Before a classification algorithm can do its magic, we need to train it by showing thousands of cat and non-cat images. The general principle in machine learning algorithms is to treat feature vectors as points in higher dimensional space. Then it tries to find planes or surfaces (contours) that separate higher dimensional space in a way that all examples from a particular class are on one side of the plane or surface. To build a predictive model we need neural networks. The neural network is a system of hardware and software similar to our brain to estimate functions that depend on the huge amount of unknown inputs.

I'm a software engineer and I've taken up a new hobby of Archery. On the side I've been experimenting with some basic classifiers in scikit-learn. A project I've gotten interested in is something to convert photos of archery targets post-shots into XY coordinate systems. As a first step my goal is just to tell if an image is an archery target at all. Doing some research it seems like TF image recognition might be an approach to take.

I've been making some TensorFlow examples for my website, fomoro.com, While it's fresh in my head, I wanted to write up an end-to-end description of what it's like to build a machine learning app, and more specifically, how to make your own reverse image search. For this demo, the work is ⅓ data munging/setup, ⅓ model development and ⅓ app development. At a high-level, I use TensorFlow to create an autoencoder, train it on a bunch of images, use the trained model to find related images, and display them with a Flask app. In the last post, I talked model development and training.

Google has made the app open-source so any developer can adopt it. It can perform chores like object detection, face attribute recognition, fine-grained classification (recognizing a dog-breed, for instance) and landmark recognition. The tech is part of TensorFlow, Google's deep learning model that recently shrunk down to mobile size in a new version called TensorFlow Lite. MobileNets is not one-size-fits-all, as Google has actually built 16 pre-trained models "for use in mobile projects of all sizes." The larger the model, the better it is at recognizing landmarks, faces or doggos, with the most CPU-intensive ones hitting scores of between 70.7 and 89.5 percent accuracy.

Google is poised to begin a grand experiment in using machine learning to widen access to healthcare. If it is successful, it could see the company help protect millions of people with diabetes from an eye disease that leads to blindness. Last year researchers at the search and ads company announced that they had trained image recognition algorithms to detect signs of diabetes-related eye disease roughly as well as human experts. The software examines photos of a patient's retina to spot tiny aneurisms indicating the early stages of a condition called diabetic retinopathy, which causes blindness if untreated. At the 2017 WIRED Business Conference in New York City today, a leader of Google's project said that work has begun on integrating the technology into a chain of eye hospitals in India.

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.

IBM is trying to remove some of the complications related to image recognition with new tools to automate critical machine learning tasks. A major update of the company's PowerAI tools has a feature called AI Vision, an auto tuner that makes it easy to identify and classify pictures. It will also speed up image recognition by breaking down tasks over multiple clusters. AI Vision plays a big role in automating machine learning by creating a tuned model, said Sumit Gupta, vice president of machine learning. The software abstracts machine learning, and developers don't need knowledge of low-level access to frameworks to tune, train, and deploy image recognition models.

I've been making some TensorFlow demos for my website, fomoro.com, While it's fresh in my head, I wanted to write up an end-to-end description of what it's like to build a machine learning app, and more specifically, how to make your own reverse image search. For this demo, the work is ⅓ data munging/setup, ⅓ model development and ⅓ app development. At a high-level, I use TensorFlow to create an autoencoder, train it on a bunch of images, use the trained model to find related images, and display them with a Flask app. In this first post, I'm going to go over my environment and project setup and do a little bit of scaffolding.

Samsung has said the its Galaxy S8's iris scanning provides users with'airtight security', but researchers have demonstrated that it can be easily bypassed using a photograph and a contact lens. A new video has revealed that hackers can place a contact lens over a printed photo of the smartphone owner's eye to unlock the handset. Although Samsung has noted that'the patterns in your irises are unique to you and are virtually impossible to replicate' the makeshift eye is able to fool the technology - leaving many to wonder just how secure the technology really is. Samsung has said the its Galaxy S8's iris scanning provides users with'airtight security', but researchers have demonstrated that it can be easily bypassed using a photograph and a contact lens Using'a good digital with 200mm-lens' at about 16 feet (5m) from the phone owner, the team snapped the picture and then printed it out with a laser print that so was also manufactured by Samsung. But to make it look more realistic, the hackers thought of adding a contact lens on top of the print out – this'emulated the curvature of a real eye's surface'.

Pinterest announced its image recognition tool back in February, but the company has already added a number of improvements since then. Today, the company is revealing the latest addition to Lens: full dish recognition. This means that when you snap a pic of your plate with the Pinterest app, the software will find full recipes for complete dishes rather than just options based on single ingredients. This update to Lens isn't all the company is doing for aspiring cooks though. Pinterest is also adding new food filters to its search tools.