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?

Embeddings in machine learning are low-dimensional representations of complex input patterns, with the property that simple geometric operations like Euclidean distances and dot products can be used for classification and comparison tasks. The proposed meta-embeddings are special embeddings that live in more general inner product spaces. They are designed to propagate uncertainty to the final output in speaker recognition and similar applications. The familiar Gaussian PLDA model (GPLDA) can be re-formulated as an extractor for Gaussian meta-embeddings (GMEs), such that likelihood ratio scores are given by Hilbert space inner products between Gaussian likelihood functions. GMEs extracted by the GPLDA model have fixed precisions and do not propagate uncertainty. We show that a generalization to heavy-tailed PLDA gives GMEs with variable precisions, which do propagate uncertainty. Experiments on NIST SRE 2010 and 2016 show that the proposed method applied to i-vectors without length normalization is up to 20% more accurate than GPLDA applied to length-normalized ivectors.

Investment and interest in Artificial Intelligence (AI) is now white hot as we enter the second decade of the 21st century. Interestingly, AI is now almost a passé term. Machine Learning and advanced pattern recognition is now gaining notoriety as the "new" approach to apply the concept of AI, having been successfully applied to many problems in academia and industry. Several use cases have been identified delivering legitimate Return on Investment. These recent advances can be attributed to the presence of greater access to more data, coupled with new, more powerful computer hardware and processing power.

Summary: Image processing is a rapidly evolving field with immense significance in science and engineering. One of the latest applications of Image processing is in Intelligent Character Recognition (ICR). Intelligent Character Recognition is the computer translation of handwritten text into machine-readable and machine-editable characters. It is an advanced version of Optical Character Recognition system that allows fonts and different styles of handwriting to be recognized during processing with high accuracy and speed. ICR, in combination with OCR and OMR (Optical Mark Recognition), is used in forms processing. Forms processing is a process by which one can capture information entered into different data fields filled in forms and convert it to an editable text.

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.

You can still find and save the original image by clicking on the thumbnail in the search results, then right-clicking the image and selecting "Open Image in New Tab." There you'll find the original you would have found using the recently deceased "View Image" button.

Today we're launching some changes on Google Images to help connect users and useful websites. This will include removing the View Image button. The Visit button remains, so users can see images in the context of the webpages they're on. The Search by Image button is also being removed. Reverse image search *still works* through the way most people use it, from the search bar of Google Images.