The promise of artificial intelligence (AI) with human-level cognition has been beyond our grasp for the past sixty years. Over the past decade, advances in AI have created breakthroughs in many of the field’s most historically intractable challenges: speech-to-text, language translation, image and pattern recognition. With such advances, how far can we possibly be from surmounting all remaining obstacles and building machines that not only think like humans but machines that will improve our lives by reducing drudgery and by solving problems great and small?

In this study, Professor Song-Chun Zhu of UCLA offers his perspective on the history, current state, and path forward for next generation cognitive AI. He dispels the myth that human- level AI is already solved; in some regards, he argues, we have barely begun. He also imagines and illustrates a future in which humans and machines collaborate with a kind of mutual "understanding." Written in plain language, this study seeks to draw AI newcomers into the field, while including enough technical material to keep an AI insider engaged.

In all, Professor Zhu explains a paradigm shift that moves away from big data for small tasks towards small data for big tasks. In the process, Professor Zhu challenges today’s "ABCD" conception of AI:

AI ≠ Big Data + Computing Power + Deep Learning

To appreciate the differences between human and artificial intelligences, let's, for a moment, compare crows and parrots. Like deep learning networks and other algorithms that use big data, parrots mimic the sounds of the world around them.

But does mimicry, does imitation suggest authentic learning, particularly the mastery of concepts and the application of those concepts to new contexts? It is doubtful. Similar to humans, crows, on the other hand, armed with spatio-temporal-causal reasoning, with a sense of how things work, observe the world with singular intent.

In the case of human intelligence, we are capable of imagining the thoughts of others. This capacity gives us the power to reason not only about space, time, and the physics of cause and effect, but also about the intent and values of those around us. Such social reasoning is the basis of communication and the ultimate prize of language.

Human-level AI must be built with all these capabilities.

Raised in rural China, Song-Chun Zhu completed his Ph.D. at Harvard and Brown with Professor David Mumford, a Fields Medalist. Zhu’s unique Sino-American perspective on AI covers as much range culturally as it does scientifically and informs the body of work he has built in AI over twenty-five years, including the last fifteen at UCLA.

Zhu makes a case for integrating the disparate disciplines that comprise the AI research fields and sets a course that may yet bring AI to the next level on the ladder to true human-level cognition.

If you believe human-level AI is already here because your mobile phone answers when you speak, this study will clarify just how far we have to go.

If you believe human-level AI is impossible, this work may just change your mind.