Written on 13 August 2016. Socrates started what may have been the first technology scare. In the "Phaedrus," he lamented the invention of books, which "create forgetfulness" in the soul. Instead of remembering for themselves, Socrates warned, new readers were blindly trusting in "external written characters." The library was ruining the mind. Needless to say, the printing press only made things worse.

Artificial intelligence (AI) is the next big thing in the world of computing, and it is expected to change the way we look at computers. Even the tools and methods used to create AI (machine learning, deep neural networks, etc.) are starting to power everything from search engines to your Facebook feed. Microsoft wants to stay ahead in that game and has announced the way it plans to do so: via the cloud. Microsoft CEO Satya Nadella has boasted that Microsoft's Azure Cloud will be the world's first AI supercomputer, and that sounds like a match made in heaven. AI, machine learning, neural networks…all of those require massive processing power, and the cloud is the perfect vehicle to deliver that power as it can utilize multiple processors and devices instead of relying on one big processor.

Dean Pomerleau can still remember his first tussle with the black-box problem. The year was 1991, and he was making a pioneering attempt to do something that has now become commonplace in autonomous-vehicle research: teach a computer how to drive. This meant taking the wheel of a specially equipped Humvee military vehicle and guiding it through city streets, says Pomerleau, who was then a robotics graduate student at Carnegie Mellon University in Pittsburgh, Pennsylvania. With him in the Humvee was a computer that he had programmed to peer through a camera, interpret what was happening out on the road and memorize every move that he made in response. Eventually, Pomerleau hoped, the machine would make enough associations to steer on its own.

The Pixel smartphones and the Google Home are amazing new Google devices. But they're not the best thing Google unveiled on stage during Tuesday's media event. You won't be able to buy it in a store by itself. It will come prepackaged in Pixels, Homes and other smart devices Google launches in the future. It's the Google Assistant, the intelligent, voice-activated AI-powered computer that can respond to intricate queries, and predict what you're going to need next.

PONG is a gloriously simple video game: you control one paddle, aiming to bounce the ball past your opponent's paddle. Artificial intelligence has learned to play it so well that it can easily beat human players. But try to get the same AI to play Breakout, a very similar paddle-based game, and it is utterly stumped. It can't reuse what it has learned about paddles and balls from Pong, and has to learn to play from scratch. Computers can learn without our guidance, but the knowledge they acquire is meaningless beyond the problem they are set.

Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to improve safety, performance, and reliability in today's modern wind turbines.

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Machine learning, which involves programs that get more accurate with experience, is fundamentally different from any kind of computing that's come before. "There's always been a simple division of labor: machines do number crunching, and humans make decisions," says Pradeep Dubey, an Intel Fellow at the company's Intel Labs division. Machine-learning programs--and in particular the high-profile deep-learning subset that can teach themselves--are different. These programs have the potential to discover new drug compounds or identify consumer trends without human intervention. For Dubey and others at Intel, it was clear that they needed to find a way to make machine-learning programs work well on Intel's architecture.

Just like a machine can be trained to help Mario navigate the course of life, a machine can be trained to provide autonomous, precise and relevant financial advisory to a bank's customers so that they can make better, more informed, decisions about their businesses. The technology is there, for Mario and for banks. The key question here is the availability of data upon which the system can learn and infer the next best action. Every time you open some of the most popular applications like Amazon, Facebook or Netflix, the most sophisticated algorithms springs into the action of analysing every little bit of data. For example, every second there are around 20,000 people on Facebook with 300 million photo uploads every day, 540 comments every 60 seconds and 293,000 status updates.

Google Home is the latest embodiment of a virtual assistant. The voice-activated speaker can help you make a dinner reservation, remind you to catch your flight, fire up your favorite playlist and even translate words for you on the fly. While the voice interface is expected to make quotidian tasks easier, it also gives the company unprecedented access to human patterns and preferences that are crucial to the next phase of artificial intelligence. Comparing an AI agent to a personal assistant, as most companies have been doing of late, makes for a powerful metaphor. It is one that is indicative of the human capabilities that most major technology companies want their disembodied helpers to adopt.