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.

One of the first questions I have asked no one in particular is: what will change when artificial intelligence is part of the fabric of day to day life? It sounds like a simple question, but it had a logical fallacy in that for the last 30 years, artificial intelligence (AI) projects were science experiments with high expectations and low delivery rates. The AI systems of the past were nowhere near as commonplace as the possibilities that were presented to us. We weren't even nearing the feats of the "The Engine," described in Jonathan Swift's Gulliver's Travels almost 300 years ago. Although Swift may have been skewering purposeless-science, The Engine actually sounds pretty useful right now. But before we get there, we have some things to solve.

Guest blog by Sebastian Raschka, originally posted here. If we tackle a supervised learning problem, my advice is to start with the simplest hypothesis space first. I.e., try a linear model such as logistic regression. If this doesn't work "well" (i.e., it doesn't meet our expectation or performance criterion that we defined earlier), I would move on to the next experiment. I would say that random forests are probably THE "worry-free" approach - if such a thing exists in ML: There are no real hyperparameters to tune (maybe except for the number of trees; typically, the more trees we have the better).

Let's punch through the noise around machine learning. The tech ecosystem is well acquainted with buzzwords. From "Web 2.0" to "cloud computing" to "mobile first" to "on-demand," it seems as though each passing year heralds the advent and popularization of new catchphrases to which fledgling companies attach themselves. But while the trends these phrases represent are real, and category-defining companies will inevitably give weight to newly coined buzzwords, so too will derivative startups seek to take advantage of concepts that remain ill-defined by experts and little-understood by everyone else. "It's clear that 9 of 10 investors have very little idea what AI is so if you're a founder raising money, you should sprinkle some AI into your pitch deck. Use of'artificial intelligence,' 'AI,' 'chatbot,' or'bot' are winners right now and might get you a little valuation bump or get the process to move quicker. If you want to drive home that you're all about that AI, use terms like machine learning, neural networks, image recognition, deep learning, and NLP. Then sit back and watch the funding roll in."

What do you get if you cross aspirin with ibuprofen? Harvard chemistry professor Alán Aspuru-Guzik isn't sure, but he's trained software that could give him an answer by suggesting a molecular structure that combines properties of both drugs. The AI program could help the search for new drug compounds. Pharmaceutical research tends to rely on software that exhaustively crawls through giant pools of candidate molecules using rules written by chemists, and simulations that try to identify or predict useful structures. The former relies on humans thinking of everything, while the latter is limited by the accuracy of simulations and the computing power required.

Demo day pitch: make your 5 minutes memorable - Duration: 8:45.

Besides your passport, what really defines your nationality these days? Is it where you were live? If it is, then despite an increase in "nationalism" (i.e., Brexit), we may see the idea of "nationality" quickly dissolve in the decades ahead… Or at least become an option you choose versus assume as a default. Residency, currency and language are rapidly being disrupted and dematerialized by technology. Increasingly, technological developments will allow us to live and work almost anywhere on the planet (and even beyond).

Japan plans to build the world's fastest supercomputer in a bid to arm its manufacturers with a platform for research that could help them develop and improve driverless cars, robotics and medical diagnostics. The Ministry of Economy, Trade and Industry will spend ¥19.5 billion ($173 million) on the previously unreported project, a budget breakdown shows, as part of a government policy to get back Japan's mojo in the world of technology. The country has lost its edge in many electronic fields amid intensifying competition from South Korea and China, which is home to the world's current best-performing machine. In a move that is expected to vault Japan to the top of the supercomputing heap, its engineers will be tasked with building a machine that can make 130 quadrillion calculations per second -- or 130 petaflops in scientific parlance -- as early as next year, sources involved in the project said. At that speed, Japan's computer would be ahead of China's Sunway Taihulight, which is capable of 93 petaflops.

What does a computer company want with a bunch of meteorologists? A few weeks ago, IBM announced it was acquiring The Weather Company, which owns Weather.com and Weather Underground, and the Wall Street Journal reported they were paying more than $2 billion for the privilege. According to The New York Times, while The Weather Company employs many atmospheric scientists and meteorologists, nearly three-quarters of its scientists work in data and computers. The Weather Company was already storing most of its data with IBM's cloud computing platform, and now Big Blue has access to all that data, which they can now sell to other companies who need to know about the weather. The ability to reliably predict the weather has always been important, but people wrongly assume that weather data is only useful to a handful of industries, like agriculture and transportation.