For about a year, Sam Fox-Hartin had worked for an on-demand concierge startup called GoButler as a "Hero," the company's term for employees who field users' requests, via text message, and then complete tasks such as booking tables at restaurants, scheduling appointments, or ordering food for delivery on their behalf. Most of these tasks, like the ones I watched Fox-Hartin maneuver when GoButler invited me to visit its New York headquarters last year, were fairly routine. But he also wrote poems. Convinced couriers to deliver dry ice. And in response to one particularly odd request, drew "some horses hanging around a campfire."

When DeepMind's AlphaGo defeated South Korean master Lee Se-dol, it was a historic stride for AI. The depth of this development, coupled with higher computing power and cheaper data storage, is moving AI into the mainstream. Perhaps the most popular application of AI today comes in the form of virtual assistants and bots, or "agents" as my good friend Shivon defines them. An agent can schedule your meetings, manage your finances, book your travels, order your meals, and more. And even though these agents are typically focused on one specific task, it's remarkable to consider how much progress we have made outsourcing mundane work for a fraction of the cost.

The latest computer games can be fantastically realistic. Surprisingly, these lifelike virtual worlds might have some educational value, too--especially for fledgling AI algorithms. Adrien Gaidon, a computer scientist at Xerox Research Center Europe in Grenoble, France, remembers watching someone play the video game Assassins Creed when he realized that the game's photo-realistic scenery might offer a useful way to teach AI algorithms about the real world. Gaidon is now testing this idea by developing highly realistic 3-D environments for training algorithms how to recognize particular real-world objects or scenarios. The idea is important because cutting-edge AI algorithms need to feed on huge quantities of data in order to learn to perform a task.

South Korea, well known for its IT infrastructure, is promising 3.5 trillion won ( 3 billion) in funding from the public and private sectors to develop artificial intelligence for corporate and university AI projects. South Korea's President Park Geun-hye assembled leaders across the country's tech industry and senior government officials in Seoul last week to announce plans to invest the amount over the next five years. It appears to be largely a reaction to the phenomenal performance of Google's algorithm AlphaGo in an historic AI-versus-human game in Seoul earlier this month, which captured the South Korean media's imagination. "Above all, Korean society is ironically lucky, that thanks to the'AlphaGo shock' we have learned the importance of AI before it is too late," the president told local reporters assembled for the meeting, describing the game as a watershed moment of an imminent "fourth industrial revolution". It also calls for the private sector to match the public sector's commitment with 2.5 trillion won ( 2.14 billion).

With ever increasing data, Machine Learning is becoming the only way to get analytics done, making it possible to glean insights from vast amounts of data. But when starting a project, it is easy to ignore a critical fact: the value of data is also time sensitive – it expires! In order to get the highest value from data, Machine Learning needs to be applied in a rapid and repeatable way, so you can go from data to insight quickly. A Machine Learning API makes this possible. In this workshop, Poul Petersen CIO of BigML will give an overview of BigML's Machine Learning API and then show real-world examples of predictive applications that can be built using Python and node.js. Several tools that have been built on top of BigML's API will be demonstrated including a loan risk assessment, real estate arbitrage, and the world's first voice controlled predictive assistant.

If anyone needed evidence that the'AI winter' is long over, they need only to look to the snowy mountain town of Davos this January. Convening for the World Economic Forum, along with important geo-political issues, the world's movers and shakers had the'fourth industrial revolution' at the top of the agenda. Amidst the discussion of the exciting digital opportunities offered by today's'sharing economy', AI was never far from view. The world's elite are clearly aware that AI is bringing about a fundamental societal shift – one that is rapidly reinventing the world in which we live. The conversations that took place at the World Economic Forum are an important reminder that digital platforms have turned our global economy and workforce into an interconnected, AI-driven machine. Over the past two decades, we have witnessed revolutionary changes in how consumers and businesses connect through technology – and consequently in the way the labour market operates.

Last term, I ended up taking three machine learning courses, along with a couple of others. Out of all of the stuff I did last term, the biggest and best thing I did was work with a few friends on a group project. Now this might sound like nonsense to most people, "how can you hear my heartrate? And you you can detect my heart rate from video? witchcraft!?". In fact you would be almost right.

A UF club is teaching students about creating systems with artificial intelligence. Nicholas Kroeger, a UF computer science sophomore, and John Henning, a UF computer science and mathematics junior, recently established an artificial intelligence club after realizing UF doesn't offer courses on AI for undergraduate students. The club, which will meet Wednesdays at 6:30 p.m., teaches students how to build computers and software capable of intelligent behavior. "There's no undergraduate classes that I saw that I could take for AI and there's no AI undergraduate major, which I think is really important," Kroeger, 19, said. "I'd like to create those later on in life if I choose to pursue a Ph.D." Bernard Marger, a UF computer engineering senior, said UF used to have an undergraduate artificial intelligence curriculum, but it discontinued when the professor retired.

In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. Thereby we will see that a neuron and a convolutional net are very similar information processing machines. While performing this comparison, I will also discuss the computational complexity of these processes and thus derive an estimate for the brains overall computational power. I will use these estimates, along with knowledge from high performance computing, to show that it is unlikely that there will be a technological singularity in this century. This blog post is complex as it arcs over multiple topics in order to unify them into a coherent framework of thought. I have tried to make this article as readable as possible, but I might have not succeeded in all places.

China's ambitions to build its own version of AlphaGo or Sophia the Robot could be a boon for voice technology company iFlytek and surveillance equipment maker Hangzhou Hikvision Digital Technology. While China lags the U.S. in hardware like advanced circuit boards that are key to becoming an artificial intelligence powerhouse, the two Chinese companies showcase the nation's emerging strength in other key technologies that are vital to helping advanced computers learn: algorithms and big data.