What is ML for Climate Change? A new “subfield” founded in 2019 is making waves, and is more accessible than I first thought
With the devolving world order, seeking positive, engaging work, I wanted to learn a) what actually is machine learning for climate change and b) are there reasonable paths for us to dive in and contribute? To quote the call for action in a paper I cite heavily later:
Groundbreaking technologies have an impact, but so do well-constructed solutions to mundane problems.
To start with, I knew there were a bunch of recent workshops on climate change and machine learning (such as ICLR 2020, ICML 2019, NeurIPs 2019 editions). When looking here, it turns out it is a centralized group of climatechange.ai. This seems good to me, but I was hoping to learn what people are actually working on.
I wrote a little script to scrape the titles and authors of the workshop proceedings and came up with this list of keywords (learned about NLP and stop words along the way).
LEARNING, USING, CLIMATE, DEEP, MACHINE, DATA, NETWORKS, CHANGE, SATELLITE, PREDICTION, IMAGERY, NEURAL, WEATHER, POWER, FORECASTING, ENERGY, TOWARDS, MODELS, CARBON, REINFORCEMENT, BASED, DETECTION, ENVIRONMENTAL, MONITORING, FLOW, VIA, DYNAMICS, FRAMEWORK, SOLAR, RISK, CLOUD, GRID, LEARNING-BASED, FOREST, CONSERVATION, SMART, ANALYSIS, OPTIMAL, MAPPING, URBAN, INTELLIGENCE, RENEWABLE
I expected some buzzword-ness, but this was pretty much a non-entity in terms of teaching me what people are working on. Some keywords that provide insight may be this subset (remove data and learning description words):
NETWORKS, SATELLITE, WEATHER, POWER, ENERGY, FLOW, SOLAR, DYNAMICS, GRID, FOREST, CONSERVATION, MAPPING, URBAN
It reads as a list of applications in the space of urban development, power systems, energy grids, conservation, and dynamic systems. After this cursory analysis, I realized I actually needed to read the 100page white paper initializing the field.
I am putting the data for paper information here. You can register for the 2020 Virtual NeurIPs conference for $100 ($25 student) and attend the next iteration on the workshop for Tackling Climate Change with Machine Learning.
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AI, Machine learning, as its title defines, is involved as a process to make the machine operate a task automatically to know more join CETPA
You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives. It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.
Machine Learning is an utilization of Artificial Intelligence (AI) that provides frameworks the capacity to naturally absorb and improve as a matter of fact without being expressly modified. AI centers round the improvement of PC programs which will get to information and use it learn for themselves.The way toward learning starts with perceptions or information, for instance , models, direct understanding, or guidance, so on look for designs in information and choose better choices afterward hooked in to the models that we give. The essential point is to allow the PCs adapt consequently without human intercession or help and modify activities as needs be.