University of California, Berkeley, researchers have measured brain waves
in participants and artificial intelligence systems -- a comparison they say provides a window into what is considered a black box of AI.

Date:
May 2, 2023
Source:
University of California - Berkeley
Summary:
New research shows that artificial intelligence (AI) systems
can process signals in a way that is remarkably similar to how
the brain interprets speech, a finding scientists say might help
explain the black box of how AI systems operate.


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New research from the University of California, Berkeley, shows that
artificial intelligence (AI) systems can process signals in a way that
is remarkably similar to how the brain interprets speech, a finding
scientists say might help explain the black box of how AI systems operate.

Using a system of electrodes placed on participants' heads, scientists
with the Berkeley Speech and Computation Lab measured brain waves as participants listened to a single syllable -- "bah." They then compared
that brain activity to the signals produced by an AI system trained to
learn English.

"The shapes are remarkably similar," said Gasper Begus, assistant
professor of linguistics at UC Berkeley and lead author on the study
published recently in the journal Scientific Reports. "That tells you
similar things get encoded, that processing is similar. " A side-by-side comparison graph of the two signals shows that similarity strikingly.

"There are no tweaks to the data," Begus added. "This is raw."
AI systems have recently advanced by leaps and bounds. Since ChatGPT
ricocheted around the world last year, these tools have been forecast
to upend sectors of society and revolutionize how millions of people
work. But despite these impressive advances, scientists have had a
limited understanding of how exactly the tools they created operate
between input and output.

A question and answer in ChatGPT has been the benchmark to measure an AI system's intelligence and biases. But what happens between those steps
has been something of a black box. Knowing how and why these systems
provide the information they do -- how they learn -- becomes essential
as they become ingrained in daily life in fields spanning health care
to education.

Begus and his co-authors, Alan Zhou of Johns Hopkins University and T.

Christina Zhao of the University of Washington, are among a cadre of
scientists working to crack open that box.

To do so, Begus turned to his training in linguistics.

When we listen to spoken words, Begus said, the sound enters our ears
and is converted into electrical signals. Those signals then travel
through the brainstem and to the outer parts of our brain. With the
electrode experiment, researchers traced that path in response to 3,000 repetitions of a single sound and found that the brain waves for speech
closely followed the actual sounds of language.

The researchers transmitted the same recording of the "bah" sound
through an unsupervised neural network -- an AI system -- that could
interpret sound.

Using a technique developed in the Berkeley Speech and Computation Lab,
they measured the coinciding waves and documented them as they occurred.

Previous research required extra steps to compare waves from the brain
and machines. Studying the waves in their raw form will help researchers understand and improve how these systems learn and increasingly come to
mirror human cognition, Begus said.

"I'm really interested as a scientist in the interpretability of these
models," Begus said. "They are so powerful. Everyone is talking about
them. And everyone is using them. But much less is being done to try
to understand them." Begus believes that what happens between input
and output doesn't have to remain a black box. Understanding how those
signals compare to the brain activity of human beings is an important
benchmark in the race to build increasingly powerful systems. So is
knowing what's going on under the hood.

For example, having that understanding could help put guardrails on increasingly powerful AI models. It could also improve our understanding
of how errors and bias are baked into the learning processes.

Begus said he and his colleagues are collaborating with other researchers
using brain imaging techniques to measure how these signals might
compare. They're also studying how other languages, like Mandarin,
are decoded in the brain differently and what that might indicate about knowledge.

Many models are trained on visual cues, like colors or written
text -- both of which have thousands of variations at the granular
level. Language, however, opens the door for a more solid understanding,
Begus said.

The English language, for example, has just a few dozen sounds.

"If you want to understand these models, you have to start with simple
things.

And speech is way easier to understand," Begus said. "I am very hopeful
that speech is the thing that will help us understand how these models
are learning." In cognitive science, one of the primary goals is to build mathematical models that resemble humans as closely as possible. The newly documented similarities in brain waves and AI waves are a benchmark on
how close researchers are to meeting that goal.

"I'm not saying that we need to build things like humans," Begus
said. "I'm not saying that we don't. But understanding how different architectures are similar or different from humans is important."
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========================================================================== Story Source: Materials provided by
University_of_California_-_Berkeley. Original written by Jason Pohl. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Gasper Begus, Alan Zhou, T. Christina Zhao. Encoding of speech in
convolutional layers and the brain stem based on language
experience.

Scientific Reports, 2023; 13 (1) DOI: 10.1038/s41598-023-33384-9 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/05/230502201343.htm

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