Jacob Hando discusses why 'Harry Potter and the Goblet of Fire' has such an impactful front cover.

MWC Barcelona 2024 comes to a close today. Here’s a look at some of the announcements that the event brought to light, from industry leaders like Cisco, Google, Intel and others, spanning artificial intelligence, telecom, security and sustainability. Telecom Cisco has partnered with Telus to launch new 5G capabilities for IoT use cases across industry […]

The government of Canada has announced significant investments to improve connectivity in British Columbia and Alberta over the past two days. That includes combined federal and provincial funding of C$112 million to bring high speed internet access to more than 22,500 households in Alta. and another $37 million for 7,500 households in B.C.. The households, […]

Boeing's Starliner spacecraft landed on Earth Saturday morning, with two test pilots left behind because of NASA's concerns that their return was too risky.

NASA has released the clearest view of Mars seen thus far, with a field of blue rocks seen on the Martian landscape on top of an ancient lake.

As many as 115 Post Office Branches and hundreds of jobs could be at risk following a strategic review held by Post Office Chairman Nigel Railton.

Three people have been taken to hospital after a car mounted the pavement and smashed into a restaurant in Piccadilly Circus, the Metropolitan Police have said.

Jill Parton, 46, suffered fatal injuries when she was hit by a freight train in Heaton Chapel in the early hours of June 3 this year, an inquest heard

Its latest roadmap suggests that if you want white fans, you should wait for an announcement in Q1 2022.

Highly efficient SoCs offer up to 10 CPU cores, 32 GPU cores, 64GB of 400GB/s RAM.

Administrative innovations in south-west Asia during the 4th millennium BCE, including the cylinder seals that were rolled on the earliest clay tablets, laid the foundations for proto-cuneiform script.

The post Study: Cylinder Seals and Sealing Practices Stimulated Invention of Writing in Ancient South-West Asia appeared first on Sci.News: Breaking Science News.

A team of scientists from the Lunar and Planetary Laboratory at the University of Arizona, TU Delft, and Caltech has developed a new method to compute how tides affect the interiors of planets and moons in the Solar System.

The post Planetary Researchers Examine Tidal Effects on Interiors of Planets and Their Moons appeared first on Sci.News: Breaking Science News.

The standard model predicted that the NASA/ESA/CSA James Webb Space Telescope would see dim signals from small, primitive galaxies.

The post New Research Questions Standard Theory of How Galaxies Formed in Early Universe appeared first on Sci.News: Breaking Science News.

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

A new variant of mpox is surging in Central Africa, raising concerns about how quickly it could spread further afield

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

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

Drugs like Ozempic and Wegovy have unexpected effects on the brain, opening up potential new ways to treat depression, anxiety, addiction and Alzheimer’s

People with cancer may have understandable reasons to follow Australian supermodel Elle Macpherson in declining chemotherapy, but the odds aren’t in their favour, warns Elle Hunt

Saline drops and sprays have already been linked to reduced cold symptoms in adults and now a study suggests they also work in children

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

The latest research on caffeine reveals why coffee and decaf can be so good for your health, but energy drinks can be lethal

Popular weight-loss medications including Ozempic and Wegovy contain a drug that seems to decrease cravings for food and drugs – and now there’s evidence that it might make exercise less rewarding, too

Weaving tales of magical fruit and vegetables into your children's stories may encourage them to eat healthy snacks

Recent research shows that anti-inflammatory diets are not as faddish as they might sound, with the power to reduce the risk of heart attacks and some cancers

Blood tests have shown that about 7 per cent of workers on dairy farms that had H5N1 outbreaks had antibodies against the disease

From seed oils to olive oil, we now have an overwhelming choice of what to cook with. Here’s how they all stack up, according to the scientific evidence

Speculation has already run rampant on who Florida Gov. Ron DeSantis will appoint to replace Sen. Marco Rubio if Rubio becomes President-elect Trump's secretary of state.

Various Cook Children’s locations are experiencing a spike in cases of salmonella, but the Texas Department of State Health Services has not reported an outbreak.     Since July, the Emergency Department at Cook Children’s Medical Center – Fort Worth has also reported increased numbers of patients with salmonella.   “In... Continue Reading

Wonder Brands Inc. is recalling various brands of bread and buns because of pieces of metal in the products. According to the Canadian Food Inspection Agency (CFIA), the recalled products were distributed in Newfoundland and Labrador, Ontario and Quebec, Canada. The brands listed in the recall include Country Harvest, D’Italiano,... Continue Reading

An in-depth analysis in the United States, covering 2002 to 2023, reveals that biological contamination and allergens are the leading causes of food recalls. The study, recently published in the Journal of Food Protection, examined more than 35,000 food and beverage recalls overseen by the U.S. Food and Drug Administration... Continue Reading

CIBUS Fresh of Noblesville, IN, is recalling CIBUS Fresh products containing Glenview Farms Spreadable Brie, 2/3lb because of a supplier notification of possible Listeria monocytogenes (products are listed below).  More information regarding the recent Brie recall can be found here. The product was distributed under the following labels: CIBUS Fresh,... Continue Reading

The Food and Drug Administration uses import alerts to enforce U.S. food safety regulations for food from foreign countries. The agency updates and modifies the alerts as needed. Recent modifications to FDA’s import alerts, as posted by the agency, are listed below. Use the chart below to view import alerts.... Continue Reading

Yu Shang Food, Inc., a Spartanburg, SC, business, over the weekend recalled 4,589 pounds of ready-to-eat (RTE) meat and poultry products, which may have been adulterated with Listeria monocytogenes, according to the U.S. Department of Agriculture’s Food Safety and Inspection Service (FSIS). The ready-to-eat meat and poultry items were produced from... Continue Reading

RFK Jr., a lawyer-politician, could replace lawyer-politician Xavier Becerra as Secretary of Health and Human  Services. Or RFK Jr could be the next Secretary of Agriculture, replacing  Tom Vilsack, a lawyer. Deputy FDA Commissioners are sometimes lawyers. Dr. Robert Califf, a cardiologist, is the outgoing FDA Commissioner. The fact that... Continue Reading

The appointment of a political outsider like Musk could help Trump cut regulations and rein in government bureaucracy, even if the moves are unpopular

Dispute risks damage to Canada's reputation as reliable trade partner, says Steven Mackinnon

Canada Post workers might soon be putting down their mailbags and grabbing picket signs

Former Education Secretary Betsy DeVos discusses what a second Trump term could mean for U.S. education on "The Story with Martha MacCallum."

Just like Eli Manning in 2004, Deion Sanders said he would tell NFL teams his son, Shedeur Sanders, won't play for them if it's not the right fit.

President-elect Trump announced on Tuesday that Kristi Noem is his pick for secretary of the Department of Homeland Security.

Senate Republicans met on Tuesday night to hear from the three candidates to succeed Mitch McConnell, and Rick Scott left with two new endorsements.

The Canadian women's soccer team was implicated in a drone scandal this past summer. But, an investigation determined drone use against opponents, predated the Paris Olympics.

Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese tech giant’s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that’s what he told IEEE Spectrum in an exclusive Q&A.

Ng’s current efforts are focused on his company Landing AI, which built a platform called LandingLens to help manufacturers improve visual inspection with computer vision. He has also become something of an evangelist for what he calls the data-centric AI movement, which he says can yield “small data” solutions to big issues in AI, including model efficiency, accuracy, and bias.

Andrew Ng on...

• What’s next for really big models
• The career advice he didn’t listen to
• Defining the data-centric AI movement
• Synthetic data
• Why Landing AI asks its customers to do the work

The great advances in deep learning over the past decade or so have been powered by ever-bigger models crunching ever-bigger amounts of data. Some people argue that that’s an unsustainable trajectory. Do you agree that it can’t go on that way?

Andrew Ng: This is a big question. We’ve seen foundation models in NLP [natural language processing]. I’m excited about NLP models getting even bigger, and also about the potential of building foundation models in computer vision. I think there’s lots of signal to still be exploited in video: We have not been able to build foundation models yet for video because of compute bandwidth and the cost of processing video, as opposed to tokenized text. So I think that this engine of scaling up deep learning algorithms, which has been running for something like 15 years now, still has steam in it. Having said that, it only applies to certain problems, and there’s a set of other problems that need small data solutions.

When you say you want a foundation model for computer vision, what do you mean by that?

Ng: This is a term coined by Percy Liang and some of my friends at Stanford to refer to very large models, trained on very large data sets, that can be tuned for specific applications. For example, GPT-3 is an example of a foundation model [for NLP]. Foundation models offer a lot of promise as a new paradigm in developing machine learning applications, but also challenges in terms of making sure that they’re reasonably fair and free from bias, especially if many of us will be building on top of them.

What needs to happen for someone to build a foundation model for video?

Ng: I think there is a scalability problem. The compute power needed to process the large volume of images for video is significant, and I think that’s why foundation models have arisen first in NLP. Many researchers are working on this, and I think we’re seeing early signs of such models being developed in computer vision. But I’m confident that if a semiconductor maker gave us 10 times more processor power, we could easily find 10 times more video to build such models for vision.

Having said that, a lot of what’s happened over the past decade is that deep learning has happened in consumer-facing companies that have large user bases, sometimes billions of users, and therefore very large data sets. While that paradigm of machine learning has driven a lot of economic value in consumer software, I find that that recipe of scale doesn’t work for other industries.

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It’s funny to hear you say that, because your early work was at a consumer-facing company with millions of users.

Ng: Over a decade ago, when I proposed starting the Google Brain project to use Google’s compute infrastructure to build very large neural networks, it was a controversial step. One very senior person pulled me aside and warned me that starting Google Brain would be bad for my career. I think he felt that the action couldn’t just be in scaling up, and that I should instead focus on architecture innovation.

“In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.”
—Andrew Ng, CEO & Founder, Landing AI

I remember when my students and I published the first NeurIPS workshop paper advocating using CUDA, a platform for processing on GPUs, for deep learning—a different senior person in AI sat me down and said, “CUDA is really complicated to program. As a programming paradigm, this seems like too much work.” I did manage to convince him; the other person I did not convince.

I expect they’re both convinced now.

Ng: I think so, yes.

Over the past year as I’ve been speaking to people about the data-centric AI movement, I’ve been getting flashbacks to when I was speaking to people about deep learning and scalability 10 or 15 years ago. In the past year, I’ve been getting the same mix of “there’s nothing new here” and “this seems like the wrong direction.”

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How do you define data-centric AI, and why do you consider it a movement?

Ng: Data-centric AI is the discipline of systematically engineering the data needed to successfully build an AI system. For an AI system, you have to implement some algorithm, say a neural network, in code and then train it on your data set. The dominant paradigm over the last decade was to download the data set while you focus on improving the code. Thanks to that paradigm, over the last decade deep learning networks have improved significantly, to the point where for a lot of applications the code—the neural network architecture—is basically a solved problem. So for many practical applications, it’s now more productive to hold the neural network architecture fixed, and instead find ways to improve the data.

When I started speaking about this, there were many practitioners who, completely appropriately, raised their hands and said, “Yes, we’ve been doing this for 20 years.” This is the time to take the things that some individuals have been doing intuitively and make it a systematic engineering discipline.

The data-centric AI movement is much bigger than one company or group of researchers. My collaborators and I organized a data-centric AI workshop at NeurIPS, and I was really delighted at the number of authors and presenters that showed up.

You often talk about companies or institutions that have only a small amount of data to work with. How can data-centric AI help them?

Ng: You hear a lot about vision systems built with millions of images—I once built a face recognition system using 350 million images. Architectures built for hundreds of millions of images don’t work with only 50 images. But it turns out, if you have 50 really good examples, you can build something valuable, like a defect-inspection system. In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.

When you talk about training a model with just 50 images, does that really mean you’re taking an existing model that was trained on a very large data set and fine-tuning it? Or do you mean a brand new model that’s designed to learn only from that small data set?

Ng: Let me describe what Landing AI does. When doing visual inspection for manufacturers, we often use our own flavor of RetinaNet. It is a pretrained model. Having said that, the pretraining is a small piece of the puzzle. What’s a bigger piece of the puzzle is providing tools that enable the manufacturer to pick the right set of images [to use for fine-tuning] and label them in a consistent way. There’s a very practical problem we’ve seen spanning vision, NLP, and speech, where even human annotators don’t agree on the appropriate label. For big data applications, the common response has been: If the data is noisy, let’s just get a lot of data and the algorithm will average over it. But if you can develop tools that flag where the data’s inconsistent and give you a very targeted way to improve the consistency of the data, that turns out to be a more efficient way to get a high-performing system.

“Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.”
—Andrew Ng

For example, if you have 10,000 images where 30 images are of one class, and those 30 images are labeled inconsistently, one of the things we do is build tools to draw your attention to the subset of data that’s inconsistent. So you can very quickly relabel those images to be more consistent, and this leads to improvement in performance.

Could this focus on high-quality data help with bias in data sets? If you’re able to curate the data more before training?

Ng: Very much so. Many researchers have pointed out that biased data is one factor among many leading to biased systems. There have been many thoughtful efforts to engineer the data. At the NeurIPS workshop, Olga Russakovsky gave a really nice talk on this. At the main NeurIPS conference, I also really enjoyed Mary Gray’s presentation, which touched on how data-centric AI is one piece of the solution, but not the entire solution. New tools like Datasheets for Datasets also seem like an important piece of the puzzle.

One of the powerful tools that data-centric AI gives us is the ability to engineer a subset of the data. Imagine training a machine-learning system and finding that its performance is okay for most of the data set, but its performance is biased for just a subset of the data. If you try to change the whole neural network architecture to improve the performance on just that subset, it’s quite difficult. But if you can engineer a subset of the data you can address the problem in a much more targeted way.

When you talk about engineering the data, what do you mean exactly?

Ng: In AI, data cleaning is important, but the way the data has been cleaned has often been in very manual ways. In computer vision, someone may visualize images through a Jupyter notebook and maybe spot the problem, and maybe fix it. But I’m excited about tools that allow you to have a very large data set, tools that draw your attention quickly and efficiently to the subset of data where, say, the labels are noisy. Or to quickly bring your attention to the one class among 100 classes where it would benefit you to collect more data. Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.

For example, I once figured out that a speech-recognition system was performing poorly when there was car noise in the background. Knowing that allowed me to collect more data with car noise in the background, rather than trying to collect more data for everything, which would have been expensive and slow.

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What about using synthetic data, is that often a good solution?

Ng: I think synthetic data is an important tool in the tool chest of data-centric AI. At the NeurIPS workshop, Anima Anandkumar gave a great talk that touched on synthetic data. I think there are important uses of synthetic data that go beyond just being a preprocessing step for increasing the data set for a learning algorithm. I’d love to see more tools to let developers use synthetic data generation as part of the closed loop of iterative machine learning development.

Do you mean that synthetic data would allow you to try the model on more data sets?

Ng: Not really. Here’s an example. Let’s say you’re trying to detect defects in a smartphone casing. There are many different types of defects on smartphones. It could be a scratch, a dent, pit marks, discoloration of the material, other types of blemishes. If you train the model and then find through error analysis that it’s doing well overall but it’s performing poorly on pit marks, then synthetic data generation allows you to address the problem in a more targeted way. You could generate more data just for the pit-mark category.

“In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models.”
—Andrew Ng

Synthetic data generation is a very powerful tool, but there are many simpler tools that I will often try first. Such as data augmentation, improving labeling consistency, or just asking a factory to collect more data.

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To make these issues more concrete, can you walk me through an example? When a company approaches Landing AI and says it has a problem with visual inspection, how do you onboard them and work toward deployment?

Ng: When a customer approaches us we usually have a conversation about their inspection problem and look at a few images to verify that the problem is feasible with computer vision. Assuming it is, we ask them to upload the data to the LandingLens platform. We often advise them on the methodology of data-centric AI and help them label the data.

One of the foci of Landing AI is to empower manufacturing companies to do the machine learning work themselves. A lot of our work is making sure the software is fast and easy to use. Through the iterative process of machine learning development, we advise customers on things like how to train models on the platform, when and how to improve the labeling of data so the performance of the model improves. Our training and software supports them all the way through deploying the trained model to an edge device in the factory.

How do you deal with changing needs? If products change or lighting conditions change in the factory, can the model keep up?

Ng: It varies by manufacturer. There is data drift in many contexts. But there are some manufacturers that have been running the same manufacturing line for 20 years now with few changes, so they don’t expect changes in the next five years. Those stable environments make things easier. For other manufacturers, we provide tools to flag when there’s a significant data-drift issue. I find it really important to empower manufacturing customers to correct data, retrain, and update the model. Because if something changes and it’s 3 a.m. in the United States, I want them to be able to adapt their learning algorithm right away to maintain operations.

In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models. The challenge is, how do you do that without Landing AI having to hire 10,000 machine learning specialists?

So you’re saying that to make it scale, you have to empower customers to do a lot of the training and other work.

Ng: Yes, exactly! This is an industry-wide problem in AI, not just in manufacturing. Look at health care. Every hospital has its own slightly different format for electronic health records. How can every hospital train its own custom AI model? Expecting every hospital’s IT personnel to invent new neural-network architectures is unrealistic. The only way out of this dilemma is to build tools that empower the customers to build their own models by giving them tools to engineer the data and express their domain knowledge. That’s what Landing AI is executing in computer vision, and the field of AI needs other teams to execute this in other domains.

Is there anything else you think it’s important for people to understand about the work you’re doing or the data-centric AI movement?

Ng: In the last decade, the biggest shift in AI was a shift to deep learning. I think it’s quite possible that in this decade the biggest shift will be to data-centric AI. With the maturity of today’s neural network architectures, I think for a lot of the practical applications the bottleneck will be whether we can efficiently get the data we need to develop systems that work well. The data-centric AI movement has tremendous energy and momentum across the whole community. I hope more researchers and developers will jump in and work on it.

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This article appears in the April 2022 print issue as “Andrew Ng, AI Minimalist.”

In this upcoming webinar, explore how to leverage the state-of-the-art high-frequency simulation capabilities of Ansys HFSS to innovate and develop advanced multiband antenna systems.

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Tactile controls are back in vogue. Apple added two new buttons to the iPhone 16, home appliances like stoves and washing machines are returning to knobs, and several car manufacturers are reintroducing buttons and dials to dashboards and steering wheels.

With this “re-buttonization,” as The Wall Street Journal describes it, demand for Rachel Plotnick’s expertise has grown. Plotnick, an associate professor of cinema and media studies at Indiana University in Bloomington, is the leading expert on buttons and how people interact with them. She studies the relationship between technology and society with a focus on everyday or overlooked technologies, and wrote the 2018 book Power Button: A History of Pleasure, Panic, and the Politics of Pushing (The MIT Press). Now, companies are reaching out to her to help improve their tactile controls.

Rachel Plotnick on...

• Researching the history of buttons
• The renaissance of physical controls
• Working with companies on “re-buttoning”

You wrote a book a few years ago about the history of buttons. What inspired that book?

Rachel Plotnick: Around 2009, I noticed there was a lot of discourse in the news about the death of the button. This was a couple years after the first iPhone had come out, and a lot of people were saying that, as touchscreens were becoming more popular, eventually we weren’t going to have any more physical buttons to push. This started to happen across a range of devices like the Microsoft Kinect, and after films like Minority Report had come out in the early 2000s, everyone thought we were moving to this kind of gesture or speech interface. I was fascinated by this idea that an entire interface could die, and that led me down this big wormhole, to try to understand how we came to be a society that pushed buttons everywhere we went.

Rachel Plotnick studies the ways we use everyday technologies and how they shape our relationships with each other and the world.Rachel Plotnick

The more that I looked around, the more that I saw not only were we pressing digital buttons on social media and to order things from Amazon, but also to start our coffee makers and go up and down in elevators and operate our televisions. The pervasiveness of the button as a technology pitted against this idea of buttons disappearing seemed like such an interesting dichotomy to me. And so I wanted to understand an origin story, if I could come up with it, of where buttons came from.

What did you find in your research?

Plotnick: One of the biggest observations I made was that a lot of fears and fantasies around pushing buttons were the same 100 years ago as they are today. I expected to see this society that wildly transformed and used buttons in such a different way, but I saw these persistent anxieties over time about control and who gets to push the button, and also these pleasures around button pushing that we can use for advertising and to make technology simpler. That pendulum swing between fantasy and fear, pleasure and panic, and how those themes persisted over more than a century was what really interested me. I liked seeing the connections between the past and the present.

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We’ve experienced the rise of touchscreens, but now we might be seeing another shift—a renaissance in buttons and physical controls. What’s prompting the trend?

Plotnick: There was this kind of touchscreen mania, where all of a sudden everything became a touchscreen. Your car was a touchscreen, your refrigerator was a touchscreen. Over time, people became somewhat fatigued with that. That’s not to say touchscreens aren’t a really useful interface, I think they are. But on the other hand, people seem to have a hunger for physical buttons, both because you don’t always have to look at them—you can feel your way around for them when you don’t want to directly pay attention to them—but also because they offer a greater range of tactility and feedback.

If you look at gamers playing video games, they want to push a lot of buttons on those controls. And if you look at DJs and digital musicians, they have endless amounts of buttons and joysticks and dials to make music. There seems to be this kind of richness of the tactile experience that’s afforded by pushing buttons. They’re not perfect for every situation, but I think increasingly, we’re realizing the merit that the interface offers.

What else is motivating the re-buttoning of consumer devices?

Plotnick: Maybe screen fatigue. We spend all our days and nights on these devices, scrolling or constantly flipping through pages and videos, and there’s something tiring about that. The button may be a way to almost de-technologize our everyday existence, to a certain extent. That’s not to say buttons don’t work with screens very nicely—they’re often partners. But in a way, it’s taking away the priority of vision as a sense, and recognizing that a screen isn’t always the best way to interact with something.

When I’m driving, it’s actually unsafe for my car to be operated in that way. It’s hard to generalize and say, buttons are always easy and good, and touchscreens are difficult and bad, or vice versa. Buttons tend to offer you a really limited range of possibilities in terms of what you can do. Maybe that simplicity of limiting our field of choices offers more safety in certain situations.

It also seems like there’s an accessibility issue when prioritizing vision in device interfaces, right?

Plotnick: The blind community had to fight for years to make touchscreens more accessible. It’s always been funny to me that we call them touchscreens. We think about them as a touch modality, but a touchscreen prioritizes the visual. Over the last few years, we’re seeing Alexa and Siri and a lot of these other voice-activated systems that are making things a little bit more auditory as a way to deal with that. But the touchscreen is oriented around visuality.

It sounds like, in general, having multiple interface options is the best way to move forward—not that touchscreens are going to become completely passé, just like the button never actually died.

Plotnick: I think that’s accurate. We see paradigm shifts over time with technologies, but for the most part, we often recycle old ideas. It’s striking that if we look at the 1800s, people were sending messages via telegraph about what the future would look like if we all had this dashboard of buttons at our command where we could communicate with anyone and shop for anything. And that’s essentially what our smartphones became. We still have this dashboard menu approach. I think it means carefully considering what the right interface is for each situation.

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Several companies have reached out to you to learn from your expertise. What do they want to know?

Plotnick: I think there is a hunger out there from companies designing buttons or consumer technologies to try to understand the history of how we used to do things, how we might bring that to bear on the present, and what the future looks like with these interfaces. I’ve had a number of interesting discussions with companies, including one that manufactures push-button interfaces. I had a conversation with them about medical devices like CT machines and X-ray machines, trying to imagine the easiest way to push a button in that situation, to save people time and improve the patient encounter.

I’ve also talked to people about what will make someone use a defibrillator or not. Even though it’s really simple to go up to these automatic machines, if you see someone going into cardiac arrest in a mall or out on the street, a lot of people are terrified to actually push the button that would get this machine started. We had a really fascinating discussion about why someone wouldn’t push a button, and what would it take to get them to feel okay about doing that.

In all of these cases, these are design questions, but they’re also social and cultural questions. I like the idea that people who are in the humanities studying these things from a long-term perspective can also speak to engineers trying to build these devices.

So these companies also want to know about the history of buttons?

Plotnick: I’ve had some fascinating conversations around history. We all want to learn what mistakes not to make and what worked well in the past. There’s often this narrative of progress, that things are only getting better with technology over time. But if we look at these lessons, I think we can see that sometimes things were simpler or better in a past moment, and sometimes they were harder. Often with new technologies, we think we’re completely reinventing the wheel. But maybe these concepts existed a long time ago, and we haven’t paid attention to that. There’s a lot to be learned from the past.

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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.

EPICS in IEEE, a service learning program for university students supported by IEEE Educational Activities, offers students opportunities to engage with engineering professionals and mentors, local organizations, and technological innovation to address community-based issues.

The following two environmentally focused projects demonstrate the value of teamwork and direct involvement with project stakeholders. One uses smart biodigesters to better manage waste in Colombia’s rural areas. The other is focused on helping Turkish olive farmers protect their trees from climate change effects by providing them with a warning system that can identify growing problems.

No time to waste in rural Colombia

Proper waste management is critical to a community’s living conditions. In rural La Vega, Colombia, the lack of an effective system has led to contaminated soil and water, an especially concerning issue because the town’s economy relies heavily on agriculture.

The Smart Biodigesters for a Better Environment in Rural Areas project brought students together to devise a solution.

Vivian Estefanía Beltrán, a Ph.D. student at the Universidad del Rosario in Bogotá, addressed the problem by building a low-cost anaerobic digester that uses an instrumentation system to break down microorganisms into biodegradable material. It reduces the amount of solid waste, and the digesters can produce biogas, which can be used to generate electricity.

“Anaerobic digestion is a natural biological process that converts organic matter into two valuable products: biogas and nutrient-rich soil amendments in the form of digestate,” Beltrán says. “As a by-product of our digester’s operation, digestate is organic matter that can’t be transferred into biogas but can be used as a soil amendment for our farmers’ crops, such as coffee.

“While it may sound easy, the process is influenced by a lot of variables. The support we’ve received from EPICS in IEEE is important because it enables us to measure these variables, such as pH levels, temperature of the reactor, and biogas composition [methane and hydrogen sulfide]. The system allows us to make informed decisions that enhance the safety, quality, and efficiency of the process for the benefit of the community.”

The project was a collaborative effort among Universidad del Rosario students, a team of engineering students from Escuela Tecnológica Instituto Técnico Central, Professor Carlos Felipe Vergara, and members of Junta de Acción Comunal (Vereda La Granja), which aims to help residents improve their community.

“It’s been a great experience to see how individuals pursuing different fields of study—from engineering to electronics and computer science—can all work and learn together on a project that will have a direct positive impact on a community.” —Vivian Estefanía Beltrán

Beltrán worked closely with eight undergraduate students and three instructors—Maria Fernanda Gómez, Andrés Pérez Gordillo (the instrumentation group leader), and Carlos Felipe Vergara-Ramirez—as well as IEEE Graduate Student Member Nicolás Castiblanco (the instrumentation group coordinator).

The team constructed and installed their anaerobic digester system in an experimental station in La Vega, a town located roughly 53 kilometers northwest of Bogotá.

“This digester is an important innovation for the residents of La Vega, as it will hopefully offer a productive way to utilize the residual biomass they produce to improve quality of life and boost the economy,” Beltrán says. Soon, she adds, the system will be expanded to incorporate high-tech sensors that automatically monitor biogas production and the digestion process.

“For our students and team members, it’s been a great experience to see how individuals pursuing different fields of study—from engineering to electronics and computer science—can all work and learn together on a project that will have a direct positive impact on a community. It enables all of us to apply our classroom skills to reality,” she says. “The funding we’ve received from EPICS in IEEE has been crucial to designing, proving, and installing the system.”

The project also aims to support the development of a circular economy, which reuses materials to enhance the community’s sustainability and self-sufficiency.

Protecting olive groves in Türkiye

Türkiye is one of the world’s leading producers of olives, but the industry has been challenged in recent years by unprecedented floods, droughts, and other destructive forces of nature resulting from climate change. To help farmers in the western part of the country monitor the health of their olive trees, a team of students from Istanbul Technical University developed an early-warning system to identify irregularities including abnormal growth.

“Almost no olives were produced last year using traditional methods, due to climate conditions and unusual weather patterns,” says Tayfun Akgül, project leader of the Smart Monitoring of Fruit Trees in Western Türkiye initiative.

“Our system will give farmers feedback from each tree so that actions can be taken in advance to improve the yield,” says Akgül, an IEEE senior member and a professor in the university’s electronics and communication engineering department.

“We’re developing deep-learning techniques to detect changes in olive trees and their fruit so that farmers and landowners can take all necessary measures to avoid a low or damaged harvest,” says project coordinator Melike Girgin, a Ph.D. student at the university and an IEEE graduate student member.

Using drones outfitted with 360-degree optical and thermal cameras, the team collects optical, thermal, and hyperspectral imaging data through aerial methods. The information is fed into a cloud-based, open-source database system.

Akgül leads the project and teaches the team skills including signal and image processing and data collection. He says regular communication with community-based stakeholders has been critical to the project’s success.

“There are several farmers in the village who have helped us direct our drone activities to the right locations,” he says. “Their involvement in the project has been instrumental in helping us refine our process for greater effectiveness.

“For students, classroom instruction is straightforward, then they take an exam at the end. But through our EPICS project, students are continuously interacting with farmers in a hands-on, practical way and can see the results of their efforts in real time.”

Looking ahead, the team is excited about expanding the project to encompass other fruits besides olives. The team also intends to apply for a travel grant from IEEE in hopes of presenting its work at a conference.

“We’re so grateful to EPICS in IEEE for this opportunity,” Girgin says. “Our project and some of the technology we required wouldn’t have been possible without the funding we received.”

A purpose-driven partnership

The IEEE Standards Association sponsored both of the proactive environmental projects.

“Technical projects play a crucial role in advancing innovation and ensuring interoperability across various industries,” says Munir Mohammed, IEEE SA senior manager of product development and market engagement. “These projects not only align with our technical standards but also drive technological progress, enhance global collaboration, and ultimately improve the quality of life for communities worldwide.”

For more information on the program or to participate in service-learning projects, visit EPICS in IEEE.

On 7 November, this article was updated from an earlier version.