Open the tech news on any given day and you’re almost guaranteed to find something about conversational AI or Natural Language Processing (NLP). This is the tech that powers chatbots, virtual assistants and the likes as they mimic human interaction. As this blog has noted, complex language models have come on leaps and bounds recently, and our future as users is becoming clear: we’ll be holding (reasonably) natural conversations with non-human bots on a regular basis, and for a variety of reasons.
The shadows on the cave wall — if not yet the fully realized Platonic form of conversational AI — can already be made out. Want banking tips? Ask Erica. Legal advice? There are bots like April. Want to engage your students? Juji thinks it can help.
If you’re of a certain generation, you might remember the Tamagotchi; the Japanese pocket-sized “pet simulation game” that became the chief obsession of 90s kids bored of yo-yos and other fleeting trends. The Tamagotchi lived mostly in the grubby hands or lint-filled pockets of its owners but, for social currency, could be paraded before envious or competitive enthusiasts.
Oddly, these oviparous virtual critters weren’t remotely animallike in their appearance, and could be intolerably demanding at times. Neglect to feed them, clean up after them, or tend to them when sick and — as many of us found out — very soon you’d be left with nothing but a dead LCD blob. But even the best cared-for Tamagotchi(s?) had certain obsolescence looming in their futures, once their needlessly complex lifecycle was complete: egg, baby, child, teen, adult, death.
“How do we get humans to trust in all this AI we’re building?”, asked Affectiva CEORana El-Kaliouby, at the prestigious NYT New Work Summit at Half Moon Bay last week. She had already assumed a consensus that trust-building was the correct way to proceed, and went on to suggest that, rather than equipping users and consumers with the skills and tools to scrutinize AI, we should instead gently coax them into placing more unearned faith in data-driven artifacts.
But how would this be accomplished? Well, Affectiva are “on a mission to humanize technology”, drawing upon machine and deep learning to “understand all things human.” All things human, El-Kaliouby reliably informed us, would include our emotions, our cognitive state, our behaviors, our activities. Note: not to sense, or to tentatively detect, but to understand those things in “the way that humans can.”