Even as powerful as today's algorithms are, we're further from autonomous general intelligence than you'd imagine.
My first patent and third company, Genomic Acceleration, was an algorithm in microprocessor form. Its goal was simple: match long strands of digital DNA together into a whole. This was just before the completion of the Human Genome Project in 2003. As with all"classic algorithms," its output was deterministic: it produced the same result for the same input every time. It would always finish, and it always produced the optimal, correct result.
Much of the business world, and nearly all healthcare decisions, are still made based on deterministic"rules." If your heart rate is above 100 bpm, you'll be classified as high risk. However, a person's health may have hundreds or thousands of variables—genetics, age, test results, medical history, family history, mental state and more.
Mint's core algorithm categorized financial transactions and allowed people to set budgets on gas or groceries, get email alerts when they overspent, optimize credit card rewards or airline points and a host of other features. It came about, as many good inventions do, out of frustration—specifically, the frustration of repeatedly telling Quicken that Safeway is"groceries" or Starbucks is"coffee.
Machine learning allows you to deal with the fuzziness and uncertainty of the real world. That's a huge advantage, but it comes with a big downside; whereas a classic algorithm is"right" 100% of the time, machine learning will always be"wrong" some of the time.
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