General Electric's healthcare division will partner with the corporate parent of two of Harvard University's teaching hospitals to develop artificial intelligence products for medicine. The goal: to leverage the company's dominant position in medical imaging into a new ownership of medical AI. "We make machines but that's not really the business," says John Flannery, the chief executive of GE Healthcare, says he tells his team. "The business is what kind of solution can we put together that gets a better clinical and economic outcome?" GE Healthcare is one of the main manufacturers of imaging devices used in radiology–things like PET and MRI scanners.

Deep learning made the headlines when the UK's AlphaGo team beat Lee Sedol, holder of 18 international titles, in the Go board game. Go is more complex than other games, such as Chess, where machines have previously crushed famous players. The number of potential moves explodes exponentially so it wasn't possible for computers to use the same techniques used in Chess. In learning Go, the computer would have to create millions of games, competing against itself and discovering new strategies that humans may never have considered. Deep learning itself isn't that new, and researchers have been working on algorithms for many years, refining the approach and developing new algorithms.

Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. CEO Sundar Pichai and other top executives brought Google's audacious ambition into sharper focus Wednesday at an annual conference attended by more than 7,000 developers who design apps to work with its wide array of digital services. Among other things, Google unveiled new ways for its massive network of computers to identify images, as well as recommend, share, and organize photos. It also is launching an attempt to make its voice-controlled digital assistant more proactive and visual while expanding its audience to Apple's iPhone, where it will try to outwit an older peer, Siri. The push marks another step toward infusing nearly all of Google's products with some semblance of artificial intelligence -- the concept of writing software that enables computers to gradually learn to think more like humans.

Looking through this lens, ML seems to be a lot like statistical modelling. In statistical modelling, we collect data, verify that it is clean -- in other words, that we have completed, corrected, or deleted any incomplete, incorrect, or irrelevant parts of the data -- and then use this clean dataset to test hypotheses and make predictions and forecasts. The idea behind statistical modelling is the attempt to represent complex issues in relatively generalizable terms, which is to say, terms that explain most events studied. Effectively, we programme the algorithm to perform certain functions based on the data we submit. Put differently, the algorithm is static.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. Google is getting more serious about chips. On Wednesday at its annual developers conference, the tech giant announced the second generation of its custom chip, the Tensor Processing Unit, optimized to run its deep learning algorithms.

The debate surrounding the use of artificial intelligence in marketing is usually a controversial one. In the eyes of pop culture, advanced technology, like artificial intelligence, evokes views of imminent dystopias. One of the most memorable books I read recently was Dave Eggers' chilling dystopia The Circle, which was released as a movie last month. The story revolves around ambitious young go-getter Mae Holland who joins a growing tech company that aims to complete the "circle" of information sharing (which would eliminate any notions of privacy). This is a world in which advanced technology is a ubiquitous and seamless part of everyday life, so much so that we don't stop to question its existence.

Watson, IBM's supercomputer, is most well known for beating two quizmasters on popular quiz show Jeopardy! in 2011. The impressive artificially intelligent software was developed to advance machine learning capabilities, including natural language processing, reasoning and knowledge retrieval. Watson can access information from an endless list of sources, from literature to databases. As AI continues to attract investment and R&D, it will impact our lives in so many ways. It's not surprising, then, that Watson has rather expanded its repertoire since its Jeopardy!

This company has developed a new anti-cancer drug (against pancreatic, breast, liver or brain cancer) called BPM 31510, which has been discovered by an algorithm. The major technology companies are using millions of people data to find treatments. In addition to the start-ups, all major technology companies have already begun to apply Big Data and artificial intelligence to the service of health. Big Data and artificial intelligence, combined with genetic analysis, allow researchers to search for and find patterns among patients with rare diseases, who may be separated by distance but carry the same mutation.

In a move that could shift the course of multiple technology markets, Google will soon launch a cloud computing service that provides exclusive access to a new kind of artificial-intelligence chip designed by its own engineers. CEO Sundar Pichai revealed the new chip and service this morning in Silicon Valley during his keynote at Google I/O, the company's annual developer conference. This new processor is a unique creation designed to both train and execute deep neural networks--machine learning systems behind the rapid evolution of everything from image and speech recognition to automated translation to robotics. Google says it will not sell the chip directly to others. Instead, through its new cloud service, set to arrive sometime before the end of the year, any business or developer can build and operate software via the internet that taps into hundreds and perhaps thousands of these processors, all packed into Google data centers.

Democratization of Artificial intelligence, Microsoft's promise to take the AI and Machine learning from the ivory towers and make it accessible for all, is starting to take shape quite effectively. Let's face it; resource constraints around AI/ML is a real problem. Most companies with real-world AI use cases just don't have enough runway to build their own artificial intelligence offerings, and Microsoft cognitive services provide a sophisticated yet easy to use abstraction which fills this gap. Microsoft has also announced AI as an MVP category (http://aka.ms/AIMVP) Being a Microsoft MVP for Data Platforms, I have had the front row seat to see how Cognitive Services, a collection of powerful APIs and toolkits unfold to fulfill the promise of AI democratization.