4 ms·

The most efficient way to deal with this asymmetry: 1. Pick a random sample of someone's claims and statements 2. Research them thoroughly, so as to figure ou

by whack 8y ago

The most efficient way to deal with this asymmetry: 1. Pick a random sample of someone's claims and statements 2. Research them thoroughly, so as to figure out if they are bullshit 3. If a sufficiently large percentage of their statements are bullshit, do not bother listening to anything else they say. Not even to "give them a fair hearing", because it will occupy all your time and energy. 4. Re-calibrate every 5-10 years, in case the person/organization has changed over that period The above algorithm is particularly useful in dealing with the steady stream of bullshit that arises out of particular media outlets and political figures.

dhimes 8y ago
I'm stealing this and spreading it as far as I can.
anoncake 8y ago
The problem with that is that other people will listen to their bullshit and, chances are, nobody will contradict them. So the bullshit spreads.
nabla9 8y ago
When you can identify the source of bullshit to single entity, it's easier to notice and ignore. What is really hard to recognize is bullshit that just "floats around" without single source.
O_H_E 8y ago
Nice that might be a worthwhile mindset, although I think 10 years might be a bit much. Edit: I don't think I am qualified to say that, as I didn't live my second 10 yet PS: Well you got to my no-BS list
mlthoughts2018 8y ago
This can be difficult though. For example, there is a frequent phenomenon on HN where certain posts arise for purposes of cheerleading the specific programming languages Nim and Julia. A ton of the comments, disguising as good natured, that give more or less unqualified praise to these languages are actually mostly bullshit, eapecially two very specific strands of discussion, which are usually about Nim or Julia capabilities compared with Cython, and to a lesser degree sometimes Julia’s multi dispatch templating patterns. It is so hard to wade through those comments and follow down every link and cultish argument or counter-counter-argument, meanwhile you’ve got people claiming that perfectly reasonable criticisms of those languages are themselves the “real” bullshit. It turns into such a convoluted mess that everybody is certain that everybody else’s claims are the bullshit and that their own claims (which other people think are the bullshit) are genuinely refuting bullshit. By the end of it, a strategy like yours becomes very hard because the time complexity of the research task gets intractable even for a small subset of claims. Like literally once I had to go and read a ~30 page published research article to learn that a certain Julia differential equations library happens to use a particular multi dispatch templating pattern to offer a way to treat uncertainty modeling as a built-in, domain-specific construct, but that this was not some type of advantage of Julia or anything (since you could do literally the same thing at the same level of abstraction in Cython), and nor was it even a required aspect of the differential equation domain problem, it was just a sort of extra feature that worked out in that library, but which could be implemented (arguably more simply) without any of the multi dispatch behavior at all. Blerg. I had to wade into all that, ten levels deep in some nested conversation with hard to believe one-sided support of Julia, all just to learn that one single point could be refuted!
- speedplane 8y ago
  > Blerg. I had to wade into all that, ten levels deep in some nested conversation with hard to believe one-sided support of Julia, all just to learn that one single point could be refuted! What's exactly the problem here? You had to study a subject before discussing it? Isn't that the goal?
  - mlthoughts2018 8y ago
    No, it was that on the surface the claims being made seemed flimsy, but the overall discussion was so full of debate and forceful counter-claims of which party is or isn’t bullshitting that as an outside reader I couldn’t tell anything about it. I didn’t have to read deeply into one niche topic to form an informed opinion about it. Rather I had to read deeply into one niche topic just to even close the loop on understanding if a basic sense that someone was bullshitting about a generic, high-level thing was warranted in a simple heuristic sort of way. Basically, the convoluted bullshit acts like an obfuscator that makes you need to do an unreasonable amount of legwork to get to a point you can safely trust just a generic appraisal of whether something is / isn’t bullshit. This is a problem because bandwidth is limited. You can’t go reading 30 page articles every time you want to decide which of two general strands of a conversation have any merit for further engagement, and the noise pumped by bullshitters (especially when they are claiming they’re not the bullshitter and that it’s really you or someone else) makes this harder and harder all the time.
bliblah 8y ago
That is a good way of solving the problem on a personal scale. ' But lets now add a social media platform where it has become easier than ever get a soapbox to thousands of people. This is where we reach a critical mass of bullshit dissemination where even if 99% of people disregard said bullshit you have a large magnitude of people will listen and continue to disseminate it on their own creating a very effective bullshit chamber which will continue on a feedback loop. Technology has lowered the barrier of bullshit much greater than the barrier to refute bullshit.
ChrisRackauckas 8y ago
The problem is that posting anonymous bullshit is still effective to on some people. For example, there is someone who frequents Hacker News and likes to go around saying that features arising from Julia's type system don't "just work". He/she will post all of the time about how we are lying about all of our claims, how when we say it didn't take work it actually did, and how the features that come out are useless. The specific mention is about uncertainty propagation in our differential equation solver library. It happened automatically because of the type system. There's evidence of the thread where we found out, since a user notified us that it works: https://discourse.julialang.org/t/differentialequations-jl-and-measurements-jl/6350 https://discourse.julialang.org/t/differentialequations-jl-a... We have since been investigating the pharmacometric implications of this methodology and are seeing good promise in a paper that is coming out. But then of course someone will come along and say "Cython can do it too". Yes, every Turing complete language can do everything. The point is how a 0 line of code feature led to a new avenue of research, and Julia's dependent compilation process allowed this to be a very optimized implementation. Of course, this little to no effect solution is pretty cool and something to share! However, as stated in the resource of this HN thread, bullshit propagates fast, so this other person had already come to this thread to start ragging on this work long before I could get here (since we were at a pharmacometrics conference getting feedback from the FDA about the uncertainty propagation algorithm! Just to show its real, check the dates of ACoP 2018). So let me add a corollary to this principle: any sufficiently widespread work will have an anonymous disgruntled bullshitter that hates on it. I guess you're supposed to learn it's a compliment that someone runs around spending their time doing this to you. But hey... I wasn't going to respond anymore but the thread's focus was just way too perfect. See you next time mlthoughts2018: maybe the name will change soon to make it harder to bop the bullshit.
Gibbon1 8y ago
This is very similar to a principal I read from a guy that predicted correctly that Iraq had no chemical or nuclear weapons program. Simple: If a source has been found to lie apply a weight of zero.
speedplane 8y ago
Sorry, but that's not a very robust bullshit spotting algorithm (BSA). Here's a bullshit generation algorithm (BGA) that will beat your spotting algorithm every time. 1. Mostly speak truths. 2. Scatter your bullshit sparsely so it remains relatively difficult to detect. 3. Wrap your bullshit with caveats or innuendo so you can argue others misinterpreted you. When someone interprets your bullshit as bullshit, counter that they are bullshitting by intentionally misunderstanding your bullshit. 4. When you get caught in complete bullshit, double down on it with such conviction and fury that you convince people by force that it isn't bullshit. I could try listing real life implementations of the above BGA, but it would likely surpass HN's character limit.

by whack 8y ago

dhimes 8y ago

I'm stealing this and spreading it as far as I can.

anoncake 8y ago

The problem with that is that other people will listen to their bullshit and, chances are, nobody will contradict them. So the bullshit spreads.

nabla9 8y ago

When you can identify the source of bullshit to single entity, it's easier to notice and ignore. What is really hard to recognize is bullshit that just "floats around" without single source.

O_H_E 8y ago

Nice that might be a worthwhile mindset, although I think 10 years might be a bit much. Edit: I don't think I am qualified to say that, as I didn't live my second 10 yet PS: Well you got to my no-BS list

mlthoughts2018 8y ago

This can be difficult though. For example, there is a frequent phenomenon on HN where certain posts arise for purposes of cheerleading the specific programming languages Nim and Julia. A ton of the comments, disguising as good natured, that give more or less unqualified praise to these languages are actually mostly bullshit, eapecially two very specific strands of discussion, which are usually about Nim or Julia capabilities compared with Cython, and to a lesser degree sometimes Julia’s multi dispatch templating patterns. It is so hard to wade through those comments and follow down every link and cultish argument or counter-counter-argument, meanwhile you’ve got people claiming that perfectly reasonable criticisms of those languages are themselves the “real” bullshit. It turns into such a convoluted mess that everybody is certain that everybody else’s claims are the bullshit and that their own claims (which other people think are the bullshit) are genuinely refuting bullshit. By the end of it, a strategy like yours becomes very hard because the time complexity of the research task gets intractable even for a small subset of claims. Like literally once I had to go and read a ~30 page published research article to learn that a certain Julia differential equations library happens to use a particular multi dispatch templating pattern to offer a way to treat uncertainty modeling as a built-in, domain-specific construct, but that this was not some type of advantage of Julia or anything (since you could do literally the same thing at the same level of abstraction in Cython), and nor was it even a required aspect of the differential equation domain problem, it was just a sort of extra feature that worked out in that library, but which could be implemented (arguably more simply) without any of the multi dispatch behavior at all. Blerg. I had to wade into all that, ten levels deep in some nested conversation with hard to believe one-sided support of Julia, all just to learn that one single point could be refuted!

speedplane 8y ago
> Blerg. I had to wade into all that, ten levels deep in some nested conversation with hard to believe one-sided support of Julia, all just to learn that one single point could be refuted! What's exactly the problem here? You had to study a subject before discussing it? Isn't that the goal?
- mlthoughts2018 8y ago
  No, it was that on the surface the claims being made seemed flimsy, but the overall discussion was so full of debate and forceful counter-claims of which party is or isn’t bullshitting that as an outside reader I couldn’t tell anything about it. I didn’t have to read deeply into one niche topic to form an informed opinion about it. Rather I had to read deeply into one niche topic just to even close the loop on understanding if a basic sense that someone was bullshitting about a generic, high-level thing was warranted in a simple heuristic sort of way. Basically, the convoluted bullshit acts like an obfuscator that makes you need to do an unreasonable amount of legwork to get to a point you can safely trust just a generic appraisal of whether something is / isn’t bullshit. This is a problem because bandwidth is limited. You can’t go reading 30 page articles every time you want to decide which of two general strands of a conversation have any merit for further engagement, and the noise pumped by bullshitters (especially when they are claiming they’re not the bullshitter and that it’s really you or someone else) makes this harder and harder all the time.

bliblah 8y ago

That is a good way of solving the problem on a personal scale. ' But lets now add a social media platform where it has become easier than ever get a soapbox to thousands of people. This is where we reach a critical mass of bullshit dissemination where even if 99% of people disregard said bullshit you have a large magnitude of people will listen and continue to disseminate it on their own creating a very effective bullshit chamber which will continue on a feedback loop. Technology has lowered the barrier of bullshit much greater than the barrier to refute bullshit.

ChrisRackauckas 8y ago

The problem is that posting anonymous bullshit is still effective to on some people. For example, there is someone who frequents Hacker News and likes to go around saying that features arising from Julia's type system don't "just work". He/she will post all of the time about how we are lying about all of our claims, how when we say it didn't take work it actually did, and how the features that come out are useless. The specific mention is about uncertainty propagation in our differential equation solver library. It happened automatically because of the type system. There's evidence of the thread where we found out, since a user notified us that it works: https://discourse.julialang.org/t/differentialequations-jl-and-measurements-jl/6350 https://discourse.julialang.org/t/differentialequations-jl-a... We have since been investigating the pharmacometric implications of this methodology and are seeing good promise in a paper that is coming out. But then of course someone will come along and say "Cython can do it too". Yes, every Turing complete language can do everything. The point is how a 0 line of code feature led to a new avenue of research, and Julia's dependent compilation process allowed this to be a very optimized implementation. Of course, this little to no effect solution is pretty cool and something to share! However, as stated in the resource of this HN thread, bullshit propagates fast, so this other person had already come to this thread to start ragging on this work long before I could get here (since we were at a pharmacometrics conference getting feedback from the FDA about the uncertainty propagation algorithm! Just to show its real, check the dates of ACoP 2018). So let me add a corollary to this principle: any sufficiently widespread work will have an anonymous disgruntled bullshitter that hates on it. I guess you're supposed to learn it's a compliment that someone runs around spending their time doing this to you. But hey... I wasn't going to respond anymore but the thread's focus was just way too perfect. See you next time mlthoughts2018: maybe the name will change soon to make it harder to bop the bullshit.

Gibbon1 8y ago

This is very similar to a principal I read from a guy that predicted correctly that Iraq had no chemical or nuclear weapons program. Simple: If a source has been found to lie apply a weight of zero.

speedplane 8y ago

Sorry, but that's not a very robust bullshit spotting algorithm (BSA). Here's a bullshit generation algorithm (BGA) that will beat your spotting algorithm every time. 1. Mostly speak truths. 2. Scatter your bullshit sparsely so it remains relatively difficult to detect. 3. Wrap your bullshit with caveats or innuendo so you can argue others misinterpreted you. When someone interprets your bullshit as bullshit, counter that they are bullshitting by intentionally misunderstanding your bullshit. 4. When you get caught in complete bullshit, double down on it with such conviction and fury that you convince people by force that it isn't bullshit. I could try listing real life implementations of the above BGA, but it would likely surpass HN's character limit.