Some examples that come to mind:
Brand Influencer — "The Algorithm" prevents exposure, sales, etc.
Customer Service — Explaining warranty status, other than "because it's the least we have to do legally".
Mathematician — The length of pi is continually increasing, and there doesn't appear to be an end.
Software Engineering — Thoroughly understanding a codebase in a reasonable amount of time.
Huh? How is this an unsolved problem? It's known that pi is an irrational number, so it doesn't have an "end". We can always compute more digits. Or did you mean something different?
Every solution I've tried is either too narrow (e.g. works on one table or kind of data only), too broad (too much boilerplate), hard to plug into existing data without massive ETL (SAP, Oracle APEX..) or cloud-based apps which are fine for a mom and pop store but basically useless for scenarios with millions of entries.
Chemistry/materials science: room-temperature superconductivity is probably the big one. There are a whole host of problems in the energy space which would benefit from improvement; while "electricity+CO2+water => fuel" is feasible at the moment it's uneconomic. Can it be done at close to the theoretical minimum energy input in a plant that's scalable and cheap to build?
IC design: is continually solving previously unsolved problems like EUV lithography, but has struggled for years with trying to go "3D" to overcome density issues. Also, is photonic computing feasible and would it achieve lower energy usage?
This is our version of P=NP, and similarly, there's a $1M prize for finding a solution.
https://en.m.wikipedia.org/wiki/Navier–Stokes_existence_and_...
Physics/Astrophysics/Cosmology: Dark Matter and Dark Energy - are clearly observed, no satisfactory explanation exists.
Astrophysics (and Science in general?): very knowledgable specialized domain experts have issues learning from each other: too many different concepts, relations, methods. Even if universe is shared, representations are not easy to map on it at once. Astrophysics is especially bad in this.
Mathematics: A generalized solution to n-order partial differential equations. Laplace, Chebyshev, Hamilton, Wave equations, sure, ok, yes. But I mean any n-order partial differential, not the 'lucky' cases. You get that, and you've solved a great deal of the outstanding problems in other fields (hydrodynamics, optics, economics, turbulence). I'm not sure up to date on this, and it may have been proved that a general solution is actually impossible, but I'm not sure.
Physics: Braver grad students. Quantum Gravity is a long was off, we need much bigger machines or a constellation of satellites that use the sky as the particle accelerator. As such, having these brilliant minds labor on obscure portions on some niche interpretation of physics, well, it's useless really. The data will make it all clear as day, whenever the data gets here. Until then, the grad students need to be braver and strike out on their own, leaving the golden age behind. We need them to work on other things. I know that's tough as nails, but I think it's what is needed. Great work has been done in neuroscience by physicists (despite their best efforts :P), and the quants on Wall Street are a meme now. More of that, but in, I dunno, weaving or something.
Also, having some agreement on code scanning. Every time security settles on a code scanning tool, engineering gets a million findings. This results in arguing about whether potential risks are actually vulnerabilities rather than improving security.
* Professional Software Developer Certification. Software developers do not have an industry recognized certification or accreditation program. Every other professional industry has this. Truck drives have this. Here are some specialized subcategories.
- Web Development
- Security Remediation
- Operating Systems and Systems Automation
- Applied Mathematics Applications
- Data Management
* Energy Efficient Hydrogen Capture from Water. Currently it takes more energy the shatter a water molecule than you would gain from burning the resulting hydrogen. Liquefied hydrogen is a wonder fuel whose energy efficient combustion yields water as its waste product and could power spacecraft deep in space.
* Obesity. Obesity is caused by a combination of 3 things: insufficient exercise, preference for carbs over fats as the primary energy source, and unhealthy fat sources. The third one can be solved with a combination of science, agriculture, and economics.
* Mental Health Therapy. There are a tremendous number of people who need mental healthy medicine but never get it (for many reasons). By tremendous I mean an utterly astonishingly significant percentage of the population. Those who do get medicine are often prescribed drugs instead of therapy when therapy is generally more effective and doesn't have negative side effects. Also the sheer quantity of mental health medications is detectable in the public water supply.
* Rapid Oil Metabolism. Oil is a necessary part of the modern economy. Crude oil is refined to make plastics, and so it will be with us well into the future. Oil spills are nasty though. It would be nice if there were micro-organisms that could consume oil so that oil pools could be removed organically in months instead of decades/centuries.
* Space Entry. We are currently limited to using rockets to enter space (or exit Earth). That is horribly fuel inefficient. Any alternative would most certainly be cleaner and more energy efficient, but there aren't alternatives yet.
"Just use Anaconda/pipenv/the-Python-installer/Docker/etc" isn't a great answer, because they probably tried one of those six months ago, got into a weird state and can't remember what it did or where it put things.
Obligatory XKCD: https://xkcd.com/1987/
i.e. with H as Heaviside operator, T the threshold, and * the convolution operator, prove that the following can hold for some kernels A,B,C and signal D:
A * H[B * D-T] = C*D
IMO, the best solution is probationary hiring (e.g. 3mo contract-to-hire), but that means you have to leave your current job first and then spend the next year bouncing through a number of "temp" jobs until you find a good fit.
Examples: p-values, Bayes Factors, credible regions, a likelihood ratio. These are all quite different!