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Please join the Annual AI & Quantum Summit, hosted by CSAIL Alliances and the MIT Center for Quantum Engineering (MIT CQE).  This event is in-person at MIT with a virtual option. 

 

On October 23rd, 2025, CSAIL and MIT experts will gather to explore how the field of quantum computing is changing, how AI innovation is molding quantum’s trajectory, and what business leaders should keep in mind as theory becomes reality.

 

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"Meschers" can create multi-dimensional versions of objects that break the laws of physics with convoluted geometries, such as buildings you might see in an M.C. Escher illustration (left) and objects that are shaded in impossible ways (center and right) (Credits: Alex Shipps/MIT CSAIL, using assets from Pixabay and the researchers).
CSAIL article

M.C. Escher’s artwork is a gateway into a world of depth-defying optical illusions, featuring “impossible objects” that break the laws of physics with convoluted geometries. What you perceive his illustrations to be depends on your point of view — for example, a person seemingly walking upstairs may be heading down the steps if you tilt your head sideways.

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alt="A system capable of generating images normally requires a tokenizer, which compresses and encodes visual data, along with a generator that can combine and arrange these compact representations in order to create novel images. MIT researchers discovered a new method to create, convert, and “inpaint” images without using a generator at all. This image shows how an input image can be gradually modified by optimizing tokens (Credits: Image courtesy of the authors)."
CSAIL article

AI image generation — which relies on neural networks to create new images from a variety of inputs, including text prompts — is projected to become a billion-dollar industry by the end of this decade. Even with today’s technology, if you wanted to make a fanciful picture of, say, a friend planting a flag on Mars or heedlessly flying into a black hole, it could take less than a second. However, before they can perform tasks like that, image generators are commonly trained on massive datasets containing millions of images that are often paired with associated text. Training these generative models can be an arduous chore that takes weeks or months, consuming vast computational resources in the process.

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The “PhysicsGen” system can multiply a few dozen VR demonstrations into nearly 3,000 simulations per machine for mechanical companions like robotic arms and hands (Credit: Alex Shipps/MIT CSAIL using photos from the researchers).
CSAIL article

When ChatGPT or Gemini gives what seems to be an expert response to your burning questions, you may not realize how much information it relies on to give that reply. Like other popular artificial intelligence (AI) models, these chatbots rely on backbone systems called foundation models that train on billions or even trillions of data points.

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Ray and Maria Stata Center exterior
CSAIL article

MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has announced a new direction for its long-standing FinTech research initiative, now FinTechAI@CSAIL, to highlight the central role artificial intelligence is playing in shaping the future of finance.

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A robotic arm learns to understand its own body (Credit: Courtesy of the researchers).
CSAIL article

In an office at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), a soft robotic hand carefully curls its fingers to grasp a small object. The intriguing part isn’t the mechanical design or embedded sensors – in fact, the hand contains none. Instead, the entire system relies on a single camera that watches the robot’s movements and uses that visual data to control it.

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Top row, left to right: Matthew Caren, April Qiu Cheng, Arav Karighattam, and Benjamin Lou. Bottom row, left to right: Isabelle Quaye, Albert Qin, Ananthan Sadagopan, and Gianfranco (Franco) Yee (Credits: Photos courtesy of the Hertz Foundation).
CSAIL article

The Hertz Foundation announced that it has awarded fellowships to eight MIT affiliates. The prestigious award provides each recipient with five years of doctoral-level research funding (up to a total of $250,000), which gives them an unusual measure of independence in their graduate work to pursue groundbreaking research.

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alt="SketchAgent uses a multimodal language model to turn natural language prompts into sketches in a few seconds. It can doodle on its own or through collaboration, drawing with a human or incorporating text-based input to sketch each part separately (Credits: Alex Shipps/MIT CSAIL, with AI-generated sketches from the researchers)."
CSAIL article

When you’re trying to communicate or understand ideas, words don’t always do the trick. Sometimes the more efficient approach is to do a simple sketch of that concept — for example, diagramming a circuit might help make sense of how the system works.

But what if artificial intelligence could help us explore these visualizations? While these systems are typically proficient at creating realistic paintings and cartoonish drawings, many models fail to capture the essence of sketching: its stroke-by-stroke, iterative process, which helps humans brainstorm and edit how they want to represent their ideas.