AI for a Better Earth #AIForGood

While the number of instances of wildfire globally are not increasing, the area devastated due to such fires is increasing day by day.

"Once the fire gets to be a crown fire and it's two football fields or larger, it's nearly impossible to put it out until the weather changes. "You're spitting on a campfire."

For giant insurance companies, it makes sense to predict the occurrence of wildfires, but the sheer number of factors influencing wildfires makes it very hard. 

Artificial Intelligence could be just the right technology to make out correlations from wide variety of data points and sources to point out the future occurrence of these devastating incidences.

An Artificial Intelligence Program has been developed in collaboration with scientists and engineers at MIT Massachusetts which could predict wildfire, up to six months in advance.

Short terms weather forecasts are really what is termed as 'crapshoot'. 

The short term weather forecast or sub-seasonal ones instead rely on a lot of variables which would include the local meteorological factors, as well as the large global phenomena like El Nino.

Now researchers are using Machine Learning to get insights from the troves of historical weather and climate data, without which short term weather forecasts cannot be produced authentically.

"Weather patterns repeat throughout the seasons and from year to year and that therefore pattern recognition can and should inform longer-term forecasts."

British Geological Society has 3 million fossils, gathered over 150 years, at thousands of sites across the country. However, this massive set of 'geological data' is not much of use, sitting in boxes in the warehouse.

Now scientists world over are trying to create, what is being termed Deep-time Digital Earth (DDE), a network of earth science databases. In layman's term, 'geological google'.

"Once earth scientists are freed of the drudgery of combing scattered collections, he says, they will have time for more important challenges, such as answering “questions about the evolution of life, materials, geography, and climate in deep time.”

While wind is helping us achieve the non-renewable energy goals, it is a highly un-predictable source of energy, where output could vary between a matter of hours. For a grid which is being fed by a wind energy farm, it is imperative to know, how much energy would be produced at what time, so that it could be optimally stored and then delivered to the consumer.

Now AI is being used to predict the output of wind farms using Machine Learning algorithms, so that delivery of energy output can be scheduled, making them more 'viable'.

"We can’t eliminate the variability of the wind, but our early results suggest that we can use machine learning to make wind power sufficiently more predictable and valuable.

This approach also helps bring greater data rigor to wind farm operations, as machine learning can help wind farm operators make smarter, faster and more data-driven assessments of how their power output can meet electricity demand.”

Dismantling old ammunition is a hazardous and time consuming activity, putting at peril both man and material at risk. While they pose a material risk, there is a bigger risk in terms of damage to environment, as old ammunition pile up everywhere on earth. Proper dismantling of ammunition could lead to recovery of precious metal components, which could then by re-used.

Now robots are being programmed through AI to achieve this task.

"This is exactly the kind of thing to use robotics for—to get humans out of harm's way. Let the automation of robots do what they do well, and have humans make advanced decisions on safety."

“One-fifth of carbon emissions comes from illegal logging. And I felt if we want to offer support for the tribes, one of the best ways is to use technology.

“The moment a chainsaw goes off, our mic in the trees picks it up and we can then alert local rangers to stop people in the act.

“We basically take an old cellphone and put it in a box and, using the powerful microphone in most smartphones, listen to the sounds of the forest and detect chainsaws and alert local people.”

One device in a tree can help protect three sq km of rainforest. “That equals taking 3,000 cars off the road and 15,000 tons of CO2.