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A new age of data means embracing the edge



A new age of data means embracing the edge

Artificial intelligence holds an enormous promise, but to be effective, it must learn from massive sets of data—and the more diverse the better. By learning patterns, AI tools can uncover insights and help decision-making not just in technology, but also pharmaceuticals, medicine, manufacturing, and more. However, data can’t always be shared—whether it’s personally identifiable, holds proprietary information, or to do so would be a security concern—until now.

“It’s going to be a new age.” Says Dr. Eng Lim Goh, senior vice president and CTO of artificial intelligence at Hewlett Packard Enterprise. “The world will shift from one where you have centralized data, what we’ve been used to for decades, to one where you have to be comfortable with data being everywhere.”

Data everywhere means the edge, where each device, server, and cloud instance collect massive amounts of data. One estimate has the number of connected devices at the edge increasing to 50 billion by 2022. The conundrum: how to keep collected data secure but also be able to share learnings from the data, which, in turn, helps teach AI to be smarter. Enter swarm learning.

Swarm learning, or swarm intelligence, is how swarms of bees or birds move in response to their environment. When applied to data Goh explains, there is “more peer-to-peer communications, more peer-to-peer collaboration, more peer-to-peer learning.” And Goh continues, “That’s the reason why swarm learning will become more and more important as …as the center of gravity shifts” from centralized to decentralized data.

Consider this example, says Goh. “A hospital trains their machine learning models on chest X-rays and sees a lot of tuberculosis cases, but very little of lung collapsed cases. So therefore, this neural network model, when trained, will be very sensitive to what’s detecting tuberculosis and less sensitive towards detecting lung collapse.” Goh continues, “However, we get the converse of it in another hospital. So what you really want is to have these two hospitals combine their data so that the resulting neural network model can predict both situations better. But since you can’t share that data, swarm learning comes in to help reduce that bias of both the hospitals.”

And this means, “each hospital is able to predict outcomes, with accuracy and with reduced bias, as though you have collected all the patient data globally in one place and learned from it,” says Goh.

And it’s not just hospital and patient data that must be kept secure. Goh emphasizes “What swarm learning does is to try to avoid that sharing of data, or totally prevent the sharing of data, to [a model] where you only share the insights, you share the learnings. And that’s why it is fundamentally more secure.”

Show notes and links:

Full transcript:

Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma. And this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. Our topic today is decentralized data. Whether it’s from devices, sensors, cars, the edge, if you will, the amount of data collected is growing. It can be personal and it must be protected. But is there a way to share insights and algorithms securely to help other companies and organizations and even vaccine researchers?

Two words for you: swarm learning.

My guest is Dr. Eng Lim Goh, who’s the senior vice president and CTO of artificial intelligence at Hewlett Packard Enterprise. Prior to this role, he was CTO for a majority of his 27 years at Silicon Graphics, now an HPE company. Dr. Goh was awarded NASA’s Exceptional Technology Achievement Medal for his work on AI in the International Space Station. He has also worked on numerous artificial intelligence research projects from F1 racing, to poker bots, to brain simulations. Dr. Goh holds a number of patents and had a publication land on the cover of Nature. This episode of Business Lab is produced in association with Hewlett Packard Enterprise. Welcome Dr. Goh.

Dr. Eng Lim Goh: Thank you for having me.

Laurel: So, we’ve started a new decade with a global pandemic. The urgency of finding a vaccine has allowed for greater information sharing between researchers, governments and companies. For example, the World Health Organization made the Pfizer vaccine’s mRNA sequence public to help researchers. How are you thinking about opportunities like this coming out of the pandemic?

Eng Lim: In science and medicine and others, sharing of findings is an important part of advancing science. So the traditional way is publications. The thing is, in a year, year and a half, of covid-19, there has been a surge of publications related to covid-19. One aggregator had, for example, the order of 300,000 of such documents related to covid-19 out there. It gets difficult, because of the amount of data, to be able to get what you need.

So a number of companies, organizations, started to build these natural language processing tools, AI tools, to allow you to ask very specific questions, not just search for keywords, but very specific questions so that you can get the answer that you need from this corpus of documents out there. A scientist could ask, or a researcher could ask, what is the binding energy of the SARS-CoV-2 spike protein to our ACE-2 receptor? And can be even more specific and saying, I want it in units of kcal per mol. And the system would go through. The NLP system would go through this corpus of documents and come up with an answer specific to that question, and even point to the area of the documents, where the answer could be. So this is one area. To help with sharing, you could build AI tools to help go through this enormous amount of data that has been generated.

The other area of sharing is sharing of a clinical trial data, as you have mentioned. Early last year, before any of the SARS-CoV-2 vaccine clinical trials had started, we were given the yellow fever vaccine clinical trial data. And even more specifically, the gene expression data from the volunteers of the clinical trial. And one of the goals is, can you analyze the tens of thousands of these genes being expressed by the volunteers and help predict, for each volunteer, whether he or she would get side-effects from this vaccine, and whether he or she will give good antibody response to this vaccine? So building predictive tools by sharing this clinical trial data, albeit anonymized and in a restricted way.

Laurel: When we talk about natural language processing, I think the two takeaways that we’ve taken from that very specific example are, you can build better AI tools to help the researchers. And then also, it helps build predictive tools and models.

Eng Lim: Yes, absolutely.

Laurel: So, as a specific example of what you’ve been working on for the past year, Nature Magazine recently published an article about how a collaborative approach to data insights can help these stakeholders, especially during a pandemic. What did you find out during that work?

Eng Lim: Yes. This is related, again, to the sharing point you brought about, how to share learning so that the community can advance faster. The Nature publication you mentioned, the title of it is “Swarm Learning [for Decentralized and Confidential Clinical Machine Learning]”. Let’s use the hospital example. There is this hospital, and it sees its patients, the hospital’s patients, of a certain demographic. And if it wants to build a machine learning model to predict based on patient data, say for example a patient’s CT scan data, to try and predict certain outcomes. The issue with learning in isolation like this is, you start to evolve models through this learning of your patient data biased to what’s the demographics you are seeing. Or in other ways, biased towards the type of medical devices you have.

The solution to this is to collect data from different hospitals, maybe from different regions or even different countries. And then combine all these hospitals’ data and then train the machine learning model on the combined data. The issue with this is that privacy of patient data prevents you from sharing that data. Swarm learning comes in to try and solve this, in two ways. One, instead of collecting data from these different hospitals, we allow each hospital to train their machine learning model on their own private patient data. And then occasionally, a blockchain comes in. That’s the second way. A blockchain comes in and collects all the learnings. I emphasize. The learnings, and not the patient data. Collect only the learnings and combine it with the learnings from other hospitals in other regions and other countries, average them and then send back down to all the hospitals, the updated globally combined averaged learnings.

And by learnings I mean the parameters, for example, of the neural network weights. The parameters which are the neural network weights in the machine learning model. So in this case, no patient data ever leaves an individual hospital. What leaves the hospital is only the learnings, the parameters or the neural network weights. And so, when you sent up your locally learned parameters, and what you get back from the blockchain is the global averaged parameters. And then you update your model with the global average, and then you carry on learning locally again. After a few cycles of these sharing of learnings, we’ve tested it, each hospital is able to predict, with accuracy and with reduced bias, as though you have collected all the patient data globally in one place, and learned from it.

Laurel: And the reason that blockchain is used is because it is actually a secure connection between various, in this case, machines, correct?

Eng Lim: There are two reasons, yes, why we use blockchain. The first reason is the security of it. And number two, we can keep that information private because, in a private blockchain, only participants, main participants or certified participants, are allowed in this blockchain. Now, even if the blockchain is compromised, what is only seen are the weights or the parameters of the learnings, not the private patient data, because the private patient data is not in the blockchain.

And the second reason for using a blockchain, it is as opposed to having a central custodian that does the collection of the parameters, of the learnings. Because once you appoint a custodian, an entity, that collects all these learnings, if one of the hospitals becomes that custodian, then you have a situation where that appointed custodian has more information than the rest, or has more capability than the rest. Not so much more information, but more capability than the rest. So in order to have a more equitable sharing, we use a blockchain. And in the blockchain system, what it does is that randomly appoints one of the participants as the collector, as the leader, to collect the parameters, average it and send it back down. And in the next cycle, randomly, another participant is appointed.

Laurel: So, there’s two interesting points here. One is, this project succeeds because you are not using only your own data. You are allowed to opt into this relationship to use the learnings from other researchers’ data as well. So that reduces bias. So that’s one kind of large problem solved. But then also this other interesting issue of equity and how even algorithms can perhaps be less equitable from time to time. But when you have an intentionally random algorithm in the blockchain assigning leadership for the collection of the learnings from each entity, that helps strip out any kind of possible bias as well, correct?

Eng Lim: Yes, yes, yes. Brilliant summary, Laurel. So there’s the first bias, which is, if you are learning in isolation, the hospital is learning, a neural network model, or a machine learning model, more generally, of a hospital is learning in isolation only on their own private patient data, they will be naturally biased towards the demographics they are seeing. For example, we have an example where a hospital trains their machine learning models on chest x-rays and sees a lot of tuberculosis cases. But very little of lung collapsed cases. So therefore, this neural network model, when trained, will be very sensitive to what’s detecting tuberculosis and less sensitive towards detecting lung collapse, for example. However, we get the converse of it in another hospital. So what you really want is to have these two hospitals combine their data so that the resulting neural network model can predict both situations better. But since you can’t share that data, swarm learning comes in to help reduce that bias of both the hospitals.

Laurel: All right. So we have an enormous amount of data. And it keeps growing exponentially as the edge, which is really any data generating device, system or sensor, expands. So how is decentralized data changing the way companies need to think about data?

Eng Lim: Oh, that’s a profound question. There is one estimate that says that by next year, by the year 2022, there will be 50 billion connected devices at the edge. And this is growing fast. And we’re coming to a point that we have an average of about 10 connected devices potentially collecting data, per person, in this world. Given that situation, the center of gravity will shift from the data center being the main location generating data to one where the center of gravity will be at the edge in terms of where data is generated. And this will change dynamics tremendously for enterprises. You will therefore see the need for these devices that are out there where this enormous amount of data generated at the edge with so much of these devices out there that you’ll reach a point where you cannot afford to backhaul or bring back all that data to the cloud or data center anymore.

Even with 5G, 6G and so on. The growth of data will outstrip that, will far exceed that of the growth in bandwidth of these new telecommunication capabilities. As such, you’ll reach a point where you have no choice but to push the intelligence to the edge so that you can decide what data to move back to the cloud or data center. So it’s going to be a new age. The world will shift from one where you have centralized data, what we’ve been used to for decades, to one where you have to be comfortable with data being everywhere. And when that’s the case, you need to do more peer-to-peer communications, more peer-to-peer collaboration, more peer-to-peer learning.

And that’s the reason why swarm learning will become more and more important as this progresses, as the center of gravity shifts out there from one where data is centralized, to one where data is everywhere.

Laurel: Could you talk a little bit more about how swarm intelligence is secure by design? In other words, it allows companies to share insights from data learnings with outside enterprises, or even within groups in a company, but then they don’t actually share the actual data?

Eng Lim: Yes. Fundamentally, when we want to learn from each other, one way is, we share the data so that each of us can learn from each other. What swarm learning does is to try to avoid that sharing of data, or totally prevent the sharing of data, to [a model] where you only share the insights, you share the learnings. And that’s why it is fundamentally more secure, using this approach, where data stays private in the location and never leaves that private entity. What leaves that private entity are only the learnings. And in this case, the neural network weights or the parameters of those learnings.

Now, there are people who are researching the ability to deduce the data from the learnings, it is still in research phase, but we are prepared if it ever works. And that is, in the blockchain, we do homomorphic encryption of the weights, of the parameters, of the learnings. By homomorphic, we mean when the appointed leader collects all these weights and then averages them, you can average them in the encrypted form so that if someone intercepts the blockchain, they see encrypted learnings. They don’t see the learnings themselves. But we’ve not implemented that yet, because we don’t see it necessary yet until such time we see that being able to reverse engineer the data from the learnings becomes feasible.

Laurel: And so, when we think about increasing rules and legislation surrounding data, like GDPR and California’s CCPA, there needs to be some sort of solution to privacy concerns. Do you see swarm learning as one of those possible options as companies grow the amount of data they have?

Eng Lim: Yes, as an option. First, if there is a need for edge devices to learn from each other, swarm learning is there, is useful for it. And number two, as you are learning, you do not want the data from each entity or participant in swarm learning to leave that entity. It should only stay where it is. And what leaves is only the parameters and the learnings. You see that not just in a hospital scenario, but you see that in finance. Credit card companies, for example, of course, wouldn’t want to share their customer data with another competitor credit card company. But they know that the learnings of the machine learning models locally is not as sensitive to fraud data because they are not seeing all the different kinds of fraud. Perhaps they’re seeing one kind of fraud, but a different credit card company might be seeing another kind of fraud.

Swarm learning could be used here where each credit card company keeps their customer data private, no sharing of that. But a blockchain comes in and shares the learnings, the fraud data learning, and collects all those learnings, averaged it and giving it back out to all the participating credit card companies. So this is one example. Banks could do the same. Industrial robots could do the same too.

We have an automotive customer that has tens of thousands of industrial robots, but in different countries. Industrial robots today follow instructions. But in the next generation robots, with AI, they will also learn locally, say for example, to avoid certain mistakes and not repeat them. What you can do, using swarm learning is, if these robots are in different countries where you cannot share data, sensor data from the local environment across country borders, but you’re allowed to share the learnings of avoiding these mistakes, swarm learning can therefore be applied. So you now imagine a swarm of industrial robots, across different countries, sharing learnings so that they don’t repeat the same mistakes.

So yes. In enterprise, you can see different applications of swarm learning. Finance, engineering, and of course, in healthcare, as we’ve discussed.

Laurel: How do you think companies need to start thinking differently about their actual data architecture to encourage the ability to share these insights, but not actually share the data?

Eng Lim: First and foremost, we need to be comfortable with the fact that devices that are collecting data will proliferate. And they will be at the edge where the data first lands. What’s the edge? The edge is where you have a device, and where the data first lands electronically. And if you imagine 50 billion of them next year, for example, and growing, in one estimate, we need to be comfortable with the fact that data will be everywhere. And to design your organization, design the way you use data, design the way you access data with that concept in mind, i.e., moving from one which we are used to, that is data being centralized most of the time, to one where data is everywhere. So the way you access data needs to be different now. You cannot now think of first aggregating all the data, pulling all the data, backhauling all the data from the edge to a centralized location, then work with it. We may need to switch to a scenario where we are operating on the data, learning from the data while the data are still out there.

Laurel: So, we talked a bit healthcare and manufacturing. How do you also envision the big ideas of smart cities and autonomous vehicles fitting in with the ideas of swarm intelligence?

Eng Lim: Yes, yes, yes. These are two big, big items. And very similar also, you think of a smart city, it is full of sensors, full of connected devices. You think of autonomous cars, one estimate puts it at something like 300 sensing devices in a car, all collecting data. A similar way of thinking of it, data is going to be everywhere, and collected in real time at these edge devices. For smart cities, it could be street lights. We work with one city with 200,000 street lights. And they want to make every one of these street lights smart. By smart, I mean ability to recommend decisions or even make decisions. You get to a point where, as I’ve said before, you cannot backhaul all the data all the time to the data center and make decisions after you’ve done the aggregation. A lot of times you have to make decisions where the data is collected. And therefore, things have to be smart at the edge, number one.

And if we take that step further beyond acting on instructions or acting on neural network models that have been pre-trained and then sent to the edge, you take one step beyond that, and that is, you want the edge devices to also learn on their own from the data they have collected. However, knowing that the data collected is biased to what they are only seeing, swarm learning will be needed in a peer-to-peer way for these devices to learn from each other.

So, this interconnectedness, the peer-to-peer interconnectedness of these edge devices, requires us to rethink or change the way we think about computing. Just take for example two autonomous cars. We call them connected cars to start with. Two connected cars, one in front of the other by 300 yards or 300 meters. The one in front, with lots of sensors in it, say for example in the shock absorbers, senses a pothole. And it actually can offer that sensed data that there is a pothole coming up to the cars behind. And if the cars behind switch on to automatically accept these, that pothole shows up on the car behind’s dashboard. And the car behind just pays maybe 0.10 cent for that information to the car in front.

So, you get a situation where you get these peer-to-peer sharing, in real time, without needing to send all that data first back to some central location and then send back down then the new information to the car behind. So, you want it to be peer-to-peer. So more and more, I’m not saying this is implemented yet, but this gives you an idea of how thinking can change going forward. A lot more peer-to-peer sharing, and a lot more peer-to-peer learning.

Laurel: When you think about how long we’ve worked in the technology industry to think that peer-to-peer as a phrase has come back around, where it used to mean people or even computers sharing various bits of information over the internet. Now it is devices and sensors sharing bits of information with each other. Sort of a different definition of peer-to-peer.

Eng Lim: Yeah. Thinking is changing. And peer, the word peer, peer-to-peer, meaning it has the connotation of a more equitable sharing in there. That’s the reason why a blockchain is needed in some of these cases so that there is no central custodian to average the learnings, to combine the learnings. So you want a true peer-to-peer environment. And that’s what swarm learning is built for. And now the reason for that, it’s not because we feel peer-to-peer is the next big thing and therefore we should do it. It is because of data and the proliferation of these devices that are collecting data.

Imagine tens of billions of these out there, and every one of these devices getting to be smarter and consuming less energy to be that smart and moving from one where they follow instructions or infer from the pre-trained neural network model given to them, to one where they can even advance towards learning on their own. But knowing that these devices are so many of them out there, therefore each of them are only seeing a small portion. Small is still big if you combine that all of them, 50 billion of them. But each of them is only seeing a small portion of data. And therefore, if they just learn in isolation, they’ll be highly biased towards what they’re seeing. As such, there must be some way where they can share their learnings without having to share their private data. And therefore, swarm learning. As opposed to backhauling all that data from the 50 billion edge devices back to these cloud locations, the data center locations, so they can do the combined learning.

Laurel: Which would cost certainly more than a fraction of a cent.

Eng Lim: Oh yeah. There is a saying, bandwidth, you pay for. Latency, you sweat for. So it’s cost. Bandwidth is cost.

Laurel: So as an expert in artificial intelligence, while we have you here, what are you most excited about in the coming years? What are you seeing that you’re thinking, that is going to be something big in the next five, 10 years?

Eng Lim:

Thank you, Laurel. I don’t see myself as an expert in AI, but a person that is being tasked and excited about working with customers on AI use cases and learning from them. The diversity of these different AI use cases and learning from them–some leading teams directly working on the projects and overseeing some of the projects. But in terms of the excitement, actually may seem mundane. And that is, the exciting part is that I see AI. The ability for smart systems to learn and adapt, and in many cases, provide decision support to humans. And in other more limited cases, make decisions in support of humans. The proliferation of AI is in everything we do, many things we do—certain things maybe we should limit—but in many things we do.

I mean, let’s just use the most basic of examples. How this progression could be. Let’s take a light switch. In the early days, even until today, the most basic light switch is one where it is manual. A human goes ahead, throws the switch on, and the light comes on. And throws the switch off, and the light goes off. Then we move on to the next level. If you want an analogy, more next level, where we automate that switch. We put a set of instructions on that switch with a light meter, and set the instructions to say, if the lighting in this room drops to 25% of its peak, switch on. So basically, we gave an instruction with a sensor to go with it, to the switch. And then the switch is now automatic. And then when the lighting in the room drops to 25% of its peak, of the peak illumination, it switches on the lights. So now the switch is automated.

Now we can even take a step further in that automation, by making the switch smart, in that it can have more sensors. And then through the combinations of sensors, make decisions as to whether the switch the light on. And the control all these sensors, we built a neural network model that has been pre-trained separately, and then downloaded onto the switch. This is where we are at today. The switch is now smart. Smart city, smart street lights, autonomous cars, and so on.

Now, is there another level beyond that? There is. And that is when the switch not just follows instructions or not just have a trained neural network model to decide in a way to combine all the different sensor data, to decide when to switch the light on in a more precise way. It advances further to one where it learns. That’s the key word. It learns from mistakes. What would be the example? The example would be, based on the neural network model it has, that was pre-trained previously, downloaded onto the switch, with all the settings. It turns the light on. But when the human comes in, the human says I don’t need the light on here this time around, the human switches the light off. Then the switch realizes that it actually made a decision that the human didn’t like. So after a few of these, it starts to adapt itself, learn from these. Adapt itself so that you can switch a light on to the changing human preferences. That’s the next step where you want edge devices that are collecting data at the edge to learn from those.

Then of course, if you take that even further, all the switches in this office or in a residential unit, learn from each other. That will be swarm learning. So if you then extend the switch to toasters, to fridges, to cars, to industrial robots and so on, you will see that doing this, we will clearly reduce energy consumption, reduce waste, and improve productivity. But the key must be, for human good.

Laurel: And what a wonderful way to end our conversation. Thank you so much for joining us on the Business Lab.

Eng Lim: Thank you Laurel. Much appreciated.

Laurel: That was Dr. Eng Lim Goh, senior vice president and CTO of artificial intelligence at Hewlett Packard Enterprise, who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review, overlooking the Charles River. That’s it for this episode of Business Lab, I’m your host, Laurel Ruma. I’m the director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology. And you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at The show is available wherever you get your podcasts. If you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Collective Next. Thanks for listening.

This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review’s editorial staff.


How the idea of a “transgender contagion” went viral—and caused untold harm



How the idea of a “transgender contagion” went viral—and caused untold harm

The ROGD paper was not funded by anti-trans zealots. But it arrived at exactly the time people with bad intentions were looking for science to buoy their opinions.

The results were in line with what one might expect given those sources: 76.5% of parents surveyed “believed their child was incorrect in their belief of being transgender.” More than 85% said their child had increased their internet use and/or had trans friends before identifying as trans. The youths themselves had no say in the study, and there’s no telling if they had simply kept their parents in the dark for months or years before coming out. (Littman acknowledges that “parent-child conflict may also explain some of the findings.”) 

Arjee Restar, now an assistant professor of epidemiology at the University of Washington, didn’t mince words in her 2020 methodological critique of the paper. Restar noted that Littman chose to describe the “social and peer contagion” hypothesis in the consent document she shared with parents, opening the door for biases in who chose to respond to the survey and how they did so. She also highlighted that Littman asked parents to offer “diagnoses” of their child’s gender dysphoria, which they were unqualified to do without professional training. It’s even possible that Littman’s data could contain multiple responses from the same parent, Restar wrote. Littman told MIT Technology Review that “targeted recruitment [to studies] is a really common practice.” She also called attention to the corrected ROGD paper, which notes that a pro-gender-­affirming parents’ Facebook group with 8,000 members posted the study’s recruitment information on its page—although Littman’s study was not designed to be able to discern whether any of them responded.

But politics is blind to nuances in methodology. And the paper was quickly seized by those who were already pushing back against increasing acceptance of trans people. In 2014, a few years before Littman published her ROGD paper, Time magazine had put Laverne Cox, the trans actress from Orange Is the New Black, on its cover and declared a “transgender tipping point.” By 2016, bills across the country that aimed to bar trans people from bathrooms that fit their gender identity failed, and one that succeeded, in North Carolina, cost its Republican governor, Pat McCrory, his job.  

Yet by 2018 a renewed backlash was well underway—one that zeroed in on trans youth. The debate about trans youth competing in sports went national, as did a heavily publicized Texas custody battle between a mother who supported her trans child and a father who didn’t. Groups working to further marginalize trans people, like the Alliance Defending Freedom and the Family Research Council, began “printing off bills and introducing them to state legislators,” says Gillian Branstetter, a communications strategist at the American Civil Liberties Union.

The ROGD paper was not funded by anti-trans zealots. But it arrived at exactly the time people with bad intentions were looking for science to buoy their opinions. The paper “laundered what had previously been the rantings of online conspiracy theorists and gave it the resemblance of serious scientific study,” Branstetter says. She believes that if Littman’s paper had not been published, a similar argument would have been made by someone else. Despite its limitations, it has become a crucial weapon in the fight against trans people, largely through online dissemination. “It is astonishing that such a blatantly bad-faith effort has been taken so seriously,” Branstetter says.

Littman plainly rejects that characterization, saying her goal was simply to “find out what’s going on.” “This was a very good-faith attempt,” she says. “As a person I am liberal; I’m pro-LGBT. I saw a phenomenon with my own eyes and I investigated, found that it was different than what was in the scientific literature.” 

One reason for the success of Littman’s paper is that it validates the idea that trans kids are new. But Jules Gill-Peterson, an associate professor of history at Johns Hopkins and author of Histories of the Transgender Child, says that is “empirically untrue.” Trans children have only recently started to be discussed in mainstream media, so people assume they weren’t around before, she says, but “there have been children transitioning for as long as there has been transition-related medical technology,” and children were socially transitioning—living as a different gender without any medical or legal interventions—long before that.

Many trans people are young children when they first observe a dissonance between how they are identified and how they identify. The process of transitioning is never simple, but the explanation of their identity might be.

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Inside the software that will become the next battle front in US-China chip war



screenshot of KiCad software for circuit board design and prototyping

EDA software is a small but mighty part of the semiconductor supply chain, and it’s mostly controlled by three Western companies. That gives the US a powerful point of leverage, similar to the way it wanted to restrict access to lithography machines—another crucial tool for chipmaking—last month. So how has the industry become so American-centric, and why can’t China just develop its own alternative software? 

What is EDA?

Electronic design automation (also known as electronic computer-aided design, or ECAD) is the specialized software used in chipmaking. It’s like the CAD software that architects use, except it’s more sophisticated, since it deals with billions of minuscule transistors on an integrated circuit.

Screenshot of KiCad, a free EDA software.


There’s no single dominant software program that represents the best in the industry. Instead, a series of software modules are often used throughout the whole design flow: logic design, debugging, component placement, wire routing, optimization of time and power consumption, verification, and more. Because modern-day chips are so complex, each step requires a different software tool. 

How important is EDA to chipmaking?

Although the global EDA market was valued at only around $10 billion in 2021, making it a small fraction of the $595 billion semiconductor market, it’s of unique importance to the entire supply chain.

The semiconductor ecosystem today can be seen as a triangle, says Mike Demler, a consultant who has been in the chip design and EDA industry for over 40 years. On one corner are the foundries, or chip manufacturers like TSMC; on another corner are intellectual-property companies like ARM, which make and sell reusable design units or layouts; and on the third corner are the EDA tools. All three together make sure the supply chain moves smoothly.

From the name, it may sound as if EDA tools are only important to chip design firms, but they are also used by chip manufacturers to verify that a design is feasible before production. There’s no way for a foundry to make a single chip as a prototype; it has to invest in months of time and production, and each time, hundreds of chips are fabricated on the same semiconductor base. It would be an enormous waste if they were found to have design flaws. Therefore, manufacturers rely on a special type of EDA tool to do their own validation. 

What are the leading companies in the EDA industry?

There are only a few companies that sell software for each step of the chipmaking process, and they have dominated this market for decades. The top three companies—Cadence (American), Synopsys (American), and Mentor Graphics (American but acquired by the German company Siemens in 2017)—control about 70% of the global EDA market. Their dominance is so strong that many EDA startups specialize in one niche use and then sell themselves to one of these three companies, further cementing the oligopoly. 

What is the US government doing to restrict EDA exports to China?

US companies’ outsize influence on the EDA industry makes it easy for the US government to squeeze China’s access. In its latest announcement, it pledged to add certain EDA tools to its list of technologies banned from export. The US will coordinate with 41 other countries, including Germany, to implement these restrictions. 

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Bright LEDs could spell the end of dark skies



a satellite view of Earth on the hemisphere away from the sun with city lights visible

A global view of Earth assembled from data acquired by the Suomi National Polar-orbiting Partnership (NPP) satellite.


Specifications in the current proposal provide a starting point for planning, including a color temperature cutoff of 3,000 K in line with Pittsburgh’s dark-sky ordinance, which passed last fall. However, Martinez says that is the maximum, and as they look for consultants, they’ll be taking into account which ones show dark-sky expertise. The city is also considering—budget and infrastructure permitting—a “network lighting management system,” a kind of “smart” lighting that would allow them to control lighting levels and know when there is an outage. 

Martinez says there will be citywide engagement and updates on the status as critical milestones are reached. “We’re in the evaluation period right now,” she says, adding that the next milestone is authorization of a new contract. She acknowledges there is some “passionate interest in street lighting,” and that she too is anxious to see the project come to fruition: “Just because things seem to go quiet doesn’t mean work is not being done.”

While they aren’t meeting with light pollution experts right now, Martinez says the ones they met with during the last proposal round—Stephen Quick and Diane Turnshek of CMU— were “instrumental” in adopting the dark-sky ordinance.

In recent months, Zielinska-Dabkowska says, her “baby” has been the first Responsible Outdoor Light at Night Conference, an international gathering of more than 300 lighting professionals and light pollution researchers held virtually in May. Barentine was among the speakers. “It’s a sign that all of this is really coming along, both as a research subject but also something that attracts the interest of practitioners in outdoor lighting,” he says of the conference.

There is more work to be done, though. The IDA recently released a report summarizing the current state of light pollution research. The 18-page report includes a list of knowledge gaps to be addressed in several areas, including the overall effectiveness of government policies on light pollution. Another is how much light pollution comes from sources other than city streetlights, which a 2020 study found accounted for only 13% of Tucson’s light pollution. It is not clear what makes up the rest, but Barentine suspects the next biggest source in the US and Europe is commercial lighting, such as flashy outdoor LED signs and parking lot lighting. 

Working with companies to reduce light emissions can be challenging, says Clayton Trevillyan, Tucson’s chief building officer. “If there is a source of light inside the building, technically it’s not regulated by the outdoor lighting code, even if it is emitting light outside,” Trevillyan says. In some cases, he says, in order to get around the city’s restrictions, businesses have suspended illuminated signs inside buildings but aimed them outside. 

Light pollution experts generally say there is no substantial evidence that more light amounts to greater safety.

For cities trying to implement a lighting ordinance, Trevillyan says, the biggest roadblocks they’ll face are “irrelevant” arguments, specifically claims that reducing the brightness of outdoor lighting will cut down on advertising revenue and make the city more vulnerable to crime. The key to successfully enforcing the dark-sky rules, he says, is to educate the public and refuse to give in to people seeking exceptions or exploiting loopholes. 

Light pollution experts generally say there is no substantial evidence that more light amounts to greater safety. In Tucson, for example, Barentine says, neither traffic accidents nor crime appeared to increase after the city started dimming its streetlights at night and restricting outdoor lighting in 2017. Last year, researchers at the University of Pennsylvania analyzed crime rates alongside 300,000 streetlight outages over an eight-year period. They concluded there is “little evidence” of any impact on crime rates on the affected streets—in fact, perpetrators seemed to seek out better-lit adjacent streets. Barentine says there is some evidence that “strategically placed lighting” can help decrease traffic collisions. “Beyond that, things get murky pretty quickly,” he says.

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