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The dual PhD problem of today’s startups

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Re: The dual PhD problem of today’s startups

#11

Post by michaelhoffman »

dhairya wrote: post #7

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 a…

As 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).

Re: The dual PhD problem of today’s startups

#12

Post by dnautics »

crmrc114 wrote: post #2

This is not a new problem when people look at technology all day. You are basically saying that you have seen so many birds that there can never be a black swan. Not without complex xy and z factors. This is a perspective problem that can tie you down into some interesting thoughts such as "There is nothing else to be invented new." -or- "The innovation will happen here, in this little corner, where I and others say…

> and no one understood the applications it would have to future technology until much later on

Huh? FHSS was a specific wartime effort with a wartime goal that, aside from that its modern application is mostly civilian, is not terribly distant from its intended use case.

Re: The dual PhD problem of today’s startups

#13

Post by pnathan »

The solution involves large companies, not startups. You have an infrastructure and a pool of highly qualified applications that aren't going make or break their personal finances on these problems. These are classic coordination problems, and it requires people skilled at this aspect.

Large companies don't talk about their work in this area much for a while for a variety of reasons. Bell Labs was a thing once....

It's sort of a solvedish problem if you imagine that you are not bound by "silicon valley 2-person startup" rules.

Re: The dual PhD problem of today’s startups

#14

Post by SQueeeeeL »

dhairya wrote: post #7

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 a…

Most 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

Re: The dual PhD problem of today’s startups

#15

Post by digitallogic »

> Today’s startups have a biologist talking about wet labs on one side and an AI specialist waxing on about GPT-3 on the other, or a cryptography expert negotiating their point of view with a securities attorney. There is constant and serious translation required between these domains, translation that (I would argue mostly) prevents the fusion these fields need in order for new startups to be built.

Is that all that different from a software engineer with little customer facing experience teaming up with a non-technical cofounder who does?

Re: The dual PhD problem of today’s startups

#16

Post by seebetter »

This is analogous to suggesting Elon should had skipped Zip2 and X.com/Paypal and went direct to building rockets.

Ideally the lucky few who make a ton of cash on easy software apps etc should be using that capital to risk solving hard problems and developing new sciences and technologies.

Re: The dual PhD problem of today’s startups

#17

Post by starfallg »

mindcrime wrote: post #3

Through the first two paragraphs of this article, I thought it was going to be another silly rant bemoaning the lack of "real innovation" today. That is, another riff on the "They promised us flying cars, we got 140 characters" kind of rant. One of the upsides of this job is that you get to see everything going on out there in the startup world. One of the downsides of this job is seeing just how many ideas out there…

>We’ve gone through the generation of startups you can do as a dropout from high school or college, hacking a social network out of PHP scripts or assembling a computer out of parts at a local homebrew club.

None of that was really innovative, yet ended up with massive commercial success. MS-DOS was the not first PC operating system, Facebook was not the first social network on the web, Apple was not the first PC or smartphone maker.

So I don't think anything has changed there at all. You can still create a massively successful venture bringing something out to market in a way that is somewhat incrementally 'better' than what is on offer without having multiple PhDs in different fields on your founding team.

And it's pointless comparing that to the type of startup that is trying to creating something that is completely 'novel' from the intersection of 2 or more technical fields. Managing that type of complexity isn't something new either - it is fairly routine in academia to apply tools from one field to another - which is also how a lot of innovation happened historically as well as how many startups got started. Historically these type of ventures are high risk and the reason we are seeing a growing number of these is more a testament to how saturated the startup ecosystem is and how research increasingly is driven by venture capital rather than by academia and industry.

Re: The dual PhD problem of today’s startups

#18

Post by dhairya »

SQueeeeeL wrote: post #14
dhairya wrote: post #7

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 a…

Most 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 do…

That'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-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.

Re: The dual PhD problem of today’s startups

#19

Post by crmrc114 »

dnautics wrote: post #12
crmrc114 wrote: post #2

This is not a new problem when people look at technology all day. You are basically saying that you have seen so many birds that there can never be a black swan. Not without complex xy and z factors. This is a perspective problem that can tie you down into some interesting thoughts such as "There is nothing else to be invented new." -or- "The innovation will happen here, in this little corner, where I and others say…

> and no one understood the applications it would have to future technology until much later on Huh? FHSS was a specific wartime effort with a wartime goal that, aside from that its modern application is mostly civilian, is not terribly distant from its intended use case.

There is actually a whole backstory to people ignoring the idea because she was a woman etc. The navy initially turned down the technology when she presented it. More the the main point I made above- people did not know what they were looking at because they could not think of how important it would be or how it could evolve.

Re: The dual PhD problem of today’s startups

#20

Post by TrackerFF »

Interestingly enough, things like Machine Learning grew out of domain fields. A couple of decades ago, there were few - if any - dedicated programs for Machine Learning. The research and grads came mostly from domain-specific fields, like Computer Vision, Signal Processing, Computational Biology / Chemistry, Statistics, Applied Math, etc. Then when things got more cohesive, the most important parts formed into a more or less "pure" field of Machine Learning.

Today, you can study Machine Learning without having to focus on any particular domain (well, other than stats and applied math, which lays the foundation for the theory).

But, yes, it is tough and demanding to find people that have deep / expert knowledge in both their respective domain, AND machine learning / data science / AI.

I think maybe one way to do it is to just look after domain experts, and learn them enough about ML and DS (if they lack the background) to work as generalists. Enough that they can read and discuss it.

And then, you hire ML scientists and engineers to do the nitty-gritty work, with the input and feedback from the domain experts.

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