The World Health Organization has declared mpox a public health emergency of international concern – a new variant of the virus has caused an outbreak in Central and West Africa and spread to Sweden

When the fever, pains and pus-filled lesions of an mpox infection strike, how dangerous is it and how can it be treated?

Surrounding ourselves with greenery can do wonders for our physical and mental wellbeing. Kathy Willis reveals just what kinds of plants are best for our brains and bodies, and why

Promising new vaccines are designed to be given to patients at risk of developing Alzheimer's disease. If they perform well in clinical trials, they have the potential to one day rid society of dementia

Despite the limitations of Alzheimer's drugs like lecanemab, this new class of treatments and a group of experimental vaccines are paving the way to a world without dementia

Stem cells made in the lab may one day aid cancer treatment by reducing our reliance on donors

They fight invaders, clear debris and tend neural connections, but sometimes microglia go rogue. Preventing this malfunction may offer new treatments for brain conditions including Alzheimer's

Medical clowns, who play with children in hospitals, may help them be discharged sooner by reducing their heart rates

A woman's brain was scanned throughout her pregnancy, adding to the growing body of evidence that dramatic remodelling takes place in preparation for motherhood

Modern life disrupts the circadian rhythms controlling our biology – increasing our risk of developing conditions ranging from diabetes to dementia. Lynne Peeples's new book The Inner Clock explores and offers solutions

A new covid-19 variant called XEC may spread more easily than past variants, but current vaccines are still effective against it

A flurry of studies has found microplastics in nearly every organ in the human body, from the brain to the testicles. But very few have revealed whether these tiny bits of plastic impact our health

Mosquito larvae die after consuming hemp leaves because they react strongly to the cannabidiol in the foliage. The discovery might lead to the development of a new pesticide to control mosquito numbers

The compound creatine, a popular exercise supplement that only occurs naturally in animal products, could one day be produced in edible plants

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The end of Moore’s Law is looming. Engineers and designers can do only so much to miniaturize transistors and pack as many of them as possible into chips. So they’re turning to other approaches to chip design, incorporating technologies like AI into the process.

Samsung, for instance, is adding AI to its memory chips to enable processing in memory, thereby saving energy and speeding up machine learning. Speaking of speed, Google’s TPU V4 AI chip has doubled its processing power compared with that of its previous version.

But AI holds still more promise and potential for the semiconductor industry. To better understand how AI is set to revolutionize chip design, we spoke with Heather Gorr, senior product manager for MathWorks’ MATLAB platform.

How is AI currently being used to design the next generation of chips?

Heather Gorr: AI is such an important technology because it’s involved in most parts of the cycle, including the design and manufacturing process. There’s a lot of important applications here, even in the general process engineering where we want to optimize things. I think defect detection is a big one at all phases of the process, especially in manufacturing. But even thinking ahead in the design process, [AI now plays a significant role] when you’re designing the light and the sensors and all the different components. There’s a lot of anomaly detection and fault mitigation that you really want to consider.

Heather GorrMathWorks

Then, thinking about the logistical modeling that you see in any industry, there is always planned downtime that you want to mitigate; but you also end up having unplanned downtime. So, looking back at that historical data of when you’ve had those moments where maybe it took a bit longer than expected to manufacture something, you can take a look at all of that data and use AI to try to identify the proximate cause or to see something that might jump out even in the processing and design phases. We think of AI oftentimes as a predictive tool, or as a robot doing something, but a lot of times you get a lot of insight from the data through AI.

What are the benefits of using AI for chip design?

Gorr: Historically, we’ve seen a lot of physics-based modeling, which is a very intensive process. We want to do a reduced order model, where instead of solving such a computationally expensive and extensive model, we can do something a little cheaper. You could create a surrogate model, so to speak, of that physics-based model, use the data, and then do your parameter sweeps, your optimizations, your Monte Carlo simulations using the surrogate model. That takes a lot less time computationally than solving the physics-based equations directly. So, we’re seeing that benefit in many ways, including the efficiency and economy that are the results of iterating quickly on the experiments and the simulations that will really help in the design.

So it’s like having a digital twin in a sense?

Gorr: Exactly. That’s pretty much what people are doing, where you have the physical system model and the experimental data. Then, in conjunction, you have this other model that you could tweak and tune and try different parameters and experiments that let sweep through all of those different situations and come up with a better design in the end.

So, it’s going to be more efficient and, as you said, cheaper?

Gorr: Yeah, definitely. Especially in the experimentation and design phases, where you’re trying different things. That’s obviously going to yield dramatic cost savings if you’re actually manufacturing and producing [the chips]. You want to simulate, test, experiment as much as possible without making something using the actual process engineering.

We’ve talked about the benefits. How about the drawbacks?

Gorr: The [AI-based experimental models] tend to not be as accurate as physics-based models. Of course, that’s why you do many simulations and parameter sweeps. But that’s also the benefit of having that digital twin, where you can keep that in mind—it’s not going to be as accurate as that precise model that we’ve developed over the years.

Both chip design and manufacturing are system intensive; you have to consider every little part. And that can be really challenging. It’s a case where you might have models to predict something and different parts of it, but you still need to bring it all together.

One of the other things to think about too is that you need the data to build the models. You have to incorporate data from all sorts of different sensors and different sorts of teams, and so that heightens the challenge.

How can engineers use AI to better prepare and extract insights from hardware or sensor data?

Gorr: We always think about using AI to predict something or do some robot task, but you can use AI to come up with patterns and pick out things you might not have noticed before on your own. People will use AI when they have high-frequency data coming from many different sensors, and a lot of times it’s useful to explore the frequency domain and things like data synchronization or resampling. Those can be really challenging if you’re not sure where to start.

One of the things I would say is, use the tools that are available. There’s a vast community of people working on these things, and you can find lots of examples [of applications and techniques] on GitHub or MATLAB Central, where people have shared nice examples, even little apps they’ve created. I think many of us are buried in data and just not sure what to do with it, so definitely take advantage of what’s already out there in the community. You can explore and see what makes sense to you, and bring in that balance of domain knowledge and the insight you get from the tools and AI.

What should engineers and designers consider when using AI for chip design?

Gorr: Think through what problems you’re trying to solve or what insights you might hope to find, and try to be clear about that. Consider all of the different components, and document and test each of those different parts. Consider all of the people involved, and explain and hand off in a way that is sensible for the whole team.

How do you think AI will affect chip designers’ jobs?

Gorr: It’s going to free up a lot of human capital for more advanced tasks. We can use AI to reduce waste, to optimize the materials, to optimize the design, but then you still have that human involved whenever it comes to decision-making. I think it’s a great example of people and technology working hand in hand. It’s also an industry where all people involved—even on the manufacturing floor—need to have some level of understanding of what’s happening, so this is a great industry for advancing AI because of how we test things and how we think about them before we put them on the chip.

How do you envision the future of AI and chip design?

Gorr: It’s very much dependent on that human element—involving people in the process and having that interpretable model. We can do many things with the mathematical minutiae of modeling, but it comes down to how people are using it, how everybody in the process is understanding and applying it. Communication and involvement of people of all skill levels in the process are going to be really important. We’re going to see less of those superprecise predictions and more transparency of information, sharing, and that digital twin—not only using AI but also using our human knowledge and all of the work that many people have done over the years.

Boston Dynamics is the master of dropping amazing robot videos with no warning, and last week, we got a surprise look at the new electric Atlas going “hands on” with a practical factory task.

This video is notable because it’s the first real look we’ve had at the new Atlas doing something useful—or doing anything at all, really, as the introductory video from back in April (the first time we saw the robot) was less than a minute long. And the amount of progress that Boston Dynamics has made is immediately obvious, with the video showing a blend of autonomous perception, full body motion, and manipulation in a practical task.

We sent over some quick questions as soon as we saw the video, and we’ve got some extra detail from Scott Kuindersma, senior director of Robotics Research at Boston Dynamics.

If you haven’t seen this video yet, what kind of robotics person are you, and also here you go:

Atlas is autonomously moving engine covers between supplier containers and a mobile sequencing dolly. The robot receives as input a list of bin locations to move parts between.

Atlas uses a machine learning (ML) vision model to detect and localize the environment fixtures and individual bins [0:36]. The robot uses a specialized grasping policy and continuously estimates the state of manipulated objects to achieve the task.

There are no prescribed or teleoperated movements; all motions are generated autonomously online. The robot is able to detect and react to changes in the environment (e.g., moving fixtures) and action failures (e.g., failure to insert the cover, tripping, environment collisions [1:24]) using a combination of vision, force, and proprioceptive sensors.

Eagle-eyed viewers will have noticed that this task is very similar to what we saw hydraulic Atlas (Atlas classic?) working on just before it retired. We probably don’t need to read too much into the differences between how each robot performs that task, but it’s an interesting comparison to make.

For more details, here’s our Q&A with Kuindersma:

How many takes did this take?

Kuindersma: We ran this sequence a couple times that day, but typically we’re always filming as we continue developing and testing Atlas. Today we’re able to run that engine cover demo with high reliability, and we’re working to expand the scope and duration of tasks like these.

Is this a task that humans currently do?

Kuindersma: Yes.

What kind of world knowledge does Atlas have while doing this task?

Kuindersma: The robot has access to a CAD model of the engine cover that is used for object pose prediction from RGB images. Fixtures are represented more abstractly using a learned keypoint prediction model. The robot builds a map of the workcell at startup which is updated on the fly when changes are detected (e.g., moving fixture).

Does Atlas’s torso have a front or back in a meaningful way when it comes to how it operates?

Kuindersma: Its head/torso/pelvis/legs do have “forward” and “backward” directions, but the robot is able to rotate all of these relative to one another. The robot always knows which way is which, but sometimes the humans watching lose track.

Are the head and torso capable of unlimited rotation?

Kuindersma: Yes, many of Atlas’s joints are continuous.

How long did it take you folks to get used to the way Atlas moves?

Kuindersma: Atlas’s motions still surprise and delight the team.

OSHA recommends against squatting because it can lead to workplace injuries. How does Atlas feel about that?

Kuindersma: As might be evident by some of Atlas’s other motions, the kinds of behaviors that might be injurious for humans might be perfectly fine for robots.

Can you describe exactly what process Atlas goes through at 1:22?

Kuindersma: The engine cover gets caught on the fabric bins and triggers a learned failure detector on the robot. Right now this transitions into a general-purpose recovery controller, which results in a somewhat jarring motion (we will improve this). After recovery, the robot retries the insertion using visual feedback to estimate the state of both the part and fixture.

Were there other costume options you considered before going with the hot dog?

Kuindersma: Yes, but marketing wants to save them for next year.

How many important sensors does the hot dog costume occlude?

Kuindersma: None. The robot is using cameras in the head, proprioceptive sensors, IMU, and force sensors in the wrists and feet. We did have to cut the costume at the top so the head could still spin around.

Why are pickles always causing problems?

Kuindersma: Because pickles are pesky, polarizing pests.

Research expeditions conducted at sea using a rotating gravity machine and microscope found that the Earth’s oceans may not be absorbing as much carbon as researchers have long thought.

Oceans are believed to absorb roughly 26 percent of global carbon dioxide emissions by drawing down CO₂ from the atmosphere and locking it away. In this system, CO₂ enters the ocean, where phytoplankton and other organisms consume about 70 percent of it. When these organisms eventually die, their soft, small structures sink to the bottom of the ocean in what looks like an underwater snowfall.

This “marine snow” pulls carbon away from the surface of the ocean and sequesters it in the depths for millennia, which enables the surface waters to draw down more CO₂ from the air. It’s one of Earth’s best natural carbon-removal systems. It’s so effective at keeping atmospheric CO₂ levels in check that many research groups are trying to enhance the process with geoengineering techniques.

But the new study, published on 11 October in Science, found that the sinking particles don’t fall to the ocean floor as quickly as researchers thought. Using a custom gravity machine that simulated marine snow’s native environment, the study’s authors observed that the particles produce mucus tails that act like parachutes, putting the brakes on their descent—sometimes even bringing them to a standstill.

The physical drag leaves carbon lingering in the upper hydrosphere, rather than being safely sequestered in deeper waters. Living organisms can then consume the marine snow particles and respire their carbon back into the sea. Ultimately, this impedes the rate at which the ocean draws down and sequesters additional CO₂ from the air.

The implications are grim: Scientists’ best estimates of how much CO₂ the Earth’s oceans sequester could be way off. “We’re talking roughly hundreds of gigatonnes of discrepancy if you don’t include these marine snow tails,” says Manu Prakash, a bioengineer at Stanford University and one of the paper’s authors. The work was conducted by researchers at Stanford, Rutgers University in New Jersey, and Woods Hole Oceanographic Institution in Massachusetts.

Oceans Absorb Less CO₂ Than Expected

Researchers for years have been developing numerical models to estimate marine carbon sequestration. Those models will need to be adjusted for the slower sinking speed of marine snow, Prakash says.

The findings also have implications for startups in the fledgling marine carbon geoengineering field. These companies use techniques such as ocean alkalinity enhancement to augment the ocean’s ability to sequester carbon. Their success depends, in part, on using numerical models to prove to investors and the public that their techniques work. But their estimates are only as good as the models they use, and the scientific community’s confidence in them.

“We’re talking roughly hundreds of gigatonnes of discrepancy if you don’t include these marine snow tails.” —Manu Prakash, Stanford University

The Stanford researchers made the discovery on an expedition off the coast of Maine. There, they collected marine samples by hanging traps from their boat 80 meters deep. After pulling up a sample, the researchers quickly analyzed the contents while still on board the ship using their wheel-shaped machine and microscope.

The researchers built a microscope with a spinning wheel that simulates marine snow falling through sea water over longer distances than would otherwise be practical.Prakash Lab/Stanford

The device simulates the organisms’ vertical travel over long distances. Samples go into a wheel about the size of a vintage film reel. The wheel spins constantly, allowing suspended marine-snow particles to sink while a camera captures their every move.

The apparatus adjusts for temperature, light, and pressure to emulate marine conditions. Computational tools assess flow around the sinking particles and custom software removes noise in the data from the ship’s vibrations. To accommodate for the tilt and roll of the ship, the researchers mounted the device on a two-axis gimbal.

Slower Marine Snow Reduces Carbon Sequestration

With this setup, the team observed that sinking marine snow generates an invisible halo-shaped comet tail made of viscoelastic transparent exopolymer—a mucus-like parachute. They discovered the invisible tail by adding small beads to the seawater sample in the wheel, and analyzing the way they flowed around the marine snow. “We found that the beads were stuck in something invisible trailing behind the sinking particles,” says Rahul Chajwa, a bioengineering postdoctoral fellow at Stanford.

The tail introduces drag and buoyancy, doubling the amount of time marine snow spends in the upper 100 meters of the ocean, the researchers concluded. “This is the sedimentation law we should be following,” says Prakash, who hopes to get the results into climate models.

The study will likely help models project carbon export—the process of transporting CO₂ from the atmosphere to the deep ocean, says Lennart Bach, a marine biochemist at the University of Tasmania in Australia, who was not involved with the research. “The methodology they developed is very exciting and it’s great to see new methods coming into this research field,” he says.

But Bach cautions against extrapolating the results too far. “I don’t think the study will change the numbers on carbon export as we know them right now,” because these numbers are derived from empirical methods that would have unknowingly included the effects of the mucus tail, he says.

Marine snow may be slowed by “parachutes” of mucus while sinking, potentially lowering the rate at which the global ocean can sequester carbon in the depths.Prakash Lab/Stanford

Prakash and his team came up with the idea for the microscope while conducting research on a human parasite that can travel dozens of meters. “We would make 5- to 10-meter-tall microscopes, and one day, while packing for a trip to Madagascar, I had this ‘aha’ moment,” says Prakash. “I was like: Why are we packing all these tubes? What if the two ends of these tubes were connected?”

The group turned their linear tube into a closed circular channel—a hamster wheel approach to observing microscopic particles. Over five expeditions at sea, the team further refined the microscope’s design and fluid mechanics to accommodate marine samples, often tackling the engineering while on the boat and adjusting for flooding and high seas.

In addition to the sedimentation physics of marine snow, the team also studies other plankton that may affect climate and carbon-cycle models. On a recent expedition off the coast of Northern California, the group discovered a cell with silica ballast that makes marine snow sink like a rock, Prakash says.

The crafty gravity machine is one of Prakash’s many frugal inventions, which include an origami-inspired paper microscope, or “foldscope,” that can be attached to a smartphone, and a paper-and-string biomedical centrifuge dubbed a “paperfuge.”

In physical books, yellowing pages are usually a sign of age. But brand-new users of Amazon’s Kindle Colorsofts, the tech giant’s first color e-reader, are already noticing yellow hues appearing at the bottoms of their displays.

Since the complaints began the trickle in, Amazon has reportedly suspended shipments and announced that it is working to fix the issue. (As of publication of this article, the US $280 Kindle had an average 2.6 star rating on Amazon.) It’s not yet clear what is causing the discoloration. But while the issue is new—and unexpected—the technology is not, says Jason Heikenfeld, an IEEE Fellow and engineering professor at the University of Cincinnati. The Kindle Colorsoft, which became available on 30 October, uses “a very old approach,” says Heikenfeld, who previously worked to develop the ultimate e-paper technology. “It was the first approach everybody tried.”

Amazon’s e-reader uses reflective display technology developed by E Ink, a company that started in the 1990s as an MIT Media Lab spin off before developing its now-dominant electronic paper displays. E Ink is used in Kindles, as well as top e-readers from Kobo, reMarkable, Onyx, and more. E Ink first introduced Kaleido—the basis of the Colorsoft’s display—five years ago, though the road to full-color e-paper started well before.

How E-Readers Work

Monochromatic Kindles work by applying voltages to electrodes in the screen that bring black or white pigment to the top of each pixel. Those pixels then reflect ambient light, creating a paper-like display. To create a full-color display, companies like E Ink added an array of filters just above the ink. This approach didn’t work well at first because the filters lost too much light, making the displays dark and low resolution. But with a few adjustments, Kaleido was ready for consumer products in 2019. (Other approaches—like adding colored pigments to the ink—have been developed, but these come with their own drawbacks, including a higher price tag.)

Given this design, it initially seemed to Heikenfeld that the issue would have stemmed from the software, which determines the voltages applied to each electrode. This aligned with reports from some users that the issue appeared after a software update.

But industry analyst Ming-Chi Kuo suggested in a post on X that the issue is due to the e-reader’s hardware. Amazon switched the optically clear adhesive (OCA) used in the Colorsoft to a material that may not be so optically clear. In its announcement of the Colorsoft, the company boasted “custom formulated coatings” that would enhance the color display as one of the new e-reader’s innovations.

In terms of resolving the issue, Kuo’s post also stated that “While component suppliers have developed several hardware solutions, Amazon seems to be leaning toward a software-based fix.” Heikenfeld is not sure how a software fix would work, apart from blacking out the bottom of the screen.

Amazon did not reply to IEEE Spectrum’s request for comment. In an email to IEEE Spectrum, E Ink stated, “While we cannot comment on any individual partner or product, we are committed to supporting our partners in understanding and addressing any issues that arise.”

The Future of E-Readers

It took a long time for color Kindles to arrive, and the future of reflective e-reader displays isn’t likely to improve much, according to Heikenfeld. “I used to work a lot in this field, and it just really slowed down at some point, because it’s a tough nut to crack,” Heikenfeld says.

There are inherent limitations and inefficiencies to working with filter-based color displays that rely on ambient light, and there’s no Moore’s Law for these displays. Instead, their improvement is asymptotic—and we may already be close to the limit. Meanwhile, displays that emit light, like LCD and OLED, continue to improve. “An iPad does a pretty damn good job with battery life now,” says Heikenfeld.

At the same time, he believes there will always be a place for reflective displays, which remain a more natural experience for our eyes. “We live in a world of reflective color,” Heikenfeld says.

This is story was updated on 12 November 2024 to correct that Jason Heikenfeld is an IEEE Fellow.

The IEEE Board of Directors shapes the future direction of IEEE and is committed to ensuring IEEE remains a strong and vibrant organization—serving the needs of its members and the engineering and technology community worldwide—while fulfilling the IEEE mission of advancing technology for the benefit of humanity.

This article features IEEE Board of Directors members ChunChe “Lance” Fung, Eric Grigorian, and Christina Schober.

IEEE Senior Member ChunChe “Lance” Fung

Director, Region 10: Asia Pacific

Joanna Mai Yie Leung

Fung has worked in academia and provided industry consultancy services for more than 40 years. His research interests include applying artificial intelligence, machine learning, computational intelligence, and other techniques to solve practical problems. He has authored more than 400 publications in the disciplines of AI, computational intelligence, and related applications. Fung currently works on the ethical applications and social impacts of AI.

A member of the IEEE Systems, Man, and Cybernetics Society, Fung has been an active IEEE volunteer for more than 30 years. As a member and chair of the IEEE Technical Program Integrity and Conference Quality committees, he oversaw the quality of technical programs presented at IEEE conferences. Fung also chaired the Region 10 Educational Activities Committee. He was instrumental in translating educational materials to local languages for the IEEE Reaching Locals project.

As chair of the IEEE New Initiatives Committee, he established and promoted the US $1 Million Challenge Call for New Initiatives, which supports potential IEEE programs, services, or products that will significantly benefit members, the public, the technical community, or customers and could have a lasting impact on IEEE or its business processes.

Fung has left an indelible mark as a dedicated educator at Singapore Polytechnic, Curtin University, and Murdoch University. He was appointed in 2015 as professor emeritus at Murdoch, and he takes pride in training the next generation of volunteers, leaders, teachers, and researchers in the Western Australian community. Fung received the IEEE Third Millennium Medal and the IEEE Region 10 Outstanding Volunteer Award.

IEEE Senior Member Eric Grigorian

Director, Region 3: Southern U.S. & Jamaica

Sean McNeil/GTRI

Grigorian has extensive experience leading international cross-domain teams that support the commercial and defense industries. His current research focuses on implementing model-based systems engineering, creating models that depict system behavior, interfaces, and architecture. His work has led to streamlined processes, reduced costs, and faster design and implementation of capabilities due to efficient modeling and verification. Grigorian holds two U.S. utility patents.

Grigorian has been an active volunteer with IEEE since his time as a student member at the University of Alabama in Huntsville (UAH). He saw it as an excellent way to network and get to know people. He found his personality was suited for working within the organization and building leadership skills. During the past 43 years as an IEEE member, he has been affiliated with the IEEE Aerospace and Electronic Systems (AESS), IEEE Computer, and IEEE Communications societies.

As Grigorian’s career has evolved, his involvement with IEEE has also increased. He has been the IEEE Huntsville Section student activities chair, as well as vice chair, and chair. He also was the section’s AESS chair. He served as IEEE SoutheastCon chair in 2008 and 2019, and served on the IEEE Region 3 executive committee as area chair and conference committee chair, enhancing IEEE members’ benefits, engagement, and career advancement. He has significantly contributed to initiatives within IEEE, including promoting preuniversity science, technology, engineering, and mathematics efforts in Alabama.

Grigorian’s professional achievements have been recognized with numerous awards from employers and local technical chapters, including with the 2020 UAH Alumni of Achievement Award for the College of Engineering and the 2006 IEEE Region 3 Outstanding Engineer of the Year Award. He is a member of the IEEE–Eta Kappa Nu honor society.

IEEE Life Senior Member Christina Schober

Director, Division V

Katie Fears/Brio Art

Schober is an innovative engineer with a diverse design and manufacturing engineering background. With more than 40 years of experience, her career has spanned research, design, and manufacturing sensors for space, commercial, and military aircraft navigation and tactical guidance systems. She was responsible for the successful transition from design to production for groundbreaking programs including an integrated flight management system, the Stinger missile’s roll frequency sensor, and the designing of three phases of the DARPA atomic clock. She holds 17 U.S. patents and 24 other patents in the aerospace and navigation fields.

Schober started her career in the 1980s, at a time when female engineers were not widely accepted. The prevailing attitude required her to “stay tough,” she says, and she credits IEEE for giving her technical and professional support. Because of her experiences, she became dedicated to making diversity and inclusion systemic in IEEE.

Schober has held many leadership roles, including IEEE Division VIII Director, IEEE Sensors Council president, and IEEE Standards Sensors Council secretary. In addition to her membership in the IEEE Photonics Society, she is active with the IEEE Computer Society, IEEE Sensors Council, IEEE Standards Association, and IEEE Women in Engineering.

She is also active in her local community, serving as an invited speaker on STEM for the public school system and was a volunteer at youth shelters. Schober has received numerous awards including the IEEE Sensors Council Lifetime Contribution Award and the IEEE Twin Cities Section’s Young Engineer of the Year Award. She is an IEEE Computer Society Gold Core member, a member of the IEEE–Eta Kappa Nu honor society and received the IEEE Third Millennium Medal.

A super-hurricane is barreling toward Florida, gaining strength from a number of sources.

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