The first man vs. machine showdown happened on a checkers board.
In 1961, Arthur Samuel, the father of “machine learning,” taught an artifical intelligence (AI) program to beat the fourth-ranked checkers player in the country.
Samuel had both academia and private tech to thank for his success. His career took him from MIT to Bell Telephone Laboratories to the University of Illinois to IBM, where his checker program demonstration raised the company’s stock by 15 points. At his last gig, he taught PhD students at Stanford.
Today, machine learning experts are training programs to take on much bigger challenges than checkers—from trading stocks to predicting social unrest.
Pairing Data with Talent
But data scientists would be smart to follow Samuel’s lead, and break down barriers between academia and private tech. Only by pairing the resources and real-world data of the private sector with the talent at colleges and universities can we unlock the full potential of AI and machine learning.
Private tech companies know that some of the greatest AI minds of our time are walking around university campuses. According to Carnegie Mellon University’s Dean Andrew Moore, AI students are “worth somewhere between $5 million and $10 million to a company’s bottom line.”
It’s no wonder, then, that big companies are heavily recruiting PhD candidates. Over the past 10 years, nearly 20 percent more data science PhD students have taken industry jobs. Collaborating on data science projects is a great way to connect university students to real-world opportunities and private companies to the talent they are looking for.
Solving Real-World Challenges
By sharing their resources and data libraries, researchers and companies are much more likely to discover AI solutions that improve human lives. A machine-learning model at Stanford sorted through 50 million images in two weeks—a task that would take a human 15 years.
Researchers are using such unprecedented computational power to solve real-world problems. The cancer center at University of North Carolina at Chapel Hill, for example, is using a commercially available AI system to quickly sort through research papers and identify treatment options that doctors might not have considered. Unlike humans, the system can digest all 8,000 medical papers that are published daily.