The concept of what defines Artificial Intelligence has changed over time, but at the core there has always been the idea of building machines which are capable of thinking like humans. These machines may or may not be able to replace humans completely in certain fields but they have the capabilities to be more than just a program. They can outgrow themselves on the basis of previous results and observations.
After all, human beings have proven uniquely capable of interpreting the world around us and using the information we pick up to effect change. If we want to build machines to help us to this more efficiently, then it makes sense to use ourselves as a blueprint!
Artificial Intelligence, then, can be thought of as simulating the capacity for abstract, creative, deductive thought – and particularly the ability to learn – using the digital, binary logic of computers.
Research and development work in AI is split between two branches.
- Applied AI – Applied Artificial Intelligence is the one which uses the principles of simulating human thought to carry out one specific task.
- Generalized AI – Generalized AI is the one which seeks to develop machine intelligences that can turn their hands to any task, much like a person.
This is something which is a major problem in various parts of the world. Especially ones which are either dependent on climatic conditions for their natural produce like agricultural products and the one which are prone to catastrophes / hazards that can be triggered by climate change. There are various countries whose Gross Domestic Product(GDP) is dependent on quantity of rains in different parts. Similarly, a lot of countries plan their disaster management based on their geographical location which affects a major chunk of their population.
Need of AI in Climate Change
Now, you can easily understand, the scope of Artificial Intelligence in Climatic Change predictions. Human made structures and technologies go as per their design. Even if there are variations in desired results, it is easily identifiable, and hence can be easily resolved. Things get quite difficult when Nature comes into picture. It is never easy to predict how nature is going to take the course of action in coming future. There are various possible parameters which directly or indirectly affects climatic change but the calculations prove to be less accurate and many a times the deviations in results is too high to compromise.
Now, with the help of Artificial Intelligence, these calculations have become smarter. The parameters that affect climatic changes varies to a large extent and based on regions, these parameters change. For example, monsoon in one region can be based on its proximity to coastal region while in some region it can be defined because of its proximity to mountains. There is no exact equation which can predict climatic changes in two regions with geographically different conditions. This is where the need of AI roots itself.
Three areas of climate and weather research seem to be benefitting most from the surge in AI. Let us study about them first.
First, machine-learning algorithms trained on data from extreme climate events have succeeded in identifying tropical cyclones and atmospheric rivers—the latter of which can dump dangerous amounts of precipitation on an area but aren’t always easy for humans to identify (paper).
Second, AI is also being used to analyze strengths and weaknesses among the dozens of models the IPCC uses to investigate climate change. An algorithm that weights the results of individual models can produce an analysis that’s more reliable than any one model would be on its own.
Third, Meteorologists are also increasingly using AI to help predict how long a storm might last, or whether it will produce damaging hail, for example.
Of course, the trouble with using such sophisticated software is that, as we have pointed out before, computers are very bad at telling you how they arrived at their decisions. It’s one of the biggest problems in Artificial Intelligence and some weather and climate modelers are rightly concerned about relying too much on “black box” AI systems to draw conclusions about how the climate is changing or to make forecasts about when and where extreme weather might strike next.
Challenges for AI in Climate changes and environment
With climate change, exploring and protecting the global commons will be phenomenally difficult. Decisions must be taken in a context full of uncertainty and partial insight, and the whole issue is ripe for political polarization. However, there is at least one big difference between climate change and AI. In the case of climate change, it is convenient for most
However, the big lesson of climate change is not to leave the consensus building too late. As a society we need to be debating this widely, learning from the successes and failures of the climate change debate. We need to explore what limitations on AI would benefit humankind, and how to establish the global governance to achieve them.
We live in interesting times, with AI set to bring immense benefits, but we must debate potential pitfalls before it’s too late.
Like any other technology, AI comes with various benefits as well as risks. Because of benefits, risks cannot be ignored. Security is another major concern in this field as well. This is a type of “deep learning” that allows machines to process information for themselves on a very sophisticated level, allowing them to perform complex functions. Big data is speeding up the AI development process, and we may be seeing more integration of AI technology in our everyday lives relatively soon. While much of this technology is still rudimentary at the moment, but we can expect better results especially in field of climatic changes.