Millions
of people, many of whom have never thought much about computer science, are
experimenting with generative AI models such as the eminently conversational
ChatGPT and creative image generator DALL-E. While these products reflect less
of a technological breakthrough than AI’s emergence into the public
consciousness, the traction they have found is guiding massive investment
streams—investment shaping how this technology will be applied for years to
come.
For
those of us who have long been bullish on AI’s potential to transform society,
especially in key areas such as health and medicine, recent months have felt
very much like science fiction has come to life.
However,
as delightful as it is to explore these capabilities—GPT-4 for example exceeded
the passing score by 20 points on the U.S. medical licensing exam—the results
of doing so mainly serve to highlight their shortcomings. The ability to read,
retain and regurgitate all such data on demand makes today’s AI good at everything—but
great at nothing.
There’s
no question that AI is poised to irrevocably change how we look to prevent and
treat illness. Doctors will cede documentation to AI scribes; primary care
providers will lean on chatbots for triage; near-endless libraries of predicted
protein structures will supercharge drug development. However, to truly
transform these fields, we should invest in creating an ecosystem of
models—say, “specialist” AIs—that learn like our best physicians and drug developers
do today.
Getting
to the top of a field typically begins with years of intensive information
upload, often via formal schooling, followed by some form of apprenticeship;
years devoted to learning, mostly in person, from the field’s most accomplished
practitioners. It’s a nearly irreplaceable process: Most of the information a
medical resident gleans by listening and watching a high-performing surgeon,
for example, isn’t spelled out in any textbook.
It’s
particularly challenging to gain the intuition, often acquired through
schooling and experience, that helps determine the best answer in a complex
situation. This is true for artificial intelligence and people alike, but for
AI, the issue is exacerbated by the way it currently learns and how technologists
are currently approaching the opportunity and challenge. By studying thousands
to millions of labeled data points—examples of “right” and “wrong”—current
advanced neural network architectures are able to figure out what makes one
choice better than another. Rather than learning solely from massive amounts of
data and expecting a single generative model to solve all problems, we should
train AI by using models that stack on top of each other—first biology, then
chemistry, then layer on top of those foundations data points specific to
health care or drug design, for example.
Pre-med
students aim to become doctors, but their coursework starts with the basics of
chemistry and biology rather than the finer points of diagnosing disease.
Without those foundational courses, their ability to one day provide
high-quality health care would face significant limits. Similarly, a scientist
who designs a new therapeutic undergoes years of studying chemistry and
biology, followed by PhD studies, followed by working under the tutelage of
expert drug designers. This style of learning can help develop a sense for how
to navigate decisions involving subtle differences, which, especially at the
molecular scale, really matter. For example, estrogen and testosterone differ only
slightly, but have dramatically different impacts on human health.
Developing
these stacked AI models with hierarchies of latent spaces—simplified maps of
complex data to help AI models understand patterns and relationships—would
reflect an understanding or predictive capability for each foundational
element. I believe this may initially parallel human education and educational
paradigms, but will likely in time specialize to develop new types of expertise
in AI learning. These stacked models could develop in ways analogous to
cortexes in the human brain. But, whereas humans have a visual cortex and a
motor cortex, AI could have a biology cortex and a drug design cortex—in both
cases, neural architectures specialized for specific tasks.
Ironically,
creating an AI that specializes in a particular domain such as health care may
be easier to create than something more akin to HAL 9000, with typical
human-level knowledge across fields. And, in fact, we need specialist AIs in
specific domains more than an overarching AI that can do anything an average
human can do. I anticipate the creation of not a single specialist AI but many,
with a diversity of approaches in coding, data, and testing, such that these
models could provide a second (or third, or fourth) opinion when necessary.
In
parallel, we must rip AI from its online moorings and plunge it into the world
of atoms. We should be equipping our most skilled human specialists with
wearables to gather nuanced, real-world interactions for AI to learn from, just
as our up-and-coming academic and industry stars do. The most complex and
uncertain aspects of addressing health and medicine simply don’t exist fully in
the world of bits.
Exposing
these specialist AIs to the perspective of a diverse range of top practitioners
will be a must to avoid replicating dangerous biases. But AI is less of a black
box than popular imagination suggests; the human decision-making we depend on
today, as I’ve noted previously, is arguably more opaque. We can’t let fear of
perpetrating human biases limit our willingness to explore how AI can help us
democratize the expertise of our human specialists, who are unfortunately
unscalable.
Given
the neural networks underpinning artificial intelligence, these specialist AIs
may gain knowledge even faster than we’d expect through meta-learning—or
learning to learn—and take us humans along for the ride. The nature of AI lets
us do something we simply can’t with people; take them apart piece by piece and
study each little bit. By building systems to plumb the inner works of
specialist AIs, we’ll create a learning flywheel. Eventually, specialist AIs
could shift beyond the role of domain expert into teachers to the next
generation of specialists—human and AI alike.
