Associate Professor Julian Togelius works at the intersection of artificial intelligence (AI) and games--a largely unexplored juncture that he has shown can be the site of visionary and mind-expanding research. Could games provide a better AI test bed than robots, which--despite the way they excite public imagination--can be slow, unwieldy and expensive? According to him, the answer is resoundingly yes. "I'm teaching computers to be more creative than humans," he says. Togelius, a member of the NYU Tandon School of Engineering's Department of Computer Science and Engineering, is at the forefront of the study of procedural content generation (PCG)--the process of creating game content (such as levels, maps, rules, and environments) by employing algorithms, rather than direct user input.

Although the machine-learning discipline is more than 30 years old, the nascent technology is poised for a serious growth spurt that may put puberty to shame. "As machine learning becomes more mainstream and there's more understanding of how the technology works, I thing we will see exponential growth," said Drew Warren, president and CEO of smart-data processing vendor Recognos Financial. "As organizations' demand increases and grows more powerful, we see more organizations allotting more money to bring its development to the next level." Warren attributes machine learning's increased pace of evolution to better and more abundant enabling technologies, improved performance, and the rise of more specialized vendors that have the potential to reconfigure the technology's linear improvement curve into an exponential one. The advent of open-source resources like Google's TensorFlow machine-learning library has made development much easier, noted Nitin Rakesh, president and CEO of technology and knowledge-processing outsourcing provider Syntel.

Could you invent an autonomous hacking system that could find and fix vulnerabilities in computer systems before criminals could exploit them, and without any human being involved? That's the challenge faced by seven teams competing in Darpa's Cyber Grand Challenge in August. Each of the teams has already won 750,000 for qualifying and must now put their hacking systems up against six others in a game of "capture the flag". The software must be able to attack the other team's vulnerabilities as well as find and fix weaknesses in their own software – all while protecting its performance and functionality. The winning team will walk away with 2m.

A little-known lab in Menlo Park, California is responsible for many of the most exciting technologies we've seen over the last half-century. Initially founded in 1946 by Stanford University as The Stanford Research Institute, it's now separate from the university and goes by SRI International. But it's always been a non-profit dedicated to research and development. With 4,000 patents to its credit, SRI is fairly well-known in Silicon Valley, but most consumers have no idea it's been behind the scenes helping with everything from the computer mouse to the Siri voice assistant in your iPhone.

Apple's purchase of Emotient last month, a California-based firm that uses Artificial Intelligence (AI) to interpret emotions from facial expressions, signals that big changes are coming to mobile devices. The move means that another tech giant has entered the AI arms race alongside Google and Facebook. This fact alone should have brands learning about the new technologies and integrations available to better engage with consumers on an emotional level. AI's capacity to respond to mood, gesture, natural language, and other complex human behaviors solves many issues facing consumers and brands, including, discovery, attention, and ease of product/service use. The big three" tech companies – Google, Apple and Facebook -- are leveraging AI in different ways to gain a competitive advantage in an economy where attention is a scarce resource.

Last week artificial intelligence, for the first time in history, secured a definitive victory over a grandmaster Go player. While chess playing humans were outpaced by computer brains almost two decades ago, Go is multitudes more complex than chess, with an estimated 10761 possible games (Chess tops out around 10120). Given this complexity, experts didn't expect artificial intelligence to be able to beat a master Go player for another ten years. But this news shouldn't send Go players into a panic about the coming robot insurrection. Chess players, for the most part, have chilled out about the fact that computers are now much better than we are.

Chip Somodevilla / GettyRobot parrots aren't the only reason to look over your shoulder. Artificial intelligence is going bananas right now. Google made headlines with it huge victory in the ancient game of Go a few weeks ago. And AI is entering into the marketplace at a historic rate, changing industries as complex as Wall Street in the process. If you get the feeling that we're at the start of a tidal wave, you might be right -- take it from Nvidia CEO Jen-Hsun Huang.

It's only March and already we've seen a computer beat a Go grandmaster and a self-driving car crash into a bus. The world is waking up to the ways in which a combination of "deep learning" artificial intelligence and robotics will take over most jobs. But if we don't want our robot servants to rise up and kill us in our beds, maybe we should delete the video of us beating their grandparents with hockey sticks. Thanks to science fiction, we know that the first thing AI will do is take over the defence grid and nuke us all. In Harlan Ellison's 1967 story I Have No Mouth, and I Must Scream – one of the most brutal depictions of an AI-dominated world – an AI called AM, constructed to fight a nuclear war, kills off most of the human race, keeping five people as playthings.

Are we looking for an algorithm that "If we all die, it would keep trading"? Should we be worried that electronic trading is mushrooming like airplane traffic, while we are not paying that much attention? Today, I'll look for AI pigments of incremental changes in algorithmic trading, first on Wall Street and then outside, in the Fintech startup world. I am not including the HFT space because it is a particular space driven by speed and merits a separate post because of its politically sensitive angle (Michael Lewis's babe). Renaissance Tech and Two Sigma, are probably the most recognizable names in old fashioned quant trading space.

It's a weird feeling, cruising around Silicon Valley in a car driven by no one. I am in the back seat of one of Google's self-driving cars – a converted Lexus SUV with lasers, radar and low-res cameras strapped to the roof and fenders – as it maneuvers the streets of Mountain View, California, not far from Google's headquarters. I grew up about five miles from here and remember riding around on these same streets on a Schwinn Sting-Ray. Now, I am riding an algorithm, you might say – a mathematical equation, which, written as computer code, controls the Lexus. The car does not feel dangerous, nor does it feel like it is being driven by a human. It rolls to a full stop at stop signs (something no Californian ever does), veers too far away from a delivery van, taps the brakes for no apparent reason as we pass a line of parked cars. I wonder if the flaw is in me, not the car: Is it reacting to something I can't see? The car is capable of detecting the motion of a cat, or a car crossing the street hundreds of yards away in any direction, day or night (snow and fog can be another matter). "It sees much better than a human being," Dmitri Dolgov, the lead software engineer for Google's self-driving-car project, says proudly. He is sitting behind the wheel, his hands on his lap. As we stop at the intersection, waiting for a left turn, I glance over at a laptop in the passenger seat that provides a real-time look at how the car interprets its surroundings. On it, I see a gridlike world of colorful objects – cars, trucks, bicyclists, pedestrians – drifting by in a video-game-like tableau. Each sensor offers a different view – the lasers provide three-dimensional depth, the cameras identify road signs, turn signals, colors and lights. The computer in the back processes all this information in real time, gauging the speed of oncoming traffic, making a judgment about when it is OK to make a left turn.