The Quantified Olympian: Wearables for Elite Athletes

Baseball pitchers, cyclists, and other competitors seek an edge with new gadgets

4 min read
Illustration: Bryan Christie Design
Illustration: Bryan Christie Design

human os icon

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

Modeling Microfluidic Organ-on-a-Chip Devices

Register for this webinar to enhance your modeling and design processes for microfluidic organ-on-a-chip devices using COMSOL Multiphysics

1 min read
Comsol Logo
Comsol

If you want to enhance your modeling and design processes for microfluidic organ-on-a-chip devices, tune into this webinar.

You will learn methods for simulating the performance and behavior of microfluidic organ-on-a-chip devices and microphysiological systems in COMSOL Multiphysics. Additionally, you will see how to couple multiple physical effects in your model, including chemical transport, particle tracing, and fluid–structure interaction. You will also learn how to distill simulation output to find key design parameters and obtain a high-level description of system performance and behavior.

Keep Reading ↓Show less