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Comment by jackblemming | Hacker News Reader

If we're going to pull random numbers from where the sun doesn't shine, why don't we say the work of a hundred engineers? How about a billion?

I've actually used ChatGPT and copilot and I can tell you it's not even 10% more productive, so I'm curious how you ended up with your estimate.

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They said “could potentially”. They’re just making the argument (true IMO) that technology doesn’t have to replace a class of employee to have a huge effect. We still have farmers, but 1% of the population instead of >50%.

Or, it could be like how assemblers didn’t get rid of software engineers but helped fuel the fire. Who knows.

>could potentially

"Potentially" we could explore the galaxy or have nanobots and Star Trek fabricators. What does it matter? ChatbotGPT is no closer to AGI than my keyboard autocomplete, and copilot is useless for any task whose answer cannot be googled in 20s.

The problem I'm seeing is that "engineers" are not interchangeable. For some folks, the job is all straight coding. For others, it's understanding requirements, talking to stakeholders and figuring out their requirements, figuring out how to make a clean design taking lots of other factors in mind.

It's not that ChatGPT is incapable... it's that it's not inherently going to make me significantly more productive.

Where did you pull 10% from then? That also seems like you're reaching up your ass. As with all of these arguments, context matters. Both of you need to say what you're doing with these tools. I've personally found the combination to range from "utterly useless" (writing Rust for microcontrollers) to "I could see this eliminating jobs" (CRUD API work).

It's just an example to make the point easier to understand

A single engineer with [some near future LLM] could potentially do the work of [X] engineers right now.

Because ChatGPT is not self driven and has no ability to do things. I can quite easily imagine one person doing 10 peoples work with this tool. I can’t imagine them being able to type fast enough to do 100 jobs.

>I've actually used ChatGPT and copilot and I can tell you it's not even 10% more productive

Use your imagination.

Do you think that LLMs have hit their plateau? Not likely I'd say. With all the billions suddenly rushing into the space, you really think that ChatGPT v.X isn't going to replace you? Risky bet.

> Do you think that LLMs have hit their plateau? Not likely I'd say.

Mmmm, I don't know.

I think LLMs are running up against fundamental limitations in the approach (hallucinations in particular) that will not be fixed unless truly new techniques are discovered.

So I do actually think we're likely hitting a plateau.

how did you reach those conclusions?

Good question!

So LLMs fundamentally do not encode the nature of facts and data. They're simply (well, no, extremely sophisticated) text prediction engines. There's no embedded comprehension. And that's basic to the approach companies like OpenAI are taking.

That's why it's so easy to find factual errors in the generated text from these models: they can create text that's pleasing, but that's as much as they can do.

The next leap, to generate language that's also accurate, will require new techniques to bake in actual understanding.

Until then, these models will always have issues with hallucinations.

Or, at least, that's my expectation. Certainly nothing we've seen in GPT 3, 3.5, or Bing, which is rumoured to be based on something close to GPT 4, indicates any advances in this area.

Perhaps, but just like humans some are more adept at tasks than others. And an AGI using LLM's is completly feasable, for example on a quick thought a LLM could be trained on many LLM all specifically designed for tasks, and verfied against other LLMs, etc

That is all true. However, the next step seems to be simple: build a second AI which has the ability to understand facts, but doesn't need all the fancy language generation capabilities.

The second AI only needs to confirm correct, provide a correct fact instead, or some other pre-agreed solution.

  USER: How many people live in Germany?
  CHATGPT: About 83 million people live in Germany.
  TRUTHAI: Correct.

  USER: How many people live in France?  
  CHATGPT: There are 12 people who live in France.
  TRUTHAI: Wrong. There are about 68 million people who live in France.
  CHATGPT: There are about 68 million people who live in France.

  USER: What is the answer to the ultimate question of life, the universe, and everything.
  CHATGPT: The answer to the ultimate question of life, the universe, and everything is 42.
  TRUTHAI: Insufficient data for meaningful answer.
  CHATGPT: The answer to the ultimate question of life, the universe, and everything is 42.

> However, the next step seems to be simple: build a second AI which has the ability to understand facts, but doesn't need all the fancy language generation capabilities.

I'm not convinced that will work, but let's say it could. First, you'd have to build that oracle and the problem is, as far as I'm aware, we don't know how to do that. That'd be one of those new techniques I was referring to.

Well, I'm not sure about that. For pure facts, Google is able to answer pretty quickly. Search for 'population of Austria-Hungary in 1914', or 'shakespeare's birthday', or 'official languages of the Philippines', and Google can provide that data.

Obviously, there's work to be done still, and I'd be wary of making Google that 'oracle', but the development there would seem to be incremental (teach it more facts), rather than creating some other completely new concept.

I genuinely don't agree. Google can handle basic facts like that, sure, but there's a wide range of factual queries where Google is unable to provide direct answers like that because that's actually quite hard in the general case. After all, if it were easy, they'd already be doing it.

But hey, I guess we'll see!

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