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This assumes that ML and AI research will continue to be silo'd outside of domain specific research. But it's not the case in academia and also increasingly in
by dhairya 6y ago
This assumes that ML and AI research will continue to be silo'd outside of domain specific research. But it's not the case in academia and also increasingly in industry. You have computational neuroscience, bioinformatics, and many other traditional disciplines which have not only incorporated ML/AI methods but also pushed the fundamental methods research forward. We're increasingly seeing interdisciplinary methods and research becoming the norm in academia. In undergrad, nearly all the social science classes and all the hard science classes had some sort programming and quantitative methods requirement. Even in industry we're seeing interesting multi-disciplinary work.Many interesting innovations in time series ML methods have come from the algorithmic trading firms and medical research community has made contributions to computer vision and unsupervised learning approaches.
I had a colleague who during her PhD in particle physics wrote from high performance parallel computation frameworks from the ground up in C which was better than Hadoop and Spark in performance. And at my last enterprise AI startup, our CTO had come from a computational neuroscience background. Whether these folks end up in creating startups is a different question, but the talent definitely exists.
The more difficult problem is how to evaluate multi-disciplinary startups and businesses. There usually isn't good empirical evidence unless they follow a more established business model.
- michaelhoffman 6y agoAs a computational biologist I have gained sufficient expertise in both computation and biology to know that most of the magic AI biomed stuff proposed by people who have expertise in only one of these areas is utter nonsense. Two PhDs that don't speak the same language isn't a great solution, but one PhD who is a jack-of-both-trades isn't the only alternative either. I feel like I've done well with alternating collaborations with biologists who don't have a computational focus, and quantitative methods folks who don't necessarily have a focus in genomics (what we work on).
- rabidrat 6y agoMaybe you need 3 PhDs for 2 fields: one to go deep in each field, and one 'jack-of-both-trades' to mediate and translate between them.
- not2b 6y agoYes, I think that works best. You need an engineering leader who has some understanding of all of the important problem areas the company faces, even though he/she is unlikely to be the company's leading expert in any of them, or more than one of them, so good decisions can be made.
- CarbyAu 6y agoSo a "Science Communication" degree?
- SQueeeeeL 6y agoMost of the "interdisciplinary" research I see is just one lab sending a dataset they generated from experiments to some ML collaborator, who just run some fairly low hanging Logistic Regression and RF on the data. I don't see a lot of places where people with deep statistical/computational understanding tackle the problems with gathering data, and working on understanding underlying processes. A lot of this comes down to how short PhD programs actually are, learning both neurology, GPU programming (not just plug and chug torch) and statistics would probably take 8-10 years; and most people/schools won't bare that commitment
- dhairya 6y agoThat's fair. It's hard to generalize as there is definitely a mixture of good and bad research out there. As a counter point I had to opportunity to observe the Summer Workshop on the Dynamic Brain (https://alleninstitute.org/what-we-do/brain-science/events-training/summer-workshop-dynamic-brain/#details https://alleninstitute.org/what-we-do/brain-science/events-t...) which had fascinating interdisciplinary research at the intersection of computer vision, ML, data science, and foundational neuroscience research. There many other programs and research groups I can mention that do quality interdisciplinary research and train interdisciplinary students.