blueblob

And legacy c/opengl, where the variables names tell you the return type...

I also stole jump script, but instead of aliasing, I just renamed the function to j instead of jump. I still kept mark as is because I don't use it anywhere near as often as j.

It's not that they aren't trying, it's that when you reinvent the wheel, you have to do more work. Microsoft is introducing `tensorflow-directml` to avoid this problem by implementing a CUDA equivalent in directX. AMD has ROCm, but it's not well supported because it's not integrated upstream in `tensorflow`.

From a basic binary search, this is very intuitive. Your explanation was well written.

Sounds a bit like radix sort?

I played a lot of these games around ~2007. Excellent collection.

Continuous tasting and integration

That would be a testable hypothesis that you have not tested. Anybody can do whataboutism but it isn't a meaningful argument. If it's your belief that the literature is biased by 'green' funding, prove it.

I think it is because the proprietary part for them is the data, not the particular algorithm. They benefit more from other people making advances on their technology because they have the data to get more benefit than anyone else. If they kept it to themselves, they would get no "free" advancement. So they trade-off the secret of the technique in the hope that others will advance the technique, making their data more valuable.

I also use ublock origin, but it doesn't imply that there are not ads.

There are ads in the old version too. They show up as sponsored posts though I don't know if they are as egregious because I also use old.

The only thing that I feel I miss out on in using old.reddit.com is the predictions. The /r/muaythai has a predictions tournament that's pretty fun but there's no way to get notifications that there are new predictions to be made with the old interface (as far as I know).

I feel exactly the opposite. AI has not yet posed any significant threats to humanity other than issues with the way people choose to use it (tracking citizens, violating privacy, etc.).

So far, we have task-driven AI/ML. It solves a problem you tell it to solve. Then you, as the engineer, need to make sure it solves the problem correctly enough for you. So it really still seems like it would be a human failing if something went wrong.

So I'm wondering why there is so much concern that AI is going to destroy humanity. Is the theoretical AI that's going to do this even going to have the actuators to do so?

Philosophically, I don't have an issue with the debate, but the "AI will destroy the world" side doesn't seem to have any tangible evidence. It seems to me that people seem to take it as a given that it's possible AI could eliminate all of humanity and they do not support that argument in the least. From my perspective, it appears to be fearmongering because people watched and believed Terminator. It appears uniquely out-of-touch.

As someone not in the field of audio engineering, I had no idea what this product is. The website focused on describing all the features without giving a simple 1-2 line description of what the product even is.

Personalization would be to have Spotify give you ads for a Rolex right after you purchased a Rolex. At least the way that kind of thing usually works for me is that I always get the recommendations after the purchase (definitely not a Rolex for me).

Why is that interesting though? I can just as easily put a backdoor in preprocessing before passing it to the algorithm. Outside of machine learning, you can do the same thing anywhere. This doesn't appear to be anything new, it's citing an article that's not even peer reviewed yet. It's just not good writing, in my opinion.

Oh good, another fearmongering piece about AI and Machine Learning. Nobody's ever seen an original thought like that before.

It turns out that your google cache page doesn't render the page, but the retrieved source does actually contain all of the content. If you view page source, you get:

How Candidates Can Signal Sincerity in an Era of Cynicism

---------------------------------------------------------

[[Download data and study materials from OSF]](https://osf.io/rj3aw/)

Principal investigators:

Scott Clifford

University of Houston

*Email:* scliffor\@central.uh.edu

*Homepage:* <https://scottaclifford.com/>

Elizabeth Simas

University of Houston

*Email:* ensimas\@uh.edu

*Homepage:* <https://www.elizabethsimas.com/>

\

*Sample size:* 769

*Field period:* 01/03/2020-06/26/2020

Abstract

Partisan polarization has reached historical highs, while politicians' credibility has reached historical lows. For example, recent polls suggest that as few as 8% of Americans think that politicians believe most of the stances that they take on issues. This extreme level of cynicism threatens to break a fundamental link in representation. If candidates cannot credibly convey their positions, then voters cannot evaluate them on policy. Yet, we know little about the strategies politicians might take to convey the credibility of their claims. In this paper, we investigate whether politicians can signal credibility by taking extreme positions or by justifying their stances in moral terms. Across three experiments, we show that moral justifications tend to enhance credibility, while extreme positions do not. In a fourth study, we show that while extreme stances increase polarization in candidate ratings, moral justifications do not. Taken together, our findings suggest that moral justifications are a useful strategy to enhance credibility without contributing to rising levels of polarization.

Hypotheses

- H1: Candidates taking extreme issue positions will be perceived as more sincere.

- H2: Issue stances justified with moral language will be perceived as more sincere.

Experimental Manipulations

A within-subjects vignette experiment. Respondents will be asked to evaluate three hypothetical politicians, each taking a stance on a particular issue. Within each candidate profile, the stance will be randomly assigned to one of four conditions in a 2x2 design. The stance will be either extreme or moderate and moral or pragmatic.

Outcomes

An index of the following questions:

- Do you think this candidate truly believes in {stance}, or is just saying what some people want to hear?

- In your opinion, how committed do you think this candidate is to {stance}?

- In your opinion, how likely is it that this candidate will be a leader on {stance}?

- In your opinion, how likely do you think it is that this candidate will flip-flop on {stance} in the future?

Summary of Results

As expected, the moral justification is perceived as significantly more credible than the pragmatic justification (b = .02, p = .002). The extreme position, on the other hand, is seen as slightly, but not significantly less credible than the more moderate position (b = -.007, p = .295). Thus, consistent with Study 1, moral justifications increase credibility, but extreme positions do not. Additionally, we find no evidence of an interaction between the treatments.

References

Paper presented at the the 2019 Texas American Politics Symposium (TAPS).

Not the parent commenter, but I agree with the sentiment. The selected portion of the earlier comment has multiple pieces that I disagree with: 1) A PhD isn't about writing, and 2) you are not expected to become a professor once you get a PhD in the US.

A PhD isn't really about writing, it's about research. Writing is merely the means of demonstrating that you know how to do research. The way that you break something down involves finding a gap in understanding or a portion of a problem that has not been addressed. Then you find an insight that leads to potential solutions to address the gap. Then you run experiments based around the insight. It is fundamentally a way of thinking, not a way of writing. That's why it's a Doctorate of Philosophy. The actual topic that you cover for your thesis is not as important as the demonstration that you know how to think about research. It's moderately unlikely that you will be working on the same problem in industry.

It's commonplace, at least in engineering programs, to ask whether you're going to go into industry or academia. Research positions in industry typically go to PhDs or Masters with a lot of experience. It is much harder to break into the research world without a PhD in the US. So I believe the original statement:

> a full-time professionalized pursuit under the assumption you will be a professor

is overly specific. Your future could be a professor, a researcher/scientist, or an entrepreneur.

It trades off utility for managing scope creep. Keeping this code base tiny necessarily means that anything that uses it would need to write more code. If the point is a demonstration instead of a usable library, then that is good because it is much easier to follow without excess complexity.

I agree with your sentiment. People like to complain about tools. The nice things about bash (and even sh) are that you can combine a bunch of simple tools together quickly because the syntax doesn't get in the way of it. The language designers didn't intend for people to be writing thousands of lines of scripts; they intended them to be using them locally and focused on interactivity. So naturally the syntax is nice for interactive work. It's a stretch to pretend that working with python paths is easier than working with shell paths, just like it's a stretch to say your bash machine learning library is better than a python one. The author should let things excel at what they're good at and stop pretending everything needs to be unified.

Making one oversimplification means you need to be very particular? The point stands that some people that look at this tool will likely expect something very different than what it is. If this tool gets enough prominence, it won't be a problem. If it doesn't, it will be a misleadingly named software stack. Worse things have definitely happened.

Well, the really ironic part of it is that the machine learning models using transformers were literally conceived for converting between languages. So this would be quite the valid use case. Unfortunately, it makes the name more confusing.

Perhaps, but it's also a service you are unlikely to want to outsource if your data is proprietary.

The search does not recognize what are individual components nor what should be in the title of a recipe. If I search for "white bread," I get recipes that don't include white bread as an ingredient and are not for white bread. I get mussel recipes that include "white wine" and say to dip bread in the broth. It's ultimately not a useful search.

I was gonna say the same thing except for reviews of recipes in comment sections. I made this chili recipe but didn't have tomatoes so I used ketchup and I didn't have beef so I used lobster tail, it was disgusting. 1 star.

I saw an article that touched on this for the pinephone (not the pro) and they said that it made and received calls fine but did not originally have dtmf support. If you need to check your voicemail and enter a passcode or use an automated system to pick up your prescription, you could not use it for that. It seems that has been ironed out depending on which OS you put on it.

Seems to me it would be better to have the backup pull from production so the scripts cannot be reverse engineered. Maybe it's just 6 in one and a half dozen in the other though.

I am not the author so I can only speculate. He's anticipating that they will be distributing an out-of-date package and it's simply another place that has to be synchronized. He's saying that there isn't actually a need to host it elsewhere as it can be installed via pip through pypy (his preferred host) which will have up-to-date code. He doesn't want to get bug reports for outdated code.

Maybe the demo server needs to be restarted, because it still does not work.

Is use-after-free ever really defined behavior?