Is it a fair assumption to say the Intelligence Spectrum is a core concept that underpins AGI safety . I often feel the idea is the first buy in to introduction AGI safety text. And due to my prior beliefs and lack of emphasis on it in Safety introductions I often bounce off the entire field.
Sorry for the length.
Before i ask the my question I think its important to give some background as to why I'm asking it. if you feel that its not important you can skip to the last 2 paragraphs.I'm by all means of the word layman. At computer science, AI let alone AI safety. I am on the other hand someone who is very enthusiastic about learning more about AGI and everything related to it. I discovered this forum a while back and in my small world this is the largest repository of digestible AGI knowledge. I have had some trouble understanding introductory topics because i would have some questions/contentions about a certain topic and never get answers to them after I finish reading the topic. I'm guessing its assumed knowledge on part of the writers. So the reason I'm asking this question is to clear up some initial outsider misunderstandings and after that benefit from decades of acquired knowledge that this forum has to offer. Here goes.
There is a certain old criticism against Gofai that goes along the lines-A symbolic expression 'Susan'+'Kitchen'= 'Susan has gone to get some food' can just as be replaced by symbols 'x'+'y'='z' .Point being that simply describing or naming something Susan doesn't capture the idea of Susan even if the expression works in the real world.That is 'the idea of Susan' is composed by a specific face,body type,certain sounds, hand writing, pile dirty plates in the kitchen, the dog MrFluffles, a rhombus, etc. It is composed of anything that invokes the idea Susan in a general learning agent. Such that if that agent sees a illuminated floating rhombus approaching the kitchen door the expression's result should still be 'Susan has gone to get some food'. I think for brevities sake I don't have to write down the Gofai's response to this specific criticism. The important thing to note is that they ultimately failed in their pursuits.
Skip to current times and we have Machine learning. It works! There are no other methods that even come close to its results. Its doing things Gofai pundits couldn't even dream of. It can solve Susan expressions given the enough data. Capturing 'the idea of Susan' well enough and showing signs of improvement every single year. And if we look at the tone of every AI expert, Machine learning is the way to AGI. Which means Machine Learning inspired AGI is what AI_Safety is currently focused on. And that brings me to my contention surrounding the the idea of goals and rewards, the stuff that keep the Machine Learning engine running.
So to frame my contention, For simplicity we make a hypothetical machine learning model with the goal(mathematical expression) stated as:
'Putting'+ 'Leaves'+ 'In'+'Basket'='Reward/Good'. Lets name this expression 'PLIBRG' for short.
Now the model will learn the idea of 'Putting' well enough along with the ideas of the remaining compositional variables given good data and engineering. But 'PLIBRG' is itsself a human idea.The idea being 'Clean the Yard'. I assume we can all agree that no matter how much we change and improve the expression 'PLIBRG' it will never fully represent the idea of 'Clean the Yard'. This to me becomes similar to the Susan problem that Gofai faced way back. For comparison sake -
1)-'Susan'+'Kitchen'= 'Susan has gone to get some food'|
is similar to
'Yard'+'Dirty'='Clean the Yard'|
2)-'Susan' is currently composed by:a name/a description|
is similar to
'Clean the Yard' is currently composed by: 'PLIBRG' expression|
3)-'Susan' should be composed by:a face,body type,certain sounds, MrFluffles,rhombus|
is similar to
'Clean the Yard' should be composed by:no leaves on grass,no sand on pavements,put leaves in bin,fill the bird feed,avoid leaning on Mr Hick's fence while picking leaves|
The only difference between the two being Machine learning has another lower level of abstraction. So the very notion of having a system with a goal results in the Susan problem .As the goal a human-centric idea, has to be described mathematically or algorithmically.A potential solution is to make the model learn the goal itself. But isn't this just kicking the can the up levels of abstractions? How many levels should we go up until the problem disappears. I know that in my own clumsy way that I've just described the AI Alignment problem. My point being: Isn't it the case that if we solve the Alignment problem we could use that solution to solve Gofai. And if previously Gofai failed to solve this problem what are the chances Machine learning will.
To rebuttal the obvious response like 'but humans have goals'. In my limited knowledge I would heavily disagree. Sexual arousal doesn't tell you what to do. Most might alleviate themselves, but some will self harm, some ignore the urge, some interpret it as a religious test and begin praying/meditation etc. And there is nothing wrong with doing any of the above in evolutionary terms.In a sense sexual arousal doesn't tell you to do anything in particular, it just prompts you to do what you usually do when it activates. The idea that our species propagates because of this is to me a side effect rather than an intentional goal.
So given all that I've said above my question is: Why are we putting goals in a AI/AGI when we have never been able to fully describe any 'idea' in programmable terms for the last 60 years. Is it because its the only currently viable way to achieve AGI. Is it because of advancements in machine learning.Has there been some progress that shows that we can algorithmically describe any human goal. Why is it obvious to everyone else that goals are a necessary building block in AGI. Have I completely lost the plot.
As I said above I'm a layman. So please point out any misconception you find.