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A new paper by MIT CSAIL researchers maps the many software-engineering tasks beyond code generation, identifies bottlenecks, and highlights research directions to overcome them. The goal: to let humans focus on high-level design, while routine work is automated (Credits: Alex Shipps/MIT CSAIL, using assets from Shutterstock and Pixabay).
CSAIL article

Imagine a future where artificial intelligence quietly shoulders the drudgery of software development: refactoring tangled code, migrating legacy systems, and hunting down race conditions, so that human engineers can devote themselves to architecture, design, and the genuinely novel problems still beyond a machine’s reach. Recent advances appear to have nudged that future tantalizingly close, but a new paper by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and several collaborating institutions argues that this potential future reality demands a hard look at present-day challenges. 

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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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Course starts September 22, 2025

Cybersecurity is no longer just a technical issue—it’s a strategic imperative as threats grow more complex and persistent. Technical leaders must understand how systems are constructed, how to detect breaches, and how to implement policies that protect long-term resilience.

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The first successful organ transplant was less than 75 years ago. Despite significant progress since then, many patients still fall through the gaps of what remains a complicated procedure (Credits: Alex Ouyang/MIT Jameel Clinic).
CSAIL article

In 1954, the world’s first successful organ transplant took place at Brigham and Women’s Hospital, in the form of a kidney donated from one twin to the other. At the time, a group of doctors and scientists had correctly theorized that the recipient’s antibodies were unlikely to reject an organ from an identical twin. One Nobel Prize and a few decades later, advancements in immune-suppressing drugs increased the viability of and demand for organ transplants. Today, over 1 million organ transplants have been performed in the United States, more than any other country in the world.

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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.