Abram, On 7/22/08, Abram Demski <[EMAIL PROTECTED]> wrote: > > "Problem Statement: What are the optimal functions, derived from > real-world observations of past events, the timings of their comings > and goings, and perhaps their physical association, to extract each > successive parameter containing the maximum amount of information (in > a Shannon sense) usable in reconstructing the observed inputs." > > I see it now! It is typically very useful to decompose a problem into > sub-problems that can be solved either independently or with simple > well-defined interaction. What you are proposing is such a > decomposition, for the very general problem of compression. "Find an > encoding scheme for the data in dataset X that minimizes the number of > bits we need" can be split into subproblems of the form "find a > meaning for the next N bits of an encoding that maximizes the > information they carry". The general problem can be solved by applying > a solution to the simpler problem until the data is completely > compressed.
Yes, we do appear to be on the same page here. The challenge is that there seems to be a prevailing opinion that these don't :stack" into multi-level structures. The reason that this hasn't been tested seems obvious from the literature - computers are now just too damn slow, but people here seem to think that there is another more basic reason, like it doesn't work. I don't understand this argument either. *Richard, perhaps you could explain?* "However, it still fails to consider temporal clues, unless of course > you just consider these to be another dimension." > > Why does this not count as a working solution? It might be. Note that delays from axonal transit times could quite easily and effectively present inputs "flat" with time presented as just another dimension. Now, the challenge of testing a theory with an additional dimension, that already clogs computers without the additional dimension. Ugh. Any thoughts? Perhaps I should write this up and send it to the various people working in this area. Perhaps people with the present test beds could find a way to test this, and the retired math professor would have a better idea as to exactly what needed to be optimized. Steve Richfield ================= > On Tue, Jul 22, 2008 at 1:48 PM, Steve Richfield > <[EMAIL PROTECTED]> wrote: > > Ben, > > On 7/22/08, Benjamin Johnston <[EMAIL PROTECTED]> wrote: > >>> > >>> You are confusing what PCA now is, and what it might become. I am more > >>> interested in the dream than in the present reality. > >> > >> That is like claiming that multiplication of two numbers is the answer > to > >> AGI, and then telling any critics that they're confusing what > multiplication > >> is now with what multiplication may become. > > > > > > Restating (not copying) my original posting, the challenge of effective > > unstructured learning is to utilize every clue and NOT just go with > static > > clusters, etc. This includes temporal as well as positional clues, > > information content, etc. PCA does some but certainly not all of this, > but > > considering that we were talking about clustering here just a couple of > > weeks ago, ratcheting up to PCA seems to be at least a step out of the > > basement. > > > > I think that perhaps I mis-stated or was misunderstood in my "position". > No > > one has "the answer" yet, but given recent work, I think that perhaps the > > problem can now be stated. Given a problem statement, it (hopefully) > should > > be "just some math" to zero in on the solution. OK... > > > > Problem Statement: What are the optimal functions, derived from > real-world > > observations of past events, the timings of their comings and goings, and > > perhaps their physical association, to extract each successive parameter > > containing the maximum amount of information (in a Shannon sense) usable > in > > reconstructing the observed inputs. IMHO these same functions will be > > exactly what you need to recognize what is happening in the world, what > you > > need to act upon, which actions will have the most effect on the world, > etc. > > PCA is clearly NOT there (e.g. it lacks temporal consideration), but > seems > > to be a step closer than anything else on the horizon. Hopefully, given > the > > "hint" of PCA, we can follow the path. > > > > You should find an explanation of PCA in any elementary linear algebra or > > statistics textbook. It has a range of applications (like any transform), > > but it might be best regarded as an/the elementary algorithm for > > unsupervised dimension reduction. > > > > Bingo! However, it still fails to consider temporal clues, unless of > course > > you just consider these to be another dimension. > > > > When PCA works, it is more likely to be interpreted as a comment on the > > underlying simplicity of the original dataset, rather than the power of > PCA > > itself. > > > > Agreed, but so far, I haven't seen any solid evidence that the world is > NOT > > simple, though it appears pretty complex until you understand it. > > > > Thanks for making me clarify my thoughts. > > > > Steve Richfield > > > > ________________________________ > > agi | Archives | Modify Your Subscription > > > ------------------------------------------- > agi > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/ > Modify Your Subscription: > https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com > ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=108809214-a0d121 Powered by Listbox: http://www.listbox.com
