The gas price for a trip is monotonically increasing in the distance driven.In real life, many pairs of variables exhibit monotonic relationships. Second, when we say the output increases, we do not mean it's strictly increasing-we mean that it does not decrease, because the output can remain the same. The two can be converted into one another simply by negation (multiplying by -1). We can also have monotonically decreasing features, whose increase always leads to a decrease in the model. there are two places where the above definition is imprecise.įirst, the features here are monotonic increasing. So what is a monotonic model? Loosely speaking, a monotonic model is an ML model that has some set of features ( monotonic features) whose increase always leads the model to increase its output.

Monotonic models make AI more “logical.”Įditor’s note: Readers looking to take their own first step in understanding ML basics are encouraged to read our introductory article on ML. 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.

ML performance would be more logical and humanly understandable.

Machine learning (ML) systems would be more robust to adversarial attacks and unexpected situations.

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.

Without going into details just yet, with the proper monotonic AI model: 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. I believe the answer to these questions is yes. 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 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. 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. Most concerning to us humans is not that the AI will make a mistake, but how “illogical” the mistake will be. 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. 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. 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. Many are excited about the future of AI and humanity. AI is fast becoming an amazing asset, having achieved superhuman levels of performance in domains such as image recognition, Go, and even poker.

Monotonic sequence meaning