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The article is terrible - it is effectively saying "being objective" is not good for achieving that "objective". This is complete non-sense without untangling t

The article is terrible - it is effectively saying "being objective" is not good for achieving that "objective". This is complete non-sense without untangling the epistemic roots for said words, the author may have had. It's apparent that it's actually saying that being too "greedy" (i.e too low a horizon) can be bad for achieving an objective. This is well known to both Control theorists, RL practitioners, and many other non-STEM people (in marketing/politics/sociology etc.). This triviality has nothing at all to do with Zen or any of the things Westerners take for being "rustic" and "warm and fuzzy".

dwaltrip 9y ago
The video referenced by the article is actually very intriguing and well done. It's basically just talking about the incredible power of broadly pursuing novelty and interestingness, as opposed to a specific goal.
- barrkel 9y ago
  AIUI, it's about having an objective function which measures something other than the delta to a pre-defined goal, but rather a higher-level more abstract function. Another phrase for it would be "unknown unknowns": if your success criteria are too focused, you'll miss out on solutions that you couldn't conceive of before they're discovered. The "Objective" criticised in this article is like a "known unknown".
  - mfoy_ 9y ago
    More than that, even when they discovered an interesting result, they couldn't go back and create an objective based algorithm that could return to that result... even though it was known to be possible. (I mean, they obviously knew the specific steps taken, but they couldn't create an algorithm that could re-find them with a specific result in mind.)
jhgb 9y ago
I'm confused, is it about "objective" as in the adjective, or about "objective" as "goal"? Perhaps my English is not good enough but I fail to see the first in the text.
bitwize 9y ago
> This is complete non-sense without untangling the epistemic roots for said words, the author may have had. Ah, but you see when Plato was saying pharmakon (drug, poison) what he actually meant was pharmakos (scapegoat); since he didn't actually say pharmakos, by its absence that must be the true meaning!
manmal 9y ago
Have you read or watched what the picbreeder author is actually about? It's not about a greater horizon. It's that you only get "valuable results" by using a heuristic that only looks at the very next steps, and completely ignoring any goals that would lie further ahead. You could say that he's promoting a greedy search with very broad success parameters. An example: You plan to be a millionaire, and you currently have nothing in the bank. How do you get there? Any plan would most probably not work, if executed in a rigid manner (or we would all look up the recipe on the internet and be millionaires). You end up trying all kinds of jobs, investments, take opportunities when they arise, etc.. and finally you realize that you probably will not ever make it. Or you get lucky and make your million - who knows. Picbreeder showed that it's quite rare to hit the very desired result you wanted. However, if you were more flexible regarding your goal, at every step, then you might take the job that agrees the most with you, meet a lot of great people, travel if you are able, start a family.. and end up without a million in the bank but with a satisfying life situation. With pic breeder, certain shapes and beautiful pictures emerged that were not intended or planned from the start. The algorithm went along with whatever was there, and optimized on that. Looking at the millionaire example again - if you already had 500k, then doubling that might not be that hard (?), or not that many steps away. People keep saying that the more money you have, the easier it is to earn - think opportunities like buying an apartment in a firesale and reselling it for 1.5 as much. For me, it's a very counter intuitive way of looking at things. It goes very deep against the wanting of goals or things in our current society. OTOH, it somehow makes sense that things work this way, because every step in our step-by-step plans is SO dependent on things beyond our control. Agile development methods remind me of this very much: Try telling a customer not familiar with agile that the software she orders might look completely different than she imagines it right now - most will think you are crazy or want to cheat them. But you might recognize problems in every sprint, and adapt the end goal accordingly.
- orclev 9y ago
  I find an interesting parallel with hill-climbing and local optima. In your example, the reason everyone isn't a millionaire is mostly to do with the fact that that's a very noisy problem space, there are far far too many random variables at play, not to mention there's a finite supply of money, it's literally impossible for everyone to be a millionaire (well, you could inflate the currency to the point where a million dollars is nearly worthless, but that's just playing semantic games). To me, one of the big takeaways here is that it's important not to be so hyper-focused on the local problem space that you overlook potentially better solutions and end up at a local optimum to your overall detriment. As for the rest of the article, it's mostly hand-wavy garbage.
visarga 9y ago
> It's apparent that it's actually saying that being too "greedy" (i.e too low a horizon) can be bad for achieving an objective. As in Reinforcement Learning, it's an exploration vs exploitation tradeoff.
gwern 9y ago
It would be interesting to see if you could improve on the results supposedly disproving having an 'objective'. I haven't sat through the full 40 minutes of the video and I don't see any writeups on the Picbreeder website (which appears to have not had any news updates since 2011), so I am guessing that what he did was using a CNN image classifier and do greedy maximization over the various options on each step; it will surprise no one in RL that this does not work as it is not a convex problem. But a RL CNN trained to instead predict human choices (imitation learning) or do RL learning with rewards just from the final step (perhaps Christiano's preference learning setup?) might do much better. After all, all the comments about the folly of greed apply just as well to choosing moves in Go, but you don't need to abandon 'the myth of winning' to win Go. It's simply about having an excellent heuristic for promising directions and keeping options open.
- Nomentatus 9y ago
  I my case, my greatest flaw in playing GO was losing sight of the objective - i.e. gaining more space everywhere. Instead I would zoom in too tightly on wherever the last few moves had happened; win battles and lose the war. I suspect that's very common in newbie GO players. But I still like the article, creativity is a different task with different pitfalls.
  - gwern 9y ago
    Interestingly, the main description of AlphaGo v2/Master's play by the Go players & experts on /r/baduk & commentaries elsewhere is exactly that: compared to the human Go players, it is very willing to disengage from a local fight to make a move elsewhere globally, often ending josekis early just to get in the first move elsewhere.

dwaltrip 9y ago

The video referenced by the article is actually very intriguing and well done. It's basically just talking about the incredible power of broadly pursuing novelty and interestingness, as opposed to a specific goal.

barrkel 9y ago
AIUI, it's about having an objective function which measures something other than the delta to a pre-defined goal, but rather a higher-level more abstract function. Another phrase for it would be "unknown unknowns": if your success criteria are too focused, you'll miss out on solutions that you couldn't conceive of before they're discovered. The "Objective" criticised in this article is like a "known unknown".
- mfoy_ 9y ago
  More than that, even when they discovered an interesting result, they couldn't go back and create an objective based algorithm that could return to that result... even though it was known to be possible. (I mean, they obviously knew the specific steps taken, but they couldn't create an algorithm that could re-find them with a specific result in mind.)

jhgb 9y ago

I'm confused, is it about "objective" as in the adjective, or about "objective" as "goal"? Perhaps my English is not good enough but I fail to see the first in the text.

bitwize 9y ago

> This is complete non-sense without untangling the epistemic roots for said words, the author may have had. Ah, but you see when Plato was saying pharmakon (drug, poison) what he actually meant was pharmakos (scapegoat); since he didn't actually say pharmakos, by its absence that must be the true meaning!

manmal 9y ago

Have you read or watched what the picbreeder author is actually about? It's not about a greater horizon. It's that you only get "valuable results" by using a heuristic that only looks at the very next steps, and completely ignoring any goals that would lie further ahead. You could say that he's promoting a greedy search with very broad success parameters. An example: You plan to be a millionaire, and you currently have nothing in the bank. How do you get there? Any plan would most probably not work, if executed in a rigid manner (or we would all look up the recipe on the internet and be millionaires). You end up trying all kinds of jobs, investments, take opportunities when they arise, etc.. and finally you realize that you probably will not ever make it. Or you get lucky and make your million - who knows. Picbreeder showed that it's quite rare to hit the very desired result you wanted. However, if you were more flexible regarding your goal, at every step, then you might take the job that agrees the most with you, meet a lot of great people, travel if you are able, start a family.. and end up without a million in the bank but with a satisfying life situation. With pic breeder, certain shapes and beautiful pictures emerged that were not intended or planned from the start. The algorithm went along with whatever was there, and optimized on that. Looking at the millionaire example again - if you already had 500k, then doubling that might not be that hard (?), or not that many steps away. People keep saying that the more money you have, the easier it is to earn - think opportunities like buying an apartment in a firesale and reselling it for 1.5 as much. For me, it's a very counter intuitive way of looking at things. It goes very deep against the wanting of goals or things in our current society. OTOH, it somehow makes sense that things work this way, because every step in our step-by-step plans is SO dependent on things beyond our control. Agile development methods remind me of this very much: Try telling a customer not familiar with agile that the software she orders might look completely different than she imagines it right now - most will think you are crazy or want to cheat them. But you might recognize problems in every sprint, and adapt the end goal accordingly.

orclev 9y ago
I find an interesting parallel with hill-climbing and local optima. In your example, the reason everyone isn't a millionaire is mostly to do with the fact that that's a very noisy problem space, there are far far too many random variables at play, not to mention there's a finite supply of money, it's literally impossible for everyone to be a millionaire (well, you could inflate the currency to the point where a million dollars is nearly worthless, but that's just playing semantic games). To me, one of the big takeaways here is that it's important not to be so hyper-focused on the local problem space that you overlook potentially better solutions and end up at a local optimum to your overall detriment. As for the rest of the article, it's mostly hand-wavy garbage.

visarga 9y ago

> It's apparent that it's actually saying that being too "greedy" (i.e too low a horizon) can be bad for achieving an objective. As in Reinforcement Learning, it's an exploration vs exploitation tradeoff.

gwern 9y ago

It would be interesting to see if you could improve on the results supposedly disproving having an 'objective'. I haven't sat through the full 40 minutes of the video and I don't see any writeups on the Picbreeder website (which appears to have not had any news updates since 2011), so I am guessing that what he did was using a CNN image classifier and do greedy maximization over the various options on each step; it will surprise no one in RL that this does not work as it is not a convex problem. But a RL CNN trained to instead predict human choices (imitation learning) or do RL learning with rewards just from the final step (perhaps Christiano's preference learning setup?) might do much better. After all, all the comments about the folly of greed apply just as well to choosing moves in Go, but you don't need to abandon 'the myth of winning' to win Go. It's simply about having an excellent heuristic for promising directions and keeping options open.

Nomentatus 9y ago
I my case, my greatest flaw in playing GO was losing sight of the objective - i.e. gaining more space everywhere. Instead I would zoom in too tightly on wherever the last few moves had happened; win battles and lose the war. I suspect that's very common in newbie GO players. But I still like the article, creativity is a different task with different pitfalls.
- gwern 9y ago
  Interestingly, the main description of AlphaGo v2/Master's play by the Go players & experts on /r/baduk & commentaries elsewhere is exactly that: compared to the human Go players, it is very willing to disengage from a local fight to make a move elsewhere globally, often ending josekis early just to get in the first move elsewhere.