AI Ramblings: News / Comments / Seeing the forest from the trees (of "open problems")

Seeing the forest from the trees (of "open problems")

Anyone having had an interest in AI for a while has surely collected a list of "open problems" or "key problems" like the one Peter Turney recently blogged about.

I haven't made an exact tally of mine but among the 2000-3000 papers for which I kept a record there are probably over 50 which I can certainly call "key problems", so far so good then what the heck do I do with this now?

Pick one which I deem among the most importants and try to "work on it"?
Mmmmm...
How likely is it that I will do better than the original researchers?
I would need to have an edge over them on something.
Which "thing"?
Some of these guys have spent a lifetime on their "favorite problem" and I am not even sure I really grasp the matter they are delving, though I can spot it as an important point.
Some are utterly brilliant Matthew Brand, Dominic Widdows, Kenny Easwaran (only a random sample from memory, those are a dime a dozen...)
Some are both utterly brilliant and spent a lifetime on hard problems Grothendieck, Feferman.
Am I (or almost anyone else) really hoping to "magically" make a breakthrough where those people seem to be lingering over?

Yet, yet...

What they are all doing is tackling the trees, some of those among the hardest trees.
For reasons of competitive research (previous post) they have to produce evidence of their smartness and hard work.
This is some kind of depth first search, a hit or miss, the most brilliant get some nuggets the less brilliant or just infortunate get nada.
In any case this doesn't shed much light over the whole landscape, the forest...

What else could be done, instead of forcefully digging even deeper?

I see two options:

prioritizing the problems, looking for the one(s) which are likely to have precedence over others in the (hypothetically assumed) path toward solutions.
reframing one or more know problems into a different question by "stepping back" and trying to find a more general framework within which the pending problems will be seen as only a special case of a more general question (going "meta", see about Jacques PITRAT, the only usefull reference I found since he is not an english speaker).

As usual I "just have" to do it...

Submitted by Kevembuangga

KevembuanggaonMonday 24 December 2007 - 17:57:20

comment: 2

Comments

Anonymous @ 08 Mar : 19:23
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Jacques Pitrat wrote in 2009 a book about his view (meta-knowledge oriented) about A.I. in his book Artificial Beings

Kevembuangga @ 10 Mar : 17:07
Reply to this

No, I haven't read the full book only an excerpt and the paper which is available at his home site, nothing appeared of much interest to me.
The idea of iterating the meta-levels until something "stabilizes" is absolutely brilliant but he seem to have been lost in his own inspiration.
I downloaded his latest program Malice CAIA's solver with which I did run the preset demos on an AMD64 but beside the demos its an humongous useless piece of junk.
I emailed Pitrat for more infos and he responded but did not seem to want to share more, only to brag about his results:

Je ne sais si vous avez vous-même essayé de résoudre ces problèmes ou
d'écrire des programmes pour les résoudre. Si oui, cela m'intéresserait
de savoir si en tant que humain vous arrivez à des solutions meilleures
que celles trouvées par CAIA; le logiciel en donne la structure
générale, c'est à dire l'arborescence qu'il développe. De même avez-vous
écrit des programmes plus performants que ceux écrits par CAIA ? Cela
m'intéresserait aussi de le savoir.
Avoir mis en ligne ce logiciel permet donc de juger la qualité des
résultats de l'approche que j'ai entreprise et la valeur de CAIA en tant
que chercheur en IA. La plupart des problèmes que je donne sont
difficiles pour un humain,
etc.. etc...

Is he old fashioned and secretive or confused and lost in his own mess, difficult to say...

[ edited 10 Mar : 17:10 ]