To start off, let’s listen to Jack Ma. We have a bright future with AI relieving some of the many burdens for humans. It seems as though the market is on the rise and that AI will be a part of our society more and more. According to this site, in 2020 the AI market is expected to grow by 54%, likewise in 2021. This could lead to some advancement in many fields, one of which is climate change.
Firstly, many use machine learning (ML) and AI synonymously, I know this is frowned upon by some. Although after reading this article I got the sense that ML is a part of AI but without the ability to perceive, and behave. Also in this article, ML is referred to as a part of a “broader AI”. So, for simplicity ML is in this article seen as a form of AI.
The massive amount of data that exists on climate change warrants the use of AI and is in fact used to some extent already. For instance, pictures of coral reefs are analyzed using ML to determine their status. Also, data on temperature and humidity is used to determine the wellbeing of forests.
One of the sectors that dump the most greenhouse gas into the atmosphere is the electric power sector, to reduce this we need green energy. One problem with many renewable sources though is reliability. For instance, solar power needs the sun, wind power needs wind, and so on. However, using ML to predict the weather, or adjust the propellers of windmills according to the wind, can decrease some of the fluctuations and maximize the output in renewable energies.
There is also hope that the entire electrical grid will become a “smart grid” in the future. Making it possible for the grid to (without human involvement) detect outages and redirect electricity to avoid large blackouts. Also, it could detect massive storms and solar flares and adapt accordingly. It would also be easier to include renewable energy into a smart grid. This would make electricity more reliable and efficient, which is good for the economy and the environment.
There is a lot of data related to climate change that are used to model and “predict” the future. But by using AI or ML to analyze data, predictions could be done with higher accuracy and maybe at a lower cost. One example of an area that needs better modeling is the Antarctic ice sheets that lack the accuracy needed.
There are other areas where AI or ML can be used, including urban planning and optimizing energy use in buildings. It can also be used in discovering new materials, which might speed up the process and lead to new findings that could for instance create solar fuels to store solar energy.
So, it seems as AI could be used in many areas, all from smart sustainable cities to finding new materials to help us adapt to climate change.