The design template used in the latest iteration of the Google Image Search algorithm.
The algorithm was introduced by Google last year to speed up images search.
The image search engine uses machine learning to generate images that can be used in search results, such as those for search results for ‘dog’, ‘cat’, ‘car’ and ‘puppy’.
The algorithm then compares the image with other images to see if the image matches the query.
The image searches are then sent to Google’s image search system for further processing.
Google has said that it has seen a large increase in image search queries in the last six months, which may be the result of the image search algorithm’s improved accuracy.
In an interview with the BBC, Google CEO Sundar Pichai said that the search engine was able to improve its image search by 1.5x over the past six months.
He added that the algorithm was not being used on all images.
The Google image search is now used in more than 70% of the search results and is the top result on Google when people type in ‘cat’.
Google’s search algorithm is now more accurate than ever, according to data from Google Trends.
In the Google image searches, the algorithm is able to produce more accurate images that do not have the same scale as other images.
Google’s algorithm is based on the assumption that the size of the original image should be equal to the size for a human in the image, as shown in this chart: Google is also working on a tool that can automatically generate images from images of the same subject, in an attempt to improve the image quality.
Google has also said that, for the first time in a long time, images of dogs have become the most searched image in the world.
Image search has been on the rise over the last couple of years, with search queries increasing by around 50% every year, according the BBC.
Image search is also the second-most searched image, after pictures of cats.