New Heights of Profitability From Artificial Data-Crunchers
If you need an example of one of the many ways artificial intelligence is transforming our lives, look no further than research labs.
These days, the problems we expect scientists to tackle and the discoveries they’re called on to make are more complex than ever.
Even the best and brightest need help now and then.
So, in 2009, scientists at Aberystwyth University in Wales created Adam, an artificial-intelligence program that functions as a robotic biologist.
Adam was first tested on yeast. Yeast is a well-studied organism, but it has “orphan genes” that no one could figure out which enzymes they create.
Adam looked at the genomes of other organisms. Then it formulated hypotheses, designed experiments to test its theories, and conducted experiments in an automated lab. It found three genes that, together, accounted for one of yeast’s enzymes. Human researchers then confirmed the result in the usual way.
It was the first time that a scientific discovery had been made entirely by artificial intelligence. The robot even published a paper in a scientific journal about its findings. (No word whether the paper was written by a digital scribe, like I wrote about last week.)
But what’s Adam without Eve?
The same scientists responsible for Adam, followed it up with Eve, whose mission is to discover new drugs.
Currently, drugs are found by a tedious series of “brute-force” trials, testing one chemical cocktail after another to see what effect each has on a particular illness. Instead, Eve can analyze a malady’s biochemistry and concoct new drug formulas that may have the greatest chance of success.
For example, Eve quickly proved her worth by discovering that a drug with cancer-fighting abilities also might work against malaria.
It’s a hopeful beginning. Tropical illnesses kill millions each year, mostly in poor countries. But the market isn’t lucrative enough to justify the years of research needed to find cures. If the search can be automated, investments in successful research and development could be modest enough to make these new drugs financially viable for poor nations.
New markets would open and small pharma companies could find profitable niches.
Today’s artificial-data crunchers can drive companies to new heights of profitability, and do it at lightning speeds.