[HN Gopher] Understanding science funding in tech, 2011-2021
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Understanding science funding in tech, 2011-2021
Author : luu
Score : 27 points
Date : 2022-03-04 17:43 UTC (1 days ago)
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| ninesnines wrote:
| As someone involved in a lot of neuroscience initiatives, I think
| the integration of science funding in technology is really just
| so exciting. I cannot wait to see what the field brings in the
| next 50 years (if we all make it that long ;-) )
| brilee wrote:
| Great read! Another phenomenon that's not explicitly mentioned,
| but is also important: the industry lab. DeepMind, Google Brain,
| FAIR, IBM Research, MSR all have big industry labs that mostly
| focus on CS/ML research, but the small percentage of that overall
| effort that spills over into the sciences is still significant.
| AlphaFold is well-known but there are many other contributions
| being made.
| lmeyerov wrote:
| Agreed. DeepMind's AlphaFold (graph neural nets that solved
| protein folding according to the community's own definition) is
| amazing...
|
| ...But as a former academic, it also a huge warning bell on the
| current effective capability gap between academia and the big
| ~5 tech co's. The field is lucky Google liked the problem, but
| there isn't a satisfying reason academics didn't do it first
| and aren't now ahead. I keep coming to problems at the
| structural layers like low $ vs tech (brain drain), localized
| ownership incentives (small efforts), broken granting
| treadmill, etc.
|
| The US gov spends so much here, and can do more, but the core
| seems broken. The crypto discussion in the article is fun, but
| the crypto aspect felt a bit of a headfake: it showed more that
| many scientists want a rethink, and while DAO isnt necessarily
| a good idea long-term (and giving little money today), it is
| air cover for such a rethink.
| derbOac wrote:
| Speaking as someone who's done the whole tenure thing etc,
| the gap between what has happened in the nonacademic and
| academic sector in the last decade or so has been mind-
| boggling to me at times, at least in my field. It's not even
| the big 5 tech corporations. Trends in tech that are
| completely and overwhelmingly obvious, even normal to daily
| life, get treated like some kind of bleeding edge
| "newfangled" thing. As a result those in the academic field
| end up looking like luddites, even though it should be the
| other way around. Years after you suggest something normal,
| after something is well-established, someone gets a grant for
| something that should have been done years earlier, and it's
| praised as being cutting edge or something.
|
| I have so much to say about this. Rigor is absolutely
| essential but something is seriously broken about incentives
| in publicly funded research.
| lmeyerov wrote:
| Yes!
|
| To your point, I've been watching the slow uptake of basic
| ideas in science departments. In this case, neural networks
| are _still_ largely looked at either with suspicion (wrong
| principles) or as magic (too difficult) by top non-CS
| departments at top institutions. There are exceptions, but
| they 're the ones that prove the rule.
|
| The big ~5 (Apple, Microsoft, Google, Facebook, maybe
| Amazon), and then an even bigger universe of Chinese
| companies + consulting companies, are interesting to me
| because they solve scaling these ideas. They've been
| putting money into projects that academic teams largely
| aren't, collecting global-scale data that these teams
| aren't, and doing global-scale real-world experiments that
| academics aren't.
|
| Ex: For something like healthcare, there isn't a good
| reason for Flatiron Health to be a leader while, to almost
| a rounding error, most real-world clinical data researchers
| are data-poor & AI-poor. While scientists are squabbling
| around open access for _papers_ , imagine if the
| NIH+HHS+CDC+... required all publicly-funded genomic +
| health _data_ be unified into a national database and the
| DOE provided AI compute infra for working with it. Instead,
| we have an area PI at every research university getting
| grants to make believe that their regional hospital network
| 's tiny genome/ehr database will be the one that becomes
| that.
| t_serpico wrote:
| Unifying clinical and biological data is an incredibly
| challenging task for a variety of reasons. Private sector
| is much more well suited to tackle such problems because
| of the engineering and coordination required.
| lmeyerov wrote:
| I'd have agreed with you 5-10 years ago.
|
| - I'm interacting with and hearing a bunch of regional
| tech companies, hospital networks, and worse, consultant
| shops, doing what you're saying. For the most part,
| they're not that special. There are variants with unique
| twists (edge compute/ai/crypto/reselling/graph/..), but
| ultimately, not that many, and generally prioritize the
| same obvious data sources (epic, ..). Genome data isn't
| as standardized/centralized as top EHRs afaict, but even
| there, we're see common data formats crop up & getting
| popular. Likewise, every hospital network IT group is
| independently having to reinvent the wheel on things like
| access for their researchers.
|
| - I'm not advocating they do all the things. Industry has
| value. (Ex: Imagine trifacta for everyone!). But this is
| a commons issue, and when industry owns it, that's a
| problem (ex: HHS groups ensnared by Palantir, or today's
| balkanized approach to real-world data). But for meat and
| potatoes of EHRs/labs/genomes, as part of the continued
| digital transform these systems are doing, targeting
| baseline standards & timely data submission isn't that
| crazy. Likewise, VA and other ~federated national groups
| already do have (bad) centralized database + compute
| facilities. I hear about their problems every ~night ;-)
| But as a baseline, a lot of what is a scramble today
| would become easy.
|
| Getting back to the main point. The top 5 tech co's are
| quite used to working at nation/global scale for data
| stuff, including for PII & AI, so there's technical
| precedent. Smaller nations actually already do centralize
| this stuff - so it's not even without precedent in terms
| of government. The US gov is intentionally spending much
| more money on doing it much tinier & weaker.
| derbOac wrote:
| I was happy to see this but it was difficult to discern what the
| underlying arguments were, and I think the author maybe
| overreaches a bit. It took me reading through a lot of what they
| wrote to understand they seem to be arguing for more researcher-
| based (as opposed to project-based funding). I although I
| completely agree, I think a lot of what's cited is a little
| misleading or one-sided. I also think in general, focusing too
| much on any single funding mechanism could backfire.
|
| In one of the pieces, for example, they discuss the frequently
| cited Dutch (?) study showing that people just above and below
| the cutoff in grant scores (presumably the same in actual
| proposal quality) end up differing in subsequent funding and
| probability of attaining full professorship. They argue that
| citation rates don't differ, though, claiming this is a more
| important metric, and using it as an example of again, how the
| individual researcher is paramount.
|
| However, doesn't that pattern just sort of point out problems
| with the funding effects? That is, it seems to be saying that
| those with slightly lower scores were more likely to give up
| trying at all, and were less likely to obtain material resources
| and recognition, _even though_ they were just as impactful
| scientifically speaking, at least as far as citations are
| concerned. It 's also the case that this grant funding mechanism
| is just one way in which structural factors can affect the career
| trajectories of talented scientists -- there are many others.
|
| I've grown very suspicious of many of the sorts of studies the
| author cites, mostly because most of the problems I've seen in
| biomedical academics game the system so subtly but pervasively.
| That is, many of the hypothetical patterns they point to could
| just as easily be explained by any number of causal explanations
| once you start to include corruption and deceit in your models.
| The models they discuss are largely predicated on different
| hypotheses about talent, and ignore manipulation of the system
| metrics dishonestly.
|
| The whole "funding of individual investigators" model seems
| promising but also should be setting off alarm bells everywhere.
| Isn't this the point of tenure? If we are talking about funding
| models like this seriously, what does that say about the actual
| state of tenured positions and what's going on? It has this
| comical quality, like "if only there were some system of funding
| people without strings attached so they could pursue their own
| intellectual interests..."
|
| You could make the argument that there aren't enough tenured
| positions, which is fair. But what about how those tenured
| positions are funded? They are seen now as largely dependent on
| obtaining funding through typical means, which then means you're
| selecting based on ability to bring in income through traditional
| grant projects. But then why not just do that?
|
| It's obvious something is broken.
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