AI’s progress isn’t the same as creating human intelligence in machines

AI and robotics concept - Chad Hagen

AI’s progress isn’t the same as creating human intelligence in machines
MIT Technology Review, June 28, 2022
Artificial intelligence
by Oren Etzioni

“Honorees from this year’s 35 Innovators list are employing AI to find new molecules, fold proteins, and analyze massive amounts of medical data.”


Spoiler alert: Our annual Innovators Under 35 contest isn’t actually about what a small group of smart young people have been up to (although that’s certainly part of it). It’s really about where technology is headed next. As you read about what problems this year’s winners have set out to solve, you’ll also glimpse the near future of AI, biotech, materials, computing, and the fight against climate change. To connect the dots, we asked five experts—all judges or former winners—to write short essays about where they see the most promise, and the biggest potential roadblocks, in their respective fields. We hope the list inspires you and gives you a sense of what to expect in the years ahead.

Innovators Under 35 2022: AI and robots

The term “artificial intelligence” really has two meanings. AI refers both to the fundamental scientific quest to build human intelligence into computers and to the work of modeling massive amounts of data. These two endeavors are very different, both in their ambitions and in the amount of progress they have made in recent years.


Scientific AI, the quest to both construct and understand human-level intelligence, is one of the most profound challenges in all of science; it dates back to the 1950s and is likely to continue for many decades.


Data-centric AI, on the other hand, began in earnest in the 1970s with the invention of methods for automatically constructing “decision trees” and has exploded in popularity over the last decade with the resounding success of neural networks (now dubbed “deep learning”). Data-centric artificial intelligence has also been called “narrow AI” or “weak AI,” but the rapid progress over the last decade or so has demonstrated its power.



The bulk of the rapid progress today is in this data-centric AI, and the work of this year’s 35 Innovators Under 35 winners is no exception. While data-centric AI is powerful, it has key limitations: the systems are still designed and framed by humans. A few years ago, I wrote an article for MIT Technology Review called “How to know if artificial intelligence is about to destroy civilization.” I argued that successfully formulating problems remains a distinctly human capability. Pablo Picasso famously said, “Computers are useless. They only give you answers.”


We continue to anticipate the distant day when AI systems can formulate good questions—and shed more light on the fundamental scientific challenge of understanding and constructing human-level intelligence.

Innovators Under 35 – AI and robots:

The remarkable progress in AI shouldn’t be confused with creating human intelligence in machines.


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About the Author:

Oren Etzioni is CEO of the Allen Institute for AI and a judge for this year’s 35 Innovators competition.