Startup Events
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IP Boot Camp—Build Your Fortress, Plot Your Exit, from CSAIL Alliances and VMS
You have an invention. What's your strategy for turning it into a fortress of value?

Join this highly-anticipated collaborative event between CSAIL Alliances and Venture Mentoring Service: MIT innovation community's essential boot camp on intellectual property, now featuring a critical new perspective on corporate engagement, acquisition, and licensing!

This is more than a legal lecture, it’s a high-level masterclass for every startup founder, seasoned inventor, and mentor who wants to maximize the commercial potential of their work.

alt="Encoding-Decoding Constellations by Rebecca Lin (Credit: Jimmy Day/MIT Media Lab)."
CSAIL article
Polyglot geometry

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.

Given the prompt “Make me a chair” and feedback “I want panels on the seat,” the robot assembles a chair and places panel components according to the user prompt (Credits: Courtesy of the researchers).
CSAIL article
“Robot, make me a chair”

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.

CSAIL’s approach uses an LLM to plan how to answer complex reasoning tasks, then divides the legwork of that strategy among smaller language models. Their method helps LMs provide more accurate responses than leading LLMs and approach the precision of top reasoning systems, while being more efficient than both (Credit: Alex Shipps/MIT CSAIL).
CSAIL article
New method enables small language models to solve complex reasoning tasks

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.

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Dual-use Ventures IAP Course

Hosted by our friends at Mission Innovation X, this stop in the CSAIL Alliances Startup Success Journey focuses on dual-use and what that means for technology.

 

(Not sure what we're talking about? Learn about the Start Up Success Journey and MIT IAP).

 

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IAP Course: Nexus II: The Nexus of Games and AI

This MIT IAP credited course is offered by CSAIL Alliances member Sony Interactive Entertainment (the team behind PlayStation). Curious about IAP? Learn more.

 

alt="The automated, multimodal approach developed by MIT researchers interprets artificial vision models that evaluate the properties of images (Credits: iStock)."
CSAIL article
Guided learning lets "untrainable" neural networks realize their potential

Even networks long considered “untrainable” can learn effectively with a bit of a helping hand. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have shown that a brief period of alignment between neural networks, a method they call guidance, can dramatically improve the performance of architectures previously thought unsuitable for modern tasks.

MIT researchers are teaching robots to understand their own limits while still achieving their goals, ensuring the machines move safely and never overextend themselves (Credits: Maximilian Stölzle and Joey Impoza Roberts).
CSAIL article
New control system teaches soft robots the art of staying safe

Imagine having a continuum soft robotic arm bend around a bunch of grapes or broccoli, adjusting its grip in real time as it lifts the object. Unlike traditional rigid robots that generally aim to avoid contact with the environment as much as possible and stay far away from humans for safety reasons, this arm senses subtle forces, stretching and flexing in ways that mimic more of the compliance of a human hand. Its every motion is calculated to avoid excessive force while achieving the task efficiently. In MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and Laboratory for Information and Decisions Systems (LIDS) labs, these seemingly simple movements are the culmination of complex mathematics, careful engineering, and a vision for robots that can safely interact with humans and delicate objects.

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