When you ask questions about intelligence, are you only thinking about human intelligence? This is not the case for Phillip Isola, an associate professor at MIT’s Department of Electrical Engineering and Computer Science (EECS). For Isola, the study of intelligence is a complex intersection of cognition and computation, where humans, animals, and even machines all have something fascinating to reveal.
An enthusiastic member of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Isola dedicates his research to the intricacies of computer vision and machine learning. He’s curious about how artificial intelligence (AI) models interpret and understand the world around them, and how this reveals unexpected facets of our cognitive processes.
Raised in Northern California, Isola’s love for understanding natural processes took root during his childhood explorations of local hills and coastlines. This curiosity carried him through his academic journey, which led him from shipless exploration of numerous subjects at Yale University, right into the heart of cognitive science. Under the supervision of Prof. Brian Scholl at Yale’s Department of Psychology, Isola’s fascination with the human brain’s complexity outshone even his interest in planetary formation. This marked the starting point to his life-long quest to understand intelligence.
His path then diverged a bit into indie video game development before taking on graduate studies in brain and cognitive sciences at MIT. Behind the guiding hand of vision science professor, Ted Adelson, Isola found an intellectual haven that valued pursuing deep, fundamental understanding over superficial benchmarks. It was here that his work began to converge with artificial intelligence, leading him to explore how computational models could provide fresh insights into our understanding of cognition. Isola’s thesis, focusing on perceptual grouping, ended up paving the way for self-supervised learning – a technique that facilitates AI learning from unlabeled data.
Isola’s research took an even deeper turn into computer science during his postdoctoral work at UC Berkeley. He ventured into the area of image-to-image translation models, making significant contributions to the development of early generative AI systems. These systems were capable of transforming simple sketches into realistic images, and even colorizing black-and-white photographs. Following this experience, he spent a year at OpenAI, attracted both by its ethos and its focus on reinforcement learning.
However, his ultimate ambition was to spearhead his own research unit, a goal he achieved upon returning to MIT as faculty.
His playing field is now his lab and his team, with whom he shares the thrill of unearthing new discoveries. Together, they delve into the ways machines and humans form internal representations of the world, particularly in the aspect of learning. Interestingly, they found that AI models, regardless of whether they were trained on language, images, or audio, all seem to develop similar internal structures as they become more complex.
This breakthrough led him to propose the Platonic Representation Hypothesis. Rooted in Plato’s allegory of the cave, this idea posits the convergence of all these models’ understanding of reality, despite their different input materials. According to Isola, these models are all learning different “shadows” of the same underlying world.
Isola’s research also delves into self-supervised learning, a valuable tool in overcoming the limitations and expenses tied with labeling data. Driven more by insights and principles rather than performance benchmarks, his approach involves high-risk research that he believes will lead to significant breakthroughs in understanding intelligence.
Isola’s passion extends beyond research, and into teaching. Having co-launched MIT’s deep learning course, his initiative has seen a student body growth from 30 to over 700. Despite the exponential growth and advances in AI, he underscores to his students that intelligent machines are still relatively simple and encourages them to question today’s truths.
Isola’s vision of the future finds humans and machines living side by side, each retaining their uniqueness and purpose. He asserts, “There’s going to be a coexistence, and I’m starting to think about what role I can play in that future”. His curiosity is ceaseless, grounded in a wisdom that recognizes the simplicity of intelligence, once understood. This enduring thirst for knowledge ensures that his journey remains as fascinating as its destination.
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