EFF has been at the forefront of defending civil liberties in the digital age, with our activism team working across state, federal, and local levels to safeguard everyone's rights in the rapidly evolving tech landscape. As federal action on technology policy often lags, many are looking to state governments to lead the way in addressing tech-related issues. 

Drawing insights from the State of State Technology Policy 2024 report by NYU’s Center on Technology Policy and EFF's own experiences advocating in state legislatures, this blog offers a breakdown on why you should care about state policy, the number of bills passed around the country, and a look forward to the coming challenges and trends in state-level tech policy.

Why Should You Care?

State governments are increasingly becoming key players in tech policy, moving much faster than the federal government. This has become especially apparent in 2024, when states enacted significantly more legislation regulating technology than in previous years. 

“Why?,” you may ask. State legislatures were the most partisan they’ve been in decades in 2024, where we saw a notable increase in the presence of "trifecta" governments—states where one political party controls both chambers of the legislature and the governorship. With this unified control, states can pass laws more easily and quickly. 

Forty states operated under such single-party rule in 2024, the most in at least three decades. Amongst the 40 trifecta states, 29 states also had veto-proof supermajorities, meaning legislation can pass regardless of gubernatorial opposition. This overwhelming single-party control helped push through new tech regulations, with the Center on Technology Policy reporting that 89% percent of all tech-related bills passed in trifecta states. Even with shifts in the 2024 elections, where at least two states—Michigan and Minnesota—lost their trifectas, the trend of state governments driving technology policy is unlikely to slow down anytime soon.

2024 in Numbers: A Historic Year for State Tech Policy

According to the State of State Technology Policy 2024 report by NYU’s Center on Technology Policy:

• 238 technology-related bills passed across 46 states, marking a 163% increase from the previous year.
• 20 states passed 28 privacy-related bills, including 7 states enacting laws similar to the industry supported Washington Privacy Act.
• 18 states passed laws regulating biometric data, with 2 states introducing genetic privacy protections.
• 23 states passed 48 laws focused on “online child safety,” primarily targeting age verification for adult content and regulating social media.
• 41 states passed 107 bills regulating AI.
• 22 states passed laws addressing Non-Consensual Intimate Images (NCII) and child sexual abuse material (CSAM) generated or altered by AI or digital means.
• 17 states enacted 22 laws regulating the use of generative AI in political campaigns.
• 6 states created 19 new commissions, task forces, and legislative committees to assess the impact of AI and explore its regulation or beneficial use. For example, California created a working group to guide the safe use of AI in education.
• 15 states passed 18 bills related to funding AI research or initiatives. For example, Nebraska allocated funds to explore how AI can assist individuals with dyslexia.
• 3 states made incremental changes to antitrust laws, while 6 states joined federal regulators in pursuing 6 significant cases against tech companies for anticompetitive practices.
• California passed the most tech-related legislation in 2024, with 26 bills, followed by Utah, which passed 13 bills.

Looking Ahead: What to Expect in 2025

2025 will be a critical year for state tech policy, and we expect to see several trends persist: state governments will continue to prioritize technology policy, leveraging their political compositions to enact new laws faster than the federal government. We expect state legislatures to continue ongoing efforts to regulate AI, online child safety, and other pressing issues, with states taking a proactive role in shaping the future of tech regulation. We also should recognize that states and local governments are technology users, and that their procurement and use of technology itself is a form of tech policy. States are also likely to introduce legislation around the procurement and use of emerging technologies like AI and facial recognition by government agencies, aiming to set clear standards and ensure transparency in their adoption—an issue the EFF plans to monitor and address in more detail in future blog posts and resources. Legislative priorities will be influenced by federal inaction or shifts in policy, as states step in to fill gaps and drive national discussions on digital rights.

Much depends on the direction of federal leadership. Some states may push forward with their own tech regulations. Others may hold off, waiting for federal action. We might also see some states act as a counterbalance to federal efforts, particularly in areas like platform content moderation and data privacy, where the federal government could potentially impose restrictive policies. 

For a deep dive on how the major tech issues fared in 2024 and our expectations for 2025, check out our blog post: Key Issues Shaping State-Level Tech Policy.

EFF will continue to be at the forefront, working alongside lawmakers and advocacy partners to ensure that digital rights remain a priority in state legislatures. As state lawmakers take on critical issues like privacy protections and facial recognition technology, we’ll be there to help guide these conversations and promote policies that address real-world harms. 

We encourage our supporters to join us in these efforts—your voice and activism are crucial in shaping a future where tech serves the public good, not just corporate interests. To stay informed about ongoing state-level tech policy and to learn how you can get involved, follow EFF’s updates and continue championing digital rights with us. 

Eleven MIT faculty, including nine from the School of Engineering and two from the School of Science, were awarded the Presidential Early Career Award for Scientists and Engineers (PECASE). More than 15 additional MIT alumni were also honored. 

Established in 1996 by President Bill Clinton, the PECASE is awarded to scientists and engineers “who show exceptional potential for leadership early in their research careers.” The latest recipients were announced by the White House on Jan. 14 under President Joe Biden. Fourteen government agencies recommended researchers for the award.

The MIT faculty and alumni honorees are among 400 scientists and engineers recognized for innovation and scientific contributions. Those from the School of Engineering and School of Science who were honored are:

• Tamara Broderick, associate professor in the Department of Electrical Engineering and Computer Science (EECS), was nominated by the Office of Naval Research for her project advancing “Lightweight representations for decentralized learning in data-rich environments.”
   
• Michael James Carbin SM ’09, PhD ’15, associate professor in the Department of EECS, was nominated by the National Science Foundation (NSF) for his CAREER award, a project that developed techniques to execute programs reliably on approximate and unreliable computation substrates.
   
• Christina Delimitrou, the KDD Career Development Professor in Communications and Technology and associate Professor in the Department of EECS, was nominated by the NSF for her group’s work on redesigning the cloud system stack given new cloud programming frameworks like microservices and serverless compute, as well as designing hardware acceleration techniques that make cloud data centers more predictable and resource-efficient.
   
• Netta Engelhardt, the Biedenharn Career Development Associate Professor of Physics, was nominated by the Department of Energy for her research on the black hole information paradox and its implications for the fundamental quantum structure of space and time.
   
• Robert Gilliard Jr., the Novartis Associate Professor of Chemistry, was selected based the results generated from his 2020 National Science Foundation CAREER award entitled: "CAREER: Boracycles with Unusual Bonding as Creative Strategies for Main-Group Functional Materials.”
   
• Heather Janine Kulik PD ’09, PhD ’09, the Lammot du Pont Professor of Chemical Engineering, was nominated by the NSF for her 2019 proposal entitled “CAREER: Revealing spin-state-dependent reactivity in open-shell single atom catalysts with systematically-improvable computational tools.”
   
• Nuno Loureiro, professor in the Department of Nuclear Science and Engineering, was nominated by the NSF for his work on the generation and amplification of magnetic fields in the universe.
   
• Robert Macfarlane, associate professor in the Department of Materials Science and Engineering, was nominated by the Department of Defense (DoD)’s Air Force Office of Scientific Research. His research focuses on making new materials using molecular and nanoscale building blocks.
   
• Ritu Raman, the Eugene Bell Career Development Professor of Tissue Engineering in the Department of Mechanical Engineering, was nominated by the DoD for her ARO-funded research that explored leveraging biological actuators in next-generation robots that can sense and adapt to their environments.
   
• Ellen Roche, the Latham Family Career Development Professor and associate department head in the Department of Mechanical Engineering, was nominated by the NSF for her CAREER award, a project that aims to create a cutting-edge benchtop model combining soft robotics and organic tissue to accurately simulate the motions of the heart and diaphragm.
   
• Justin Wilkerson, a visiting associate professor in the Department of Aeronautics and Astronautics, was nominated by the Air Force Office of Scientific Research (AFOSR) for his research primarily related to the design and optimization of novel multifunctional composite materials that can survive extreme environments.

Additional MIT alumni who were honored include: Elaheh Ahmadi ’20, MNG ’21; Ambika Bajpayee MNG ’07, PhD ’15; Katherine Bouman SM ’13, PhD ’17; Walter Cheng-Wan Lee ’95, MNG ’95, PhD ’05; Ismaila Dabo PhD ’08; Ying Diao SM ’10, PhD ’12; Eno Ebong ’99; Soheil Feizi- Khankandi SM ’10, PhD ’16; Mark Finlayson SM ’01, PhD ’12; Chelsea B. Finn ’14; Grace Xiang Gu SM ’14, PhD ’18; David Michael Isaacson PhD ’06, AF ’16; Lewei Lin ’05; Michelle Sander PhD ’12; Kevin Solomon SM ’08, PhD ’12; and Zhiting Tian PhD ’14.

At EFF, we’re big fans of the Creative Commons project, which makes copyright work in empowering ways for people who want to share their work widely. EFF uses Creative Commons licenses on nearly all of our public communications. To highlight the importance of open licensing as a tool for building a shared culture, we are upgrading the license on our website to the latest version, Creative Commons Attribution 4.0 International.

Open licenses like Creative Commons are an important tool for sharing culture and learning. They allow artists and creators a simple way to encourage widespread, free distribution of their work while keeping just the rights they want for themselves—such as the right to be credited as the work’s author, the right to modify the work, or the right to control commercial uses.

Without tools like Creative Commons, copyright is frequently a roadblock to sharing and preserving culture. Copyright is ubiquitous, applying automatically to most kinds of creative work from the moment they are “fixed in a tangible medium.” Copyright carries draconian penalties unknown in most areas of U.S. law, like “statutory damages” with no proof of harm and the possibility of having to pay the rightsholder’s attorney fees. And it can be hard to learn who owns a copyright in any given work, given that copyrights can last a century or more. All of these make it risky and expensive to share and re-use creative works, or sometimes even to preserve them and make them accessible to future generations.

Open licensing helps culture and learning flourish. With many millions of works now available under Creative Commons licenses, creators and knowledge-seekers have reassurance that these works of culture and learning can be freely shared and built upon without risk.

The current suite of Creative Commons licenses has thoughtful, powerful features. It’s written to work effectively in many countries, using language that can be understood in the context of different copyright laws around the world. It addresses legal regimes other than copyright that can interfere with free re-use of creative materials, like database rights, anti-circumvention laws, and rights of publicity or personality.

And importantly, the 4.0 licenses also make clear that giving credit to the author (something all of the Creative Commons licenses require) can be done in various ways, and that technical failures don't expose users to lawsuits by copyright trolls.

At EFF, we want our work to be seen and shared widely. That’s why we’ve made our content available under Creative Commons licenses for many years. Today, in that spirit, we are updating the license for most materials on our website, www.eff.org, to Creative Commons Attribution 4.0 International.

From crafting complex code to revolutionizing the hiring process, generative artificial intelligence is reshaping industries faster than ever before — pushing the boundaries of creativity, productivity, and collaboration across countless domains.

Enter the MIT Generative AI Impact Consortium, a collaboration between industry leaders and MIT’s top minds. As MIT President Sally Kornbluth highlighted last year, the Institute is poised to address the societal impacts of generative AI through bold collaborations. Building on this momentum and established through MIT’s Generative AI Week and impact papers, the consortium aims to harness AI’s transformative power for societal good, tackling challenges before they shape the future in unintended ways.

“Generative AI and large language models [LLMs] are reshaping everything, with applications stretching across diverse sectors,” says Anantha Chandrakasan, dean of the School of Engineering and MIT’s chief innovation and strategy officer, who leads the consortium. “As we push forward with newer and more efficient models, MIT is committed to guiding their development and impact on the world.”

Chandrakasan adds that the consortium’s vision is rooted in MIT’s core mission. “I am thrilled and honored to help advance one of President Kornbluth’s strategic priorities around artificial intelligence,” he says. “This initiative is uniquely MIT — it thrives on breaking down barriers, bringing together disciplines, and partnering with industry to create real, lasting impact. The collaborations ahead are something we’re truly excited about.”

Developing the blueprint for generative AI’s next leap

The consortium is guided by three pivotal questions, framed by Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and co-chair of the GenAI Dean’s oversight group, that go beyond AI’s technical capabilities and into its potential to transform industries and lives:

1. How can AI-human collaboration create outcomes that neither could achieve alone?
2. What is the dynamic between AI systems and human behavior, and how do we maximize the benefits while steering clear of risks?
3. How can interdisciplinary research guide the development of better, safer AI technologies that improve human life?

Generative AI continues to advance at lightning speed, but its future depends on building a solid foundation. “Everybody recognizes that large language models will transform entire industries, but there's no strong foundation yet around design principles,” says Tim Kraska, associate professor of electrical engineering and computer science in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-faculty director of the consortium.

“Now is a perfect time to look at the fundamentals — the building blocks that will make generative AI more effective and safer to use,” adds Kraska.

"What excites me is that this consortium isn’t just academic research for the distant future — we’re working on problems where our timelines align with industry needs, driving meaningful progress in real time," says Vivek F. Farias, the Patrick J. McGovern (1959) Professor at the MIT Sloan School of Management, and co-faculty director of the consortium.

A “perfect match” of academia and industry

At the heart of the Generative AI Impact Consortium are six founding members: Analog Devices, The Coca-Cola Co., OpenAI, Tata Group, SK Telecom, and TWG Global. Together, they will work hand-in-hand with MIT researchers to accelerate breakthroughs and address industry-shaping problems.

The consortium taps into MIT’s expertise, working across schools and disciplines — led by MIT’s Office of Innovation and Strategy, in collaboration with the MIT Schwarzman College of Computing and all five of MIT’s schools.

“This initiative is the ideal bridge between academia and industry,” says Chandrakasan. “With companies spanning diverse sectors, the consortium brings together real-world challenges, data, and expertise. MIT researchers will dive into these problems to develop cutting-edge models and applications into these different domains.”

Industry partners: Collaborating on AI’s evolution

At the core of the consortium’s mission is collaboration — bringing MIT researchers and industry partners together to unlock generative AI’s potential while ensuring its benefits are felt across society.

Among the founding members is OpenAI, the creator of the generative AI chatbot ChatGPT.

“This type of collaboration between academics, practitioners, and labs is key to ensuring that generative AI evolves in ways that meaningfully benefit society,” says Anna Makanju, vice president of global impact at OpenAI, adding that OpenAI “is eager to work alongside MIT’s Generative AI Consortium to bridge the gap between cutting-edge AI research and the real-world expertise of diverse industries.”

The Coca-Cola Co. recognizes an opportunity to leverage AI innovation on a global scale. “We see a tremendous opportunity to innovate at the speed of AI and, leveraging The Coca-Cola Company's global footprint, make these cutting-edge solutions accessible to everyone,” says Pratik Thakar, global vice president and head of generative AI. “Both MIT and The Coca-Cola Company are deeply committed to innovation, while also placing equal emphasis on the legally and ethically responsible development and use of technology.”

For TWG Global, the consortium offers the ideal environment to share knowledge and drive advancements. “The strength of the consortium is its unique combination of industry leaders and academia, which fosters the exchange of valuable lessons, technological advancements, and access to pioneering research,” says Drew Cukor, head of data and artificial intelligence transformation. Cukor adds that TWG Global “is keen to share its insights and actively engage with leading executives and academics to gain a broader perspective of how others are configuring and adopting AI, which is why we believe in the work of the consortium.”

The Tata Group views the collaboration as a platform to address some of AI’s most pressing challenges. “The consortium enables Tata to collaborate, share knowledge, and collectively shape the future of generative AI, particularly in addressing urgent challenges such as ethical considerations, data privacy, and algorithmic biases,” says Aparna Ganesh, vice president of Tata Sons Ltd.

Similarly, SK Telecom sees its involvement as a launchpad for growth and innovation. Suk-geun (SG) Chung, SK Telecom executive vice president and chief AI global officer, explains, “Joining the consortium presents a significant opportunity for SK Telecom to enhance its AI competitiveness in core business areas, including AI agents, AI semiconductors, data centers (AIDC), and physical AI,” says Chung. “By collaborating with MIT and leveraging the SK AI R&D Center as a technology control tower, we aim to forecast next-generation generative AI technology trends, propose innovative business models, and drive commercialization through academic-industrial collaboration.”

Alan Lee, chief technology officer of Analog Devices (ADI), highlights how the consortium bridges key knowledge gaps for both his company and the industry at large. “ADI can’t hire a world-leading expert in every single corner case, but the consortium will enable us to access top MIT researchers and get them involved in addressing problems we care about, as we also work together with others in the industry towards common goals,” he says.

The consortium will host interactive workshops and discussions to identify and prioritize challenges. “It’s going to be a two-way conversation, with the faculty coming together with industry partners, but also industry partners talking with each other,” says Georgia Perakis, the John C Head III Dean (Interim) of the MIT Sloan School of Management and professor of operations management, operations research and statistics, who serves alongside Huttenlocher as co-chair of the GenAI Dean’s oversight group.

Preparing for the AI-enabled workforce of the future

With AI poised to disrupt industries and create new opportunities, one of the consortium’s core goals is to guide that change in a way that benefits both businesses and society.

“When the first commercial digital computers were introduced [the UNIVAC was delivered to the U.S. Census Bureau in 1951], people were worried about losing their jobs,” says Kraska. “And yes, jobs like large-scale, manual data entry clerks and human ‘computers,’ people tasked with doing manual calculations, largely disappeared over time. But the people impacted by those first computers were trained to do other jobs.”

The consortium aims to play a key role in preparing the workforce of tomorrow by educating global business leaders and employees on generative AI evolving uses and applications. With the pace of innovation accelerating, leaders face a flood of information and uncertainty.

“When it comes to educating leaders about generative AI, it’s about helping them navigate the complexity of the space right now, because there’s so much hype and hundreds of papers published daily,” says Kraska. “The hard part is understanding which developments could actually have a chance of changing the field and which are just tiny improvements. There's a kind of FOMO [fear of missing out] for leaders that we can help reduce.”

Defining success: Shared goals for generative AI impact

Success within the initiative is defined by shared progress, open innovation, and mutual growth. “Consortium participants recognize, I think, that when I share my ideas with you, and you share your ideas with me, we’re both fundamentally better off,” explains Farias. “Progress on generative AI is not zero-sum, so it makes sense for this to be an open-source initiative.”

While participants may approach success from different angles, they share a common goal of advancing generative AI for broad societal benefit. “There will be many success metrics,” says Perakis. “We’ll educate students, who will be networking with companies. Companies will come together and learn from each other. Business leaders will come to MIT and have discussions that will help all of us, not just the leaders themselves.”

For Analog Devices’ Alan Lee, success is measured in tangible improvements that drive efficiency and product innovation: “For us at ADI, it’s a better, faster quality of experience for our customers, and that could mean better products. It could mean faster design cycles, faster verification cycles, and faster tuning of equipment that we already have or that we’re going to develop for the future. But beyond that, we want to help the world be a better, more efficient place.”

Ganesh highlights success through the lens of real-world application. “Success will also be defined by accelerating AI adoption within Tata companies, generating actionable knowledge that can be applied in real-world scenarios, and delivering significant advantages to our customers and stakeholders,” she says.

Generative AI is no longer confined to isolated research labs — it’s driving innovation across industries and disciplines. At MIT, the technology has become a campus-wide priority, connecting researchers, students, and industry leaders to solve complex challenges and uncover new opportunities. “It's truly an MIT initiative,” says Farias, “one that’s much larger than any individual or department on campus.”

David L. Darmofal SM ’91, PhD ’93 will serve as MIT’s next vice chancellor for undergraduate and graduate education, effective Feb. 17. Chancellor Melissa Nobles announced Darmofal’s appointment today in a letter to the MIT community.

Darmofal succeeds Ian A. Waitz, who stepped down in May to become MIT’s vice president for research, and Daniel E. Hastings, who has been serving in an interim capacity.

A creative innovator in research-based teaching and learning, Darmofal is the Jerome C. Hunsaker Professor of Aeronautics and Astronautics. Since 2017, he and his wife Claudia have served as heads of house at The Warehouse, an MIT graduate residence.

“Dave knows the ins and outs of education and student life at MIT in a way that few do,” Nobles says. “He’s a head of house, an alum, and the parent of a graduate. Dave will bring decades of first-hand experience to the role.”

“An MIT education is incredibly special, combining passionate students, staff, and faculty striving to use knowledge and discovery to drive positive change for the world,” says Darmofal. “I am grateful for this opportunity to play a part in supporting MIT’s academic mission.”

Darmofal’s leadership experience includes service from 2008 to 2011 as associate and interim department head in the Department of Aeronautics and Astronautics, overseeing undergraduate and graduate programs. He was the AeroAstro director of digital education from 2020 to 2022, including leading the department’s response to remote learning during the Covid-19 pandemic. He currently serves as director of the MIT Aerospace Computational Science and Engineering Laboratory and is a member of the Center for Computational Science and Engineering (CCSE) in the MIT Stephen A. Schwarzman College of Computing.

As an MIT faculty member and administrator, Darmofal has been involved in designing more flexible degree programs, developing open digital-learning opportunities, creating first-year advising seminars, and enhancing professional and personal development opportunities for students. He also contributed his expertise in engineering pedagogy to the development of the Schwarzman College of Computing’s Common Ground efforts, to address the need for computing education across many disciplines.

“MIT students, staff, and faculty share a common bond as problem solvers. Talk to any of us about an MIT education, and you will get an earful on not only what we need to do better, but also how we can actually do it. The Office of the Vice Chancellor can help bring our community of problem solvers together to enable improvements in our academics,” says Darmofal.

Overseeing the academic arm of the Chancellor’s Office, the vice chancellor’s portfolio is extensive. Darmofal will lead professionals across more than a dozen units, covering areas such as recruitment and admissions, financial aid, student systems, advising, professional and career development, pedagogy, experiential learning, and support for MIT’s more than 100 graduate programs. He will also work collaboratively with many of MIT’s student organizations and groups, including with the leaders of the Undergraduate Association and the Graduate Student Council, and administer the relationship with the graduate student union.

“Dave will be a critical part of my office’s efforts to strengthen and expand critical connections across all areas of student life and learning,” Nobles says. She credits the search advisory group, co-chaired by professors Laurie Boyer and Will Tisdale, in setting the right tenor for such an important role and leading a thorough, inclusive process.

Darmofal’s research is focused on computational methods for partial differential equations, especially fluid dynamics. He earned his SM and PhD degrees in aeronautics and astronautics in 1991 and 1993, respectively, from MIT, and his BS in aerospace engineering in 1989 from the University of Michigan. Prior to joining MIT in 1998, he was an assistant professor in the Department of Aerospace Engineering at Texas A&M University from 1995 to 1998. Currently, he is the chair of AeroAstro’s Undergraduate Committee and the graduate officer for the CCSE PhD program.

“I want to echo something that Dan Hastings said recently,” Darmofal says. “We have a lot to be proud of when it comes to an MIT education. It’s more accessible than it has ever been. It’s innovative, with unmatched learning opportunities here and around the world. It’s home to academic research labs that attract the most talented scholars, creators, experimenters, and engineers. And ultimately, it prepares graduates who do good.”

This is yet another story of commercial spyware being used against journalists and civil society members.

The journalists and other civil society members were being alerted of a possible breach of their devices, with WhatsApp telling the Guardian it had “high confidence” that the 90 users in question had been targeted and “possibly compromised.”

It is not clear who was behind the attack. Like other spyware makers, Paragon’s hacking software is used by government clients and WhatsApp said it had not been able to identify the clients who ordered the alleged attacks...

The president's new tariffs on Canada, Mexico and China could hit the solar, battery, wind and electric vehicle industries particularly hard.

A new list of funding conditions also requires recipients to follow federal policy on immigration enforcement.

President Donald Trump's purge of diversity initiatives has affected both federal agencies and the institutions they fund.

A majority on the court indicated the case would not be treated as precedent for future cases.

Perils from rising temperatures are pushing people out of their homes and upending the real estate market, according to climate modeling firm First Street.

The department’s action follows similar moves taken by the Fed and FDIC.

The Royal Bank of Canada said leaving Net-Zero Banking Alliance won’t change its focus on “supporting our clients to help them address climate change."

The former central banker says he’ll get rid of Canada’s consumer carbon tax if he wins the Liberal leadership race.

Colombia’s environment minister had been frantically courting officials in Washington to secure initial funding to help transition the nation’s economy away from fossil fuels toward green investments.

Milder winters across the Northern Hemisphere are helping rats thrive in ever denser metropolitan areas.

The neural network artificial intelligence models used in applications like medical image processing and speech recognition perform operations on hugely complex data structures that require an enormous amount of computation to process. This is one reason deep-learning models consume so much energy.

To improve the efficiency of AI models, MIT researchers created an automated system that enables developers of deep learning algorithms to simultaneously take advantage of two types of data redundancy. This reduces the amount of computation, bandwidth, and memory storage needed for machine learning operations.

Existing techniques for optimizing algorithms can be cumbersome and typically only allow developers to capitalize on either sparsity or symmetry — two different types of redundancy that exist in deep learning data structures.

By enabling a developer to build an algorithm from scratch that takes advantage of both redundancies at once, the MIT researchers’ approach boosted the speed of computations by nearly 30 times in some experiments.

Because the system utilizes a user-friendly programming language, it could optimize machine-learning algorithms for a wide range of applications. The system could also help scientists who are not experts in deep learning but want to improve the efficiency of AI algorithms they use to process data. In addition, the system could have applications in scientific computing.

“For a long time, capturing these data redundancies has required a lot of implementation effort. Instead, a scientist can tell our system what they would like to compute in a more abstract way, without telling the system exactly how to compute it,” says Willow Ahrens, an MIT postdoc and co-author of a paper on the system, which will be presented at the International Symposium on Code Generation and Optimization.

She is joined on the paper by lead author Radha Patel ’23, SM ’24 and senior author Saman Amarasinghe, a professor in the Department of Electrical Engineering and Computer Science (EECS) and a principal researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Cutting out computation

In machine learning, data are often represented and manipulated as multidimensional arrays known as tensors. A tensor is like a matrix, which is a rectangular array of values arranged on two axes, rows and columns. But unlike a two-dimensional matrix, a tensor can have many dimensions, or axes, making tensors more difficult to manipulate.

Deep-learning models perform operations on tensors using repeated matrix multiplication and addition — this process is how neural networks learn complex patterns in data. The sheer volume of calculations that must be performed on these multidimensional data structures requires an enormous amount of computation and energy.

But because of the way data in tensors are arranged, engineers can often boost the speed of a neural network by cutting out redundant computations.

For instance, if a tensor represents user review data from an e-commerce site, since not every user reviewed every product, most values in that tensor are likely zero. This type of data redundancy is called sparsity. A model can save time and computation by only storing and operating on non-zero values.

In addition, sometimes a tensor is symmetric, which means the top half and bottom half of the data structure are equal. In this case, the model only needs to operate on one half, reducing the amount of computation. This type of data redundancy is called symmetry.

“But when you try to capture both of these optimizations, the situation becomes quite complex,” Ahrens says.

To simplify the process, she and her collaborators built a new compiler, which is a computer program that translates complex code into a simpler language that can be processed by a machine. Their compiler, called SySTeC, can optimize computations by automatically taking advantage of both sparsity and symmetry in tensors.

They began the process of building SySTeC by identifying three key optimizations they can perform using symmetry.

First, if the algorithm’s output tensor is symmetric, then it only needs to compute one half of it. Second, if the input tensor is symmetric, then algorithm only needs to read one half of it. Finally, if intermediate results of tensor operations are symmetric, the algorithm can skip redundant computations.

Simultaneous optimizations

To use SySTeC, a developer inputs their program and the system automatically optimizes their code for all three types of symmetry. Then the second phase of SySTeC performs additional transformations to only store non-zero data values, optimizing the program for sparsity.

In the end, SySTeC generates ready-to-use code.

“In this way, we get the benefits of both optimizations. And the interesting thing about symmetry is, as your tensor has more dimensions, you can get even more savings on computation,” Ahrens says.

The researchers demonstrated speedups of nearly a factor of 30 with code generated automatically by SySTeC.

Because the system is automated, it could be especially useful in situations where a scientist wants to process data using an algorithm they are writing from scratch.

In the future, the researchers want to integrate SySTeC into existing sparse tensor compiler systems to create a seamless interface for users. In addition, they would like to use it to optimize code for more complicated programs.

This work is funded, in part, by Intel, the National Science Foundation, the Defense Advanced Research Projects Agency, and the Department of Energy.

Nature Climate Change, Published online: 03 February 2025; doi:10.1038/s41558-024-02232-7

Assessments of the aggregate impacts of climate change on the global economy are widely varying and diverge depending on the method employed. It is essential to understand the mechanisms behind the differing estimates and identify a robust range. Only then could these estimates meaningfully inform and guide climate actions.

Interesting.

Blog moderation policy.

If you’ve got lawyers and a copyright, the law gives you tremendous power to silence speech you don’t like. Copyright’s statutory damages can be as high as $150,000 per work infringed, even if no actual harm is done. This makes it far too dangerous to rely on the limitations and exceptions to fair use, as you may face a financial death sentence if a court decides you got it wrong. Most would-be speakers back down in the face of such risks, no matter now legitimate their use. The Digital Millennium Copyright Act provides an incentive for platforms to remove content on your say-so, without a judge ever reviewing your papers. The special procedures and damages available to copyright owners make it one of the most appealing mechanisms for removing unwanted speech from the internet.

Copyright owners have intimidated researchers away from disclosing that their software spies on users or is full of bugs that make it unsafe. When a blockbuster entertainment product inspires people to tell their own stories by depicting themselves in the same world or costumes, a letter from the studio’s lawyers will usually convince them to stay silent. And whose who sell software write their own law into End User License Agreements and can threaten any user who disobeys them with copyright damages.

Culture has always been a conversation, not a product that is packaged up for consumption.

These are only a few of the ways that copyright is a civil liberties nightmare in the modern age, and only a few of the abuses of copyright that we fight against in court. Copyright started out as a way for European rulers to ensure that publishers remained friendly to the government, and we still see this dynamic in the cozy relationship between Hollywood and the US military and police forces. But more and more it’s been a way for private entities that are already powerful to prevent both market competition and contrary ideas from challenging their dominance.

The imbalance of power between authors and the owners of mass media is the main reason that authors only get a small share of the value they create. Copyright is at its best when it protects a creator from being beaten to market by those who own mass media channels, giving them some leverage to negotiate. With that small bit of leverage, they can get paid something rather than nothing, though the publishing deals in highly concentrated industries are famously one-sided.

But, too often, we see copyright at its worst instead, and there is no good reason for copyright law to be as broad and draconian as it is now. It lasts essentially forever, as you will probably be dead before any new works you cherished as a child will enter the public domain. It is uniquely favored by the courts as a means for controlling speech, with ordinary First Amendment considerations taking a back seat to the interests of content owners. The would-be speaker has to prove their right to speak: for example, by persuading a court that they were making a fair use. And the penalties for a court deciding your use was infringing are devastating. It’s even used as a supposed justification for spying on and filtering the internet. Anyone familiar with automated copyright controls like ContentID on YouTube knows how restrictive they tend to be.

Bizarrely, copyright has grown so broad that it doesn’t just bar others from reproducing a work or adapting it into another medium such as film, it even prevents making original stories with a character or setting “owned” by the copyright owner. For the vast majority of our history, humans have built on and retold one another’s stories. Culture has always been a conversation, not a product that is packaged up for consumption.

The same is true for innovation, with a boom in software technology coming before copyright was applied to software. And, thanks to free software licenses that remove the default, restrictive behavior of copyright, we have communities of scrappy innovators building tools that we all rely upon for a functioning internet. When the people who depend upon a technology have a say in creating it and have the option to build their own to suit their needs, we’re much more likely to get technology that serves our interests and respects our privacy and autonomy. That's far superior to technology that comes into our homes as an agent of its creators, seeking to exploit us for advertising data, or limit our choices of apps and hardware to serve another’s profit motive.

EFF has been at the vanguard for decades, fighting back against copyright overreach in the digital world. More than ever, people need to be able to tell their stories, criticize the powerful and the status quo, and to communicate with technologies that aren’t censored by overzealous copyright bots.