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.

Artificial Intelligence (AI) was born at a workshop organized by John McCarthy in 1956 at Dartmouth. Ever since, AI has been a branch of computer science that studies the properties of intelligence and aims to synthesize it. Over the past 60 years, the field has seen at least 4 peaks and troughs (called "winters") of interest and sponsorship. During much of this time, researchers in the field have been stymied not only by the lack of funding, but by the lack of data, hardware and algorithms. In the last 5 years, however, all this has changed.

A recent post highlighted how artificial intelligence (AI) is already playing important roles in health care, and concluded that expanded use of AI may be ready for us before we are ready for it. One example of the kind of problem we'll face is: who would we sue if care that an AI recommended or performed went wrong? Because, you know, always follow the money. Last week Stanford's One Hundred Year Study On Artificial Intelligence released its 2016 report, looking at the progress and potential of AI, as well as some recommendations for public policy. The report urged that we be cautious about both too little regulation and too much, as the former could lead to undesirable consequences and the latter could stifle innovation.

The field of artificial intelligence got its start at a conference at Dartmouth in 1956. Optimism ran high and it was believed that machines would be able to do the work of humans within 20 years. Alas, it was not to be. By the 1970's, funding dried up and the technology entered the period known as the AI winter. Clearly, much has changed since then.