Source/Disclaimer: Am graduate student in Boston area who works in Computer Engineering. I am certain I am overlooking some important info that others should add.
You're right in Saying that Stanford, UCB, UW, and other schools have had a much stronger track record producing deep learning models that have revolutionized certain fields (img classification, segmentation, nlp, etc.). I think MIT has hired some seriously talented researchers who have chosen to invest their time into different avenues of research that align more closely with the "engineering" side of deep learning.
For example, Eyeriss[1] kickstarted the AI accelerator race. Halide[2] is a DSL+runtime used as the basis in a lot of deep learning compilation tools, like Tensor Comprehensions[3]. I don't think you're grossly misinformed, I just think that while other schools have invested in the theory, MIT is betting on a level of abstraction that's a bit lower.
You're right in Saying that Stanford, UCB, UW, and other schools have had a much stronger track record producing deep learning models that have revolutionized certain fields (img classification, segmentation, nlp, etc.). I think MIT has hired some seriously talented researchers who have chosen to invest their time into different avenues of research that align more closely with the "engineering" side of deep learning.
For example, Eyeriss[1] kickstarted the AI accelerator race. Halide[2] is a DSL+runtime used as the basis in a lot of deep learning compilation tools, like Tensor Comprehensions[3]. I don't think you're grossly misinformed, I just think that while other schools have invested in the theory, MIT is betting on a level of abstraction that's a bit lower.
1 = http://eyeriss.mit.edu/ 2 = https://halide-lang.org/ 3 = https://research.fb.com/blog/2018/02/announcing-tensor-compr... 4 = https://openai.com/blog/ai-and-compute/