4 ms·

This may be slightly OT, but my layperson perspective is for a pre-eminent STEM university, that MIT always seems to lag behind its rivals in deep learning and

by caycep 7y ago

This may be slightly OT, but my layperson perspective is for a pre-eminent STEM university, that MIT always seems to lag behind its rivals in deep learning and the latest ml advances were spearheaded at Stanford/Silicon Valley outfits, the Montreal/Toronto academic communities, Caltech and to some extent in the UK. Whereas its background should have made it one of the leaders in the field. Am I overlooking something, or just gross misinformed? Did MIT culturally stay married for a while to the Minsky school of deterministic AI vs. the Big Data statistical approach?

dwrodri 7y ago
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. 1 = http://eyeriss.mit.edu/ http://eyeriss.mit.edu/ 2 = https://halide-lang.org/ https://halide-lang.org/ 3 = https://research.fb.com/blog/2018/02/announcing-tensor-comprehensions/ https://research.fb.com/blog/2018/02/announcing-tensor-compr... 4 = https://openai.com/blog/ai-and-compute/ https://openai.com/blog/ai-and-compute/
lonelappde 7y ago
Big Data requires Big Computers (and Big Brother), which came from Google. MIT did't have the $100Billion to spend on hardware and create the culture.
mrdrozdov 7y ago
Publications at NeurIPS/ICML is a reasonable proxy for contribution to the field. https://medium.com/@chuvpilo/ai-research-rankings-2019-insights-from-neurips-and-icml-leading-ai-conferences-ee6953152c1a https://medium.com/@chuvpilo/ai-research-rankings-2019-insig... In 2019, the top 5 institutions were: 1. Google (USA) — 167.3 2. Stanford University (USA) — 82.3 3. MIT (USA) — 69.8 4. Carnegie Mellon University (USA) — 67.7 5. UC Berkeley (USA) — 54.0
- etrk 7y ago
  Citations tend to be a better indicator of impact. There are some interesting analysis here [1]. MIT has the most citations, and they've also published the most papers by far. If considering citations per paper, MIT falls in the middle of the pack. Toronto is an outlier with a very high number of citations per paper. NeurIPs is a big conference covering lots of topics, though. All of this says relatively little about MIT's true impact on deep learning in the last eight years. [1] https://www.microsoft.com/en-us/research/project/academic/articles/neurips-conference-analytics/ https://www.microsoft.com/en-us/research/project/academic/ar...

by caycep 7y ago

dwrodri 7y ago

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. 1 = http://eyeriss.mit.edu/ http://eyeriss.mit.edu/ 2 = https://halide-lang.org/ https://halide-lang.org/ 3 = https://research.fb.com/blog/2018/02/announcing-tensor-comprehensions/ https://research.fb.com/blog/2018/02/announcing-tensor-compr... 4 = https://openai.com/blog/ai-and-compute/ https://openai.com/blog/ai-and-compute/

lonelappde 7y ago

Big Data requires Big Computers (and Big Brother), which came from Google. MIT did't have the $100Billion to spend on hardware and create the culture.

mrdrozdov 7y ago

Publications at NeurIPS/ICML is a reasonable proxy for contribution to the field. https://medium.com/@chuvpilo/ai-research-rankings-2019-insights-from-neurips-and-icml-leading-ai-conferences-ee6953152c1a https://medium.com/@chuvpilo/ai-research-rankings-2019-insig... In 2019, the top 5 institutions were: 1. Google (USA) — 167.3 2. Stanford University (USA) — 82.3 3. MIT (USA) — 69.8 4. Carnegie Mellon University (USA) — 67.7 5. UC Berkeley (USA) — 54.0

etrk 7y ago
Citations tend to be a better indicator of impact. There are some interesting analysis here [1]. MIT has the most citations, and they've also published the most papers by far. If considering citations per paper, MIT falls in the middle of the pack. Toronto is an outlier with a very high number of citations per paper. NeurIPs is a big conference covering lots of topics, though. All of this says relatively little about MIT's true impact on deep learning in the last eight years. [1] https://www.microsoft.com/en-us/research/project/academic/articles/neurips-conference-analytics/ https://www.microsoft.com/en-us/research/project/academic/ar...