When it comes to artificial intelligenceMIT and IBM were there at the beginning: laying foundational work and creating some of the first programs — AI predecessors — and theorizing how machine “intelligence” might come to be.
Todaycollaborations like the MIT-IBM Watson AI Labwhich launched eight years agoare continuing to deliver expertise for the promise of tomorrow’s AI technology. This is critical for industries and the labor force that stand to benefitparticularly in the short term: from $3-4 trillion of forecast global economic benefits and 80 percent productivity gains for knowledge workers and creative tasksto significant incorporations of generative AI into business processes (80 percent) and software applications (70 percent) in the next three years.
While industry has seen a boom in notable modelschiefly in the past yearacademia continues to drive the innovationcontributing most of the highly cited research. At the MIT-IBM Watson AI Labsuccess takes the form of 54 patent disclosuresan excess of 128,000 citations with an h-index of 162and more than 50 industry-driven use cases. Some of the lab’s many achievements include improved stent placement with AI imaging techniquesslashing computational overheadshrinking models while maintaining performanceand modeling of interatomic potential for silicate chemistry.
“The lab is uniquely positioned to identify the ‘right’ problems to solvesetting us apart from other entities,” says Aude Olivalab MIT director and director of strategic industry engagement in the MIT Schwarzman College of Computing. “Furtherthe experience our students gain from working on these challenges for enterprise AI translates to their competitiveness in the job market and the promotion of a competitive industry.”
“The MIT-IBM Watson AI Lab has had tremendous impact by bringing together a rich set of collaborations between IBM and MIT’s researchers and students,” says Provost Anantha Chandrakasanwho is the lab’s MIT co-chair and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “By supporting cross-cutting research at the intersection of AI and many other disciplinesthe lab is advancing foundational work and accelerating the development of transformative solutions for our nation and the world.”
Long-horizon work
As AI continues to garner interestmany organizations struggle to channel the technology into meaningful outcomes. A 2024 Gartner study finds that“at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025,” demonstrating ambition and widespread hunger for AIbut a lack of knowledge for how to develop and apply it to create immediate value.
Herethe lab shinesbridging research and deployment. The majority of the lab’s current-year research portfolio is aligned to use and develop new featurescapacitiesor products for IBMthe lab’s corporate membersor real-world applications. The last of these comprise large language modelsAI hardwareand foundation modelsincluding multi-modalbio-medicaland geo-spatial ones. Inquiry-driven students and interns are invaluable in this pursuitoffering enthusiasm and new perspectives while accumulating domain knowledge to help derive and engineer advancements in the fieldas well as opening up new frontiers for exploration with AI as a tool.
Findings from the AAAI 2025 Presidential panel on the Future of AI Research support the need for contributions from academia-industry collaborations like the lab in the AI arena: “Academics have a role to play in providing independent advice and interpretations of these results [from industry] and their consequences. The private sector focuses more on the short termand universities and society more on a longer-term perspective.”
Bringing these strengths togetheralong with the push for open sourcing and open sciencecan spark innovation that neither could achieve alone. History shows that embracing these principlesand sharing code and making research accessiblehas long-term benefits for both the sector and society. In line with IBM and MIT’s missionsthe lab contributes technologiesfindingsgovernanceand standards to the public sphere through this collaborationthereby enhancing transparencyaccelerating reproducibilityand ensuring trustworthy advances.
The lab was created to merge MIT’s deep research expertise with IBM’s industrial R&D capacityaiming for breakthroughs in core AI methods and hardwareas well as new applications in areas like health carechemistryfinancecybersecurityand robust planning and decision-making for business.
Bigger isn't always better
Todaylarge foundation models are giving way to smallermore task-specific models yielding better performance. Contributions from lab members like Song Hanassociate professor in the MIT Department of Electrical Engineering and Computer Science (EECS)and IBM Research’s Chuang Gan help make this possiblethrough work such as once-for-all and AWQ. Innovations such as these improve efficiency with better architecturesalgorithm shrinkingand activation-aware weight quantizationletting models like language processing run on edge devices at faster speeds and reduced latency.
Consequentlyfoundationvisionmultimodaland large language models have seen benefitsallowing for the lab research groups of OlivaMIT EECS Associate Professor Yoon Kimand IBM Research members Rameswar PandaYang Zhangand Rogerio Feris to build on the work. This includes techniques to imbue models with external knowledge and the development of linear attention transformer methods for higher throughputcompared to other state-of-the-art systems.
Understanding and reasoning in vision and multimodal systems has also seen a boon. Works like “Task2Sim” and “AdaFuse” demonstrate improved vision model performance if pre-training takes place on synthetic dataand how video action recognition can be boosted by fusing channels from past and current feature maps.
As part of a commitment to leaner AIthe lab teams of Gregory Wornellthe MIT EECS Sumitomo Electric Industries Professor in EngineeringIBM Research’s Chuang Ganand David CoxVP for foundational AI at IBM Research and the lab’s IBM directorhave shown that model adaptability and data efficiency can go hand in hand. Two approachesEvoScale and Chain-of-Action-Thought reasoning (COAT)enable language models to make the most of limited data and computation by improving on prior generation attempts through structured iterationnarrowing in on a better response. COAT uses a meta-action framework and reinforcement learning to tackle reasoning-intensive tasks via self-correctionwhile EvoScale brings a similar philosophy to code generationevolving high-quality candidate solutions. These techniques help to enable resource-conscioustargetedreal-world deployment.
“The impact of MIT-IBM research on our large language model development efforts cannot be overstated,” says Cox. “We’re seeing that smallermore specialized models and tools are having an outsized impactespecially when they are combined. Innovations from the MIT-IBM Watson AI Lab help shape these technical directions and influence the strategy we are taking in the market through platforms like watsonx.”
For examplenumerous lab projects have contributed featurescapabilitiesand uses to IBM’s Granite Visionwhich provides impressive computer vision designed for document understandingdespite its compact size. This comes at a time when there’s a growing need for extractioninterpretationand trustworthy summarization of information and data contained in long formats for enterprise purposes.
Other achievements that extend beyond direct research on AI and across disciplines are not only beneficialbut necessary for advancing the technology and lifting up societyconcludes the 2025 AAAI panel.
Work from the lab’s Caroline Uhler and Devavrat Shah — both Andrew (1956) and Erna Viterbi Professors in EECS and the Institute for DataSystemsand Society (IDSS) — along with IBM Research’s Kristjan Greenewaldtranscends specializations. They are developing causal discovery methods to uncover how interventions affect outcomesand identify which ones achieve desired results. The studies include developing a framework that can both elucidate how “treatments” for different sub-populations may play outlike on an ecommerce platform or mobility restrictions on morbidity outcomes. Findings from this body of work could influence the fields of marketing and medicine to education and risk management.
“Advances in AI and other areas of computing are influencing how people formulate and tackle challenges in nearly every discipline. At the MIT-IBM Watson AI Labresearchers recognize this cross-cutting nature of their work and its impactinterrogating problems from multiple viewpoints and bringing real-world problems from industryin order to develop novel solutions,” says Dan HuttenlocherMIT lab co-chairdean of the MIT Schwarzman College of Computingand the Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science.
A significant piece of what makes this research ecosystem thrive is the steady influx of student talent and their contributions through MIT’s Undergraduate Research Opportunities Program (UROP)MIT EECS 6A Program, and the new MIT-IBM Watson AI Lab Internship Program. Altogethermore than 70 young researchers have not only accelerated their technical skill developmentbutthrough guidance and support by the lab’s mentorsgained knowledge in AI domains to become emerging practitioners themselves. This is why the lab continually seeks to identify promising students at all stages in their exploration of AI’s potential.
“In order to unlock the full economic and societal potential of AIwe need to foster ‘useful and efficient intelligence,’” says Sriram RaghavanIBM Research VP for AI and IBM chair of the lab. “To translate AI promise into progressit’s crucial that we continue to focus on innovations to develop efficientoptimizedand fit-for-purpose models that can easily be adapted to specific domains and use cases. Academic-industry collaborationssuch as the MIT-IBM Watson AI Labhelp drive the breakthroughs that make this possible.”
