Since you did the hard work of parsing rich metadata already, it would be even cooler if your network visualization oriented nodes by some of this information. Here the 'hiveplot' idea (https://hiveplot.com/ ) is often even more useful than e.g. springloaded or UMAP based layouts; clustering into semantically-meaningful categories into axes (say, city or arrondissement? years open? cuisine? an explicit phylogeny from oldest culinary grandparents to youngest?) then choosing a coordinate to localize nodes on the axes (total node degree? prix? "les plus" tags?...) automatically compels us think about salient features of the data.
Agreed, idiosyncratic voice is so life- and mind- affirming in papers. (Do you mind sharing examples of three papers that you did enjoy slowly and change your conceptual life?)
For those in (or soon traveling to) Southern California, the LA County Museum of Art is now featuring a lovely computer art exhibit that includes physical outputs from Harold Cohen, among other works from algorithmic luminaries of art: https://www.lacma.org/art/exhibition/coded-art-enters-comput...
"When art critics get together they talk about Form and Structure and Meaning. When artists get together they talk about where you can buy cheap turpentine." — Picasso.
^Culture is good at romanticizing the "dreamer" as divorced from (& higher than) the "doer"/implementer. Picasso might protest. This post is helpful in inviting us examine this instinct/tradition.
But that good contrariness doesn't excuse us from being more thoughtful about the chain of deduction underlying the titular claim. First, many big ideas—Maxwell's equations of electromagnetism; that software should be Free as in both Beer and Software; that unruly Democracy could possibly be sometimes a (messily, weirdly) good way of organizing people—might naturally require more doers/thinkers to implement than just the mind (few minds?) who happened to crystallize it. Non-unitary stoichiometries for progress are the rule, not the exception. Nudging a culture is already a many-body problem, & because details matter, and details scale exponentially with levels of abstraction, a project's success can improve with the number of engineering minds adding leverage to advance it.
If we take the above earnestly—that making ideas useful usually requires more people than who happened to express an idea—then noticing that more job postings exist for implementory/engineering roles than for "science" roles actually says nearly nothing about whether we as a technological culture are out of balance with science vs engineering, up to how poorly we know about the typical ratio of implementers versus dramers.
It could be that there are plenty of good "scientific" ideas in circulation; maybe what separates us from progress is earnest implementation, reflected by empirical over-demand for engineers (as this post seems to mainly argue). The aggressive scaling laws for improving AI (along existing paradigms but broader compute) are tempting support for this conclusion.
But personally, I think it comes down to your position on this underlying question. Do you believe that fundamentally _better_ data paradigms--eg those that actually compute differently & more (super)humanly--will come from ideas already articulated in the conceptual universe? Or do you think that the key to smarter data science, if it exists, has yet to be invented and may little resemble the ideas dominantly in circulation?
If the latter, then we may most desperately need data _scientists_, in addition to engineers! In the sense that society would totally benefit from generating 1 new idea from “science,” even if 1000+ had been funded but not panned out.
This is true on a global, pro-social sense, but also likely on an individual basis: if you are a thinker than surely partly what matters is what role could maximize your ∂impact/∂effort, and probabilistically, science of data is at least competitive with engineering if you think the future will look different than the present and needs to be invented.
Tao is likely inviting us (just as many physical/probabilistic laws do) to view any arbitrary function as relatively "thicker"/"fuzzier" than an infinitely-thin, infinitely-tall spike function at a certain value: the Dirac delta function (https://en.wikipedia.org/wiki/Dirac_delta_function). If you convolve ≡ integrate this Dirac delta function (located at some value x) against any function g(t), by construction the integral is zero everywhere except at t=x, so the result is an infinitely thin slice of g at x, exactly g(x) (the 'sifting property,' https://math.stackexchange.com/questions/1015498/convolution...).
Now imagine you begin to thicken/fuzz the spike; now you begin to accumulate the behavior of g(t) not just exactly at x, but also at points nearby, getting a schmeared representation of g. Deforming our spike to an arbitrary function of interest in this way gives an arbitrary convolution schmear.
While possible, the notion that asymptomatic[†] cases are due to a separate less-pathogenic strain is both indeed a possibility researchers are thinking about, and yet also not (a priori) the only or perhaps most probable explanation for the wide variance in clinical outcomes we see. Other important, perhaps dominant, factors include heterogeneity in people's immune responses to virus--common in other conditions--and (possibly) a dose-dependence (e.g. if you are exposed to a high viral load e.g. by intense or prolonged exposure, some reports (but far too few for definiteness yet) are that clinical outcomes may be poorer). Though there are different COVID strains in circulation (see the amazing data tracking of https://nextstrain.org/ncov), with regards to the proposed hypothesis: there is no evidence that these strains show any difference in virulence (see e.g. the perspective of Francois Balloux at UCL: https://twitter.com/BallouxFrancois/status/12395362423558225...).
Many groups are attempting to scale up environmental genomic testing for COVID (see again the nextstrain site).
[†] Note that currently, researchers are rather vigorously debating the true proportion of asymptomatic cases (or distinguishing them from pre-symptomatic cases)--we need more widespread e.g. antibody-based testing to answer this more confidently than we can by indirectly fitting coarse time-series to simplified models.
Lest others also experience HN's hug-of-death of the original source, Google's cached version (https://webcache.googleusercontent.com/search?q=cache:bkjo_-...) works as interim alternative version of the original PDF before the link returns or e.g. Wayback Machine has a chance to archive it.
(This aside, heed @asdfasgasdgasdg's very prudent note of caution; other independent and more reputable replications are essential.
As of this writing there are at least 19 such hydroxychloroquine trials in US (https://clinicaltrials.gov/ct2/results?term=Hydroxychloroqui...) and more beyond (though WHO clinical trial site listing them is also apparently under critically-heavy load).)
This article presumes the premise that "what you love"/"your passion" is the same as "what you can love"/the set of all "passions" you have not yet discovered.
This is untrue.
Especially for young people, the amount of time that you have been alive is small compared to your lifetime. What you currently know to be interesting is correspondingly a small subset of the number of things you can find interesting over a lifetime, and an even smaller subset of the things which you could find fulfilling to work on with many lifetimes.
(For those who have lived longer, your life experience makes it even more likely you can identify fulfilling connections/facets of the universe to study.)
The challenge is to find the intersection between what you can be riveted to work on, and what society values (in whatever its flawed wisdom) or can be invited to value.
This is not trivial, but the statistics of the universe are on your side.
What sort of society would we be if e.g. Nikola Tesla/Jame Clark Maxwell/Mozart/etc. had followed this advice?
To aspire is human, powerful, fulfilling. To eat is practical. It is possible to do both.
Society needs people who persist in that pursuit.
Also, not only will you change, but if you invest in something, your taste toward it will change as well. It's a nice trick for people looking for a passion: invest yourself in anything that has depth and you don't have, and you may end up getting passionate about it.
We often have it backward, trying to "feel like it" to do things. But it's one of the tricky things in life: you may very well have to do things so you can feel like it.
I started learning how to program so I could automate some of my business processes. In the beginning I hated every minute of it. I would avoid working on the project because it sucked but as soon as I made up my mind that I would do this or die trying, it took being in the right mood off the table. It now became, OK what's the next problem I need to solve to complete my project. I ended up falling in love with the process and now I'm consistently "programming" (more debugging?) for hours until I find a solution. I used to hate that there was so much to learn, now it excites me that there are so many new ways to improve.
Also, it's very difficult to like something when you suck at it. I've learned this playing video games and sports. First there's the grind to improve. Once you start getting better and understand the mechanics better, you automatically start having fun. And most often, when people don't like anything, it's often the grind associated they don't like not the thing itself. Take math and literature for example.
There are plenty of stories of artists doing both, it's just really f-ing hard. I'd argue especially for artists, but this is probably true of any passion. [1]
Philip Glass for instance:
"While working, I suddenly heard a noise and looked up to find Robert Hughes, the art critic of Time magazine, staring at me in disbelief. 'But you're Philip Glass! What are you doing here?' It was obvious that I was installing his dishwasher and I told him I would soon be finished. 'But you are an artist,' he protested. I explained that I was an artist but that I was sometimes a plumber as well and that he should go away and let me finish."
> Society needs people who persist in that pursuit.
The persistence is key.
Am I a guitar player or a software developer? We all know I'm a hack developer - in contrast - I feel like I really know what I'm doing with music. Most would assume that because music doesn't pay my bills then I'm not a musician. Compare to a hobbyist/non-paid developer who code-binges at night.
Thanks for the link! It’s similar but the one I was referring to was just a blip about Glass, and more about artists (of all kinds). Even writers I believe. Gets the point across though.
> This article presumes the premise that "what you love"/"your passion" is the same as "what you can love"/the set of all "passions" you have not yet discovered. This is untrue.
A while ago, an article was posted here on HN on paper jam engineering[1] which I believe really demonstrates this point. Few people aspire to become paper jam engineers (or even know of their existence), but it seems to be a very rewarding field.
Yes. Even when it comes to development, I find that often the project you are most passionate about is not one that you can be paid for, at least long term. So even if you love software, you may still need to pursue the fun parts in your spare time.
It means the universe it vast, diverse, and ever changing. You are changing too, and your life is a function of time and space, so you will have a lot of opportunities for happiness. That's doesn't remove the hard things or the sadness, but it's here and it's a good skill to practice to be able to leverage it.
This is a richly intriguing phenomenon, with interesting implications!
For those interested in reading more, this article (on stochastic resonance's potential importance in biological sensing) is edifying:
https://www.physik.uni-augsburg.de/theo1/hanggi/Papers/282.p...
(Hänggi, Peter. "Stochastic resonance in biology: how noise can enhance detection of weak signals and help improve biological information processing." ChemPhysChem 3.3 (2002): 285-290.)
(Perhaps this is an example of how biological systems can value accurate sensation so highly that they invent ingenious sensing schemes to achieve high performance.)
Do you have a link (e.g. on arxiv or elsewhere) that describes your approach using ML for image reconstruction in greater detail?
How would you recommend building up one's combined intuition in optical theory, the relevant ML techniques, and the biological substances themselves, to the level where you can innovate in this task as you have done?
Also, for those interested in the concept of building better images using higher fidelity simulations of the microscope itself, presumably Andrew meant studies along these lines: https://arxiv.org/abs/1702.07336
That paper has the right general idea, you need a generative model of the scope and if you're clever you can use that to improve your reconstructions. But it's missing a couple key things that it workable in practice. One missing ingredient is a probabilistic model of the thing you're imaging. The others are secret ;)
Secret because I have a dream for a crazy startup based on this idea which I don't have the means to do now. Although I generally hate being secretive about, like, knowledge and for sure the value creation happens during execution - but just humor me this time, ok?
> How would you recommend building up one's combined intuition in optical theory, the relevant ML techniques, and the biological substances themselves, to the level where you can innovate in this task as you have done?
Well, I'm flattered but I haven't done any substantial innovation here... My recommendation would be to become a theorist, learn math and physics and work your way up the hierarchy. You need to understand the whole picture, how it works on each level, and how the levels fit together - then you can run thought experiments. A good generative model of the world.
> One missing ingredient is a probabilistic model of the thing you're imaging
Ooh, this (making the prior for the true image signal more informative by incorporating knowledge of the structure of the signal) is clever.
Here, when you say model, you mean a description that is based on the (bio)physics of your sample? (E.g., knowing that objects being imaged obey diffusion equations informs your maximum likelihood estimation of the true signal?)
> Secret because I have a dream for a crazy startup based on this idea which I don't have the means to do now. Although I generally hate being secretive about, like, knowledge and for sure the value creation happens during execution - but just humor me this time, ok?
Of course; such is the right (and joy) of an innovator to define how one's own idea is disseminated/actualized! (~:
It is intriguing that you feel your idea has the character of best being pursued via a startup (addressing some crucial unmet commercial need), rather than via the academic model (e.g. transformative Nature publication) more commonly used for improved microscopy techniques.
Best of luck in this pursuit; I look forward to seeing your startup's innovations someday soon!
> You need to understand the whole picture, how it works on each level, and how the levels fit together - then you can run thought experiments. A good generative model of the world.
This idea of developing a "good generative model of the world" is a beautiful aspiration for all of us to have. Thanks for your insights!
