Because they’re fucking terrible at designing tools to solve problems, they are obviously less and less good at pretending this is an omnitool that can do everything with perfect coherency (and if it isn’t working right it’s because you’re not believing or paying hard enough)

why don't they program them

AI models aren't programmed traditionally. They're generated by machine learning. Essentially the model is given test prompts and then given a rating on its answer. The model's calculations will be adjusted so that its answer to the test prompt will be closer to the expected answer. You repeat this a few billion times with a few billion prompts and you will have generated a model that scores very high on all test prompts.

Then someone asks it how many R's are in strawberry and it gets the wrong answer. The only way to fix this is to add that as a test prompt and redo the machine learning process which takes an enormous amount of time and computational power each time it's done, only for people to once again quickly find some kind of prompt it doesn't answer well.

There are already AI models that play chess incredibly well. Using machine learning to solve a complexe problem isn't the issue. It's trying to get one model to be good at absolutely everything.

...or a simple counter to count the r in strawberry. Because that's more difficult than one might think and they are starting to do this now.

Because the LLMs are now being used to vibe code themselves.

If you pay for chatgpt you can connect it with wolfrenalpha and it's relays the maths to it

I think they're trying to do that. But AI can still fail at that lol

They are starting to do this. Most new models support function calling and can generate code to come up with math answers etc

This is where MCP comes in. It's a protocol for LLMs to call standard tools. Basically the LLM would figure out the tool to use from the context, then figure out the order of parameters from those the MCP server says is available, send the JSON, and parse the response.

why don't they program them to look up math programs and outsource chess to other programs when they're asked for that stuff?

Because the AI doesn't know what it's being asked, it's just a algorithm guessing what the next word in a reply is. It has no understanding of what the words mean.

"Why doesn't the man in the Chinese room just use a calculator for math questions?"

From a technology standpoint, nothing is stopping them. From a business standpoint: hubris.

To put time and effort into creating traditional logic based algorithms to compensate for this generic math model would be to admit what mathematicians and scientists have known for centuries. That models are good at finding patterns but they do not explain why a relationship exists (if it exists at all). The technology is fundamentally flawed for the use cases that OpenAI is trying to claim it can be used in, and programming around it would be to acknowledge that.

why don't they program them to look up math programs and outsource chess to other programs when they're asked for that stuff?

They will, when it makes sense for what the AI is designed to do. For example, ChatGPT can outsource image generation to an AI dedicated to that. It also used to calculate math using python for me, but that doesn't seem to happen anymore, probably due to security issues with letting the AI run arbitrary python code.

ChatGPT however was not designed to play chess, so I don't see why OpenAI should invest resources into connecting it to a chess API.

I think especially since adding custom GPTs, adding this kind of stuff has become kind of unnecessary for base ChatGPT. If you want a chess engine, get a GPT which implements a Stockfish API (there seem to be several GPTs that do). For math, get the Wolfram GPT which uses Wolfram Alpha's API, or a different powerful math GPT.