Posted by Sara Robinson, Developer Advocate I've been playing around with Google's machine learning tools recently, especially image recognition. By far, my favorite feature of the Cloud Vision API is landmark detection: you send the API a picture of a popular (or obscure) landmark and it returns JSON with the name of the landmark, its latitude / longitude coordinates, a bounding box indicating where the landmark was found in the image, and even more landmark metadata. To see the Vision API in action and understand how it works behind the scenes, let's look at some landmarks from around the world. First, let's see what a JSON response looks like. We'll start with this image of the Eiffel Tower: If at first glance you thought this was a picture of the Eiffel Tower in Paris, you were wrong (don't worry, I was too).

Currently, the two largest companies in legal data-driven research are LexisNexis and Westlaw. They have databases that contain huge numbers of case details, and often serve as the default starting point for legal researchers. However, they are not a resource for running advanced analytical tools. Others are filling this gap in the market though. Brainspace, for example, is applying more analytically driven tools to unstructured data in ways that companies have previously not been able to do, and one application that it's been used for successfully is legal files.

A new personalized search engine helps you explore what you would look like with brown hair, curly hair or in a different time period. Ira Kemelmacher-Shlizerman, a computer vision researcher at University of Washington, developed the image recognition software using a TITAN X GPU and the cuDNN-accelerated Caffe deep learning framework to train the models and for inference. Ira presented her paper at this week's SIGGRAPH 2016 and the search engine will be publicly available later this year. Dreambit is also able to predict what a child might look like when they are forty years old or with red hair, black hair, or even a shaved head. "It's hard to recognize someone by just looking at a face, because we as humans are so biased towards hairstyles and hair colors," said Kemelmacher-Shlizerman. "With missing children, people often dye their hair or change the style so age-progressing just their face isn't enough. This is a first step in trying to imagine how a missing person's appearance might change over time."

The Amazon Echo, a voice-controlled virtual assistant, is seen at it's product launch for Britain and Germany in London, Britain, September 14, 2016. What's all the fuss about the voice-activated home speaker that Amazon is due to release in the UK and Germany in late September? This gadget has been available in the US for over a year and has proven a minor hit, with sales estimates between 1.6m and 3m. But these figures belie the potential impact this kind of artificial intelligence device could have on our lives in the near future. Echo doesn't just let you switch on your music by voice command. It's the first of what will be several types of smart home appliances that work beyond simple tasks like playing music or turning on a light.

Five companies have been the first to sign up with Grip to provide integrated business event networking apps via the API including Eventfuel, ITM Mobile, Attendease, Goomeo and Loopd. Grip's artificial intelligence powered matchmaking platform, which is currently taking the events world by storm, can now integrate with any event app or website through REST APIs. This makes it possible for event businesses to easily integrate Grip into their products. Tim Groot, CEO of Grip explains: "By launching our API, we have widened the scope of our unique event matchmaking solution so that it can be used via a company's own website or existing digital apps. It provides a seamless and simple solution for companies keen to use our matchmaking engine in the optimum way and who want to integrate it with their existing solutions."

Online shopping has the potential to be so much smarter. If you stumble across a rug or lamp you like in a photo, shouldn't it be easier to track down where to buy it? One home design website is aiming to do just that, with the help of artificial intelligence. Palo Alto-based Houzz is a platform for people who want to remodel or redesign their homes and are looking for inspiration. Consumers and design professionals alike can upload photos of their completed projects, where they can then tag specific furniture and accessories offered by merchants Houzz partners with--letting other users easily buy any product they see and love.

A machine-learning technique that has already given computers an eerie ability to recognize speech and categorize images is now creeping into industries ranging from computer security to stock trading. If the technique works in those areas, it could create new opportunities but also displace some workers. Deep learning, as the technique is known, involves applying layers of calculations to data, such as sound or images, to recognize key features and similarities. It offers a powerful way for machines to recognize similarities that would normally be abstruse to a computer: the same face seen from different angles, for instance, or a word spoken in different accents (see "10 Breakthrough Technologies 2013: Deep Learning"). The mathematical principles that underlie deep learning are relatively simple, but when combined with huge quantities of training data and computer systems capable of powerful parallel computations, the technique has resulted in dramatic progress in recent years, especially in voice and image recognition.

Machine Learning sounds like science fiction, but it's already present in our daily lives. From Siri's voice recognition to Tesla's self-driving cars, Machine Learning is behind many of today's innovations. If you're looking to take your programming skills to the next level, the Analytics, Machine Learning & NLP in Python Course can help you add Machine Learning to your repertoire, and it's on sale for just 45. Taught by a team of seasoned SIlicon Valley experts, this course will train you in Machine Learning's essential tools like NLP and Python. You'll learn in a fun and exciting manner as animations break down complex programming topics for you.

Great, you've just identified some of the characteristics of a good generalization: a relatively simple, abstract, less detailed, model that is consistent with (or fits, or explains) the observations, and is more broadly applicable beyond the cases you have seen. That's how humans think, but how does this help us design a computer algorithm that generalizes well? This helps us in a couple of ways. Firstly, it gives us a framework for designing an ML algorithm. Just like humans build mental models based on their observations, an ML algorithm should ingest training data and output a model that fits, or explains the training data well. A model is the mathematical analog to the human idea of a "concept" or "mental model"; it's the mathematical formalization of what we have been informally calling "rules" until now. A model is essentially a function takes as input the characteristics (i.e.