The year 2017 arrives and we humans are still in charge. The machines haven't taken over yet, but they are gaining on us. Google's DeepMind AlphaGo computer program recently beat the world champ at Go, a complex board game, while Japanese researchers plan to build the world's fastest supercomputer for use on artificial intelligence projects. It will do 130 quadrillion calculations per second, which is, um, really, really fast. She can explain it better than we can.

A big thank you to those of you who have been following the blog for some time now, and welcome to all of you joining for the first time in 2017! I spent the holiday break fine-tuning my writing and publishing process. The biggest difference regular readers will notice is that I've figured out a way to keep writing in Markdown, but have LaTeX math rendered in the blog as well as in the email newsletter (where I can't rely on the MathJax.js Previously I've been writing math expressions using plain HTML, which gets quite tedious, and also makes it hard to include some formulas! The LaTeX version looks much better, especially on the blog site. These don't look as crisp as the MathJax versions on the blog itself, but are still better than what we had before.

My speech will be about the most important about the grand theme of the 1st century which is the rise of artificial intelligence which is going o transform every aspect of our civilization and before we will look at the content rillettes have a brief look at the previous century what was the most important thing in the previous century the journal nature in 1999 made a list of the most influential inventions after twenty century and number one of class once the invention from 1908 which made the 20th century stand out among our centuries ever in the history of mankind because it was the one that drove the population explosion from 1.6 billion people in the year nineteen hundred too soon 10 billion it's a chemical thing and a high pressure and high temperature nitrogen is extracted from thin air to make still 500 million tons of artificial fertilizer for a year now without that stuff half of humankind would not even exist this planet could sustain at most four billion people without that one invention billions and billions and billions would never have lived without it and soon two out of three people on this planet will depend on this one single mention nothing else was remotely as influential as an however the way I explosion of the present century is going to be much more impactful and grander than that because that we are not talking about smaller numbers such as for or 10 but we are talking about trillions of trillions and this has a lot to do with the fact that computers are getting faster by a factor of 10 per euro per five years and this trend has held at least since nineteen forty one man cannot souza built the first working program controlled computer and nineteen forty one seventy five years gone every five years since then computers became roughly 10 times cheaper which means that now we have a factor of a million billions and this trend has been running for a long time but only recently we have approached the computational power of a small animal brain and in the near future for the first time for a thousand euros.

On Tuesday, the White House released a chilling report on AI and the economy. It began by positing that "it is to be expected that machines will continue to reach and exceed human performance on more and more tasks," and it warned of massive job losses. Yet to counter this threat, the government makes a recommendation that may sound absurd: we have to increase investment in AI. The risk to productivity and the US's competitive advantage is too high to do anything but double down on it. This approach not only makes sense, but also is the only approach that makes sense.

This book of projects highlights how TensorFlow can be used in different scenarios – this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with Tensors. Simply pick a project that is in line with your environment and get stacks of information on how to implement TensorFlow in production. Rodolfo Bonnin is a systems engineer and PhD student at Universidad Tecnológica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni Stuttgart, Germany.

Not a day goes by when we don't hear of something related to artificial intelligence in the news. But AI (sometimes confused with machine learning, which is simply a technique within AI) wouldn't be where it is today if it weren't for one seminal event in 2016: AlphaGo beating Lee Sedol. In March last year, an AI program that trained itself to play the ancient game of Go beat the 18-time world champion. The reason it was such a feat for AI, was because Go is about feel, strategic judgment and winning multiple battles across the board – and a computer cannot simply memorise all possible combinations of board pieces, assess the situation, construct and execute a strategy to win, like chess. So the programmers of AlphaGo, from Google DeepMind, set up the basic heuristics of the game, allowed AlphaGo to analyse previous games and then split its brain so it could play itself millions of times.

Note that there is no reason, in principle (at least that I'm aware of) that "formal logic" might not ultimately turn out to be an emergent aspect of a sufficiently complex ANN. Given that our brain can do formal logic, and does at times, and is "just" neural networks as far as we know, it seems likely. That said, in the short-term, I'm guessing we'll have hybrid systems that explicitly feature various AI techniques that have been developed over the years, sharing some representations (of concepts, knowledge, etc.). Maybe the "thought vectors" that Geoffrey Hinton has spoken of could be a step down that path. I'm also curious to see if something like rule induction using CN2 might not play a role.

Earlier in this series, we learned why IBM is focusing on investments and acquisitions in the IoT (Internet of Things) space. Let's see how AI (artificial intelligence) and ML (machine learning) could drive the expected $2 trillion in spending during the next new computing cycle. The SMAC (social, mobile, analytics, and cloud) revolution is rapidly transforming the technology space. The influx of data, the majority of which is unstructured, coupled with the advances in processing power and cognitive technology, has led to the necessity of machine learning to facilitate better-informed decisions. In today's scenario, understanding the content of images as well as organizing and extracting relevant information from raw media and data pose a significant challenge. The specialty of deep learning is that it can be deployed in structured and unstructured data and context.

On the west coast of Australia, Amanda Hodgson is launching drones out towards the Indian Ocean so that they can photograph the water from above. The photos are a way of locating dugongs, or sea cows, in the bay near Perth--part of an effort to prevent the extinction of these endangered marine mammals. The trouble is that Hodgson and her team don't have the time needed to examine all those aerial photos. There are too many of them--about 45,000--and spotting the dugongs is far too difficult for the untrained eye. Deep learning is remaking Google, Facebook, Microsoft, and Amazon.

Definitely! Asynchronous Deep Reinforcement Learning has really set a new standard to Deep Reinforcement Learning. One of its most important features is that you train a "global" model asynchronously through several agents, which basically means that you can have anywhere in between 8-32 agents training and sharing a global model at the same time. It requires far less memory than e.g. Experience Replay and also allows the agent to potentially converge much faster as it explores a broader state space asynchronously.