Stop Calling Everything AI, Machine-Learning Pioneer Says

Michael I. Jordan explains why today’s artificial-intelligence systems aren’t actually intelligent

6 min read
Michael I. Jordan
Photo: Peg Skorpinski

THE INSTITUTE Artificial-intelligence systems are nowhere near advanced enough to replace humans in many tasks involving reasoning, real-world knowledge, and social interaction. They are showing human-level competence in low-level pattern recognition skills, but at the cognitive level they are merely imitating human intelligence, not engaging deeply and creatively, says Michael I. Jordan, a leading researcher in AI and machine learning. Jordan is a professor in the department of electrical engineering and computer science, and the department of statistics, at the University of California, Berkeley.

He notes that the imitation of human thinking is not the sole goal of machine learning—the engineering field that underlies recent progress in AI—or even the best goal. Instead, machine learning can serve to augment human intelligence, via painstaking analysis of large data sets in much the way that a search engine augments human knowledge by organizing the Web. Machine learning also can provide new services to humans in domains such as health care, commerce, and transportation, by bringing together information found in multiple data sets, finding patterns, and proposing new courses of action.

“People are getting confused about the meaning of AI in discussions of technology trends—that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans," he says. “We don't have that, but people are talking as if we do."

Jordan should know the difference, after all. The IEEE Fellow is one of the world's leading authorities on machine learning. In 2016 he was ranked as the most influential computer scientist by a program that analyzed research publications, Science reported. Jordan helped transform unsupervised machine learning, which can find structure in data without preexisting labels, from a collection of unrelated algorithms to an intellectually coherent field, the Engineering and Technology History Wiki explains. Unsupervised learning plays an important role in scientific applications where there is an absence of established theory that can provide labeled training data.

Jordan's contributions have earned him many awards including this year's Ulf Grenander Prize in Stochastic Theory and Modeling from the American Mathematical Society. Last year he received the IEEE John von Neumann Medal for his contributions to machine learning and data science.

In recent years, he has been on a mission to help scientists, engineers, and others understand the full scope of machine learning. He says he believes that developments in machine learning reflect the emergence of a new field of engineering. He draws parallels to the emergence of chemical engineering in the early 1900s from foundations in chemistry and fluid mechanics, noting that machine learning builds on decades of progress in computer science, statistics, and control theory. Moreover, he says, it is the first engineering field that is humancentric, focused on the interface between people and technology.

“While the science-fiction discussions about AI and super intelligence are fun, they are a distraction," he says. “There's not been enough focus on the real problem, which is building planetary-scale machine learning–based systems that actually work, deliver value to humans, and do not amplify inequities."


As a child of the '60s, Jordan has been interested in philosophical and cultural perspectives on how the mind works. He was inspired to study psychology and statistics after reading British logician Bertrand Russell's autobiography. Russell explored thought as a logical mathematical process.

“Thinking about thought as a logical process and realizing that computers had arisen from software and hardware implementations of logic, I saw a parallel to the mind and the brain," Jordan says. “It felt like philosophy could transition from vague discussions about the mind and brain to something more concrete, algorithmic, and logical. That attracted me."

Jordan studied psychology at Louisiana State University, in Baton Rouge, where he earned a bachelor's degree in 1978 in the subject. He earned a master's degree in mathematics in 1980 from Arizona State University, in Tempe, and in 1985 a doctorate in cognitive science from the University of California, San Diego.

When he entered college, the field of machine learning didn't exist. It had just begun to emerge when he graduated.

“While I was intrigued by machine learning," he says, “I already felt at the time that the deeper principles needed to understand learning were to be found in statistics, information theory, and control theory, so I didn't label myself as a machine-learning researcher. But I ended up embracing machine learning because there were interesting people in it, and creative work was being done."

In 2003 he and his students developed latent Dirichlet allocation, a probabilistic framework for learning about the topical structure of documents and other data collections in an unsupervised manner, according to the Wiki. The technique lets the computer, not the user, discover patterns and information on its own from documents. The framework is one of the most popular topic modeling methods used to discover hidden themes and classify documents into categories.

Jordan's current projects incorporate ideas from economics in his earlier blending of computer science and statistics. He argues that the goal of learning systems is to make decisions, or to support human decision-making, and decision-makers rarely operate in isolation. They interact with other decision-makers, each of whom might have different needs and values, and the overall interaction needs to be informed by economic principles. Jordan is developing “a research agenda in which agents learn about their preferences from real-world experimentation, where they blend exploration and exploitation as they collect data to learn from, and where market mechanisms can structure the learning process—providing incentives for learners to gather certain kinds of data and make certain kinds of coordinated decisions. The beneficiary of such research will be real-world systems that bring producers and consumers together in learning-based markets that are attentive to social welfare."


In 2019 Jordan wrote “Artificial Intelligence—The Revolution Hasn't Happened Yet," published in the Harvard Data Science Review. He explains in the article that the term AI is misunderstood not only by the public but also by technologists. Back in the 1950s, when the term was coined, he writes, people aspired to build computing machines that possessed human-level intelligence. That aspiration still exists, he says, but what has happened in the intervening decades is something different. Computers have not become intelligent per se, but they have provided capabilities that augment human intelligence, he writes. Moreover, they have excelled at low-level pattern-recognition capabilities that could be performed in principle by humans but at great cost. Machine learning–based systems are able to detect fraud in financial transactions at massive scale, for example, thereby catalyzing electronic commerce. They are essential in the modeling and control of supply chains in manufacturing and health care. They also help insurance agents, doctors, educators, and filmmakers.

Despite such developments being referred to as “AI technology," he writes, the underlying systems do not involve high-level reasoning or thought. The systems do not form the kinds of semantic representations and inferences that humans are capable of. They do not formulate and pursue long-term goals.

“For the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations," he writes. “We will need well-thought-out interactions of humans and computers to solve our most pressing problems. We need to understand that the intelligent behavior of large-scale systems arises as much from the interactions among agents as from the intelligence of individual agents."

Moreover, he emphasizes, human happiness should not be an afterthought when developing technology. “We have a real opportunity to conceive of something historically new: a humancentric engineering discipline," he writes.

Jordan's perspective includes a revitalized discussion of engineering's role in public policy and academic research. He points out that when people talk about social science, it sounds appealing, but the term social engineering sounds unappealing. The same holds true for genome science versus genome engineering.

“I think that we've allowed the term engineering to become diminished in the intellectual sphere," he says. The term science is used instead of engineering when people wish to refer to visionary research. Phrases such as just engineering don't help.

“I think that it's important to recall that for all of the wonderful things science has done for the human species, it really is engineering—civil, electrical, chemical, and other engineering fields—that has most directly and profoundly increased human happiness."


Jordan says he values IEEE particularly for its investment in building mechanisms whereby communities can connect with each other through conferences and other forums.

He also appreciates IEEE's thoughtful publishing policies. Many of his papers are available in the IEEE Xplore Digital Library.

“I think commercial publishing companies have built a business model that is now ineffectual and is actually blocking the flow of information," he says. Through the open-access journal IEEE Access, he says, the organization is “allowing—and helping with—the flow of information."

IEEE membership offers a wide range of benefits and opportunities for those who share a common interest in technology. If you are not already a member, consider joining IEEE and becoming part of a worldwide network of more than 400,000 students and professionals.

The Conversation (9)

At the moment, we are simply applying rather rudimentary maths and stats to perform some statistical analysis that helps discover patterns hitherto invisible to human eyes (or brains). Given the impressive power and speed of modern computers, we can do it a scale and speed...and accuracy, that humans can't match. Despite all that, we are clearly not any close to the notion of intelligence in the sense of "artificial intelligence". There is a need to educate all in business, especially the leaders, on what is real AI away from the portrayal in movies and popular media.

2 Replies
Joshua Stern 21 Sep, 2021

Just saw this in current Spectrum, and wanted to say it's very good. I was never one to make the mistake of overestimating current ML as a complete AI technology, but given all the hype it's clear the explanation is needed! However, what remains is a bit of a mystery, which is why ML works as far as it does, if we even have the concepts and language to state how far it does work. Is it a key technology for one aspect of AI, or not? Is there a statistical foundation for it, or do neural networks work "just because"? Getting clear on these matters may also clarify where to look for the completion, complement.

Deborah Hagar 26 Oct, 2021

Outstanding overview of the failure to achieve the full potential of AI. The human intelligence has been minimized and neutralized, not explored. Your perspective, leadership, and advocacy of human-centric engineering is what is needed - thank you!

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Can This DIY Rocket Program Send an Astronaut to Space?

Copenhagen Suborbitals is crowdfunding its crewed rocket

15 min read
Five people stand in front of two tall rockets. Some of the people are wearing space suits and holding helmets, others are holding welding equipment.

Copenhagen Suborbitals volunteers are building a crewed rocket on nights and weekends. The team includes [from left] Mads Stenfatt, Martin Hedegaard Petersen, Jørgen Skyt, Carsten Olsen, and Anna Olsen.

Mads Stenfatt

It was one of the prettiest sights I have ever seen: our homemade rocket floating down from the sky, slowed by a white-and-orange parachute that I had worked on during many nights at the dining room table. The 6.7-meter-tall Nexø II rocket was powered by a bipropellant engine designed and constructed by the Copenhagen Suborbitals team. The engine mixed ethanol and liquid oxygen together to produce a thrust of 5 kilonewtons, and the rocket soared to a height of 6,500 meters. Even more important, it came back down in one piece.

That successful mission in August 2018 was a huge step toward our goal of sending an amateur astronaut to the edge of space aboard one of our DIY rockets. We're now building the Spica rocket to fulfill that mission, and we hope to launch a crewed rocket about 10 years from now.

Copenhagen Suborbitals is the world's only crowdsourced crewed spaceflight program, funded to the tune of almost US $100,000 per year by hundreds of generous donors around the world. Our project is staffed by a motley crew of volunteers who have a wide variety of day jobs. We have plenty of engineers, as well as people like me, a pricing manager with a skydiving hobby. I'm also one of three candidates for the astronaut position.

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