Trump Enforces Major Changes, Job Cuts – Americans Protest
In his first month back in office, President Donald Trump has made swift and dramatic changes to the federal government. His administration has implemented new policies, sparked national protests, and removed thousands of federal workers from their positions. Here’s a look at the major developments so far. Government Restructuring and Policy Shifts One of President Trump’s first moves was placing a hiring freeze on federal agencies, preventing most new hires unless deemed essential. His administration has also focused on reducing government size, cutting budgets, and eliminating positions considered unnecessary. A controversial decision was the appointment of Elon Musk to lead a newly created agency, the Department of Government Efficiency (DOGE). Musk, known for his business ventures, has taken an aggressive approach to downsizing, particularly targeting agencies like the Department of Energy and USAID. However, the legality of his appointment has been challenged, and lawsuits are already in motion. Nationwide Protests Erupt The rapid pace of these changes has led to widespread public outrage. Across the country, protests have erupted, with many government workers voicing concerns over job security and the impact of mass layoffs. San Francisco saw a particularly large demonstration, where people gathered to criticize cuts they fear will weaken essential services. A growing movement among federal workers, operating under the slogan “Hold the line, don’t resign,” encourages employees to stay in their roles to prevent further dismantling of government institutions. Activists argue that privatization and deep budget cuts could result in inefficiency and a decline in the quality of public services. Federal Workforce Reductions The Trump administration’s effort to streamline government operations has already resulted in thousands of job losses. The Department of Veterans Affairs has let go over 1,000 employees, leading to concerns about how it will continue to provide services to former military personnel. The Department of Education has also seen major cuts, with at least 60 employees dismissed, particularly in areas related to civil rights and student financial aid. Meanwhile, the Small Business Administration has reduced its workforce by nearly 20%, terminating around 720 employees. What Comes Next? With just under 30 days in office, Trump’s policies are already reshaping the federal government. His administration promises that these changes will create a more efficient system, while critics argue they threaten essential services and public sector stability. As protests continue and legal battles unfold, the next few months will determine the long-term effects of these actions.
The future of photography: how AI-generated images are redefining portraits
Artificial Intelligence (AI) is transforming the world of photography, pushing the boundaries of realism and creative expression. With AI-generated images now capable of producing hyper-realistic visuals that leave viewers questioning their authenticity, we’re entering a new era where digital creations challenge traditional photography in both artistry and accessibility. From Hyper-Realism to Unconventional Beauty AI-generated images can be designed to mimic real-life features or go beyond them, exploring new aesthetics and expressions. Recently, digital artist Moritz Stellmacher shared an AI-generated human portrait that was so realistic, it had viewers on social media guessing whether it was a photograph of a real person. This uncertainty highlights the advanced realism of AI tools today, which can blur the lines between real and artificial in ways we haven’t seen before. This new flexibility in creating faces and features might soon influence what society deems attractive. Traditionally, the modeling industry has relied on specific beauty standards like symmetry and clear skin, but AI-generated models can now embody striking, unconventional aesthetics that challenge these norms. As viewers grow accustomed to digitally crafted faces, demand for diversity and uniqueness in visual media may rise, leading to a broader representation of beauty. AI vs. Human Models: A Shift in the Modeling Industry The modeling industry is already adapting to the impact of AI. Companies can now create AI models tailored to specific advertising needs without the costs and logistics involved in traditional photography. This shift is significant in industries like e-commerce, where AI-generated images can be modified instantly to fit seasonal or demographic trends. By using AI, brands can generate a wide range of looks quickly and economically, making AI an attractive option for digital campaigns. However, human models continue to bring irreplaceable qualities like authentic emotion, unique expressions, and genuine connection, which AI models struggle to replicate fully. This blend of AI and human photography might encourage a future where both forms coexist, each serving distinct purposes and enhancing the variety of visual media. The Competitive Edge of AI: Mass Production and A/B Testing A standout advantage of AI-generated images is their ability to be mass-produced at almost no cost, a game-changer for e-commerce and online advertising. Traditional photoshoots require considerable time, expense, and coordination, while AI can produce thousands of unique images in minutes. This efficiency allows companies to quickly adapt visuals to consumer preferences, boosting engagement in competitive markets. In addition, AI facilitates large-scale A/B testing—analyzing hundreds of variations of product photos to determine which ones lead to higher conversions. With data-driven design insights, businesses can refine their marketing strategies for maximum impact, an approach that would be costly and time-consuming with conventional photography. AI-Enhanced Artistic Expression Beyond Physical Limits Unlike traditional photography, which is bound by real-world physics and environments, AI-generated images offer unlimited artistic freedom. Artists and brands can create surreal, gravity-defying visuals that defy physical laws and blend various art styles, unlocking new realms of creativity. This capacity to break conventional rules of composition and design allows for imaginative visual storytelling, helping brands and creators engage audiences in exciting new ways. AI’s Role in a Diverse Visual Landscape As AI technology becomes more integrated into the world of photography, we’re likely to see a more diverse visual landscape where AI-generated images complement human photography. While AI offers speed, scalability, and innovation, human models and photographers will continue to bring authenticity, emotion, and a personal touch to images. Together, AI and traditional photography will shape a future where beauty is more inclusive, varied, and captivating than ever before.
Puzzling out climate change
Shreyaa Raghavan’s journey into solving some of the world’s toughest challenges started with a simple love for puzzles. By high school, her knack for problem-solving naturally drew her to computer science. Through her participation in an entrepreneurship and leadership program, she built apps and twice made it to the semifinals of the program’s global competition. Her early successes made a computer science career seem like an obvious choice, but Raghavan says a significant competing interest left her torn. “Computer science sparks that puzzle-, problem-solving part of my brain,” says Raghavan ’24, an Accenture Fellow and a PhD candidate in MIT’s Institute for Data, Systems, and Society. “But while I always felt like building mobile apps was a fun little hobby, it didn’t feel like I was directly solving societal challenges.” Her perspective shifted when, as an MIT undergraduate, Raghavan participated in an Undergraduate Research Opportunity in the Photovoltaic Research Laboratory, now known as the Accelerated Materials Laboratory for Sustainability. There, she discovered how computational techniques like machine learning could optimize materials for solar panels — a direct application of her skills toward mitigating climate change. “This lab had a very diverse group of people, some from a computer science background, some from a chemistry background, some who were hardcore engineers. All of them were communicating effectively and working toward one unified goal — building better renewable energy systems,” Raghavan says. “It opened my eyes to the fact that I could use very technical tools that I enjoy building and find fulfillment in that by helping solve major climate challenges.” With her sights set on applying machine learning and optimization to energy and climate, Raghavan joined Cathy Wu’s lab when she started her PhD in 2023. The lab focuses on building more sustainable transportation systems, a field that resonated with Raghavan due to its universal impact and its outsized role in climate change — transportation accounts for roughly 30 percent of greenhouse gas emissions. “If we were to throw all of the intelligent systems we are exploring into the transportation networks, by how much could we reduce emissions?” she asks, summarizing a core question of her research. Wu, an associate professor in the Department of Civil and Environmental Engineering, stresses the value of Raghavan’s work. “Transportation is a critical element of both the economy and climate change, so potential changes to transportation must be carefully studied,” Wu says. “Shreyaa’s research into smart congestion management is important because it takes a data-driven approach to add rigor to the broader research supporting sustainability.” Raghavan’s contributions have been recognized with the Accenture Fellowship, a cornerstone of the MIT-Accenture Convergence Initiative for Industry and Technology. As an Accenture Fellow, she is exploring the potential impact of technologies for avoiding stop-and-go traffic and its emissions, using systems such as networked autonomous vehicles and digital speed limits that vary according to traffic conditions — solutions that could advance decarbonization in the transportation section at relatively low cost and in the near term. Raghavan says she appreciates the Accenture Fellowship not only for the support it provides, but also because it demonstrates industry involvement in sustainable transportation solutions. “It’s important for the field of transportation, and also energy and climate as a whole, to synergize with all of the different stakeholders,” she says. “I think it’s important for industry to be involved in this issue of incorporating smarter transportation systems to decarbonize transportation.” Raghavan has also received a fellowship supporting her research from the U.S. Department of Transportation. “I think it’s really exciting that there’s interest from the policy side with the Department of Transportation and from the industry side with Accenture,” she says. Raghavan believes that addressing climate change requires collaboration across disciplines. “I think with climate change, no one industry or field is going to solve it on its own. It’s really got to be each field stepping up and trying to make a difference,” she says. “I don’t think there’s any silver-bullet solution to this problem. It’s going to take many different solutions from different people, different angles, different disciplines.” With that in mind, Raghavan has been very active in the MIT Energy and Climate Club since joining about three years ago, which, she says, “was a really cool way to meet lots of people who were working toward the same goal, the same climate goals, the same passions, but from completely different angles.” This year, Raghavan is on the community and education team, which works to build the community at MIT that is working on climate and energy issues. As part of that work, Raghavan is launching a mentorship program for undergraduates, pairing them with graduate students who help the undergrads develop ideas about how they can work on climate using their unique expertise. “I didn’t foresee myself using my computer science skills in energy and climate,” Raghavan says, “so I really want to give other students a clear pathway, or a clear sense of how they can get involved.” Raghavan has embraced her area of study even in terms of where she likes to think. “I love working on trains, on buses, on airplanes,” she says. “It’s really fun to be in transit and working on transportation problems.” Anticipating a trip to New York to visit a cousin, she holds no dread for the long train trip. “I know I’m going to do some of my best work during those hours,” she says. “Four hours there. Four hours back.”
What Americans Need to Know About the 2025 Tax Law Changes
As 2025 tax season begins, a wave of new tax law changes is rolling in—and for millions of Americans, understanding these updates is crucial for smart financial planning. Whether you’re a wage earner, a small business owner, or preparing for retirement, here’s a breakdown of the most important shifts in the U.S. tax landscape this year. 1. The Standard Deduction Has Increased The IRS has raised the standard deduction again to adjust for inflation. For the 2025 tax year: Single filers can now deduct $15,000, up from $13,850 in 2024. Married couples filing jointly can deduct $30,000, up from $27,700. Heads of household can deduct $22,500. This change means fewer people may itemize their deductions, simplifying the filing process for many. 2. Child Tax Credit Adjustments The Child Tax Credit has been updated to provide continued support to families. For 2025: The maximum credit is $2,000 per qualifying child under age 17. The refundable portion (Additional Child Tax Credit) is up to $1,700. Phase-out thresholds begin at $200,000 for single filers and $400,000 for joint filers. 3. Changes for Gig and Freelance Workers Self-employed individuals and gig economy workers should take note of 1099-K reporting thresholds: The reporting threshold for third-party platforms (like Venmo, Etsy, or Uber) is $5,000, delayed from the originally planned $600 implementation. More people will receive 1099-K forms and must report that income. The IRS has also released updated guidance on how to categorize and deduct expenses related to freelance work. 4. Retirement Contribution Limits Have Increased To help Americans save more for retirement, contribution limits have gone up: 401(k): You can now contribute up to $23,500. Catch-Up (Age 50+): Additional $7,500. Special Catch-Up (Ages 60–63): Additional $11,250. IRA: The limit remains at $7,000 for those under 50, and $8,000 for those 50 and older. These increases are part of the Secure 2.0 Act changes. 5. EV and Green Energy Incentives Expanded If you’re going green in 2025, the tax code has some perks for you: Electric vehicle (EV) credits offer up to $7,500 for new EVs. Used EVs may qualify for up to $4,000, or 30% of the sale price. Home energy upgrades such as solar panels, efficient windows, and heat pumps may qualify for credits up to 30% of the cost. 6. Capital Gains and Investment Income While capital gains tax rates remain unchanged, the income thresholds have increased: 0% rate: Applies to taxable income up to $47,025 (single) or $94,050 (married filing jointly). 15% rate: Applies to income between $47,025 and $518,900 (single) or $94,050 and $583,750 (joint). 20% rate: Applies to income above those thresholds. The Net Investment Income Tax (NIIT) of 3.8% still applies to individuals earning more than $200,000 and couples over $250,000. 7. Expiring Provisions to Watch Several provisions from the 2017 Tax Cuts and Jobs Act (TCJA) are set to expire after 2025. Though these changes aren’t effective yet, experts suggest planning ahead: Individual income tax rate reductions Doubling of the standard deduction Increased estate tax exemption If Congress doesn’t act, many Americans could see higher tax bills in 2026. Navigating tax season can be tricky, but staying informed is half the battle. With these 2025 tax changes in mind, now’s the time to adjust your withholdings, revisit your deductions, and make sure you’re not leaving money on the table. As always, consult a trusted tax professional for personalized advice. Stay tuned to Readovia for more essential financial updates and smart money insights all year long.
MIT students’ works redefine human-AI collaboration
Imagine a boombox that tracks your every move and suggests music to match your personal dance style. That’s the idea behind “Be the Beat,” one of several projects from MIT course 4.043/4.044 (Interaction Intelligence), taught by Marcelo Coelho in the Department of Architecture, that were presented at the 38th annual NeurIPS (Neural Information Processing Systems) conference in December 2024. With over 16,000 attendees converging in Vancouver, NeurIPS is a competitive and prestigious conference dedicated to research and science in the field of artificial intelligence and machine learning, and a premier venue for showcasing cutting-edge developments. The course investigates the emerging field of large language objects, and how artificial intelligence can be extended into the physical world. While “Be the Beat” transforms the creative possibilities of dance, other student submissions span disciplines such as music, storytelling, critical thinking, and memory, creating generative experiences and new forms of human-computer interaction. Taken together, these projects illustrate a broader vision for artificial intelligence: one that goes beyond automation to catalyze creativity, reshape education, and reimagine social interactions. Be the Beat “Be the Beat,” by Ethan Chang, an MIT mechanical engineering and design student, and Zhixing Chen, an MIT mechanical engineering and music student, is an AI-powered boombox that suggests music from a dancer’s movement. Dance has traditionally been guided by music throughout history and across cultures, yet the concept of dancing to create music is rarely explored. “Be the Beat” creates a space for human-AI collaboration on freestyle dance, empowering dancers to rethink the traditional dynamic between dance and music. It uses PoseNet to describe movements for a large language model, enabling it to analyze dance style and query APIs to find music with similar style, energy, and tempo. Dancers interacting with the boombox reported having more control over artistic expression and described the boombox as a novel approach to discovering dance genres and choreographing creatively. A Mystery for You “A Mystery for You,” by Mrinalini Singha SM ’24, a recent graduate in the Art, Culture, and Technology program, and Haoheng Tang, a recent graduate of the Harvard University Graduate School of Design, is an educational game designed to cultivate critical thinking and fact-checking skills in young learners. The game leverages a large language model (LLM) and a tangible interface to create an immersive investigative experience. Players act as citizen fact-checkers, responding to AI-generated “news alerts” printed by the game interface. By inserting cartridge combinations to prompt follow-up “news updates,” they navigate ambiguous scenarios, analyze evidence, and weigh conflicting information to make informed decisions. This human-computer interaction experience challenges our news-consumption habits by eliminating touchscreen interfaces, replacing perpetual scrolling and skim-reading with a haptically rich analog device. By combining the affordances of slow media with new generative media, the game promotes thoughtful, embodied interactions while equipping players to better understand and challenge today’s polarized media landscape, where misinformation and manipulative narratives thrive. Memorscope “Memorscope,” by MIT Media Lab research collaborator Keunwook Kim, is a device that creates collective memories by merging the deeply human experience of face-to-face interaction with advanced AI technologies. Inspired by how we use microscopes and telescopes to examine and uncover hidden and invisible details, Memorscope allows two users to “look into” each other’s faces, using this intimate interaction as a gateway to the creation and exploration of their shared memories. The device leverages AI models such as OpenAI and Midjourney, introducing different aesthetic and emotional interpretations, which results in a dynamic and collective memory space. This space transcends the limitations of traditional shared albums, offering a fluid, interactive environment where memories are not just static snapshots but living, evolving narratives, shaped by the ongoing relationship between users. Narratron “Narratron,” by Harvard Graduate School of Design students Xiying (Aria) Bao and Yubo Zhao, is an interactive projector that co-creates and co-performs children’s stories through shadow puppetry using large language models. Users can press the shutter to “capture” protagonists they want to be in the story, and it takes hand shadows (such as animal shapes) as input for the main characters. The system then develops the story plot as new shadow characters are introduced. The story appears through a projector as a backdrop for shadow puppetry while being narrated through a speaker as users turn a crank to “play” in real time. By combining visual, auditory, and bodily interactions in one system, the project aims to spark creativity in shadow play storytelling and enable multi-modal human-AI collaboration. Perfect Syntax “Perfect Syntax,” by Karyn Nakamura ’24, is a video art piece examining the syntactic logic behind motion and video. Using AI to manipulate video fragments, the project explores how the fluidity of motion and time can be simulated and reconstructed by machines. Drawing inspiration from both philosophical inquiry and artistic practice, Nakamura’s work interrogates the relationship between perception, technology, and the movement that shapes our experience of the world. By reimagining video through computational processes, Nakamura investigates the complexities of how machines understand and represent the passage of time and motion.
Explained: Generative AI’s environmental impact
In a two-part series, MIT News explores the environmental implications of generative AI. In this article, we look at why this technology is so resource-intensive. A second piece will investigate what experts are doing to reduce genAI’s carbon footprint and other impacts. The excitement surrounding potential benefits of generative AI, from improving worker productivity to advancing scientific research, is hard to ignore. While the explosive growth of this new technology has enabled rapid deployment of powerful models in many industries, the environmental consequences of this generative AI “gold rush” remain difficult to pin down, let alone mitigate. The computational power required to train generative AI models that often have billions of parameters, such as OpenAI’s GPT-4, can demand a staggering amount of electricity, which leads to increased carbon dioxide emissions and pressures on the electric grid. Furthermore, deploying these models in real-world applications, enabling millions to use generative AI in their daily lives, and then fine-tuning the models to improve their performance draws large amounts of energy long after a model has been developed. Beyond electricity demands, a great deal of water is needed to cool the hardware used for training, deploying, and fine-tuning generative AI models, which can strain municipal water supplies and disrupt local ecosystems. The increasing number of generative AI applications has also spurred demand for high-performance computing hardware, adding indirect environmental impacts from its manufacture and transport. “When we think about the environmental impact of generative AI, it is not just the electricity you consume when you plug the computer in. There are much broader consequences that go out to a system level and persist based on actions that we take,” says Elsa A. Olivetti, professor in the Department of Materials Science and Engineering and the lead of the Decarbonization Mission of MIT’s new Climate Project. Olivetti is senior author of a 2024 paper, “The Climate and Sustainability Implications of Generative AI,” co-authored by MIT colleagues in response to an Institute-wide call for papers that explore the transformative potential of generative AI, in both positive and negative directions for society. Demanding data centers The electricity demands of data centers are one major factor contributing to the environmental impacts of generative AI, since data centers are used to train and run the deep learning models behind popular tools like ChatGPT and DALL-E. A data center is a temperature-controlled building that houses computing infrastructure, such as servers, data storage drives, and network equipment. For instance, Amazon has more than 100 data centers worldwide, each of which has about 50,000 servers that the company uses to support cloud computing services. While data centers have been around since the 1940s (the first was built at the University of Pennsylvania in 1945 to support the first general-purpose digital computer, the ENIAC), the rise of generative AI has dramatically increased the pace of data center construction. “What is different about generative AI is the power density it requires. Fundamentally, it is just computing, but a generative AI training cluster might consume seven or eight times more energy than a typical computing workload,” says Noman Bashir, lead author of the impact paper, who is a Computing and Climate Impact Fellow at MIT Climate and Sustainability Consortium (MCSC) and a postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Scientists have estimated that the power requirements of data centers in North America increased from 2,688 megawatts at the end of 2022 to 5,341 megawatts at the end of 2023, partly driven by the demands of generative AI. Globally, the electricity consumption of data centers rose to 460 terawatts in 2022. This would have made data centers the 11th largest electricity consumer in the world, between the nations of Saudi Arabia (371 terawatts) and France (463 terawatts), according to the Organization for Economic Co-operation and Development. By 2026, the electricity consumption of data centers is expected to approach 1,050 terawatts (which would bump data centers up to fifth place on the global list, between Japan and Russia). While not all data center computation involves generative AI, the technology has been a major driver of increasing energy demands. “The demand for new data centers cannot be met in a sustainable way. The pace at which companies are building new data centers means the bulk of the electricity to power them must come from fossil fuel-based power plants,” says Bashir. The power needed to train and deploy a model like OpenAI’s GPT-3 is difficult to ascertain. In a 2021 research paper, scientists from Google and the University of California at Berkeley estimated the training process alone consumed 1,287 megawatt hours of electricity (enough to power about 120 average U.S. homes for a year), generating about 552 tons of carbon dioxide. While all machine-learning models must be trained, one issue unique to generative AI is the rapid fluctuations in energy use that occur over different phases of the training process, Bashir explains. Power grid operators must have a way to absorb those fluctuations to protect the grid, and they usually employ diesel-based generators for that task. Increasing impacts from inference Once a generative AI model is trained, the energy demands don’t disappear. Each time a model is used, perhaps by an individual asking ChatGPT to summarize an email, the computing hardware that performs those operations consumes energy. Researchers have estimated that a ChatGPT query consumes about five times more electricity than a simple web search. “But an everyday user doesn’t think too much about that,” says Bashir. “The ease-of-use of generative AI interfaces and the lack of information about the environmental impacts of my actions means that, as a user, I don’t have much incentive to cut back on my use of generative AI.” With traditional AI, the energy usage is split fairly evenly between data processing, model training, and inference, which is the process of using a trained model to make predictions on new data. However, Bashir expects the electricity demands of generative AI inference to eventually dominate since these models are becoming ubiquitous in so many applications, and the electricity
Algorithms and AI for a better world
Amid the benefits that algorithmic decision-making and artificial intelligence offer — including revolutionizing speed, efficiency, and predictive ability in a vast range of fields — Manish Raghavan is working to mitigate associated risks, while also seeking opportunities to apply the technologies to help with preexisting social concerns. “I ultimately want my research to push towards better solutions to long-standing societal problems,” says Raghavan, the Drew Houston Career Development Professor in MIT’s Sloan School of Management and the Department of Electrical Engineering and Computer Science and a principal investigator at the Laboratory for Information and Decision Systems (LIDS). A good example of Raghavan’s intention can be found in his exploration of the use AI in hiring. Raghavan says, “It’s hard to argue that hiring practices historically have been particularly good or worth preserving, and tools that learn from historical data inherit all of the biases and mistakes that humans have made in the past.” Here, however, Raghavan cites a potential opportunity. “It’s always been hard to measure discrimination,” he says, adding, “AI-driven systems are sometimes easier to observe and measure than humans, and one goal of my work is to understand how we might leverage this improved visibility to come up with new ways to figure out when systems are behaving badly.” Growing up in the San Francisco Bay Area with parents who both have computer science degrees, Raghavan says he originally wanted to be a doctor. Just before starting college, though, his love of math and computing called him to follow his family example into computer science. After spending a summer as an undergraduate doing research at Cornell University with Jon Kleinberg, professor of computer science and information science, he decided he wanted to earn his PhD there, writing his thesis on “The Societal Impacts of Algorithmic Decision-Making.” Raghavan won awards for his work, including a National Science Foundation Graduate Research Fellowships Program award, a Microsoft Research PhD Fellowship, and the Cornell University Department of Computer Science PhD Dissertation Award. In 2022, he joined the MIT faculty. Perhaps hearkening back to his early interest in medicine, Raghavan has done research on whether the determinations of a highly accurate algorithmic screening tool used in triage of patients with gastrointestinal bleeding, known as the Glasgow-Blatchford Score (GBS), are improved with complementary expert physician advice. “The GBS is roughly as good as humans on average, but that doesn’t mean that there aren’t individual patients, or small groups of patients, where the GBS is wrong and doctors are likely to be right,” he says. “Our hope is that we can identify these patients ahead of time so that doctors’ feedback is particularly valuable there.” Raghavan has also worked on how online platforms affect their users, considering how social media algorithms observe the content a user chooses and then show them more of that same kind of content. The difficulty, Raghavan says, is that users may be choosing what they view in the same way they might grab bag of potato chips, which are of course delicious but not all that nutritious. The experience may be satisfying in the moment, but it can leave the user feeling slightly sick. Raghavan and his colleagues have developed a model of how a user with conflicting desires — for immediate gratification versus a wish of longer-term satisfaction — interacts with a platform. The model demonstrates how a platform’s design can be changed to encourage a more wholesome experience. The model won the Exemplary Applied Modeling Track Paper Award at the 2022 Association for Computing Machinery Conference on Economics and Computation. “Long-term satisfaction is ultimately important, even if all you care about is a company’s interests,” Raghavan says. “If we can start to build evidence that user and corporate interests are more aligned, my hope is that we can push for healthier platforms without needing to resolve conflicts of interest between users and platforms. Of course, this is idealistic. But my sense is that enough people at these companies believe there’s room to make everyone happier, and they just lack the conceptual and technical tools to make it happen.” Regarding his process of coming up with ideas for such tools and concepts for how to best apply computational techniques, Raghavan says his best ideas come to him when he’s been thinking about a problem off and on for a time. He would advise his students, he says, to follow his example of putting a very difficult problem away for a day and then coming back to it. “Things are often better the next day,” he says. When he’s not puzzling out a problem or teaching, Raghavan can often be found outdoors on a soccer field, as a coach of the Harvard Men’s Soccer Club, a position he cherishes. “I can’t procrastinate if I know I’ll have to spend the evening at the field, and it gives me something to look forward to at the end of the day,” he says. “I try to have things in my schedule that seem at least as important to me as work to put those challenges and setbacks into context.” As Raghavan considers how to apply computational technologies to best serve our world, he says he finds the most exciting thing going on his field is the idea that AI will open up new insights into “humans and human society.” “I’m hoping,” he says, “that we can use it to better understand ourselves.”
Making the art world more accessible
In the world of high-priced art, galleries usually act as gatekeepers. Their selective curation process is a key reason galleries in major cities often feature work from the same batch of artists. The system limits opportunities for emerging artists and leaves great art undiscovered. NALA was founded by Benjamin Gulak ’22 to disrupt the gallery model. The company’s digital platform, which was started as part of an MIT class project, allows artists to list their art and uses machine learning and data science to offer personalized recommendations to art lovers. By providing a much larger pool of artwork to buyers, the company is dismantling the exclusive barriers put up by traditional galleries and efficiently connecting creators with collectors. “There’s so much talent out there that has never had the opportunity to be seen outside of the artists’ local market,” Gulak says. “We’re opening the art world to all artists, creating a true meritocracy.” NALA takes no commission from artists, instead charging buyers an 11.5 percent commission on top of the artist’s listed price. Today more than 20,000 art lovers are using NALA’s platform, and the company has registered more than 8,500 artists. “My goal is for NALA to become the dominant place where art is discovered, bought, and sold online,” Gulak says. “The gallery model has existed for such a long period of time that they are the tastemakers in the art world. However, most buyers never realize how restrictive the industry has been.” From founder to student to founder again Growing up in Canada, Gulak worked hard to get into MIT, participating in science fairs and robotic competitions throughout high school. When he was 16, he created an electric, one-wheeled motorcycle that got him on the popular television show “Shark Tank” and was later named one of the top inventions of the year by Popular Science. Gulak was accepted into MIT in 2009 but withdrew from his undergrad program shortly after entering to launch a business around the media exposure and capital from “Shark Tank.” Following a whirlwind decade in which he raised more than $12 million and sold thousands of units globally, Gulak decided to return to MIT to complete his degree, switching his major from mechanical engineering to one combining computer science, economics, and data science. “I spent 10 years of my life building my business, and realized to get the company where I wanted it to be, it would take another decade, and that wasn’t what I wanted to be doing,” Gulak says. “I missed learning, and I missed the academic side of my life. I basically begged MIT to take me back, and it was the best decision I ever made.” During the ups and downs of running his company, Gulak took up painting to de-stress. Art had always been a part of Gulak’s life, and he had even done a fine arts study abroad program in Italy during high school. Determined to try selling his art, he collaborated with some prominent art galleries in London, Miami, and St. Moritz. Eventually he began connecting artists he’d met on travels from emerging markets like Cuba, Egypt, and Brazil to the gallery owners he knew. “The results were incredible because these artists were used to selling their work to tourists for $50, and suddenly they’re hanging work in a fancy gallery in London and getting 5,000 pounds,” Gulak says. “It was the same artist, same talent, but different buyers.” At the time, Gulak was in his third year at MIT and wondering what he’d do after graduation. He thought he wanted to start a new business, but every industry he looked at was dominated by tech giants. Every industry, that is, except the art world. “The art industry is archaic,” Gulak says. “Galleries have monopolies over small groups of artists, and they have absolute control over the prices. The buyers are told what the value is, and almost everywhere you look in the industry, there’s inefficiencies.” At MIT, Gulak was studying the recommender engines that are used to populate social media feeds and personalize show and music suggestions, and he envisioned something similar for the visual arts. “I thought, why, when I go on the big art platforms, do I see horrible combinations of artwork even though I’ve had accounts on these platforms for years?” Gulak says. “I’d get new emails every week titled ‘New art for your collection,’ and the platform had no idea about my taste or budget.” For a class project at MIT, Gulak built a system that tried to predict the types of art that would do well in a gallery. By his final year at MIT, he had realized that working directly with artists would be a more promising approach. “Online platforms typically take a 30 percent fee, and galleries can take an additional 50 percent fee, so the artist ends up with a small percentage of each online sale, but the buyer also has to pay a luxury import duty on the full price,” Gulak explains. “That means there’s a massive amount of fat in the middle, and that’s where our direct-to-artist business model comes in.” Today NALA, which stands for Networked Artistic Learning Algorithm, onboards artists by having them upload artwork and fill out a questionnaire about their style. They can begin uploading work immediately and choose their listing price. The company began by using AI to match art with its most likely buyer. Gulak notes that not all art will sell — “if you’re making rock paintings there may not be a big market” — and artists may price their work higher than buyers are willing to pay, but the algorithm works to put art in front of the most likely buyer based on style preferences and budget. NALA also handles sales and shipments, providing artists with 100 percent of their list price from every sale. “By not taking commissions, we’re very pro artists,” Gulak says. “We also allow all artists to participate, which is unique in this
Top Home Improvement Trends for 2025: What’s In and What’s Out
As we head into 2025, the world of home improvement is evolving. With shifting design trends, new technologies, and growing environmental awareness, homeowners are investing in spaces that combine functionality, comfort, and sustainability. If you’re planning any updates or renovations this year, here are the biggest trends to consider—and what’s starting to fade. 1. What’s Fading: The All-White Everything Trend For a long time, white walls, white kitchens, and minimalist designs ruled the home improvement scene. But in 2025, this trend is losing its appeal as homeowners seek warmth, texture, and more vibrant, expressive design. Excessive Minimalism: Rooms that feel sterile and overly simplistic are being replaced by spaces that encourage comfort, individuality, and personality. Impersonal Decor: Mass-produced, generic furniture is being swapped out for pieces that reflect personal style, whether vintage, eclectic, or custom-designed. 2. Smart Homes Aren’t Just for Tech Enthusiasts Technology has continued to transform how we live in our homes. In 2025, smart home technology is becoming more accessible and functional, extending beyond simple security systems. Voice-Controlled Devices: Smart speakers and virtual assistants like Alexa and Google Assistant are now controlling everything from lighting to thermostats and even kitchen appliances. Home Automation: Homeowners are embracing automation with systems that learn their habits and adjust temperature, lighting, and security settings without manual input. Smart Kitchens: AI-powered appliances that can suggest recipes, order groceries, and even cook food are becoming standard in many homes. 3. Maximizing Small Spaces with Multi-Functional Furniture As real estate prices rise and space becomes more limited, homeowners are looking for ways to maximize the space they have. Multi-functional furniture is a solution that’s here to stay. Foldable and Expandable Furniture: Pieces like foldable dining tables, expandable couches, and beds that transform into desks help make the most of small living spaces. Hidden Storage Solutions: Under-bed storage, built-in shelves, and even furniture that doubles as storage are gaining popularity for their ability to keep homes organized and clutter-free. 4. Bold Colors and Customization in Interior Design While neutral tones dominated for years, 2025 is seeing a return of bold colors, personalized touches, and unique design choices in home interiors. Vibrant Hues: Expect to see shades like deep blues, rich greens, and bold oranges making their way into living rooms, kitchens, and bedrooms. Custom Decor: Homeowners are opting for custom furniture, hand-made art, and personalized details to make their homes truly one-of-a-kind. 5. Outdoor Living Spaces as Extensions of the Home As the lines between indoor and outdoor living continue to blur, outdoor spaces are becoming more like fully functional rooms of the house. Outdoor Kitchens: Full outdoor kitchens with grills, sinks, and refrigerators are becoming more common for those who love entertaining or dining al fresco. Fire Pits and Lounges: Comfortable seating areas and fire pits are essential for cozy evenings in the backyard. Zen Gardens and Relaxation Areas: More homeowners are designing outdoor spaces dedicated to relaxation, incorporating elements like water features, greenery, and quiet spots for reflection. 6. Sustainable Living Is More Than a Trend Eco-friendly renovations are no longer a “nice to have” but a “must have.” Homeowners are increasingly seeking to reduce their carbon footprint with sustainable upgrades that save energy and water while boosting home value. Energy-Efficient Appliances: From smart refrigerators to dishwashers that use less water and energy, the demand for energy-efficient appliances continues to rise. Solar Panels: Solar energy is becoming a standard choice for homeowners looking to cut energy costs and reduce reliance on fossil fuels. Water Conservation: Low-flow toilets, rainwater harvesting systems, and drought-resistant landscaping are all becoming more popular. Wrapping Up Whether you’re looking to reduce your environmental impact, embrace cutting-edge tech, or create a space that reflects your personal style, 2025 is the year of transformation for home improvement. By focusing on sustainability, smart technology, and customizable design, you can create a space that’s not only functional but also a true reflection of who you are.
Q&A: The climate impact of generative AI
Vijay Gadepally, a senior staff member at MIT Lincoln Laboratory, leads a number of projects at the Lincoln Laboratory Supercomputing Center (LLSC) to make computing platforms, and the artificial intelligence systems that run on them, more efficient. Here, Gadepally discusses the increasing use of generative AI in everyday tools, its hidden environmental impact, and some of the ways that Lincoln Laboratory and the greater AI community can reduce emissions for a greener future. Q: What trends are you seeing in terms of how generative AI is being used in computing? A: Generative AI uses machine learning (ML) to create new content, like images and text, based on data that is inputted into the ML system. At the LLSC we design and build some of the largest academic computing platforms in the world, and over the past few years we’ve seen an explosion in the number of projects that need access to high-performance computing for generative AI. We’re also seeing how generative AI is changing all sorts of fields and domains — for example, ChatGPT is already influencing the classroom and the workplace faster than regulations can seem to keep up. We can imagine all sorts of uses for generative AI within the next decade or so, like powering highly capable virtual assistants, developing new drugs and materials, and even improving our understanding of basic science. We can’t predict everything that generative AI will be used for, but I can certainly say that with more and more complex algorithms, their compute, energy, and climate impact will continue to grow very quickly. Q: What strategies is the LLSC using to mitigate this climate impact? A: We’re always looking for ways to make computing more efficient, as doing so helps our data center make the most of its resources and allows our scientific colleagues to push their fields forward in as efficient a manner as possible. As one example, we’ve been reducing the amount of power our hardware consumes by making simple changes, similar to dimming or turning off lights when you leave a room. In one experiment, we reduced the energy consumption of a group of graphics processing units by 20 percent to 30 percent, with minimal impact on their performance, by enforcing a power cap. This technique also lowered the hardware operating temperatures, making the GPUs easier to cool and longer lasting. Another strategy is changing our behavior to be more climate-aware. At home, some of us might choose to use renewable energy sources or intelligent scheduling. We are using similar techniques at the LLSC — such as training AI models when temperatures are cooler, or when local grid energy demand is low. We also realized that a lot of the energy spent on computing is often wasted, like how a water leak increases your bill but without any benefits to your home. We developed some new techniques that allow us to monitor computing workloads as they are running and then terminate those that are unlikely to yield good results. Surprisingly, in a number of cases we found that the majority of computations could be terminated early without compromising the end result. Q: What’s an example of a project you’ve done that reduces the energy output of a generative AI program? A: We recently built a climate-aware computer vision tool. Computer vision is a domain that’s focused on applying AI to images; so, differentiating between cats and dogs in an image, correctly labeling objects within an image, or looking for components of interest within an image. In our tool, we included real-time carbon telemetry, which produces information about how much carbon is being emitted by our local grid as a model is running. Depending on this information, our system will automatically switch to a more energy-efficient version of the model, which typically has fewer parameters, in times of high carbon intensity, or a much higher-fidelity version of the model in times of low carbon intensity. By doing this, we saw a nearly 80 percent reduction in carbon emissions over a one- to two-day period. We recently extended this idea to other generative AI tasks such as text summarization and found the same results. Interestingly, the performance sometimes improved after using our technique! Q: What can we do as consumers of generative AI to help mitigate its climate impact? A: As consumers, we can ask our AI providers to offer greater transparency. For example, on Google Flights, I can see a variety of options that indicate a specific flight’s carbon footprint. We should be getting similar kinds of measurements from generative AI tools so that we can make a conscious decision on which product or platform to use based on our priorities. We can also make an effort to be more educated on generative AI emissions in general. Many of us are familiar with vehicle emissions, and it can help to talk about generative AI emissions in comparative terms. People may be surprised to know, for example, that one image-generation task is roughly equivalent to driving four miles in a gas car, or that it takes the same amount of energy to charge an electric car as it does to generate about 1,500 text summarizations. There are many cases where customers would be happy to make a trade-off if they knew the trade-off’s impact. Q: What do you see for the future? A: Mitigating the climate impact of generative AI is one of those problems that people all over the world are working on, and with a similar goal. We’re doing a lot of work here at Lincoln Laboratory, but its only scratching at the surface. In the long term, data centers, AI developers, and energy grids will need to work together to provide “energy audits” to uncover other unique ways that we can improve computing efficiencies. We need more partnerships and more collaboration in order to forge ahead. If you’re interested in learning more, or collaborating with Lincoln Laboratory on these efforts, please contact Vijay Gadepally.