Shutterstock is flexing its AI muscles with the news that the stock photo giant is introducing new computer-vision search smarts to its platform. The company, which is headquartered in New York's Empire State Building, went public back in 2012 and now offers more than 70 million images for bloggers and media outlets -- which can make searching for specific assets challenging. Of course, the trusty old keyword search tool is effective to an extent, but what if you want to find images that are similar to one you have in your possession? Or what if you want alternative images based on color schemes, mood, or shapes? This is where Shutterstock's new reverse image search comes into play.

This smart cutlery could be a dieter's worst nightmare, or the weight loss tool you've been dreaming about. Spün claims to be the world's most intelligent utensil, using a combination of image recognition and motion sensing technology to measure food as users eat it. The device, which has an interchangeable stainless steel spoon or fork head, weighs each mouthful of food to calculate the calories and nutrients being eaten. Spün (pictured) claims to be the world's most intelligent utensil that is capable of tracking the calories and nutrients in each mouthful. The information is logged in a smartphone app, which can help dieters set goals and monitor their progress.

Facebook is rolling out a new feature that will automatically describe the content of photos to blind and visually impaired users. Called automatic alternative text, the feature uses artificial intelligence to identify visual content and provide a description for people using screen readers. While scrolling through Facebook, blind and visually impaired users will hear the name of the person followed by the word "photo" when they scroll past an image post by a user. Automatic alt text will then describe a list of themes of the image, such as "three people, smiling, outdoors" or "two people, smiling, sunglasses, sky, tree, outdoor". According to Facebook, more than two million photos are shared on social media every day, yet as content becomes more visual, many blind and visually impaired users are left feeling excluded.

Many of us may bemoan the constant photos of babies, food or sunsets on Facebook, but blind users don't have that luxury. Until now, visually impaired people on the site could only use audio descriptions to explain they were'looking' at a photo, without any other details. Now, Facebook has started using artificial intelligence and image recognition to reveal what is shown in these photos. Until now, visually impaired people on Facebook could only use audio descriptions to explain they were'looking' at a photo, without any other details. Now, Facebook has started using artificial intelligence and image recognition to reveal what is shown in these photos (examples pictured).

Machine Learning and Data Mining in Pattern Recognition Event on 2016-07-09 00:00:00 Machine Learning and Data Mining in Pattern Recognition is happening on 09 Jul 2016 at Leonardo Hotel New York, United States Of America.

For designers and photographers, selecting and laying out photos is often subjective, requiring a keen sense of color and composition. Using a computer algorithm, the stock footage site Shutterstock hopes to make that process easier. It now offers a reverse image search tool that analyzes the pixels in a photo and returns images that are similar in "look and feel" to the original without requiring a user to type in keywords to search. Dragging a photo of a stained-glass cathedral window into the search box, the company demonstrates in a video, produces a series of related images that more closely match the original in color and composition. The new search engine works by using a customized convolutional neural network, a type of machine learning tool that is modeled on how the brain's visual cortex, especially that of animals, processes images.

The platform will be the integral part of Image Search Engine for Image Referral Network and Image Ad Network, to automate generation and placement of highly-relevant targeted ads based on images in a large scale for the first time in the industry. ZAC's AI Discovery platform can also be used for other types of images, data, or objects, e.g., clothing, purse, accessories, medical images, satellite images, and biometrics. ZAC has an impressive team of scientists and developers. The software development is headed by Saied Tadayon, a veteran software developer and scientist, who got PhD from Cornell at age 23. One of ZAC's inventors is Prof. Lotfi A. Zadeh ("The Father of Fuzzy Logic"), a pioneer computer scientist at U.C. Berkeley.

Don't forget to check the FAQ. It has links to lots of good resources including Elements of Statistical Learning which is free online and one of the standard texts (Murphy and Bishop are the other two standard texts). Yaser Abu-Mostafa wrote a book called "Learning from Data" which I enjoyed quite a bit but wasn't listed in the FAQ. It doesn't cover many algorithms (just one or two I think) but does a good job providing an approachable introduction to theoretical aspects of ML that aren't always covered in the standard texts. Colah's blog isn't updated often but all the articles are fantastic You may also want to follow Yann LeCun on facebook. He posts stuff about ML on a fairly regular basis.

Last week we previewed this in "How Machine Learning APIs are Being Used to Predict Startup Success." Can there be a quantifiable way to hedge investors' risk and ensure they are betting on the right horse? According to the startup "jury" algorithm PreSeries, it's mathematically probable to predict which startup is most likely to succeed and that startup is Novelti. This startup which uses online machine-learning algorithms to convert Internet of Things sensor data into real-time intelligence, machine learning and pattern recognition was predicted to be successful with an 87 percent likelihood. Novelti beat out four other predictive analytics and artificial intelligence competitors--Intranetum, Emotion Research Lab, Datatrics and restb--at the PAPIs Connect conference for machine learning and predictive APIs.

Artificial intelligence is not as advanced as you may think. Sure, it's technologically advanced, but it lacks a general understanding of what's socially acceptable to say, the real world and the way humans interact. In the last couple of months, Microsoft has had a couple of failed attempts with artificial intelligence. The first involved an image recognition app called Fetch!, which looks at photos of dogs to identify its breed. People, of course, started to use the app to determine what breed of dog people resemble.