US: IBM is planning to invest in developing machine learning ‘Macroscopes’ to analyze the complexities of the physical world. According to the company predictions, within five years, such technology will “help us understand the Earth’s complexity in infinite detail.”
The investment in the project is determined by the company’s goal of managing the world’s resources and commercial endeavors that use those resources by applying machine learning algorithms across an array of data sources.
This will include geospatial data (weather, soil, water, etc.) as well as data about economic, social and political conditions. The idea is to manage things like food, water, and energy with much greater precision. All of this dovetails rather nicely under IBM’s “Smarter Planet” mantra.
The work to develop this macroscope technology is being done by a team of scientists in IBM Research’s Physical Analytics group. Hendrik Hamann, the group’s research manager describes the work as an intersection between big data and physics – a field that he refers to as “physical analytics.”
That’s probably a more useful term than macroscopes, a metaphorical reference to something intended to measure very large things. It’s the analytics capability, rather than the measurement aspect that is at the heart of the technology.
“My team’s expertise in physical models, machine-learning, sensors, data curation and big data technologies has been put to use in applications dealing with renewable energy, precision agriculture and energy management,” writes Hamann.
“We are now leading the company’s research in the quickly developing area of the Internet of Things (IoT), an extension of the classical internet of computers to any physical object.”
Dealing with IoT data is a massive challenge. According to Gartner’s estimates, in 2015 there were more than 6.4 billion IoT devices in service, and those were being added to at the rate of about 5.5 million new devices each day. That works out to about a 30 percent increase year-over-year. Given the amount of streaming data that represents (tens of exabytes per month), no single system is capable storing it, much less analyzing it.