(p. A21) If you read Google’s public statement about Google Duplex, you’ll discover that the initial scope of the project is surprisingly limited. It encompasses just three tasks: helping users “make restaurant reservations, schedule hair salon appointments, and get holiday hours.”
Schedule hair salon appointments? The dream of artificial intelligence was supposed to be grander than this — to help revolutionize medicine, say, or to produce trustworthy robot helpers for the home.
The reason Google Duplex is so narrow in scope isn’t that it represents a small but important first step toward such goals. The reason is that the field of A.I. doesn’t yet have a clue how to do any better.
. . .
The narrower the scope of a conversation, the easier it is to have. If your interlocutor is more or less following a script, it is not hard to build a computer program that, with the help of simple phrase-book-like templates, can recognize a few variations on a theme. (“What time does your establishment close?” “I would like a reservation for four people at 7 p.m.”) But mastering a Berlitz phrase book doesn’t make you a fluent speaker of a foreign language. Sooner or later the non sequiturs start flowing.
. . .
To be fair, Google Duplex doesn’t literally use phrase-book-like templates. It uses “machine learning” techniques to extract a range of possible phrases drawn from an enormous data set of recordings of human conversations. But the basic problem remains the same: No matter how much data you have and how many patterns you discern, your data will never match the creativity of human beings or the fluidity of the real world. The universe of possible sentences is too complex. There is no end to the variety of life — or to the ways in which we can talk about that variety.
. . .
Today’s dominant approach to A.I. has not worked out. Yes, some remarkable applications have been built from it, including Google Translate and Google Duplex. But the limitations of these applications as a form of intelligence should be a wake-up call. If machine learning and big data can’t get us any further than a restaurant reservation, even in the hands of the world’s most capable A.I. company, it is time to reconsider that strategy.
For the full commentary, see:
Gary Marcus and Ernest Davis. “A.I. Is Harder Than You Think.” The New York Times (Saturday, May 19, 2018): A21.
(Note: ellipses added.)
(Note: the online version of the commentary has the date May 18, 2018.)