It is helpful to take a step back and frankly assess the strengths and weaknesses of today’s AI in order to better focus resources and research efforts going forward. In each of the areas discussed below, promising work is already underway at the frontiers of the field to make the next generation of artificial intelligence more high-performing and robust….
Humans’ “common sense” is a consequence of the fact that we develop persistent mental representations of the objects, people, places and other concepts that populate our world—what they’re like, how they behave, what they can and cannot do.
Deep neural networks do not form such mental models. They do not possess discrete, semantically grounded representations of, say, a house or a cup of coffee. Instead, they rely on statistical relationships in raw data to generate insights that humans find useful….
Real-world environments entail a continuous stream of incoming data. New information becomes available incrementally; circumstances change over time, sometimes abruptly. Humans are able to dynamically and smoothly incorporate this continuous input from their environment, adapting their behavior as they go. In the parlance of machine learning, one could say that humans “train” and “deploy” in parallel and in real-time. Today’s AI lacks this suppleness….
Today’s machine learning is at its core a correlative tool. It excels at identifying subtle patterns and associations in data. But when it comes to understanding the causal mechanisms—the real-world dynamics—that underlie those patterns, today’s AI is at a loss….
Going back to its inception, the field of artificial intelligence—and indeed, the field of statistics more broadly—has been architected to understand associations rather than causes. This is reflected in the basic mathematical symbols we use.
“The language of algebra is symmetric: If X tells us about Y, then Y tells us about X,” says AI luminary Judea Pearl, who for years has been at the forefront of the movement to build AI that understands causation. “Mathematics has not developed the asymmetric language required to capture our understanding that if X causes Y that does not mean that Y causes X.”…
The challenge of building AI that shares, and reliably acts in accordance with, human values is a profoundly complex dimension of developing robust artificial intelligence. It is referred to as the alignment problem.
As we entrust machine learning systems with more and more real-world responsibilities—from granting loans to making hiring decisions to reviewing parole applications—solving the alignment problem will become an increasingly high-stakes issue for society. Yet it is a problem that defies straightforward resolution….
Part of the problem is that human values are nuanced, amorphous, at times contradictory; they cannot be reduced to a set of definitive maxims. This is precisely why philosophy and ethics have been such rich, open-ended fields of human scholarship for centuries.
For other posts on AI ethics, see here.
Leave A Comment