A new tool makes it easier for database users to perform complicated statistical analyses of tabular data without the need to know what is going on behind the scenes.

The impact of artificial intelligence will never be equitable if there’s only one company that builds and controls the models (not to mention the data that go into them). Unfortunately, today’s AI models are made up of billions of parameters that must be trained and tuned to maximize performance for each use case, putting the most powerful AI models out of reach for most people and companies.

Someday, you may want your home robot to carry a load of dirty clothes downstairs and deposit them in the washing machine in the far-left corner of the basement. The robot will need to combine your instructions with its visual observations to determine the steps it should take to complete this task.

The internet is awash in instructional videos that can teach curious viewers everything from cooking the perfect pancake to performing a life-saving Heimlich maneuver.

When you’re trying to understand which diseases or physical traits you’re predisposed to, the answers are sprinkled across your DNA. One powerful method for decoding this genetic forecast is polygenic scores, which give patients estimates of their risk for a condition and the likelihood of having physical characteristics (phenotypes, like being tall). Researchers seek to improve the accuracy of these cumulative predictions to account for most of the known genetic contributions.

Your heart is a tireless organ that beats about 3 billion times over an average lifetime and is simply essential for life. Unsurprisingly, cardiovascular disease is the leading cause of death worldwide, costing millions of lives each year. This relentless condition primarily damages the heart, which is divided into four main chambers: the right atrium, left atrium, right ventricle, and left ventricle. Understanding the functions and vulnerabilities of these chambers is crucial in the fight against heart disease.

Imagine a slime-like robot that can seamlessly change its shape to squeeze through narrow spaces, which could be deployed inside the human body to remove an unwanted item.

The allure of whales has stoked human consciousness for millennia, casting these ocean giants as enigmatic residents of the deep seas. From the biblical Leviathan to Herman Melville's formidable Moby Dick, whales have been central to mythologies and folklore. And while cetology, or whale science, has improved our knowledge of these marine mammals in the past century in particular, studying whales has remained a formidable a challenge.

When MIT professor and now Computer Science and Artificial Intelligence Laboratory (CSAIL) member Peter Shor first demonstrated the potential of quantum computers to solve problems faster than classical ones, he inspired scientists to imagine countless possibilities for the emerging technology. Thirty years later, though, the quantum edge remains a peak not yet reached.

In biomedicine, segmentation involves annotating pixels from an important structure in a medical image, like an organ or cell. Artificial intelligence models can help clinicians by highlighting pixels that may show signs of a certain disease or anomaly.