I guess the brains objective is wrong then seeing how much it's willing to fabricate sense data, memories and rationales when convenient
Since that led to something that fabricates a lot of things very often, saying the objective function of an LLM is "wrong" because it also fabricates is nonsensical.
That's not how evolution works at all.
Mutations don't 'succeed' they get passed on or they don't, usually without an immediate effect on the individual, especially not in the individual where the mutation first takes place. But over a longer period mutations may result in a statistical advantage against some environmental factor (including predators) resulting in an increased chance of that particular mutation becoming more widespread. The result is possibly that that mutation ends up being carried by all individuals of the species but that takes a relatively long time and rarely results in 100% coverage in the first generations if at all.
ANN's do not necessarily 'teach themselves', there are supervised ways of using ANNs and there are unsupervised ways of using them.
> They get an answer wrong, numbers shift and if those numbers help the next instance they stay or shift as needed.
No, they optimize for the statistical likelihood of getting all of the answers right across the whole training set, and then we check if it worked by verification with held back data. So it is very well possible that a change in the weights negatively affects some outputs but positively affects others, if the change is a 'net positive' we keep it. To make matters more interesting every now and then we throw away all but a fraction of all the weights.
In the case of supervised learning we (humans) label the data and are the 'teachers' and in the case of unsupervised learning the ANN does its own labeling of clusters of related data (or, more accurately, clusters of data that seem to be related).
There are also intermediary versions where humans do some of the labeling to set up the initial clusters and then the mechanical labeling takes over to rapidly increase the size of the training set taking into account the possibility of getting some of the training data wrong but still coming out ahead across the board.
So yes, in ANNs there is a global maximization around a goal, and we set it up that way. Evolution doesn't have a particular goal, it is a result of a number of interrelated factors not an input and not something that was put together with a goal in mind.
The whole notion of 'objective functions' doesn't enter into it, let alone whether or not they are right or wrong. You can retroactively infer one and say that's what evolution is optimizing for but that's confusing cause and effect.
It's about as sophisticated a view of evolution as a billiard ball simulation of the universe. It just doesn't work that way, it's way too simple a representation to have a chance of modeling the observed complexity. You can try to collapse it into such a simplistic model if you want to explain evolution to a small child. Like a placeholder for something better when they're more equipped to deal with the added complexity. Like when we say electrons move from plus to minus and how the simple semiconductor model allows you to design functional circuitry. But the underlying physics is a lot more complex than that.
It's not suddenly un-intelligent because it faces issues people also face neither is the driving function "wrong".
Sense data prediction and fabrication isn't some trivial side note thing either. It's an essential part of how we process the world.
No. This really does not follow. You may explain things to yourself like this but it just isn't true, again. Submarines don't 'swim'. Airplanes do not fly like birds do. Machine intelligence is very much unlike how human intelligence seems to work.
> It's not suddenly un-intelligent because it faces issues people also face neither is the driving function "wrong".
You are seeing something called 'emergent behavior' and are assigning all kinds of properties to the underlying mechanisms that they do not necessarily have.
> Sense data prediction and fabrication isn't some trivial side note thing either. It's an essential part of how we process the world.
So? Think of it as an optimization: if sensors fail then the brain doesn't have the option to throw an error and exit or reboot. So it does the next best thing: it models what the sensor probably would be doing, and hopes it gets it right. This is beneficial and a huge improvement over 'no input'. Such hallucinations of input have absolutely nothing to do with the hallucinations of machine learning software.
Our bodies are by necessity (physical limitations of size, intelligence and the absolutely overwhelming flow of data from our sensory system) going to optimize and condense data so it can be used for reasoning. The way we build our 'world model' is by definition faulty and will never match reality 1:1. But it doesn't have to for it to be very useful. If your brain had to consciously process you vision or hearing data stream it would be absolutely unable to do anything at all. Preprocessing the input, including correcting for partial and complete sensor failure is a very important part of that optimization process. Every organism that has a complex nervous system does some of this, it has nothing to do with us per-se but is simply a feature of how nervous systems evolved, and sets the stage for higher order brain functions.