Our brains make vision seem easy. It doesn't take any effort for humans to tell apart a lion and a jaguar, read a sign, or recognize a human's face. But these are actually hard problems to solve with a computer: they only seem easy because our brains are incredibly good at understanding images. In the last few years, the field of machine learning has made tremendous progress on addressing these difficult problems. In particular, we've found that a kind of model called a deep convolutional neural network can achieve reasonable performance on hard visual recognition tasks -- matching or exceeding human performance in some domains.

It is a fundamental, but still elusive question whether methods based on quantum mechanics, in particular on quantum entanglement, can be used for classical information processing and machine learning. Even partial answer to this question would bring important insights to both fields of both machine learning and quantum mechanics. In this work, we implement simple numerical experiments, related to pattern/images classification, in which we represent the classifiers by quantum matrix product states (MPS). Classical machine learning algorithm is then applied to these quantum states. We explicitly show how quantum features (i.e., single-site and bipartite entanglement) can emerge in such represented images; entanglement characterizes here the importance of data, and this information can be practically used to improve the learning procedures. Thanks to the low demands on the dimensions and number of the unitary matrices, necessary to construct the MPS, we expect such numerical experiments could open new paths in classical machine learning, and shed at same time lights on generic quantum simulations/computations.

Microsoft today announced several improvements to its pre-built AI tools for companies, with a focus on improving facial recognition, custom image classification, and understanding important entities. The updates are included in the company's suite of Cognitive Services -- APIs that help developers deliver intelligent capabilities even if they don't have a great deal of AI expertise. The three updated services -- Microsoft's Custom Vision Service, Face API, and Bing Entity Search -- are designed to make AI easier for companies that can't keep a professional data scientist on staff. That's important, given the limited number of AI experts currently available, how much they cost to hire, and how complicated the task of rolling your own AI capabilities can be. The Custom Vision Service is now in paid public beta.

There are already lots of great articles covering these topics (for example here or here). And this isn't a discussion about whether AI will enslave humankind or merely steal all our jobs. You can find plenty of speculation and some premature fearmongering elsewhere. Instead, this post is a detailed description of how to get started in Machine Learning by building a system that is (somewhat) able to recognize what it sees in an image. I'm currently on a journey to learn about Artificial Intelligence and Machine Learning.

A while back, I was reading an article posted on Facebook, about Clovis people found alive and well living in Florida, with a picture featuring tribesmen (see below.) The quality of the picture was poor, and the URL was very suspicious: baynews9.com.ddwg.clonezone.link, as to make it appear that it was from Baynews9.com. It turned out that the picture (and thus the whole story) was fake: these people are real people living in Peru, see here for a Youtube video about them. My question is how to detect that a story is fake? The picture might have metadata embedded in it, allowing the data scientist to find the real source, unless it is a screenshot.

Artificial intelligence is becoming a centralised part of our everyday lives, even if we don't realise it. In fact, half of the people who encounter AI don't know they are doing so. AI is quietly blending into the background while aiming to improve how we interact with technology. From chatbots on websites that can answer our queries to personalised voice assistants on our devices, machine learning techniques improve continuously. Machine learning is creating better image tagging and recognition in an understated yet vital way.

There are many different ways to do image recognition. Google recently released a new Tensorflow Object Detection API to give computer vision everywhere a boost. I added a second phase for this project where I used the Tensorflow Object Detection API on a custom dataset to build my own toy aeroplane detector. So what was the experience like? First lets understand the API.

Someone got in touch with us recently asking for some advice on image detection algorithms, so let's see what we can do! They already know what algorithms they want to use, so let's start with those. Hang on no, for the uninitiated, let's start with what even is an image detection algorithm?

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.

People online are upset over a new decision from Google that makes it a little harder to download photos. The search giant removed its popular "view image" feature Thursday as a part of a legal settlement. The feature previously allowed users to download and save photos without having to navigate through to the pictures' web pages. Today we're launching some changes on Google Images to help connect users and useful websites. This will include removing the View Image button.