Expanding the MIT-IBM Watson AI Lab’s network of neurons

Students and postdocs from across MIT and locally attend a networking event to learn how to become involved in meaningful applicable research projects with real-world impacts.

On October 6, nearly 50 undergraduate and graduate students and postdocs, primarily from MIT, attended the MIT-IBM Watson AI Lab’s networking event. The goal was to connect young researchers with domain experts across the Lab for applied research through the MIT 6-A program offered through the EECS Alliance and the Lab’s summer 2023 internship; the event also helped to give the students a feel for what the Lab has to offer.

The event kicked off with an introduction from David Cox, IBM director of the Lab and director of exploratory AI research at IBM, who provided insights into the Lab’s structure and how the work fits into the larger picture of global machine learning innovation. The Lab, Cox says, is part of IBM Research, which is one of the oldest and largest research labs around the world; part of IBM’s storied history includes their researchers receiving several Nobel Prizes and Turing Awards — that’s more than most countries. “It’s a testament to this long commitment to charting the future of computing,” says Cox. While IBM Research has many different areas of work, such as cloud, AI and security, the Lab is within a part called exploratory science. “So, our job is to be very academic in our goals to chart frontiers, invent new methods, and look at new technologies with fresh eyes, and we focus on artificial intelligence.” In this field, MIT and IBM can trace their roots back to a 1956 workshop, when the term “artificial intelligence” was first coined, and officially joined forces to found the Lab in 2017. Today, in the evolution of machine learning, the Lab’s work sits on the tail-end of narrow AI (emerging and specific, limited-use technologies) and before general AI (revolutionary, coming around 2050 and beyond), in a class Cox described as broad AI, which is disruptive and pervasive. This is characterized by systems that have multi-task, multimodal, multi-domain uses and are easier to broadly apply different problems. Working in this unique area of broad AI, the Lab is considering if the systems we’re building are explainable, secure, ethical and unbiased, able to learn from small data, and have efficient computing infrastructure.

The roughly 70 projects undertaken in the Lab at any one time are jointly conceived and executed, and the research output routinely appears in top AI conferences and journals. Providing a 30,000-foot view, Cox provided a proverbial tour of the research portfolio, consisting of foundation models (like self-supervised learning), synthetic data (generating new data that also helps with security), multimodal research, fluid intelligence (reason and logic), accelerated discovery (chemistry, materials science, climate change), AI for business and decision-making (forecasting supply chains, causal discovery, healthcare, and logistics), efficient AI (hardware/software co-design, cheaper to run, lower energy requirements), and trusted AI and robustness (safe and fair systems, unbiased models, robust to adversarial attacks, human-AI interactions, and explainable systems).

One of the special aspects of the Lab is that it brings together an academic institution and industry, which has the advantage of making real impacts for business. A benefit of this unique structure is that, Cox says, “… we built this member program, where external companies come and co-invest with us…to use cutting edge AI to solve problems that they are facing in their businesses” — an opportunity 6-A program students would be able to leverage for their career growth.

As an integral part of the Lab and the future of the research community, event attendees—the students and early career researchers—were invited to engage with the Lab’s researchers and explore current applied projects through demonstrations, including privacy preserving synthetic data generationstrolling cities that generated realistic city images, creating a molecular grammar for chemical generation and discovery, building giant language model test room (GLTR) that can detect computer-generated text, modeling individual fairness, and an adversarial t-shirt that can make someone invisible to a person detection computer vision model. These showcased many pillars of the Lab’s research and the solutions that have come out of them.

Students expressed interests and backgrounds spanning mathematics, computer science, robotics, cybersecurity, neuroscience, hardware, natural language processing, quantum computing, economics, healthcare, computer vision, algorithms, and software engineering, to name a few. Drawn in, they inquired about overcoming roadblocks in projects, how research questions and problems evolved, how certain results were achieved and the thinking behind it, and how their background would fit into a particular team and area of inquiry.

“It was great to see such a high level of engagement from such young researchers,” says Aude Oliva, MIT director of the MIT-IBM Watson AI Lab and director of strategic industry engagement in the MIT Stephen A. Schwarzman College of Computing. “As the pace of computing and machine learning innovation skyrockets, students and early-career researchers are and will continue to be vital contributors of novel ideas and creative solutions to new research questions and industry problems in AI and computing.”

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