Artificial intelligence (AI)

is defined as the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans. The coinage of the term AI dates back to 1956. Over the past decades, it has been applied to various fields such as healthcare, banking, E-commerce, logistics, education, and even entertainment.
Essentially a way to replicate human intelligence in machines, While A.I’s effects on the industries mentioned above are outstanding, it is often considered a baneful hazard to the environment and its sustainability. 
Which couldn’t be further from the truth…A.I, or more specifically Machine Learning, allows us to provide machines with the capability to learn and improve from experience without being explicitly programmed. Now before you start having Skynet flashbacks, HEAR ME OUT, when it comes to its applications in the good of humanity, Machine Learning does wonders.

Deep learning, a form of machine learning, is able to learn without human supervision, drawing from data that is unstructured.We feed input to a neural network that predicts the outcome. Although artificial neural networks are analogous to our brain’s neural networks, they are not related in any way.
Neural networks are layers through which data is transformed. It is analogous to coding— You write the first program, it’s not very good so you have to optimize it. Then it’s better but still bad, so you refactor certain parts or delete them. You keep fine-tuning it until you are satisfied with the end result.
Likewise, neural networks adjust the input parameters on their own until they are happy with the outcome. Deep Learning algorithms can be applied to unsupervised tasks.
Tesla, for instance, uses deep neural networks to detect roads, cars, objects, and people in video feeds from cameras installed around the vehicle. It combines two of the most very popular A.I fields of study, Machine Learning with Computer Vision.

We can predict and prevent natural disasters!

The Implementation of Deep Learning can help us detect and observe hazards such as hurricanes, tremors and storms. Natrual disasters that we he have no control over are becoming less terrifying than ever.
Researchers at the Image Processing Laboratory (IPL) of the University of Valencia, in collaboration with the University of Oxford and the Phi-Lab of the European Space Agency (ESA), have developed and sent a neural-networks model for flood detection into space.
WorldFloods provides valuable information for anticipating both the time and severity of impact. The system implements Deep Learning to achieve Surface Water Mapping that allows information to be processed on board. “On-board processing offers a solution to reduce the amount of data to be transmitted by reducing large images from sensors to smaller data products”, says Gonzalo Mateo (IPL).

 Satellite imagery after segmentation results

A.I now enables authorities in Cape Canaveral, Florida (a city that is very exposed to coastal flooding), to act proactively, and take the necessary measures to evacuate people and facilitate rescue missions, even protecting wild-life and endangered species in the area from the event of a flood.

How Google Maps uses A.I to reduce carbon footprint

Artificial intelligence is used to facilitate navigation and thus reduce CO2 emissions. The implementation of A.I in Google Maps for instance, provides detailed information about traffic flows, presenting shorter, faster routes. Consequently, fewer emissions.eco-friendly routes let you choose the route with the lowest carbon footprint

co-friendly routes let you choose the route with the lowest carbon footprint

Empowering renewable energy
It is expected that by 2050 natural gas usage will be phased out. Some countries already started passing laws restricting the use of gas, including the United Kingdom, which has announced that by 2025 all new homes will be banned from installing gas and oil boilers. 
Electricity is opted for as the main alternative to gas in the short term and nuclear energy in the long term. Before we adopt nuclear energy as our main source of energy, we have to pass through the phase of electrification, which already started some years ago. To begin with, the worldwide number of battery electric vehicles in use increased from 1.2 million in 2016 to 6.8 million in 2020. 
Moreover, over the next decade, most heating systems will be electrified and the number of solar panels and wind tribunes will greatly rise, which could threaten the collapse of the grid. With the right usage of Machine Learning we will be able to solve most of these problems, managing decentralized grids by balancing supply and demand, storing power, and ensuring a continuously functional and accessible grid. Problems of the future could very much be solved decades before their imminent threat!

Towards global flood mapping onboard low cost satellites with machine learning – Scientific Reports
Spaceborne Earth observation is a key technology for flood response, offering valuable information to decision makers…