This time, Sebastian explains the difference between Deep Learning and "regular" machine learning. That's an interesting question, and I try to answer this is a very general way. The tl;dr version of this is: Deep learning is essentially a set of techniques that help we to parameterize deep neural network structures, neural networks with many, many layers and parameters. And if we are interested, a more concrete example: Let's start with multi-layer perceptrons (MLPs)… On a tangent: The term "perceptron" in MLPs may be a bit confusing since we don't really want only linear neurons in our network. Using MLPs, we want to learn complex functions to solve non-linear problems.

German engineering powerhouse Siemens today announced that it has set up an innovation unit named'next47' (as the company was founded back in 1847) to "foster disruptive ideas more vigorously and to accelerate the development of new technologies", more specifically in the fields of artificial intelligence, blockchain, autonomous machines and what it calls'decentralized electrification'. Effective October 1, 2016, the unit intends to pool all of Siemens' existing startup activities, fuelled by 1 billion in funding for the first five years. Siemens CTO Siegfried Russwurm will head the new unit on an acting basis. "Siemens itself was a startup in 1847 – founded in a rear courtyard in Berlin," said Joe Kaeser, president and CEO of Siemens. "With next47, we're living up to our company founder's ideals and creating an important basis for fostering innovation as we continue Siemens' development." Next47 will have offices in Berkeley, Shanghai and Munich and cover all regions of the world from those locations.

One additional note: unlike the wall-of-equations that make up most machine learning papers, the IML literature is profoundly inviting and largely friendly to non-experts. I encourage you to dive into the original papers wherever a particular topic piques your interest. I've gathered links to all of the papers in Knox's syllabus here to make doing so especially convenient.

In a 1942 short story called Runaround, science fiction author Isaac Asimove formulated his famous'Three Laws of Robotics'. As per the Handbook of Robotics, 56th Edition, 2058 AD, the three laws are: A robot may not injure a human being or, through inaction, allow a human being to come to harm; robot must obey the orders given it by human beings except where such orders would conflict with the First Law; and a robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. While Asimove created these laws as a literary device - both to provide an ethical framework for sentient machines that were smarter than humans, and to find drama in situations where, inevitably, the laws came across a loophole, or became self-contradictory - today's world needs ethical guidelines for machine intelligence that can soon become so smart that they leave humans far behind. In a piece at Slate.com, Nadella, lays down his own laws for AI. AI must be designed to assist humanity.

Today was a day for the history books. The UK has voted to leave the European Union and opened a deep crack in the heart of Europe. As a consequence of this result, Prime Minister David Cameron will step down by October urging for a fresh leadership. At this point nobody knows the repercussions of these results. Will the Brexit hurt the economy of the UK and ignite a new recession?

In sympathy with yesterday's post about AI as presented in films, consider this recent article from the Wall Street Journal: Artificial Intelligence Experts are in High Demand. A list of mostly machine learning experts is produced as evidence for the topic of the article. There is an unfortunate trend being presented to the public in this space in which the term'artificial intelligence' is being used to draw readers with stories of real technical achievements in the space of machine learning and machine perception (recognizing a cat in a image is not an act of artificial intelligence), movies are being produced that romanticize a form of unobtainable AI, and the two are being tied together with stories of impending doom (Musk, Hawking). All this is done with little or no investment in helping us establish what we really mean – and need – in an artificial intelligence. If artificial intelligence experts were in high demand, then linguistics, philosophers, sociologists, etc. should be very happy – not just ML peeps.

The purpose of this book is to help to spread TensorFlow knowledge among engineers who want to expand their wisdom in the exciting world of Machine Learning. We believe that anyone with an engineering background might require from now on Deep Learning, and Machine Learning in general, to apply it in their work. As the title indicates, it is a first contact with TensorFlow in order to get started with Deep Learning programming. The book has a practical nature, and therefore it reduces the theoretical part as much as possible, assuming that the reader has some basic understanding about Machine Learning.

Here are upcoming meetings and conferences, for July 2016 and beyond. Save 10% with the KDNUGGETS registration code. Aug 29 - Sep 1, Image Processing, Computer Vision and Machine Learning based on Optimization and PDE. Use code CDOINSUR to save 10% on registration. Sep 23, MLconf Atlanta Machine Learning Conference - mention "KDNuggets" and save 18%.

Apps that help you translate different languages can be extraordinary helpful, but sometimes you want to understand a foreign language immediately. In a decade, that could very well be a reality thanks to advancements in artificial intelligence, Greg Corrado, the co-founder of Google's deep learning project dubbed Google Brain, said in a roundtable discussion with journalists Thursday. In 10 years, it will be possible to have a meeting where everyone speaks a different language, but can understand one another by wearing special earbuds, he said. "That is totally science fiction today, but it's the kind of thing I'd take the 10-year bet on," he said. To make that technology a reality, there will need to be advancements in a type of artificial intelligence called machine learning, or training computers to learn on their own.

In 1973, the statistician Francis Anscombe devised a fascinating demonstration showing why data should always be plotted before it is analyzed. The demonstration consisted of four data sets that had almost identical statistical properties. By this measure they are essentially the same. But when plotted, the data sets look entirely different. Anscombe's quartet, as it has become known, shows how good graphics allow people to analyze data in a different way, to think and talk about it on another level. Most scientists recognize the importance of good graphics for communicating complex ideas.