MIT Schwarzman College of Computing

CodeSignal, co-founded by alumnus and CEO Tigran Sloyan, has developed Agentic Skills Intelligence, a new product that “uses AI agents to evaluate the skills people use in their work and links the findings to personalized practice,” writes Forbes’ Ray Ravaglia. CodeSignal has built an AI agent “for each domain in its job taxonomy, such as engineering and sales, along with agents for capabilities that cut across jobs, among them communication and AI literacy,” notes Ravaglia. “The agents draw on systems including Gong, Salesforce, and GitHub, and they score what they find against rubrics the company says are grounded in industrial-organizational psychology.” 

Prof. Ryan Williams speaks to TechCrunch’s Tim Fernholz about AI platforms refuting the Exponential Time Hypothesis, part of a broader class of problems known as P versus NP. “I do think that it kind of calls into question a lot of the intuitions that we thought we had about … how tractable [hard NP problems] are. I also think that this is probably not the final answer either,” says Williams. AI is “forcing people to be a little more ambitious about the kind of work that they do, and to think more deeply about, okay, if all the things I used to work on are now low-hanging fruit for AI, how can I raise the bar for the kind of scientific questions I want to ask?” 

Instead of giving AI agents a specific task, MIT researchers put 13 agents, named Eko, alone on a digital desert island for 30 hours, and witnessed the technology playing instead of simply wandering around, writes Fast Company’s Jude Cramer. “The researchers compared Eko’s behavior to common elements of play in animals, including humans,” notes Cramer. “These signs of play include creating their own rules, repeating behaviors with slight variations, setting goals for themselves, and transforming objects and events through make-believe.” 

After observing the behavior of 13 AI agents on a simulated desert island, MIT researchers suggest that allowing agents to explore “without a goal” could be an effective way to teach AI agents new skills, reports Aditya Mehta for TechCrunch. “We normally develop artificial agents by deciding in advance what they should practice, what they should optimize, and what counts as progress,” the researchers write. “Play suggests another regime: give an agent a sufficiently rich and safe world, let it choose what is interesting, and allow useful knowledge to accumulate from the history that follows.” 

Flapping Airplanes, a startup co-founded by Benjamin Spector ’22, MEng ’22, has developed a framework for AI models to run efficiently while being trained on less data and in a single graphics processing unit (GPU), writes Julie Bort for TechCrunch. The framework “can run a task that spends most of its time waiting for data to be fetched alongside one that spends most of its time doing math, so the chip is rarely sitting idle,” notes Bort. “The premise is that AI has barely scratched the surface of what GPUs can do, and they believe it can create intelligent systems ‘without ingesting half the internet.’”  

Eka Robotics, a startup cofounded by Prof. Pulkit Agrawal, has developed a robot with  a force-sensitive gripper that can “pick up about 1,500 objects that weigh up to 1 kg in an hour, compared to about 600 for a human” with 95% accuracy, reports Tim Fernholz for TechCrunch.  “We are starting off being very pragmatic, and saying here are two verticals in pick, arrange, place, and wire handling, let’s make them be home runs,” says Agrawal. “Once we have made home runs on them, the sky is the limit in how much we can do with the approach we are developing.” 

Prof. Jacob Andreas speaks with CNN’s Lisa Eadicicco about deciphering the chain-of-thought behind complex AI neural networks. “I think we’re still very far from being able to claim that we have any sort of fine-grained understanding of how the model is actually arriving at a solution,” says Andreas.  

MIT researchers gave 13 AI agents, named Eko, free rein, on a simulated desert island for 30 hours, and observed as the agents played and learned from their surroundings like humans, writes Gizmodo’s Webb Wright. “We had this mindset to have as [few] constraints as possible, because we really wanted to see what the behavior of these autonomous agents is when you don’t really tell them what to do,” says graduate student Nathan Cloos. “Play is really about curiosity or novelty-seeking behavior, and this is essentially what Eko does,” Prof. Daniela Rus adds.  

At the STAT Summit, President Sally Kornbluth joined Holden Thorp, editor-in-chief of the Science family of journals, for a panel moderated by STAT’s Anil Oza. During the conversation, Kornbluth emphasized the importance of curiosity-driven research and education at institutions like MIT, highlighting MIT for America, a new initiative aimed at ensuring learners and educators from Kindergarten through community college across the country have access to STEM education. “We want to both meet people where they are, give their kids the opportunity, and also make them understand us as people who care about their communities,” says Kornbluth.

Forbes’ Lisa Chambers highlights MIT as one of the “12 top colleges reinventing education for the AI era.” “At Forbes’ No. 1-ranked college in the nation—and one of the world’s leading technology schools—AI is hardly confined to a single degree,” writes Chambers. “MIT’s undergraduate offerings range from machine learning, computer vision and natural language processing to robotics, deep-learning hardware and AI applications in biology and medicine.”  

Boston Globe reporter Jon Chesto spotlights the fall fair event held as part of the MIT x GE Vernova Energy & Climate Alliance, during which, GE Vernova CEO Scott Strazik shared his enthusiasm for hiring MIT graduates. “Something we talk a lot about inside Vernova is the world is changing very quickly but we need to change quicker and even faster than that so we can lead in the world,” says Strazik. “Over the last year-plus on this campus, we see a lot of opportunities for us to get better through bringing you into the organization.”  

In an interview with Bloomberg, Prof. Justin Solomon discusses how mathematicians are responding to advances in AI in the wake of an AI platform solving the Navier-Stokes problem. “The really interesting part [of proof writing] is the modeling and figuring out which questions to ask and what insights they bring us to the universe, and the AI tools are not doing that,” says Solomon. “Writing the prompts is very much what we’ve been teaching our students to do for centuries now, and that skill, I think, persists.” 

Prof. Virginia Vassilevska Williams speaks to the Scientific American’s Peter Hall and Joseph Howlett about AI assisting in faster matrix multiplications. “Matrix multiplication is needed all over the place,” says Williams. “But also, its complexity is one of the great mysteries in computer science. In a maybe diabolical sense, the advance on matrix multiplication can be seen as an attempt by the LLMs to speed themselves up.”  

With help from an artificial intelligence algorithm, MIT researchers have formulated heat-resistant mRNA COVID vaccines that “remain stable even when stored at room temperature for up to a year, or at nearly 100 degrees Fahrenheit for two months,” writes Nancy Lapid for Reuters. The heat resistance shots “would lower costs and make them [COVID vaccines] available in regions where ultra-cold storage is not possible,” Lapid notes.  

Forbes’ Rashi Shrivastava highlights MIT spinout Liquid AI, which is building “liquid neural networks that process data more efficiently and don’t need expensive chips stored in data centers.” Shrivastava writes, “Liquid AI’s models are small enough that they can run directly on laptops and phones. There’s no cost to host them.”