Date Posted: 6/14/2021

In a profile of his current research, Kilian Weinberger, Associate Professor of Computer Science in the Ann S. Bowers College of Computing and Information Science at Cornell, reflects on "writing algorithms that can draw inferences from patterns in data." Read an excerpt here from Jackie Swift's "What Makes a Machine Intelligent?"

"Artificial intelligence (AI) is a mainstay of science fiction. From the rogue computer HAL in 2001: A Space Odyssey, to Iron Man Tony Stark’s stalwart assistant, JARVIS, the potential and the peril of AI has been imagined and explored for decades. But how close are we to these fictional presentations?

“In some sense, the field of computer science has, for a long time, been really far behind these grand expectations for artificial intelligence that you see in movies,” says Kilian Q. Weinberger, Computer Science. “Only lately, for better or worse, have we caught up with some of this stuff that’s been promised in science fiction.”

Weinberger is an expert on machine learning, the algorithm-driven processes that enable AI to analyze and draw inferences from patterns in data. “Machine learning tries to answer the question of how to make computers learn from experience,” he says. “Instead of writing a concrete software program of how exactly to do something, which is the traditional way to program computers, I write a program that can learn, and then I show it examples of what I want it to do. It’s a very different approach.”

AI All Around Us

Machine learning is used in many day-to-day applications we take for granted—from the phone cameras we rely on to identify and optimize human faces in the photos we take, to spam filters that capture objectionable email before it hits our inboxes.

“In the case of something like face recognition, it’s very hard to write a program using the traditional approach where I would have to say exactly what a face looks like,” says Weinberger. “I could say, ‘It has two eyes and a nose and a mouth,’ but what if someone has only one eye? Or what if they have an eye patch or glasses or they’re wearing a hat? And then, of course, there is the question of what an eye or a nose looks like.”

Rather than explicitly describing the attributes of a human face, Weinberger would write a learning algorithm that detects patterns. He would then show it examples: 10,000 images with faces in them clearly identified, followed by 100,000 images in which no faces appear, he explains. “The algorithm would try to find patterns that are present on faces and not present on not-face images,” he says. “By seeing more and more examples, it would get better and better at identifying faces.”

Continue reading

Also, see the related article "Autonomous cars using human-style behaviour to see, learn, find Sarah Connor" in Drive:

According to Kilian Weinberger from Cornell University’s Computer Science division who is exploring the technology, neural network algorithms are key to making the two separate cameras piece together imagery to interpret surroundings.

Though this technology isn’t exactly new, it is the first time the process has been explained explicitly and is the most likely interpretation to have real-world applications.

It was achieved using machine learning, which essentially enables the artificial intelligence to learn from its experiences. Rather than providing code for the software to follow, machine learning uses examples of how to react in a given situation, which allows the software to notice patterns and react accordingly.

But not unlike 'learning machines' in science fiction, the artificial intelligence can become overly confident in its perceptions and decide that it’s always right. After being given examples to ‘view’ and not making mistakes, the system will assume it always understands a given situation correctly and doesn’t react. 

Of course, this could have detrimental real-world outcomes, so Weinberger and his team are currently developing a ‘spectrum’ of accuracy, which is set to better depict just how well their system understands a given situation.

Sufficed to say, full self-driving and learning capability is still some time away.

In related news, read about Weinberger as finalist for a prize from the Blavatnik Family Foundation and the New York Academy of Sciences.