This pairs well a paper comes from the Dunedin longitudinal study, which has been following ~1000 people who were born in 1972/1972 in one area of New Zealand: "Deep-seated psychological histories of COVID-19 vaccine hesitance and resistance" [0]
The paper includes the interesting finding that vaccine resistant/hesitant and vaccine acceptant individuals had very different (not to mention statistically significant) personality profiles at 18 years of age.
From the abstract:
"Vaccine-resistant and vaccine-hesitant participants had histories of adverse childhood experiences that foster mistrust, longstanding mental-health problems that foster misinterpretation of messaging, and early-emerging personality traits including tendencies toward extreme negative emotions, shutting down mentally under stress, nonconformism, and fatalism about health. Many vaccine-resistant and -hesitant participants had cognitive difficulties in comprehending health information. Findings held after control for socioeconomic origins. Vaccine intentions are not short-term isolated misunderstandings. They are part of a person's style of interpreting information and making decisions that is laid down before secondary school age. Findings suggest ways to tailor vaccine messaging for hesitant and resistant groups."
How cool! The paper was last revised on Arxiv over the summer; this blog post announces that MetNet3 is now powering weather predictions across google devices and services. (I'll bet it gets picked up and used for electricity demand forecasting if it hasn't already.)
From the paper:
> While ground based radars provide dense precipitation measurements, observations
that MetNet-3 uses for the other variables come from just 942 points that correspond to weather stations spread out across Continental United Stated (CONUS).
I don't know a thing about weather prediction, but the fact MetNet-3 can do it using data from less than 1000 points across the continental US is surprising.
The other line that stood out to me was:
> On a high level, MetNet-3 neural network consists of three parts: topographical embeddings, U-Net backbone and a MaxVit transformer for capturing long-range interactions.
If I understand it correctly, MetNet-3 is sort of abstractly treating 'predicting the weather at each geographical patch' like a very big computer vision problem.
While Michael Lewis never says it outright, the picture painted in his book Going Infinite suggests that SBF has a few things going on. Self-delusion and rationalization, ego, etc., but he also might be on one or more neurodivergent spectra.
This was a fascinating read. The well-meaning and hard-working author went through several iterations of trying to make a profit on 'academic-knowledge-graph-adjacent' products, but things ultimately fell through.
The article describes two separate things likely to appeal to HN readers. The first is that there is a lot of tacit knowledge not captured in scientific publications. The second is that the author and his team, despite best efforts, never found product-market fit.
To the first point: it wasn't until I got to graduate school that I realized that the scientific literature isn't exactly 'An accurate record of true facts.' It is instead the paper-trail of a slow-moving conversation among researchers, where old ideas are slowly jettisoned and new ideas are evaluated and tried on.
To the second point, a few reactions:
* If your primary market / audience is graduate students or post-docs, good luck! You can't sell to people who don't have money: grad students are paid around minimum wage, postdocs its slightly better. If I had to sell a science or research-adjacent product, I'd either sell to entire departments or colleges / campuses. This is likely a pretty protracted sales process and doesn't seem pleasant.
* I wonder if Law would be a better place to start. Either selling to law-firms directly (building tools for generating internal knowledge graphs when they are given 80k documents in the discovery process), or particularly for IP lawyers. IP lawyers have the money and expertise to have their skills augmented by AI-powered literature searches. I have to imagine it's not just patents they read when looking for prior art.
* I wonder if the author and his team tried to solve too broad of a problem: they never seem to have gotten hyper-specific, and built something from the bottom up.
To be honest, while the author's depiction of academic publishing is mostly not wrong, they make it sound much worse than it actually is. Folk knowledge is a thing, but papers do contain most of the valuable knowledge if you know how to read them.
I think 95% of this person's failure to monetize their product comes from trying to sell it to an audience that is just quite broke, and the rest is probably mostly post hoc rationalization. Not only grad students and postdoc wages are low, in many countries (not the US) professors aren't well paid either (and buying software subscriptions from grant funds is often not allowed or difficult due to crazy bureaucracy).
As a full professor myself, I almost don't buy software for work. I suffer the torture of Microsoft Office, which my institution is subscribed to, I'm subscribed to Overleaf with grant money (for now, but I might be forced to cancel depending on how the funding goes) and I pay for ChatGPT out of pocket because trying to use grant money for that is bureaucratic hell. That's all. It would take a really transformative piece of software for me to subscribe to something else.
Quite a lot of things. A (probably non-exhaustive, off the top of my head) of things where it saves me the most time:
- Bureaucracy. Writing silly boilerplate, e.g. data management plans or gender perspective statements in grant proposals.
- Cutting or expanding text (we routinely have lots of forms and submissions where you need to write a text in a given word or character range).
- Polite emails in English to people I don't know much (e.g. "Write a polite professional email reminding this person that the deadline for reviewing paper Y expired yesterday...")
- Brainstorming. "Give me 10 ideas about research direction in topic X". It won't give great ideas, but it's good to set the mind rolling.
- Routine scripts/code used in experiments and papers: write a Python script to make a box plot with such and such data, or to take a file in this format and strip this unneeded content, etc. The typical kind of code that appears a lot in research, is trivial to code but consumes time and ChatGPT does it in seconds.
- Suggest titles (paper titles, grant proposal titles, etc.).
- Suggest ideas for exercises or exam questions (e.g. write an assignment that can be solved with the coin change algorithm but involves no coins or currency).
- How to do X in Excel (although the problem here is that my Excel is in Spanish - why, why did they decide to translate function names? - and it's not that good at that - but anyway, it's very useful).
The productivity boost is very noticeable, well worth the cost, even if it hurts to pay out of pocket for a tool used at work.
Corporate filings. Business intelligence. There is value in those areas. Science is too esoteric. And boy there are a lot of papers that don’t really signify anything at all but they fill up some pages and add to somebody’s paper count.
It is a hoot that they sell access to individual scientific papers for $35 because if you think one will help you with some commercial problem you have the odds are the real value is $0.00.
As for corporate fillings, there are lots of strong products already, also data in those fillings is rather limited.
"Business intelligence" is very broad term, maybe it is possible to find market fit there, since area is moving very fast, but hard to judge without seeing specifics.
I happen to run a corporate filings product[1] so I'm curious to know in what way you find the data in filings limited. There are financial statements (ex. balance sheet) & disclosures (ex. litigation) in 100+ page annual reports so our tool makes it easier to find them. We also do AI (ex. sentiment analysis) and diffs (ex. redline / blackline) which yield their own insights.
> so I'm curious to know in what way you find the data in filings limited.
to me every filling has maybe 20 essential numbers which are interesting: balance sheet, income statement and major sectors, everything else is some generic boilerplate, and there are dozens of services which will already sell it for cheap.
I worked at a place where we developed information extraction systems that could be customized to the needs of particular customers. This was before transformers so the technology wasn't 100% ready.
Think of a global aircraft manufacturer turning maintenance documentation into a knowledge graph, a global clothing and shoes retailer building a model of what social media thinks about them, etc. I told other employees that our product could generate enough value for one customer that it would be worth it for one to buy us and... that's what happened.
I'm an Engineer (not a scientist) not sure how much is applicable to other fields but when I've been interested in what the "current state of topic X is" I have looked for recently published thesis on the topic, a good thesis will have summarized the papers for me (gone through all the churn of papers and highlighted the key points).
I think this is kind of what the author attempted to build (i.e. something that spits out the Literature review portion of a thesis.)
I think that's probably why graduate students were excited - they are the people who have to write a thesis at the end of the day.
I'm curious to know how the soundwaves can be used to cause cavitation at a precise depth, while not causing cavitation along the entire wave pathway. Can anyone explain?
The wave would be focused by the transducer (analog of a lens) so that the energy flux is highest at the focal point and presumably low enough not to cavitate outside the focal area. I.e. magnifying glass and the sun kinda thing.
I think the way it works is you have sound from multiple sources, and you time the sound waves so that the peaks and troughs all hit the targeted area at the same time. Each individual 'beam' is harmless, but at the point of intersection they add up and can do damage.
I am a graduate student and I TA for various professors' undergraduate classes in a STEM, but not engineering, field.
After spending about a decade outside of academia, I wonder about the phenomenon of class attendance dropping off as the semester or quarter progresses. Previously, I would have chalked this up to students being irresponsible etc. And while there are certainly cases of this happening, and 18-22 year olds aren't finished (psychologically) maturing... I wonder if one reason for the drop-off in student attendance is simply that students don't perceive certain classes to be that valuable for them / their goals / the rest of their life. I know a few undergrads who have taken graduate level seminars and are struggling to get their first job. Some professors who have no experience outside of Academia aren't exactly equipped to impart deeper wisdom.
As a recent university graduate, yes you're exactly right. I know plenty of people who'd skip class in order to grind Leetcode or job interview prep or whatever. It did make it so CS students in general were less passionate about their subject than my peers in other subjects.
Once you've learned what you can from online resources and textbooks, doing projects -- from Kaggle, etc. -- is a good way to practice applying what you've learned.
Also, this isn't best metric as "miles" in a city != "miles" on a freeway.