Dr. John Gall was my pediatrician! When I was young, I was interested in astronomy and he gave me a membership in the astronomy book club. I only learned of his work on system theory after his death.
You can look him up on Google scholar: https://scholar.google.com/citations?user=baVVmaoAAAAJ&hl=en
In 2020/2021 he has at least 3 first author papers and a number of co-authored papers and apparently he wrote an impressive thesis. Even if he wanted to continue in academia, in a number of STEM fields, it would be sufficient for a degree. It's not a question of putting in time, it's about output.
I looked at a PDF of the bill that was linked to in the WaPo version of the article. It probably doesn't matter until the House passes its version, but there are funds for AI workforce development for example that might be of interest to those early in their careers.
FWIW, this was actually in my wish List on Amazon, so I will eventually get around to buying it. I have Amazon unlimited, so I don't know how much this will help you--the book's description caught my eye and I think it came up as a recommendation. I'll write a review when I get around to reading it (I have some other books in the queue). Good luck!
At it's best, that's what scientific reports is (in a number of subfields)--a place to put research that is reviewed for technical correctness. There may be debate about how well it succeeds, but I think it's useful. I would prefer to have more papers out where people worked on something, found it wasn't necessarily exciting, but it was solid work and it saves other people time.
@eloff I think our replies crossed. I think that the question is whether you are still at a net positive as a result of inflation (consider the counterfactual).
In what sense? In a world of fiat currency, post-Bretton Woods, what does money mean? I am not an expert by any means, but to me, it seems like the USD has value (ignore the rest of the world for a moment) because the US issues taxes that it says can only be paid with this wacky tokens called dollars and if you don't pay your taxes, then bad things happen. The rest follows from there. I think that you are saying (correct me if I'm wrong) that printing money leads to inflation which lowers what you can exchange your tokens for (maybe it takes more of them to buy apples). So, the first question is whether that is actually true. We have seen a fair amount of printing of money in recent years, but fairly low inflation. It's not obvious to me at what point that changes. The second question is whether inflation if it does occur is net bad/good and for who. I think we can agree Weimar Republic style hyperinflation is bad. However, let's say that inflation is 1% is that good or bad? For who? I think this question doesn't have meaning on an absolute scale. I think it has meaning relative to growth (and the productive capability of the full economy). Let's say that the economy is growing at say 6%, but we have 1% inflation--I'm fairly happy with that. But, let's say that we have 2% growth and 1% inflation--I'm less happy. Deflation is deadly (which has historically been the fate of gold based currencies), so we could argue that having a bit of inflation is insurance that we pay to avoid disaster. But, what about beyond that? The typical argument is that it helps drive consumption and encourages investment. Do you disagree?
I guess it's just a different way of thinking that I'm unfamiliar with. I have turned down other job offers (30-40% salary increase--not 2x) because I like where I am--coworkers, projects, autonomy, city, etc.
I guess I'm weird. If I were offered 6 months of severance to quit a job at a successful company where I enjoyed the work and my coworkers, why would I take the offer? I've turned down offers that paid over 30% more because I like where I am. I don't think it's so uncommon.
I agree with you about the decision not to reflexively fire those responsible for the list. Though for the conversation to veer towards white supremacy and for ~30% of the company to leave, I do wonder if something else was going on there. I could be a bad judge of humans, but if it was just one guy overreacting, I would think that fewer people would have quit.
I was debating about whether to comment on this--whether I could add any useful signal to the noise. My thought is that is a tragedy. Let me start with the business case. It resulted in around 30% of the company resigning. As a client, that would disturb me. What is the likelihood of new features being added? Security patches? Maintenance? Also, AFAIKT, Basecamp isn't just a product, but a brand, with the founders writing several books on work and this basically wipes out that brand.
But to the issue itself. I've read what I can about it and it's tragic. I'll start by saying that I believe the founders had the right to make the call that they did and give them credit for offering severance packages to those who disagreed with their decision. However, the whole affair is a disaster and a cautionary tale. It seems to have started out with good intent. A group of employees wanted to create a more equitable workplace. They found a list of "funny" customer names and realized that this was a bad thing to have. The founders agreed--mocking your customers is a bad idea. The question is what happened next. I was unaware of the idea of the pyramid of hate. It kinda reminds me of Yoda “Fear is the path to the dark side … fear leads to anger … anger leads to hate … hate leads to suffering.” It looks like the implication is not that funny names === The Holocaust, but rather that a certain context is required for something like The Holocaust to occur. I believe that idea is true, but am not sure if this was a productive path to take. It seemed like the company had decided that the list was bad, to discontinue it, and to move on. The call not to fire whoever made the list doesn't seem to be a bad one. Contexts change and people make mistakes. If people evolve that should be encouraged. Where it seems that things fell apart is in how the CEOs responded to this. It's a really difficult issue and it would seem that discussion could have led to some mutual understanding. Moving to cancel social-political discussion in response (and to announce it in a blog post instead of internally) strikes me as where things took a tragic turn. While it's possible to have a policy of not discussing partisan politics at work (for example, it's not allowed for federal employees), not having discussion of equity issues at work is more problematic. Because for some employees, it's not just a question of doing their job and talking about these things in their own time--because for some, it effects their ability to do their jobs. For an in-person company, there can be something as simple as bathrooms. I'm a heterosexual male and never thought about there being a need for unisex bathrooms until an LGBTQ+ group at work pointed it out. Now in our building we have some. It doesn't effect me, but it means a lot to them. I can picture a woman wondering if she didn't get a promotion because there was something that she wasn't doing at work or if it was because of her gender--for her, it's not an abstract sociopolitical question--it's a question that has an impact on her ability at work. Or, if she's in a meeting and she finds that she's being ignored when she speaks, but men of similar ability are not, then it's not just an abstract issue, but a question of how she gets her job done. I am not even saying that if it happens that men are doing this that they are sexist--they might simply be oblivious to it. By my personality, I have a tendency to talk over people and have to remind myself to let other people talk (I think it's a good idea because then I can hear ideas that I might not have considered and sometimes save myself from doing something dumb). I hope that it's something that I never get called into HR about and would much rather a coworker told me if it was a problem. I think there's a worthwhile set of discussions about workplace culture that are worth having about what it means to be professional in the workplace. Of course there are cases that can go too far. But, a lot of it just strikes me as being polite to people. If someone wants to be called Robert instead of Bob, then if it makes him happy, ok. Other things are related to questions of cognitive bias. For example, if if I want to hire someone, it's pretty natural to use my network. However, maybe I can surface better candidates by searching more broadly. Am I using a good set of criteria in hiring?
But, back to the list. Without seeing it, it's not possible to judge whether was it just in bad taste to mock customers period, or did it have racial overtones in the "funny" names for some racial groups. A lot of the interpretation hinges on that. Also, the perception of whether it was racially motivated or not (unless it was particularly blatant could also vary depending on if one was a member of the targeted group or not. But from the exchange on white supremacy to occur and for such a large fraction of the company to quit, then it would suggest that there were deeper issues at the company and that the CEOs didn't want to engage with those issues.
I have seen another approach (also with STEM PhDs), where it isn't an income share, but rather that the incubator gets a referral fee if the student is hired--similar to a recruiter. Now, why do this? The math behind most of data science is rather basic for a number of PhD physicists--I would say the greatest weakness on the math front is that many will know probability, but not Bayesian statistics. However, the level of coding will be mixed with many not knowing about source control and a number coding in say Matlab rather than Python. Now, some have said that people can learn this on their own, which comes to the next reason. Physics graduate students and postdocs can put in extreme hours and their supervisors can expect those hours. 100+ hour weeks happen (especially for experimentalists--a number enter industry and it takes readjustment to figure out what to do with spare time). By formally leaving and joining an incubator, they give themselves space/time to learn--it's also a pretty intentional act where they have to decide that they are leaving the field (which can be psychologically difficult). The next is job interviews--the typical physicist has no idea what a data science interview will be like and coaching will help them a lot. Finally, there is networking, where the incubator may have connections with hiring managers where they can at least get people into interviews. I have seen people do either the incubator route or the DIY route and for those who have gone with the incubator (where they don't pay and the business model isn't a percentage of their salary coming from their pocket, but rather a recruiting fee going to the incubator from the company they get a job at), then they seem fairly satisfied with their experience.
Is the problem the grant process or the lack of cash to award? There are pre-proposals for say department of energy that are only a couple of pages so you can see if you should waste your time on an application. I am not sure about other agencies. You do have grants that go across groups of researchers (MURIs/MRSECS/etc) On the tangent that you mention, I sadly agree with you about the difficulty of dealing with bad statistical methods. I am curious about your thoughts on peer review.
While this seems to give the impression that science is not functioning, I don't see this as the case. I will give an example from my field. Before Covid struck, I was at a conference in Japan. There was a talk about an interesting superconductor. Someone repeated some of the earlier measurements and determined that they were misinterpreted--that the measurement technique led to heating the material and thus the misinterpretation of the results and that the material wasn't so interesting after all. I think this was say 15-20 years after the original measurement. During the Q&A session, someone asked the original author what he thought. He stood up and said that the new measurement was correct. To me, that says science is working--it may work slowly, but over time, it does correct. Sure, there are people that take a long time convince (and sometimes they are never convinced), but science itself eventually corrects--but the challenge of new measurements that contradict the current consensus is that it's not that each one is correct--some are just measurement errors. For example, there was the case of a recent experiment that seemed to show faster than light travel of neutrinos. The authors presented it with the idea that there was a glitch somewhere that they couldn't find. After a flurry of papers (some with exotic new theories), they eventually were able to find the electronics glitch. It would have been exciting if they were correct--but they weren't--and science again worked.
Great! So it's not just me! As I understand the paper, their main point seems to be that as you extrapolate out in time, you need to not just handle the physics of CO2 that is already in the atmosphere, but predict say how much populations will grow, as well as how much GDP will grow, and how CO2 emissions will be effected by that. So, say if there is an economic recession, then there will be less CO2 produced than if you predicted just say steady growth. This seems to be a case where I could see different assumptions (some reasonable) leading to different results--it's a far cry from say the breast cancer-vs skin-cancer mistake that he leads with
Is this true though? Wouldn't it be rather boring if the standard model continues work? In the end, Josephson won. Even the example that the author here mentions with breast cancer-vs-skin cancer was eventually discovered to be wrong. The main question is the time scale.
This is a challenging issue. I see a few reasons that companies want to increase demographic diversity. One would be that some companies are trying to address more demographically diverse markets and having more diverse teams may reveal more opportunities, or at least help to prevent blunders. For example, if you have people from many skin tones on your team, it's natural for then to say attempt to use a product on themselves and not fix it. If you don't have someone on the team (or at least in a test group) from those backgrounds, you may not think to test broadly--not that anyone was actively trying to be discriminatory--just that it's pretty natural to test things on yourself and within your network. Another reason is to have a stronger work force. A natural human thing to do is to recruit within networks--who did I go to school with? Work with? Where did I go to school? But, if I restrict myself to that pool, maybe I miss out on excellent candidates who went elsewhere. For example, I once found an amazing intern from a tiny college that I never heard of--but he was amazing (now staff at Carnegie Mellon). Finally, there are questions of social justice--organizations should reach out to populations that are underrepresented and ask if their recruiting/hiring processes have blind spots. On the recruiting side, are we reaching outside of our normal channels to recruit? For example, one of my coworkers took on an intern from a community college to do some physics/software project. The intern did well and transferred to the local state school and kept working--as a result, they got several job offers based on that work. On the hiring side, does the current methodology result in good hires? Are relevant characteristics being considered? For example, we sometimes bring on postdocs and as part of the review someone screens them based on their undergraduate grades. To me, this is dumb--no idea where it came from, I care about what they did during their PhD, not if they got a B in calculus as a teenager. It rates right up there with how fast they can run a 400 m race. Not terribly predictive of their ability to do research and probably results in the loss of some good candidates (for example, I met a young woman who went to a crap high school, spent college recovering from crap high school--she went to a small university for her masters and did incredibly well, and now she's at Harvard doing publishing good papers during her PhD--under the current system, she would probably not make it pass that screen). What happens once you have people in the institution is a longer discussion--basically, are people promoted fairly (based on performance)? Are these evenly applied?
It reminds me of a graduate quantum class I had. The professor gave what he called "infinite time length exams". We would come in during the afternoon and we could stay as long as we wanted to work on the exam--though we had to slip it under his door by say 9:00am. You could bring as many books as you wanted (apparently at one point you could bring "anything" and someone brought a professor--but that could just be a story). But, if you didn't finish in a reasonable time, you probably weren't going to get the answer.