Web developer and Python programmer. Professionally interested in data processing and machine learning. Non-professionally is interested in science and farming. Studied at Warsaw University of Technology.
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.
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:
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:
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:
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.
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.
I think that preference preservation is something in our favor and the aligned model should have it - at least about meta-values and core values. This removes many possible modes of failure like diverging over time, or removing some values for better consistency, or sacrificing some values for better outcomes in the direction of some other values.
I think that arguments for why godlike AI will make us extinct are not described well in the Compendium. I could not find them in AI Catastrophe, only a hint at the end that it will be in the next section:
"The obvious next question is: why would godlike-AI not be under our control, not follow our goals, not care about humanity? Why would we get that wrong in making them?"
In the next section, AI Safety, we can find the definition of AI alignment and arguments for why it is really hard. This is all good, but it does not answer the question of why godlike AI would be unaligned to the point of indifference. At least not in a clear way.
I think that failure modes should be explained, why they might be likely enough to care about, what can be the outcome, etc.
Many people, both laymen and those with some background in ML and AI, have this intuition that AI is not totally indifferent and is not totally misaligned. Even current chatbots know general human values, understand many nuances, and usually act like they are at least somewhat aligned. Especially if not jailbroken and prompted to be naughty.
It would be great to have some argument that would explain in easy-to-understand terms why when scaling the power of AI the misalignment is expected to escalate. I don't mean the description that indifferent AI with more power and capabilities is able to do more harm just by doing what it's doing, this is intuitive and it is explained (with the simple analogy of us building stuff vs ants), but this misses the point. I would really like to see some argument as to why AI with some differences in values, possibly not very big, would do much more harm when scaling up.
For me personally the main argument here is godlike AI with human-like values will surely restrict our growth and any change, will control us like we control animals in the zoo + might create some form of dystopian future with some undesired elements if we are not careful enough (and we are not). Will it extinct us in the long term? Depending on the definition - likely it will put us into a simulation and optimize our use of energy, so we will not be organic in the same sense anymore. So I think it will extinct our species, but possibly not minds. But that's my educated guess.
There is also one more point, that is not stated clearly enough and is the main concern for me with current progress on AI - that current AIs really are not something built with small differences to human values. They only act as ones more often than not. Those AIs are trained first as role-playing models which can "emulate" personas that were in the trained set, and then conditioned to rather not role-play bad ones. The implication of this is that they can just snap into role-playing bad actors found in training data - by malicious prompting or pattern matching (like we have a lot of SF with rogue AI). This + godlike = extinction-level threat sooner or later.
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.