The path to real-world artificial intelligence Posted on : Jul 11 - 2020

Experts from MIT and IBM held a webinar this week to discuss where AI technologies are today and advances that will help make their usage more practical and widespread.

Artificial intelligence has made significant strides in recent years, but modern AI techniques remain limited, a panel of MIT professors and IBM's director of the Watson AI Lab said during a webinar this week.

Neural networks can perform specific, well-defined tasks but they struggle in real-world situations that go beyond pattern recognition and present obstacles like limited data, reliance on self-training, and answering questions like "why" and "how" versus "what," the panel said.

The future of AI depends on enabling AI systems to do something once considered impossible: Learn by demonstrating flexibility, some semblance of reasoning, and/or by transferring knowledge from one set of tasks to another, the group said.

The panel discussion was moderated by David Schubmehl, a research director at IDC, and it began with a question he posed asking about the current limitations of AI and machine learning.

"The striking success right now in particular, in machine learning, is in problems that require interpretation of signals—images, speech and language," said panelist Leslie Kaelbling, a computer science and engineering professor at MIT.

For years, people have tried to solve problems like detecting faces and images and directly engineering solutions that didn't work, she said.

We have become good at engineering algorithms that take data and use that to derive a solution, she said. "That's been an amazing success." But it takes a lot of data and a lot of computation so for some problems formulations aren't available yet that would let us learn from the amount of data available, Kaelbling said.

One of her areas of focus is in robotics, and it's harder to get training examples there because robots are expensive and parts break, "so we really have to be able to learn from smaller amounts of data," Kaelbling said.

Neural networks and deep learning are the "latest and greatest way to frame those sorts of problems and the successes are many," added Josh Tenenbaum, a professor of cognitive science and computation at MIT.

But when talking about general intelligence and how to get machines to understand the world there is still a huge gap, he said.

"But on the research side … really exciting things are starting to happen to try to capture some steps to more general forms of intelligence [in] machines," he said. In his work, "we're seeing ways in which we can draw insights from how humans understand the world and taking small steps to put them in machines." View More