A lot happened this week deserving of attention in the AI space. The Guardian wrote an article with GPT-3 and again demonstrated that no matter what OpenAI paid to train and create the language model, the free marketing might be worth more. After losing the JEDI cloud contract appeal with the Pentagon, Amazon appointed Keith Alexander to its board — the man who oversaw the National Security Agency mass surveillance revealed by Edward Snowden leaks in 2013. And Portland passed the strictest facial recognition bans in U.S. history, outlawing government and business use of the technology.
However, AI Weekly attempts to reach into the zeitgeist and highlight important events on people’s minds. This week without question it’s the smoke that’s hung over the western United States and the underlying issue of climate change.
In the Bay Area Wednesday, the density of the smoke effectively blocked out the sun and cast a deep, dark orange or red light. The eerie scenery drew comparisons to Blade Runner 2049, among other sci-fi scenarios.
Someone put Bladerunner 2049 music to drone footage of San Francisco and at first I didn’t know whether to be amazed or horrified. This is very much horrifying. pic.twitter.com/XQTv4qrE93
— Omar Jimenez (@OmarJimenez) September 10, 2020
That day the temperature was forecast to reach the high 80s or 90s, but temperatures dropped to around 60, a nearly 30 degree decline in heat in a single day. Street lights stayed on most of the day and disoriented people felt like it was the night during the middle of the day. For some people living here, San Francisco turning into Mars was a breaking point, a stressor after weeks of smoke and a heat wave. All that on top of ongoing efforts toward progress toward racial justice, economic recovery, and a cure to COVID-19.
A quick recap: The August complex fire in northern California is now the largest in state history. CalFire said in its daily report today that 26 times as many acres have burned this year compared to the same time period in 2019. At one point on Wednesday, the National Weather Service Bay Area tweeted that conditions were “beyond our models.” In Oregon, Governor Kate Brown said yesterday that 900,000 acres burned in three days. In a typical year, she said, the state loses 500,000 acres to fire. Today, unhealthy air quality stretched the entirety of the west coast of the United States, from San Diego to Seattle. On Friday, Portland recorded the worst air quality in the world. In neighboring states, Salt Lake City recorded historically bad air quality, and Denver residents were impacted as well.
The destruction and health risks posed to people by natural disasters is just one way to see the consequences of climate change. The global environment is also showing strain: The World Wildlife Fund said in its annual Living Planet report monitoring 21,000 species of animals that wildlife population levels are down nearly 70% in the past 50 years. That loss of biodiversity poses a threat to global food supplies.
So what can we do about it? There are major initiatives underway to apply machine learning to address the problem, such as Climate Change AI. The group of AI researchers explores solutions to climate change and adjacent problems like human displacement food insecurity. In the past month, group members have discussed the kinds of startups that can combat climate change, the group is assembling a dataset wish list from researchers to inform what kind of data researchers desire to train models and solve problems.
In June 2019, researchers from more than a dozen organizations teamed up to release a paper with more than 800 references that attempts to cover the vast swath of ways in which machine learning can help combat climate change. Areas of focus in the full paper and interactive summary include practical models for electric systems and smart cities as well as long-term projects with uncertain impact, like CO2 sequestration or engineering a planetary control system.
At a Climate Change AI workshop held at the NeurIPS conference last year, researchers talked about the possibility of making AI a zero carbon industry and the sort of cultural changes needed for the machine learning community to focus more of its attention on climate change. Calculators for figuring out the carbon footprint of a machine learning model were also circulated at the conference. Another Climate Change AI workshop is scheduled to take place in December at NeurIPS.
There’s also the work of WattTime, a nonprofit organization that reduces a household’s carbon footprint by automating when electric vehicles, thermostats, and appliances are active based on when renewable energy is available. Algorithms to determine those times are trained using data from the EPA’s continuous pollution monitoring system. The tech is currently available in California where about 33% of power today comes from renewable energy as part of the Self-Generation Incentive Program, WattTime creator Gavin McCormick told VentureBeat.
“Nobody knows about the U.S. continuous emissions monitoring system, but it’s been live since the ’70s, and it’s why organizations like mine can write increasingly sophisticated AI algorithms to integrate more renewable energy and do what we do,” McCormick told VentureBeat in a phone call.
Last year, WattTime received a grant from Google.org’s AI Impact Challenge to see whether computer vision can track power plant emissions outside the U.S. from satellite imagery. In July, WattTime joined nine organizations and Al Gore to form Climate Trace, a group that wants to trace emissions in major sectors of the economy in the world like power plants and shipping. Climate Trace’s goal is to make that data available to the public by June 2021, leading up to the next round of international climate negotiations.
When I was growing up, the idea that technology could change the world was an idyllic dream. In years past, there’s been a fair share of disillusionment in part due to startups solving problems that don’t exist, mass surveillance, a general lack of funding for diverse startup founders, and a laundry list of transgressions by Big Tech companies too long to list here.
But if you’re a person who feels helpless about all the recent disaster, and you want to do something to change things, numerous projects (including the above) need volunteers. According to a Yale Climate Change Communication survey released in April, people are ready to get involved in efforts to compel action by elected officials to address climate change.
The word apocalypse came up a little too often for my liking this week. It’s easy to feel things are dire, chaotic, and out of control — because they are — but nobody should believe there’s nothing they can do to make change. Technology alone won’t save us. People have to vote for elected officials whose policies take climate change seriously, and individuals can take action.
Know any other projects at the intersection of climate change and machine learning? Send news tips to Khari Johnson and Kyle Wiggers and AI editor Seth Colaner — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI Channel.
Thanks for reading,
Senior AI Staff Writer
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