The AI Renaissance of recent years has led many to ask how this technology can help with one of the greatest threats to humanity: climate change. A new investigation document written by some of the most famous thinkers in the field, tries to answer this question and gives some examples of how machine learning can help prevent human destruction.
The proposed use cases are varied, ranging from the use of AI and satellite images to better follow deforestation, to the development of new materials that can replace steel and cement (the production of which accounts for nine percent of global greenhouse gas emissions).
But despite this variety, the paper (that we saw through MIT Technology Review) returns time and time again to a few broad areas of application. A prominent feature of this is the use of machine vision to monitor the environment; use of data analysis to find inefficiencies in emission-heavy industries; and the use of AI to model complex systems, such as our own climate on earth, so that we can better prepare ourselves for future changes.
The document's authors – including DeepMind CEO Demis Hassabis, Turing award winner Yoshua Bengio and Google Brain co-founder Andrew Ng – say that AI could be "invaluable" in mitigating and preventing the worse effects of climate change, but note that it is not a & # 39; silver bullet & # 39; and political action is desperately needed.
"Technology alone is not enough," write the article authors, led by David Rolnick, a postdoctoral fellow at the University of Pennsylvania. "(T) e technologies that would reduce climate change have been available for years, but have largely not been adopted by society. Although we hope that ML will be useful in reducing the costs associated with climate action, humanity should also decide to act. "
In total, the paper suggests 13 fields where machine learning can be deployed (from which we have selected eight examples), categorized by the time frame of their potential impact and whether the technology involved is sufficiently developed to reap certain rewards. You can read the entire paper for yourself hereor browse our list below.
- Build better electricity systems. Electricity systems are "flooded with data", but too little is being done to benefit from this information. Machine learning can help by predicting electricity generation and demand, allowing suppliers to better integrate renewable resources into national networks and reduce waste. Google's English laboratory, DeepMind, has already demonstrated this type of work, with the help of AI to predict the energy production of wind farms.
- Monitoring of agricultural emissions and deforestation. Greenhouse gases are not only emitted by engines and power plants – a large part comes from the destruction of trees, peat areas and other plants that have captured carbon through the process of photosynthesis over millions of years. Deforestation and unsustainable agriculture cause this carbon to be released into the atmosphere again, but with the help of satellite images and AI we can determine exactly where this is happening and protect these natural carbon sinks.
- Make new low-carbon materials. The authors of the article note that nine percent of all global greenhouse gas emissions come from the production of concrete and steel. Machine learning can help reduce this number by helping to develop low-carbon alternatives to these materials. AI helps scientists discover new materials by allowing them to model the properties and interactions of never before seen chemical compounds.
- Predict extreme weather conditions. Many of the greatest effects of climate change in the coming decades will be driven by hugely complex systems, such as cloud changes and ice sheet dynamics. These are exactly the kind of problems that AI is great at. Modeling these changes will help scientists predict extreme weather conditions, such as droughts and hurricanes, which in turn will help governments protect against their worst consequences.
- Make transport more efficient. The transport sector accounts for a quarter of global energy-related CO2 emissions, two-thirds of which are generated by road users. As with electricity systems, machine learning can make this sector more efficient, reducing the number of wasted journeys, increasing vehicle efficiency and shifting freight to low-carbon options such as rail transport. AI could also reduce car use through the use of shared, autonomous vehicles, but the authors note that this technology is still not proven.
- Reduce wasted energy from buildings. The energy used in buildings accounts for another quarter of the world's CO2 emissions from energy, and presents part of "the lowest hanging fruit" for climate action. Buildings last a long time and are rarely equipped with new technology. By adding just a few smart sensors for monitoring air temperature, water temperature and energy consumption, energy consumption can be reduced by 20 percent in a single building, and large-scale projects that monitor entire cities could have an even greater impact.
- Geoengineer a more reflective earth. This use case is probably the most extreme and speculative of all those mentioned, but it is one of some scientists are hopeful about it. If we can find ways to make clouds more reflective or create artificial clouds with the help of spray cans, we can reflect more of the sun's heat back into space. That's a big one as However, modeling the possible side effects of all schedules is hugely important. AI could help with this, but the authors of the article note that there are still significant & # 39; governance challenges & # 39; ahead.
- Give individuals tools to reduce their ecological footprint. According to the authors of the article, it is "a common misconception that individuals cannot take meaningful action against climate change." But people must know how they can help. Machine learning can help by calculating a person's ecological footprint and marking small changes that they can make to reduce them – such as using public transport more; buy meat less often; or reducing the electricity consumption in their home. Adding individual actions can create a large cumulative effect.