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.
Teaching AI to communicate sounds like humans do
Whether you’re describing the sound of your faulty car engine or meowing like your neighbor’s cat, imitating sounds with your voice can be a helpful way to relay a concept when words don’t do the trick. Vocal imitation is the sonic equivalent of doodling a quick picture to communicate something you saw — except that instead of using a pencil to illustrate an image, you use your vocal tract to express a sound. This might seem difficult, but it’s something we all do intuitively: To experience it for yourself, try using your voice to mirror the sound of an ambulance siren, a crow, or a bell being struck. Inspired by the cognitive science of how we communicate, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have developed an AI system that can produce human-like vocal imitations with no training, and without ever having “heard” a human vocal impression before. To achieve this, the researchers engineered their system to produce and interpret sounds much like we do. They started by building a model of the human vocal tract that simulates how vibrations from the voice box are shaped by the throat, tongue, and lips. Then, they used a cognitively-inspired AI algorithm to control this vocal tract model and make it produce imitations, taking into consideration the context-specific ways that humans choose to communicate sound. The model can effectively take many sounds from the world and generate a human-like imitation of them — including noises like leaves rustling, a snake’s hiss, and an approaching ambulance siren. Their model can also be run in reverse to guess real-world sounds from human vocal imitations, similar to how some computer vision systems can retrieve high-quality images based on sketches. For instance, the model can correctly distinguish the sound of a human imitating a cat’s “meow” versus its “hiss.” In the future, this model could potentially lead to more intuitive “imitation-based” interfaces for sound designers, more human-like AI characters in virtual reality, and even methods to help students learn new languages. The co-lead authors — MIT CSAIL PhD students Kartik Chandra SM ’23 and Karima Ma, and undergraduate researcher Matthew Caren — note that computer graphics researchers have long recognized that realism is rarely the ultimate goal of visual expression. For example, an abstract painting or a child’s crayon doodle can be just as expressive as a photograph. “Over the past few decades, advances in sketching algorithms have led to new tools for artists, advances in AI and computer vision, and even a deeper understanding of human cognition,” notes Chandra. “In the same way that a sketch is an abstract, non-photorealistic representation of an image, our method captures the abstract, non-phono–realistic ways humans express the sounds they hear. This teaches us about the process of auditory abstraction.” The art of imitation, in three parts The team developed three increasingly nuanced versions of the model to compare to human vocal imitations. First, they created a baseline model that simply aimed to generate imitations that were as similar to real-world sounds as possible — but this model didn’t match human behavior very well. The researchers then designed a second “communicative” model. According to Caren, this model considers what’s distinctive about a sound to a listener. For instance, you’d likely imitate the sound of a motorboat by mimicking the rumble of its engine, since that’s its most distinctive auditory feature, even if it’s not the loudest aspect of the sound (compared to, say, the water splashing). This second model created imitations that were better than the baseline, but the team wanted to improve it even more. To take their method a step further, the researchers added a final layer of reasoning to the model. “Vocal imitations can sound different based on the amount of effort you put into them. It costs time and energy to produce sounds that are perfectly accurate,” says Chandra. The researchers’ full model accounts for this by trying to avoid utterances that are very rapid, loud, or high- or low-pitched, which people are less likely to use in a conversation. The result: more human-like imitations that closely match many of the decisions that humans make when imitating the same sounds. After building this model, the team conducted a behavioral experiment to see whether the AI- or human-generated vocal imitations were perceived as better by human judges. Notably, participants in the experiment favored the AI model 25 percent of the time in general, and as much as 75 percent for an imitation of a motorboat and 50 percent for an imitation of a gunshot. Toward more expressive sound technology Passionate about technology for music and art, Caren envisions that this model could help artists better communicate sounds to computational systems and assist filmmakers and other content creators with generating AI sounds that are more nuanced to a specific context. It could also enable a musician to rapidly search a sound database by imitating a noise that is difficult to describe in, say, a text prompt. In the meantime, Caren, Chandra, and Ma are looking at the implications of their model in other domains, including the development of language, how infants learn to talk, and even imitation behaviors in birds like parrots and songbirds. The team still has work to do with the current iteration of their model: It struggles with some consonants, like “z,” which led to inaccurate impressions of some sounds, like bees buzzing. They also can’t yet replicate how humans imitate speech, music, or sounds that are imitated differently across different languages, like a heartbeat. Stanford University linguistics professor Robert Hawkins says that language is full of onomatopoeia and words that mimic but don’t fully replicate the things they describe, like the “meow” sound that very inexactly approximates the sound that cats make. “The processes that get us from the sound of a real cat to a word like ‘meow’ reveal a lot about the intricate interplay between physiology, social reasoning, and communication in the evolution of language,” says Hawkins, who wasn’t
Donald Trump Taps Dr. Oz to Head U.S. Medicaid & Medicare
President-elect Donald Trump has nominated Dr. Mehmet Oz, a well-known television personality and surgeon, to lead the Centers for Medicare and Medicaid Services (CMS). Oz, who will oversee programs impacting millions of Americans, is known for his media presence and health advocacy. However, some have criticized his promotion of treatments lacking scientific support. Trump’s selection of Oz highlights his unconventional approach to leadership, blending celebrity influence with healthcare reform goals. Related Podcast Trump Taps Dr. Oz for Medicaid & Medicare President-elect Donald Trump has nominated Dr. Mehmet Oz, a well-known television personality and surgeon, to lead the Centers for Medicare and Medicaid Services (CMS).
Whoopi Goldberg launches All Women’s Sports Network (AWSN)
Trailblazing actress and activist Whoopi Goldberg is breaking new ground in sports media with the launch of the All Women’s Sports Network (AWSN), a groundbreaking television channel dedicated exclusively to women’s sports. In collaboration with Jungo TV, AWSN aims to provide a platform for female athletes to shine, offering live coverage of major leagues such as UEFA, FIBA, WTA, and WNBL. This bold initiative is already available in 65 countries, reaching a potential audience of over 2 billion people, and has successfully debuted in parts of Asia and the Middle East. The launch of AWSN comes at a critical time, as women’s sports continue to gain momentum but still receive only a fraction of the media coverage given to men’s sports. Goldberg’s vision, which she has nurtured for 16 years, seeks to address this disparity by celebrating the talent, dedication, and athleticism of women across a wide range of sports, including soccer, basketball, tennis, cricket, and even curling. By amplifying these stories, AWSN hopes to inspire future generations of athletes and create a more equitable playing field. Related Podcast Whoopi launches women’s sports network Whoopi Goldberg is breaking new ground in sports media with the launch of the All Women’s Sports Network (AWSN) – the first global television channel dedicated exclusively to women’s sports. In collaboration with Jungo TV, AWSN is now available in 65 countries, providing live coverage of major women’s sports leagues. Goldberg’s initiative is not only a cultural milestone but also a timely response to the growing interest in women’s sports. With record-breaking viewership for events like the FIFA Women’s World Cup and increased investments from brands and organizations, AWSN is poised to meet the demand for more diverse and inclusive sports programming. It also serves as a powerful reminder of the need for consistent and widespread media representation for female athletes, who often perform at the highest levels with minimal recognition. As AWSN expands globally, Goldberg’s mission to champion women in sports sends a resounding message: athletic excellence knows no gender. The All Women’s Sports Network stands as a testament to the power of visibility and the importance of creating platforms that recognize and celebrate the achievements of women, on and off the field. Through AWSN, Goldberg is not only transforming sports media but also paving the way for a more inclusive future.
Trump poised for unrestricted leadership
Donald Trump has made history once again. Nearly eight years after his surprising win over Hillary Clinton, and four years after Joe Biden’s term removed him from office, Trump is ready to return to the White House. With a strong showing in key early voting states and improved support across the country, Trump declared he has a “powerful mandate” to lead. “This will truly be America’s golden age,” he told a cheering crowd in West Palm Beach, Florida. A Stronger Conservative Movement Trump’s victory signals a continued shift in U.S. politics toward conservative populism—a movement that began with his 2016 election and appeared at risk after his 2020 defeat. Now, this movement looks stronger than ever. Trump now has the chance to build a new administration and act on the promises he’s made for a brighter American future. This administration will also be supported by a Republican-controlled Senate, which makes confirming Trump’s political appointees easier, including his Cabinet and judges. Although results for the House of Representatives aren’t final, Trump predicted a Republican win there too. Having a Republican-led Congress will help him advance his plans, including a complete federal overhaul by appointing loyalists to key positions across the government. Joining Trump in his administration are big names like billionaire Elon Musk, vaccine skeptic Robert F. Kennedy Jr., former Democrat Tulsi Gabbard, entrepreneur Vivek Ramaswamy, and others in his unique coalition. Four Years to Fulfill Promises Trump has also pledged new tariffs to protect U.S. industries, targeted tax breaks, and large-scale deportation of undocumented immigrants. On foreign policy, he promises to quickly end conflicts in Ukraine and Gaza, focusing on America’s interests. By January, his administration will handle these global challenges. Vice President Kamala Harris, Democrats, and some of Trump’s former advisors warn that his policies may disrupt the economy and society, possibly even affecting global stability. They worry that a second Trump term could be even less restrained than his first. Trump himself admitted his second term might “be rough at times,” but he promised good results in the end. A majority of voters, it seems, agreed. With a Republican Congress, Trump could reverse many of the policies from the last four years and pass conservative laws on taxes, spending, trade, and immigration, aiming to leave a lasting mark on government. A Remarkable Comeback Trump’s victory marks a surprising comeback for someone who left office after the January 6 incident, seemingly with his reputation in trouble. After intense criticism from both Democrats and some Republicans, Trump spent four years working his way back to the top. During that time, he faced legal challenges, including criminal indictments, felony convictions, civil judgments related to assault claims, and major fines for his business. Yet, he pushed forward, securing the Republican nomination and focusing on his campaign. Though his rallies could sometimes be unfocused, Trump built a skilled team. Polls showed voters trusted him on immigration and the economy, and his campaign stayed on these issues. Being aligned with voter concerns proved crucial, as many Americans—and people in democracies worldwide—were increasingly anti-incumbent. Trump’s campaign mobilized rural voters and cut into Democratic strongholds in urban areas. Preliminary exit polls show he even gained ground with younger, Hispanic, and Black voters, groups that usually vote Democrat. Although Trump’s team initially struggled when Biden left the race, leaving him to face Kamala Harris, he soon found his stride, riding the wave of anti-incumbent sentiment back to the White House. Now, with four more years ahead, Trump has a more organized political team ready to turn his promises into lasting policies. We’ll be following his presidency closely, and reporting on his progress here at Readovia. Related Podcast Trump Reclaims White House Throne Donald Trump has defeated Kamala Harris and will return to the White House in January 2025. Sources attribute Trump’s victory to key wins in swing states and suggest that inflation may have been a contributing factor. In this discussion, we highlight the significance of Trump’s victory, including his position as the first convicted felon to win the presidency.