AI in the Era of Climate Change: Solution or Problem?

AI could help save energy but in the near term AI datacenters will use more energy.

This article was written for ACM News and published on 12 May 2025.

With the rise of deep learning since 2010, the number of computations needed to train artificial intelligence (AI) models has doubled about every six months. Enabling that growth requires a lot of energy, and in practice most of that comes from fossil fuels.

On the other hand, there are also countless ways AI can help save energy, which raises the pressing question of whether AI will lead to a net increase or decrease in energy consumption and, subsequently, what effect that will have on climate change.

That question was at the center of the session “AI in the Era of Climate Change: Solution or Problem?” at the AAAS Annual Meeting in Boston in February.

The complete article can be read here.

AI Risks for Democracy, the Economy, and Civil Rights

The lack of clarity on whether a speech, image, or video is real or artificial is at the center of the debate over AI's benefit to society.

This article was written for ACM News and published on 1 April 2025.

Since January’s launch of DeepSeek R1, the Chinese open-source variant of OpenAI’s GPT-o1, the metaphor of the ‘AI race’ has dominated the geopolitical discussion about artificial intelligence (AI) technology. The debate on the real impact of AI on society and the best ways for AI to benefit society as a whole has faded into the background.

However, these topics were at the heart of the conference session “Risks from AI to the Economy and Society” at the recent American Association for the Advancement of Science annual meeting in Boston.

The complete article can be read here.

Rethinking Social Media’s Future

Decentralization and open protocols for social technologies get a call for consideration.

This article was written for ACM News and published on 1 May 2025.

It was only 15 to 20 years ago that social media platforms like Facebook, Twitter (now X), YouTube, and Instagram were hailed as drivers of social connections, multipliers of knowledge and experiences, and even as catalysts for better-functioning democracy. In fact, Time magazine chose Facebook co-founder Mark Zuckerberg as its 2010 Person of the Year, “for changing how we all live our lives.”

“Sure, there are lots of benefits of social technologies,” said Sarita Schoenebeck, a professor in the School of Information at the University of Michigan. “They allow people to participate, to enjoy social relations online, to advocate and to learn new things. But now the harms feel very urgent and growing: disinformation, harassment, quality of information, security, mental health. Are social technologies now actually helping society? And if not, what to do about it?”

The complete article can be read here.

How Liquid Networks Make Robots Smarter

Liquid networks can learn to associate cause and effect, which makes them suitable for robots and other real-world applications.

This article was written for ACM News and published on 24 April 2025.

When Daniela Rus and her collaborators looked at how a deep neural network made decisions in the vision system of their laboratory’s self-driving car, they noticed that its attention was focused on the entire image, even the bushes and trees at the side of the road. “But that’s not how people drive,” said Rus in her office in the Massachusetts Institute of Technology (MIT)’s Computer Science and Artificial Intelligence Laboratory (CSAIL), of which she is the director. “We usually look at the road horizon and the sides of the road.”

Traditionally AI and robotics have been largely two separate fields, Rus explained. “AI has been amazing us with its decision-making and reasoning, but it is confined in the digital space. Robots have physical presence but are generally pre-programmed and not intelligent. We are aiming to bridge the separation between AI and robots by developing what I call ‘physical AI’. Physical AI uses AI’s power to understand text, images, and video to make a real-world machine smarter. And those machines can be any physical platform: a sensor, a robot, or a power grid.”

The complete article can be read here.