Home » News and Events » News » Professor Judea Pearl codes languages to challenge paradigms of computer science

Professor Judea Pearl codes languages to challenge paradigms of computer science

Pearl, an emeritus professor of computer science at UCLA, built his career by challenging generally accepted conventions in the field of artificial intelligence (AI).

When people upgraded to newer computers, Pearl continued to use his 20-year-old Wyse terminal.

“I always have my internet when everyone else is complaining about it,” he said while typing code onto the green and black screen to load his email.

When computer scientists said computers could only understand true or false, Pearl found a way to make them understand uncertainty.

When computer scientists said machines should be answering the question, “What?” Pearl said they should be answering the question, “Why?”

“He understands people aren’t necessarily going to agree with him, but when (Pearl) explains his logic, it all just seems so obvious,” said Kaoru Mulvihill, Pearl’s office assistant.

Mulvihill puttered around Pearl’s office, dusting first edition Francis Bacon books and the leather chair in the corner that Pearl sleeps in to maintain his strict nighttime working hours.

The walls are decorated with honorary degrees from universities that he can’t keep track of. Yellowed pages of scientific manuscripts are neatly slotted in bins around his desk, and trophies cover any flat surface that isn’t occupied by a stack of books.

The A.M. Turing Award, the most prestigious prize awarded to computer scientists by the Association for Computing Machinery, secures its spot in the center of Pearl’s bookcase. Pearl won the Turing award in 2011 for his probabilistic model of artificial intelligence.

The original article can be found here.

In 2020, Professor Pearl is also awarded as World Leader in AI World Society by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Judea Pearl is an AI pioneer to contribute on Causal Inference for AI transparency, which is one of important AIWS.net topics on AI Ethics.