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At Last, First Light for the James Webb Space Telescope Nears

The most ambitious space instrument ever will let us see back almost to the big bang

3 min read
Image of the James Webb Space Telescope.
Photo: Chris Gunn/NASA

Back in 1990, after significant cost overruns and delays, the Hubble Space Telescope was finally carried into orbit aboard the space shuttle. Astronomers rejoiced, but within a few weeks, elation turned to dread. Hubble wasn't able to achieve anything like the results anticipated, because, simply put, its 2.4-meter mirror was made in the wrong shape.

For a time, it appeared that the US $5 billion spent on the project had been a colossal waste. Then NASA came up with a plan: Astronauts would compensate for ­Hubble's distorted main mirror by adding small secondary mirrors shaped in just the right way to correct the flaw. Three years later, that fix was carried out, and Hubble's mission to probe some of the faintest objects in the sky was saved.

Fast-forward three decades. Later this year, the James Webb Space Telescope is slated to be launched. Like Hubble, the Webb telescope project has been plagued by cost overruns and delays. At the turn of the millennium, the cost was thought to be $1 billion. But the final bill will likely be close to $10 billion.

Unlike Hubble, the Webb telescope won't be orbiting Earth. Instead, it will be sent to the Earth-Sun L2 Lagrange point, which is about 1.5 million kilometers away in the opposite direction from the sun. This point is cloaked in semidarkness, with Earth shadowing much of the sun. Such positioning is good for observing, but bad for solar powering. So the telescope will circle around L2 instead of positioning itself there.

The downside will be that the telescope will be too distant to service, at least with any kind of spacecraft available now. “Webb's peril is that it will be parked in space at a place that we can't get back to if something goes wrong," says Eric Chaisson, an astrophysicist at Harvard who was a senior scientist at Johns Hopkins Space Telescope Science Institute when Hubble was commissioned. “Given its complexity, it's hard to believe something won't, though we can all hope, as I do, that all goes right."

Why then send the Webb telescope so far away?

The answer is that the Webb telescope is intended to gather images of stars and galaxies created soon after the big bang. And to look back that far in time, the telescope must view objects that are very distant. These objects will be extremely faint, sure. More important, the visible light they gave off will be shifted into the infrared.

For this reason, the telescope's detectors are sensitive to comparatively long infrared wavelengths. And those detectors would be blinded if the body of the telescope itself was giving off a lot of radiation at these wavelengths due to its heat. To avoid that, the telescope will be kept far from the sun at L2 and will be shaded by a tennis-court-size sun shield composed of five layers of aluminum- and silicon-coated DuPont Kapton. This shielding will keep the mirror of the telescope at a chilly 40 to 50 kelvins, considerably colder than Hubble's mirror, which is kept essentially at room temperature.

Clearly, for the Webb telescope to succeed, everything about the mission has to go right. Gaining confidence in that outcome has taken longer than anyone involved in the project had envisioned. A particularly troubling episode occurred in 2018, when a shake test of the sun shield jostled some screws loose. That and other problems, attributed to human error, shook legislators' confidence in the prime contractor, Northrop Grumman. The government convened an independent review board, which uncovered fundamental issues with how the project was being managed.

In 2018 testimony to the House Committee on Science, Space, and Technology, Wesley Bush, then the CEO of Northrop Grumman, came under fire when the chairman of the committee, Rep. Lamar Smith (R-Texas), asked him whether Northrop Grumman would agree to pay for $800 million of unexpected expenditures beyond the nominal final spending limit.

Naturally, Bush demurred. He also marshalled an argument that has been used to justify large expenditures on space missions since Sputnik: the need to demonstrate technological prowess. “It is especially important that we take on programs like Webb to demonstrate to the world that we can lead," said Bush.

During the thoroughgoing reevaluation in 2018, launch was postponed to March of 2021. Then the pandemic hit, delaying work up and down the line. In July of 2020, launch was postponed yet again, to 31 October 2021.

Whether Northrop Grumman will really hit that target is anyone's guess: The company did not respond to requests from IEEE Spectrum for information about how the pandemic is affecting the project timeline. But if this massive, quarter-century-long undertaking finally makes it into space this year, astronomers will no doubt be elated. Let's just hope that elation over a space telescope doesn't again turn into dread.

This article appears in the January 2021 print issue as “Where No One Has Seen Before."

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We’re Training AI Twice as Fast This Year as Last

New MLPerf rankings show training times plunging

5 min read
Racks in a data center

Google’s cloud based TPU v4 Pods turned in some impressive results.


According to the best measures we’ve got, a set of benchmarks called MLPerf, machine-learning systems can be trained nearly twice as quickly as they could last year. It’s a figure that outstrips Moore’s Law, but also one we’ve come to expect. Most of the gain is thanks to software and systems innovations, but this year also gave the first peek at what some new processors, notably from Graphcore and Intel subsidiary Habana Labs, can do.

The once-crippling time it took to train a neural network to do its task is the problem that launched startups like Cerebras and SambaNova and drove companies like Google to develop machine-learning accelerator chips in house. But the new MLPerf data shows that training time for standard neural networks has gotten a lot less taxing in a short period of time. And that speedup has come from much more than just the advance of Moore’s Law.

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In the quest for human-level intelligent AI, Meta is betting on self-supervised learning

6 min read
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Meta AI’s masked auto-encoder for computer vision was trained on images that were mostly obscured [left]. Yet its reconstructions [center] were remarkably close to the original images [right].


Meta’s chief AI scientist, Yann LeCun, doesn’t lose sight of his far-off goal, even when talking about concrete steps in the here and now. “We want to build intelligent machines that learn like animals and humans,” LeCun tells IEEE Spectrum in an interview.

Today’s concrete step is a series of papers from Meta, the company formerly known as Facebook, on a type of self-supervised learning (SSL) for AI systems. SSL stands in contrast to supervised learning, in which an AI system learns from a labeled data set (the labels serve as the teacher who provides the correct answers when the AI system checks its work). LeCun has often spoken about his strong belief that SSL is a necessary prerequisite for AI systems that can build “world models” and can therefore begin to gain humanlike faculties such as reason, common sense, and the ability to transfer skills and knowledge from one context to another. The new papers show how a self-supervised system called a masked auto-encoder (MAE) learned to reconstruct images, video, and even audio from very patchy and incomplete data. While MAEs are not a new idea, Meta has extended the work to new domains.

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Take the Lead on Satellite Design Using Digital Engineering

Learn how to accelerate your satellite design process and reduce risk and costs with model-based engineering methods

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