And then the machines came for the doctors
by David Atkins
You remember IBM's Watson, the Jeopardy champion computer program? It diagnoses cancer now, and better than doctors do. Read this, and think about what it means for public policy and the future of privacy, the world economy and capitalism itself:
The first stages of a planned wider deployment, IBM's business agreement with the Memorial Sloan-Kettering Cancer Center in New York and American private healthcare company Wellpoint will see Watson available for rent to any hospital or clinic that wants to get its opinion on matters relating to oncology. Not only that, but it'll suggest the most affordable way of paying for it in America's excessively-complex healthcare market. The hope is it will improve diagnoses while reducing their costs at the same time.
Two years ago, IBM announced that Watson had "learned" the same amount of knowledge as the average second-year medical student. For the last year, IBM, Sloan-Kettering and Wellpoint have been working to teach Watson how to understand and accumulate complicated peer-reviewed medical knowledge relating to oncology. That's just lung, prostate and breast cancers to begin with, but with others to come in the next few years). Watson's ingestion of more than 600,000 pieces of medical evidence, more than two million pages from medical journals and the further ability to search through up to 1.5 million patient records for further information gives it a breadth of knowledge no human doctor can match.
According to Sloan-Kettering, only around 20 percent of the knowledge that human doctors use when diagnosing patients and deciding on treatments relies on trial-based evidence. It would take at least 160 hours of reading a week just to keep up with new medical knowledge as it's published, let alone consider its relevance or apply it practically. Watson's ability to absorb this information faster than any human should, in theory, fix a flaw in the current healthcare model. Wellpoint's Samuel Nessbaum has claimed that, in tests, Watson's successful diagnosis rate for lung cancer is 90 percent, compared to 50 percent for human doctors.
The implications of this are staggering on several fronts. Most policy makers are fighting over sand castles while an oncoming tide is surging at them.
The first and most obvious point is the mechanization of the global workforce. This has been happening for quite some time, obviously, but to be frank, the people who get elected to office don't tend to be the machinists, seamstresses and booksellers who have already lost their jobs to mechanization and deskilling of the labor force. So no one has done anything about it: these workers have blithely been told to get a better education and a white collar job, instead.
But a funny thing is going to happen when the machines start taking the jobs of doctors, lawyers, stockbrokers, managers and professors. We're not quite there yet, but the day is coming very soon when many of what had traditionally been considered untouchable jobs will be done just as effectively or better by machines. Diagnostics and radiology will be handled by machine, with basic examination and nursing work the most common medical professions. Humans won't be needed for legal services beyond the courtroom and mediation room itself, computer programs will pick investments better than any human, employee evaluation and workforce structuring will be better assessed by analytics than by any middle manager, and mass online education programs will render teachers and professors little more than test proctors and homework readers. None of which assumes the actual intelligent robotic AI of science fiction, which is a whole other story and is also likely coming sooner than we think. Some people see this as utopia, some as dystopia. But either way, it's coming and coming soon.
Consider my profession: I moderate focus groups, testing political messages, advertising ideas, products, websites and what not. You would think that sort of job could not be mechanized. You would be wrong. It's already happening, albeit slowly. Within decades my job will not exist. It will be replaced by human beings wearing biometric monitors, being watched for infinitesimal and involuntary changes in body heat, sweat, positioning, eye movement, heart rates, even brain scans as they react to stimuli. People will play with products, watch advertisements, scan a fake store shelf, use a test website, and the biometrics will know instantly whether the message/ad/product will succeed or fail. People can lie, even to themselves; their involuntary reactions cannot. Some low skilled worker will have to serve as proctor, of course, but the job of moderator will be dead. The analysis, part of which makes up my current job, will also be done by machine.
When the machines and the Internet start taking the white collar jobs, look for a moral panic and rethinking of the capitalist bargain that should have started 30 years ago, but didn't because blue collar workers have no political power.
There are also major implications for privacy. When a computer can look at your medical history and lab results and determine your likelihood of illness better than any doctor, people will flock to have their personal medical histories on a national database. When mothers can know the location and health of their children at all times through a simple mobile device, they will attach it. When an Amber Alert can stop a driverless car in its tracks at a moment's notice, voters will give politicians the power to enable that. When computer programs can manage the likelihood of economic bubbles and recessions based on economic inputs, the pressure to record everyone's transactions into such a system will be enormous.
Sooner rather than later, Big Data is going to know everything about us and we will give that power happily and willingly for the sake of safety and convenience--whether it be to corporations or to the government.
The three-way economic and privacy balance between the public, the government and the private business sector will be forever changed by all of this. Soon we'll go from the natural 95% employment rate we were in the 1990s, to a natural 90% employment rate that we are today, to a natural 70% employment rate or worse. The graph of that change will be parabolic, not linear. And rather than a curse, the power of Big Data will increasingly be seen a comforting source of reliability and promise in a world shocked by mass unemployment and climate disaster.
Will we be ready for that future? Maybe. But only we start planning for it today. If not, the plutocrats and the snoops will get there to take advantage of it before the public can.
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