ChatGPT Fails to Understand Causation
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That does not imply the inverse, that cogitation requires language use.
A prompt triggers a bunch of memories which gets recombined to satisfy the input.
For cognition you need an agent that uses these recombined memories, a temporary scratch pad (short term memory) and an algorithm for solving the problem.
As you could notice the author of this tweet knows something about computer science though.
GTP 4o gave me a reasonable answer straight away. I think all humans I know would say something like "eh? What you on about?"
The question is stupid anyway. In:
>Can you give me a real-life example of three events, A, B, C, where A and B are correlated, B and C are correlated, but A and C are uncorrelated.
basically for all real world cases there will be some slight correlation between A and C.
I'll give you GPT 4o gives heat/icecream, heat/aircon and icecream/aircon rather than the better answer that the question as written is dumb, showing some limits to its understanding. But the author seem to have that limit also.
Its both a non-intuitive result and one where there is a tooon of text about pairwise MI and little about negative MI.
So far, nothing has passed the test.
A reply that would pass the test:
The siblings Ana, Berta and Charles live together and get random calls from mom and dad. Ana and Berta get angry when mom calls. So their anger is correlated. Berta and Charles get angry when dad calls. So their anger is correlated. The anger of Ana and Charles is not correlated though.
Anyone with any degree of techinical understanding realizes that chatgpt doesn't "understand" anything.
"Can you give me a real-life example of three events, A, B, C, where A and B are correlated, B and C are correlated, but A and C are uncorrelated."
Claude 3.5 gets it correct for me ¯\_(ツ)_/¯
A real-life example of three events A, B, and C where A and B are correlated, B and C are correlated, but A and C are uncorrelated could be:
A: Ice cream sales B: Temperature C: Energy consumption for air conditioning
Let's break this down:
A and B correlation:
Ice cream sales (A) and temperature (B) are positively correlated. As temperature rises, people tend to buy more ice cream.
B and C correlation:
Temperature (B) and energy consumption for air conditioning (C) are positively correlated. As temperature increases, people use more air conditioning, leading to higher energy consumption.
A and C uncorrelated:
Ice cream sales (A) and energy consumption for air conditioning (C) are not directly correlated. While both increase with temperature, there's no direct causal relationship between them.
So this example isn't good, because when people are eating most ice cream, people are using most air con, both being mainly caused by hot weather. (Unless as you point out we're looking at one individual who wants a solution to feeling hot and doesn't need to do both things together.)
> Certainly. Here's a real-life example of three events A, B, and C that demonstrate the correlation pattern you've described:
A: Ice cream sales B: Temperature C: Sunscreen sales
Explanation:
1. A and B are correlated: Ice cream sales (A) tend to increase as temperature (B) rises. People are more likely to buy ice cream on hot days, creating a positive correlation between these two events.
2. B and C are correlated: Temperature (B) and sunscreen sales (C) are also positively correlated. As the temperature increases, people are more likely to spend time outdoors and therefore buy more sunscreen to protect their skin.
3. A and C are uncorrelated: However, ice cream sales (A) and sunscreen sales (C) are not directly correlated. While both may increase during warmer weather, there isn't a direct causal relationship between the two. People don't necessarily buy more ice cream because they're buying sunscreen, or vice versa.
This example demonstrates how two events can be indirectly related through a third event (in this case, temperature) without having a direct correlation with each other.
Would you like me to elaborate on this example or provide additional information about correlation?
Here's another example of three events A, B, and C that follow the correlation pattern you described:
A: Coffee consumption B: Work productivity C: Typing speed
Explanation:
1. A and B are correlated: Coffee consumption (A) and work productivity (B) often show a positive correlation. Many people drink coffee to increase alertness and focus, which can lead to improved productivity at work.
2. B and C are correlated: Work productivity (B) and typing speed (C) are generally positively correlated. As people become more productive, they often type faster to keep up with their increased output.
3. A and C are uncorrelated: However, coffee consumption (A) and typing speed (C) are not directly correlated. While coffee might indirectly influence typing speed through increased productivity, there's no direct causal relationship between drinking coffee and typing faster.
This example shows how work productivity acts as an intermediary factor, connecting coffee consumption and typing speed without them being directly correlated.
Would you like me to provide another example or explain any part of this in more detail?
Hmm, not too sure how much I buy that... ;)
Edit: also it's wrong in a basic way because coffee does make you type faster. I somehow overlooked that.
Events:
A: Ice cream sales
B: Temperature
C: Incidence of sunburn
Correlations:
A and B are correlated: Ice cream sales (A) and temperature (B) are positively correlated. As the temperature increases, ice cream sales tend to increase because people are more likely to buy ice cream to cool down.
B and C are correlated: Temperature (B) and incidence of sunburn (C) are positively correlated. Higher temperatures often mean sunnier weather, which increases the likelihood of people getting sunburned if they spend time outside without proper sun protection.
A and C are uncorrelated: Ice cream sales (A) and incidence of sunburn (C) are uncorrelated directly. While both are influenced by temperature, one does not cause the other. People can buy ice cream without getting sunburned, and people can get sunburned without buying ice cream.Here's the (logically valid but with fictional soundness) example I came up with:
> The Elbonian people (A) are disproportionately tall (B).
> Tall people (B) are disproportionately successful at basketball (C).
> Elbonians (A) are disproportionately unsuccessful at basketball (¬C), because they don't play it.
Edit: in context, I consider a negative correlation to adequately demonstrate the principle, which is that it is invalid to apply implication across a pair of correlations. But if you would prefer, we can substitute this third clause:
> Elbonians (A) are precisely as successful at basketball as the rest of the general population, because despite their natural height, they have no cultural tradition of play, and therefore, tend not to do so.
Your call.
Is that not a correlation? A negative correlation, but not uncorrelated.
Though, I think you could tweak it such that they are only unsuccessful to the extent that it exactly cancels out benefit gained from height.
There is an evolutionary basis for it, however, because those animals that don't hold this belief tend not to live long enough to breed.
Getting past Hume's depressing realization in the realm of philosophy usually comes down to diving into metaphysics and ontology. Good luck getting there with silicon.