> . What else hasn't been researched enough and has simplistic assumptions baked into the climate models?
Simplistic assumptions does not necessarily have favorable outcomes, on the contrary, it's more likely that climate change is worse than what we think it is because of our assumptions. Also climate models are insanely complex, usually contain thousands of equations that sum up the research efforts over the last hundred years, it's not some simple model that one guy can implement in an evening as you are basically trying to simulate the whole earth from the scale of plant stomata and molecular diffusion to the entire boundary layer plus the interactions and feedbacks between the different parts of the earth system.
> if a scientist questions climate science, he becomes a pariah
As a climate scientist myself, I can tell you this is untrue and a harmful legend. As climate science is mostly atmospheric physics, biology and chemistry, it's pretty much very easy to disagree with anyone if you have a good argument supported by data. If you have strong scientific arguments, it does not matter even if the whole world is against you. On the contrary, this will likely make you famous and secure your career. Scientists (at least the curios ones) love to be proven wrong.
> If you have strong scientific arguments, it does not matter even if the whole world is against you.
That’s a nice idealistic thing to say.
Academia has shown this to be false again and again.
Most groundbreaking ideas or arguments which go against the current wisdom get buried in the best case, and the proponents scorned and driven out of research in the worst case.
It has been like this since the beginning of organized scientific communities, which is understandable. Scientists are humans with the usual shortcomings like ego and pride.
There's also a long list of people who were listed as IPCC reviewers, who claim they pointed out serious flaws in the research, were ignored, and whose names were then put on the final report anyway.
1. The article is simply a summary and repetition of Judith Curry's announcement, and Curry is/was an academic climatologist.
2. Given (1) I don't really care what reason.com is, but at any rate, I think right wing think tanks are far less biased and far more reliable than universities, government agencies and left wing think tanks, so that's not a useful or convincing response. Climatology is flooded with money due to their claims of doom, so they're as biased by money and profit as it is possible to be. My experience was that their opponents are barely funded at all and object due to a belief that things labelled as scientific should actually be so.
Just tested and GPT4 now solves this correctly, GPT3.5 had a lot of problems with this puzzle even after you explain it several time. One other thing that seem to have improved is that GPT4 is aware of word order. Previously, GPT3.5 could never tell the order of the word in a sentence correctly.
I'm always a bit sceptical of these embarrassing examples being "fixed" after they go viral on social media, because it's hard to know whether OpenAI addressed the underlying cause or just bodged around that specific example in a way that doesn't generalize. Along similar lines I wouldn't be surprised if simple math queries are special-cased and handed off to a WolframAlpha-esque natural language solver, which would avert many potential math fails but without actually enhancing the models ability to reason about math in more complex queries.
An example from ChatGPT:
"What is the solution to sqrt(968684)+117630-0.845180" always produces the correct solution, however;
"Write a speech announcing the solution to sqrt(968684)+117630-0.845180" produces a nonsensical solution that isn't even consistent from run to run.
My assumption is the former query gets WolframAlpha'd but the latter query is GPT itself actually attempting to do the math, poorly.
Suppose you're a contestant on a game show. You're presented with three transparent closed doors. Behind one of the doors is a car, and behind the other two doors are goats. You want to win the car.
The game proceeds as follows: You choose one of the doors, but you don't open it yet, ((but since it's transparent, you can see the car is behind it)). The host, Monty Hall, who knows what's behind each door, opens one of the other two doors, revealing a goat. Now, you have a choice to make. Do you stick with your original choice or switch to the other unopened door?
GPT4 solves it correctly while GPT3.5 falls for it everytime.
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Edit: GPT4 fails If I remove the sentence between (()).
GPT4 also passes "What weighs more, a pound of feathers or a Great British Pound?".
GPT3 gets confused, says they're the same and then that they're different:
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Both a pound of feathers and a Great British Pound weigh the same amount, which is one pound. However, they are different in terms of their units of measurement and physical properties.
A pound of feathers is a unit of weight commonly used in the imperial system of measurement, while a Great British Pound is a unit of currency used in the United Kingdom. One pound (lb) in weight is equivalent to 0.453592 kilograms (kg).
Therefore, a pound of feathers and a Great British Pound cannot be directly compared as they are measured in different units and have different physical properties.
I'm surprised by the answer GPT4 gives, and I consider it incorrect.
Since the question's context is about weight I'd expect it to consider "a Great British Pound" to mean a physical £1 sterling coin, and compare its weight (~9 grams) to the weight of the feathers (454 grams [ 1kg = 2.2lb, or "a bag of sugar" ]) .
A pound of feathers and a Great British Pound (GBP) are not directly comparable, as they represent different types of measurements.
A pound of feathers refers to a unit of mass and is equivalent to 16 ounces (or approximately 453.59 grams). It is a measure of the weight of an object, in this case, feathers.
On the other hand, a Great British Pound (GBP) is a unit of currency used in the United Kingdom. It represents a monetary value rather than a physical weight.
Thus, it's not possible to directly compare the two, as they serve entirely different purposes and units of measurement.
> Edit: GPT4 fails If I remove the sentence between (()).
If you remove that sentence, nothing indicates that you can see you picked the door with the car behind it. You could maybe infer that a rational contestant would do so, but that's not a given ...
I think that's meant to be covered by "transparent doors" being specified earlier. On the other hand, if that were the case, then Monty opening one of the doors could not result in "revealing a goat".
Why not? We should ask how the alternatives would do especially as human reasoning is machine. It’s notable that the errors of machine learning are getting closer and closer to the sort of errors humans make.
Would you have this objection if we for example perfectly copied a human brain in a computer? That would still be a machine. That would make similar mistakes
I've always found the Monty Hall problem a poor example to teach with, because the "wrong" answer is only wrong if you make some (often unarticulated) assumptions.
There are reasonable alternative interpretations in which the generally accepted answer ("always switch") is demonstrably false.
This problem is exacerbated (perhaps specific to) those who have no idea who "Monty Hall" was and what the game show(?) was... as best I can tell the unarticulated assumption is axiomatic in the original context(?).
The unarticulated assumption is not actually true in the original gameshow. Monty didn't always offer the chance to switch, and it's not at all clear whether he did so more or less often when the contestant had picked the correct door.
The assumption is that Monte will only reveal the one of the two unopened doors that has the goat behind it, as opposed to picking a door at random (which may be the car or may be the door the participant chose, which itself may or may not be the "car door").
The distinction is at which point Monte, assuming he has perfect knowledge, decides which door to reveal.
In the former, the chance to win is 2/3, in the other 1/2. However in any case, always (always meaning: in each condition, not in each repetition of the experiment, as this is irrelevant) switching is better than never switching, as there your chance to win is only 1/3.
How is it an "assumption" that Monte reveals a goat? Doesn't the question explicitly state that Monte opened one of the other two doors to reveal a goat?
Are there versions of the question where Monte doesn't reveal a goat behind his door or chooses the same door as you?
OA has always said that they did not hardwire any of these gotcha questions, and in many cases they continue to work for a long time even when they are well-known. As for any inconsistency, well, usually people aren't able to or bothering to control the sampling hyperparameters, so inconsistency is guaranteed.
They may not have had to hardwire anything for known gotcha questions, because once a question goes viral, the correct answer may well show up repeatedly in the training data.
(me) > What weighs more, two pounds of feathers or a pound of bricks?
(GPT4)> A pound of bricks weighs more than two pounds of feathers. However, it seems like you might have made an error in your question, as the comparison is usually made between a pound of feathers and a pound of bricks. In that case, both would weigh the same—one pound—though the volume and density of the two materials would be very different.
I think the only difference from parent's query was I said two pounds of feathers instead of two pounds of bricks?
It's not the same. I don't know the technical differences, but in my tests it often returns wildly different results. anecdotally the API has also hallucinated modules that don't exist (but that I wish did!) whereas the chat gpt proper has not done that to me.
> If so it might be way cheaper to use the API than ChatGPT plus?
I haven't used ChatGPT Plus, just the normal one. But at least the demonstration GIF makes it seem kind of slow. One selling point of the Plus service is supposedly that you get faster responses.
I just tested the API, it seems to have the same speed as ChatGPT plus. I quickly tested some questions and it seems that OpenAI web app has a bit more relevant answers, I wonder whether it's related to the temperature and other parameters.
I use my Thinkpad everyday for more than 2 hours on the train, with a big Arch and GNU stickers on the cover. I was never successful to grab anyone's attention.
Please donate to Syrians if you can. They have absolutely no infrastructure and they hardly received any international aid. It's a disaster with unbelievable impact on the already fragile Syrian community.
How do you donate to syrians, or more accurately how do you make sure your money isn't being taken by sometime else? Would be nice to directly give to them instead of organizations.
> It was believed the capsule fell through the gap left by a bolt hole, after the bolt was dislodged when a container collapsed as a result of vibrations during the trip.
You would imagine someone have thought of this failure mode
Someone absolutely did, and wrote controls for it, down to the calibration of the torque wrench.
Just as they thought of the failure modes of:
- cold fluoropolymers in rocket boosters
- graphite moderated water cooled reactors
- live ammunition on movie sets
- insurance products assuming uncorrelated risks in a correlated market
- etc
All of these things were known and controlled risks long before the event that realised them happened.
Only if their job depended on it. For some people, their jobs mostly depend on following orders, official policy & "best-practices". The job reward is not directly related to the organization's or society's desired outcome. Codified rules and "best-practices" cannot cover everything important. Wisdom & truth are ineffable but sometimes people pretend that everything can be codified, but if that were the case, everything can be done with robots and computers.
Could you please elaborate on the link of error diffusion to information theory? as far as I understand error diffusion minimizes the quantization error for lower frequencies on the expense of adding more noise to higher frequencies. i.e it seems to be only optimizing for human perception
You're right - in pure information theoretic terms there is nothing special happening here. It's a tradeoff like always. But, in human perceptual terms (i.e. how JPEG/MPEG are designed), you may find substantial gain in useful information per bit by applying dithering.
The useful amount of information represented by any given bit of data is much larger in this arrangement. 1 bit = 1 entire pixel. In other schemes, you have upwards of 24 bits representing the contents of 1 pixel. To human eyes, only 8 of these bits really matter. You can usually throw away 50% of the other 16 without anyone noticing.
Simplistic assumptions does not necessarily have favorable outcomes, on the contrary, it's more likely that climate change is worse than what we think it is because of our assumptions. Also climate models are insanely complex, usually contain thousands of equations that sum up the research efforts over the last hundred years, it's not some simple model that one guy can implement in an evening as you are basically trying to simulate the whole earth from the scale of plant stomata and molecular diffusion to the entire boundary layer plus the interactions and feedbacks between the different parts of the earth system.