MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to test whenever a model is deployed in a new setting.
Gemstones like precious opal are beautiful to look at and deceivingly complex. As you look at such gems from different angles, you’ll see a variety of tints glisten, causing you to question what color the rock actually is. It’s iridescent thanks to something called structural color — microscopic structures that reflect light to produce radiant hues.
Generative artificial intelligence models have left such an indelible impact on digital content creation that it’s getting harder to recall what the internet was like before it. You can call on these AI tools for clever projects such as videos and photos — but their flair for the creative hasn’t quite crossed over into the physical world just yet.
MIT researchers have developed a new method for designing 3D structures that can be transformed from a flat configuration into their curved, fully formed shape with only a single pull of a string.
Whether you’re a scientist brainstorming research ideas or a CEO hoping to automate a task in human resources or finance, you’ll find that artificial intelligence (AI) tools are becoming the assistants you didn’t know you needed. In particular, many professionals are tapping into the talents of semi-autonomous software systems called AI agents, which can call on AI at specific points to solve problems and complete tasks.
Most languages use word position and sentence structure to extract meaning. For example, “The cat sat on the box,” is not the same as “The box was on the cat.” Over a long text, like a financial document or a novel, the syntax of these words likely evolves.
To innovate as a technologist, you need to be a polyglot—fluent in multiple languages of problem-solving, able to synthesize ideas across domains, reframing puzzles to visualize different outcomes, and revealing the questions that have yet to be asked.
Computer-aided design (CAD) systems are tried-and-true tools used to design many of the physical objects we use each day. But CAD software requires extensive expertise to master, and many tools incorporate such a high level of detail they don’t lend themselves to brainstorming or rapid prototyping.
As language models (LMs) improve at tasks like image generation, trivia questions, and simple math, you might think that human-like reasoning is around the corner. In reality, they still trail us by a wide margin on complex tasks. Try playing Sudoku with one, for instance, where you fill in numbers one through nine in such a way that each appears only once across the columns, rows, and sections of a nine-by-nine grid. Your AI opponent will either fail to fill in boxes on its own or do so inefficiently, though it can verify if you’ve filled yours out correctly.
