The machine learning community has coalesced around Google's TensorFlow library. Interestingly, one major holdout was DeepMind, which did most of its research on the Torch7 library. Then, late last month, DeepMind announced it was moving to TensorFlow as well -- it was already using it for portions of AlphaGo. Why does any of this matter? Well, with standardization comes the opportunity for optimization, and Google has gone wild with optimization in this case.

Given that the reality of AI may be fast approaching, it's of the utmost importance that we work out what might a future with artificial intelligence might look like. Last year, an open letter with signatories including Stephen Hawking and Nick Bostrom called for AI to be of demonstrable benefit to humanity, or risk something that exceeds our ability to control it. AI, as conceived of in popular culture, does not yet exist, even if autonomous and expert systems do. Smartphones might not be supercomputers, but they are called "smartphones" for good reason, in terms of how their operating systems function. Equally, we are happy to talk about a computer game's "AI", but gamers quickly learn to take advantage of its limitations and inability to "think" creatively.

It's a known fact that bagging (an ensemble technique) works well on unstable algorithms like decision trees, artificial neural networks and not on stable algorithms like Naive Bayes. The well known ensemble algorithm Random forest thrives on the ability of bagging technique which leverages the'instability' of decisions trees, to help build a better classifier. Even though, random forest attempts to handle the issues caused by highly correlated trees, does it completely solve the issue? Can the decision trees be made more unstable than what random forest does, so that the learner be even more accurate? If trees are sufficiently deep, they have very low bias.

Some of McCarthy's colleagues in neighboring departments, however, were more interested in how intelligence is implemented in humans (and other animals) first. Noam Chomsky and others worked on what became cognitive science, a field aimed at uncovering the mental representations and rules that underlie our perceptual and cognitive abilities. Chomsky and his colleagues had to overthrow the then-dominant paradigm of behaviorism, championed by Harvard psychologist B.F. Skinner, where animal behavior was reduced to a simple set of associations between an action and its subsequent reward or punishment. The undoing of Skinner's grip on psychology is commonly marked by Chomsky's 1959 critical review of Skinner's book Verbal Behavior, a book in which Skinner attempted to explain linguistic ability using behaviorist principles. Skinner's approach stressed the historical associations between a stimulus and the animal's response -- an approach easily framed as a kind of empirical statistical analysis, predicting the future as a function of the past.

First there was TensorFlow, Google's machine learning framework. Then there was SyntaxNet, a neural network framework Google released to help developers build applications that understand human language. What comes next is anyone's guess, but one thing is clear: Google is aggressively open-sourcing the smarts behind some of its most promising AI technology. Despite giving it away for free, however, Google is also apparently betting that "artificial intelligence will be its secret sauce," as Larry Dignan details. That "sauce" permeates a bevy of newly announced Google products like Google Home, but it's anything but secret.

The artificial-intelligence routine is far from perfect, of course. Google says Parsey McParseface reaches about 94 percent accuracy identifying the root of an English sentence taken from a newspaper. As for the competition, you can play with a version of the Stanford NLP parser here. A simpler, but much faster parser is spaCy, which has a demo here. You have todownload Parsey McParseface to your computer; it's a little tricky.)

Google has designed and deployed a custom chip for driving its machine learning technologies. The chips are custom made to work with TensorFlow, Google's machine learning platform. The technology was stealthily developed, and is already deployed for Street View, Inbox Smart Reply and RankBrain, which is the brains behind delivering more relevant search results. The chips are named Tensor Processing Units (TPU). It delivers better performance optimisation per unit of electricity consumption, an order of magnitude better than any other product in the market.

Teaching the machines involve a structural process where every stage builds a better version of the machine.

It happens hundreds of times a day: We press snooze on the alarm clock, we pick a shirt out of the closet, we reach for a beer in the fridge. In each case, we conceive of ourselves as free agents, consciously guiding our bodies in purposeful ways. But what does science have to say about the true source of this experience? In a classic paper published almost 20 years ago, the psychologists Dan Wegner and Thalia Wheatley made a revolutionary proposal: The experience of intentionally willing an action, they suggested, is often nothing more than a post hoc causal inference that our thoughts caused some behavior. The feeling itself, however, plays no causal role in producing that behavior. This could sometimes lead us to think we made a choice when we actually didn't or think we made a different choice than we actually did.

At the Stanford shopping center in Palo Alto, California, there is a new sheriff in town – and it's an egg-shaped robot. Outside Tiffany & Co, an unfortunate man holding a baby finds himself in the robot's path. It bears down on him, a little jerkily, like a giant Roomba. The man dodges but the robot's software is already trying to avoid him, so they end up on a collision course. "I've seen Terminator," the man says, half to himself and half to the amused crowd, "and that is some Skynet-ass shit."