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Lots of condescension here, but supposing that overnight returns are in fact on average substantially greater than intraday returns, what is the layman-friendly, non-conspiracy-theory explanation of this phenomenon?
Well, I do aging research (mostly from a computational+biochemical perspective). I've met most/all of the important players in the field, and it baffles me how this important area of research continues to be a backwater, as far as the public's concerned.
It's hard for me personally to think of something more important than aging, so if I were to expand outwards, it would be to pursue the same goal, but maybe with fewer constraints. In general, I'd work towards streamlining and automating certain aspects of it. Technologically, the field is in the Dark Ages. There are realistically ~200-300 (max: 5000 including subordinates and techs) people in the entire world working on this seriously, which is fairly mind-boggling, considering that it is the primary risk factor for cardiovascular disease, cancer, and indeed COVID-19, along with many other diseases and the more transhumanist and futurist implications.
The problem is that if you communicate to the public at the level of normal scientific certainty -- with all the methodological and statistical caveats -- it's very hard to generate the moral authority needed to push sweeping mandates on a population.
Political and scientific leaders knew this, and they made a decision to exaggerate the level of confidence they had or should have had in several of these matters. No one seriously expected leadership to have complete knowledge from day 1, but that's not the criticism. Nor is the criticism that facts change on the ground in fast-moving situations. Of course they do.
The criticism is that they knowingly overstated their factual case at the time so that they could implement their chosen strategies, even to the point of suppressing legitimate scientific dissent, and are now unconvincingly trying to use "facts on the ground change", "science learns over time", and "of course we couldn't have been expected to know everything" as excuses for those decisions.
If you're making very confident policy-guiding assertions to the public on behalf of Science (TM), and when you're right, it's evidence of how great Science is, and when you're wrong, it's because Science is a process of continual revision and uncertain information, that creates a bit of moral hazard. It works internally in science, where there are no consequences for being wrong other than wasted time, but not in the real world where there are real consequences for being wrong.
"...Scientists go looking for trouble."
It is several repeated and very costly attempts that I made to do just that which leads me to give the advice I did.
The pyramid quote is an interesting one. Obviously there is a tension between being passionate about an idea/goal/cause but not being overly siloed. It seems the best-case scenario is: pick your passion, find some people who're thinking in the same general direction, and compromise the vision among yourselves.
Let's just say that the thought of solving some of the problems I'm interested in from outside academia has occurred to me. But I'm sure it's not all sunshine and rainbows on the outside, either, and moving from academia whose primary motivator is risk aversion to something like a startup is an extreme culture shock, the more so because my objective would be building something real, rather than bilking gullible VCs into an acquihire.
Really good thoughts there.
It's a good question, and I don't like posting excessively long comments and didn't have time to make it concise, so here's an attempt at an answer:
I think science is too big a thing to have a small set of "core features", and the question of how to usefully define "honesty" in a scientific context is another big topic, but reading about "bullshit" (the term of art that has its own literature, not the colloquialism) is a good place to start thinking about it.
I would suggest that fraud is one of the rarest types of dishonesty, because people who are both smart and dishonest have less risky ways to proceed, and that such people are very glad fraud exists, because it misdirects attention away from their arguably more damaging and prevalent methods. Feynman has a passage about how honesty in science is more a state of mind, which I agree with. But really, the techniques to be dishonest with low risk are the same in science, journalism, politics, and business.
My field isn't sociology of science though; these are just views from the genomics trenches.
Very early on, I noticed that graduate students tend to be idealistic, postdocs extremely cynical, and faculty ruthlessly pragmatic perhaps to the point of occasional shortsightedness. Clearly, something about this progression is expected and normal. I'm a postdoc now, so I'm right on schedule.
I think the way it ultimately works is that you have to be disillusioned from the grade-school fairy tales told to the public about how science works before you can learn to live and work in the environment that actually exists rather than the one you wish existed.
"Never question a scientific superior?" Not parsing that concept, please elaborate.
tech < grad student < postdoc < junior faculty < full prof < Big Guy/Gal < Nobel Laureate < NIH Director
People above you in that chain will accept limited feedback on methods to attain their chosen goals and will greatly resent questions about whether their selected goals are worthwhile/realistic/rational, or whether their gestalt vision of the field's conventional wisdom is correct.
My big eye-openers (some from postdoc) were more about the sociology of science than the day-to-day productivity:
- Even the most blatantly wrong and illogical published work can only be displaced by another publication that explains/does the same phenomenon better; i.e., people are going to keep believing in phlogiston until someone shows them oxygen. If you simply point out inconsistencies in phlogiston theory, in person or in writing, they may well make a variety of unwanted psychological deductions about you.
- Similarly, nobody actually enjoys being around critics or enduring criticism, and therefore you will observe many senior scientists partially avoiding the major downsides of being a critic by artfully concealing criticisms inside what sounds to the uninitiated like mutual affirmation sessions. You have to listen very closely and learn the lingo to pick this up.
- Never question a scientific superior (other than maybe a direct mentor or very close colleague) with any other approach besides "I have a helpful suggestion about how you can maybe reach your intended destination better/faster/more precisely". Regardless of where that destination might be, such as off a cliff or into a wall.
- The opinion/fact ratio you are allowed to have as a scientist is directly and very strongly correlated with seniority, H-index, and so on.
- The incentive structure of scientific publication is such that there are big rewards for being right on an important question, bigger the earlier you are to the party, and little to no penalties for being wrong, so long as the error cannot be provably and directly linked to fraud. There are a variety of interesting consequences to this incentive structure.
Thanks to you, and others, for sharing. I hadn't yet resorted to looking in the consumer space. In research (and presumably clinical)-land, the costs are substantially higher.
I'll be looking into it further to figure out whether there is some tradeoff here, or if it is just typical cost bloat for medicine/academia.
I work in an adjacent area and agree this is all good advice.
OP, how did you even get the sequence to begin with? I have a friend who has an immunodeficiency which is almost certainly due to a rare genetic disorder and want to do a very similar thing. Despite contacting his physician, fellow researchers, and even my institution's president -- with friend's full cooperation -- no one is willing to pay for it.
I'm at my wit's end to the point that I'm starting to think the only viable option is paying for it out of pocket, but it's not cheap.
A question you might want to ponder is: suppose you isolate the problem to a single missense/nonsense/truncation mutation in a protein that seems likely to cause the phenotype. How do you plan to use that information? In theory, there is gene therapy, but in reality, given how much effort I have had to go through just to get this fellow sequenced -- and I'm a PhD working in genomics with a lot of contacts -- creating a custom one-off gene therapy solution seems like it would be a very tremendous undertaking.
There is a very difficult problem here in that rare or "personalized" disease treatments are: A) not profitable, so drug companies have no interest, B) there are mountains of paperwork, IRBs, consent waivers, etc, involved in developing an experimental therapeutic, C) by definition you cannot do a proper clinical trial on a one-off, and D) it requires several different types of expertise to pull such a thing off. Sadly this means that it almost never happens, even though I suspect there are a lot of severe and lifelong genetic disorders which could be diagnosed and treated with technology available today.
Based on my experience so far, I suspect that even if you were to hand his physician very strong evidence that "the problem is caused by this specific single mutation", the response will be "OK, thanks". You should not make strong assumptions about them being able to take it from there. All this is based on the best-case scenario of it being a single variant in a coding region; if the disorder is caused by multiple variants at different loci, anything you find will probably not be actionable.
So, we have a climate crisis, uncontrolled health care and college costs, decades of pointless war, mass incarceration, and a variety of other crises that cause a lot of death and suffering.
Yet isn't it strange that the one crisis we have chosen to pull out all the societal stops for, to radically reorient all of society and put it in stasis for, is COVID-19. Odd coincidence that most of the first list affects young people, COVID-19 primarily affects old people, and the political leadership of the developed world happens to be comprised of old people.
And the subset of old people responsible for handling the pandemic hasn't even managed to do that properly. This is the same category of people who hollowed out unions, induced globalization, and generally kicked out the ladder beneath them in a variety of ways.
I do care about my parents/grandparents, I've been vaccinated, and I'd wear a mask around an old person. That's the absolute maximum I'm willing to do voluntarily and feel fine about that. I'd even venture to suggest that if someone has a problem and demands that all of society radically realign itself to fix/prevent it in a way that's disproportionate to society's other needs, it's not society that's being selfish.
One of our graduate students is thrilled about this paper, although he tends to do that with any new CS advance that seems sensational and that we barely understand (we do bioinformatics). He said that it stood to reason that if it can mmult 100GB/s/core, then we could matrix multiply 12TB in a minute!
Could you translate into practitioner-level language what are the practical limitations of this method; specifically, what would the error rates induced by approximation be under some practical scenarios, when would it make sense and not make sense to use it, etc? There is a complex equation in the paper describing the theoretical error bounds, but I have no idea whether in some practical scenario multiplying some normally distributed variables, whether that would mean a 0.1%, 1%, 5%, 10% error.
Personally I think it only makes sense to use this kind of method in some real-time algorithm where speed is of the essence, the downstream results of the mmult are themselves used in some other approximation (like many ML applications), and emphatically not to make the process of drawing biological conclusions from painstakingly derived data a few minutes faster for the analyst.
I fear that you have made an impressive, but dangerous, tool to people who don't know what they're doing.
My gut reaction if I were Jeremy would be to say that "This is absolutely absurd; there is no way that claiming 'X's opinion is wrong' is in itself a CoC violation, and if it somehow is, then the CoC is ridiculous. The fact that such an accusation was even able to be made under the CoC and not immediately dismissed makes CoCs look ridiculous. Why are you wasting everyone's time?"
He goes a very, very different route. I think that different route gave him a better outcome. I think it is worth thinking about why that is. We are not talking about an accusation of sexual harassment or assault here, we are talking about an almost textbook case of how CoC accusations can be overextended to absurdity. Yet he chooses to defend CoCs.
One possible explanation of this is that he is trying to draw a distinction between things that should be "legitimate" CoC violations, and his case. Another possible explanation is that he is trying to say that "Look, I strongly believe that <murder should be illegal>, really strongly. That makes me one of the good guys. Would a good guy commit murder?" The latter take is more cynical, but I think it would be more effective than my gut reaction.
Yes, I think you're right. He refers to "the two reporters" (and previously mentions that there were two complaints).
EDIT: My reading was wrong, see below.
The thing that seemed to me oddest about all this is that Jeremy says the committee spoke to two reporters before hearing his side of the story, and before issuing a judgment. It is not totally clear if they were speaking to those two reporters about his case.
I would imagine that an obvious way for CoC committees to minimize their own liability would be to at least avoid talking to reporters before they have issued a judgment. Really, the process should be totally private until a guilty judgment has been made, and if the person is declared innocent, then the whole thing should disappear without ever having been made public.
It was a very effective response from Jeremy. He clearly describes his side of the story and convincingly explains why he thought he did nothing wrong.
What is a little more troubling is that he (A) describes codes of conduct in detail and repeatedly affirms his allegiance to the general idea behind CoCs, including describing "previous sexual assault allegations" as "behaviors" he "strongly agrees" should be stopped. (B) He goes some way towards portraying himself as a victim, describing in graphic detail his lack of "emotional resilience". I wonder if he realized what a thin line he was walking with (A), because he now has a "previous CoC violation allegation" on his permanent record, regardless of his acquittal.
In other words, he very strongly backs the spirit, letter, and zeitgeist of CoCs before describing why he thinks he did not violate them in this specific case. I think these were very wise tactical moves, although they leave a bad taste in my mouth, and I can't help but wonder if this approach affected the outcome. I don't recall seeing an apology ever given, let alone changes instituted, for CoC violation allegations.
The issue, amazingly, is Fox News and it’s ilk. Yet, the conversation here is the NYT.
That is because practically everyone on HN agrees Fox is bad, biased, etc. Therefore the debate is going to be implicitly about how bad the NYT is in relation to Fox.
A related factor is that it's hard for an educated person to get suckered by Fox. There are too many garish infographics and obvious nutjobs. It just does not give even a superficial impression of being Legitimate and Unbiased and Supported by the Best Experts. But the NYT does, and that's what makes it more dangerous.
If I go into "Uncle Cletus's Homeopathy Clinick", I kind of deserve whatever I get. But if another con man has a convincingly faked (or even real) Harvard M.D., then sets about poisoning lots of people through incompetence and apathy and greed, then everyone insists it can't possibly be his fault because he has an M.D. from Harvard...
...you can see why "Uncle Cletus is the real problem here" can seem nonresponsive. It is not even especially obvious to me which is "worse", "Uncle Cletus" or Fake M.D., even if we grant that Fake M.D. is somewhat better at medicine. I know I personally could get suckered by the latter but not the former, making the latter more dangerous to me.
It's still used quite heavily in research also (medical is what I'm familiar with). NCBI puts a lot of critical data on FTP, as do individual researchers.
In the case of NCBI, the actual transfer protocol usually used is Aspera, but you use the FTP server to find what files you are going to download using Aspera (which is CLI only AFAIK). Thus in this workflow, browser support for FTP is actually important.
I would say that the reason it hasn't changed is that it does its job fine. It serves files. It's one of the simplest ways to do so, and I lament that in my sphere the biggest apparent replacement is Dropbox. I agree with one comment above that "legacy" code for FAANG is apparently "features which we can't monetize".
Interesting because in the original post, he declares his resolve to quit HN and declares that he's not visited it in one whole month. "Yeah, right", I thought, and indeed in the intervening 3 years, he came back.
I'd wager most of us have needed a break at some point. I myself have gone through at least 4 accounts since 2006. I periodically "delete" them by setting minaway to 999999999. The act of starting over with a new account with zero fake internet points does help keep their value in perspective, though.
Seconded. I work as a data analyst in medical research (bioinformatics postdoc). I am often introduced as a statistician, even though I'm not, because I can do a bit more than a t-test.
The situation in research is exactly as you describe -- we are figureheads who are put into place and highly pressured to confirm whatever hypothesis a PI wants for their latest grant or paper. They would never ask us to commit fraud, only perhaps to "double check" an analysis 10 times until it shows what they want to see.
If I were working for a company, this would at least be understandable, as companies don't even have a theoretical commitment to truth and scientific integrity, and there are no real consequences to a faulty analysis.
But it is immensely galling to see in research. Here we are, paid by the public to supposedly pursue truth and improve human health, and instead the job is to constantly be finding ways to avoid fraud and fabrication without pissing off the collaborator. The result is, as you say, useless analyses if the analyst is honest, and fabrications if they are not.
There is absolutely no doubt in my mind that this is one of the key reasons the ROI on science has declined drastically in the last few decades. It makes me laugh bitterly every time I see (increasingly frequently) political exhortations for plebeians to "trust the science".
Even middle schoolers are taught that there are "reliable sources" and "unreliable sources". Wikipedia (pretty good usually) is not considered a reliable source, let alone Twitter or Facebook.
Even a reliable source, like an academic article or a nationally syndicated newspaper, can be wrong, and are, frequently. Basic logic and rationality would be enough for people to realize that "I saw it on Facebook" has basically no informational value.
If anything, therefore it is the "reliable" sources which should be fact-checked, since they have greater credibility and influence, and because no one with any sense would believe something just because they saw it on Facebook and Twitter.
The fact that many people appear to do so says, to me, that when people are on Facebook and Twitter, they really don't care about the truth of the matter, they are looking for things to fit their confirmation bias. Therefore we would expect "fact checking" on such platforms to accomplish very little, except for irritating people who are semi-consciously already aware that they are consuming right-wing propaganda, and see left-leaning propaganda as informationally equivalent, just a product for a different audience.
I'd gently suggest that, were you to look around, you'd see others behaving in similar manner; but, for whatever reason, you are noticing it more with this chap...confirmation bias.
I agree, I'm well aware of the possibility and am trying to combat it the best I can. But @CarbyAu is right, no one trained me in how to deal with this kind of situation and it is just inherently difficult for everyone.
That said, from his side, if I were a member of a $GROUP that had certain negative stereotypes about it, I'd consider it wise for my own personal interests to try to avoid playing directly into the stereotype.
Not sure if this will help, but I'll share my very recent story of how ageism looks from the other (youngish people) side, which contains some pitfalls to avoid.
I'm 32, and the technical lead of a mid-sized research computing / ML team in quasi-academia. I'm the one who tells the boss whether a hire is technically competent, performing up to standard, is meshing well with the team, etc. So I don't make hire/fire decisions myself but have substantial influence on them.
Usually, when we hire people > 30, they are PhDs with a specific specialty, like statistics. But recently we hired a guy from industry in his early 50s to do some programming, web dev, and light ML. He had, obviously a long CV with programming and some practice with ML/statistics, although nothing related to our field. Here are some of the things this guy has done in the ~1 months before and since his hiring:
1. He frequently bullshits in presentations and meetings, pretending to know things he doesn't know.
2. Very shortly after joining, he has recommended we radically rebuild several of our systems in different, "better" ways -- ways which he's familiar with. For example, we have a web app on AWS Ubuntu, and he has repeatedly asked why we can't just run a Windows server.
3. If he doesn't know something, he insists on getting step-by-step tutorials from technical people in the lab, but tutorials in how to do things HIS way. For example, we all use Linux and an SSH client, but he wasted 2 hours of my time asking how to SSH into AWS using PuTTY on Windows, how to copy files using WinSCP, etc, since of course he only uses Windows. He claims he'll learn Linux eventually, but wants to use what he knows "for now", "so that he can more rapidly produce results".
4. The first time I met the guy in person (not immediately due to COVID), he had plopped himself and his laptop down in my desk, without asking, and even readjusted my chair settings, and complained about my office being messy. I'm #2 in seniority...
Any one of these alone would be...annoying, but all together he is almost a caricature of every ageist stereotype in tech. He expects a level of respect he feels is due his experience level, while simultaneously he resists learning anything about the way his new organization does things...presumably because he knows better?
It puts me in an awkward situation because I'm kind of his technical supervisor. If he were a new graduate student, I would tell him to cut out the bullshit, figure things out, and quit wasting my time (and commandeering my desk). Since he is almost twice my age it is too awkward to read him the riot act, though, and I don't really know how to deal with this.
My takeaway is that an older person in a new tech job should really, really avoid displaying arrogance and entitlement. Respect is earned, not given; even if you've spent 30+ years in the field, we whippersnappers don't know how competent you are (or not), until it's demonstrated. I'm not saying you made any of these mistakes, but sometimes it is easy to do some of these things unconsciously. Probably you are perfectly competent, but people like this guy are working against you.
I do have to comment that the diction used is a bit dramatic.
Fair. I wouldn't use this kind of language when talking to colleagues in person, for sure. In fact, I wouldn't address the subject at all.
But, at the same time, I think this is more just a human problem, not a PI specific problem. Moreover, I think a lot can still be gained from such a document...
Absolutely. I think the OP has good intentions and believes what he writes. But I wanted to warn prospective grad students not to take this kind of thing completely at face value.
To avoid giving the impression that I had a horrible advisor who twisted me into cynicism, I should say:
My advisor was definitely one of the "idealists". I was his first graduate student. He always treated me well, with respect and reasonable expectations, and we are friends to this day. He did have some of the weaknesses you mention. He looked out for me as well as could reasonably be expected, but he did occasionally throw me to the wolves if the stakes were high enough -- for example, if we had a collaborator who was giving us substantial money, and they asked me to do the impossible or the unreasonable, he'd tell me to grin and bear it, and do my best, rather than informing the collaborator about reality.
In short, he was way above average, but still, his interests and mine occasionally came into conflict. But it can get so much worse -- I have seen numerous graduate students and postdocs absolutely exploited (department chairs and big shots are the most frequent offenders), and the most vulnerable targets were always those who assumed that we are all but brothers-in-arms in the great Scientific Enterprise.
What I mean to say is that even if a grad student lucks into or intelligently selects a good advisor, idealism is still a problem because as your collaborations and career expand, the probability approaches 1 that you will run into someone who will absolutely exploit you if given the chance. Someone who has enough leverage on an otherwise good advisor can also exploit a student by proxy. Students should be prepared for this inevitability.
In my view, when we read a document like the OP, what we are mainly getting is a window into how a PI likes to view himself -- i.e., the benevolent master lovingly and altruistically shepherding his apprentices into independence -- rather than any relevant form of reality. I'm sure OP came by this delusion honestly, but one of the primary qualifications to become a PI is the ability to spin, and no one is easier to spin than oneself.
An effective PhD advisor/thesis isn't wandering the woods to find something. It's a guided coaching exercise, with an outcome in mind.
There is a tradeoff here. One of Eric's selling points for his lab is obviously that students get a lot of intellectual freedom to develop their own ideas. This is the "wandering the woods" approach, which was my advisor's as well. The advantage is the freedom, as well as the responsibility and intellectual independence that comes from exercising it. The disadvantage is that it can lead to a lot of wasted time and dead ends.
The "guided coaching exercise", when taken to the opposite extreme, results in an advisor that hands a project with clearly defined goals and outcomes to the student. That kind of approach usually, in my experience, leads to more and higher-impact papers. It does not develop the student very well, but it does develop their CV, which is quite important, and once that student becomes a postdoc, it is do-or-die time, and every CV item helps.
But if we just view this advice as "how should someone doing a project conceptualize their project", this is good advice. Although you would be astonished how many PIs don't follow it. I've had PIs want to write papers before seeing a single figure. I've had countless collaborators send me data to analyze without any clear idea what sort of results/analysis they want, without a hypothesis even.
Hard disagree. I made every possible mistake that can be made related to being too idealistic about grad school; for example, I believed:
- My advisor and collaborators have my best interests at heart
- My primary role in graduate school is to develop novel, useful, reproducible ideas
- Grants, fellowships, and stipends are generous donations freely given in order to enable the above
- Quality is more important than quantity
These kinds of sentiments caused more damage to my career than any other mistakes I have made (fortunately, I survived...so far). When someone gives you money, they definitely expect something in return, even if that something is not always clearly stated, and that something is almost always related to the donor's own career advancement.
There are PIs who absolutely prey on this kind of idealism. They can find certain kinds of idealistic students, use them up, and discard them. Graduate students should be told from day one that they need to look out for their own interests, because no one else will. I'm sure there are exceptions, but they are just that.
The best that can be reasonably hoped for from an advisor-advisee relationship is a clear understanding that it is a mutually beneficial transaction with bidirectional expectations. It makes me uncomfortable that the OP document obscures this fact.
they aren't paying you to advance their career
What are they paying you for, then?
Well, if the AI turns out to be benevolent, it could end aging and disease, enable interplanetary or interstellar travel, end all relevant forms of scarcity, and liberate us to focus on artistic or hedonistic pursuits for our 10,000-year lifespans.
If the AI turns out to be malevolent...well, I have a different take than most on this. Conditional on me dying, I've always thought that the two best ways to go would be: 1) falling into a black hole or 2) liquidated by the AGI. It's a lot less prosaic than dying of cancer and at least you could content yourself, while being reprocessed into paper clips, that you have (possibly) died giving birth to the next phase of evolution.
Unpublished data, I'm afraid, from a collaborator. But the manuscript is very close to submission. I'd expect to see it in 3-6 months.
It is honestly very hard to know about this kind of thing, because the results can vary based on how the analysis is done. But there is a severe publication bias effect in the sense that authors who see the clock giving a less than clean "story" tend to double and triple check their work and ultimately rarely to publish.
I have seen data going both ways on clock-disease associations, published and unpublished, so I do not know what the truth is, only enough to caution HN readers that a robust clock-disease association should not be taken as established truth.
Thanks very much for that correction. I have no idea how I got the idea that the Commonwealth is so much smaller than it is.
So I retract the points I made based on that false assertion.
Unfortunately the relationship between clock readouts and actual health state is tenuous at best, and is really more of a postulate than an established fact.
It is true that Horvath and others have shown that clock deltas (between predicted and chronological age) correlate with all-cause mortality. There has been surprisingly little work on clock deltas and specific disease risk.
But these are correlational or observational studies. What is not known is whether, or how often, an intervention that changes the clock will also change lifespan or healthspan. There are far too many grants and papers that just assume this is true. Usually the argument is something like this: intervention X improves health, intervention X lowers clock age, therefore the clock is a good biomarker of aging.
The classical Horvath clock has something like 350 loci all spread out in the genome, often in regions that have no obvious functional significance.
Imagine the extreme case (which has been the subject of more than one grant proposal): take one of the clock loci 100 kbp from the nearest gene, forcibly alter its methylation state specifically. Will this change clock age? By definition, yes. Will it alter health at all? Very likely not. People seem unaware of the almost certain fact that these loci are not causal in aging, they are readouts of upstream biological states.
Also, there are numerous inconvenient facts, such as the fact that the if you compare groups of the same age, one group with and one without various neurological diseases such as Alzheimer's, the group with neurological disease appear to be younger by the clock.
I would beware of this area, there is so much hype around it and claims have far outstripped evidence by this point.