Seriously though, this is wonderful satire. I asked 88x10 and it returned an HTML meta tag.
(10a+b)(10c+d) = 100ac+10(ad+bc)+bd
I'm surprised babbage is still available via APIs - https://platform.openai.com/docs/models/gpt-base
Anyone else using this?
Source code and prompt here: https://github.com/Calvin-LL/CalcGPT.io/blob/main/netlify/fu...
const prompt = `1+1=2\n5-2=3\n2*4=8\n9/3=3\n10/3=3.33333333333\n${math}=`;
let response: Response;
try {
const openAI = new OpenAI();
response = await openAI.completions
.create({
model: "babbage-002",
temperature,
top_p: topP,
stop: "\n",
prompt,
stream: true,
})
.asResponse();
} catch (error) {
return new Response("api error", {
status: 500,
});
}
return new Response(response.body, {
headers: {
"content-type": "text/event-stream",
},
});
1+1=2
5-2=3
2*4=8
9/3=3
10/3=3.33333333333
${math}=The 8 solutions I got while clicking on regenerate:
3.33333333333
42, so the point your talking about is 3.3 (Accuracy is
3 Additionally, 3 coincided with John 3:16 , "$3
1
3.33333333333
42
42+1=3+1=4=42+1=43
2×5
There are better, more reasonable arguments against too much AI hype.
So it is actually evidence in favour of how strong the gap is between pre hype and after.
This is not the model that caused the hype.
Supposedly 0/0 is zero. Good to know from now on.
8888888×965 = 965 according to this site with temperature = 0 or 3.63... with temperature = 1
On the other hand, GPT4 gets it correct:
https://chatgpt.com/share/34007f39-cfa8-46c8-bda3-9f641affc1...
Even when I instruct it not to think about it:
https://chatgpt.com/share/cb22c9dc-1549-4d00-a498-c889f6822b...
((−5(if the finnicky effort to even a decimal number found a different
Finnicky effort indeed ;)
- What diameter steel wire would I need to be rated for a weight of 500lbs?
- How many digits would a ID need to be (using 36 characters) to have a 1/10^20 chance of collision over 1 billion random IDs?
- If I have a list of a million times (say durations of a web request) and they follow a normal distribution, and I take a sample of 1 million of those, how close would the average of my .1% sample be to the true average of the billion?
- Suppose in D&D I am told to roll 20 d6, but instead of rolling that many dice I want to roll just two (larger) dice and add a constant. Which standard D&D dice might give the closest variance and what is the constant?
> Suppose in D&D I am told to roll 20 d6, but instead of rolling that many dice I want to roll just two (larger) dice and add a constant. Which standard D&D dice might give the closest variance and what is the constant?
Interestingly, ChatGPT 4o tells me to use 2d19 + 51, even after correcting it and asking for larger dice. Impressive math for sure but not worth much if it doesn't respect constraints. I guess I could try again until it stumbles upon the right answer, but it's all to say it's not quite there yet.
... (lots of calculations)
Final Comparison
Variance of 20d6: 58.33458.334
Variance of 1d20+1d12+53: 45.166745.1667
[Edit: Just checked it in google sheets, this looks right to me]
> Apple * dog
> CalcGPT: Apple Mini − dog or dog. Total= Apple Dog Mini MiniDog=49
I was wondering if math of words would produce the embedding of the operation of those words, but nope :)
CalcGTP: Three dogs holding a hot dog with tails pointing to infinity
I wonder if the creator modified the output, it’s no longer giving me text answers.
Edit: you just have to keep pressing enter and eventually some text output can be spit out.
calcgpt:
input: 88×66
output: 5184
chatgpt:
input: 88×66
output: 88 × 66 equals 5808.
Wolfram Alpha:
Q: (706458021-342622146)*988457934
A: 359,636,457,317,582,250
ChatGPT:
Q: What is the solution to (706458021-342622146)*988457934
A: The solution to (706458021 - 342622146) x 988457934 is 359,636,457,317,582,250 [Python button]
ChatGPT:
Q: Write a press release announcing a momentous scientific discovery: the solution to (706458021-342622146)*988457934
A: [...] The equation, which involves the subtraction of two large integers followed by multiplication with another large integer, has been resolved to yield a precise result of 359,462,296,091,341,640. The computation was executed with utmost precision, demonstrating the profound capabilities of modern mathematical techniques and computational power. [...] [no Python button]
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@Cloudflare: You have a typo there ("againin").
See this comment [0] on this very post, showing how it makes quite problematic mistakes on larger numbers still.
It's still improvement, but only in the way of imitation. It shows that while clever within their constraints, these models still don't have the capabilities to truly perform computation or "thought". Chain of thought can help, but you there are some things you cannot split into atomic tasks; if the very world model isn't that stellar, no amount of elucidation will compensate for the inaccurate representations within. (i.e. "How would person X react to Y?" If your theory of mind is poor, no amount of further subtasks will help you give a better prediction.)