All of Chris Land's Comments + Replies

You're correct, time handicaps (e.g. 2m vs. 5m) are more common than pawn/piece handicaps. Mostly for in-person play.

Master vs. Amateur handicaps can look crazy: 2m vs. 15m and -QRR is a slight advantage for the master simply because most amateurs are not used to playing with the clock. Another M v. A handicap is 'capped pawn': amateur picks a pawn, checkmate must be delivered with that pawn (pre-promotion). It's a bit like having two Kings, as if that pawn is captured the game is lost.

It's a playground for testing ideas associated with Deception. Naturally there are other ways and other arenas. The rules for this arena are fun and flexible (perhaps no deceivers some of the time!), but still limited to discussing only the quality of particular chess moves in a specific positions. Quality as compared to a hidden but soon-revealed 'perfect' answer.

As far as lessons, I expect Player will have the most valuable post-game perspective. How easy is it to judge quality of Advice? In what ways does advice look different if it's Deceptive? Does it even look different? Given a reasonably strong Opponent, most any human advice appears 'Deceptive' with no such intent.

Another post-Internet chess form also features text-based influence: Vote Chess. Players on each team discuss via private msg board (no engines). Everyone has 24 hours (say) to choose a preferred legal move. There's no built-in deception, however on large teams there is an equivalent to saboteurs as many voters choose impulsively. A sample game with 400+ per team: https://www.chess.com/votechess/game/117834

2PoignardAzur
I get an "Oops! You don't have access to this page" error.

Very interested in C, also B. I'm an over-the-board FM. Available many evenings (US) but not all. I enjoy recreational deception (e.g. Mafia / Werewolf) but I'm much better at chess than detecting or deploying verbal trickery.

Additional thoughts:

  1. Written chess commentary by 'weak' players tends to be true but not the most relevant. After 1.e4 Nf6 2.e5, a player might say "Black can play 2...Nc6 developing the N and attacking the pawn on e5". True, but this neglects 3.exf6. This scales upwards. My commentary tends to be very relevant but I miss things tha

... (read more)
1Zane
Sounds like a good strategy! ...although, actually, I would recommend you delete it before all the potential As read it and know what to look out for.

Re: section 4.3.4 theories of humor

In my 2021 book Why Funny Is Funny, I introduce Clash Theory as a new 'grand theory of humor'. I believe it's much more precise than other theories, but I'm the creator of it so of course I'd say something like that that. The first five chapters are readable online. Click Read Sample (Kindle edition):

https://www.amazon.com/Why-Funny-comprehensive-hilarious-theoretical-ebook/dp/B091GP5Y54

A somewhat related point: it's only very recently (2023) that chess engines have begun competently mimicking the error patterns of human play. The nerfings of previous decades were all artificial.

I'm an FM and play casual games vs. the various nerfed engines at chess.com. The games are very fast (they move instantly) but there's no possibility of time loss. Not the best way to practice openings but good enough.

The implication for AI / AGI is that humans will never create human-similar AI. Everything we make will be way ahead in many areas and way behind in... (read more)

3[anonymous]
The implication for AI / AGI is that humans will never create human-similar AI. Everything we make will be way ahead in many areas and way behind in others Is this not a mere supervised learning problem?  You're saying, for some problem domain D, you want to predict the probability distribution of actions a Real Human would emit when given a particular input sample.   This is what a GPT is, it's doing something very close to this, by predicting, from the same input text string a human was using, what they are going to type next.   We can extend this, to video, and obviously first translate video of humans to joint coordinates, and from sounds they emit back to phonemes, then do the same prediction as above. We would expect to get an AI system from this method that approximates the average human from the sample set we trained on.  This system will be multimodal and able to speak, run robotics, and emit text. Now, after that, we train using reinforcement learning, and that feedback can clear out mistakes, so that the GPT system is now less and less likely to emit "next tokens" that the consensus for human knowledge believes is wrong.  And the system never tires and the hardware never miscalculates.  And we can then use machine based RL - have robots attempt tasks in sim and IRL, autonomously grade them on how well the task was done.  Have the machine attempt to use software plugins, RL feedback on errors and successful tool usage.  Because the machinery can learn on a larger scale due to having more time to learn than a human lifetime, it will soon exceed human performance. And we also have more breadth with a system like this than any single individual living human. But I think you can see how, if you wanted to, you could probably find a solution based on the above that emulates the observable outputs of a single typical human.

'Humor' is universal. It's the same kind of cognitive experience everywhere and every time it happens. This despite the fact that individual manifestations diverge wildly and even contradict. It's true even though every example of humor (meaning, a thing some observers find funny) is also a thing that other observers find not funny.

Hm, that doesn't seem true to me. With friendship people derive value from simply sharing space and engaging in conversation, neither of which involve consumable physical objects.

Space for conversation is a form of shelter. But I will concede to condense a highly-condensed line of argument further to remove the trickiest examples: art/music/software/friendship/justice. Software is abstract; it's also not physical in an obvious sense. It does rest on a foundation of physical objects (chips, wiring) capable of using electricity in a controlled and orderly wa... (read more)

Life requires physical consumption: oxygen, water, food. Consumption also includes deterioration through use, for further life-required values like clothing, shelter, transportation, security. Even highly abstract values like art/music/software/friendship/justice all rest on a foundation of consumable physical objects. Production is transformation of physical matter into consumable form.

Wealth is everything produced but not yet consumed. Money is easily exchangeable wealth.

The idea of wealth can be extended into intellectual or spiritual or poetic realms. But the root of the idea of wealth is the physical requirements for life.

3Adam Zerner
Hm, that doesn't seem true to me. With friendship people derive value from simply sharing space and engaging in conversation, neither of which involve consumable physical objects. What about things that we want but that don't require production, like swimming in the ocean or enjoying the sound of birds chirping? PG calls out that money isn't actually wealth. Is he using a non-standard definition? Does a standard definition even exist?

Interesting that AlphaGo plays strongly atypical or totally won positions 'poorly' and therefore isn't a reliable advice-giver for human players. Chess engines have similar limitations with different qualities. First, they have no sense of move-selection difficulty. Strong human players learn to avoid positions where finding a good move is harder than normal. The second point is related: in winning positions (say, over +3.50 or under -3.50), the human move-selection goal shifts towards maximizing winning chances by eliminating counterplay. E.g., in a queen... (read more)

3Ege Erdil
Yeah, that matches my experience with chess engines. Thanks for the comment. It's probably worthwhile to mention that people have trained models that are more "human-like" than AlphaGo by various parts of the training process. One improvement they made on this front is that they changed the reward function so that while almost all of the variance in reward is from whether you win or lose, how many points you win by still has a small influence on the reward you get, like so: This is obviously tricky because it could push the model to take unreasonable risks to attempt to win by more and therefore risk a greater probability of losing the game, but the authors of the paper found that this particular utility function works well in practice. On top of this, they also took measures to widen the training distribution by forcing the model to play handicap games where one side gets a few extra moves, or forcing the model to play against weaker or stronger versions of itself, et cetera. All of these serve to mitigate the problems that would be caused by distributional shift, and in this case I think they were moderately successful. I can confirm from having used their model myself that it indeed makes much more "human-like" recommendations, and is very useful for humans wanting to analyze their games, unlike pure replications of AlphaGo Zero such as Leela Zero.

Hoo, my entry Rainforest, Rainforest
When you gonna run out of time, my Rony?
Hoo, you eat both grass and seeds, grass and seeds
Two meals on which you can dine, my Rony

Hope you never stop, keep it up, such a fertile find
Try to get away from a touch predatory kind
My, my, my, my, woo!

M-m-m-my poor Rony

Flying with a speed of four, speed of four
Flying all the way to Grassland, my Rony
Find another place to thrive, place to thrive
Assuming you can survive, my Rony

Hope you never stop, keep it up, reach stability
Validate ecosys-stemic suitability
My, my, my, my, woo!

M... (read more)

> As a concept, “culture” is notoriously slippery and expansive.
The definition I find useful: "Culture is a set of shared preferences among choices." Your points don't seem to be altered with this definition swap-in. But if I'm wrong, that would be more interesting.
 

3David Hugh-Jones
I think shared is too broad. You like Coke, I like Coke - we share that. But it's shared because we both have sugar-loving taste buds. To be cultural, you need something more. Hence the biologists' emphasis on the transmission mechanism via learning. Does it matter? My argument is that a lot of what gets called "Western culture" is really just "stuff that is appealing to human taste buds", in a broad sense. So yes, it is spreading, but no cultural learning is required. Coca Cola sells Coke, people in India like it and buy it; but this doesn't have implications for things that are actually cultural, such as attitudes to gender, political values, etc.

All of them, but also none of them.

It has successfully explained (to my own satisfaction only) every humor example I've ever encountered, including extreme outliers. It's a reasonably comprehensive examination of all causes of humor response variability (but maybe there are some I missed). Clash Theory explains, predicts response, and assists construction both in editing and in generating.

However, independent experimental testing of Clash Theory has never been done. Not yet. I would like it to, but I've found my wishes are seldom granted immediately. I've ... (read more)