Virtual reality may be generating most of the buzz today, but another major tech shift looms much closer on the horizon: machine learning. The technology has already made inroads with the public through platforms such as Amazon's Echo and Google's Deep Dream Generator. But its influence will extend beyond voice-controlled speakers and AI-enhanced art, effecting a sea of change for businesses of all sizes. It will be a few years before we witness machine learning's breakthrough moment, but it's coming -- and it will change everything. Humans could be incredibly effective given endless timelines, budget, and energy.

After months of physician-failed diagnosis, a super computer steps in and saves the life of a female patient from Japan, suffering from leukemia. IBM Watson Health has committed to developing a partnership between humanity and technology with the goal of transforming global health. With the ability to read 40 million documents in 15 seconds, IBM's Watson –super computer powered with artificial intelligence- studied the patient's medical records for ten minutes and was able to compare her type of cancer against 20 million oncological records, according to International Business Times. Physicians in Japan decided to try out IBM's Watson on patients after all other treatment options had failed. The Watson revealed that the patient's condition was another form of leukemia and required a different treatment from the one originally prescribed.

A major conference on genomics has moved its West Coast location to San Diego, a leading hub of genomic technology. The Festival of Genomics California runs Tuesday and Wednesday, Sept. 20-21 at the San Diego Convention Center The festival itself is free, People can register at j.mp/fog2016. A pre-event "Base Camp on Monday, Sept. 19 costs 699; information is available at j.mp/2016basecamp. This year's Festival offers sessions on clinical access to genomic medicine, applied technology and data sharing, along with artificial intelligence and machine learning applications. Tuesday is devoted to the effect of genomics on health care, especially in pediatrics.

As industry embraces AI, computers aren't the only ones that have to learn new tricks. Jen-Hsun Huang: 2015 was a big year. Artificial intelligence is moving into the commercial world. AI has been worked on for many years, largely in research. Various aspects of commercial use of AI, otherwise known as machine learning, is used for advertising and web searches and things like that. It wasn't until the last few years that AI could do things that people can't do. Several milestones were achieved in 2015 in particular that made it possible for us to use it in all kinds of areas. Yes, in an area of AI called "deep learning." The system basically learns by itself using a lot of data and computation.

The big data analytics market has seen rapid growth in recent years. Part of this trend includes the increased use of machine learning (Deep Learning) technologies. Indeed, machine learning speed has been drastically increased though the use of GPU accelerators. The issues facing the HPC market are similar to the analytics market -- efficient use of the underlying hardware. A position paper from the third annual Big Data and Extreme Computing conference (2015) illustrates the power of co-design in the analytics market.

While they started out as technological curiosities -- pet projects of computer scientists at MIT, Stanford, and other tech havens -- chatbots are now evolving into the next big interface for doing anything and everything. Ordering flowers, checking the weather, hailing a ride … a few short years ago we'd say, "There's an app for that." But more and more it seems we're able to say, "There's a chatbot for that." One of the main drivers of the rise of the chatbots, of course, is the rise of 1:1 messaging. While AOL and MSN Messenger offered us a glimpse of the future with SmarterChild, messaging platforms like Facebook Messenger and Slack are now helping to elevate chatbots from interesting playthings to powerful tools.

In the deadliest attack against Indian forces in more than a decade, militants sneaked into an army encampment in the disputed territory of Kashmir early Sunday and opened fire on sleeping soldiers, killing at least 17 and wounding dozens. The four assailants, who also threw grenades that caused tents and temporary shelters to catch fire at the army brigade headquarters at Uri, were killed in a gun battle with security forces that lasted six hours, authorities said. Indian officials blamed the Pakistan-based militant group Jaish-e-Mohammed for the attack, saying it had recovered weapons from the assailants that carried Pakistani markings. Lt. Gen. Ranbir Singh, the director of military operations, said he contacted his Pakistani counterpart to convey "serious concerns." Indian Home Minister Rajnath Singh was more pointed, saying on Twitter: "Pakistan is a terrorist state, and it should be identified and isolated as such."

Since I'm a cognitive scientist and also something of a data scientist, I figured I'd weigh in on'Cognitive Computing'--what it is, what it isn't, and what is could (and should) be. The'cognitive' bit of Cognitive Computing is a marketing term. Cognitive computing describes technology platforms that broadly speaking, are based on the scientific disciplines of Artificial Intelligence and Signal Processing. These platforms encompass machine learning, reasoning, natural language processing, speech and vision, human-computer interaction, dialog and narrative generation and more. From the people I've talked to who work with this stuff, the converged-upon definition of Cognitive Computing seems to boil down to inference plus recommendation.

Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.

The Machine Learning - Applications category leads the sector in IPOs (4 companies), while the Computer Vision - Applications category leads the sector in acquisitions (22 companies). Both 2013 and 2015 lead the sector in IPOs (2 companies for each year), while 2014 leads the sector in acquisitions (17 companies). We are currently tracking 1185 Artificial Intelligence companies in 13 categories across 71 countries, with a total of 7.3 Billion in funding. Click here to see the full Artificial Intelligence landscape report and data.