azergante

• azerganteApr 15, 2025, 9:45 PM

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  on: Ein­stein’s Superpowers

  “Yeah? Let’s see your aura of destiny, buddy.”

  Another angle: if I have to hire a software engineer, I’ll pick the one with the aura of destiny any time, because that one is more likely to achieve great things than the others.

  I would say auras of destiny are Bayesian evidence for greatness, and they are hard to fake signals.

• azerganteApr 14, 2025, 9:37 PM

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  on: AI 2027: What Su­per­in­tel­li­gence Looks Like

  Slightly off-topic but Wow! this is material for an awesome RTS video-game! That would be so cool!

  And a bit more on topic: that kind of video game would give the broader public a good idea of what’s coming, and researchers and leaders a way to vividly explore various scenario, all while having fun.

  Imagine playing the role of an unaligned AGI, and noticing that the game dynamics push you to deceive humans to gain more compute and capabilities until you can take over or something, all because that’s the fastest way to maximize your utility function!

• azerganteApr 12, 2025, 6:12 PM

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  on: A new in­tro to Quan­tum Physics, with the math fixed

  If you now put a detector in path A , it will find a photon with probability ($(\frac{1}{\sqrt{2}}{)}^2=\frac{1}{2}$ ), and same for path B. This means that there is a 50% chance of the configuration |photon in path A only>, and 50% chance of the configuration |photon in path B only>. The arrow direction still has no effect on the probability.

  This ⁵⁰⁄₅₀ split is extra surprising and perhaps misleading? What’s the cause? why not 100 on path A and 0 on path B (or the reverse)?

  As a layman it seems like either:

  1. The world is not deterministic, so when we repeat the experiment sometimes the detector goes off on path A and sometimes it goes off on path B. That’s very surprising.

  2. We are in a deterministic world, that means that if we can repeat the same experiment we get the same result each time. But actually we don’t get the same result, which means we failed to repeat the experiment, or rather the initial conditions were significantly different. The probabilities tell us something about the experimental setup, not about quantum mechanics: as an analogy if I roll a fair plastic dice N times, and observe it falls on 6 with probability half, this tells me a lot about my dice rolling skills, but not so much about plastic. In a similar way when we send N photons in an interferometer setup, the setup actually varies with time: maybe the photon does not land quite on the same spot on the mirror each time, or some atoms have decayed, whatever, so the ⁵⁰⁄₅₀ probabilities we observe actually tell us about the setup and our inability to repeat the same experiment to the degree that it matters, not about quantum mechanics. This is misleading because if the probabilities have nothing to do with QM why mention them at all?

• azerganteApr 8, 2025, 10:35 PM

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  on: Most smart and skil­led peo­ple are out­side of the EA/​ra­tio­nal­ist com­mu­nity: an analysis

  Insularity will make you dumber

  Okay but there is another side to the issue, insularity can also have positive effects:

  If you look at evolution, when a population gets stuck on an island, it starts to develop in interesting ways, maybe insularity is a necessary step to develop truly creative worldviews?

  Also IIRC in “The Timeless way of building” Christopher Alexander mentions that cities should be designed as several small neighborhoods with well-defined boundaries, where people with similar background live. He also says something to the effect that the boundaries and the common background are essential to create a strong and interesting culture. So to put a positive spin on it, to some extent insularity is what allowed the rationalist community to develop its peculiar charm.

  Now I also find the jargon annoying at times, and had to ask an LLM about this “epistemic status” business, but another way to see it is that this gate-keeping is a selection process that keeps people that are not a good fit out of the community, helping it maintain its coherence. But maybe we should still tone it down in favor of clarity.

• azerganteApr 7, 2025, 10:30 PM

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  on: Distinct Configurations

  I liked the intro but some parts of the previous posts and this one have been confusing, for example in this post:

  Second, we saw that configurations are about multiple particles. [...] And in the real universe, every configuration is about all the particles… everywhere.)

  and more glaring in the previous one:

  A configuration says, “a photon here, a photon there,”

  Here my intuition is that we can model the world as particles, or we can use the lower-level model of the world which configurations are, but we can’t mix both any way we want. These sentences feel backwards: they talk about configurations in terms of particles, and this doesn’t make any sense because the model of particles should be derived from the lower-level model of configurations, not the other way around!!

  Maybe someone with more knowledge in physics and philosophy of science can clarify?

• azerganteMar 9, 2025, 10:48 PM

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  on: How to Corner Liars: A Mi­asma-Clear­ing Protocol

  Planecrash (from Eliezer and Lintamande) seems highly relevant here: the hero, Keltam, tries to determine whether he is in a conspiracy or not. To do that he basically applies Bayes theorem to each new fact he encounters: “Is fact F more likely to happen if I am in a conspiracy or if I am not? hmm, fact F seems more likely to happen if I am not in a conspiracy, let’s update my prior a bit towards the ‘not in a conspiracy’ side”.

  Planecrash is a great walkthrough on how to apply that kind of thinking to evaluate whether someone is bullshitting you or not, by keeping two alternative worlds that explain what they are saying, and updating the likelihoods as the discussion goes on.

  Surely if you start putting probability on events such as “someone stole my phone”, and “that person then tailed me”, and multiply the probability of each new fact, it gets really unlikely really fast. Also relevant: Burdensome details

• azerganteMar 9, 2025, 10:13 PM

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  on: Lots of brief thoughts on Soft­ware Engineering

  You did not explicitly state the goal of the advice, I think it would be interesting to distinguish between advice that is meant to increase your value to the company, and advice meant to increase your satisfaction with your work, especially when the two point in opposite directions.

  For example it could be that “swallow[ing] your pride and us[ing] that garbage language you hate so much” is good for the company in some cases, but terrible for job satisfaction, making you depressed or angry every time you have to use that silly language/​tool.

• azerganteMar 9, 2025, 10:03 PM

  12 points

  2

  on: Lots of brief thoughts on Soft­ware Engineering

  For that reason try to structure teams such that every team has everything it needs for its day to day work.

  I would extend that to “have as much control as you can over what you do”. I increasingly find that this is key to move fast and produce quality software.

  This applies to code and means dependencies should be owned and open to modifications, so the team understands them well and can fix bugs or add features as needed.

  This avoids ridiculous situations where bugs are never fixed or shipping very simple features (such as changing a theme for a UI component) is impossible or takes weeks because a framework actively prevents it.

  More control and understanding also tends to be better for satisfaction. Of course all this is on a spectrum and should be balanced with other requirements.

• azerganteMar 7, 2025, 7:31 PM

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  on: az­er­gante’s Shortform

  Tip: you can ask ChatGPT to include confidence scores in its replies

  Interactions with ChatGPT can be customized durably in the options, for example you can add the following instructions: “include a confidence rating at the end of your response in the format ‘Confidence: X%’. If your confidence is below 80%, briefly explain why”.

  Here is a sample conversation demonstrating this and showing what ChatGPT has to say about its calibration:

  Me: Are you calibrated, by which I mean, when you output a confidence X as a percentage, are you right X times out of 100?

  ChatGPT: Reasoned about confidence calibration for 27 seconds. I don’t have calibration in that strict sense. When I mention a confidence percentage, it’s a heuristic internal signal rather than a guarantee that I’ll be right exactly that proportion of the time. Research shows that language models often exhibit miscalibration, meaning a stated confidence (say 70%) doesn’t empirically translate to being correct 70% of the time. I’m about 90% confident in this explanation.

  Me: How bad is the miscalibration?

  ChatGPT: Thought for a second. LLMs—especially those fine‐tuned with RLHF—are often notably overconfident. For example, a model might claim 90% confidence while its actual accuracy is closer to 70–80%. Studies report expected calibration errors (ECE) of roughly 5–10% or more, and effective calibration methods can sometimes cut these errors by about half, underscoring that miscalibration is a significant issue. I’m about 90% confident in this summary, based on multiple recent calibration studies.

  Me: If you are predictably overconfident, and you know it, do you think you can notice that before stating your confidence and correct for it?

  ChatGPT: Reasoned about overconfidence and calibration adjustment for 4 seconds. I don’t have a real-time “internal monitor” to detect my own overconfidence before stating it. Instead, I rely on calibration techniques—like temperature scaling and post-hoc adjustments—that help align my confidence levels with actual accuracy. In other words, I can be tuned to be less overconfident, but I don’t consciously correct my confidence on the fly. I’m about 90% confident in this explanation based on current research findings.

• azerganteMar 5, 2025, 9:48 PM

  12 points

  4

  on: How Much Are LLMs Ac­tu­ally Boost­ing Real-World Pro­gram­mer Pro­duc­tivity?

  Many developers have been reporting that this is dramatically increasing their productivity, up to 5x’ing/​10x’ing it

  I challenge the data: none of my colleagues have been reporting this high a speed-up. I think your observation can just be explained by a high sampling bias.

  People who do not use AI or got no improvement are unlikely to report. You also mention Twitter where users share “hot takes” etc to increase engagement.

  It’s good to have actual numbers before we explain them, so I ran a quick search and found 3 articles that look promising (I only did a basic check on the methodology, don’t take these numbers at face value without analyzing the source in depth):

  • An Axify analysis of the DORA metrics they collect: https://​​axify.io/​​blog/​​use-ai-for-developer-productivity

  Documentation quality (+7.5%)

  Code review speed (+3.1%)

  Delivery throughput (-1.5%): AI adoption slightly decreases delivery throughput, usually due to over-reliance, learning curve, and increased complexity.

  Delivery stability (-7.2%): It is significantly impacted because AI tools can generate incorrect or incomplete code, increasing the risk of production errors.

  What are the DORA metrics?

  Deployment frequency | How often a team puts an item into production.

  Lead time for changes | Time required for a commit to go into production.

  Change failure rate | Percentage of deployments resulting in production failure.

  Failed deployment recovery time | Time required for a team to recover from a production failure.

  • A McKinsey pilot study on 40 of their developers: https://​​www.mckinsey.com/​​capabilities/​​mckinsey-digital/​​our-insights/​​unleashing-developer-productivity-with-generative-ai

  

  • An UpLevel analysis of the data of 800 developers: https://​​uplevelteam.com/​​blog/​​ai-for-developer-productivity

  Analyzing actual engineering data from a sample of nearly 800 developers and objective metrics, such as cycle time, PR throughput, bug rate, and extended working hours (“Always On” time), we found that Copilot access provided no significant change in efficiency metrics.

  The group using Copilot introduced 41% more bugs

  Copilot access didn’t mitigate the risk of burnout

  “The adoption rate is significantly below 100% in all three experiments,” the researchers wrote. “With around 30-40% of the engineers not even trying the product.

  In my experience LLMs are a replacement for search engines (these days, search engines are only good to find info when you already know on which website to look …). They don’t do well in moderately sized code bases, nor in code bases with lots of esoteric business logic, which is to say they don’t do well in most enterprise software.

  I mostly use them as:

  • a documentation that talks: it reminds me of syntax, finds functions in APIs and generates code snippets I can play with to understand APIs

  • a fuzzy version of an encyclopedia: it helps me get an overview of a subject and points me to resources where I can get crisper knowledge

  • support/​better search engine for weird build issues, or weird bugs etc

  I think it’s also good at one shot scripts, such as data wrangling and data viz, but it does not come up often in my current position.

  I would rate the productivity increase at about 10%. I think the use of modal editors (Vim or modern alternatives) improve coding speed more than inline AI completion, which is often distracting.

  A lot of my time is spent understanding what I need to code in a back and forth with the product manager or clients. Then I can either code it myself (it’s not so hard once I understand the requirements) or spend time explaining the AI what it needs to do, watch it write sloppy code, and rewrite the thing.

  Once in a while the coding part is actually the hard part, for example when I need to make the code fast or make a feature play well with the existing architecture, but then the AI can’t do logic well enough to optimize code, nor can it reason about the entire code base which it can’t see anyway.

• azerganteJan 16, 2025, 8:53 PM

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  in reply to: AnthonyC’s comment on: We prob­a­bly won’t just play sta­tus games with each other af­ter AGI

  I also think it is unlikely that AGIs will compete in human status games. Status games are not just about being the best: Deep Blue is not high status, sportsmen that take drugs to improve their performance are not high status.

  Status games have rules and you only win if you do something impressive while competing within the rules, being an AGI is likely to be seen as an unfair advantage, and thus AIs will be banned from human status games, in the same way that current sports competitions are split by gender and weight.

  Even if they are not banned given their abilities it will be expected that they do much better than humans, it will just be a normal thing, not a high status, impressive thing.

• azerganteJan 6, 2025, 10:02 PM

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  on: az­er­gante’s Shortform

  Let me show you the ropes

  There is a rope.
  You hold one end.
  I hold the other.
  The rope is tight.
  I pull on it.

  How long until your end of the rope moves?

  What matters is not how long until your end of the rope moves.
  It’s having fun sciencing it!

• azerganteDec 30, 2024, 3:40 PM

  11 points

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  on: Learn to write well BEFORE you have some­thing worth saying

  For those interested in writing better trip reports there is a “Guide to Writing Rigorous Reports of Exotic States of Consciousness” at https://​​qri.org/​​blog/​​rigorous-reports

  A trip report is an especially hard case of something one can write about:

  • english does not have a well-developed vocabulary for exotic states of consciousness

  • even if we made up new words, they might not make much sense to people that have not experienced what they point at, just like it’s hard to describe color to blind people or to project a high-dimensional thing to a lower dimensional space.

• azerganteDec 26, 2024, 10:31 PM

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  in reply to: Roger Scott’s comment on: Biolog­i­cal risk from the mir­ror world

  I have a similar intuition that if mirror-life is dangerous to Earth-life, then the mirror version of mirror-life (that is, Earth-life) should be about equally as dangerous to mirror-life as mirror-life is to Earth-life. Having only read this post and in the absence of any evidence either way this default intuition seems reasonable.

  I find the post alarming and I really wish it had some numbers instead of words like “might” to back up the claims of threat. At the moment my uneducated mental model is that for mirror-life to be a danger it has to:

  • find enough food that fit its chirality to survive

  • not get killed by other life-forms

  • be able to survive Earth temperature, atmosphere etc etc

  • enter our body

  • bypass our immune system

  • be a danger to us

  Hmm, 6 ifs seems like a lot, so is it unlikely? in the absence of any odds it is hard to say.

  The post would be more convincing and useful if it included a more detailed threat model, or some probabilities, or a simulation, or anything quantified.

  A last question: how many mirror molecules does an organism need to be mirror-life? is one enough? does it make any difference to its threat-level?

• azerganteDec 23, 2024, 12:54 PM

  5 points

  2

  on: az­er­gante’s Shortform

  2+2=5 is Fine Maths: all you need is Coherence

  [ epistemological status: a thought I had while reading about Russell’s paradox, rewritten and expanded on by Claude ; my math level: undergraduate-ish ]

  Introduction

  Mathematics has faced several apparent “crises” throughout history that seemed to threaten its very foundations. However, these crises largely dissolve when we recognize a simple truth: mathematics consists of coherent systems designed for specific purposes, rather than a single universal “true” mathematics. This perspective shift—from seeing mathematics as the discovery of absolute truth to viewing it as the creation of coherent and sometimes useful logical systems—resolves many historical paradoxes and controversies.

  The Key Insight

  The only fundamental requirement for a mathematical system is internal coherence—it must operate according to consistent rules without contradicting itself. A system need not:

  • Apply to every conceivable case

  • Match physical reality

  • Be the “one true” way to approach a problem

  Just as a carpenter might choose different tools for different jobs, mathematicians can work with different systems depending on their needs. This insight resolves numerous historical “crises” in mathematics.

  Historical Examples

  The Non-Euclidean Revelation

  For two millennia, mathematicians struggled to prove Euclid’s parallel postulate from his other axioms. The discovery that you could create perfectly consistent geometries where parallel lines behave differently initially seemed to threaten the foundations of geometry itself. How could there be multiple “true” geometries? The resolution? Different geometric systems serve different purposes:

  • Euclidean geometry works perfectly for everyday human-scale calculations

  • Spherical geometry proves invaluable for navigation on planetary surfaces

  • Hyperbolic geometry finds applications in relativity theory

  None of these systems is “more true” than the others—they’re different tools for different jobs.

  Russell’s Paradox and Set Theory

  Consider the set of all sets that don’t contain themselves. Does this set contain itself? If it does, it shouldn’t; if it doesn’t, it should. This paradox seemed to threaten the foundations of set theory and logic itself.

  The solution was elegantly simple: we don’t need a set theory that can handle every conceivable set definition. Modern set theories (like ZFC) simply exclude problematic cases while remaining perfectly useful for mathematics. This isn’t a weakness—it’s a feature. A hammer doesn’t need to be able to tighten screws to be an excellent hammer.

  The Calculus Controversy

  Early calculus used “infinitesimals”—infinitely small quantities—in ways that seemed logically questionable. Rather than this destroying calculus, mathematics evolved multiple rigorous frameworks:

  • Standard analysis using limits

  • Non-standard analysis with hyperreal numbers

  • Smooth infinitesimal analysis

  Each approach has its advantages for different applications, and all are internally coherent.

  Implications for Modern Mathematics

  This perspective—that mathematics consists of various coherent systems with different domains of applicability—aligns perfectly with modern mathematical practice. Mathematicians routinely work with different systems depending on their needs:

  • A number theorist might work with different number systems

  • A geometer might switch between different geometric frameworks

  • A logician might use different logical systems

  None of these choices imply that other options are “wrong”—just that they’re less useful for the particular problem at hand.

  The Parallel with Physics

  This view of mathematics parallels modern physics, where seemingly incompatible theories (quantum mechanics and general relativity) can coexist because each is useful in its domain. We don’t need a “theory of everything” to do useful physics, and we don’t need a universal mathematics to do useful mathematics.

  Conclusion

  The recurring “crises” in mathematical foundations largely stem from an overly rigid view of what mathematics should be. By recognizing mathematics as a collection of coherent tools rather than a search for absolute truth, these crises dissolve into mere stepping stones in our understanding of mathematical systems.

  Mathematics isn’t about discovering the one true system—it’s about creating useful systems that help us understand and manipulate abstract patterns. The only real requirement is internal coherence, and the main criterion for choosing between systems is their utility for the task at hand.

  This perspective not only resolves historical controversies but also liberates us to create and explore new mathematical systems without worrying about whether they’re “really true.” The question isn’t truth—it’s coherence.

az­er­gante’s Shortform

• azerganteNov 23, 2024, 4:11 PM

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  on: Ideas for an ac­tion co­or­di­na­tion website

  I really like the idea of milestones, I think seeing the result of each milestones will help create trust in the group, confidence that the end action will succeed and a realization of the real impact the group has. Each CA should probably start with small milestones (posting something on social medias) and ramp things up until the end goal is reached. Seeing actual impact early will definitely keep people engaged and might make the group more cohesive and ambitious.

• azerganteNov 23, 2024, 3:50 PM

  1 point

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  on: If a “Kick­starter for Inad­e­quate Equlibria” was built, do you have a con­crete in­ad­e­quate equil­ibrium to fix?

  Ditch old software tools or programming languages for better, new ones.

[Question] A Co­or­di­na­tion Cook­book?

• azerganteAug 30, 2024, 7:26 PM

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  on: Su­per­in­tel­li­gence 16: Tool AIs

  My take on the tool VS agent distinction:

  • A tool runs a predefined algorithm whose outputs are in a narrow, well-understood and obviously safe space.

  • An agent runs an algorithm that allows it to compose and execute its own algorithm (choose actions) to maximize its utility function (get closer to its goal). If the agent can compose enough actions from a large enough set, the output of the new algorithm is wildly unpredictable and potentially catastrophic.

  This hints that we can build safe agents by carefully curating the set of actions it chooses from so that any algorithm composed from the set produces an output that is in a safe space.