5 ms·

Exponential curves don't last for long fortunately, or the universe would have turned into a quark soup. The example of COVID is especially ironic, considering

Exponential curves don't last for long fortunately, or the universe would have turned into a quark soup. The example of COVID is especially ironic, considering it stopped being a real concern within 3 years of its advent despite the exponential growth in the early years. Those who understand exponentials should also try to understand stock and flow.

smohare 1y ago
Stopped being a concern primarily due to heavy vaccination campaigns though. It is still raging, just not nearly as many people are dying. The immunity from infection these days is pretty paltry.
nkrisc 1y ago
Reminds me a bit of the "ultraviolet catastrophe". > The ultraviolet catastrophe, also called the Rayleigh–Jeans catastrophe, was the prediction of late 19th century and early 20th century classical physics that an ideal black body at thermal equilibrium would emit an unbounded quantity of energy as wavelength decreased into the ultraviolet range. [...] > The phrase refers to the fact that the empirically derived Rayleigh–Jeans law, which accurately predicted experimental results at large wavelengths, failed to do so for short wavelengths. https://en.wikipedia.org/wiki/Ultraviolet_catastrophe https://en.wikipedia.org/wiki/Ultraviolet_catastrophe
- analog31 1y ago
  Right. Nobody believed that the intensity would go to infinity. What they believed was that the theory was incomplete, but they didn't know how or why. And the solution required inventing a completely new theory.
analog31 1y ago
It's possible to understand both exponential and limiting behavior at the same time. I work in an office full of scientists. Our team scrammed the workplace on March 10, 2020. To the scientists, it was intuitively obvious that the curve could not surpass 100% of the population. An exponential curve with no turning point is almost always seen as a sure sign that something is wrong with your model. But we didn't have a clue as to the actual limit, and any putative limit below 100% would need a justification, which we didn't have, or some dramatic change to the fundamental conditions, which we couldn't guess. The typical practice is to watch the curve for any sign of a departure from exponential behavior, and then say: "I told you so." ;-) The first change may have been social isolation. In fact that was pretty much the only arrow in our quivers. The second change was the vaccine, which changed both the infection rate and the mortality rate, dramatically.
- Earw0rm 1y ago
  I'm curious as to whether the consensus is that the observed behaviour of COVID waves was ever fully and satisfactorily explained - the tend to grow exponentially but then seemingly saturate at a much lower point than a naïve look at the curve might suggest?
  - analog31 1y ago
    It would probably be hard to do. The really huge factor may be easier to study, since we know where and when every vaccine dose was administered. The behavioral factors are likely to be harder to measure, and would have been masked by the larger effect of vaccination. We don't really know the extent of social isolation over geography, demographics, time, etc..
    - Earw0rm 1y ago
      There's human behavioural factors yes, but I was kinda wondering about the virus itself, the R number seemed to fluctuate quite a bit, with waves peaking fast and early and then receding equally quickly.. I know there were some ideas around asymptomatic spread and superspreaders (both people with highly connected social graphs, and people shedding far more active virus than the median), I just wondered whether anyone had built a model that was considered to have accurately reproduced the observed behaviour of number of positive tests and symptomatic cases, and the way waves would seemingly saturate after infecting a few % of the population.
  - adornKey 1y ago
    To those interested in numbers it was explained early - even on TV. Anyone interested saw that it was going like a seasonal flue wave. Numbers were following strict mathematics. My area was early - the numbers peaked right before people started to go crazy - the rest was censorship - There was a lot of fakery going on by using very soft numbers. Very often they used reporting date instead of infection date.. and some numbers were delayed 9 months... So most curves out there were seriously flawed. But if you were really interested you could see real epidemiological curves - but you had to do real work to find the numbers. Strict mathematics of a seasonal virus was something people didn't want to see - and this is still the consensus...
FrustratedMonky 1y ago
Exponentials exist in their environment. Didn't Covid stop because we ran out of people to infect. Of course it can't keep going exponential, because there aren't exponential people to infect. What is this limit on AI? It is technology, energy, something. All these things can be over-come, to keep the exponential going. And of course, systems also break at the exponential. Maybe AI is stopped by the world economy collapsing. AI advancement would be stopped, but that is cold comfort to the humans.
- HexDecOctBin 1y ago
  > What is this limit on AI? Gulf money, for one. DoD budget would be another. Booms are economic phenomena, not technological phenomena. When looking for a limiting factor of a boom, think about the money taps.
- bawolff 1y ago
  > What is this limit on AI? It is technology, energy, something. All these things can be over-come, to keep the exponential going. That's kind of begging the question. Obviously if all the limitations on AI can be overcome growth would be exponential. Even the biggest ai skeptic would agree. The question is, will it?
- timmytokyo 1y ago
  >What is this limit on AI? Data. Think of our LLMs like bacteria in a Petri dish. When first introduced, they achieve exponential growth by rapidly consuming the dish's growth medium. Once the medium is consumed, growth slows and then stops. The corpus of information on the Internet, produced over several decades, is the LLM's growth medium. And we're not producing new growth medium at an exponential rate.
tehjoker 1y ago
Long COVID is still a thing, the nAbs immunity is pretty paltry because the virus keeps changing its immunity profile so much. T-cells help but also damage the host because of how COVID overstimulates them. A big reason people aren't dying like they used to is because of the government's strategy of constant infection which boosts immunity regularly* while damaging people each time, that plus how Omicron changed SARS-CoV-2's cell entry mechanism to avoid cell-cell fusion (syncytia) that caused huge over-reaction in lung tissue. If you think COVID isn't still around: https://www.cdc.gov/nwss/rv/COVID19-national-data.html https://www.cdc.gov/nwss/rv/COVID19-national-data.html * one might call this strategy forced vaccination with a known dangerous live vaccine strain lol

smohare 1y ago

Stopped being a concern primarily due to heavy vaccination campaigns though. It is still raging, just not nearly as many people are dying. The immunity from infection these days is pretty paltry.

nkrisc 1y ago

Reminds me a bit of the "ultraviolet catastrophe". > The ultraviolet catastrophe, also called the Rayleigh–Jeans catastrophe, was the prediction of late 19th century and early 20th century classical physics that an ideal black body at thermal equilibrium would emit an unbounded quantity of energy as wavelength decreased into the ultraviolet range. [...] > The phrase refers to the fact that the empirically derived Rayleigh–Jeans law, which accurately predicted experimental results at large wavelengths, failed to do so for short wavelengths. https://en.wikipedia.org/wiki/Ultraviolet_catastrophe https://en.wikipedia.org/wiki/Ultraviolet_catastrophe

analog31 1y ago
Right. Nobody believed that the intensity would go to infinity. What they believed was that the theory was incomplete, but they didn't know how or why. And the solution required inventing a completely new theory.

analog31 1y ago

It's possible to understand both exponential and limiting behavior at the same time. I work in an office full of scientists. Our team scrammed the workplace on March 10, 2020. To the scientists, it was intuitively obvious that the curve could not surpass 100% of the population. An exponential curve with no turning point is almost always seen as a sure sign that something is wrong with your model. But we didn't have a clue as to the actual limit, and any putative limit below 100% would need a justification, which we didn't have, or some dramatic change to the fundamental conditions, which we couldn't guess. The typical practice is to watch the curve for any sign of a departure from exponential behavior, and then say: "I told you so." ;-) The first change may have been social isolation. In fact that was pretty much the only arrow in our quivers. The second change was the vaccine, which changed both the infection rate and the mortality rate, dramatically.

Earw0rm 1y ago
I'm curious as to whether the consensus is that the observed behaviour of COVID waves was ever fully and satisfactorily explained - the tend to grow exponentially but then seemingly saturate at a much lower point than a naïve look at the curve might suggest?
- analog31 1y ago
  It would probably be hard to do. The really huge factor may be easier to study, since we know where and when every vaccine dose was administered. The behavioral factors are likely to be harder to measure, and would have been masked by the larger effect of vaccination. We don't really know the extent of social isolation over geography, demographics, time, etc..
  - Earw0rm 1y ago
    There's human behavioural factors yes, but I was kinda wondering about the virus itself, the R number seemed to fluctuate quite a bit, with waves peaking fast and early and then receding equally quickly.. I know there were some ideas around asymptomatic spread and superspreaders (both people with highly connected social graphs, and people shedding far more active virus than the median), I just wondered whether anyone had built a model that was considered to have accurately reproduced the observed behaviour of number of positive tests and symptomatic cases, and the way waves would seemingly saturate after infecting a few % of the population.
- adornKey 1y ago
  To those interested in numbers it was explained early - even on TV. Anyone interested saw that it was going like a seasonal flue wave. Numbers were following strict mathematics. My area was early - the numbers peaked right before people started to go crazy - the rest was censorship - There was a lot of fakery going on by using very soft numbers. Very often they used reporting date instead of infection date.. and some numbers were delayed 9 months... So most curves out there were seriously flawed. But if you were really interested you could see real epidemiological curves - but you had to do real work to find the numbers. Strict mathematics of a seasonal virus was something people didn't want to see - and this is still the consensus...

FrustratedMonky 1y ago

Exponentials exist in their environment. Didn't Covid stop because we ran out of people to infect. Of course it can't keep going exponential, because there aren't exponential people to infect. What is this limit on AI? It is technology, energy, something. All these things can be over-come, to keep the exponential going. And of course, systems also break at the exponential. Maybe AI is stopped by the world economy collapsing. AI advancement would be stopped, but that is cold comfort to the humans.

HexDecOctBin 1y ago
> What is this limit on AI? Gulf money, for one. DoD budget would be another. Booms are economic phenomena, not technological phenomena. When looking for a limiting factor of a boom, think about the money taps.
bawolff 1y ago
> What is this limit on AI? It is technology, energy, something. All these things can be over-come, to keep the exponential going. That's kind of begging the question. Obviously if all the limitations on AI can be overcome growth would be exponential. Even the biggest ai skeptic would agree. The question is, will it?
timmytokyo 1y ago
>What is this limit on AI? Data. Think of our LLMs like bacteria in a Petri dish. When first introduced, they achieve exponential growth by rapidly consuming the dish's growth medium. Once the medium is consumed, growth slows and then stops. The corpus of information on the Internet, produced over several decades, is the LLM's growth medium. And we're not producing new growth medium at an exponential rate.

tehjoker 1y ago

Long COVID is still a thing, the nAbs immunity is pretty paltry because the virus keeps changing its immunity profile so much. T-cells help but also damage the host because of how COVID overstimulates them. A big reason people aren't dying like they used to is because of the government's strategy of constant infection which boosts immunity regularly* while damaging people each time, that plus how Omicron changed SARS-CoV-2's cell entry mechanism to avoid cell-cell fusion (syncytia) that caused huge over-reaction in lung tissue. If you think COVID isn't still around: https://www.cdc.gov/nwss/rv/COVID19-national-data.html https://www.cdc.gov/nwss/rv/COVID19-national-data.html * one might call this strategy forced vaccination with a known dangerous live vaccine strain lol