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DateDate: 25-06-2019, 07:26
It is reported by Sotsportal with reference to Hi-Tech +.
“The report, edited by David Rolnik from the University of Pennsylvania, is divided into 13 areas, from energy to agriculture and climatology,” the author noted.
The recommendations are divided into three categories: “highly efficient” for problems well suited for machine learning; “Long-term” - for solutions that do not pay off before 2040; and "risky" - methods with less predictable results. MIT Technology Review has selected recommendations from the category of the most effective.
Evaluation of the need for electricity.
If we are going to switch to renewable energy, we should more accurately calculate the need for electricity today and in the long term. Already there are algorithms that predict energy consumption, but they can be improved.
New materials.
Scientists need materials that store, collect and use energy more efficiently, but the discovery process is usually too slow and inaccurate. Machine learning could develop, for example, solar fuel that absorbs the energy of sunlight, or find a replacement for steel, which is responsible for 10% of all greenhouse gas emissions.
Optimization of freight and deliveries.
Transportation of goods around the world is a complex and often inefficient process, requiring coordination of a lot of parameters. Machine learning is able to find ways to minimize the number of journeys and associated emissions.
Acceleration of the transition to electric transport. Thanks to the algorithms, automakers can increase the range of trips on a single charge and prepare the grid for the load.
Improving the efficiency of buildings.
Smart control systems could significantly reduce the energy consumption of buildings, taking into account the weather forecast, the number of inhabitants and combining them with other factors: heating, cooling, ventilation and lighting.
Other recommendations from the same category: creation of opportunities for the development of precise agricultural technology, tracking the process of deforestation and changing the consumer's attitude to the environment to a more careful one.