Machine Learning

Machine Learning is disrupting science research: Here’s how

It’s rare that one technology has the power to improve an entire industry, but machine learning is doing just that for scientists.

Regardless of the specific subjects they study, machine learning allows them to make discoveries faster than they otherwise could. As such, it’s possible to make rapid progress that could benefit society at large in ways not even imagined yet.

Speeding up Clustering and Ranking of Huge Amounts of Data

Scientists often process data through means called clustering and ranking. Clustering involves focusing on things sharing common characteristics. Then, ranking puts them in order of importance as defined by certain parameters.

One of the tremendously helpful things machine learning can do is cluster and rank quantities of data that are much bigger than people could handle without extremely time-intensive processes. Then, scientists can arrange data in systematic ways and potentially find things out about it that they’d otherwise miss.

Running Experiments and Building Models Faster Than Before

Users of machine learning platforms can also quickly tweak components to run new queries during experiments.

A traditional method of experimentation is to change one variable at a time and then determine which one brings about the expected or necessary changes. But that sometimes means carrying out thousands of experiments to cover all the variables and potential combinations. As a result, there’s a likelihood that some combinations will ultimately be useless.

By using machine learning methods, researchers can gain knowledge about the most impactful variables. Then, they can turn their attention to those characteristics and might reach the same conclusions after only a couple dozen experiments. The extraordinarily fast processing of machine learning gives scientists more time to test hypotheses.

In other cases, scientists have to build models to meet precise standards. Without machine learning, that takes months, not to mention extremely powerful computers. However, some of today’s machine learning tools achieve the same results in a matter of hours.

Researchers have also devised a machine learning technique they call iterative random forests. They say it’s most applicable to highly complex systems that were previously extraordinarily difficult concerning finding relationships between variables. The team believes their method could benefit any type of science known for such complexity.

Applying Machine Learning to Climate Data

Machine learning facilitates the efforts of all scientists and helps them make discoveries more efficiently. However, there are also cases where people in specific science fields depend on machine learning to reach more definitive conclusions.

Meteorologists apply machine learning to study the climate and mention that the technology can uncover air pressure patterns, tropical cyclones and other weather-related phenomena in ways that are sometimes more effective than past methods.

A 2016 study found that nine meteorologists from the United States’ National Weather Service chose artificial intelligence (AI) algorithms about 75 percent of the time when making storm duration forecasts and being given the option between newer methods and conventional ones.

But even though such technology can figure out things faster than humans, it won’t replace them. Researchers don’t always know how machine learning arrives at its results and may feel reluctant to trust it when giving details about impending disasters.

Providing a Structure for Theoretical Science

Because theoretical science involves mathematical models that rationalize and explain natural phenomena, it’s also a good fit for machine learning applications — but the possibilities for practical uses are still in the early stages. Eventually, machine learning could give the mathematic structure for scientific theories while theorists add the meaning.

The technology could also help people in this branch of science know when to expect stable correlations.

Studying Biological Data With Fewer Errors

Machine learning platforms also assist biologists, particularly by helping them sort through cells and differentiate them based on defined parameters.

One platform called DeepVariant transforms genomic data that later gets analyzed as images. One researcher achieved an error rate of nearly two percent when working with plants, whereas the typical error rate could be around 20 percent.

Depending on Machine Learning for Particle Physics

Research teams specializing in particle physics use machine learning as they attempt to uncover some of the universe’s biggest mysteries. Some of the algorithms they use even get smarter on their own by learning from their performance and improving upon it.

In one experiment aiming to find out why matter so outweighs antimatter in the universe, machine learning makes up to 70 percent of the decisions about which data is worth keeping and which should be discarded.

Significantly Changing Science for the Better

These examples show that machine learning benefits science as a whole as well as offers worthy applications for particular disciplines. Soon, the technology could forever change how all scientists work and mean that the world benefits from what they find out.

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