Image
Scaling laws enable researchers to use smaller LLMs to predict the performance of a significantly bigger target model, thus allowing better allocation of computational power (Credits: Adobe Stock).
CSAIL article

When researchers are building large language models (LLMs), they aim to maximize performance under a particular computational and financial budget. Since training a model can amount to millions of dollars, developers need to be judicious with cost-impacting decisions about, for instance, the model architecture, optimizers, and training datasets before committing to a model. To anticipate the quality and accuracy of a large model’s predictions, practitioners often turn to scaling laws: using smaller, cheaper models to try to approximate the performance of a much larger target model. The challenge, however, is that there are thousands of ways to create a scaling law.

Image
Fetal SMPL was trained on 20,000 MRI volumes to predict the location and size of a fetus and create sculpture-like 3D representations. The approach could enable doctors to precisely measure things like the size of a baby’s head and compare these metrics with healthy fetuses at the same age (Credits: Alex Shipps and Yingcheng Liu/MIT CSAIL).
CSAIL article

For pregnant women, ultrasounds are an informative (and sometimes necessary) procedure. They typically produce two-dimensional black-and-white scans of fetuses that can reveal key insights, including biological sex, approximate size, and abnormalities like heart issues or cleft lip. If your doctor wants a closer look, they may use magnetic resonance imaging (MRI), which uses magnetic fields to capture images that can be combined to create a 3D view of the fetus.

Image
“Our system can turn a seemingly static, abstract image into an attention-catching animation,” says MIT PhD student Ticha Sethapakdi, a lead researcher on the FabObscura project. “The tool lowers the barrier to entry to creating these barrier-grid animations, while helping users express a variety of designs that would’ve been very time-consuming to explore by hand” (Credits: Courtesy of the researchers).
CSAIL article

Whether you’re an artist, advertising specialist, or just looking to spruce up your home, turning everyday objects into dynamic displays is a great way to make them more visually engaging. For example, you could turn a kids’ book into a handheld cartoon of sorts, making the reading experience more immersive and memorable for a child.

Image
A new software and hardware toolkit called SustainaPrint can help users strategically combine strong and weak filaments to achieve the best of both worlds. Instead of printing an entire object with high-performance plastic, the system analyzes a model, predicts where the object is most likely to experience stress, and reinforces those zones with stronger material (Credits: Alex Shipps/MIT CSAIL, using assets from Pixabay and the researchers).
CSAIL article

3D printing has come a long way since its invention in 1983 by Chuck Hull, who pioneered stereolithography, a technique that solidifies liquid resin into solid objects using ultraviolet lasers. Over the decades, 3D printers have evolved from experimental curiosities into tools capable of producing everything from custom prosthetics to complex food designs, architectural models, and even functioning human organs. 

Image
A brain, a DNA strand, and binary code shine across a bluish, glowing background (Credit: Adobe Stock).
CSAIL article

Most people recognize Alzheimer’s from its devastating symptoms such as memory loss, while new drugs target pathological aspects of disease manifestations, such as plaques of amyloid proteins. Now a sweeping new study in the Sept. 4 edition of Cell by MIT researchers shows the importance of understanding the disease as a battle over how well brain cells control the expression of their genes..  The study paints a high-resolution picture of a desperate struggle to maintain healthy gene expression and gene regulation where the consequences of failure or success are nothing less than the loss or preservation of cell function and cognition.

Image
"VaxSeer" can predict dominant flu strains and identify the most protective vaccine candidates. The tool uses deep learning models trained on decades of viral sequences and lab test results to simulate how the flu virus might evolve and how the vaccines will respond (Image: Alex Gagne).
CSAIL article

Every year, global health experts are faced with a high-stakes decision: which flu strains should go into the next seasonal vaccine? The choice must be made months in advance, long before flu season even begins, and it can often feel like a race against the clock. If the selected strains match those that circulate, the vaccine will likely be highly effective. But if the prediction is off, protection can drop significantly, leading to (potentially preventable) illness and strain on healthcare systems.

Image
“Solving robotics is a long-term agenda,” MIT professor Russ Tedrake reflected. “It may take decades. But the debate itself is healthy. It means we’re testing our assumptions and sharpening our tools. The truth is, we’ll probably need both data and models - but which takes the lead, and when, remains unsettled” (Credit: ChatGPT).
CSAIL article

When the IEEE International Conference on Robotics and Automation (ICRA) first convened 40 years ago, the robotics community shared a clear vision: robots would one day combine elegant mathematical models with advanced computation to handle complex tasks. Four decades later, the community is divided over how to reach that goal. That divide was on full display this May in Atlanta, where ICRA marked its anniversary with a unique closing keynote: a live Oxford-style debate on whether “data will solve robotics and automation.”