Climate change is the biggest problem that the life on this planet is facing today. It will need every possible situation including technologies like Machine Learning and Artificial Intelligence. Here are 5 ways machine learning can help combat global climate change. Carbon Tracker is an independent financial think-tank working toward the UN goal of preventing new coal plants from being built by 2020. By monitoring coal plant emissions with satellite imagery, Carbon Tracker can use the information it gathers to convince the finance industry that carbon plants aren't profitable.
Slowing down climate change is an urgent matter. If we fail, our world will face a more extensive crisis than we experienced because of the global COVID-19 pandemic. When artificial intelligence (AI) technology helps solve a problem, problem-solving can be done quicker, and the solution is often one that would have taken longer for humans to discover. There's no time to waste: atmospheric CO2 levels are the highest ever (even with significant drops from the stay-at-home orders for COVID-19), average sea levels are rising (3 inches in the last 25 years alone), and 2019 was the hottest year on record for the world's oceans. Artificial intelligence isn't a silver bullet, but it can certainly help us reduce greenhouse gas (GHG) emissions in various ways.
At the intersection of machine learning and energy consumption stands an incredibly powerful force with the potential to transform the way we globally produce and consume energy. So powerful in fact, that the concept of merging machine learning and renewable resources has been named the "energy internet" by economic theorist and author Jeremy Rifkin or "digital efficiency" by Intel and GE. Digital twin tech, or a virtual representation of a product, is a critical concept in IoT that's still being sorted out. You forgot to provide an Email Address. This email address doesn't appear to be valid.
At the intersection of machine learning and energy consumption stands an incredibly powerful force with the potential to transform the way we globally produce and consume energy. So powerful in fact, that the concept of merging machine learning and renewable resources has been named the "energy internet" by economic theorist and author Jeremy Rifkin or "digital efficiency" by Intel and GE.
Artificial intelligence (AI) machine learning can have a considerable carbon footprint. Deep learning is inherently costly, as it requires massive computational and energy resources. Now researchers in the U.K. have discovered how to create an energy-efficient artificial neural network without sacrificing accuracy and published the findings in Nature Communications on August 26, 2020. The biological brain is the inspiration for neuromorphic computing--an interdisciplinary approach that draws upon neuroscience, physics, artificial intelligence, computer science, and electrical engineering to create artificial neural systems that mimic biological functions and systems. The human brain is a complex system of roughly 86 billion neurons, 200 billion neurons, and hundreds of trillions of synapses.