Today in Washington DC, leading US and UK scientists are meeting to share dispatches from the frontiers of machine learning – an area of research that is creating new breakthroughs in artificial intelligence (AI). Their meeting follows the publication of a set of principles for beneficial AI that emerged from a conference earlier this year at a place with an important history. In February 1975, 140 people – mostly scientists, with a few assorted lawyers, journalists and others – gathered at a conference centre on the California coast. A magazine article from the time by Michael Rogers, one of the few journalists allowed in, reported that most of the four days' discussion was about the scientific possibilities of genetic modification. Two years earlier, scientists had begun using recombinant DNA to genetically modify viruses. The Promethean nature of this new tool prompted scientists to impose a moratorium on such experiments until they had worked out the risks.

Four years ago, Neil Mehta was among the 15 million people who watched Ken Jennings and Brad Rutter--the world's greatest "Jeopardy!" "Jeopardy!" is a television game show in which the host challenges contestants with answers for which they must then supply the questions--a task that involves some seriously complicated cognition. Artificial-intelligence experts described Watson's triumph as even more extraordinary than IBM supercomputer Deep Blue's history-making 1997 defeat of chess grandmaster Garry Kasparov. To an AI aficionado, Watson was a tour de force of language analysis and machine reasoning. To Mehta, a physician and professor at the world-renowned Cleveland Clinic, Watson was a question unto itself: What might be possible were Watson's powers turned to medicine? "I love technology, and I was rooting for Watson," says Mehta. "I knew that the world was changing.

Information technologies are accelerating at an exponential rate, ushering in the fourth industrial revolution. This is a digital revolution and the pace of change is unprecedented. This revolution incorporates machine learning (think parallel processing and neural networks) and the concept of self-assembly or self-programmability. As technologies continue to advance, they accelerate the progress of other technologies, and so on, and so on. Thanks to parallel processing, big data, cloud technology, and advanced algorithms, Artificial Intelligence (AI) and machine learning are becoming more powerful.

I didn't worry too much when computers beat humans at checkers, chess, or Go. It was, after all, only a matter of time before someone built a powerful computer with a vast database of known game situations. But now that machines are beating professionals at poker -- a game of imperfect information -- a question must be asked: Is artificial intelligence starting to threaten people in creative jobs? In terms of game complexity -- the number of allowed positions reachable in the course of a game -- no-limit Texas Hold'em poker, isn't an AI researcher's worst nightmare. The chess game tree has 10 to the 120th degree nodes. The one for Go has 10 to the 170th degree.

DeepMind is a British AI startup which was relatively unknown until it was bought by Google for around $600 million in 2014. Since then DeepMind has continued to refine its neural-network driven technology which has broken new frontiers with machine learning, particularly deep learning. Perhaps DeepMind's most famous accomplishment so far is being the brains behind AlphaGo, the first computer program to beat a professional human player of the board game Go. AlphaGo was developed by feeding DeepMind's machine learning algorithms with 30 million moves from historical tournament data, and then having it play against itself and learn from each defeat or victory. DeepMind's work is based on a solid grounding in neuroscience.

To date, no computer has ever beaten the world's best poker players when it comes to Texas Hold'Em. However, that could all be about to change during a twenty-day contest in Pittsburgh where even some of the best specialists are saying they don't have a chance this time and it's all down to artificial intelligence. The new AI machine in question was designed by two computer scientists at Carnegie Mellon and is called Libratus which is Latin for balanced. Dong Kim is a professional poker player who specializes in Texas Hold'Em and is among the best in the world at this game. Although he typically competes against others on high-stakes internet sites or Las Vegas casinos, this time Kim's traveled to Pittsburgh to try and defeat the mighty Libratus even though he feels doubtful.

They cover more than 70% of the earth's surface and the sheer size of the oceans makes tracking and measuring life under water an enormous task. New advances in satellite observation, open data and machine learning now allow us to process the massive amounts of data being produced. And they could not have come at a better time for protecting life under water, which is United Nations' Sustainable Development Goal No. 14 (SDG14). Last year was particularly challenging for most life underneath our world's oceans. Despite the dearth of bad news for the world's oceans, there is hope that 2017 can turn the tide for life under the sea thanks, in part, to machine learning.

Through techniques like machine learning, our devices are finally beginning to understand us on a much more fundamental level than ever before. Though true artificial intelligence is still not here quite yet, contextual data storage combined with the simplicity of almost perfected speech recognition has changed the way we interact with our devices, and will only be iterated upon until technology is so seamless that we will forget we are even using it. It seems quite clear that AI will be the future, but what about AR? Will augmented reality integrate with artificial intelligence to make our lives as simplistic as possible? Let's take a look a few possible scenarios, along with technologies on the market today that seem to be headed towards this transition. Artificial intelligence is used to describe a technology that can make decisions based on varying efficiency algorithms.

Parag Mital is director of machine intelligence at Kadenze, as well as an artist and interdisciplinary researcher obsessed with the nature of information, representation and attention. Artificial intelligence allows machines to reason and interact with the world, and it's evolving at a breakneck pace. Many advances in AI can be attributed to machine learning, which works by tapping massive computing power to crunch through enormous amounts of digitized data. Now consider that most of our data, the best minds in the business and more computing power than you could ever imagine sit with just a handful of companies. For these reasons, only a few companies in the world are best situated to understand the true potential -- and the current limits -- of AI.

How can we compare education and artificial intelligence? Well, some of you may say that we should include more courses related to artificial intelligence (AI). Yes, we should include artificial intelligence in the course, but I'm not talking about this type of connection here. I'm trying to emphasize on the advancement of AI here, and why education should be like AI in this article. AI is everywhere nowadays from our cars to our pockets.