The danger, some experts warn, is that A.I. will run into a technical wall and eventually face a popular backlash — a familiar pattern in artificial intelligence since that term was coined in the 1950s. With deep learning in particular, researchers said, the concerns are being fueled by the technology’s limits.
Deep learning algorithms train on a batch of related data — like pictures of human faces — and are then fed more and more data, which steadily improve the software’s pattern-matching accuracy. Although the technique has spawned successes, the results are largely confined to fields where those huge data sets are available and the tasks are well defined, like labeling images or translating speech to text.
The technology struggles in the more open terrains of intelligence — that is, meaning, reasoning and common-sense knowledge. While deep learning software can instantly identify millions of words, it has no understanding of a concept like “justice,” “democracy” or “meddling.”
Researchers have shown that deep learning can be easily fooled. Scramble a relative handful of pixels, and the technology can mistake a turtle for a rifle or a parking sign for a refrigerator.
Paet III
Paet III
