541–550 of 723 posts
Just added Claude 3 to Chat at https://double.bot if anyone wants to try it for coding. Free for now and will push Claude 3 for autocomplete later this afternoon. From my early tests this seems like the first API alternative to GPT4. Huge!
(Chromium 87.0.4280.144 (Jan. 2021), plus security patches up to 119.0.6045.160 (Nov. 2023).)
Opus (and all prior Claude models) still do not correctly answer the Sally question, unfortunately: Q: Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have? A: Okay, let's organize the information we have: Sally is a girl. Sally has 3 brothers. Each of Sally's brothers has 2 sisters. Now, let's think about this step by step: We know that Sally is a girl, so she is a sister to he…
This is what my local LLama 13B Q5 had to say on this: > There are two ways to approach this problem. First, we can count each person by gender and then add them up. Second, we can use the given information about the siblings and their genders to find a quicker solution. > Let's go with the second method: > 1. Sally has one sister (herself). > 2. Each brother has two sisters. So, there are 3 sisters for each of her b…
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I've tested with the Mixtral on LMSYS direct chat, gen params may vary a bit of course. In my experience running it locally it's been a lot more finicky to get it to work consistently compared to non-MoE models so I don't really keep it around anymore. 3.5-turbo's coding abilities are not that great, specialist 7B models like codeninja and deepseek coder match and sometimes outperform it.
There is also Mistral-next, which they claim that it has advanced reasoning abilities, better than ChatGPT-turbo. I want to use it at some point to test it. Have you tried Mistral-next? Is it no good? You were talking about reasoning and i replied about coding, but coding requires some minimal level of reasoning. In my experience using both models to code, ChatGPT-turbo and Mixtral are both great. >3.5-turbo's coding…
They say it's more "concise" whatever that's supposed to mean, I haven't noticed it being any more succinct than the others.
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Temperature 1 - It answered 1 sister: https://i.imgur.com/7gI1Vc9.png Temperature 0 - it answered 0 sisters: https://i.imgur.com/iPD8Wfp.png
By virtue of increasing randomness, we got the correct answer once ... a monkey at a typewriter will also spit out the correct answer occasionally. Temperature 0 is the correct evaluation.
In theory maybe, but I don't think it is in practice. It feels like each model has its own quasi-optimal temperature and other settings at which it performs vastly better. Sort of like a particle filter that must do random sampling to find the optimal solution.
What's up with the weird list of the supported countries? It isn't available in most European countries (except for Ukraine and UK) but on the other hand lot of African counties are listed... https://www.anthropic.com/claude-ai-locations
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Monkey sees moon. Monkey climbs tree. "See? Monkey is closer to moon than before. To reach moon, monkey just needs taller tree." How long before monkey finds tall enough tree to reach moon?
We're rapidly approaching the compute capacity of the human brain in individual server racks. This "moon" is neither unreachable nor is there any doubt that we will cross the threshold soon.
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Why is this a trick or somehow inferior to getting the AI model to be able to do it natively? Most humans also can’t reliably do complex arithmetic without the use of something like a calculator. And that’s no trick. We’ve built the modern world with such tools. Why should we fault AI for doing what we do? To me, training the AI use a calculator is not just a trick for hype, it’s exciting progress.
It would be exciting if the LLM knew it needed a calculator for certain things and went out and got it. If the human supervisors are pre-screening the input and massaging what the LLM is doing that is a sign we don't understand LLMs enough to engineer them precisely and can't count on them to be aware of their own limitations, which would seem to be a useful part of general intelligence.
Isn't that what it does, when it writes a Python program to compute the answer to the user's question?
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The question isn't whether LLMs can simulate human intelligence, I think that is well-established. Many aspects of human nature are a mystery, but a technology that by design produces random outputs based on a seed number does not meet the criteria of human intelligence.
Why? People also produce somewhat random outputs, so?
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I cant wait until this is the true disruptor in the economy: " Take this $1,000 and maximise my returns and invest it where appropriate. Goal is to make this $1,000 100X " And just let your r/wallStreetBets BOT run rampant with it...
That will only work for the first few people who try it.
Opus (and all prior Claude models) still do not correctly answer the Sally question, unfortunately: Q: Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have? A: Okay, let's organize the information we have: Sally is a girl. Sally has 3 brothers. Each of Sally's brothers has 2 sisters. Now, let's think about this step by step: We know that Sally is a girl, so she is a sister to he…
Sally has 1 sister.
Here's the logic behind this:
1. We know that Sally has 3 brothers.
2. Then we are told that each brother has 2 sisters.
3. Since Sally is one of the children in the family, there must be another sister besides Sally.
4. Therefore, Sally has 1 sister.
The number of sisters Sally has is not affected by the number of brothers she has, so the information about her brothers having 2 sisters is not relevant to finding the number of sisters Sally has.