Working through a response on mobile so it’s a bit chunked. I’ll answer each point in series but it may take a bit.

1. that’s not really what the video above claims. The presenter explicitly states that he believes GPT4 is intelligent, and that increasing the size of the LLM will make it true AGI. My entire premise here is not that an LLM is useless but that AGI is still entirely fantastical. Could an LLM conceivably be some building block of AGI? Sure, just like it could not be a building block of AGI. The “humble” position here is keeping AGI out of the picture because we have no idea what that is or how to get there, while we do know exactly what an LLM is and how it works. At its core an LLM is a complex dictionary. It is a queryable encoding of all the data that was passed through it.

Can that model be tweaked and tuned and updated? Sure. But there’s no reason to think that it demonstrates any capability out of the ordinary for “queryable encoded data”, and plenty of questions as to why natural language would be the queryable encoding of choice for an artificial intelligence. Your brain doesn’t encode your thoughts in English, or whatever language your internal thoughts use if you’re ESL+, language is a specific function of the brain. That’s why damage to language centers in the brain can render people illiterate or mute without affecting any other capacities.

I firmly believe that LLMs as a component of broader AGI is certainly worth exploring just like any of the other hundreds of forms of genetic models or specialized “AI” tools: but that’s not the language used to talk about it. The overwhelming majority of online discourse is AI maximalist, delusional claims about the impending singularity or endless claims of job loss and full replacement of customer support with ChatGPT.

Having professionally worked with GitHub Copilot for months now I can confidently say that it’s useful for the tasks that any competent programmer can do as long as you babysit it. Beyond that any programmer who can do the more complex work that an LLC can’t will need to understand the basics that an LLC generates in order to grasp the advanced. Generally it’s faster for me to just write things myself than it is for Copilot to generate responses. The use cases I’ve found where it actually saves any time are:

1. Generating documentation (has at least 1 error in every javadoc comment that you have to fix but is mostly correct). Trying documentation first and code generated from it never worked well enough to be worth doing.

2. Filling out else cases or other branches of unit test code. Once you’ve written a pattern for one test it stamps out the permutations fairly well. Still usually has issues.

3. Inserting logging statements. I basically never have to tweak these, except prompting for more detail by writing a ,

This all is expected behavior for a model that has been trained on all examples of code patterns that have ever been uploaded online. It has general patterns and does a good job taking the input and adapting it to look like the training data.

But that’s all it does. Fed more training data it does a better job of distinguishing patterns, but it doesn’t change its core role or competencies: it takes an input and tries to make it’s pattern match other examples of similar text.