Hey, *** Let me put it this way: asking "which program-trees in a deme are successful" is kind of like asking "which neurons in a neural net are successful", or "who are the John Galts of society" This is like focusing on the point dynamics in a fractal -- it identifies a set of measure zero, giving a completely faulty understanding of the fractal (dynamical system) The system dynamics is governed by measures, not by individuals. The individuals are the exception.
MOSES is more accurate, the larger the population. Having 1000 exemplars will give you far more accurate answers, than what you'd get if you tried to trim this to the top-10 most successful. Put another way: ten super-accurate, highly-evolved expert trees are less accurate than the democratic vote of 1000 simple-minded average joes, when yo just want an answer for a "typical" dataset. *** yes, I'm certainly all in favor of ensemble methods... as you'll recall that's what we are doing with our current practical applications of MOSES (to genomic data analysis), and also what we were doing in our prior applications of MOSES to financial prediction... I was typing late at night while exhausted -- in hindsight, a better phrasing would have been not "which program-trees in a deme are successful" but rather "which patterns of dependency characterize the program trees in a deme that are major contributors to the program tree ensemble" I would add though, this ensemble business is trickier when the programs are doing stuff like, say, sorting lists or controlling movements of a robot, rather than say making classification judgments. It's easy to let a bunch of programs vote on whether patient 55's DNA is in Cancer or Control, but trickier to let a bunch of navigation programs vote on what direction a robot should move next.... (Because some programs might have a misconception about how or why the robot got to its current interim location, guided there by other programs in the ensemble...)... So there is an awful lot still to be experimented with, but I would rather have this experimentation happen in the Atomspace than in a standalone MOSES codebase (and I note that this experimentation has *not* really been happening in the standalone MOSES codebase... since Aidyia stopped doing MOSES R&D, there hasn't been any innovation happening in the guts of MOSES, though we have been using MOSES for bio analytics and other stuff....) .... -- Ben On Sun, Feb 11, 2018 at 2:33 AM, Linas Vepstas <[email protected]> wrote: > > > On Sat, Feb 10, 2018 at 8:55 AM, Ben Goertzel <[email protected]> wrote: >> >> >> You are of course aware that Yidne has been working on porting Reduct >> to the URE > > > ? There does not seem to be any activity on github that would suggest > such work is underway. I strongly urge that work not be carried out in > some private corner, and then revealed to the world as a fait-accompli. > >> >> >> Fitness evaluation should not be tricky here, basically a fitness >> function can be a GroundedSchemaNode > > > Umm, fitness evaluation is "trickier" than reduct. It is the the primary > bottleneck in moses. Moshe created a lot of technology to make this > go fast. Nil also devoted a huge amount of time and attention on this. > > Reduct might feel difficult, because if you just glance at the code, it > obviously looks arcane. The fitness evaluation code looks like it's > easy by comparison. This is highly misleading. Computer software > has a kind of inversion effect, where often it is the easiest-looking > things that are the hardest. Do not be fooled about where the actual > complexity lies. > >> >> >> (Obviously the big intended payoff here is a return to the roots of >> MOSES, i.e. the use of probabilistic analysis to find patterns >> regarding which program-trees in a deme are successful. > > > Well,some caution is needed to visualize what is really happening here. > You have that probabilistic analysis already available now: you can > easily extract a measure from a collection of program trees, in several > different ways. That measure is fractal; don't imagine that its smooth. > > I just now wrote, and then deleted a long email on the actual technicalities > and experience of doing this by hand. The general upshot is that its a lot > more subtle than what one might imagine. The notion of "returning to the > roots of MOSES", is I feel, exactly the wrong direction to go in: we moved > away from those roots for a reason, and got a much stronger, faster and > more robust system as a result. > > Let me put it this way: asking "which program-trees in a deme are > successful" > is kind of like asking "which neurons in a neural net are successful", or > "who > are the John Galts of society" This is like focusing on the point dynamics > in a fractal -- it identifies a set of measure zero, giving a completely > faulty > understanding of the fractal (dynamical system) The system dynamics is > governed by measures, not by individuals. The individuals are the > exception. > > MOSES is more accurate, the larger the population. Having 1000 exemplars > will give you far more accurate answers, than what you'd get if you tried to > trim this to the top-10 most successful. Put another way: ten > super-accurate, > highly-evolved expert trees are less accurate than the democratic vote of > 1000 simple-minded average joes, when yo just want an answer for a > "typical" > dataset. > > Linas. > > -- > cassette tapes - analog TV - film cameras - you > > -- > You received this message because you are subscribed to the Google Groups > "opencog" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To post to this group, send email to [email protected]. > Visit this group at https://groups.google.com/group/opencog. > To view this discussion on the web visit > https://groups.google.com/d/msgid/opencog/CAHrUA36Qu8SR55YZGKu460c2QZnJDOC3tMyYZ%3D1g%3DSPZvSBG6g%40mail.gmail.com. > For more options, visit https://groups.google.com/d/optout. -- Ben Goertzel, PhD http://goertzel.org "In the province of the mind, what one believes to be true is true or becomes true, within certain limits to be found experientially and experimentally. These limits are further beliefs to be transcended. In the mind, there are no limits.... In the province of connected minds, what the network believes to be true, either is true or becomes true within certain limits to be found experientially and experimentally. These limits are further beliefs to be transcended. In the network's mind there are no limits." -- John Lilly -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/CACYTDBfADUzs-%2B_79J4UxLnF4JLWaRDRH_%2BA5PG1a22%3Ds-fVeg%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
