On the flip side of the enthusiasm we share for digital assistants and the progress on natural language understanding there are people like Boris Katz, a principal research scientist at MIT that believes current AI techniques aren’t enough to make Siri or Alexa truly smart:
But on the other hand, these programs are so incredibly stupid. So there’s a feeling of being proud and being almost embarrassed. You launch something that people feel is intelligent, but it’s not even close.
Katz has made key contributions to the linguistic abilities of machines. In the 1980s, he developed START
, a system capable of responding to naturally phrased queries. The ideas used in START helped IBM’s Watson win on Jeopardy!
and laid the groundwork for today’s chattering artificial servants.
Katz then moves to explain why the current state of ML and DL are not the optimal to solve natural language challenges that we have today:
In language processing, like in other fields, progress was made by training models on huge amounts of data—many millions of sentences. But the human brain would not be able to learn language using this paradigm. We don’t leave our babies with an encyclopedia in the crib, expecting them to master the language.
Boris Katz suggest a new way to teach machines language through simulated physical worlds:
I have yet to see a baby whose parents put an encyclopedia in the crib and say, “Go learn.” And this is what our computers do today. I don’t think these systems will learn the way we want them to or understand the world the way we want to.
To end the article what is a better approach for building intelligent systems:
AI research needs to build on ideas from developmental psychology, cognitive science, and neuroscience, and AI models ought to reflect what is already known about how humans learn and understand the world. Real progress will come only when researchers get out of our offices and start talking to people in other fields.
Short but thought provoking article
that you shouldn’t miss.