An acquaintance of mine recently found himself in a fairly common plight: His car broke down miles from home, and so he called for a tow.
Tow truck drivers not only ply a noble trade; they also rank among life’s most sagacious observers. During the ride to the garage, this driver told my friend that most of his calls actually involve newer cars whose electrical systems have gone awry, rendering the vehicles inoperable. Given the complexity of today’s autos, the tow man shared, he spends his days bailing out one stranded new car owner after another.
We’ve come to rely on technology in many facets of our lives, from the computers in our cars to the AI-driven home assistants sitting atop our dressers. Doctors have begun to depend on AI algorithms to facilitate diagnostics and treatment. We tend to think that such tools have advanced far more than they really have; they’re actually prone to serious mistakes.
“People worry computers will get too smart and take over the world, but the real problem is they’re too stupid and they’ve already taken over the world,” Pedro Domingos, author of The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, quipped in a 2015 interview.
The solution, said Domingos, is to make the computers smarter. In our second annual data issue, we take a look at the quest in life sciences to harness data. In our lead feature on privacy — “Who’s next?”— senior editor Larry Dobrow wrote of “a quenchless thirst” for smart AI use cases among tech and marketing execs. Yet breaches in healthcare, besides being bad for privacy, may undermine these analytic tools by prompting consumers to opt out of sharing their health info.
“Almost everything we do can be improved by the use of advanced data analytics,” one aficionado tells us in our “Minds Behind the Data” Q&A, “but we must continue to practice a fair amount of scientific skepticism,” he cautions, to understand limitations of the data.
That brings us back to marketing, where there are some legit use cases, as we uncover in “Healthcare marketing’s data debut.” However, we often hear of those in healthcare express interest in moving into AI because it’s the latest innovation, rather than because of a stated business problem. Not wanting to get left behind is not a business plan.
Was this the same message forward-minded industry folks were delivering five years ago? Perhaps. We look forward to the day when the technology catches up with peoples’ needs, and vice versa. Until then, biopharma’s embrace of all things AI should be balanced with equal parts curiosity and caution.