The American Medical Association's PAC contributed to politicians who voted against the Inflation Reduction Act, according to Harvard researchers.

Gov. Kathy Hochul has delayed action on a sweeping program to charge for greenhouse gas emissions.

The rich oil state has built a team of silver-tongued U.N. negotiators with a simple mandate: Keep plastic production growing.

A clean industry push was one of the policies halted when the government collapsed in November.

Critics see the seemingly simultaneous move by the big banks and firms to leave climate alliances as an obvious attempt to curry favor with President-elect Donald Trump.

Please join EFF for the next segment of EFFecting Change, our livestream series covering digital privacy and free speech. 

EFFecting Change Livestream Series:
Digital Rights & the New Administration
Thursday, January 16th
10:00 AM - 11:00 AM Pacific - Check Local Time
This event is LIVE and FREE!

What direction will your digital rights take under Trump and the 119th Congress? Find out about the topics EFF is watching and the effect they might have on you.

Join our panel of experts as they discuss surveillance, age verification, and consumer privacy. Learn how you can advocate for your digital rights and the resources available to you with our panel featuring EFF Senior Investigative Researcher Beryl Lipton, EFF Senior Staff Technologist Bill Budington, EFF Legislative Director Lee Tien, and EFF Senior Policy Analyst Joe Mullin.

We hope you and your friends can join us live! Be sure to spread the word, and share our past livestreams. Please note that all events will be recorded for later viewing on our YouTube page.

Want to make sure you don’t miss our next livestream? Here’s a link to sign up for updates about this series: eff.org/ECUpdates.

Quantum computing promises to solve complex problems exponentially faster than a classical computer, by using the principles of quantum mechanics to encode and manipulate information in quantum bits (qubits).

Qubits are the building blocks of a quantum computer. One challenge to scaling, however, is that qubits are highly sensitive to background noise and control imperfections, which introduce errors into the quantum operations and ultimately limit the complexity and duration of a quantum algorithm. To improve the situation, MIT researchers and researchers worldwide have continually focused on improving qubit performance. 

In new work, using a superconducting qubit called fluxonium, MIT researchers in the Department of Physics, the Research Laboratory of Electronics (RLE), and the Department of Electrical Engineering and Computer Science (EECS) developed two new control techniques to achieve a world-record single-qubit fidelity of 99.998 percent. This result complements then-MIT researcher Leon Ding’s demonstration last year of a 99.92 percent two-qubit gate fidelity. 

The paper’s senior authors are David Rower PhD ’24, a recent physics postdoc in MIT’s Engineering Quantum Systems (EQuS) group and now a research scientist at the Google Quantum AI laboratory; Leon Ding PhD ’23 from EQuS, now leading the Calibration team at Atlantic Quantum; and William D. Oliver, the Henry Ellis Warren Professor of EECS and professor of physics, leader of EQuS, director of the Center for Quantum Engineering, and RLE associate director. The paper recently appeared in the journal PRX Quantum.

Decoherence and counter-rotating errors

A major challenge with quantum computation is decoherence, a process by which qubits lose their quantum information. For platforms such as superconducting qubits, decoherence stands in the way of realizing higher-fidelity quantum gates.

Quantum computers need to achieve high gate fidelities in order to implement sustained computation through protocols like quantum error correction. The higher the gate fidelity, the easier it is to realize practical quantum computing.

MIT researchers are developing techniques to make quantum gates, the basic operations of a quantum computer, as fast as possible in order to reduce the impact of decoherence. However, as gates get faster, another type of error, arising from counter-rotating dynamics, can be introduced because of the way qubits are controlled using electromagnetic waves. 

Single-qubit gates are usually implemented with a resonant pulse, which induces Rabi oscillations between the qubit states. When the pulses are too fast, however, “Rabi gates” are not so consistent, due to unwanted errors from counter-rotating effects. The faster the gate, the more the counter-rotating error is manifest. For low-frequency qubits such as fluxonium, counter-rotating errors limit the fidelity of fast gates.

“Getting rid of these errors was a fun challenge for us,” says Rower. “Initially, Leon had the idea to utilize circularly polarized microwave drives, analogous to circularly polarized light, but realized by controlling the relative phase of charge and flux drives of a superconducting qubit. Such a circularly polarized drive would ideally be immune to counter-rotating errors.”

While Ding’s idea worked immediately, the fidelities achieved with circularly polarized drives were not as high as expected from coherence measurements.

“Eventually, we stumbled on a beautifully simple idea,” says Rower. “If we applied pulses at exactly the right times, we should be able to make counter-rotating errors consistent from pulse-to-pulse. This would make the counter-rotating errors correctable. Even better, they would be automatically accounted for with our usual Rabi gate calibrations!”

They called this idea “commensurate pulses,” since the pulses needed to be applied at times commensurate with intervals determined by the qubit frequency through its inverse, the time period. Commensurate pulses are defined simply by timing constraints and can be applied to a single linear qubit drive. In contrast, circularly polarized microwaves require two drives and some extra calibration.

“I had much fun developing the commensurate technique,” says Rower. “It was simple, we understood why it worked so well, and it should be portable to any qubit suffering from counter-rotating errors!”

“This project makes it clear that counter-rotating errors can be dealt with easily. This is a wonderful thing for low-frequency qubits such as fluxonium, which are looking more and more promising for quantum computing.”

Fluxonium’s promise

Fluxonium is a type of superconducting qubit made up of a capacitor and Josephson junction; unlike transmon qubits, however, fluxonium also includes a large “superinductor,” which by design helps protect the qubit from environmental noise. This results in performing logical operations, or gates, with greater accuracy.

Despite having higher coherence, however, fluxonium has a lower qubit frequency that is generally associated with proportionally longer gates.

“Here, we’ve demonstrated a gate that is among the fastest and highest-fidelity across all superconducting qubits,” says Ding. “Our experiments really show that fluxonium is a qubit that supports both interesting physical explorations and also absolutely delivers in terms of engineering performance.”

With further research, they hope to reveal new limitations and yield even faster and higher-fidelity gates.

“Counter-rotating dynamics have been understudied in the context of superconducting quantum computing because of how well the rotating-wave approximation holds in common scenarios,” says Ding. “Our paper shows how to precisely calibrate fast, low-frequency gates where the rotating-wave approximation does not hold.”

Physics and engineering team up

“This is a wonderful example of the type of work we like to do in EQuS, because it leverages fundamental concepts in both physics and electrical engineering to achieve a better outcome,” says Oliver. “It builds on our earlier work with non-adiabatic qubit control, applies it to a new qubit — fluxonium — and makes a beautiful connection with counter-rotating dynamics.”

The science and engineering teams enabled the high fidelity in two ways. First, the team demonstrated “commensurate” (synchronous) non-adiabatic control, which goes beyond the standard “rotating wave approximation” of standard Rabi approaches. This leverages ideas that won the 2023 Nobel Prize in Physics for ultrafast “attosecond” pulses of light.

Secondly, they demonstrated it using an analog to circularly polarized light. Rather than a physical electromagnetic field with a rotating polarization vector in real x-y space, they realized a synthetic version of circularly polarized light using the qubit’s x-y space, which in this case corresponds to its magnetic flux and electric charge.

The combination of a new take on an existing qubit design (fluxonium) and the application of advanced control methods applied to an understanding of the underlying physics enabled this result.

Platform-independent and requiring no additional calibration overhead, this work establishes straightforward strategies for mitigating counter-rotating effects from strong drives in circuit quantum electrodynamics and other platforms, which the researchers expect to be helpful in the effort to realize high-fidelity control for fault-tolerant quantum computing.

Adds Oliver, “With the recent announcement of Google’s Willow quantum chip that demonstrated quantum error correction beyond threshold for the first time, this is a timely result, as we have pushed performance even higher. Higher-performant qubits will lead to lower overhead requirements for implementing error correction.”  

Other researchers on the paper are RLE’s Helin Zhang, Max Hays, Patrick M. Harrington, Ilan T. Rosen, Simon Gustavsson, Kyle Serniak, Jeffrey A. Grover, and Junyoung An, who is also with EECS; and MIT Lincoln Laboratory’s Jeffrey M. Gertler, Thomas M. Hazard, Bethany M. Niedzielski, and Mollie E. Schwartz.

This research was funded, in part, by the U.S. Army Research Office, the U.S. Department of Energy Office of Science, National Quantum Information Science Research Centers, Co-design Center for Quantum Advantage, U.S. Air Force, the U.S. Office of the Director of National Intelligence, and the U.S. National Science Foundation.  

Angelina Wu has been taking Japanese classes at MIT since arriving as a first-year student.

“I have had such a wonderful experience learning the language, getting to know my classmates, and interacting with the Japanese community at MIT,” says Wu, now a senior majoring in computer science and engineering.

“It’s been an integral part of my MIT experience, supplementing my other technical skills and also giving me opportunities to meet many people outside my major that I likely wouldn’t have had otherwise. As a result, I feel like I get to understand a much broader, more complete version of MIT.”

Now, Wu is sharing her experience and giving back as a Global Languages Student Ambassador. At a recent Global Languages preregistration fair, Wu spoke with other students interested in pursuing Japanese studies.

“I could not be happier to help promote such an experience to curious students and the greater MIT community,” Wu says.

Global Language Student Ambassadors is a group of students who lead outreach efforts to help increase visibility for the program.

In addition to disseminating information and promotional materials to the MIT undergraduate community, student ambassadors are asked to organize and host informal gatherings for Global Languages students around themes related to language and cultural exploration to build community and provide opportunities for learning and fun outside of the classroom.

Global Languages director Per Urlaub isn’t surprised that the Student Ambassadors program is popular with both students and the MIT community.

“The Global Languages program brings people together,” he says. “Providing a caring learning environment and creating a sense of belonging are central to our mission.”

What’s also central to the Global Languages’ mission is centering students’ work and creating spaces in which language learning can help create connections across academic areas. Students who study languages may improve their understanding of the cultural facets that underlie communication across cultures and open new worlds.

“An engaging community that fosters a deep sense of belonging doesn’t just happen automatically,” Urlaub notes. “A stronger community elevates our students’ proficiency gains, and also makes language learning more meaningful and fun.”

Each student ambassador serves for a single academic year in their area of language focus. They work closely with MIT’s academic administrators to plan, communicate, and stage events.

“I love exploring the richness of the Arabic language, especially how it connects to my culture and heritage,” says Heba Hussein, a student ambassador studying Arabic and majoring in electrical science and engineering. “I believe that having a strong grasp of languages and cultural awareness will help me work effectively in diverse teams.”

Student ambassadors, alongside other language learners, discover how other languages, cultures, and countries can guide their communications with others while shaping how they understand the world.

“My Spanish courses at MIT have been a highlight of my college experience thus far — the opportunity to connect on a deeper level with other cultures and force myself out of my comfort zone in conversations is important to me,” says Katie Kempff, another student ambassador who is majoring in climate system science and engineering and Spanish.

“As a heritage speaker, learning Chinese has been a way for me to connect with my culture and my roots,” adds Zixuan Liu, a double major in biological engineering and biology, and a Chinese student ambassador, who says that as a heritage speaker, learning Chinese has been a way for her to connect with her culture and her roots.

“I would highly recommend diving into languages and culture at MIT, where the support and the community really enhances the experience,” Liu says.

Back in the old days — the really old days — the task of designing materials was laborious. Investigators, over the course of 1,000-plus years, tried to make gold by combining things like lead, mercury, and sulfur, mixed in what they hoped would be just the right proportions. Even famous scientists like Tycho Brahe, Robert Boyle, and Isaac Newton tried their hands at the fruitless endeavor we call alchemy.

Materials science has, of course, come a long way. For the past 150 years, researchers have had the benefit of the periodic table of elements to draw upon, which tells them that different elements have different properties, and one can’t magically transform into another. Moreover, in the past decade or so, machine learning tools have considerably boosted our capacity to determine the structure and physical properties of various molecules and substances. New research by a group led by Ju Li — the Tokyo Electric Power Company Professor of Nuclear Engineering at MIT and professor of materials science and engineering — offers the promise of a major leap in capabilities that can facilitate materials design. The results of their investigation are reported in a December 2024 issue of Nature Computational Science.

At present, most of the machine-learning models that are used to characterize molecular systems are based on density functional theory (DFT), which offers a quantum mechanical approach to determining the total energy of a molecule or crystal by looking at the electron density distribution — which is, basically, the average number of electrons located in a unit volume around each given point in space near the molecule. (Walter Kohn, who co-invented this theory 60 years ago, received a Nobel Prize in Chemistry for it in 1998.) While the method has been very successful, it has some drawbacks, according to Li: “First, the accuracy is not uniformly great. And, second, it only tells you one thing: the lowest total energy of the molecular system.”

“Couples therapy” to the rescue

His team is now relying on a different computational chemistry technique, also derived from quantum mechanics, known as coupled-cluster theory, or CCSD(T). “This is the gold standard of quantum chemistry,” Li comments. The results of CCSD(T) calculations are much more accurate than what you get from DFT calculations, and they can be as trustworthy as those currently obtainable from experiments. The problem is that carrying out these calculations on a computer is very slow, he says, “and the scaling is bad: If you double the number of electrons in the system, the computations become 100 times more expensive.” For that reason, CCSD(T) calculations have normally been limited to molecules with a small number of atoms — on the order of about 10. Anything much beyond that would simply take too long.

That’s where machine learning comes in. CCSD(T) calculations are first performed on conventional computers, and the results are then used to train a neural network with a novel architecture specially devised by Li and his colleagues. After training, the neural network can perform these same calculations much faster by taking advantage of approximation techniques. What’s more, their neural network model can extract much more information about a molecule than just its energy. “In previous work, people have used multiple different models to assess different properties,” says Hao Tang, an MIT PhD student in materials science and engineering. “Here we use just one model to evaluate all of these properties, which is why we call it a ‘multi-task’ approach.”

The “Multi-task Electronic Hamiltonian network,” or MEHnet, sheds light on a number of electronic properties, such as the dipole and quadrupole moments, electronic polarizability, and the optical excitation gap — the amount of energy needed to take an electron from the ground state to the lowest excited state. “The excitation gap affects the optical properties of materials,” Tang explains, “because it determines the frequency of light that can be absorbed by a molecule.” Another advantage of their CCSD-trained model is that it can reveal properties of not only ground states, but also excited states. The model can also predict the infrared absorption spectrum of a molecule related to its vibrational properties, where the vibrations of atoms within a molecule are coupled to each other, leading to various collective behaviors.

The strength of their approach owes a lot to the network architecture. Drawing on the work of MIT Assistant Professor Tess Smidt, the team is utilizing a so-called E(3)-equivariant graph neural network, says Tang, “in which the nodes represent atoms and the edges that connect the nodes represent the bonds between atoms. We also use customized algorithms that incorporate physics principles — related to how people calculate molecular properties in quantum mechanics — directly into our model.”

Testing, 1, 2 3

When tested on its analysis of known hydrocarbon molecules, the model of Li et al. outperformed DFT counterparts and closely matched experimental results taken from the published literature.

Qiang Zhu — a materials discovery specialist at the University of North Carolina at Charlotte (who was not part of this study) — is impressed by what’s been accomplished so far. “Their method enables effective training with a small dataset, while achieving superior accuracy and computational efficiency compared to existing models,” he says. “This is exciting work that illustrates the powerful synergy between computational chemistry and deep learning, offering fresh ideas for developing more accurate and scalable electronic structure methods.”

The MIT-based group applied their model first to small, nonmetallic elements — hydrogen, carbon, nitrogen, oxygen, and fluorine, from which organic compounds can be made — and has since moved on to examining heavier elements: silicon, phosphorus, sulfur, chlorine, and even platinum. After being trained on small molecules, the model can be generalized to bigger and bigger molecules. “Previously, most calculations were limited to analyzing hundreds of atoms with DFT and just tens of atoms with CCSD(T) calculations,” Li says. “Now we’re talking about handling thousands of atoms and, eventually, perhaps tens of thousands.”

For now, the researchers are still evaluating known molecules, but the model can be used to characterize molecules that haven’t been seen before, as well as to predict the properties of hypothetical materials that consist of different kinds of molecules. “The idea is to use our theoretical tools to pick out promising candidates, which satisfy a particular set of criteria, before suggesting them to an experimentalist to check out,” Tang says.

It’s all about the apps

Looking ahead, Zhu is optimistic about the possible applications. “This approach holds the potential for high-throughput molecular screening,” he says. “That’s a task where achieving chemical accuracy can be essential for identifying novel molecules and materials with desirable properties.”

Once they demonstrate the ability to analyze large molecules with perhaps tens of thousands of atoms, Li says, “we should be able to invent new polymers or materials” that might be used in drug design or in semiconductor devices. The examination of heavier transition metal elements could lead to the advent of new materials for batteries — presently an area of acute need.

The future, as Li sees it, is wide open. “It’s no longer about just one area,” he says. “Our ambition, ultimately, is to cover the whole periodic table with CCSD(T)-level accuracy, but at lower computational cost than DFT. This should enable us to solve a wide range of problems in chemistry, biology, and materials science. It’s hard to know, at present, just how wide that range might be.”

This work was supported by the Honda Research Institute. Hao Tang acknowledges support from the Mathworks Engineering Fellowship. The calculations in this work were performed, in part, on the Matlantis high-speed universal atomistic simulator, the Texas Advanced Computing Center, the MIT SuperCloud, and the National Energy Research Scientific Computing.

When sound waves reach the inner ear, neurons there pick up the vibrations and alert the brain. Encoded in their signals is a wealth of information that enables us to follow conversations, recognize familiar voices, appreciate music, and quickly locate a ringing phone or crying baby.

Neurons send signals by emitting spikes — brief changes in voltage that propagate along nerve fibers, also known as action potentials. Remarkably, auditory neurons can fire hundreds of spikes per second, and time their spikes with exquisite precision to match the oscillations of incoming sound waves.

With powerful new models of human hearing, scientists at MIT’s McGovern Institute for Brain Research have determined that this precise timing is vital for some of the most important ways we make sense of auditory information, including recognizing voices and localizing sounds.

The open-access findings, reported Dec. 4 in the journal Nature Communications, show how machine learning can help neuroscientists understand how the brain uses auditory information in the real world. MIT professor and McGovern investigator Josh McDermott, who led the research, explains that his team’s models better-equip researchers to study the consequences of different types of hearing impairment and devise more effective interventions.

Science of sound

The nervous system’s auditory signals are timed so precisely, researchers have long suspected that timing is important to our perception of sound. Sound waves oscillate at rates that determine their pitch: Low-pitched sounds travel in slow waves, whereas high-pitched sound waves oscillate more frequently. The auditory nerve that relays information from sound-detecting hair cells in the ear to the brain generates electrical spikes that correspond to the frequency of these oscillations. “The action potentials in an auditory nerve get fired at very particular points in time relative to the peaks in the stimulus waveform,” explains McDermott, who is also associate head of the MIT Department of Brain and Cognitive Sciences.

This relationship, known as phase-locking, requires neurons to time their spikes with sub-millisecond precision. But scientists haven’t really known how informative these temporal patterns are to the brain. Beyond being scientifically intriguing, McDermott says, the question has important clinical implications: “If you want to design a prosthesis that provides electrical signals to the brain to reproduce the function of the ear, it’s arguably pretty important to know what kinds of information in the normal ear actually matter,” he says.

This has been difficult to study experimentally; animal models can’t offer much insight into how the human brain extracts structure in language or music, and the auditory nerve is inaccessible for study in humans. So McDermott and graduate student Mark Saddler PhD ’24 turned to artificial neural networks.

Artificial hearing

Neuroscientists have long used computational models to explore how sensory information might be decoded by the brain, but until recent advances in computing power and machine learning methods, these models were limited to simulating simple tasks. “One of the problems with these prior models is that they’re often way too good,” says Saddler, who is now at the Technical University of Denmark. For example, a computational model tasked with identifying the higher pitch in a pair of simple tones is likely to perform better than people who are asked to do the same thing. “This is not the kind of task that we do every day in hearing,” Saddler points out. “The brain is not optimized to solve this very artificial task.” This mismatch limited the insights that could be drawn from this prior generation of models.

To better understand the brain, Saddler and McDermott wanted to challenge a hearing model to do things that people use their hearing for in the real world, like recognizing words and voices. That meant developing an artificial neural network to simulate the parts of the brain that receive input from the ear. The network was given input from some 32,000 simulated sound-detecting sensory neurons and then optimized for various real-world tasks.

The researchers showed that their model replicated human hearing well — better than any previous model of auditory behavior, McDermott says. In one test, the artificial neural network was asked to recognize words and voices within dozens of types of background noise, from the hum of an airplane cabin to enthusiastic applause. Under every condition, the model performed very similarly to humans.

When the team degraded the timing of the spikes in the simulated ear, however, their model could no longer match humans’ ability to recognize voices or identify the locations of sounds. For example, while McDermott’s team had previously shown that people use pitch to help them identify people’s voices, the model revealed that that this ability is lost without precisely timed signals. “You need quite precise spike timing in order to both account for human behavior and to perform well on the task,” Saddler says. That suggests that the brain uses precisely timed auditory signals because they aid these practical aspects of hearing.

The team’s findings demonstrate how artificial neural networks can help neuroscientists understand how the information extracted by the ear influences our perception of the world, both when hearing is intact and when it is impaired. “The ability to link patterns of firing in the auditory nerve with behavior opens a lot of doors,” McDermott says.

“Now that we have these models that link neural responses in the ear to auditory behavior, we can ask, ‘If we simulate different types of hearing loss, what effect is that going to have on our auditory abilities?’” McDermott says. “That will help us better diagnose hearing loss, and we think there are also extensions of that to help us design better hearing aids or cochlear implants.” For example, he says, “The cochlear implant is limited in various ways — it can do some things and not others. What’s the best way to set up that cochlear implant to enable you to mediate behaviors? You can, in principle, use the models to tell you that.”

On December 14, James Harr, the owner of an online store called ComradeWorkwear, announced on social media that he planned to sell a deck of “Most Wanted CEO” playing cards, satirizing the infamous “Most-wanted Iraqi playing cards” introduced by the U.S. Defense Intelligence Agency in 2003. Per the ComradeWorkwear website, the Most Wanted CEO cards would offer “a critique of the capitalist machine that sacrifices people and planet for profit,” and “Unmask the oligarchs, CEOs, and profiteers who rule our world...From real estate moguls to weapons manufacturers.”  

But within a day of posting his plans for the card deck to his combined 100,000 followers on Instagram and TikTok, the New York Post ran a front page story on Harr, calling the cards “disturbing.” Less than 5 hours later, officers from the New York City Police Department came to Harr's door to interview him. They gave no indication he had done anything illegal or would receive any further scrutiny, but the next day the New York police commissioner held the New York Post story up during a press conference after announcing charges against Luigi Mangione, the alleged assassin of UnitedHealth Group CEO Brian Thompson. Shortly thereafter, platforms from TikTok to Shopify disabled both the company’s accounts and Harr’s personal accounts, simply because he used the moment to highlight what he saw as the harms that large corporations and their CEOs cause.

Even benign posts, such as one about Mangione’s astrological sign, were deleted from Threads.

Harr was not alone. After the assassination, thousands of people took to social media to express their negative experiences with the healthcare industry, speculate about who was behind the murder, and show their sympathy for either the victim or the shooter—if social media platforms allowed them to do so. Many users reported having their accounts banned and content removed after sharing comments about Luigi Mangione, Thompson's alleged assassin. TikTok, for example reportedly removed comments that simply said, "Free Luigi." Even seemingly benign content, such as a post about Mangione’s astrological sign or a video montage of him set to music, was deleted from Threads, according to users. 

The Most Wanted CEO playing cards did not reference Mangione, and would the cards—which have not been released—would not include personal information about any CEO. In his initial posts about the cards, Harr said he planned to include QR codes with more information about each company and, in his view, what dangers the companies present. Each suit would represent a different industry, and the back of each card would include a generic shooting-range style silhouette. As Harr put it in his now-removed video, the cards would include “the person, what they’re a part of, and a QR code that goes to dedicated pages that explain why they’re evil. So you could be like, 'Why is the CEO of Walmart evil? Why is the CEO of Northrop Grumman evil?’” 

A design for the Most Wanted CEO playing cards

Many have riffed on the military’s tradition of using playing cards to help troops learn about the enemy. You can currently find “Gaza’s Most Wanted” playing cards on Instagram, purportedly depicting “leaders and commanders of various groups such as the IRGC, Hezbollah, Hamas, Houthis, and numerous leaders within Iran-backed militias.” A Shopify store selling “Covid’s Most Wanted” playing cards, displaying figures like Bill Gates and Anthony Fauci, and including QR codes linking to a website “where all the crimes and evidence are listed,” is available as of this writing. Hero Decks, which sells novelty playing cards generally showing sports figures, even produced a deck of “Wall Street Most Wanted” cards in 2003 (popular enough to have a second edition). 

A Shopify store selling “Covid’s Most Wanted” playing cards is available as of this writing.

As we’ve said many times, content moderation at scale, whether human or automated, is impossible to do perfectly and nearly impossible to do well. Companies often get it wrong and remove content or whole accounts that those affected by the content would agree do not violate the platform’s terms of service or community guidelines. Conversely, they allow speech that could arguably be seen to violate those terms and guidelines. That has been especially true for speech related to divisive topics and during heated national discussions. These mistakes often remove important voices, perspectives, and context, regularly impacting not just everyday users but journalists, human rights defenders, artists, sex worker advocacy groups, LGBTQ+ advocates, pro-Palestinian activists, and political groups. In some instances, this even harms people's livelihoods. 

Instagram disabled the ComradeWorkwear account for “not following community standards,” with no further information provided. Harr’s personal account was also banned. Meta has a policy against the "glorification" of dangerous organizations and people, which it defines as "legitimizing or defending the violent or hateful acts of a designated entity by claiming that those acts have a moral, political, logical or other justification that makes them acceptable or reasonable.” Meta’s Oversight Board has overturned multiple moderation decisions by the company regarding its application of this policy. While Harr had posted to Instagram that “the CEO must die” after Thompson’s assassination, he included an explanation that, "When we say the ceo must die, we mean the structure of capitalism must be broken.” (Compare this to a series of Instagram story posts from musician Ethel Cain, whose account is still available, which used the hashtag #KillMoreCEOs, for one of many examples of how moderation affects some people and not others.) 

TikTok reported that Harr violated the platform’s community guidelines with no additional information. The platform has a policy against "promoting (including any praise, celebration, or sharing of manifestos) or providing material support" to violent extremists or people who cause serial or mass violence. TikTok gave Harr no opportunity for appeal, and continued to remove additional accounts Harr only created to  update his followers on his life. TikTok did not point to any specific piece of content that violated its guidelines. 

These voices shouldn’t be silenced into submission simply for drawing attention to the influence that platforms have.

On December 20, PayPal informed Harr it could no longer continue processing payments for ComradeWorkwear, with no information about why. Shopify informed Harr that his store was selling “offensive content,” and his Shopify and Apple Pay accounts would both be disabled. In a follow-up email, Shopify told Harr the decision to close his account “was made by our banking partners who power the payment gateway.”  

Harr’s situation is not unique. Financial and social media platforms have an enormous amount of control over our online expression, and we’ve long been critical of their over-moderation,  uneven enforcement, lack of transparency, and failure to offer reasonable appeals. This is why EFF co-created The Santa Clara Principles on transparency and accountability in content moderation, along with a broad coalition of organizations, advocates, and academic experts. These platforms have the resources to set the standard for content moderation, but clearly don’t apply their moderation evenly, and in many instances, aren’t even doing the basics—like offering clear notices and opportunities for appeal.  

Harr was one of many who expressed frustration online with the growing power of corporations. These voices shouldn’t be silenced into submission simply for drawing attention to the influence that they have. These are exactly the kinds of actions that Harr intended to highlight. If the Most Wanted CEO deck is ever released, it shouldn’t be a surprise for the CEOs of these platforms to find themselves in the lineup.  

This is a current list of where and when I am scheduled to speak:

• I’m speaking on “AI: Trust & Power” at Capricon 45 in Chicago, Illinois, USA, at 11:30 AM on February 7, 2025. I’m also signing books there on Saturday, February 8, starting at 1:45 PM.
• I’m speaking at Boskone 62 in Boston, Massachusetts, USA, which runs from February 14-16, 2025.
• I’m speaking at the Rossfest Symposium in Cambridge, UK, on March 25, 2025.

The list is maintained on this page.

A massive Los Angeles rebuilding effort will be shaped by the president-elect who disrupted the release of disaster aid to Democratic areas during his first term.

The justices have again declined to get involved in the climate liability litigation, allowing cases to proceed to trial and potentially encouraging more local governments to sue.

Anne Idsal Austin played a role in easing regulations on the fossil fuel industry during her time at the agency in Trump’s first term.

The move aims to bolster clean energy and protect national security during a boom in artificial intelligence.

It was created in 1973 by Peter Kirstein:

So from the beginning I put password protection on my gateway. This had been done in such a way that even if UK users telephoned directly into the communications computer provided by Darpa in UCL, they would require a password.

In fact this was the first password on Arpanet. It proved invaluable in satisfying authorities on both sides of the Atlantic for the 15 years I ran the service ­ during which no security breach occurred over my link. I also put in place a system of governance that any UK users had to be approved by a committee which I chaired but which also had UK government and British Post Office representation...

The state is less than halfway toward its goal of drawing 40 percent of its power from renewable sources by 2030.

Two open letters reflect a growing anxiety about the future of federal science under the president-elect.

Two reports backed by environmental advocates found distributing money raised from a cap-and-trade program would leave households better off.