COLD
SPRING HARBOR, N.Y., June 20,
2024 /PRNewswire/ -- It reads. It talks. It collates
mountains of data and recommends business decisions. Today's
artificial intelligence might seem more human than ever. However,
AI still has several critical shortcomings. Cold Spring Harbor
Laboratory (CSHL) NeuroAI Scholar Kyle Daruwalla explains:
"As impressive as ChatGPT and all these current AI technologies
are, in terms of interacting with the physical world, they're still
very limited. Even in things they do, like solve math problems and
write essays, they take billions and billions of training examples
before they can do them well."
Daruwalla has been searching for new, unconventional ways to
design AI that can overcome such computational obstacles. And he
might have just found one.
The key was moving data. Nowadays, most of modern computing's
energy consumption comes from bouncing data around. In artificial
neural networks, which are made up of billions of connections, data
can have a very long way to go. So, to find a solution, Daruwalla
looked for inspiration in one of the most computationally powerful
and energy-efficient machines in existence—the human brain.
Daruwalla designed a new way for AI algorithms to move and
process data much more efficiently, based on how our brains take in
new information. The design allows individual AI "neurons" to
receive feedback and adjust on the fly rather than wait for a whole
circuit to update simultaneously. This way, data doesn't have to
travel as far and gets processed in real time.
"In our brains, our connections are changing and adjusting all
the time," Daruwalla says. "It's not like you pause everything,
adjust, and then resume being you."
The new machine-learning model provides evidence for a yet
unproven theory that correlates working memory with learning and
academic performance. Working memory is the cognitive system that
enables us to stay on task while recalling stored knowledge and
experiences. Daruwalla says:
"There have been theories in neuroscience of how working memory
circuits could help facilitate learning. But there isn't something
as concrete as our rule that actually ties these two together. And
so that was one of the nice things we stumbled into here. The
theory led out to a rule where adjusting each synapse individually
necessitated this working memory sitting alongside it."
Daruwalla's design may help pioneer a new generation of AI that
learns like we do. That would not only make AI more efficient and
accessible—it would also be somewhat of a full-circle moment for
neuroAI. Neuroscience has been feeding AI valuable data since long
before ChatGPT uttered its first digital syllable. Soon, it seems,
AI may return the favor.
About Cold Spring Harbor Laboratory
Founded in
1890, Cold Spring Harbor Laboratory has shaped contemporary
biomedical research and education with programs in cancer,
neuroscience, plant biology and quantitative biology. Home to eight
Nobel Prize winners, the private, not-for-profit Laboratory employs
1,000 people including 600 scientists, students and technicians.
For more information, visit www.cshl.edu
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SOURCE Cold Spring Harbor Laboratory