The Montreal AI Ethics Institute, a nonprofit research organization dedicated to defining humanity's place in an algorithm-driven world, today published the inaugural edition of its State of AI Ethics report. The 128-page multidisciplinary paper, which covers a set of areas spanning agency and responsibility, security and risk, and jobs and labor, aims to bring attention to key developments in the field of AI this past quarter. The State of AI Ethics first addresses the problem of bias in ranking and recommendation algorithms, like those used by Amazon to match customers with products they're likely to purchase. The authors note that while there are efforts to apply the notion of diversity to these systems, they usually consider the problem from an algorithmic perspective and strip it of cultural and contextual social meanings. "Demographic parity and equalized odds are some examples of this approach that apply the notion of social choice to score the diversity of data," the report reads.

Part of this exhibit will feature illusions. Some can fool people, and some can fool machines. Without full context, our brains cannot always make sense of images. Machines can see illusions we can't, but they can't see things we can! For example: without ears and a tail, a Chihuahua can easily look like a blueberry muffin!

Quick Draw is a online game developed by Google. It is a very interesting game where you are required to draw something and then it tries to guess what you have drawn. It uses a neural network artificial intelligence to guess what the drawing is. It is simple game created by Google which is AI based as part of a project known as'A.I. The game is built by Jonas Jongejan, Henry Rowley, Takashi Kawashima, Jongmin Kim, with friends at Google Creative Lab and Data Arts Team.

Quick Draw is a Game-like Tool provided by Google to explore the power of AI (Artificial Intelligence) in fun ways. Quick Draw is a game built with machine learning. You draw, and a neural network tries to guess what you're drawing. But the more you play with it, the more it will learn. It's just one example of how you can use machine learning in fun ways.

Google has a number of research projects underway aimed at making computers smarter and technically versatile. One of those projects involves teaching machines how to draw. On April 11, Google researchers released a technical paper describing "sketch-rnn", a neural network that has been trained by using thousands of crude human-drawn images to construct basic drawings of its own. One of the goals of the paper is to show that machines can be taught to draw certain things, like the sketch of a house, a tree or a dog, in a manner similar to humans. "As humans, we do not understand the world as a grid of pixels, but rather develop abstract concepts to represent what we see," wrote two of the papers authors, David Ha and Douglas Eck, who are researchers with Google Brain, the company's deep learning research group.

Sadly, not everyone can say they have an artistic flair. It's predicted that around 10,000 hours of practice are needed to be able to become proficient with a pencil and paper and most of us don't have the time (or the patience). Thankfully, Google is taking its artificial intelligence systems and applying them to artworks to make drawing easier for everyone. The tech giant has released a free web-app that allows for terrible digital drawings to be turned into recognisable objects. Called AutoDraw, the system has been dubbed as "fast drawing for everyone".

The thing about AutoDraw is that it isn't just saying, Oh, I see you've drawn a zebra, but also suggesting, And here's what that should look like, actually. This can be quite helpful if you're just a person trying to find some quick clip art. But it's also a way of erasing the lovely and nuanced evidence of how differently people see and interpret the world around them. AutoDraw is based on an earlier Google experiment, Quick Draw, which turned the training of its neural network into a simple and pleasing sketching game. Quick Draw is a little like playing Pictionary with a computer as your teammate.

Google's latest AI toy may be its most clever: an automated drawing bot that analyzes what you're doodling in real time to suggest a more polished piece of clip art to replace it. Called AutoDraw, the software is another of Google's ongoing creative machine learning demonstrations that it releases as part of its AI Experiments series. It uses the underlying technology behind the company's experimental image recognition software to identify potential objects and pairs that with a database of neat and simplistic hand-drawn images. The company bills AutoDraw as a "drawing tool for the rest of us," and by us it means aesthetically impaired individuals who couldn't doodle themselves out of a paper bag. "AutoDraw pairs the magic of machine learning with drawings from talented artists to help you draw stuff fast," says the narrator in Google's AutoDraw teaser video.

Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before. They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs. But while AI and machine learning are very much related, they are not quite the same thing. Holding AI to account: will algorithms ever be free from bias if they're created by humans? Holding AI to account: will algorithms ever be free from bias if they're created by humans?

Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before. They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs. But while AI and machine learning are very much related, they are not quite the same thing. AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while Stanford University defines machine learning as "the science of getting computers to act without being explicitly programmed". You need AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent.