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Raj Reddy Bets on Babel Fish, Gordon Bell Says No Way

Famous computer scientists are placing wagers again

3 min read
Video still of Raj Reddy
COMPUTER HISTORY MUSEUM

Some three decades ago, a group of pioneering computer scientists—Gordon Bell, John Hennessy, Ed Lazowska, Raj Reddy, Andy Van Dam, and sometimes a few others—began a tradition of placing bets against each other. 

This week, these long-time friends and competitors gathered virtually at an event held by the Computer History Museum in honor of Reddy being named a museum Fellow. In keeping with tradition, they made tech bets; this time for $2000 (years ago, their wagers had been for as much as $1000), to be paid by the losers as a donation to the museum.

Reddy predicted: “We will have a digital Babel fish [a universal translator, as described in The Hitchhiker’s Guide to the Galaxy] that will hide in your ear and translate all the world’s languages—in ten years,” he said. “Anybody will be able to watch any movie and talk to anyone in any language.” 

Or at least, he conceded, it will work for the 100 most common languages.

The names in this gaggle of wager-making technology enthusiasts are no doubt familiar to you: Bell architected the PDP-4 and PDP-6 and oversaw the development of the VAX at Digital Equipment Corp.—and cofounded the Computer History Museum. Hennessy helped write the book on RISC architectures, founded MIPS, served as president of Stanford University, and is chairman of the board of Alphabet. Lazowska, a pioneer in object-oriented computing, helped write the classic text on quantifying computer system performance. AI pioneer Reddy’s focus on speech recognition systems started computing research down the path that led to Siri and today’s other intelligent agents. Van Dam co-designed the first hypertext system and coauthored the key textbook used in the study of computer graphics (the character “Andy” in the movie Toy Story is named after him).

For many years, these five, sometimes with one or two others, sometime minus one or two, assembled at meetings of Microsoft’s Technical Advisory Board. When the talk turned to future technologies, their competitive instincts flared up. Reddy would make a prediction—something concrete, but always a reach in terms of what was currently possible. The others would jump in to bet for and against it. The stakes? Often a dinner, winner’s choice of restaurant, sometimes $1000 cash. Reddy usually lost.

A few of Reddy’s losing predictions, according to a record kept by Bell, who always bet against him:

  • By 1996 video-on-demand would be available in 5 cities of over 0.5 million people and 250,000 people will have access to the service, with a substantial number of users.
  • In 2003 AI would be thought more important than the transistor
  • In 2003 a production car that drives itself will be available for less than 20 percent more than a non-self-driving car
  • By 2002, 10,000 workstations would communicate at a speed of gigabits per second

Which brings us back to Reddy’s new Babel fish wager.  

In fairness, Reddy critiqued his own prediction. “Why I might lose?” he said. Because “technology isn’t enough, you need accessibility and ease of use. It has to be completely unintrusive, like a Babel fish, to fit in our ear, recognize the language and translate it.”

Bell, as usual, took the opposite side, betting against Reddy.

Hennessy supported Reddy’s view. “For once I think Raj could be right,” he said.

But there are “two issues I worry about,” he continued. One is “the last 10 percent problem, though the choice of 100 of the most frequent languages makes it more doable. The other is that if we don’t figure out how to extend Moore’s Law, you’ll put that thing in your ear, and it will burn your head up. One of the things we were wrong about [in the past] was that we didn’t understand how much computing power it would take to do AI.”

“There is a spectrum of cockeyed techno-optimism which Raj (Reddy) occupies.

Lazowska asked for a clarification, that is, did the envisioned device have to do the translation on board, or could it rely on the user carrying a smart phone. Reddy indicated that it could rely on whatever it wanted, as long as the system doesn’t demand the user’s attention.

“It has to be nonintrusive. I don’t want to have to think about it,” Reddy explained. “If I am talking in Hindi, you can hear me in English in real time.”

With an external gadget permitted in the mix, Lazowska was also willing to bet that Reddy’s prediction will come true.

Van Dam, however, wasn’t convinced.  “There is a spectrum of cockeyed techno-optimism,” he said, “which Raj [Reddy] occupies. I try to be a pragmatist.”

Van Dam’s concerned are the financial incentives, which would have to be significant, he said, to get a device like this to market.

It would have to “nail all 100 languages, manage background noise, [and] be robust,” he said. And, he mused, the user interface will present a big challenge for designers. “I think we will get close but not quite there,” he said, and so, though he loves the quest, he reluctantly bet against it.

Whose bets will pay off? We’ll have to wait ten years to find out.

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Quantum Error Correction: Time to Make It Work

If technologists can’t perfect it, quantum computers will never be big

13 min read
Quantum Error Correction: Time to Make It Work
Chad Hagen
Blue

Dates chiseled into an ancient tombstone have more in common with the data in your phone or laptop than you may realize. They both involve conventional, classical information, carried by hardware that is relatively immune to errors. The situation inside a quantum computer is far different: The information itself has its own idiosyncratic properties, and compared with standard digital microelectronics, state-of-the-art quantum-computer hardware is more than a billion trillion times as likely to suffer a fault. This tremendous susceptibility to errors is the single biggest problem holding back quantum computing from realizing its great promise.

Fortunately, an approach known as quantum error correction (QEC) can remedy this problem, at least in principle. A mature body of theory built up over the past quarter century now provides a solid theoretical foundation, and experimentalists have demonstrated dozens of proof-of-principle examples of QEC. But these experiments still have not reached the level of quality and sophistication needed to reduce the overall error rate in a system.

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