Machines may soon be trying to master just about anything you can do on a computer. Open AI, a nonprofit dedicated to pursuing big advances in AI and making that progress freely available to anyone, has released Universe, a platform that will let AI programs learn, through experimentation and positive reward, how to do all sorts of things on a computer. Universe will include more than a thousand games, but also desktop programs such as Web browsers. It will make it possible for AI researchers to train programs to do all sorts of new tricks, including potentially useful tasks like filling out online forms, responding to e-mails, and updating spreadsheets. But Ilya Sutskevar, cofounder and research director at OpenAI, says the motivation for developing and releasing Universe is a lot bigger.

Artificial intelligence (AI) researchers around the world will soon be able to use DeepMind's "flagship" platform to develop innovative computer systems that can learn and think for themselves. DeepMind, which was acquired by Google for £400 million in 2014, announced on Monday that it is open-sourcing its "Lab" from this week onwards so that others can try and make advances in the notoriously complex field of AI. The company says that the DeepMind Lab, which it has been using internally for some time, is a 3D game-like platform tailored for agent-based AI research. Founded in 2010, DeepMind has been developing AI agents that can master arcade games like "Space Invaders," "Pac-Man," and more recently the incredibly complex Chinese board game of Go. Describing the Lab platform in a blog post, DeepMind cofounder Shane Legg and DeepMind employees Charles Beattie, Joel Leibo, Stig Petersen, wrote: "It is observed from a first-person viewpoint, through the eyes of the simulated agent. Scenes are rendered with rich science fiction-style visuals. The available actions allow agents to look around and move in 3D. The agent's'body' is a floating orb. It levitates and moves by activating thrusters opposite its desired direction of movement, and it has a camera that moves around the main sphere as a ball-in-socket joint tracking the rotational look actions. "Example tasks include collecting fruit, navigating in mazes, traversing dangerous passages while avoiding falling off cliffs, bouncing through space using launch pads to move between platforms, playing laser tag, and quickly learning and remembering random procedurally generated environments." The DeepMind Lab aims to combine several different AI research areas into one environment. Researchers will be able to test their AI agent's abilities on navigation, memory, and 3D vision, while determining how good they are at planning and strategy. "Each are considered frontier research questions in their own right," DeepMind wrote in the blog post. "Putting them all together in one platform, as we have, represents a significant new challenge for the field." The Lab can be adapted and extended, with the possibility to create new "levels" that can be "customised with gameplay logic, item pickups, custom observations, level restarts, reward schemes, in-game messages and more." "We believe it has already had a significant impact on our thinking concerning numerous aspects of intelligence, both natural and artificial," wrote the blog posts' authors. "However, our efforts so far have only barely scratched the surface of what is possible in DeepMind Lab.

IBM is developing a platform that uses deep learning to diagnose melanoma from images of skin lesions in collaboration with dermatologists from Memorial Sloan Kettering Cancer Center. Over the past few years, we've seen IBM dive into healthcare, including the recent launch of Watson Health. From helping guide cancer therapy decisions to simply collecting and collating unbelievable amounts of health data, IBM has undertaken a number of exciting endeavors. In this work, IBM researchers describe development of a platform that uses deep learning technology to analyze images of skin lesions and identify those that could be melanoma. The technical details of how they accomplished this are described in detail in a paper recently published online by IBM in detail that goes way beyond my understanding of this space.

You probably caught video a few weeks back of Alexa speaking through Big Mouth Billy Bass, a singing fish that got popular in the late 1990s. If you haven't seen it yet, take a moment to watch and enjoy. The animatronic fish is a good reminder that Amazon's Alexa does not just come in speakers like Amazon Echo or Echo Dot. The Alexa Voice Service (AVS) can be used to put Alexa inside a wide range of devices, and since it's in the cloud, Alexa can operate without dependency on a local device for computing power. With Amazon deep learning services, the Lex bot framework, and a smarter Alexa Skills Kit announced at AWS re:Invent this week, people can expect to see and interact with Alexa in places way beyond an Echo or animatronic fish.

From deep science to real-world breakthroughs: how artificial intelligence (AI) can have a positive impact on energy management. Watch Mustafa Suleyman, co-founder of DeepMind, exclusive talk on how Google used AI in their data centres to achieve 40% of energy reduction. You can find more talks from X Energy website with the likes of; John Elkington of Volans on the "exponential mindset", Scott Balloch of BT and Caroline Hill of Land Securities on the case for leadership and more. Tiffany is Event Manager at The Crowd. For further information, please contact Tiffany - tiffany@thecrowd.me

In this video, we first go over the history of video game AI, then I introduce OpenAI's Universe, which lets you build a bot that can play thousands of different video games. It has environments for all sorts of games, from Space Invaders, to Grand Theft Auto, to Protein folding simulations. For your README, just include a 1-3 sentence description of your strategy and instructions on how to run the code.The demo code can be found in the README of the Universe repo. They are awesome!! Please subscribe! That's what keeps me going.

In OpenAI's Universe, Computers Learn to Use Apps Like Humans Do Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

OpenAI, the billion-dollar San Francisco artificial intelligence lab backed by Tesla CEO Elon Musk, just unveiled a new virtual world. It's called Universe, and it's a virtual world like no other. It's a place where AI can learn to do just about anything. Other AI labs have built similar worlds where AI agents can learn on their own. Researchers at the University of Alberta offer the Atari Learning Environment, where agents can learn to play old Atari games like Breakout and Space Invaders. Microsoft offers Malmo, based on the game Minecraft.

What is the difference between AI, Machine Learning, NLP, and Deep Learning? AI (Artificial intelligence) is a subfield of computer science that was created in the 1960s, and it was/is concerned with solving tasks that are easy for humans but hard for computers. In particular, a so-called Strong AI would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic and includes all kinds of tasks such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc. NLP (Natural language processing) is simply the part of AI that has to do with language (usually written). Machine learning is concerned with one aspect of this: given some AI problem that can be described in discrete terms (e.g.

The advertising industry has made huge strides in targeting, but there's one more big step to take to make ads truly relevant: We need to adapt each creative message so it is interactive and personalized to every single individual. The key to achieving what may be the holy grail of advertising is a thriving branch of artificial intelligence known as deep learning. It uses algorithms to mimic neural networks' capabilities to recognize and act on abstract patterns. A retailer, for example, might send ads for sweaters to one segment, ads for bathing suits to another. Soon, instead of those few variations, a brand will have a million personalized versions of an ad dynamically presented to a million different individuals.