Menu
“The diffusion model has an easier job to do, which leads to more efficiency,” he adds. While this boosts the model’s speed, the information loss that occurs during compression causes errors when the model generates a new image. But because the what is the difference between retained earnings and cash model has multiple chances to correct details it got wrong, the images are high-quality. The ability to generate high-quality images quickly is crucial for producing realistic simulated environments that can be used to train self-driving cars to avoid unpredictable hazards, making them safer on real streets. Instead of having a model make an image of a chair, perhaps it could generate a plan for a chair that could be produced. The models have the capacity to plagiarize, and can generate content that looks like it was produced by a specific human creator, raising potential copyright issues.
New models often consume more energy for training, since they usually have more parameters than their predecessors. Companies release new models every few weeks, so the energy used to train prior versions goes to waste, Bashir adds. 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. 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. While not all data center computation involves generative AI, the technology has been a major driver of increasing energy demands. 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.
This resulted in what is manufacturing overhead and what does it include a new algorithm that could classify unlabeled images 8 percent better than another state-of-the-art approach. As they arranged the table, the researchers began to see gaps where algorithms could exist, but which hadn’t been invented yet. They decided to organize I-Con into a periodic table to categorize algorithms based on how points are connected in real datasets and the primary ways algorithms can approximate those connections. The equation describes how such algorithms find connections between real data points and then approximate those connections internally. The framework they created, information contrastive learning (I-Con), shows how a variety of algorithms can be viewed through the lens of this unifying equation.
Konstantin Rusch and Daniela Rus have developed what they call “linear oscillatory state-space models” (LinOSS), which leverage principles of forced harmonic oscillators — a concept deeply rooted in physics and observed in biological neural networks. One new type of AI model, called “state-space models,” has been designed specifically to understand these sequential patterns more effectively. MIT neuroscientists find a surprising parallel in the ways humans and new AI models solve complex problems. AI supports the clean energy transition as it manages power grid operations, helps plan infrastructure investments, guides development of novel materials, and more. Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning. In this context, papers that unify and connect existing algorithms are of great importance, yet they are extremely rare.
Each algorithm aims to minimize the amount of deviation between the connections it learns to approximate and the real connections in its training data. Alshammari found that these two disparate algorithms could be reframed using the same underlying equation. She realized the clustering algorithm she was studying was similar to another classical machine-learning algorithm, called contrastive learning, and began digging deeper into the mathematics. These spaces predict where algorithms should exist, but which haven’t been discovered yet. While teachers don’t get to choose regarding AI’s existence, Reich believes it’s important that we solicit their input and involve students and other stakeholders to help develop solutions that improve learning and outcomes.
A user only needs to enter one natural language prompt into the HART interface to generate an image. But the generative artificial intelligence techniques increasingly being used to produce such images have drawbacks. You may not alter the images provided, other than to crop them to size. Even if the AI is trained on a wealth of data, people feel AI can’t grasp their personal situations. For example, people tend to favor AI when it comes to detecting fraud or sorting large datasets — areas where AI’s abilities exceed those of humans in speed and scale, and personalization is not required. The researchers tested whether the data supported their proposed “Capability–Personalization Framework” — the idea that in a given context, both the perceived capability of AI and the perceived necessity for personalization shape our preferences for either AI or humans.
One popular type of model, called a diffusion model, can create stunningly realistic images but is too slow and computationally intensive for many applications. Before the generative AI boom of the past few years, when people talked about AI, typically they were personal account examples talking about machine-learning models that can learn to make a prediction based on data. 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. 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.
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. A credit line must be used when reproducing images; if one is not provided below, credit the images to “MIT.” The team imagines that the emergence of a new paradigm like LinOSS will be of interest to machine learning practitioners to build upon. Empirical testing demonstrated that LinOSS consistently outperformed existing state-of-the-art models across various demanding sequence classification and forecasting tasks. Moreover, the researchers rigorously proved the model’s universal approximation capability, meaning it can approximate any continuous, causal function relating input and output sequences. “Our goal was to capture the stability and efficiency seen in biological neural systems and translate these principles into a machine learning framework,” explains Rusch.
A diffusion model is at the heart of the text-to-image generation system Stable Diffusion. But that focus has shifted a bit, and many researchers are now using larger datasets, perhaps with hundreds of millions or even billions of data points, to train models that can achieve impressive results. In machine learning, Markov models have long been used for next-word prediction tasks, like the autocomplete function in an email program.
“We’ve also developed pipelines for converting voxel structures into feasible assembly sequences for small, distributed mobile robots, which could help translate this work to structures at any size scale,” Smith says. He continued working on the project at the MIT Center for Bits and Atoms (CBA), directed by Gershenfeld, collaborating with graduate students Se Hwan Jeon of the Department of Mechanical Engineering and Miana Smith of CBA. To date, the researchers have used the system to create stools, shelves, chairs, a small table, and even decorative items such as a dog statue. In fact, MIT researchers have developed a speech-to-reality system, an AI-driven workflow that allows them to provide input to a robotic arm and “speak objects into existence,” creating things like furniture in as little as five minutes.
“We’ve shown that just one very elegant equation, rooted in the science of information, gives you rich algorithms spanning 100 years of research in machine learning. During the development of HART, the researchers encountered challenges in effectively integrating the diffusion model to enhance the autoregressive model. Because the diffusion model de-noises all pixels in an image at each step, and there may be 30 or more steps, the process is slow and computationally expensive. The generation process consumes fewer computational resources than typical diffusion models, enabling HART to run locally on a commercial laptop or smartphone. Their hybrid image-generation tool uses an autoregressive model to quickly capture the big picture and then a small diffusion model to refine the details of the image. In 2017, researchers at Google introduced the transformer architecture, which has been used to develop large language models, like those that power ChatGPT.
Moreover, because HART uses an autoregressive model to do the bulk of the work — the same type of model that powers LLMs — it is more compatible for integration with the new class of unified vision-language generative models. Instead, their final design of applying the diffusion model to predict only residual tokens as the final step significantly improved generation quality. They found that incorporating the diffusion model in the early stages of the autoregressive process resulted in an accumulation of errors.
These powerful machine-learning models draw on research and computational advances that go back more than 50 years. 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. They also used I-Con to show how a data debiasing technique developed for contrastive learning could be used to boost the accuracy of clustering algorithms.
“We hope the podcast will spark thought and discussion, allowing people to draw from others’ experiences,” Reich says. “The academic publishing cycle doesn’t lend itself to helping people with near-term challenges like those AI presents,” Reich says. The podcast allows Reich to share timely information about education-related updates and collaborate with people interested in helping further the work. Each episode tackles a specific area, asking important questions about challenges related to issues like AI adoption, poetry as a tool for student engagement, post-Covid learning loss, pedagogy, and book bans.
The researchers filled in one gap by borrowing ideas from a machine-learning technique called contrastive learning and applying them to image clustering. It includes everything from classification algorithms that can detect spam to the deep learning algorithms that power LLMs. After joining the Freeman Lab, Alshammari began studying clustering, a machine-learning technique that classifies images by learning to organize similar images into nearby clusters. The researchers didn’t set out to create a periodic table of machine learning.
For instance, AI appreciation is more pronounced for tangible robots than for intangible algorithms. They want a human recruiter, a human doctor who can see them as distinct from other people.” “People have a fundamental desire to see themselves as unique and distinct from other people,” Lu says. The analysis confirmed that the Capability–Personalization Framework indeed helps account for people’s preferences. To reconcile these mixed findings, Lu and his co-authors conducted a meta-analysis of 163 prior studies that compared people’s preferences for AI versus humans.
Their tool, known as HART (short for hybrid autoregressive transformer), can generate images that match or exceed the quality of state-of-the-art diffusion models, but do so about nine times faster. Popular diffusion models, such as Stable Diffusion and DALL-E, are known to produce highly detailed images. By iteratively refining their output, these models learn to generate new data samples that resemble samples in a training dataset, and have been used to create realistic-looking images. This minimal overhead of the additional diffusion model allows HART to retain the speed advantage of the autoregressive model while significantly enhancing its ability to generate intricate image details. Because the diffusion model only predicts the remaining details after the autoregressive model has done its job, it can accomplish the task in eight steps, instead of the usual 30 or more a standard diffusion model requires to generate an entire image.
Qualidade, experiência e tecnologia.
