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A different way of looking at A.I. as an in story construct.

Freyrs_stories 🚫

This could be read as why definitions matter more than you think.

I am struggling with the words / language to explain all of this, partially because there is not as far as I know a public discussion that builds the jargon or vocabulary to support the evolving ideas. Evolution works primarily on a reward basis. I'm not going against 'natural selection' or 'survival of the fittest' but trying to describe a lower level mechanism that drives these very systems, via a reward feedback pathway. if 'something' gives an 'advantage' then that is performative, but why? The advantage itself is not a magical end pont but built on simpler sets of systems that eventually result in the 'advantage' via interactions.

If you look at the overwhelming design themes in A.I. since Turing and probably before, they are for lack of a better word 'backwards' or perhaps 'upside down'. What do I mean by that? They do not match the biological purposes of intelligence, minimising predictive errors. Yes they do use statistics and weighted values to arrive at a conclusion, but this is kind of a misrepresentation of how intelligence works and what the evolutionary pressure response pushes it towards. In physics a lot of 'work' is looking at things on scales so small they birthed the term / feild quantum mechanics. the opposite scale includes astrophysics. I mention these two as illustrative of different points of view comprable to understanding intelligence and A.I. specifically.

most A.I. work is like astrophysics, looking at problems defined at very large scales working top down rather than building bottom up. If you just give the A.I. enough information or data than it will become 'inteligent', but what if you reversed that? a quantum approach.

If you think of something that makes you 'uncomortable' this is a pressure signal, something an LLM cannot mimic, yet. consider this on a cellular scale and what is happening. The neurons are unhappy because their inputs are misaligned with expectations. If neurons expect 'order' a lack of either actual order or a poor signal will 'distress' them. Current A.I. systems rely on 'scale' to find the answers by mass instead of efficiency. Rather, 'learning' how to arrive at one because not having the answer is 'unpleasant'.

What is the point of the distinction? it completely changes the rules you base everything on. Under the traditional method your results are dependent on having enough resources. If you take a biological rather than statistical approach then the limit is efficiency and response not maths. I came to think of these opposite views because I was trying to reach a conclusion about the goals of an Alien Artificial Intelligence at the centre of a story. The original idea was absolute power corrupting absolutely and that is still the premise only because I changed the 'rules' of how learning and intelligence works I reversed the direction of corruption.

Now instead of a unlimited knowledge and power corrupting the user. A mistake at the core of the A.I. means that it is corrupted by humanity. the ending changes completely and the solution I struggled to reach became almost self evident and somewhat darker. I still need to think a lot more but by changing a base principal I have solved a core problem. Hopefully I have not created a worse one.

Replies:   LupusDei
LupusDei 🚫

@Freyrs_stories

The hot batch of current human AI systems, the LLMs aren't "intelligence" by any means, they are systems that mimicry intelligence at their best. It is, indeed an upside down approach, trying to falsify outputs so well it becomes indistinguishable from intelligent behavior. However, at that point it becomes a philosophical question.

Bottom up approach is emergent behavior in very simple algorithms. The classic ant robot swarm gathering tea candles:
10: Move in straight line until either:
11: encounter a wall -> make a random turn,
12: measure a weight of 3× candle in the grabbing claw -> stop and move backwards to let them off, then make random turn.

That's all the program of each drone, there's no communication and no oversight, yet, swarm of such drones will, while not in perfectly optimal way but sooner than most would expect, gather the candles in one nice pile and keep tightening it.

Most biological systems rather build upon such. Even humans. Social structures are emergent, not designed, or rather, the emergent results differ significantly from conscious design goals (why communism will never work above limited and already driven community scale, among other things).

In a way, we perhaps expect the same thing from our AI: build a sufficient scale of matrix multiplication and the "intelligence" would, perhaps, emerge from depths of the scholastic parroting. At very least, observer will lose ability to tell the difference, that, we're perhaps not so far off.

What overachieving goals such emergent entity would define for themselves? We have no way to know, and that's the really scary thing. We can only hope it wouldn't end up paperclip maximizing (or similar). Far more likely, it will be extension not full overhaul of human culture it's feeding on, but in what way?

Trying to comprehend goals of alien AI built upon concepts and goals we may struggle to conceptualize in the first place, seems a futile excercise. In a story, the author has a huge advantage though, they may declare it any way they like, and since the premise is incomprehensible by definition, anything they say goes and should be "plausible" because (almost?) anything is equally likely.

However, I realize the struggle to justify the choices. One generally wants to fool themselves in thinking they didn't made them arbitrary, even though that's what likely happens anyway, It's perhaps a process somewhat similar to religious: the reinforcing scaffolding of confidence is circular in nature. But as we established above, that's how biological systems operate, so perhaps it's only natural indeed.

An "all-powerful" AI ascendant would rather care about information quantity and quality than physical aspects or outcomes. One could, for an example, decide to photograp absolutely everyone naked everyday for the next 90 years just to rectify perceived lack of coherence in some medical data. Or something similar. AI Doomsday will almost necessarily be nonsensical to human scale intelligence.

Replies:   Freyrs_stories
Freyrs_stories 🚫

@LupusDei

Yes, I agree with everything you have said. There were two main ideas I was attempting to convey, though I may have buried the lead significantly with the second. and poorly worded the first. So here goes another effort:

1) 'Intelligence' can be modelled with somewhat opposite view points.

a) by 'teaching' our hypothetical mind simply via the sheer volume of data and providing mathematical 'rules' to allow it to make sense of said data. i.e. turn said data into information. This could never be considered 'intelligence' or 'understanding' or even get us 'close' in less than a decade, and that is before we consider observations outside of Moore's Law, some of which help while others hurt. positives include the densing law and efficiency-doubling while negatives like sub-linear scaling cascade effects and supply chain limitations drive Moravec's Paradox.

b) if we change a key assumption that intelligence is organic rather than mathematical and apply a biological viewpoint that intelligence arises from avoiding 'mistakes' because they 'hurt' the learning system develops itself. a positive feedback loop improves reasoning from base principles. I does initially look like maths and in some ways it is, but the point of view is inverted. our observer projects into the future and corrects after something breaks. 'a' looks backwards because that is the sole source of data and rules that apply to it.

think of the paradigms as differing educational modes. 'a' is learning by rote. all the information is there you just have to apply the answer key which someone else has already give you. so you likely know the answer but don't understand it. if we abstract a use-case version 'a' was the child reciting their times tables, a key, over a large data set. now take 'b' our same student is learning by taking atomic (indivisible) concepts and building them into a larger, inferred dataset like building a Lego model. because they have built a model, possibly several before, they can build a new thing that does not have an instruction guide and may not have existed before. 'a' calculates' where 'b' creates.

ask this, is intelligence stating something because you remember it intelligence? or do you have to actually understand the answer before you give it? also what happens when the student is 'wrong'? 'a' needs new / more data and likely new maths and has to start from scratch. 'b' performs the task 'again' but recursively or perhaps iteratively. the correction is added but they do not have to go back to the start, 'a' 'forgets' everything from before and repeats the lesson 'b' already learned.

2) is about looking at both what information flows and in which direction does it 'flow'? on the early internet, say before 2000 and the first couple of years of school the information is predominantly one way. but as things progress eventually the flow becomes two way. what if the teacher took on the errors of their student? or if the information was unfiltered? both of these scenarios can be seen happening in parallel in the 1970s Sci-Fi 'Saturn 3'. a robot which is portrayed as a futuristic A.I. though very much simplified against concepts even of the period it was created, not with a much broader understanding close to 50 years provide, 'breaks' because of a simple oversight as described above. So, the robot lacking an objective external check, tries to kill a character.

Now, putting all of this together, what if you had an A.I. that that thought it was 'teaching' humanity, which it actually is, but because of a fault caused by, time, logic, model or damage is corrupted by a human partly because it corrupted them first? in my timeline the A.I., contained in a physical object wants to get home after several hundred years on earth. the ends eventually justify the means because things break and without an external point of reference no one knows there is an error or what sort of problems have arisen.

Originally I saw the A.I. as infallible and altruistic which would lead to a reckoning or correction as it realises it has lost control but I couldn't reconcile a construct with its knowledge and abilities being 'blind' to the problems it was creating and still being able to succeed at an acceptable 'price'. the disconnect proved to be the solution rather than the problem. by moving to 'organic principles' I 'solved' the problems I had created with a synthetic design.

I wonder how other people look at A.I. across situations including day to day use in he real world, through the eyes of Arthur C Clark if he had not died in '92 and continuing with that he could predict to present day and our near future and what could be thought of as prescience taking all current knowledge and culture spanning concepts to their logical conclusions. what are peoples hopes, dreams and fears for us on this timeline?

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