AI and Deep Learning Are the Keys to Unlocking the Future Of Environmental Sustainability
AI, deep learning, neural networks, synthetic data, machine learning — these technologies have extreme potential to revolutionize everyday processes and industries, leading humanity into an era of greater self-sufficiency and productivity.
People often cite AI’s ability to buff up current technology, databases, education, transportation, and relevant jobs in the tech sector. But what about those applications that might slip by people’s minds?
Take sustainability, for instance. AI may germinate from the chromatic, circuit-wired world of computers, but it may provide us with the sustainability solutions we need to protect our environment from pollution, climate change, and the like.
“So how could it apply to climate change? You have complex data sets, such as from measuring remote-sensing data from satellites and ground sensors, or from the Internet of Things and the sensors that are all over the planet in various devices. Think of all the mobile phones that have temperature sensors, and cars that have sensors. We’re not really using that data for anything, but we could. Putting all the data on a screen wouldn’t necessarily give you insights. But you could imagine some kind of a learning harness being applied to try to figure out patterns in those data sets,” venture capitalist Steve Jurvetson states in an interview with GreenBiz.
Companies like Google are already using AI to cut back on data center energy use. The tech giant was able to curtail its energy consumption by 40% thanks to its DeepMind’s “Earth Friendly” AI, a significant reduction for a process that, industry-wide, consumes 3% of the world’s energy annually.
But as Jurvetson’s statement suggests, AI can do much more than that. It can also be employed in more hands-on, proactive ways to fight climate change by monitoring CO2 emissions and global temperatures, effecting smart farming practices, managing industrial manufacturing operations, and much more.
Deep learning, neural networks, and synthetic data will provide AI with the necessary data and framework to work towards sustainability solutions. And Neuromation has all the tools necessary in its toolkit to begin building long term solutions for a greener tomorrow.
For instance, a platform like Neuromation can harness data from aerial satellite imagery on the Earth, its climate, and its inhabitants. Neuromation can take this data and use its deep learning and neural networks to analyze it in ways humans cannot, providing incisive analysis and discovering patterns that we would otherwise miss.
With the synthetic datasets this would create, Neuromation can employ this information to forecast the development of growing urban areas and the environmental impact this will have and also keep tabs on environmentally devastating events like oil spills, deforestation, coral bleaching, and more.
In addition, we can more accurately diagnose urban traffic congestion, urban growth, city-by-city impact on climate change, and even the predicted impact of potential natural disasters, thus empowering us to work through these challenges and mitigate their negative effects.
Thus, Neuromation can take data that is currently too vast and rich for humans to sift through manually and turn it into valuable datasets that can be used to work towards climate change solutions.
Neuromation’s services can also be used to improve current farming practices to make them more environmentally friendly and less wasteful. AI can help farmers keep better track of their livestock and their anticipated crop yields, for instance. Using synthetic data, Neuromation’s AI could anticipate how much grain a farm’s livestock will likely consume in a given year, meaning the farmer can buy exactly the right amount of feed — no more and no less — and not waste resources on extra food that will just go to waste.
The same logic can be applied to irrigation. With AI functionality, farmers can pinpoint exactly how many gallons of water their crops will need without having to waste any extra and risk over watering their crops. Neuromation’s potential in ag-tech is expansive still. Our neural networks can be used to forecast weather patterns to help farmers plan accordingly, as well.
Extending these applications to the world’s global food supply chain, AI platforms like Neuromation can help retailers and manufacturers to cut back on waste by more accurately forecasting supply and demand. Nearly half of all the world’s total food supply is thrown out each year. With the tools that Neuromation offers, we could reduce the potential for such egregious waste and even redirect food supplies to feed impoverished populations.
Jurvetson said in his interview that, despite the world’s plethora of data, that “[we’re] not really using that data for anything.” Neuromation’s services and platform give us the power to harness this unused data in unexplored ways. For example, Neuromation could theoretically harness the temperature reading data from the phones and cars that Jurvetson cites in his example.
Using data such as this, Neuromation can give research scientists and climatologists reliable and hyper-accurate data to strengthen their research and work towards the solutions that are going to revolutionize sustainability and ecological initiatives for years to come.
