Maybe detecting a seasonal cycle for certain compounds will be a very strong evidence. Like the amount of atmospheric CO2 in air changes with seasons on earth.
It's really a nice book and it left me feeling very optimistic about the future. The audio book has superb quality as well if you prefer that. Regarding rereading, maybe the chapter about the multiverse is dense and require rereading, the rest was OK.
I was able to reach 10^-7 mbar with a good vacuum system and I though I would have few thousand molecules, turns out there is a billion (10^9) molecules per cubic centimeter at that vacuum level.
I have an opposite experience. I'm a scientist and my research topic is accurately depicted in Wikipedia I would say better than any textbook. Of course Wikipedia has to be reductionist, it is not required to provide a full literature review for any topic. If you are doing a PhD-level research, then Wikipedia will fall short of providing the latest and finest details. Other than that, it's more than enough. I also believe it is the duty of experts to contribute to improving Wikipedia (I know many who do).
The internet was cut off for three months in my country. I had offline Wikipedia, tons of books and other media. It was more than enough for reading/media consumption but I really missed communicating with people.
Older Thinkpads, in particular Thinkpad x230 is an overall a good powerful machine selling for around 150$ second hand. It also has good screen and really awesome keyboard.
Every writing course I attended in grad school told us that the first sentence of the abstract should be understandable by any scientist, the second sentence should be comprehensible to a scientist in a related field. After that, you address the specialists community. However, it seems that more and more people are skipping the first and second sentences and jumping directly to talking to specialists. I have mixed feelings about that. On one hand, it's nice to skip the generalities but at the same time some times its nice to have some context.
Related to weather, climate science would benefit greatly from increased computational power. Current models are forced to make assumptions to predict certain processes below the grid resolution. Things like cloud processes and transport processes are usually parameterized. Similarly, computational fluid dynamics are very useful to studying climate and are limited by the available computational power.
I'm struggling to see the advantage over the arithmetic mean if I don't have a model of the true value. How is the confidence estimated for each value? given the example from the article where the variance of the temperature sensor is considered to be constant.
This is probably one of the best applications of AI in science in terms of impact so far. I can't think of any other problem with the same potential impact.
One of the things I regret most is not keeping backups of the programs I wrote as a child almost 25 years ago. They all just gone, tens of thousands of lines of code. I remember spending whole summers coding and it's all gone.
I use vimwiki this way. I changed the landing page name to `README.md` and used markdown files so I can always see it rendered beautifully on Github.
I have another vim extension that automatically commits and pushes the changes.
Same here, cultivating motivation is definitely the hardest part of the job. Academic work needs months if not years get out and have impact and, personally, I don't get to discuss my work too often with people because it's too specialized.
Every post-doc I know is plotting an escape plan, some are learning web development, some are learning data science or system administration, heck, there is even special bootcamps now for postdocs evading academia
I'm currently a postdoc in Germany, to be honest, it seems that only people who can't find a job in the industry are staying in academia. I've never met a postdoc who was happy about what they are doing, everyone one is thinking of an escape plan.
Yeah, it feels like most fields are stagnated except for biology and neuroscience. I am a postdoc right now but have considered seriously switching fields to work on something exciting.