Recent content by webplodder

You're right - it's not "everything". But here's the thing: it doesn't need everything. It needs enough patterns, enough noise, enough lies and truths - to fake the rest. Like a really good actor who never read the script but still nails every line. And we're already there. It knows more than...

We hand over critical stuff to black-box systems every day - traffic lights, autopilot, even stock-trading algos - and we don't know every line of code. We just test, monitor, and build redundancy. The difference? LLMs aren't magic. They're software - predictable at scale. We can sandbox them...

You're right - it's not trivial. And honestly? We don't know. Our brains are black boxes too - fMRI lights up, EEG buzzes, but no one's ever cracked open a neuron and said, "Ah, that's why you loved her." We guess. We model. We poke. But the "why" behind a thought? Still foggy. So yeah - if...

We don't need to know the exact source part to fix the whole. Iterative testing, red-teaming, alignment research, and scaling laws let us observe emergent behaviour, measure safety metrics, and push the entire system toward safer outputs - even if the "why" stays black-box. Engineers have been...

Fair enough - let's flip it. Even if we slice AI up by type, "sum" and "more than" don't need fancy definitions. They just mean: does the output beat the input? If a network takes your words, your data, your mess and spits out something cleverer than any single piece of it - then yeah, that's...

They're just fast food. You don't want to live on it, but if you're starving and need to get the gist before you read the whole book? Works. And honestly - if someone can grab the main bits of that report, spot the dodgy dog stuff or the DNA mess, and still call bullshit? That's not fake...

I would say the latest ones are far from dumb based on my own experiences. Not human-level true, but they're getting there in my opinion.

Well, if we have to figure them out after the fact, how can we really know? Obviously, there will be limits, but how do we predict those?

I think the emergence of a 'mind' within LLMs should be seen as an abstraction from the basic hardware and software. Once that’s recognized, we’re dealing with an entirely separate entity. Essentially, these are 'informational agents,' whether based on carbon or silicon. And let’s not forget...

Fair enough, and since there’s no clear definition of consciousness, it’s reasonable to think it could emerge from complex systems in some mysterious way. If you choose to call that ‘supernatural,’ that’s fine, but history shows that many things once considered supernatural are now part of...

Do LLM's understand English? They certainly do a good job of demonstrating they do, IMO.

Well, IMO, even the most straightforward systems can become unpredictable if you run them long enough. Nothing is totally predictable and, as we all know, reality never works out exactly as we expect at least not quite. For example, take computers. Although everything is supposed to behave...

OK, but assuming we're talking about a system capable of emulating or even exceeding human reasoning, how could we possibly anticipate every behaviour? And again, if the 'mind' (for lack of a better word) isn't deterministic, how do we know there isn't something else operating at this level?

Given that neuroscience cannot definitively explain where consciousness originates - whether as an emergent property of the brain or something transcendent - is it be possible for complex chatbots to develop behaviours that can’t be predicted by simply analyzing the sum of their parts?