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Last May, two research groups announced a breakthrough: they each grew human embryos, in the lab, longer than ever before. In doing so, they witnessed a period of human development no one had ever seen. But in the process, they crashed up against something called the '14-day rule,' a guideline set over 30 years ago that dictates what we do, and possibly how we feel, about human embryos in the lab. On this episode, join producer Molly Webster as she peers down at our very own origins, and wonders: what do we do now?

The air of machine precision casts doubt on what our eyes—or the eyes of referees—can process. As viewers, they argue, our instinct is to assume that all forms of assistance are equally effective, or that they do what they claim in the first place. But what we see on television is usually just a representation, or a calculated guess, based on thermal imaging, sound, or the work of cameras tracking an object’s velocity through air.

So what, exactly, did the algorithm “learn” about the process of dying? And what, in turn, can it teach oncologists? Here is the strange rub of such a deep learning system: It learns, but it cannot tell us why it has learned; it assigns probabilities, but it cannot easily express the reasoning behind the assignment. Like a child who learns to ride a bicycle by trial and error and, asked to articulate the rules that enable bicycle riding, simply shrugs her shoulders and sails away, the algorithm looks vacantly at us when we ask, “Why?”