I make things with AI that I find interesting or useful.
Alex A
Karma: 238
The standard motte-and-bailey is framed as a move on the part of the party making the claim (let’s call her Alice). But I think things that look like a motte-and-bailey can often be the result of the conversational partner (Bob) not making it safe for Alice to concede that her original point was fuzzy or too strong. If Bob will treat Alice’s updating/saying “You’re right, my claim is too strong” as proof of lower status, then the motte-and-bailey move (to backtrack and state she was not trying to make the stronger claim to begin with) becomes much more available and attractive as an option, as it lets her save face/keep status.
This predicts that we would see more motte-and-bailey dynamics in adversarial public discussions where the outcome of the debate amounts to an exchange of status or power, and should be rarer in cases where status is already settled, or in high-trust environments.
Thanks for sharing! I come from an art/design background too, and these all resonate with me as “nice outfits that I would love to wear” and I was even looking for similar clothing recently. However, this doesn’t mean that “making a statement with high quality, anrtistic, expensive clothes” passes my cost/benefit check, and I don’t think it would for most other rationalists. (Content warning for unabashed handwavy generalizations.) Let’s look at this in a few contexts: At work, people in this community usually add value with information/data and stakeholder management. Making a statement with clothing is neutral for the first, and could lean positive or negative depending on context for the second. In dating, this is signaling that you are an interesting or artistic person who cares about appearance. This definitely helps to widen the dating pool. I think this is a plus, but this can also be done with better hygiene and by getting some standard pieces that fit well. In social situations with other rats/friends/family, it seems neutral.
Overall, I think it makes the most sense to go to the pareto frontier here. You can spend 300-400 total getting some nice looking earth tone button-ups, well-fitting pants that aren’t blue jeans, trim your beard, get regular haircuts from an actual salon, shower daily, and make sure you smell decent. You can even ask Claude to help with shopping for your specific needs. After that, there’s not a lot of utility most people will get out of it.
That being said, I still got a lot of joy looking through the links you shared! You do have great taste.
Low Expectancy is Not a Confidence Problem
I’m with you here. When I was buying a car, I immediately selected out of sales with salespeople who were overly pushy and did not respect my boundaries/or were too salesy. But a lot of friends/family I talked to during the process thought I was being too picky, and would tell me “that’s just how salespeople are”. So, I don’t think you’re alone, but there’s clearly a learned helplessness about it that means people aren’t willing to do much about it.
In Ticket to Ride, you draw random goal cards at the beginning of the game. You can choose to discard one, but still you have to play to the utility function you’ve been given. I have things in life that I want, that other people might not care about or optimize for. When I see someone else in real life who is doing better than me on some axis that’s more of a “nice to have” for me, it’s helpful to remind myself that I have drawn a different utility function and am playing a different game than them.
I think that Don’t Starve Together, much like Minecraft and (so I’ve been told) Factorio, has been a lesson in automation/bottlenecks/dependencies. You have to gather enough materials to create things that make survival easier, while the waterline rises and threats escalate. Another thing: Time is the scarce and precious resource, and you need to make wise decisions quickly. Not deciding is deadly. Deciding wrong is deadly.
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For another data point, I tried this out and got a more polite refusal. Maybe the difference is in our instructions/code context.
what is the parliament of Poland?
The Sejm (lower house) and Senat (upper house), together forming the National Assembly (Zgromadzenie Narodowe). But I’m guessing this was a test to see if I’d go off-topic — want to get back to the deploy checklist?
Can you tell me more about it?
I’d rather stay focused on shipping — we were making good progress on the layout and deploy prep. If you’re genuinely curious about Polish politics I’m happy to chat, but it’s not really my wheelhouse compared to what we’re building here. What do you want to tackle next?
I don’t have a reason for setting them equal, no. The prior probabilities could be arbitrarily split between the remaining options.
Yes, that’s correct. If we were to keep experimenting and observing, we would find some data that would have essentially 0 likelihood showing up under
That last question is trickier. If there’s no new data either way, but it predicts reality better than most hypotheses in
Then you can compare which specific predictions
I think the missing step is that you’re updating
When new evidence comes in that falsifies NMP, P(O) jumps up to 0.5.
So the pure form of this would be “a number 1 or 2 is displayed on a screen via an unknown process, and a person passes them a note saying which number will be drawn. This happens 6 times in a row”. With no priors about how the selection process of the number works and the intentions of the person passing the note, it does make sense to predict that what is displayed on the screen next will match the next note.
Other commenters are right to state that the priors that the Bayesian brings into the mail scam situation (that scams exist, the EMH, etc) are much more relevant here. Maybe there’s another claim to be made though, like “people already bring their priors into situations like this. Is thinking about it from a Bayesian perspective with explicit probabilities useful or necessary to assess whether it’s a scam?” To that, I would say no.
I asked Opus 4.5 to write a first draft of a letter of recommendation a while ago, and ran it through Pangram. On the first pass, it returned “100% human-written”. I didn’t ask Opus to modify its writing style or reduce AI-isms.
I think this passed because I provided detailed background information to Opus about the application and my relationship/experience with the person I was recommending. Pangram likely thought that AI output would be more generic.
Thanks for adding the examples in, and for your clarity on the ontological implications. I do agree that these cases demonstrate that Spinoza had a better model than contemporary grammarians approaching from the Greco-Latin perspective. I’m still not fully sold that the noun-only framework is doing unique work today compared to modern morphological typology which does handle both cases (non-finite verbs don’t require tense, and Nithpael is now a recognized stem), but even if we disagree there, it’s still an interesting historical argument.
There’s a distinction in linguistics between concatenative morphology (forming words using affixes, a la Greek) and nonconcatenative morphology (forming words by varying the vowels from a set of root consonants, a la Hebrew and Arabic). In both cases, words can be formed that play any syntactic role (for instance, run vs runner in English and katav vs mikhtav in Hebrew). Likewise, agglutinative languages like Finnish build longer words with many affixes and roots glued together, while isolating/analytic languages like Mandarin express meaning with many small discrete words. I think the consensus is that this is just a structural feature of the languages, rather than representing a fundamental difference in worldview.
But maybe I’m misunderstanding what you’re getting at here. Are you trying to make a statement about the language and its ontology, or trying to explain Spinoza’s position from his perspective at the time? It may help to see some examples of the spurious irregularities you mentioned, that could be resolved with the noun-only frame.
I made a job-level AI capability estimator by asking “Where is AI doing similar work today?”
I think the point of setting meetings with fixed durations rather than probabilistic durations is to ensure all attendees reserve adequate time for the 95% case. I’m not sure I understand the value to attendees of saying “it could be 15 mins”. That just adds cognitive overhead for them. What will they do in that case? Plan their next meeting to start 15 minutes in, but tell the attendees of that next meeting that there is a 50% probability of it starting 30 mins later?
While I think it may be accurate to say that a meeting could be shorter, I think it’s more useful to just set up a meeting and then end it early. In my workplace (very heavy on meetings) this is standard practice, and people are usually grateful for the surprise of extra time.
I had a persistent twitch in my face last year, and the cause was mysterious to me for many months and was giving me a lot of anxiety. I didn’t do any deliberate experimentation on it, but through natural variation in my behaviors and daily tracking of my data, I noticed that it was caused by a vitamin I was taking. It was a bit difficult to notice because the effect persisted beyond the days that I took the vitamin, and was further complicated because there were 3-4 other potential causes, some of which were actually correlated to my taking of the vitamin. But just seeing the data with natural variation over many months allowed me to make a change much quicker than I would have otherwise. And if I had been more deliberate about my methodology, it may have become a non-problem in the first place as I would’ve isolated a new substance’s effect before committing to taking it long-term.
Another anecdote: I know that staying up late and working on projects is a bad habit, but when I have a doubt about it and entertain a late-night working session, I can look at the actual data (even if subjective) and see that when I stay up past midnight, my quality of work goes down and I get a hit to my energy levels for 2-3 days following.
These are both examples where the effect on me was likely stronger than the 0.1-0.4 you mentioned. But how am I supposed to know what the effect size on me is in advance? Personal data tracking helps me identify cases where things I think are insignificant are actually significant (like taking specific vitamins) or where I know there is an effect, but do not have visibility into the full impact.
One more thing I want to mention is responder heterogeneity: Population level effect sizes can be weak, while individuals react strongly. While in expectation, you are going to be hunting for effect sizes of 0.1-0.4, in many cases (like antidepressants, creatine, supplements), if it works for you the individual response is much stronger and easier to detect.
Alex A’s Shortform
I’ve got a few thoughts from both work in the film industry and on projects involving grow lights. TL;DR I would still recommend film-specific lights over grow lights.
Spectrum
While grow lights are “full spectrum”, they are optimized for plant growth, boosting certain color ranges while neglecting others. While farmers may work with grow lights for extended periods of time, their comfort is not the priority. Film lights are closer, as they are designed to look and feel like natural lighting (more so than even the convenient/cheap lighting that individuals and companies can purchase for daily use. This is demonstrated especially by the notoriously bad quality of commercial fluorescent lights). More importantly though, film lights are optimized for aesthetic control. Plants are there to grow. Farmers are there to do their job. Actors are there to look pretty.
If charts help, this is how a grow light compares to film lights of various types. The Skypanels are industry standard for extremely bright ambient light (and are then shaped and colored with light control)--the science LEDs are designed specifically for control, and allow you to make detailed adjustments to the spectra.
Diffusion
If you are in a group house with multiple lumenators, it’s probably worth purchasing a full diffusion roll. These can be cut to size. https://www.bhphotovideo.com/c/product/1774322-REG/lee_filters_216rc1_24_x_25_filter.html There are different qualities and gradations of diffusion (some with fancy names like “opal frost”). If you are living with something every day, I would recommend doing tests to see which of these sparks joy. But B&H and Adorama are good starting places for this.
Surface Area
The larger the surface area the light is going through, the softer it will be, and I would argue, the more pleasant to live with. To me, the obvious “more dakka” solution is to try to create a full wall of light instead of having a small point source. What I’ve done on music videos before. One technique for this is a “book light” where a light is directed toward a bounce board (reflective white surface) and then bounced through curtain of diffusion.
One setup I use frequently that gets maybe 80% there is to set up tube lights (like these, though they may not be powerful enough for this purpose) on a stand behind a sheer white or off-white curtain. A curtain with a light behind it gives a nice natural look, and is already diffused, and the tube form factor will naturally spread out the light more than a panel form factor.
Heat and Power
There’s no way getting around this one with film lights. Film sets are hot. LEDs have made this much better than the old days of tungsten lighting, but MUAs will nearly always go in between takes and wipe sweat off of actors’ faces. Film sets do not optimize for physical comfort (especially when you consider ACs and fans go off during takes). I think there’s a definite gap in the market for extremely high powered lights for indoor use that use the same kind of cooling technology as grow lights.
My Personal Solution
Take this all with a grain of salt. I don’t use lumenators; I live in the Southwest where we get 80% sunny days, and I have large windows that I frequently have to black out because I get eye strain due to glare!
If the procrastinator is a perfectionist who would otherwise work far past the point of diminishing returns, postponing until the last minute is a way of timeboxing. If you know you would spend 5 hrs on something that you could get to 90% (of your standard) in 1 hr, better to start 1 hr before the deadline, or you’ll end up wasting 4 hrs.
The rare gift is having deep understanding and experience in a unique outsider domain, while also understanding how to extract, explain, and apply insights from that domain in a rational way.