The XPrize’s Lunar Deadline Looms

Aspiring moon explorers now have until 2016 to win a top prize of $20 million from Google

8 min read
The XPrize’s Lunar Deadline Looms
Illustration: Matthew Hollister

Griffin is a pack mule with a mission. The four-footed spacecraft is designed to carry 1.7 metric tons of fuel in its belly. It’s girded by wide aluminum deck plates, from which robotic rovers can hang like sleeping bats. It’s built to carry time capsules and cremated remains, among other potential payloads. And one day, in the not-too-distant future, a Pittsburgh-based start-up plans to send it to the moon.

One of 18 competitors remaining in the Google Lunar XPrize, the Pittsburgh company, Astrobotic, hopes to be the first private team to make a moon landing, move 500 meters across the lunar soil, and send high-definition images and video back to Earth. If it can do all of this before any of its competitors, it stands to claim a top prize of US $20 million, provided by Google.

Keep reading...Show less

This article is for IEEE members only. Join IEEE to access our full archive.

Join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of Spectrum’s articles, podcasts, and special reports. Learn more →

If you're already an IEEE member, please sign in to continue reading.

Membership includes:

  • Get unlimited access to IEEE Spectrum content
  • Follow your favorite topics to create a personalized feed of IEEE Spectrum content
  • Save Spectrum articles to read later
  • Network with other technology professionals
  • Establish a professional profile
  • Create a group to share and collaborate on projects
  • Discover IEEE events and activities
  • Join and participate in discussions

Meta’s AI Takes an Unsupervised Step Forward

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

6 min read
A collection of 8 sets of images. In each, the left most image is partially obscured, yet recognizable as the blurry version (center) and the sharp version on the right.

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

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.

Keep Reading ↓Show less

Landsat Proved the Power of Remote Sensing

The Earth-imaging satellites have amassed a half-century of data on crops, borders, and war zones

6 min read
A satellite image shows vegetation in red tones and urban and rocky areas in grays and whites.

The first image captured on 25 July 1972 by the first Landsat satellite shows the Dallas-Fort Worth area.

Robert Simmon/USGS/NASA

On 18 September 1969, U.S. President Richard Nixon addressed the General Assembly of the United Nations. It was a difficult time in global politics, and much of his speech focused on the war in Vietnam, disputes in the Middle East, and strategic arms control. Toward the end, though, the speech took a curious and hopeful turn, as Nixon rhapsodized about the unifying potential of international cooperation in space exploration. As an example, he noted the United States was in the process of developing new satellites to survey Earth’s natural resources.

Three years later, on 23 July 1972, NASA launched what would be the first Earth Resources Technology Satellite (ERTS). It gave scientists, land managers, policymakers, and others an unprecedented view of their planet. The program has since launched eight more satellites. Renamed the Landsat program in 1975, it is now celebrating its 50th anniversary of imaging the Earth.

Keep Reading ↓Show less

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

1 min read
Keysight
Keysight

Win the race to design and deploy satellite technologies and systems. Learn how new digital engineering techniques can accelerate development and reduce your risk and costs. Download this free whitepaper now!

Our white paper covers:

Keep Reading ↓Show less