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It is unfortunate that the only way to get through the regulatory process is copious $ - but it does work. If this project were better funded, it would likely have gotten through.
There is an entire political party representing something like half the population of the US dedicated to shrinking the regulatory apparatus, including the FDA. That doesn't sound like career security to me.
My perception is that the parent commenter is referring at least partly to "subcriminal" or nearly subcriminal behaviour. There is/was nowhere to "put" this kind of individual when they come from these settings, for a whole host of reasons, ranging from: something like "it's your neighbour's mentally ill kid, you really gonna call the cops for THAT?" to "what cops?".
School shooters and "crazy people acting violently" are probably incredibly rare by comparison. Sub/urban environments probably reduce the impact of the mentally ill on their surrounding neighbours in a whole host of ways, in fact -- in no small part because treatment is much less available in rural areas. (There's more of them in a smaller area and they're more visible and increasingly less criminalized, so people get the idea that poverty and health issues of that variety are modern, urban problems.)
Keywords in the literature around this include "community impact" or "community health", but it's not my area.
I really want to know this, too.
This is cool. I hooked up Llama to an open-source TTS model for a recent project and there was lots of fun engineering that went into it.
On a different note:
I think the most useful coding copilot tools for me reduce "manual overhead" without attempting to do any hard thinking/problem solving for me (such as generating arguments and types from docstrings or vice-versa, etc.). For more complicated tasks you really have to give copilot a pretty good "starting point".
I often talk to myself while coding. It would be extremely, extremely futuristic (and potentially useful) if a tool like this embedded my speech into a context vector and used it to as an additional copilot input so the model has a better "starting point".
I'm a late adopter of copilot and don't use it all the time but if anyone is aware of anything like this I'd be curious to hear about it.
Prior evidence suggests that sedentary behaviour increases blood biomarkers of cardiometabolic dysfunction.
They studied a hunter-gatherer tribe in Tanzania and found that despite having similar patterns of sedentary behaviour, their blood biomarkers of cardiometabolic dysfunction were much lower than those in industrialized nations. The authors posit that one reason for this is that sedentary behaviour in this group of individuals does not involve furniture -- rather, it usually involves a "deep squat", and the authors show that in this position the muscles are much more engaged than when someone sits in a chair.
This is consistent with evidence that breaking up periods of sitting with movement is good for you.
Their open-access paper talks about some evolutionary context for this hypothesis [1].
[1] 10.1073/pnas.1911868117
Eh, with one confirmed injury, I'm comfortable calling it "conceivable". The testimony of the injured worker in that case even reflects the fact that a spiked tree does not guarantee injury or death to the operator -- indeed, first there seems to be substantial damage to the machinery, and only then, a risk to the operator [1]:
Alexander later filed a lawsuit against Louisiana-Pacific claiming that the band saw had been weakened from previous strikes with nails, but that he was forced to work with the saw or face dismissal.
Tree spiking is one example of this. It's so common as to be illegal in the United States [1]. Unfortunately, without strict worker protections, it's conceivable that workers would be the ones to pay for their employers' (or their upstream suppliers') illegal actions.
IANAL, so I genuinely can't answer that, but in this particular case I'd hope so, yes. More generally, I suspect circumstance dictates. Prosecutors' responsibilities under Brady do seem to be a topic of conversation in the context of "Big Data Policing" [1].
[1] https://www.uclalawreview.org/wp-content/uploads/securepdfs/...
I think there's good evidence that by the time it reaches common use, it will be used that way.
Consider ShotSpotter, which uses an array of microphones in an urban environment to detect gunshots (and often then deploy officers to the location) [1]:
A ShotSpotter expert admitted in a 2016 trial, for example, that the company reclassified sounds from a helicopter to a bullet at the request of a police department customer, saying such changes occur “all the time” because “we trust our law enforcement customers to be really upfront and honest with us.”
In this case, it seems like it's more like "evidence laundering" - a cop found a bullet (presumably through legitimate means) and would like to use the ShotSpotter results as additional evidence that the shooting took place, and so requests a re-classification of the audio recording. Even in this case, where the parallel evidentiary construction is presumably legitimate, one can imagine the problem - a jury may put more stock in a ShotSpotter result than the cop's testimony about a bullet. But in this case, the ShotSpotter "result" is due precisely to that testimony.
Never mind the fact that ShotSpotter microphones are powerful enough to pick up loud conversations [2]:
The apparent ability of ShotSpotter to record voices on the street raises questions about privacy rights and highlights another example of how emerging technologies can pose challenges to enforcing the law while also protecting civil liberties.
Predictive policing will require large-scale data collection, and policing institutions don't seem to always use it the way we'd want them to.
[1] https://www.aclu.org/news/privacy-technology/four-problems-w...
[2] https://www.southcoasttoday.com/story/news/crime/2012/01/11/...
Nope, you're thinking of regression coefficients, where you'd be correct that usually the null hypothesis is $\beta = 0$. In this case, what's being reported are odds ratios, so the null hypothesis would be that OR = 1.
The parent comment's point is that although the reported effect is significant at $\alpha = 0.05$ (the usual "95% CI" you mentioned), there are other problems that render their test of this hypothesis less than valid.
Any evidence for them being a fad? I definitely remember my parents' generation having dedicated one-on-one meetings with their managers on a regular basis, regardless of what field they were working in (skilled labourers, but none of them in tech).
Even in Western medicine (at the research stage, at least), that is not necessarily true in patients suffering from certain conditions.
There is some evidence (in the sense of evidence-based medicine) that a low-FODMAP (fermentable *saccharides) diet reduces symptoms in patients with irritable bowel syndrome. [1]
As far as vegetables go, according to one site high-FODMAP vegetables include alliums and artichokes. [2]
It is worth noting that the authors of the linked review paper caution that it is unknown whether a low-FODMAP diet may have long-term adverse effects.
[1] doi:10.2147/CEG.S86798
[2] https://www.monashfodmap.com/about-fodmap-and-ibs/high-and-l...
Yeah, that's a fair point, and I agree. I don't think it's ideal.
Hm, in what way do you find that DVC requires creating new branches for experiment tracking?
I find the following workflow works well, for example:
1. Define steps depending on a `config.yml`.
2. Run an initial experiment (with an initial config) and commit the results.
3. Update config (preserving the alternate config and using symlinks from `config.yml` to various new configs if necessary), re-run, and commit.
4. Results are then all preserved in your git history.
A timely study, certainly.
It's important to note that risk factors for mortality overlap significantly with the risk factors for other serious adverse events (see e.g. disability status [1], or HELPP syndrome [2]). In a sense, mortality (although fortunately rare as noted in other comments) is therefore a useful proxy for other severe, potentially life-changing adverse pregnancy-related medical events which are much more common than just plain death. (As an analogy, something like 10-15% of heart attacks are fatal, but many more are debilitating.)
[1] doi:10.1001/jamanetworkopen.2021.38414
[2] doi:10.1111/1471-0528.16225
I wrote some Python code recently that uses a similar data structure (Futures instead of Promises, without knowing necessarily about the data structure's use in JavaScript). It wasn't really for caching.
I modeled mine on the Django ASGI reference library's server implementation, which uses the data structure for maintaining references to stateful event consumers. Exception handling is done with a pre-scheduled long-running coroutine that looks at the map.
I'm curious about your second point -- why exactly do things get bad with high tail latency? Is it only a weakness of the data structure when used for caching? I'm having trouble picturing that.
Thanks for your comment. I'm also a computational biologist by training, but have never worked in the private sector.
I'm familiar with the idea of re-purposing drugs for rare disease treatments (most of my adjacent work has been in very early-stage academic research), but I'm curious about the financials here. Could some of the financial risk here be minimized by aggregating multiple groups of patients, all suffering from different rare diseases? From what I know about the process, the answer is yes, but I'd be curious to hear from somebody closer to the process.
Sure, but to what end? The posture of the current government of Ukraine is quite pro-EU, and the people of Ukraine tend to agree (although I suppose one could argue that these surveys are biased?).
Sorry, I hit submit before realizing it might seem rhetorical. I've updated it to clarify that no, it was not purely rhetorical, even though I doubt the veracity of the claims in the article.
What would be the motivation for American/European officials fabricating a story about the Russian military escalating the war in Ukraine?
I don't think this article is credible, but my question above is asked in good faith. There do seem to be informed people who doubt the veracity of American claims of increasing Russian hostility.
This is awesome to see!
I had a very similar idea recently (representing "big" preprocessing pipelines in Python as DAGs), but for research-scale projects. Funnily enough, I was also motivated by a time series project that has resulted in several thousand lines of gnarly preprocessing code, and growing - mostly in Pandas.
I'm not sure Hamilton would totally work for our use case, but I'll be following closely either way. Thank you for open sourcing this!
Thanks very much for answering!
Sorry, if I'd had the foresight to use a throwaway I'd drop a link to our group, but I prefer not to publicly associate my HN account with work. We're a small team of software and data engineers, machine learning scientists, and health policy folks at a large research institution in Canada that take on clients to work on stuff like this (from early stage research to approvals to deployment). I'd be happy to reach out with my contact info privately if you're interested, just let me know.
That makes sense! I wondered if it had something to do with the group component, and I agree that the customer pool is growing.
Congratulations on the launch!
This is really interesting as someone who works as part of a group who takes products similar to this to market! Thank you for sharing.
You mentioned the medication is as effective in women as it is in men. While I understand men are underserved in this space and so I respect the decision to focus on that population on that basis, I'm curious if there are business elements to that decision as well? Would you ever expand to serving women, given that they seem to be a larger potential customer pool?
Yeah, I agree completely.
Pre-made Anki decks vary in quality. Some are great. But none that I've found have the "themes" that personally keep me motivated to language learn long term.
I work on large multi-center clinical trials as a machine learning engineer. One of my projects involves the semi-automation of the detection of fraudulent data.
There's one link in the chain here missing that some people here seem to be ignoring. The authors of this post (while entirely correct) draw no link between "bad data" (which is doubtlessly responsible for a large number of "bad papers"/"bad trials") and "bad clinical practice."
I don't know a single clinician who would base their care on the findings of a single-center RCT of the kind described in this article. Or the findings of a meta-analysis of single-center RCTs, for that matter.
Bad data happens in multi-center RCTs too, and in fact that's what I'm focused on, but a lot of work already (and therefore $, for the cynical) goes into the validation of data (see [1] for a brief description). Phase III clinical trials in the west practically require a robust multi-center RCT, where systemic fraud is very difficult to perform (but not impossible [2]). By the time a Phase III trial is conducted, the efficacy of the drug can already be estimated, and the focus of the drug company (who yes, often fund these trials) is to conduct a trial which is unimpeachable in the face of a regulatory board (who are generally good at their jobs, although the revolving-door tends to reduce public trust and should be legislated away).
In short, I support most of the proposed changes to incentives around publish-or-perish. I reject the notion that these incentives are (currently) significant drivers of decreased quality of standard of care in the West. I think global governance structures, as suggested in this article, could improve understanding among both clinicians who are not necessarily scientists and the general public about just how validated a given standard of care is.
tl;dr Most good evidence-based practitioners already think this way -- not because they inherently believe fraud is rampant, necessarily, but because evidence says the kinds of studies where fraud is most prevalent are untrustworthy for other reasons.
[1] doi:10.1177/1740774512447898
I think the evidence is pretty much in on that -- namely, yes, if your data is too small, a reasonably large neural net (a.k.a. basically any computer vision model from the last 3-4 years) is perfectly capable of memorizing the training images.
The relative success of attacks on nets to extract their training data support that this happens in practice too.
Generalization performance as it stands now always has to be evaluated empirically.
The notion of a standard language is a modern idea (not in the sense of modern vs. postmodern, but historically).
The idea that the only valid basis for distinguishing between dialects is geography strikes me as odd, but suppose we accept it as a premise. Why is it less valid to allow correctness to be defined, say, with respect to local borders as opposed to the borders between countries?
There's confounding here that you're ignoring. For reference, I'm a machine learning research scientist who started in bioinformatics, initially lured by the possibility of a machine learning solution to the protein folding problem.
Google's research arm has made leaps and bounds in a particular field (deep learning) and then managed to apply it successfully to a very, very hard problem (protein folding). That other companies failed to adapt Google's successes in deep learning faster than Google is not surprising at all to me.
One might argue that the impact of academic-big pharma collaboration (in the form of funding for research projects related to CASP) is what enabled a company like Google, with no independent desire to collect the massive amounts of wetlab data required to evaluate or develop a tool like AlphaFold, to even participate.
More importantly, AlphaFold hasn't really solved the protein folding problem well enough for drug development. So, the entire debate might be moot.
Interestingly, Poe is not alone as a famous writer of fiction who became fascinated by a particular branch of zoology. About a century later, Vladimir Nabokov was obsessed with butterflies, with one of his hypotheses (then thought of as largely insignificant and incorrect) later being borne out by genetic studies -- ironic, given the man's distaste for the idea of genetics.
Nabokov himself even went so far as to say:
It is not improbable that had there been no revolution in Russia, I would have devoted myself entirely to lepidopterology and never written any novels at all.
I think the usual argument is more along the lines of: Google's greed prevents them from putting the proper controls in place to prevent these sorts of abuses of the system, because those controls would cost too much. Not so much that any particular specific instance of abuse is particularly profitable.