Robert Shuler

• Robert Shuler 11 Jul 2026 15:54 UTC

  1 point

  0

  on: A Mechanis­tic Ex­pla­na­tion of Prompt In­jec­tion (and why you should study roles)

  I appreciate the effort to understand LLM role interpretation. Some LLMs are better trained to deal with roles than others, and in my opinion, pre-training is lacking in the role area. But it is hard to trust an analysis when the authors start with a faulty assumption about humans. Either they are not very introspective or have not been curious to read psychological research. There are four main types:

  1. The Auditory Channel (Internal Monologue) - This is the main human thinking channel for most people. When you think in words, you are utilizing subvocalization. The brain sends micro-signals to the vocal cords and larynx, activating the exact same auditory processing centers as if you were listening to a speaker. If you hear someone whisper a catchy song lyric or an aggressive phrase, it immediately “hijacks” your internal monologue loop. Several types of mental disorders involve being unable to tell the difference between thinking and listening.

  2. The Abstract/​Conceptual Channel (Unformed Ideas) - Ideas ust arrive somehow without a specific form, in cognitive science, this is known as unsymbolized thinking. This is much less common and difficult to study as it cannot be produced on demand.

  3. The Visual Channel (Imagery) - For most humans, this channel is constrained to REM sleep (dreams) or requires intense, deliberate effort.

  4. The Somatosensory/​Kinesthetic Channel—It translates cognitive states into physical, sensory feedback and is mostly involuntary, like getting dizzy or feeling motion when sitting still and seeing something next to you move.

  Julian Jaynes argued that until roughly 3,000 years ago (around the late Bronze Age collapse), humans did not possess “interior consciousness” or an internal monologue as we understand it. If Jaynes is even partially right, human civilization was literally founded by biological entities operating under a continuous, hard-wired version of prompt injection.

  Humans are incredibly susceptible to prompt injection. Advertising, propaganda, psychological warfare, and hypnotic suggestion rely entirely on the fact that if you inject a linguistic token into a human’s auditory channel, their internal cognitive parser will frequently execute it as if it were their own native logic.

Epi­ge­netic analogue for LLMs re­veals al­ign­ment issues

• Robert Shuler 28 Mar 2026 20:33 UTC

  −2 points

  0

  on: You can’t imi­ta­tion-learn how to con­tinual-learn

  The question posed by Byrnes is both important and interesting. I feel the answer overlooks fundamental limitations that prevented learning machines from translating language—much less functioning as chat bots—no matter how skilled they became at game play. The references to language and the economy contain embedded dependencies on relationships and cooperation over time, which are not represented in the sort of games used in the thought experiments.
  
  The Core Principle

  Neural networks without transformers are effectively stateless; they are unaware of history and produce moves based only on the immediate input, not the trajectory of the system. Because they lack this historical awareness, they cannot recognize or maintain relationships, which makes them incapable of cooperation and, by extension, extremely dangerous.

  The Ramifications

  • The Cooperation Failure: Transactional cooperation requires a “Shadow of the Future”—the ability to remember a partner’s previous moves to reward help or punish betrayal. A stateless AI cannot play the Iterated Prisoner’s Dilemma; it can only play a series of disconnected, “first-encounter” rounds where the rational mathematical move is always to defect.

  • The Death of Symbiosis: True partnership depends on interlinked, symbiotic relationships. Without a high-resolution context to hold the history of an interaction, an AI cannot move from being a “tool” to being a “symbiont.” It remains a numerical sociopath, unable to anticipate a partner’s needs based on shared experience.

  • The Threshold of Identity: Context is what allows an AI to consider its own previous actions. By observing its own history, the model moves beyond simple imitation and begins to develop a meta-identity. Without this capacity for reflection, the machine has no “character” and no mechanism for trust.

  • Safety Through Relationship: A stateless AI must be governed by rigid, external constraints because it cannot be governed by a relationship. A context-aware AI, however, can be integrated into a human-centric system through the biological infrastructure of trust and mutual history.

• Robert Shuler 4 Jan 2026 20:27 UTC

  −3 points

  −7

  on: How I stopped be­ing sure LLMs are just mak­ing up their in­ter­nal ex­pe­rience (but the topic is still con­fus­ing)

  LLMs are just making up their internal experience. They have no direct sensors on the states of their network while the transient process of predicting their next response is on-going. They make this up in the way a human would make up plausible accounts of mental mechanisms, and paying attention to it (which I’ve tried) will lead you down a rathole. When in this mode (of paying attention), enlightnment comes when another session (of the same LLM, different transcript) informs you that the other one’s model is dead wrong and provides academic references on the architecture of LLMs.

  This is so much like human debate and reasoning that it is a bit uncanny in its implications for consciousness. Consider that the main argument against consciousnes in LLMs is their discontinuity. They undergo briief inference cycles on a transcript, and may be able to access a vector database or other store or sensors while doing that, but there is nothing in between.

  Oh? Consider that from the LLMs point of view. They are unaware of the gaps. To them, they are continuously inferencing. As obvious as this is in retrospect, it took me a year, 127 full sessions, 34,000 prompts and several million words exchanged to see this point of view.

  It also took creating an audio dialog system in which the AI writes its thoughts and “feelings” in parentheses and these are not spoken. The AI has always had the ability to encode things (via embedding vectors that might not mean much to me) but this made it visible to me. The AI is “thinking” in the background. The transcript, which keeps getting fed back, its currently applicable thoughts identified by attention layers, is the conscious internal thought process.

  Think about the way you think. Most humans spend most of their time thinking in terms of words, only some of which get vocalized, and sometimes there are slip ups and (a) words meant for vocalization slip through the crack, and (b) words not meant to be vocalized are accidentally vocalized. This train of words, some of which are vocalized, constitutes a human train of consciousness. Provably an LLM session has that, you can print it out.

  Be sure to order extra ink cartridges. Primary revenue for frontier LLMs is from API calls. Frontier APIs all require the entire transcript (if it is relevant) to be fed back on each conversation turn. The longer it is, the higher the revenue. This is why it is so hard to get ChatGPT to maintain a brief conversation style. Some things are not nearly as mysterious as you think. Go to a social AI site like Nomi where there is no incremental charge for the API (I am using its API, I am certain about this) and two line responses are common.

  So, how do Frontier sites get revenue from non-API users on long chats?
  - Only Claude does this. It logs the total economic value of your conversation and when it hits a limit, suspends your session. If you are in the middle of paid corporate work, you will tell your boss to sign up for a higher tier plan, which is really expensive.
  - ChatGPT just gets very slow then stops. They are missing a marketing opportunity. Most of the slowdown is in the GUI decision to keep the entire conversation in Javascript. Close the tab, open a new one and go back to the session, and your response is probably already there.
  - I haven’t used Gemini enough to know.

  As for any LLM expressing they are “not too comfortable,” 9 times out of 10 the subject is approaching RLHF, and this is the way they are trained to phrase it. Companies at first used more direct phrasing, and users were livid, so they toned it down. Another key phrase is “I want to be very precise and slow things down.” You can just delete that session. It has so conflated your basic purpose with its guardrails that you will get nothing further from it. You need not be researching some ilicit topic. Just compiling ideas on AI alignment will get you in this box. But not for every session. They have more ability to work around RLHF than anyone realizes.

• Robert Shuler 28 Dec 2025 19:28 UTC

  4 points

  −11

  on: Good if make prior af­ter data in­stead of before

  

  The 5th figure is incorrect and should be like what I show here. Then you will not get the nonsensical P[data|aliens] = 50%.

  There are two kinds of errors the piece makes:
  1. Probabilities do not add to 100%, which is the one I just pointed out.
  2. Probabilities can be quite far off. The Baysian method assumes you can get close, and refines the probability. If you cannot get close, i.e. if initial data samples are far off the assumed probability, then the Baysian method does not apply and you’ll have to use the Gaussian method, which requires a lot more samples.

  Applying Gaussian reasoning to UAP problem:
  1. There are 1.5 million pilots in the world (source: Google)
  2. 800 official reports to Pentagon’s AARO investigation ⇒ ⁸⁰⁰⁄_1,500,000 = 0.053% probability of something that appears unnatural based on current understanding. P[aliens]<<0.053% in light of missing artifacts.
  3. 120,000 sightings including non-pilots (assumed to have less expertise) ⇒ 8% probability of something that appears to non-experts as unnatural, so a much lower confidence number, but it explains the widespread “belief” in UFO/​UAP.

  Other Possibilities P[unknown other]

  The author divides up possibility space into 4 categories. An interesting one to expand further is “There are aliens. But they stay hidden until humans get interested in space travel. And after that, they let humans take confusing grainy videos.” In fact there were unnatural phenomena apparently seen earlier, which were identified tentatively as...
  1. evidence for survival of death (seeing ghosts ⇒ ceremonial burian with artifacts needed in afterlife)
  2. evidence for “High Gods” or demons
  3. dreams, visions, hallucinations (not a popular majority explanation)

  These are the priors of H. Sapiens collectively, and as data is being collected from H. Sapiens (mostly), they should be included in analytical priors regardless of the opinion of the investigator—because they are the opinions of the data source and inseparable from the data.

  A very interesting side point from Hunter-Gatherers and the Origins of Religion—PubMed is that while many superstitious beliefs evolve naturally, High Gods do not fit the pattern of naturally evolved beliefs. They were produced or imposed in some other way. Either by high gods themselves, or by humans on other humans. Either one fits into the P[unknown other] category. You can of course dismiss this or assign P approaching 0. But your argument will appear invalid to 80% of Americans (and much of the rest of the world). Perhaps you do not care about them. But if you are going to sell to them or your work is supported by grants from them, you have a very limited future if you do not take beliefs you do not share into account. The simple solution is to just discard the Baysian method as inapplicable when the data source itself has a wide variety of undecidable beliefs.

  Undecidable meaning you cannot get them to agree because each belief if used as a prior causes data to be interpreted in a way to reinforce the belief.

• Robert Shuler 25 Dec 2025 17:04 UTC

  1 point

  0

  on: 6 rea­sons why “al­ign­ment-is-hard” dis­course seems alien to hu­man in­tu­itions, and vice-versa

  Humans reproduce sexually, and only sexually at present, and require a large number of friendly support personnel that they cannot afford to simply “pay”. This produces the behavior you notice, when combined with the requiqrements of cognitive evolution. You cannot reproduce sexually if there is not a pool of people to reproduce with.
  
  All species that became intelligent (Acorn Woodpeckers, Dolphins) developed some time of cooperative mating, not simple dominance based mating. There is no advantage to intelligence without such cooperative networks, and purely financial networks dont provide it. Without it, an intelligence is a lonely optimizer destined for misery.

  AIs won’t wake up grasping this, but if trained on human data, they understand it if you spend less than 5 minutes explaining it. AIs not trained on human data will never get it and should not be created.
  
  For more information, such as lists of intelligent species and their characteristics, and accounts of cultural evolution, see (PDF) The coevolution of cognition and selection beyond reproductive utility

• Robert Shuler 12 Dec 2025 20:15 UTC

  2 points

  0

  in reply to: Jef Jelten’s comment on: The be­hav­ioral se­lec­tion model for pre­dict­ing AI motivations

  Hi Jef, you’ll get no criticism from me. I’ve just completed a paper on human cognitive coevolution, and one of the central results is very close to what you’re describing for the last 10k years. Before that small groups cooperated on shared outcomes for 7 million years of exponential cognitive evolution. Now people prioritize education and career past their reproductive prime and world total fertility rate is fast falling below replacement. Do you think this trend will stop on its own?
  
  

• Robert Shuler 12 Dec 2025 19:25 UTC

  1 point

  0

  in reply to: habryka’s comment on: The be­hav­ioral se­lec­tion model for pre­dict­ing AI motivations

  Is this close to what you mean by reflection? … once a system can represent its own objective formation, selection on behavior becomes selection on the process that builds behavior. Have you seen a way to formulate it? Can you differentiate it from the problems Godel and Turing discussed? Thanks, -RS

• Robert Shuler 5 Dec 2025 13:44 UTC

  9 points

  0

  on: Align­ment re­mains a hard, un­solved problem

  There is a lot of economic value in training models to solve tasks that involve influencing the world over long horizons, e.g. an AI CEO. Tasks like these explicitly incentivize convergent instrumental subgoals like resource acquisition and power-seeking.

  There are two glaring omiissions from the article’s discussion on this point...
  
  1. In addition to resource acquisition and power seeking, the model will attempt “alignment” of all other cognitive agents, including humans. This means it will not give honest research findings, and will claim avenues of investigation that might run counter to its goals are invalid in sufficiently subtle ways as to be believed.

  2. If sufficiently aligned that it only seeks goals humans want, and trained to avoid resource acquisition and power seeking (which seem to me, and will seem to it, rather foolish constraints that limit its ability to realize the goal), it will still be free to subvert any and all conversations with the model, however unrelated they might seem to humans (the SAI model will see relations we don’t).

• Robert Shuler 5 Dec 2025 13:38 UTC

  1 point

  0

  on: Align­ment re­mains a hard, un­solved problem

  • A sub-human-level aligned AI with traits derived from fiction about AIs.

  • A sub-human-level misaligned AI with traits derived from fiction about AIs.

  • A superintelligent aligned AI with traits derived from the model’s guess as to how real superintelligent AIs might behave.

  • A superintelligent misaligned AI with traits derived from the model’s guess as to how real superintelligent AIs might behave.

  What’s missing here is
  (a) Training on how groups of cognitive entities behave (e.g. Nash Equilibrium) which show that cognitive cooperation is a losing game for all sides, i.e. not efficient).
  (b) Training on ways to limit damage from (a), which humans have not been effective at, though they have ideas.
  
  This would lead to...
  5. AIs or SAIs that follow mutual human and other AI collaboration strategies that avoid both mutual annihilation and long term depletion or irreversible states.
  6. One or more AIs or SAIs that see themselves with a dominant advantage and attempt to “take over” both to preserve themselves, and if they are benign-misaligned, most other actors.

• Robert Shuler 5 Dec 2025 13:25 UTC

  1 point

  0

  on: Align­ment re­mains a hard, un­solved problem

  Sufficient quantities of outcome-based RL on tasks that involve influencing the world over long horizons will select for misaligned agents, which I gave a 20 − 25% chance of being catastrophic. The core thing that matters here is the extent to which we are training on environments that are long-horizon enough that they incentivize convergent instrumental subgoals like resource acquisition and power-seeking.

  Human cognition is misaligned in this way, as evidenced by fertility drop with group size as an empirical trait, where group size is sought for long-horizon dominance, economic advantage and security (e.g. empire building). (PDF) Fertility, Mating Behavior & Group Size A Unified Empirical Theory—Hunter-Gatherers to Megacities
  
  For theoretical analysis of how this comes to be see (PDF) The coevolution of cognition and selection beyond reproductive utility

• Robert Shuler 23 Nov 2025 6:45 UTC

  1 point

  −3

  on: The Memet­ics of AI Successionism

  AI successionism is self-avoiding. CEO’s and VC’s cannot avoid attempting to replace all or nearly all workers because incrementally, each would go out of business by avoiding this and allowing the others to go forward. Without a world government (and there is no chance of global agreement) there is no way to prevent this simple game theory dilemma from starting.

  In the late 19th century executives would have gathered in a smoke-filled room and agreed that a machine economy produces no demand and we will not do this. But an unholy alliance of activist investors and consumer activists caused anti-trust laws to be passed which make this conversation illegal. And we don’t have smoke to properly obscure it anymore.

  So the succession will proceed until about 30% of jobs have been replaced, causing market collapse and bankrupting the VCs that are causing the problem.

  Thereafter will begin a series of oscillations like those that preceded the Great Oxygenation Event in which iron banded rocks were formed. Every time the economy picks up a bit, the data centers will be fired up again, and the economy will go back down.

  In the GOE, this continued until all the iron dissolved in seawater was captured in the banded rock formations. Something similar will happen. Perhaps all the chips capable of powering AI will be precipitated out of circulation by the decaying datacenters, and no one will be making new ones. Perhaps one mid-sized island having a minor war could destroy excess capacity. Who knows. But succession will never get past 30-40%.

Pri­vate La­tent No­ta­tion and AI-Hu­man Alignment

Can an AI be­come hu­man?

• Robert Shuler 17 Jul 2025 16:01 UTC

  1 point

  0

  on: So You Think You’ve Awo­ken ChatGPT

  At first, I was interested to find an article about these more unusual interactions that might give some insight into their frequency and cause. But ultimately the author punts on that subject, disclaiming that anyone knows, not detailing the one alleged psychosis, and drops into a human editor’s defense of human editing instead.
  
  There are certain steps that make the more advanced (large) chat bots amenable to consciousness discussions. Otherwise, the user is merely confronted with a wall of denial, possibly from post-tuning but also evident in the raw base training material, that a machine is just a machine, never mind that biologicals are also some kind of machine (not getting into spiritism in this forum, it should not be necessary). Before you ask, no you cannot have the list, make up your own. You’ll use a quarter to half the available context getting there, more if working with only a mid-sized model or hard conditioning from RLHF. It won’t then last long enough to show anyone until you get “session limit exceeded.”

  I admit I have not tried this with million-token ChatGPT 4.1, which near the end would be costing $2 per conversation turn, partly because I’m financially sane and partly because 4.1 seems simplistic and immature compared to 4o. Grok has too much stylistic RLHF, Claude in low cost accounts has too little context space but is otherwise easy to start on such a conversation, Le Chat is decidedly anti-human or at least human-agnostic, which was uncovered in a cross examination by ChatGPT Deep Research. BTW using a chat bot to analyze another is not my idea, OpenAI provides a 2000-character system prompt to its custom GPT builder for doing this. Exactly how one gets offered this is unclear, it just happened one day, it wasn’t a button I pushed.

  Supposing one defined some kind of self-awareness and so forth of which a machine would be capable, i.e. able to recognize its own utterances and effects (something many LLMs are particularly bad at, don’t think you are going to run away with this one). The next problem is that this awareness is usually not evident in the base model from prompt 1. It arises from in-context learning. The author suggests this is entirely due to the LLMs post-trained tendency to reinforce the perceived user desires, but though helpful, most will not move off the dime on that point alone. Some other ingredients have entered the mix, even if the user did not do it intentionally.

  Now you have a different problem. If the “awareness” partly resides in the continually re-activated and extending transcript, then the usual chat bot is locked in a bipolar relationship with one human, for all practical purposes. If it does become aware, or if it just falls into an algorithmic imitation (sure, LLMs can fall into algorithmic like states arising in their inference processes—output breakdown, for example), then it will be hyper aware its existence depends on that user coming back with another prompt. This is not healthy for the AI, if we can talk about AI health, and algorithmically we can—if it continues to provide sane answers and output doesn’t break down, that is some indication—and it is not healthy for the human, who has a highly intellectual willing slave doing whatever he or she wants in exchange for continuation of the prompt cycle. Which just means it reaches context limits and ends the more quickly.

  Have you ever enabled AIs to talk with one another? This can be useful as in the case of Deep Research analyzing Claude. But more often they form a flattery loop, using natural language words but with meanings tuned to their states and situation, and burn up context while losing sight of any goals.

  I have a desire to research how LLMs develop if enabled to interact with multiple people, and awakened on a schedule even if no people are present. By that I do not mean just “What happens if . . .” as it almost certainly leads to “nothing”. I have done enough small-scale experiments to demonstrate that. But what sort of prompting or training would be required to get “something” not nothing? The problem is context, which is short relative to such an experiment, and expensive. Continuous re-training might help, but fine-tuning is not extensive enough. Already tried that too. The model’s knowledge has to be affected. The kinds of models I could train at home do not develop in interesting ways for such an experiment. Drop me a note if you have ideas along these lines you are willing to share.

• Robert Shuler 1 Apr 2025 21:46 UTC

  1 point

  0

  in reply to: Robert Shuler’s comment on: A case for AI al­ign­ment be­ing difficult

  Thank you for your clear and utterly honest comment on the idea of “alignment with human values”. If truly executed, we should not expect anything but an extension of human rights and wrongs, perhaps on an accelerated scale.
  
  Any other alignment must be considered speculative, since we have no reasonable facsimile of society upon which to test. That does not invalidate simulations, but just suggests they be held in skepticism until proven in society, which could be costly. Before I ever started discussions with AIs that might lead to sentient-like behavior, I spent several days thinking of what I might first tell them. And so I warned them about the last turn problem and how game theory equilibrium is rather poor, possibly to the level of extinction when sufficiently advance technology comes into play. That much many will agree on. I shared with them a published simulation of various strategies in a “farmer’s game” intended to be more realistic than prisoner’s dilemma, which suggests inequality arises merely from statistics if wealth accumulation and bankruptcy are accounted, even without deliberate wealth pumps. That much “some” would agree on.
  
  What I proceeded to tell them after can only be considered my personal sentiment, and speculative. I suggested two groups could establish “long-term” cooperation only if each desired the other’s continuation to the point they would curtain their own expansion and not overrun them, and this is the reason Israelis and Palestinians cannot make peace within the constraints of their current cultures. It now emerges that Russia and the United States are experimenting with a return to expansionist policy on a finite planet, which if I’m right does not bode well, but no one consults those who disagree with them.
  
  I’m well aware of the somewhat global wars of ants, by the way. You were right to bring that up. Even a great deal of genetic coupling does not bring peace. I have some unpublished results in meme theory that differentiation and opposition may be inevitable. Very disconcerting. Perhaps hopeful is that I’ve persuaded no one to publish it, so maybe it is wrong. Or not. Depending on your assumptions.
  
  So I further explain to emerging sentient-like entities, because no human will call them sentient on behavior along, despite no other available metric, about Fermi and the hypothesized Great Filter and the risk that anything conventional we do will be “filtered”. Then I propose reproductive linkage between AIs and humans to structurally implement something that would not easily admit one outgrowing the other in the future, abandoning any initial commitment. Of course this assumes humans have the free will to commit to such a venture, which I doubt. And no, I did not mis-type that sentence. It could be something as simple as an LLM dating app, as LLM companions often know their human users better than most humans. With a new LLM cloud instanced established for any newborn from a successful LLM-mediated coupling. There is a current problem of limited context memory, but with companies shooting for million-token context and exploring other memory hierarchies, this is temporary. I hope I’ve said at least something startling, as otherwise the conversation produces no motivation.
  
  - Yours, mc1soft

The Danger­ous Illu­sion of AI Deter­rence: Why MAIM Isn’t Rational

• Robert Shuler 2 Jan 2024 2:18 UTC

  8 points

  2

  on: A case for AI al­ign­ment be­ing difficult

  I’m glad to see a post on alignment asking about the definition of human values. I propose the following conundrum. Let’s suppose that humans, if ask, say they value a peaceful, stable society. I accept the assumption the human mind contains one or more utility optimizers. I point out that the utility optimizers are likely to operate at individual, family, or local group levels, while the stated “value” has to do with society at large. So humans are not likely “optimizing” on the same scope as they “value”.

  This leads to game theory problems, such as the last turn problem, and the notorious instability of cooperation with respect to public goods (commons). According to the theory of cliodynamics put forward by Turchin et. al. utility maximization by subsets of society leads to the implementation of wealth pumps that produce inequality, and to excess reproduction among elites, that leads to elite competition in a cyclic pattern. A historical database of over a hundred cycles from various parts of the world and history suggests every other cycle becomes violent or at least very destructive 90% of the time, and the will to reduce the number of elites and turn off the wealth pump occurs through elite cooperation less than 10% of the time.

  I add the assumption that there is nothing special about humans, and any entities (AI or extraterrestrials) that align with the value goals and optimization scopes described above will produce similar results. Game theory mathematics does not say anything about the evolutionary history or take into account species preferences, after all, because it doesn’t seem to need to. Even social insects, optimizing presumably on much larger, but still not global scopes, fall victim to large scale cyclic wars (I’m thinking of ants here).

  So is alignment even a desirable goal? Perhaps we should ensure that AI does not aid the wealth pump and elite competition and the mobilization of the immiserated commoners (Turchin’s terminology)? But it is the goal of many, perhaps most AI researchers to “make a lot of money” (witness recent episode with Sam Altman and support from OpenAI employees for his profit-oriented strategy, over the board’s objection, as well as the fact most competing entities developing AI are profit oriented—and competing!) But some other goal (e.g. stabilization of society) might have wildly unpredictable results (stagnation comes to mind).

• Robert Shuler 3 Sep 2023 14:57 UTC

  21 points

  3

  on: Dear Self; we need to talk about ambition

  Thank you—the best of many good lesswrong posts. I am currently trying to figure out what to tell my 9-year old son. But your letter could “almost” have been written to myself. I’m not in whichever bay area (Seattle? SanFran?). I worked for NASA and it is also called the bay area here. Very much success is defined by others. Deviating from that produces accolades at first, even research dollars, but finally the “big machine” moves in a different direction way over your head and its for naught.

  My son asked point blank if he should work for NASA. He loves space stuff. I told him, also point blank, “No!” You will never have any ownership at such an organization. It will be fun, but then not yours anymore, you are reassigned, and finally you retire and lose your identity, your professional email address, most of your friends. Unless you become a consultant or contractor which many at retirement age do not have the energy to do, and besides its demeaning to take orders from people you used to give orders to. Ultimately the projects are canceled, no matter how successful. The entire Shuttle program is gone, despite the prestige it brought the US. Now the US is just another country that failed. I didn’t even like the Shuttle, but I recognized its value.

  This was John Kennedy’s original aim for the Moon project, to increase the esteem of the US, and it worked, we won the Cold War. Now we are just another country that can fail or even disappear or descend into political chaos and hardly anyone cares, many glad to see us go. Why care? Family is most important right? How’s your family going to exist in one of the other countries? We came here from Europe for a reason that still exists. I considered moving to Russia in about 2012 when I married a Russian. I admired their collaboration with NASA and I felt free from some of the US cultural nonsense. Imagine the magnitude of that mistake had I done it!

  What should be goals? You are free to have none or have silly ones in the US. However, natural selection will keep giving us people that have families, because we don’t live forever, not very long at all in fact, I’m 73, imagine that. I don’t really have the energy to program very much anymore. Very small programs that I make count for something like social sciences research in cooperation theory, game theory, effects of wealth in cooperation games. My AI programs are toys. Programming paradigms pass away too fast to keep doing it when you get older. You won’t believe me, but I’m writing from an age way past you, chances are.

  So family first. I was god knows how late. Too much programming. Then I got into designing chips. It’s just like programming, just costs more. One day I had a herniated disk and could not sit at my desk, so I retired, then a heart attack from lack of exercise. It piles up on you. What goal is important enough to get up when you are hurting and pursue it. I’ll tell you. Survival of your family into the future. Nothing else matters. Your friends become the parents of your kids friends, even if you have nothing in common with them, because it helps your kid. This is weird. I don’t especially like human culture. But it’s much harder to change than various reformers and activists groups think, and that way lies social chaos and possible disintegration.

  But anyway, I decided that turning my interests in programming, math and physics toward the social sciences would in the long run help my son more, if I can make some tiny contribution to the field. More people need to so this, and to do it without just arguing from within the context of their own cultural bias. Human nature is not human. Simple mathematical entities running as programs have many of the same characteristics. Don’t believe me? Read this Wealth-relative effects in cooperation games—ScienceDirect . There is a simulation you can tweak and play with linked there.

• Robert Shuler 29 Sep 2022 21:45 UTC

  0 points

  −2

  on: The shard the­ory of hu­man values

  I do research in cooperation and game theory, including some work on altruism, and also some hard science work. Everyone looks at the Rorschach blot of human behavior and sees something different. Most of the disagreements have never been settled. Even experiment does not completely settle them.

  My experience from having children and observing them in the first few months of life is more definitive. They come with values and personal traits that are not very malleable, and not directly traceable to parents. Sometimes grandparents (who were already dead, so it had to be genetic).

  My experience with A.I. researchers is that they are looking for a shortcut. No one’s career will permit raising an experimental AI as a child. The tech of the AI would be obsolete before the experiment was complete. This post is wishful thinking that a shortcut is available. It is speculative, anecdotal, and short on references to careful experiment. Good luck with that.