Author: Lauren Hinkel | MIT-IBM Watson AI Lab<\/p>\n
The early years of faculty members\u2019 careers are a formative and exciting time in which to establish a firm footing that helps determine the trajectory of researchers\u2019 studies. This includes building a research team, which demands innovative ideas and direction, creative collaborators, and reliable resources.\u00a0<\/p>\n
For a group of MIT faculty working with and on artificial intelligence, early engagement with the MIT-IBM Watson AI Lab through projects has played an important role helping to promote ambitious lines of inquiry and shaping prolific research groups.<\/p>\n
Building momentum<\/strong><\/p>\n \u201cThe MIT-IBM Watson AI Lab has been hugely important for my success, especially when I was starting out,\u201d says Jacob Andreas \u2014 associate professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and a researcher with the MIT-IBM Watson AI Lab \u2014 who studies natural language processing (NLP). Shortly after joining MIT, Andreas jump-started his first major project through the MIT-IBM Watson AI Lab, working on language representation and structured data augmentation methods for low-resource languages. \u201cIt really was the thing that let me launch my lab and start recruiting students.\u201d\u00a0<\/p>\n Andreas notes that this occurred during a \u201cpivotal moment\u201d when the field of NLP was undergoing significant shifts to understand language models \u2014 a task that required significantly more compute, which was available through the MIT-IBM Watson AI Lab. \u201cI feel like the kind of the work that we did under that [first] project, and in collaboration with all of our people on the IBM side, was pretty helpful in figuring out just how to navigate that transition.\u201d Further, the Andreas group was able to pursue multi-year projects on pre-training, reinforcement learning, and calibration for trustworthy responses, thanks to the computing resources and expertise within the MIT-IBM community.<\/p>\n For several other faculty members, timely participation with the MIT-IBM Watson AI Lab proved to be highly advantageous as well. \u201cHaving both intellectual support and also being able to leverage some of the computational resources that are within MIT-IBM, that\u2019s been completely transformative and incredibly important for my research program,\u201d says Yoon Kim \u2014 associate professor in EECS, CSAIL, and a researcher with the MIT-IBM Watson AI Lab \u2014 who has also seen his research field alter trajectory. Before joining MIT, Kim met his future collaborators during an MIT-IBM postdoctoral position, where he pursued neuro-symbolic model development; now, Kim\u2019s team develops methods to improve large language model (LLM) capabilities and efficiency.\u00a0<\/p>\n One factor he points to that led to his group\u2019s success is a seamless research process with intellectual partners. This has allowed his MIT-IBM team to apply for a project, experiment at scale, identify bottlenecks, validate techniques, and adapt as necessary to develop cutting-edge methods for potential inclusion in real-world applications. \u201cThis is an impetus for new ideas, and that\u2019s, I think, what\u2019s unique about this relationship,\u201d says Kim.<\/p>\n Merging expertise<\/strong><\/p>\n The nature of the MIT-IBM Watson AI Lab is that it not only brings together researchers in the AI realm to accelerate research, but also blends work across disciplines. Lab researcher and MIT associate professor in EECS and CSAIL Justin Solomon describes his research group as growing up with the lab, and the collaboration as being \u201ccrucial \u2026 from its beginning until now.\u201d Solomon\u2019s research team focuses on theoretically oriented, geometric problems as they pertain to computer graphics, vision, and machine learning.\u00a0<\/p>\n Solomon credits the MIT-IBM collaboration with expanding his skill set as well as applications of his group\u2019s work \u2014 a sentiment that\u2019s also shared by lab researchers Chuchu Fan, an associate professor of aeronautics and astronautics and a member of the Laboratory for Information and Decision Systems, and Faez Ahmed, associate professor of mechanical engineering. \u201cThey [IBM] are able to translate some of these really messy problems from engineering into the sort of mathematical assets that our team can work on, and close the loop,\u201d says Solomon. This, for Solomon, includes fusing distinct AI models that were trained on different datasets for separate tasks. \u201cI think these are all really exciting spaces,\u201d he says.<\/p>\n \u201cI think these early-career projects [with the MIT-IBM Watson AI Lab] largely shaped my own research agenda,\u201d says Fan, whose research intersects robotics, control theory, and safety-critical systems. Like Kim, Solomon, and Andreas, Fan and Ahmed began projects through the collaboration the first year they were able to at MIT. Constraints and optimization govern the problems that Fan and Ahmed address, and so require deep domain knowledge outside of AI.\u00a0<\/p>\n Working with the MIT-IBM Watson AI Lab enabled Fan\u2019s group to combine formal methods with natural language processing, which she says, allowed the team to go from developing autoregressive task and motion planning for robots to creating LLM-based agents for travel planning, decision-making, and verification. \u201cThat work was the first exploration of using an LLM to translate any free-form natural language into some specification that robot can understand, can execute. That\u2019s something that I\u2019m very proud of, and very difficult at the time,\u201d says Fan. Further, through joint investigation, her team has been able to improve LLM reasoning\u00ad \u2014 work that \u201cwould be impossible without the IBM support,\u201d she says.\u00a0 \u00a0<\/p>\n Through the lab, Faez Ahmed\u2019s collaboration facilitated the development of machine-learning methods to accelerate discovery and design within complex mechanical systems. Their Linkages<\/a> work, for instance, employs \u201cgenerative optimization\u201d to solve engineering problems in a way that is both data-driven and has precision; more recently, they\u2019re applying multi-modal data and LLMs to computer-aided design. Ahmed states that AI is frequently applied to problems that are already solvable, but could benefit from increased speed or efficiency; however, challenges \u2014 like mechanical linkages that were deemed \u201calmost unsolvable\u201d \u2014 are now within reach. \u201cI do think that is definitely the hallmark [of our MIT-IBM team],\u201d says Ahmed, praising the achievements of his MIT-IBM group, which is co-lead by Akash Srivastava and Dan Gutfreund of IBM.<\/p>\n What began as initial collaborations for each MIT faculty member has evolved into a lasting intellectual relationship, where both parties are \u201cexcited about the science,\u201d and \u201cstudent-driven,\u201d Ahmed adds. Taken together, the experiences of Jacob Andreas, Yoon Kim, Justin Solomon, Chuchu Fan, and Faez Ahmed speak to the impact that a durable, hands-on, academia-industry relationship can have on establishing research groups and ambitious scientific exploration.<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"