Otherwise, you'll have a lot of systems dependent on these orchestrators creating hard-to-debug mistakes up and down the pipeline. With software, you can reach a state where it does what you tell it to without having to worry if some model adjustment or API change is going to break the output.
If they solve that, then yes. Otherwise, what I personally expect is a lot of businesses rushing into implementing "agents" only to backpedal later when they start to have negative material effects on bottom lines.
People want us to be at "Her" levels of AI, but we're at a far earlier stage. We can fake certain aspects of that (using TTS), but blindly trusting an AI to run everything is going to be a big mistake in the short-term. And in order for the inevitability of what you describe to take place, the predecessor(s) to that have to work in a way that doesn't scare people and businesses away.
The plowing of money and hype into the current forms of AI (not to mention the gaslighting about their ability) makes me think the real inevitability is a meltdown in the next 5-10 years which leads to AI-hesitancy on a mass scale.
The problem with your "close to the metal" assertion is that this has been parroted about every iteration of LLMs thus far. They've certainly gotten better (impressively so), but again, it doesn't matter. By their very nature (whether today or ten years from now), they're a big risk at the business level which is ultimately where the rubber has to hit the road.
At the moment people are so wooed by the confidence of current LLMs that they forget that there's all sorts of types of AI models. I think the key is going to be to have them work together, each doing the part they're good at.
This is where reasoning models come in. Train models on many logical statements then give them enough time to produce a chain of thoughts that’s indistinguishable from “understanding” and “thinking”.
I’m not sure why this leap is so hard for some people to make.
So obviously completely full of shit.
> If you can find a way to make the context window on the scale of the human brain, you may be able to mostly mitigate this.
Human brains have a much smaller context window than AI do. We can't pay attention to the last 128,000 concepts that filtered past our sensory systems — our conscious considerations are for about seven things.
There's a lot of stuff that we don't yet understand well enough to reproduce with AI, but context length is the wrong criticism for these models.
You're right. What I'm getting at is the overall speed, efficiency, and accuracy of the storage, retrieval, and processing capability of the human brain.
Then you have to recreate all of the services on top of the AWS.
Then you have to deal with regulations and certifications.
Then you have to convince decision makers to go against their own interests. “No one ever got fired for Amazon”.
Then you have to convince corporations to spend money to migrate.
I think it is, yes.
It was interviewed under that name on one of the UK's main news broadcasts almost immediately after it came out. Few hundred million users. Anecdotes about teachers whose students use it to cheat.
But who knows. I was surprising people about the existence of Wikipedia as late as 2004, and Google Translate's augmented reality mode some time around the start of the pandemic.
Maybe try some humility. You're not helping yourself with the bragging about frankly underwhelming and common (here) experience.
As have 10s of thousands of other people who could invert a btree on the whiteboard….