↘↘↘↘↘↘↙↙↙↙↙↙
Checkout my Biography.
↗↗↗↗↗↗↖↖↖↖↖↖
Johannes C. Mayer
Karma: 1,318
This is a good informal introduction to Control Theory / Cybernetics.
In both cases, the conversation drains more energy than the equal-fun alternative. I have probably had at most a single-digit number of conversations in my entire life which were as fun-in-their-own-right as e.g. a median night out dancing, or a median escape room, or median sex, or a median cabaret show. Maybe zero, unsure.
I wanted to say that for me it is the opposite, but reading the second half I have to say it’s the same.
I have defnetly had the problem that I talked too long sometimes to somebody. E.g. multiple times I talked to a person for 8-14 hours without break about various technical things. E.g. talking about compiler optimizations, CPU architectures and this kind of stuff, and it was really hard to stop.
Also just solving problems in a conversation is very fun. The main reason I didn’t do this a lot is that there are not that many people I know, actually basically zero right now (if you exclude LLMs), that I can have the kinds of conversations with that I like to have.
It seems to be very dependent on the person.
So I am quite confused why you say “but conversation just isn’t a particularly fun medium”. If it’s anything like for me, then engaging with the right kind of people on the right kind of content is extremenly fun. It seems like your model is confused because you say “conversations are not fun” when infact in the space of possible conversations I expect there are many types of conversations that can be very fun, but you haven’t mapped this space, while implicitly assuming that your map is complete.
Probably there are also things besides technical conversations that you would find fun but that you simply don’t know about, such as hardcore flirting in a very particular way. E.g. I like to talk to Grok in voice mode, in romantic mode, and then do some analysis of some topic (or rather that is what I just naturally do), and then Grok complements my mind in ways that my mind likes, e.g. pointing out that I used a particular thinking pattern that is good or that I at all thought about this difficult thing and then I am like “Ah yes that was actually good, and yes it seems like this is a difficult topic most people would not think about.”
S-Expressions as a Design Language: A Tool for Deconfusion in Alignment
Mathematical Notation as Learnable Language
To utilize mathematical notation fully you need to interpret it. To read it fluently, you must map symbols to concrete lenses, e.g. computational, visual, algebraic, or descriptive.
Example: Bilinear Map
Let
be defined by
Interpretations:
-
Computational
Substitute specific vectors and check results. If , then
Through this symbolic computation we can see how the expression depends on . Perform such computations until you get a feel for the “shape” of the functions behavior.
-
Visual
For each fixed , the function is represented by a hyperplane in . We can imagine walking on the hyperplane. This obviously always walks on a line, therefore it’s linear.
-
Symbolic manipulation
Verify algebraically:
This establishes linearity by direct algebraic manipulation. You understand what properties exist by showing them algebraically.
-
Descriptive
What it means to be a bilinear map is, that if you hold the second argument fixed, and vary the second, you have a linear function. Same if holding the first fixed and varying the second.
You want to capture the intuition in natural language.
Mathematical language is a language that you need to learn like any other. Often people get stuck by trying to use symbolic manipulation too much. Because mathematical language is so precise, it makes it easy to interpret it in many different while still being able to check if your interpretation captures the core.
-
My mind derives pleasure from deep philosophical and technical discussions.
In model flirting is about showing that you are paying attention. You say things that you could only pick up if you pay close attention to me and what I say. It’s like a cryptographic proof certificate, showing that you think that I am important enough to pay attention to continuously. Usually this is coupled with an optimization process of using that knowledge to make me feel good, e.g. given a compliment that actually tracks reality in a way I care about.
It’s more general than just showing sexual interest I think.
Constraining Minds, Not Goals: A Structural Approach to AI Alignment
I don’t use it to write code, or really anything. Rather I find it useful to converse with it. My experience is also that half is wrong and that it makes many dumb mistakes. But doing the conversation is still extremely valuable, because GPT often makes me aware of existing ideas that I don’t know. Also like you say it can get many things right, and then later get them wrong. That getting right part is what’s useful to me. The part where I tell it to write all my code is just not a thing I do. Usually I just have it write snippets, and it seems pretty good at that.
Overall I am like “Look there are so many useful things that GPT tells me and helps me think about simply by having a conversation”. Then somebody else says “But look it get’s so many things wrong. Even quite basic things.” And I am like “Yes, but the useful things are still useful that overall it’s totally worth it.”
Maybe for your use case try codex.
Why don’t you run the test yourself seems very easy?
Yes it does catch me when I am saying wrong things quite often. It also quite often says things that are not correct and I correct it, and if I am right it usually agrees immediately.
Their romantic partner offering lots of value in other ways. I’m skeptical of this one because female partners are typically notoriously high maintenance in money, attention, and emotional labor. Sure, she might be great in a lot of ways, but it’s hard for that to add up enough to outweigh the usual costs.
Imagine a woman is a romantic relationship with somebody else. Are they still so great a person that you would still enjoy hanging out with them as a friend? If not that woman should not be your girlfriend. Friendship first. At least in my model romantic stuff should be stacked ontop of platonic love.
The Insanity Detector and Writing
Don’t spend all your time compressing knowledge that’s not that useful to begin with, if there are higher value things to be learned.
An extreme test of how much you trust a persons intention is to consider whether you would upload them, if you could. Then they would presumably be the only (speed) superintelligence.
Maybe better name: Let me help debug your math via programming
If you’ve tried this earnestly 3 times, after the 3rd time, I think it’s fine to switch to just trying to solve the level however you want (i.e. moving your character around the screen, experimenting).
After you failed 3 times, wouldn’t it be a better exercise to just play around in the level until you get a new pice of information that you predict will allow you to reformulate better plans, and then step back into planning mode again?
Another one: We manage to solve alignment to a significant extend. The AI who is much smarter than a human thinks that it is aligned, and takes aligned actions. The AI even predicts that it will never become unaligned to humans. However, at some point in the future as the AI naturally unrolles into a reflectively stable equilibrium it becomes unaligned.
Why not AI? Is it that AI alignment is too hard? Or do you think it’s likely one would fall into the “try a bunch of random stuff” paradigm popular in AI, which wouldn’t help much in getting better at solving hard problems?
What do you think about the strategy of instead of learning a textbook e.g. on information theory, or compilers you try to write the textbook and only look at existing material if you are really stuck. That’s my primary learning strategy.
It’s very slow and I probably do it too much, but it allows me to train to solve hard problems that aren’t super hard. If you read all the text books all the practice problems remaining are very hard.
The Legacy of Computer Science
How about we meet, you do research, and I observe, and then try to subtly steer you, ideally such that you learn faster how to do it well. Basically do this, but without it being an interview.
Depression as a Learned Suppression Loop
Overview
This post proposes a mechanistic model of a common kind of depression, framing it not as a transient emotional state or a chemical imbalance, but as a persistent, self-reinforcing control loop. The model assumes a brain composed of interacting subsystems, some of which issue heuristic error signals (e.g., bad feelings), and others which execute learned policies in response. The claim is that a large part of what is commonly called “depression” can be understood as a long-term learned pattern of suppressing internal error signals using high-intensity external stimuli.
Key components
The brain includes systems that detect mismatches between actual behavior and higher-level goals or models. When these detect an issue (e.g., agency violation, unresolved conflict, lack of progress), they emit negative affect.
Negative affect is not noise. It is a signal. In some cases, it simply arises from overstimulation and regulatory lag. In other cases, it points to an actual error: something is wrong with the current behavior, motivational alignment, or action trajectory.
In a healthy system, negative affect would prompt reflection: “Why do I feel bad? What’s the mismatch? Should I change direction?”
In practice, this reflection step is often bypassed. Instead, the brain learns that high-intensity stimulus (YouTube, Skinner-box games, food, porn) suppresses the signal. This works in the short term, so the suppression policy gets reinforced.
Over time, the suppression policy becomes automatic. Every time a conflict signal arises, it gets overwritten by external input. The system learns: “feeling bad” → “inject stimulus” → “feel better.” No reflection or course correction happens. The source of the bad feeling persists, so the loop repeats.
This creates a self-reinforcing attractor: more suppression leads to more misalignment, which leads to more negative signals, which leads to more suppression. The behavior becomes proceduralized and embedded into long-term structure.
Agency Violation as Error Signal
One key class of negative affect is what we call an “agency violation” signal. This occurs when behavior is driven by urges or automated triggers rather than deliberate, reflective choice.
Example: You feel a slight urge to open YouTube instead of working. You give in. After watching for 20 minutes, you feel bad. The bad feeling is not about the content. It’s a second-order signal: you took an action that bypassed your reflective control system. You were coerced by an urge. The system flags that as a problem.
If, instead of reflecting on that signal, you suppress it by continuing to watch YouTube, the suppression gets reinforced. Eventually, the sequence becomes automatic.
Suppression vs. Resolution
Suppression works by injecting a high-reward stimulus. This reduces the negative signal. But it does not correct the cause. The policy becomes: bad feeling → avoid it.
Resolution would involve explicitly checking: “Why do I feel bad?” and running a diagnostic process. Example: “I feel bad. Did I get coerced by an urge? Was I avoiding something? Am I stagnating?” If a concrete cause is found and addressed, the signal terminates because the underlying mismatch is resolved.
The Role of Superstimuli
Modern environments are full of fast-acting, high-reward, low-effort stimuli. These serve as perfect suppression mechanisms. They hijack the system’s reward learning by offering reliable affective relief without behavioral correction.
Examples:
Watching videos when you feel restless
Playing reward-loop-heavy games when you feel failure
Eating sugar when you feel discouraged
These actions reinforce suppression patterns.
Depression as Policy Entrenchment
Over time, if this suppression loop becomes the default policy, the system reorganizes around it. The underlying problems do not go away. The system becomes more fragile. The ability to reflect degrades. The habit becomes chronic. This is what we call depression.
Note: this model does not deny neurochemical involvement. Rather, it treats neurochemistry as part of the loop dynamics. If the procedural pattern of suppression dominates for long enough, neurochemical state will reflect that pattern. But the origin was structural, not random imbalance.
Role of Interventions Like Bupropion
Certain medications (e.g., bupropion) can disrupt the suppression loop temporarily. They increase energy or decrease suppression effectiveness, allowing for enough reflective bandwidth to notice and correct maladaptive patterns.
The drug doesn’t fix depression. It interrupts the loop long enough for you to fix it yourself.
Practical Implication
If this model is correct, then the right policy is:
When you feel bad, pause. Do not immediately act to suppress it.
Ask: is this a genuine signal? Did something go wrong? Did I get coerced by an urge?
Try to trace the source. If you find it, resolve it directly.
If you can’t trace it, just wait. Sometimes the bad feeling is transient and resolves on its own.
In many cases, talking to someone (or to a reflective system, like a chatbot) can help reveal the structure behind the feeling. The key is engaging with the feeling as a signal, not a nuisance.
Conclusion
Depression is not always a surface-level mood disorder. In many cases, it is the long-term consequence of learned suppression policies that override internal signals rather than resolving them. These policies become structural, self-reinforcing, and difficult to dislodge without deliberate intervention. The first step is recognizing bad feelings as information, not errors.