This 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. So far we have trained it on a few hundred concepts, and we hope to add more over time. We made this as an example of how you can use machine learning in fun ways.

Defined as a way to provide computers with the ability to learn without the need for programming, the term Machine Learning seems to be this year's big buzzword. In fact today you'd be hard-pressed to find a security vendor that doesn't claim to incorporate some form of machine learning or AI technology into their threat prevention solution. So how much is true and how much is smoke and mirrors? Despite being a concept first envisioned back in the 1950s by Alan Turing, the lack of computing power has held research teams from making any significant jump towards realising the technology. Nowadays we all rely on machine learning without even knowing it – Google's search engine is powered by machine learning APIs, as is Facebook's personalised news feed.

We are at an incredible moment in technology history. Thanks to cloud computing power, more advanced algorithms and the availability of massive amounts of data, the artificial intelligence (AI) field has exploded – allowing computer scientists to create technology many of us only dreamed about just a few years ago. Using deep learning, computers today can recognize the words in a conversation about as well as a person does and provide real-time translation. Thanks to advances in fields such as reinforcement learning, we are making tangible progress in the effort to build systems that have true artificial intelligence. At Microsoft, we believe everyone deserves to be able to take advantage of these breakthroughs, in both their work and personal lives.

We have discussed some AI topics in the previous posts, and it should seem now obvious the extraordinary disruptive impact AI had over the past few years. However, what everyone is now thinking of is where AI will be in five years time. I find it useful then to describe a few emerging trends we start seeing today, as well as make few predictions around machine learning future developments. The following proposed list does not want to be either exhaustive or truth-in-stone, but it comes from a series of personal considerations that might be useful when thinking about the impact of AI on our world. Companies like Vicarious or Geometric Intelligence are working toward reducing the data burden needed to train neural networks.

Want to play around with Google's AI projects? Well, now you can thanks to a website which makes the company's tinkering with artificial intelligence available for the public to sample. If you head over to the AI Experiments web page, you'll find eight different experiments on offer, designed to illustrate what machine learning can do and appeal to a wider audience (with the option to submit your own work, to boot, if you've concocted anything along these lines yourself). The web-based apps include one which is an experiment in seeing whether a neural network can recognise a sketch. With Quick Draw, you simply doodle something on your touchscreen and the AI gives its best guess as to what that object is.

As part of Google's slew of artificial intelligence announcements today, the company is releasing a number of AI web experiments powered by its cloud services that anyone can go and play with. One -- called Quick, Draw! -- gives you a prompt to draw an image of a written word or phrase in under 20 seconds with your mouse cursor in such a way that a neural network can identify it. Quick, Draw! is a great way to familiarize yourself with how neural networks work to identify objects and text in photos, which is one of the most common forms of AI-guided software techniques we see daily on platform's like Facebook and Google Photos. As you start to craft the doodle, Quick, Draw!'s software automaton will start yelling out words and phrases it thinks you're trying to illustrate. As you get closer to the finished product, the voice starts to become a good indication of how your drawing could be misinterpreted as something else. If you're on point, however, the neural network will hone in on the object and guess correctly.

OpenAI, the nonprofit artificial intelligence research organization co-founded by tech visionaries Elon Musk, Sam Altman, Greg Brockman, and Ilya Sutskever, announced Tuesday that it was partnering with Microsoft to advance its work in AI. As part of the deal, announced via a blog post, Microsoft Azure will act as the primary cloud platform for OpenAI. In the post, Microsoft said that it is "committed to democratizing AI and making it accessible to everyone," and that is a mission that they share with OpenAI. As part of the deal, Microsoft isn't just providing infrastructure, it will also help OpenAI "advance their research and create new tools and technologies that are only possible with the cloud," the post said. The reasoning behind OpenAI's decision to partner with Microsoft, according to the post, is due to Microsoft's focus on deep learning, open source technologies, and the capabilities available in tools such as Azure Batch, Azure Machine Learning, and the Microsoft Cognitive Toolkit.

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For millennia, we surpassed the other intelligent species with which we share our planet--dolphins, porpoises, orangutans, and the like--in almost all skills, bar swimming and tree-climbing. In recent years, though, our species has created new forms of intelligence, able to outperform us in other ways. One of the most famous of these artificial intelligences (AIs) is AlphaGo, developed by Deepmind. In just a few years, it has learned to play the 4,000-year-old strategy game, Go, beating two of the world's strongest players. Other software developed by Deepmind has learned to play classic eight-bit video games, notably Breakout, in which players must use a bat to hit a ball at a wall, knocking bricks out of it.

Sure, you may know that neural networks are spicing up your photos and translating languages, but what if you want a better appreciation of how they function? It just launched an AI Experiments site that puts machine learning to work in a direct (and often entertaining) way. The highlight by far is Giorgio Cam -- put an object in front of your phone or PC camera and the AI will rattle off a quick rhyme based on what it thinks it's seeing. Other experiments are worth checking out, although you may need to compile the code or watch to get a feel for them. Quick, Draw! is effectively AI Pictionary; Infinite Drum Machine gathers sounds by similarity, and lets you sequence them into simple tracks; Bird Sounds uses neural networking to arrange and visualize calls based on their qualities (say, shrill versus melodic).