The venture capital community is seeing literally hundreds, if not thousands, of new companies focused on applying artificial intelligence to resolving business issues and improving consumer experience on and offline. This explosion of interest, on both sides of the Atlantic, is driven by the well-known trends of big data, faster processing speeds, more bandwidth and increasing broadband access. The market numbers are staggering: a financial services firm issued a 300-page report in 2015 explaining why the AI market is projected to grow to $153 billion by 2020: that's $83 billion for robotics and $70 billion for AI-based analytics. The joke in the VC industry is that any start-up that claims to use AI will expect at least a 20 percent valuation premium. There will indeed be great value created but also lots of wasted experiments.

The Machine Learning Canvas is a template for developing new (or documenting existing) intelligent systems based on data and machine learning. It is a visual chart with elements describing the key aspects of such systems: the value proposition, the data to learn from (to create predictive models), the utilization of predictions (to create proposed value), requirements and measures of performance. It assists teams of data scientists, software engineers, product and business managers, in aligning their activities. This tutorial will help you get into the right mindset to go beyond the current hype around machine learning, beyond proofs of concept, and to clearly see how this technology can have an actual impact in your domain. I'll present the general structure of the Canvas, the different boxes it is composed of and the associated questions to answer. We'll see how to fill it in iteratively on a churn prevention example.

Enter a bookstore, while they still exist. Walk toward the philosophy section, toward shelves of fat books by Plato, Nietzsche, Spinoza. Perhaps you browse through their pages before putting them back in their place, respectfully but with a bit of a yawn. More appealing, perhaps: the books at the front of the store, the best sellers, the ones that portend crises (Rise of the Robots: Technology and the Threat of a Jobless Future); others advise on surviving one (Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence). The business best sellers are more gung-ho about the changes to come: Zero to One: Notes on Startups, or How to Build the Future.

In March 2016, AlphaGo, a computer program developed by Google's London-based DeepMind subsidiary, beat leading professional Go player Lee Se-dol in four games out of five. It was a result that surprised the Go and technology communities in equal measure, and one that has been heralded as a breakthrough in artificial intelligence. The rules of Go are simple, but the sheer number of possible moves available to the players means 2,500-year-old game is considered significantly harder than chess. IBM's Deep Blue computer beat chess champion Garry Kasparov in 1997 but, until 2015, Go programs had only managed to play as well as good amateurs. As significant as AlphaGo's level of competence is the way it was achieved. Rather than basing its decisions on explicit rules about the relative value of different moves, as chess computers do, AlphaGo "taught" itself how to play well, running millions of game simulations against versions of itself and gradually adjusting its algorithms to achieve better results.

Over the past few decades, artificial intelligence, or AI, has morphed from science fiction into an integral part of 21st century life. And what we've seen so far is just the beginning. Experts expect its use to skyrocket in coming years, and market researcher IDC forecasts that by 2020 spending on AI will rise nearly 500 percent to $47 billion from current levels. As Goldman Sachs (GS) noted in a recent report to clients, AI's potential appears boundless. IBM's (IBM) Jeopardy-playing supercomputer Watson may be the technology's best-known example.

At BlizzCon earlier this month in Anaheim, California, Blizzard announced an ambitious new project in collaboration with DeepMind, a leading artificial intelligence research company acquired by Google in 2014. After creating the AlphaGo AI that bested the world's top Go player earlier this year, DeepMind's next groundbreaking challenge will be StarCraft II. If DeepMind is able to build an AI that could learn how to beat top players such as Byun "ByuN" Hyun Woo in the complex real-time strategy, tactics and resource management of this game, it would be a giant step forward in AI research. And with DeepMind's interest in using its research to solve hard problems in areas such as healthcare and energy efficiency on a massive scale, this Starcraft II project could impact the whole world. Soon after AlphaGo's Go victory, there were signs that DeepMind would take on StarCraft next. This was not lost on legendary StarCraft player/commentator and former competitive chess player Dan "Artosis" Stemkosi, for whom StarCraft seemed like the logical next step for AI research after games like chess and Go.

Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: "If you have to do this task more than 3 times just write a script and automate it." By now, you may have already started wondering about this blog. I haven't written anything for more than half a year! Okay, musings on social network platforms aside, that's not true: I have written something – about 400 pages to be precise. This has really been quite a journey for me lately. And regarding the frequently asked question "Why did you choose Python for Machine Learning?"

Google DeepMind's artificial intelligence team, alongside researchers at the University of California, Berkeley, has trained AI machines to interact with objects in order to evaluate their properties without any prior awareness of physical laws. The research project drew inspiration from child development and sought to train AI to mirror human capacity to interact with physical objects and infer properties such as mass, friction, and malleability. The study, entitled Learning to perform physics experiments via deep reinforcement learning, explained that while recent advances in AI have achieved'superhuman performance' in complex control problems and other processing tasks, the machines still lack a common sense understanding of our physical world – 'it is not clear that these systems can rival the scientific intuition of even a young child.' Lead researcher Misha Denil and his team set about various trials in different virtual environments in which the AI was faced with a series of blocks and tasked with assessing their properties. In the first simulation, called Which is Heavier, the AI was given a set of four blocks which were the same size but varied in mass.

The way we watch sport, the way we shop, the way we meet each other, the way we interact with machine intelligence -- everything we can imagine is now possible by adding a crystal clear layer of visual genius over our real world. The Fourth Transformation is based on two years of research and about 400 interviews with technologists and business decision makers. It explains the technology and product landscape on a level designed to be interesting and useful to business thinkers and general audiences. Mostly it talks about how VR and AR are already being used, or will be used in the next one-to-three years. It explains how this massive and fundamental transformation will be driven, nit just by Millennials, but by the generation following them, which the authors have named the Minecraft Generation.

ORLANDO, Fla., October 18, 2016 View All Press Releases Gartner Reveals Top Predictions for IT Organizations and Users in 2017 and Beyond Analysts Explore the Digital Future at Gartner Symposium/ITxpo 2016, October 16-20 in Orlando Gartner, Inc. today revealed its top predictions for 2017 and beyond. Gartner's top predictions for 2017 examine three fundamental effects of continued digital innovation: experience and engagement, business innovation, and the secondary effects that result from increased digital capabilities. "Gartner's top strategic predictions continue to offer a provocative look at what might happen in some of the most critical areas of technology evolution. At the core of future outcomes is the notion of digital disruption, which has moved from an infrequent inconvenience to a consistent stream of change that is redefining markets and entire industries," said Daryl Plummer, managing vice president, chief of research and Gartner Fellow. "Last year, we said digital changes were coming fast. This year the acceleration continues and may cause secondary effects that have wide-ranging impact on people and technology."