Accelerating electrocatalyst discovery with machine learning
Researchers are paving the way to total reliance on renewable energy as they study both large- and small-scale ways to replace fossil fuels. One promising avenue is converting simple chemicals into valuable ones using renewable electricity, including processes such as carbon dioxide reduction or water splitting. But to scale these processes up for widespread use, we need to discover new electrocatalysts—substances that increase the rate of an electrochemical reaction that occurs on an electrode surface. To do so, researchers at Carnegie Mellon University are looking to new methods to accelerate the discovery process: machine learning.
https://phys.org/news/2018-09-electrocatalyst-discovery-machine.html


How machine learning and sensors are helping farmers boost yields
Agriculture is always modernizing, but most farmers struggle to collect data that’s useful—or to analyze it in useful ways. That’s changing: emerging tools for data collection and analysis are helping boost yields and make farming more sustainable, according to Sam Eathington, chief science officer at the Climate Corporation.
In the next five to 10 years, “we’re going to see an explosion of sensors and collection of data from the farm,” Eathington said.
https://www.technologyreview.com/s/612056/how-machine-learning-and-sensors-are-helping-farmers-boost-yields/


CPU vs GPU in Machine Learning
Any data scientist or machine learning enthusiast who has been trying to elicit performance of her learning models at scale will at some point hit a cap and start to experience various degrees of processing lag.
Tasks that take minutes with smaller training sets may now take more hours—in some cases weeks—when datasets get larger. You’ll need the best hardware, and while researching you will come across and may get confused with CPUs, GPUs, and ASICs.
https://www.datascience.com/blog/cpu-gpu-machine-learning