For those working with AI, the future is certainly exciting. At the same time, there is a general sense that AI suffers from one pesky flaw: AI in its current state can be unpredictably unreliable.
AI is fast becoming an amazing asset, having achieved superhuman levels of performance in domains such as image recognition, Go, and even poker. Many are excited about the future of AI and humanity. At the same time, there is a general sense that AI does suffer from one pesky flaw: AI in its current state can be unpredictably unreliable.
The classical example is the Jeopardy! IBM Challenge, during which Watson, the IBM AI, cleaned the board with ease, only to miss the “Final Jeopardy!” question, which was under the category of US Cities: “Its largest airport is named for a World War II hero; its second largest for a World War II battle.” Watson answered, “What is
Toronto?????”—the extra question marks (and low wager) indicating its doubt.
So even though AI has the capacity for fairy-tale-like performance for large periods of time
—months, years, decades even—there is always this nagging possibility that all of a sudden, it will mysteriously blunder.
Most concerning to us humans is not that the AI will make a mistake, but how “illogical” the mistake will be. In Watson’s case, someone who doesn’t know the answer to the question would “logically” try to at least guess a major US city. I believe that this is one of the main reasons we don’t yet have public adoption of self-driving cars: Even if self-driving cars may be statistically safer, we fear that their underlying AI might unexpectedly blunder in a similar sense to Watson, but with much more serious repercussions.
This got me wondering, could the right AI model fix this issue? Could the right AI have the capability to make sound decisions in critical moments, even when it doesn’t have all the answers? Such AI would be able to change the course of technology and enable us the fairy-tale-like benefits of AI…
I believe the answer to these questions is yes. I believe that mistakes like Watson’s may be avoidable with the use of improved, more logically constrained models, the early prototype of which are called monotonic machine learning models. Without going into details just yet, with the proper monotonic AI model:
A self-driving car would be safer, as the detection of even the smallest amount of human signal would always suffice to activate a safety protocol even in the presence of a large amount of other signal.
Machine learning (ML) systems would be more robust to adversarial attacks and unexpected situations.
ML performance would be more logical and humanly understandable.
I believe we are moving from an era of great growth in the computational and algorithmic power of AI to an era of finesse, effectiveness, and understanding in AI, and monotonic machine learning models are the first step in this exciting journey. Monotonic models make AI more “logical.”
Editor’s note: Readers looking to take their own first step in understanding ML basics are encouraged to read our introductory article on ML.
