At the Tackling Climate Change workshop at this year’s NeurIPS conference, some of the top minds in machine learning came together to discuss the effects of climate change on life on Earth, how AI can tackle the urgent problem, and why and how the machine learning community should join the fight.
Copyright by venturebeat.com
The panel included Yoshua Bengio, MILA director and University of Montreal professor; Jeff Dean, Google’s AI chief; Andrew Ng, cofounder of Google Brain and founder of Landing.ai; and Cornell University professor and Institute for Computational Sustainability director Carla Gomes.
The Tackling Climate Change workshop explored a wide range of topics, from the use of deep reinforcement learning to improve performance for ride-hailing services like Uber and Lyft to the application of deep learning to predict wildfire risk, detect avalanche deposits, improve plane efficiency with better wind forecasts, and conduct a global census of solar farms.
The workshop is put together by Climate Change AI, a group that hosts workshops at AI research conferences and a forum for collaboration between machine learning practitioners and people from other fields. Above: Left to right: Yoshua Bengio, Andrew Ng, Carla Gomes, Lester Mackey, and Jeff Dean
Valuing research
One essential step in better addressing the world’s pressing challenges, says Bengio, is changing the way AI research is valued.
Bengio, who talked about the development of what he calls “basic consciousness” earlier in the week, was the top-cited computer science researcher in 2018. He said the machine learning community needs to change its attitude toward the research submitted to major conferences like NeurIPS by evaluating the work’s genuine impact on the world.
“I think the sort of projects we’re talking about in this workshop can potentially be much more impactful than one more incremental improvement in GANs or something,” Bengio said.
NeurIPS organizers said Wednesday that they may make AI models’ carbon footprint part of future submission criteria for conference papers.
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“The reason we count papers is because we just decided that this was the metric we wanted to optimize, but it’s the wrong metric. We should be thinking about what, why — ‘Why am I doing this work, and what do I contribute to society?’” Bengio said.
“The current psychological and cultural mood is so focused on publication, you know, being first author and putting more things on your CV to get a good job, that it’s not healthy. It’s not something where students and researchers feel good. We feel oppressed, and we feel like we have to work [an] incredible number of hours, and so on. Once we start stepping back from this and thinking about what we can bring to the world, the value of truthful long-term research, the value of doing projects that can impact the world — like climate change — we can feel better about ourselves and our work, less stressed, and at the end of the day even [create] better science,” he said.
Going small to get big
The panel also discussed specific technical advances in machine learning that can most effectively combat climate change.
Andrew Ng, along with other panelists, called for progress on ML that works with small data sets and applications like self-supervised learning in tandem with transfer learning so that training models requires less data. […]
Read more – venturebeat.com
At the Tackling Climate Change workshop at this year’s NeurIPS conference, some of the top minds in machine learning came together to discuss the effects of climate change on life on Earth, how AI can tackle the urgent problem, and why and how the machine learning community should join the fight.
Copyright by venturebeat.com
The Tackling Climate Change workshop explored a wide range of topics, from the use of deep reinforcement learning to improve performance for ride-hailing services like Uber and Lyft to the application of deep learning to predict wildfire risk, detect avalanche deposits, improve plane efficiency with better wind forecasts, and conduct a global census of solar farms.
The workshop is put together by Climate Change AI, a group that hosts workshops at AI research conferences and a forum for collaboration between machine learning practitioners and people from other fields. Above: Left to right: Yoshua Bengio, Andrew Ng, Carla Gomes, Lester Mackey, and Jeff Dean
Valuing research
One essential step in better addressing the world’s pressing challenges, says Bengio, is changing the way AI research is valued.
Bengio, who talked about the development of what he calls “basic consciousness” earlier in the week, was the top-cited computer science researcher in 2018. He said the machine learning community needs to change its attitude toward the research submitted to major conferences like NeurIPS by evaluating the work’s genuine impact on the world.
“I think the sort of projects we’re talking about in this workshop can potentially be much more impactful than one more incremental improvement in GANs or something,” Bengio said.
NeurIPS organizers said Wednesday that they may make AI models’ carbon footprint part of future submission criteria for conference papers.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
“The reason we count papers is because we just decided that this was the metric we wanted to optimize, but it’s the wrong metric. We should be thinking about what, why — ‘Why am I doing this work, and what do I contribute to society?’” Bengio said.
“The current psychological and cultural mood is so focused on publication, you know, being first author and putting more things on your CV to get a good job, that it’s not healthy. It’s not something where students and researchers feel good. We feel oppressed, and we feel like we have to work [an] incredible number of hours, and so on. Once we start stepping back from this and thinking about what we can bring to the world, the value of truthful long-term research, the value of doing projects that can impact the world — like climate change — we can feel better about ourselves and our work, less stressed, and at the end of the day even [create] better science,” he said.
Going small to get big
The panel also discussed specific technical advances in machine learning that can most effectively combat climate change.
Andrew Ng, along with other panelists, called for progress on ML that works with small data sets and applications like self-supervised learning in tandem with transfer learning so that training models requires less data. […]
Read more – venturebeat.com
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