AI and ML could save the planet – or add more fuel to the climate fire
'Staggering amount of computation' deployed to solve big problems uses a lot of electricity
AI is killing the planet. Wait, no – it's going to save it. According to Hewlett Packard Enterprise VP of AI and HPC Evan Sparks and professor of machine learning Ameet Talwalkar from Carnegie Mellon University, it's not entirely clear just what AI might do for – or to – our home planet.
Speaking at the SixFive Summit this week, the duo discussed one of the more controversial challenges facing AI/ML: the technology's impact on the climate.
"What we've seen over the last few years is that really computationally demanding machine learning technology has become increasingly prominent in the industry," Sparks said. "This has resulted in increasing concerns about the associated rise in energy usage and correlated – not always cleanly – concerns about carbon emissions and carbon footprint of these workloads."
Sparks estimates that AI/ML workloads account for more than half of all compute demand today.
"The big issue is that many high-profile ML advances just require a staggering amount of computation," said Talwalkar, who also works as an AI researcher at HPE, citing an OpenAI blog post from 2018 that showed that the compute and energy requirements for the model had increased more than 300,000 times since 2012.
"That's a figure, at this point, that's almost four years old, but I think the trend is continuing along similar directions," he added.
- Why chasing the AI dragon may force big tech to take sustainability seriously
- Compute responsibly: Yet another IT industry sustainability drive
- Fusion won't avert need for climate change 'sacrifice', says nuclear energy expert
- Lonestar plans to put datacenters in the Moon's lava tubes
However, the fact the broader machine learning community is even thinking about the effect of AI on the climate is a promising sign, Talwalkar noted.
"This wasn't something that we were really thinking about in the machine learning community a few years ago," he said. "It's good to get ahead of this issue and put pressure on ourselves as a community."
It's not too late
Confronting the environmental ramifications of AI proliferation first requires a better understanding of the problem itself, Talwalkar explained.
"This means learning to accurately measure the exact degree to which this is a problem both in terms of the energy requirements of current AI workloads, as well as coming up with accurate predictions of what we expect future requirements to look like," he said, adding that these insights will not only help researchers understand the true cost of a workload, but also take steps to develop more efficient hardware and improve the algorithms.
"We're actively in the midst of hardware proliferation in terms of specialized hardware specifically designed for training and/or deployment of machine-learning models," he said, citing Google's tensor processing unit as an early example and pointing to ongoing efforts by Nvidia, Graphcore, Cerebras, and others to develop novel hardware for machine learning and AI workloads.
"It's tempting to throw more hardware at the problem, but I think simultaneously as a research community we're really pushing the envelope as well on the algorithmic advances," Sparks noted, highlighting the equal importance of software.
In this regard, Talwalkar argues a better understanding of how and why deep learning models work could bear fruit for optimizing the algorithms to eke out more performance from the compute resources available.
AI is in its infancy
Despite the challenges, Talwalkar remains optimistic that the community will rise to the occasion, and, as the technology matures, place less emphasis on what we can do with these workloads and increase efforts to optimize them.
"We're certainly in the early days of AI progress," he explained. "It seems like we’re seeing new applications showing up daily that are pretty amazing."
Talwalkar believes AI and ML will follow a path not unlike that of the Human Genome Project – a massively expensive endeavor that laid the groundwork for low-cost gene sequencing that has proven enormously beneficial.
And while Sparks expressed similar optimism, he doesn't expect AI/ML growth to abate any time soon. "At least for the next few years, we're going to see a lot more – not a lot less." ®