Artificial intelligence (AI) that can understand and answer any work-related question it is asked has been made available in the UK for the first time. The computer software, called Starmind, uses machine learning to understand queries, then finds answers from previous staff conversations on a subject or tracks down experts in the company who are able to help. Its creators refer to it as "brain technology", adding its aim is to become a central knowledge bank within any company, an instant database of information that can be accessed by anyone. Starmind co-founder Pascal Kaufmann said of the technology: "Thousands of human brains connected can outsmart any machine today. "But if you can find ways for humans and AI-inspired technologies to autonomously collaborate rather than focusing on ways for them to compete, you can bring out the best in both." The algorithm within the system, which was developed in Switzerland, becomes more powerful the more it is used and is able to build a map of the people in a business and the areas in which all of them are experts, or are able to provide relevant information. "Starmind acts like an artificial hyperbrain that seamlessly exists at the core of a company," Mr Kaufmann added. "The algorithm is then fuelled by the know-how stored inside the brains of everyone that engages with the system." Several major companies in Europe, including UBS and Bayer, are using the system. A new version of the software - called Starmind NOW - has also been launched. It enables the software to be accessed outside a company intranet for the first time. Starmind says that makes the technology more "intuitive and seamless" to use. Former Microsoft executive Peter Waser has also joined the company as CEO. "It's a new technology that has never been available on the market in this form," he said. "Brain technology is the latest technology in the megatrend of machine learning and artificial intelligence.

Look, Chrome is a great browser. It's feature-packed, overstuffed with fun extensions, and keeps your digital life organized across multiple devices. But maybe its insatiable resource-lust is bringing your laptop down. Maybe Google gives you the privacy willies. Or hey, it first showed up on Macs in 2009--maybe you've just got a seven-year itch.

Input: a) A set of labelled examples where every is the input vector, and is the corresponding output label. Output: The set of expected labels for all instances in . There are two ways (or rather, two philosophies) you could use, to solve this problem. Induction, in the context of learning, is the attempted discovery of rules/generalizations based on analysis of collected data. 'Attempted discovery' is the key term here – the generalizations are not facts, but approximations based on evidence you have gathered.

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.