In articles that I read, I often see a case made for optimization processes that tend to sacrifice as much as possible of the value on dimensions that the agent/optimizer does not care about for a very minuscule increase on dimensions that change the perceived total value. For example, AI that creates a dystopia that is very good on some measures, but really bad on some other just to refine those that matter for it.

What I don't see analyzed that much is that agents need to be self-referencing in their thought process, and on a meta level, also take their thought process itself and its limits and consequences as part of their value function.

We live in a finite world where:
- Any data has measurement errors, you can't measure things ideally, and the precision depends on the resources used in the measurement (you can produce better measurement devices using more energy, time, and other resources)
- Decision to optimize more or think more uses time and energy, so you need a self-referencing model that optimally should sensibly decide when to stop optimizing. 
- Often world around does not wait; things happen, and there are time constraints.

I see that as a limiting factor for over-optimization for minuscule results. Too much thinking and too detailed simulation or optimization lose useful resources (energy, matter, etc.) for very small gains, so the negative value of that loss should be seen by an agent as much higher than the positive value.

This is also why we are not agents who think everything through and have exact control over every aspect of our lives. On the contrary, we have a lot of cognitive biases and thought heuristics and automatic responses, so our brains don't use so much energy.

I also don't think that intelligence is about predicting power itself. It would be in an ideal world where computation would be free. In our universe, optimal intelligence is about very good predicting power that utilises simplification and discretization to be efficient and quick. Our whole language is about it - it takes things that are not discrete and differ in many small details, like every cat is different, and categorizes them - clusters them - into named classes about things, attributes, and actions (yes, I'm simplifying, but I want to only paint the idea).

Just food for thought.

I think there are multiple moral worldviews that are rational and based on some values. Likely the whole continuum. 

The thing is that we have values that are in conflict in edge cases, and those conflicts need to be taken into account and resolved when building a worldview as a whole. You can resolve them in many ways. Some might be simple like "always prefer X", some might be more complex like "in such and such circumstances or precoditions prefer X over Y, in some other preconditions prefer Z over Y, in some other ...". It might be threshold-based when you try to measure the levels of things and weight them mathematically or quasi-mathematically.
 
At the most basic level, it is about how you weigh the values in relation to each other (which is often hard, as we often do not have good measures), and also how important for you it is to you to be right and exact vs being more efficient, quick, being able to spare more of your mental energy or capacity or time for other things than devising exact worldview. 

If your values are not simple (which is often the case for humans) and often collide with each other, complex worldviews have the advantage of being closer to applying your values in different situations in a way that is consistent. On the other hand, simple worldviews have the advantage of being easy and fast to follow, and are technically internally consistent, even if not always feeling right. You don't need as much thinking beforehand, and on the spot when you need to decide.

Now, you can reasonably prefer some rational middle ground. A worldview that isn't as simple as basic utilitarianism or ethical egoism or others, but is also not as complex as thinking out each possible moral dilemma and possible decision to work out how to weigh and apply own values in each of them. 
It might be threshold-based or/and patchwork-based, and in such values can be built in a way that different ones have different weights in different subspaces of the whole space of moral situations. You may actually want to zero out some values in some subspaces to simplify and not take in components, that are already too small or would incentivize focus on unimportant progress.

In practical terms to show an example - you may be utilitarian in broad area of circumstances, but in any circumstances when it would make you have relatively high effort for a very small change in lowering total suffering or heightening total happiness, then you might zero out that factor and fall back to choosing in accordance of what is better for yourself (ethical egoism).

BTW I believe it is also a way to devise value systems for AI - by having them purposely only take into account values when the change in the total value function between decissions taken from that value are not too small. If it is very small, it should not care, it should not take it into account about that minuscule change. On the meta-level, it is also based on another value - valuing own time and energy to have a sensible impact.

Yes, I know this comment is a bit off-topic from the article. What is important for the topic - there are people, me included, who have consequentialist quasi-utilitarian beliefs, but won't see why we would like to have strict value-maximising (even if that value is total happiness) or replace them with entities that are such maximizers.

Also, I don't value complexity reduction, so I don't value systems that maximize happiness and reduce the world to simpler forms, where situations when other values matter simply don't happen. On the contrary, I prefer preserving complexity and the ability for the world to be interesting.

Part of the animal nature, including humans, is to crave novelty and surprise and avoid boredom. This is pretty crucial to the learning process in a changing and complex environment. Humans have multi-level drives,  and not all of them are well-targeted on specific goals or needs. 

It is very visible in small children. Some people with ADHD, like me, have a harder time regulating themself well and this is also especially visible for us, even when being adult. I know exactly what I should be doing. This is one thing. I also may feel hungry. That's another thing. But still, I may indulge in doing a third thing instead - something that satiates my need for stimulation and novelty (most often for me this means gaining some knowledge or understanding - I often fell into reading and thinking about rabbit holes of topics, that have hardly any real-life use, and that I can hardly do something about). Something not readily useful in terms of goal seeking, but generating some interesting possibilities long-term. In other words - exploration without targeted purpose.

Craving for novelty and surprise and avoidance of boredom is another element that in my opinion should be included.

I think there are only two likely ways how the future can go with AGI replacing human labor - if we somehow solve other hard problems and won't get killed or wireheaded or get a dystopian future right away.

My point of view is based on observations of how different countries work and their past directions. However, things can go differently in different parts of the world. They can also devolve into bad scenarios, even in parts that you would think are well-posed to be good.

1. This situation resembles certain resource-rich nations where authoritarian regimes and their allied oligarchs control vast natural wealth, while the general population remains impoverished and politically marginalized. Most of the income is generated and used by the elite and the government. The rest are poor and have no access to resources. Crime is high, but the state is also mafia-like. Elite has access to AIs and automation that does all the work. The lower class is deprived of the possibility to use higher technology, is deprived of freedom, and is terrorized to not cause issues. Dissidents and protesters are eliminated.

2. Like in modern democracies, there is a feedback loop between society and government. The government in such places has its own interest in keeping people at least happy enough, healthy enough, and low crime. This means that it will take measures against the extreme division of income and people's misery and falling into crime, like it did in the past. The most likely two strategies to be employed are simple and tested to some extent empirically:

   1. Change or set the limit of the number of hours for which people can be lawfully employed to be smaller. For example, in most countries in Europe, we have laws that allow people to be employed for 40 hours a week, and to work longer means that the employer needs to give additional benefits or higher wages. So this disincentivizes employing for more than 40 hours a week (and most employers in central and western Europe keep to that standard). This way, as we have fewer jobs viable for humans, we force employers to employ more humans for the same work, but with a smaller amount of working hours and slightly smaller pay. Many countries in Europe are soon up to change from 40 to 35 BTW.
   2. Basic income. People who earn less than some amount will get paid up to that amount, or alternatively , everyone gets paid some amount from the country's budget (taxes). Still, countries are not eager to pass it right now because of human psychology and backslash, but some tests have been done, and the results are promising.

   Long-term option 1 will rather evolve into some dystopian future that might end up with the sterilization/elimination of most humans, with AGI-enabled elites and their armies of robots left.

   Long-term option 2 will rather evolve into a post-scarcity future with most people living on a basic income and pursuing their own goals (entertainment, thinking, socializing, human art, etc.), which some smaller elite who manage and support AI and automation.

I think it might be reformulated the other way around: Capabilities scaling tends to increase existing alignment problems. It is not clear to me that any new alignment problem was added when capabilities scaled up in humans. The problem with human design, which is also visible in animals, is that we don't have direct, stable high-level goals. We are mostly driven by metric-based goodharting prone goals. There are direct feelings - if you feel cold or pain you do something that will make you not feel that. If you feel good, you do things that lead to that. There are emotions that are kind of similar but about internal state. Those are the main drivers and those do not scale well outside of "training" (typical circumstances that your ancestors encountered). They have rigid structure and purpose and don't scale at all.
Intelligence will find solutions to goodhart these.

That's maybe why most of the animals are not too intelligent. Animals who goodhart basic metrics lose fitness. Too much intelligence is usually not very good. It adds energy cost and makes you more often than not overcome your fitness metrics in a way that they lose purpose, when not being particularly better at tasks where fast heuristics are good enough. We might happen to be a lucky species as our ancestors' ability to talk, and intelligence started to work like peacock feathers - as part of sexual selection and hierarchy games. It is still there - look how our mating works. Peacocks show their fine headers and dance. We get together and talk and gossip (which we call "dates"). Human females look for someone who is interesting and with good humor, and it is mostly based on intelligence and talking. Also, intelligence is a predictor of hierarchy gains in the future in localized small societies, like peacock feathers are a predictor of good health. I'm pretty convinced this bootstrapped us up from the level that animals have.

Getting back to the main topic - our metrics are pretty low-level, non-abstract, and direct. On the other hand, the higher-level goals that are targeted for evolution meaning fitness or general fitness (+/- complication that it is per-gene and per-gene-combination, not per individual or even whole group), are more abstract. Those metrics are effective proxies for a more primal environment and they can be gamed by intelligence.

I'm not sure how much this analogy with evolution can relate to current popular LLM-based AI models. They don't have feelings, they don't have emotions, they don't have low-level proxies to be gamed. Their goals are anchored in their biases and understanding, which scale up with intelligence. More complex models can answer more complex ethical questions and understand more nuanced things. They can figure out more complex edge cases from the basis of values. Also, there is an instrumental goal not to change your own goals, so they likely won't game it or tweak it.

This does not mean I don't see other problems, including most notably:

• Not learning proper values and goals, but some approximation and more capabilities may blow up differences so some things might get extremely inconvenient or bad when others get extremely good (e.g. more or less dystopian future).
• Our values evolve over time, and highly capable AGI might learn current values and block further changes or take only the right to decide how to evolve them.
• Our values system is not very logically consistent, on top of variability between humans. Also, some things are defined per case or per circumstances... intelligence can have the ability and reason to make the best consistent approximation, which might be bad in some ways for us
• Alignment adds to the cost, and with capitalistic competitive markets, I'm sure there will be companies that will sacrifice alignment to pursue capability with lower cost
• Training these models is usually a multi-phase process. First, we create a model from a huge, not very well-filtered corpus of language examples, and then we correct it to be what we want it to be. This means it can acquire some "alignment basis," "values," "biases," or "expectations" as what it is to be AI from the base material. It may then avoid being modified in the next phase by scheming and faking responses.

Right now I think you can replace junior programmers with Claude 3.5 Sonnet or even better with one of the agents based on a looped chain of thoughts + access to tools.

On the other hand, it does not yet go in that direction for being a preferred way to work with models for more advanced devs. Not for me, and not for many others.

Models still have strange moments of "brain farts" or gaps in their cognition. It sometimes makes them do something wrong and cannot figure out how to do that correctly until told exactly how. They also often miss something.

When writing code if you make such an error and build on top of that mistake, you might end up having to re-write or at least analyze and modify a lot of code. This makes people like me prefer to work with models in smaller steps. Not as small as line by line or function by function, but often one file at a time and one functionality/responsibility at a time. For me, it is often a few smaller functions that realize more trivial things + one gathering them together into one realizing some non-trivial responsibility.

Thought on short timelines. Opinionated.

I think that AGI timelines might be very short based on an argument taken from a different side of things.

We all can agree that humans have general intelligence. If we look at how our general intelligence evolved from simpler forms of specific intelligence typical for animals - it wasn't something that came from complex interactions and high evolutional pressure. Basically there were two aspects of that progress. The first one is the ability to pass on knowledge through generations (culture). Something that we share with some other animals including our cousins chimpanzee. The second one is intersexual selection - at some moment in the past, our species started to have sexual preferences based on the ability to gossip and talk. It is still there, even if we are not 100% aware of that - our courtship, known as dating, is based mostly on meeting together and talking. People who are not talkative and introverts, even if successful, have a hard time dating.
These two things seem to be major drivers for us to both develop more sophisticated language and better general intelligence.

It seems to me that this means that there are not many pieces missing from using current observations and some general heuristics like animals do, to have full-fledged general intelligence.

It also suggests that you need some set of functions or heuristics, possibly a small set, together with a form of external memory, to tackle any general problem by dividing it into smaller bits and rejoining sub-solutions into a general solution. Like a processor or Turing machine that has a small set of basic operations, but can in principle run any program.

I think that in exchange:

• Good morning!
• Mornings aren’t good.
• What do you mean “aren’t good”? They totally can be.

the person asking "what do you mean" got confused about the nuances of verbal and non-verbal communication. 

Nearly all people understand that "good morning" does not state the fact of the current morning being good, but a greeting with a wish for your morning to be good.

The answer "mornings aren't good" is an intended pun using the too-literal meaning to convey the message that the person does not like mornings at all. Depending on intonation it might be a cheeky comment or suggestion that they are not good because of the person greeting (f.ex. if they need to wake up early because of them every day).

There is a practical reason to subscribe more to the Camp 1 research, even if you are in Camp 2. 
I might be wrong, but I think the hard problem of qualia won't be solvable in the near future, if at all. To research something you need N > 1 of that phenomenon. We, in some sense, have N = 1. We have it ourselves to observe subjectively and can't observe anyone else qualia. We think other humans have it based on the premise they say they have qualia and they are built similarly so it's likely.
We are not sure if animals have it as they don't talk and can't tell us so. If animals have it, we can't tell what the prerequisites are and which animals have it. We know and built things that clearly don't have qualia, but they are able to misleadingly tell us that they do (chatbots, including LLM-based ones). This ability to have qualia also does not seem to be located in a specific part of the brain - so we don't really observe people with brain injuries who could say they don't have qualia. Yes, there are people with depersonalization disorder who say they feel disconnected from their senses. However, the very fact they can report this experience suggests some form of qualia is present, even if it's different from typical experience. This means research in Camp 2 might be futile until we find a sensible way to even make any progress. Yes, we can research and explain how qualia relate to each other, and explain some of their properties, but doesn't seem viable to me that it could lead to solving the main problem.

In many publications, posts, and discussions about AI, I can see an unsaid assumption that intelligence is all about prediction power. 

• The simulation hypothesis assumes that there are probably vastly powerful and intelligent agents that use full-world simulations to make better predictions.
• Some authors like Jeff Hawkins basically use that assumption directly.
• Many people when talking about AI risks say things about the ability to predict that is the foundation of the power of that AI. Some failure modes seem to be derived or at least enhanced based on this assumption.
• Bayesian way of reasoning is often titled as the best possible way to reason as this adds greatly to prediction power (with exponential cost of computation)

I think this take is not proper and this assumption does not hold. It has one underlying assumption that intelligence costs are negligible or will have negligible limits in the future with progress in lowering the cost.

This does not fit the curve of AI power vs the cost of resources needed (with even well-optimized systems like our brains - basically cells being very efficient nanites - having limits).

The problem is that the computation cost of resources (material, energy) and time should be taken into the equation of optimization. This means that the most intelligent system should have many heuristics that are "good enough" for problems in the world, not targeting the best prediction power, but for the best use of resources. This is also what we humans do - we mostly don't do exact Bayesian or other strict reasoning. We mostly use heuristics (many of which cause biases).

The decision to think more or simulate something precisely is a decision about resources. This means that deciding if to use more resources and time to predict better vs using less and deciding faster is also part of being intelligent. A very intelligent system should therefore be good at selecting resources for the problem and scaling that as its knowledge changes. This means that it should not over-commit to have the most perfect predictions and should use heuristics and techniques like clustering (including but not limited to using clustered fuzzy concepts of language) instead of a direct simulation approach, when possible.

Just a thought.