What’s next for AI: Gary Marcus talks about the journey toward robust artificial intelligence

He was not particularly talented at writing, yet became a best-selling author. He is an academic who founded two startups — one acquired by Uber, another just scored $15 million to make building smarter robots easier. He has a humanities background, yet became one of the more prominent, and controversial, figures in AI.

If you know Gary Marcus, then you probably know it’s very hard to summarize someone like him. If you don’t, here’s your chance to change that. Gary Marcus is a scientist, best-selling author, and entrepreneur. Marcus is well known in AI circles, mostly for his critique on — and ensuing debates around — a number of topics, including the nature of intelligence, what’s wrong with deep learning, and whether four lines of code are acceptable as a way to infuse knowledge when seeding algorithms. 

Although Marcus is sometimes seen as “almost a professional critic of organizations like DeepMind and OpenAI”, he is much more than that.

As a precursor to Marcus’s upcoming keynote on the future of AI in Knowledge Connexions, ZDNet caught up with Marcus on a wide array of topics. We publish the first part of the discussion today — check back for the second part next week.

In February 2020, Marcus published a 60-page long paper titled “The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence”. In a way, this is Marcus’ answer to his own critics, going beyond critique and putting forward concrete proposals. 

Unfortunately, to quote Marcus, the world has much larger problems to deal with right now, so the paper has not been discussed as it might have been in a pre-COVID world. We agree, but we think it’s time to change that. We discussed everything from his background to the AI debates and from his recent paper to knowledge graphs.

Marcus is a cognitive psychologist by training. That may seem strange for some people: how can someone who has a background in humanities be considered one of the top minds in AI? To us, it did not seem that strange. And it made even more sense after Marcus expanded on the topic.

Marcus comes to AI from a perspective of trying to understand the human mind. As a child and teenager, Marcus programmed computers – but quickly became dissatisfied with the state of that art in the 1980s.

Marcus realized that humans were a whole lot smarter than any of the software that he could write. He skipped to the last couple of years of high school, based on a translator that he wrote that worked from Latin into English, which he said was one his first serious AI projects. But then another realization hit home:

“I could do a semester’s worth of Latin by using a bunch of tricks, but it wasn’t really very deep and there wasn’t anything else out there that was deep. This eventually led me to studying human language acquisition, and human cognitive development”.

Marcus teamed up with Steven Pinker, who was his mentor as a PhD student. They worked on how human beings acquire even simple parts of language like the past tense of English. Marcus spent a lot of time comparing neural networks that were popular then to what human children did. Those neural networks went into obscurity, and then they reemerged in 2012. 

When they reemerged, Marcus realized that they had all the same problems he had criticized in some of his early technical work. Marcus has spent a good part of the last decade trying to look at what we know about how children  learn about the world, language and so forth, and what can that tell us about what we might need to do to make progress in AI.

As an interdisciplinary cognitive scientist, Marcus has been trying to bring together what we know from many fields in order to answer some really hard questions: How does the mind work? How does it develop? How did it evolve in time?

That led him to be a writer as well, as he found that people in different fields didn’t speak each other’s languages.