Without the concepts of time, space and causality, much of common sense is impossible. We all know, for example, that any given animal’s life begins with its birth and ends with its death; that at every moment during its life it occupies some particular region in space; that two animals can’t ordinarily be in the same space at the same time ; that two animals can be in the same space at different times; and so on.

We don’t have to be taught this kind of knowledge explicitly. It is the set of background assumptions, the conceptual framework, that makes possible all our other thinking about the world.

Yet few people working in A.I. are even trying to build such background assumptions into their machines. We’re not saying that doing so is easy — on the contrary, it’s a significant theoretical and practical challenge — but we’re not going to get sophisticated computer intelligence without it.

If we build machines equipped with rich conceptual understanding, some other worries will go away. The philosopher Nick Bostrom, for example, has imagined a scenario in which a powerful A.I. machine instructed to make paper clips doesn’t know when to stop and eventually turns the whole world — people included — into paper clips.

In our view, this kind of dystopian speculation arises in large part from thinking about today’s mindless A.I. systems and extrapolating from them. If all you can calculate is statistical correlation, you can’t conceptualize harm. But A.I. systems that know about time, space and causality are the kinds of things that can be programmed to follow more general instructions, such as “A robot may not injure a human being or, through inaction, allow a human being to come to harm” (the first of Isaac Asimov’s three laws of robotics).

We face a choice. We can stick with today’s approach to A.I. and greatly restrict what the machines are allowed to do (lest we end up with autonomous-vehicle crashes and machines that perpetuate bias rather than reduce it). Or we can shift our approach to A.I. in the hope of developing machines that have a rich enough conceptual understanding of the world that we need not fear their operation. Anything else would be too risky.

Gary Marcus (@GaryMarcus), the founder and chief executive of Robust AI, and Ernest Davis, a professor of computer science at New York University, are the authors of the forthcoming book “Rebooting AI: Building Artificial Intelligence We Can Trust,” from which this essay is adapted.

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