Here’s a selection of recent headlines about artificial intelligence, picked more or less at random:
Okay, not exactly at random — I did look for more doomy-sounding headlines. But they weren’t hard to find. That’s because numerous studies indicate that negative or fear-framed coverage of AI in mainstream media tends to outnumber positive framings.
And to be clear, there are good reasons for that! From disinformation to cyberwarfare to autonomous weapons to massive job loss to the actual, flat-out end of the world (shameless plug of my book here), there are a lot of things that could go very, very wrong with AI.
But as in so many other areas, the emphasis on the negative in artificial intelligence risks overshadowing what could go right — both in the future as this technology continues to develop and right now. As a corrective (and maybe just to ingratiate myself to our potential future robot overlords), here’s a roundup of one way in which AI is already making a positive difference in three important fields.
Whenever anyone asks me about an unquestionably good use of AI, I point to one thing: AlphaFold. After all, how many other AI models have won their creators an actual Nobel Prize?
AlphaFold, which was developed by the Google-owned AI company DeepMind, is an AI model that predicts the 3D structures of proteins based solely on their amino acid sequences. That’s important because scientists need to predict the shape of protein to better understand how it might function and how it might be used in products like drugs.
That’s known as the “protein-folding problem” — and it was a problem because while human researchers could eventually figure out the structure of a protein, it would often take them years of laborious work in the lab to do so. AlphaFold, through machine-learning methods I couldn’t explain to you if I tried, can make predictions in as little as five seconds, with accuracy that is almost as good as gold-standard experimental methods.
By speeding up a basic part of biomedical research, AlphaFold has already managed to meaningfully accelerate drug development in everything from Huntington’s disease to antibiotic resistance. And Google DeepMind’s decision last year to open source AlphaFold3, its most advanced model, for non-commercial academic use has greatly expanded the number of researchers who can take advantage of it.
You wouldn’t know it from watching medical dramas like The Pitt, but doctors spend a lot of time doing paperwork — two hours of it for every one hour they actually spend with a patient, by one count. Finding a way to cut down that time could free up doctors to do actual medicine and help stem the problem of burnout.
That’s where AI is already making a difference. As the Wall Street Journal reported this week, health care systems across the country are employing “AI scribes” — systems that automatically capture doctor-patient discussions, update medical records, and generally automate as much as possible around the documentation of a medical interaction. In one pilot study employing AI scribes from Microsoft and a startup called Abridge, doctors cut back daily documentation time from 90 minutes to under 30 minutes.
Not only do ambient-listening AI products free doctors from much of the need to make manual notes, but they can eventually connect new data from a doctor-patient interaction with existing medical records and ensure connections and insights on care don’t fall between the cracks. “I see it being able to provide insights about the patient that the human mind just can’t do in a reasonable time,” Dr. Lance Owens, regional chief medical information officer at University of Michigan Health, told the Journal.
A timely warning about a natural disaster can mean the difference between life and death, especially in already vulnerable poor countries. That is why Google Flood Hub is so important.
An open-access, AI-driven river-flood early warning system, Flood Hub provides seven-day flood forecasts for 700 million people in 100 countries. It works by marrying a global hydrology model that can forecast river levels even in basins that lack physical flood gauges with an inundation model that converts those predicted levels into high-resolution flood maps. This allows villagers to see exactly what roads or fields might end up underwater.
Flood Hub, to my mind, is one of the clearest examples of how AI can be used for good for those who need it most. Though many rich countries like the US are included in Flood Hub, they mostly already have infrastructure in place to forecast the effects of extreme weather. (Unless, of course, we cut it all from the budget.) But many poor countries lack those capabilities. AI’s ability to drastically reduce the labor and cost of such forecasts has made it possible to extend those lifesaving capabilities to those who need it most.
One more cool thing: The NGO GiveDirectly — which provides direct cash payments to the global poor — has experimented with using Flood Hub warnings to send families hundreds of dollars in cash aid days before an expected flood to help themselves prepare for the worst. As the threat of extreme weather grows, thanks to climate change and population movement, this is the kind of cutting-edge philanthropy.
Even what seems to be the best applications for AI can come with their drawbacks. The same kind of AI technology that allows AlphaFold to help speed drug development could conceivably be used one day to more rapidly design bioweapons. AI scribes in medicine raise questions about patient confidentiality and the risk of hacking. And while it’s hard to find fault in an AI system that can help warn poor people about natural disasters, the lack of access to the internet in the poorest countries can limit the value of those warnings — and there’s not much AI can do to change that.
But with the headlines around AI leaning so apocalyptic, it’s easy to overlook the tangible benefits AI already delivers. Ultimately AI is a tool. A powerful tool, but a tool nonetheless. And like any tool, what it will do — bad and good — will be determined by how we use it.
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