I'm not sure, why must it be so? In cell-phones we have Apple and Android-phones. In OSes we have Linux, Windows, and Apple.
In search-engines we used to have just Google. But what would be the reason to assume that AI must similarly coalesce to a single winner-take-all? And now AI agents are much providing an alternative to Google.
>I'm not sure, why must it be so? In cell-phones...
And then described a bunch of winners in a winner take all market.
Do you see many people trying to revive any of the apple/android alternatives or starting a new one?
Such a market doesn't have to end up in a monopoly that gets broken up.
Plenty of rather sticky duopolies or otherwise severely consolidated markets and the like out there.
> why not use the AI itself to come up with a proven paradigm?
Because AI can only imitate the language it has seen. If there are no texts in its training materials about what is the best way to use multiple coding agents at the same time, then AI knows very little about that subject matter.
AI only knows what humans know, but it knows much more than any single human.
We don't know "what is the best way to use multiple coding agents" until we or somebody else does some experiments and records the findings. Buit AI is not there yet to be able to do such actual experiments itself.
I'm sorry, but the whole stochastic parrot thing is so thoroughly debunked at this point that we should stop repeating it as if it's some kind of rare wisdom.
AlphaGo showed that even pre-LLM models could generate brand new approaches to winning a game that human experts had never seen before, and didn't exist in any training material.
With a little thought and experimentation, it's pretty easy to show that LLMs can reason about concepts that do not exist in its training corpus.
You could invent a tiny DSL with brand-new, never-seen-before tokens, give two worked examples, then ask it to evaluate a gnarlier expression. If it solves it, it inferred and executed rules you just made up for the first time.
Or you could drop in docs for a new, never-seen-before API and ask it to decide when and why to call which tool, run the calls, and revise after errors. If it composes a working plan and improves from feedback, that’s reasoning about procedures that weren’t in the corpus.
You're implicitly disparaging non-LLM models at the same time as implying that LLMs are an evolution of the state of the art (in machine learning). Assuming AGI is the target (and it's not clear if we can even define it yet), LLM's or something like them, will be but one aspect. Using the example AlphaGo to laud the abilities and potential of LLM's is not warranted. They are different.
>AlphaGo showed that even pre-LLM models could generate brand new approaches to winning a game that human experts had never seen before, and didn't exist in any training material.
AlphaGo is an entirely different kind of algorithm.
Would it make sense to think that you and 5 agents form a Team? How would it be different freom a human based team? And how does it work if you have a team of humans who all use their own team of AI agents?
Not necessarily. Logically, there must have been a first writing system (even if cuneiform wasn't it), so you can't show cuneiform wasn't the first on the basis of "something must have come before it".
Right, there is a reason newspapers long ago realized multiple columns is better than full-width text. Imagine reading New York Times if it had only one full-width column. People would stop subscribing to it.
Right, if you have wide columns then you have to move eyes BOTH from left to right AND when you reach the end of the line you have to move them back to left AND down to next line. Whereas if the line is narrow enough to read without moving eyes horizontally you only need to move your eyes down after each line.
Are our brains "analog"? Or are they in fact "digital"? I would think actually more digital than analog. A synapse triggers or it does not trigger. It either triggers or not, not something in between. In this sense it is 0 or 1.
Similarly transistor-based logic is based on such thresholds, when current or voltage reaches a certain level then a state-transition happens.
The question is where should AI advance itself? Which direction? There are an infinite number of theorems that can be derived from a set of axioms. Infinite. AI can't prove them all. Somebody needs to tell it what it needs to do, and that is us.
You can't sandbox the code that is supposed to talk to your DB from your DB.
And even on client side, the sandboxing helps isolate any malicious webpage, even ones that are accidentally malicious, from other webpages and from the rest of your machine.
If malicious actors could get gmail.com to run their malicious JS on the client side through this type of supply-chain attack, they could very very easily steal all of your emails. The browser sandbox doesn't offer any protection from 1st party javascript.
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