I just registered for an event that celebrates the 35th anniversary of a particular science and engineering program, and one question they posed was, to paraphrase, "Science has changed a lot in the last 35 years. Please make three predictions about science in the next 35 years."
I'd be curious for readers' views on this. My quick take:
- There will be far more AI/machine learning/software agent-assisted activity. That seems a certainty, and hopefully it may alleviate some repetitive drudgery in certain types of research.
- Hopefully I am wrong about this, but I have a feeling that we are still trending in the direction of a widening divide between "have" and "have not" research universities, in terms of having the financial resources to do leading science and engineering research.
- Foundation investments may be a growing portion of basic research support, for good or ill. Governmental agencies will face increasing constraints on finances and pressure to concentrate more on short-term and applied work with some claimed quick benefit to economic competitiveness or national security.
Maybe it’s just my imagination, but it seems to that in the past decade, our collective awareness of various systemic problems in science has gotten much better. I feel like in the past, professional scientists subconsciously assumed that science was a pure meritocracy, and that everyone had a fairly equal chance at succeeding. Today, the younger generation at least seems more explicitly cognizant of the various unique challenges faced by racial minorities, women, people who went to ‘lower-ranked’ schools, etc… I’m pleased to see so many institutes withdrawing from the US News rankings. I hope that this is a sign that we are moving closer to true diversity, equity and inclusion.
ReplyDeleteYes , the publication area of science requires a overhaul. PhDs and postdocs are confused with this boring publications in great numbers even in good journals. It is a tsunami. Science policy makers should act on this. Can Artificial intelligence help in sorting out the rubbish from the good? AI could set up new metrics for journals?
ReplyDeleteMetrics are part of the cause of the tsunami, IMHO. Each metric will become a goal in itself.
ReplyDeleteOne needs to find a place where important work is curated. OR one should just not care about the massive info out there, but invest in a good *finding* capability (it's not the searching that matters, but the finding of what one needs...)
AI/ML will find applications in research, but I think the current obsession with applying it to *everything* will subside. I don't think it is a universal tool for all fields.
ReplyDeleteBullet number 3 is spot on. NSF's disdain for fundamental research seems to be deepening, with a rapid shift towards the flashy and the short term return. Sad. Another bullet that could be added is about the steady erosion of meritocracy by "true diversity, equity and inclusion" and what it would lead to in 35 years.
ReplyDeleteAnon @ 4:24 AM: I have a problem with your notion that DEI is incompatible with meritocracy. If anything, DEI is an intrinsic requirement for true meritocracy - you can't claim that the people who succeed are definitely the most talented and qualified people unless everyone has a fair chance to compete.
ReplyDeleteI would be very interested to hear the reasoning behind your statement, which is quite unfounded in my opinion.
Anon @ 3:22 PM: The advance of science is not dependent on any kind of ideology or religion. And true meritocracy under equal opportunity does not imply even distributions or representations of anything.
ReplyDeleteI interpret DEI to mean that everyone has equal opportunity, not that there necessarily has to be “even distributions or representations of anything”.
Delete@PPP "Equal opportunities" are pretty unfavourable these days, with "equal outcomes" preferred.
ReplyDeleteFor what it is worth, I think that we are ripe for a resurgence of privately funded research (this is already happening for e.g. quantum computing). In my opinion, publically funded research no longer delivers in many areas. This is due to a reliance on early career people to do the actual work, short sighted and bureacratic funding, and I am afraid to say, the growth of politics/D&I and 'normal' managerial types. The latter are driving out the odd-ball researchers who make the real advances.
Not a bad thing IMV, the system needs to refresh itself now and then.
Whether “equal outcome” or “equal opportunity” is what’s preferred, it’s clear to me that right now we have neither.
DeleteBut I think this digression has taken up the conversation long enough. This is my last comment on the subject (in this thread, at least).
Genuinely equal opportunity seems like it should be a non controversial goal, right? Clearly there are disagreements on how to get there. I have to say, in my opinion, if you think DEI efforts are the dominant issue affecting how research delivers these days, I think you are way off the mark. Thanks for the enlightening discussion.
ReplyDeleteA good example of ML used in materials science occurred about 25 years ago... Harshad (Harry) Bhadeshia (metallurgist) and David Mackay (ML/Inference) were discussing their science over dinner at Darwin College, Cambridge (UK). They got together and used the corpus of steel making literature as a training data set for David's ML algorithm. Optimising for hardness (wear resistance) and toughness they found a new way to process steel to produce hard, tough nanostructured bainite. This steel was so good that they used it for the rails in the Channel Tunnel rail project and the UK has been replacing their rail infrastructure over the years with bainitic steel. Bainite rails last twice as long as 'normal' rail steels. Harry and David firmly believed that their science should be open source and they never patented the process needed to make it.
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