What is the Prime Objective?

Author’s note: I have relocated from Silicon Valley to Research Triangle Park in North Carolina. Rick, the founder of Convergent Technologies, recently set up shop in Austin. He and I have a running bet on what city will be the bigger tech hub in ten years. We will keep you updated on who is winning!

The title of today’s post is not a typo. Star Trek fans know it is the prime directive—not objective. With that disclaimer, I’d like to discuss the scaling down of Watson, IBM’s flagship AI system. Due to limited success, they reduced their team and resources, and this year, they halted sales. This was surprising to me. I watched with rapt enthusiasm as Watson took down the reigning Jeopardy champions almost a decade ago. Then, it was announced that its next feat would be to topple cancer!

Why use AI . . . because scientists run on papers. Based on a Pubmed search, in 2018 alone, there were 203,607 research papers published on cancer. For a fun thought experiment, think of it this way: medical papers are relatively short at around five pages, and research papers are longer at around 12 pages. Let’s split the difference, giving a bit of favoritism to the medical papers, and call it an average of about seven pages. Then let’s assume that you are a very fast reader, taking in about a page every minute. It would take approximately three years to read the current literature on cancer if you forewent pesky things like sleep and food. Granted, all those papers are probably not relevant to a physician/scientist’s research, but there is a growing problem of how to process all that data (quality control is also an issue, but that is another topic altogether).

Enter AI, which is fast on the buzzer and can answer all those questions about famous rivers that Jeopardy tends to throw in there. If it can win at this game, why not point it at another problem and let it go? So why did it essentially fail? The tipping point came when it recommended a potentially fatal treatment for a cancer patient.

If you look at the greatest successes in AI that are dominating the headlines, you’ll note that all have one thing in common, a simple objective: win this game; get an object from point A to point B without hitting anything. With cancer, it is not so simple. My cousin is an outstanding oncologist doing amazing research at Indiana University. He is level-headed with the exception of one phrase that gives him a teeth clench, “It cures cancer.” I cannot go into enough detail here to discuss all the problems with that statement, but I can say with as much confidence as a scientist lends that if someone tells you something definitively cures cancer, he or she is knowingly or unknowingly misleading you. Some questions you might want to follow up with: What type of cancer cells? (Cancer is not monolithic.) What do you mean by cure? Do you mean prevention of occurrence, cutting off the causative agent (like HPV virus, mutant cells, genetic predisposition), treatment of existing cancer, or prevention of recurrence?

Algorithms need a clear direction to improve. Nuance in their solutions leads to muddying the waters of how the mathematical equations are trained. Additionally, the problem intended to be solved by the algorithm needs to be nearly limitlessly testable. We can’t let an AI endlessly play against patient outcomes like we could with a game like Go. “Machine learning” and “AI” are commonly used interchangeably, but there is an actual distinction. A true AI should be able to handle this nuance and thrive in situations in which it has not seen data to a specific outcome. Unfortunately, we are not there yet, but I do find it encouraging that IBM tried.

Big data is daunting and confusing, yet it is becoming more of a necessity to remain competitive in the modern workplace. We here at Convergent Technologies specialize in simplifying the seemingly opaque. We have helped many organizations of various scales implement sensible data solutions. Let us help get you there!