the test set. Data sets such as these are a convenient way to hone your skills and machine learning model development with a tried and trusted data source. It's important to keep in mind that a good data set has several features in common.

This tutorial shows how to build an image recognition service in Go using pre-trained TensorFlow Inception-V3 model. The service will run inside a Docker container, use TensorFlow Go package to process images and return labels that best describe them. Full source code is available on GitHub. Inside project's root directory create docker-compose.yaml It uses official TensorFlow Docker image as its base image.

Plenty of companies are taking advantage of machine learning to tag and search visual content. Pinterest lets you find visually-similar images in order to track down that recipe or jacket you're looking for, and Pornhub is using machine learning to automatically identify porn stars in videos. Stock image company Shutterstock, though, has developed one of the more novel implementations of this sort of technology: using machine learning to identify the layout of images. The new tool, launched today, is currently available on the company's test site, Shutterstock Labs. You can search for various elements (in the case of the link above, wine and cheese) and then move icons about to specify where you want them to appear in the image.

Radiant is a robust tool for business analytics and running sophisticated models without any need for code development. It leverages the functions and tools in R and at the same time provides a user-friendly interface. With Radiant, you can manipulate and visualize your data, run different models from simple OLS to decision trees (CART) and neural networks, and evaluate your results. The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vicent Nijs.

Using AI and deep learning to create a quick report on the statistics and quotes used should be relatively simple, scanning through complex and variable data sources to discover patterns that show whether information is correct or not. These tools could even check that images accompanying the article show the correct picture and context. A recent article on Breitbart, for instance, could see them end up in court after they used an image of Lukas Podolski, a German soccer player who has appeared for his country 130 times. The image of Podolski and another man appeared under the headline'Spanish police crack gang moving migrants on jet skis', but a 10 second Google image search would have shown that this was actually an image of Podolski on a Jet Ski trip during the Rio 2016 World Cup. A simple AI system would have picked this up almost instantly through image recognition and allowed them to avoid the embarrassment and potential law suit that Podolski is reportedly considering against them.

Data, in particular, unstructured data has been growing at a very fast pace since mid-2000's. Eighty percent of all data generated is unstructured multimedia content which fails to get focus in organizations' big data initiatives. A good portion of this multimedia content is images and videos. Readily available smart wireless devices along with the rising popularity of sharing images and videos through the internet have contributed significantly in the massive growth of this type of content. Images and videos now reflect a good portion of human knowledge, interactions and conversations.