Who advantages from artificial intelligence? This basic query, which has been especially salient throughout the AI surge of the previous few years, was front and center at a conference at MIT on Wednesday, as speakers and audience members grappled with the numerous dimensions of AI’s impact.
In one in every of the conferences’s keynote talks, journalist Karen Hao ’15 called for an altered trajectory of AI development, including a move away from the huge scale-up of knowledge use, data centers, and models getting used to develop tools under the rubric of “artificial general intelligence.”
“This scale is unnecessary,” said Hao, who has grow to be a distinguished voice in AI discussions. “You do not want this scale of AI and compute to understand the advantages.” Indeed, she added, “If we actually need AI to be broadly helpful, we urgently have to shift away from this approach.”
Hao is a former staff member at The Wall Street Journal and MIT Technology Review, and creator of the 2025 book, “Empire of AI.” She has reported extensively on the expansion of the AI industry.
In her remarks, Hao outlined the astonishing size of datasets now getting used by the most important AI firms to develop large language models. She also emphasized a number of the tradeoffs on this scale-up, corresponding to the huge energy consumption and emissions of hyper-scale data centers, which also devour large amounts of water. Drawing on her own reporting, Hao also noted the human toll from the input work that global gig-economy employees do, inputting data manually for the hyper-scale models.
Against this, Hao offered, an alternate path for AI might exist in the instance of AlphaFold, the Nobel Prize-winning tool used to discover protein structures. This represents the concept of the “small, task-specific AI model tackling a well-scoped problem that lends itself to the computational strengths of AI,” Hao said.
She added: “It’s trained on highly curated data sets that only should do with the issue at hand: protein folding and amino acid sequences. … There’s no need for fast supercomputing since the datasets are small, the model is small, and it’s still unlocking enormous profit.”
In a second keynote address, scholar Paola Ricaurte underscored the desirability of purpose-driven AI approaches, outlining quite a few conceptual keys to evaluating the usefulness of AI.
“There isn’t a sense in having technologies that aren’t going to reply to the communities which might be going to make use of them,” said Ricaurte.
She is a professor at Tecnologico de Monterrey in Mexico and a school associate at Harvard University’s Berkman Klein Center for Web and Society. Ricaurte has also served on expert committees corresponding to the Global Partnership for AI, UNESCO’s AI Ethics Experts Without Borders, and the Women for Ethical AI project.
The event was hosted by the MIT Program in Women’s and Gender Studies. Manduhai Buyandelger, this system’s director and a professor of anthropology, provided introductory remarks.
Titled “Gender, Empire, and AI: Symposium and Design Workshop,” the event was held within the conference space on the MIT Schwartzman College of Computing, with over 300 people in attendance for the keynote talks. There was also a segment of the event dedicated to discussion groups, and a day session on design, in a half-dozen different subject areas.
In her talk, Hao decried the often-vague nature of AI discourse, suggesting it impedes a more thoughtful discussion in regards to the industry’s direction.
“A part of the challenge in talking about AI is the whole lack of specificity within the term ‘artificial intelligence,’” Hao said. “It’s just like the word ‘transportation.’ You could possibly be referring to anything from a bicycle to a rocket.” Because of this, she said, “once we speak about accessing its advantages, we actually should be very specific. Which AI technologies are we talking about, and which of them do we would like more of?”
In her view, the smaller-sized tools — more akin to the bicycle, by analogy — are more useful on an on a regular basis basis. As one other example, Hao mentioned the project Climate Change AI, focused on tools that might help improve the energy efficiency of buildings, track emissions, optimize supply chains, forecast extreme weather, and more.
“That is the vision of AI that we ought to be constructing towards,” Hao said.
In conclusion, Hao encouraged audience members to be energetic participants in AI-related discourse and projects, saying the trajectory of the technology was not yet fixed, and that public interventions matter.
Citing the author Rebecca Solnit, Hao suggested to the audience that “Hope locates itself within the premise that we don’t know what’s going to occur, and that within the spaciousness of uncertainty is room to act.” She also noted, “Each one in every of you has an energetic role to play in shaping technology development.”
Ricaurte, similarly, encouraged attendees to be proactive participants in AI matters, noting that technologies will work best when the pressing on a regular basis needs of all residents are addressed.
“We’ve got the responsibility to make hope possible,” Ricaurte said.