I see no reason to impose on AGI the arbitrary restriction that it need posses the ability to learn to perform in a given domain by learning from only within that domain. An AGI should be able to, by definition, adapt itself to function across different and varied domains, using its multi-domain knowledge and experience to improve its performance in any single domain. Choosing a performance metric from only a single domain as a benchmark for an AGI is antithetical to this definition, because, e.g., software that can perform well at chess without being adaptable to other domains is not AGI, but merely narrow AI, and such simplistic single-domain benchmarks can be easily tricked by collections of well orchestrated narrow AI programs. Rather, good benchmarks should be composite benchmarks with component sub-benchmarks spanning multiple and varied domains.
A human analogue of the multi-domain AGI concept is nicely paraphrased by Robert A. Heinlein: "A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects." -dave On Wed, Oct 22, 2008 at 7:23 PM, Dr. Matthias Heger <[EMAIL PROTECTED]> wrote: > I see no argument in your text against my main argumentation, that an AGI > should be able to learn chess from playing chess alone. This I call straw > man replies. > > > > My main point against embodiment is just the huge effort for embodiment. > You could work for years with this approach and a certain AGI concept until > you recognize that it doesn't work. > > > > If you apply your AGI concept in a small and even not necessarily > AGI-complete domain you would come much faster to a benchmark whether your > concept is even worth to make difficult studies with embodiment. > > > > Chess is a very good domain for this benchmark because it is very easy to > program and it is very difficult to outperform human intelligence in this > domain. > > > > - Matthias > > > > > > > > > > *Von:* David Hart [mailto:[EMAIL PROTECTED] > *Gesendet:* Mittwoch, 22. Oktober 2008 09:43 > *An:* agi@v2.listbox.com > *Betreff:* Re: [agi] If your AGI can't learn to play chess it is no AGI > > > > Matthias, > > You've presented a straw man argument to criticize embodiment; As a > counter-example, in the OCP AGI-development plan, embodiment is not > primarily used to provide domains (via artificial environments) in which an > AGI might work out abstract problems, directly or comparatively (not to > discount the potential utility of this approach in many scenarios), but > rather to provide an environment for the grounding of symbols (which include > concepts important for doing mathematics), similar to the way in which > humans (from infants through to adults) learn through play and also through > guided education. > > 'Abstraction' is so named because it involves generalizing from the > specifics of one or more domains (d1, d2), and is useful when it can be > applied (with *any* degree of success) to other domains (d3, ...). Virtual > embodied interactive learning utilizes virtual objects and their properties > as a way of generating these specifics for artificial minds to use to build > abstractions, to grok the abstractions of others, and ultimately to build a > deep understanding of our reality (yes, 'deep' in this sense is used in a > very human-mind-centric way). > > Of course, few people claim that machine learning with the help of > virtually embodied environments is the ONLY way to approach building an AI > capable of doing and mathematics (and communicating with humans about > mathematics), but it is an approach that has *many* good things going for > it, including proving tractable via measurable incremental improvements > (even though it is admittedly still at a *very* early stage). > > -dave > > On Wed, Oct 22, 2008 at 4:20 PM, Dr. Matthias Heger <[EMAIL PROTECTED]> > wrote: > > It seems to me that many people think that embodiment is very important for > AGI. > > For instance some people seem to believe that you can't be a good > mathematician if you haven't made some embodied experience. > > > > But this would have a rather strange consequence: > > If you give your AGI a difficult mathematical problem to solve, then it > would answer: > > > > "Sorry, I still cannot solve your problem, but let me walk with my body > through the virtual world. > > Hopefully, I will then understand your mathematical question end even more > hopefully I will be able to solve it after some further embodied > experience." > > > > AGI is the ability to solve different problems in different domains. But > such an AGI would need to make experiences in domain d1 in order to solve > problems of domain d2. Does this really make sense, if every information > necessary to solve problems of d2 is in d2? I think an AGI which has to make > experiences in d1 in order to solve a problem of domain d2 which contains > everything to solve this problem is no AGI. How should such an AGI know what > experiences in d1 are necessary to solve the problem of d2? > > > > In my opinion a real AGI must be able to solve a problem of a domain d > without leaving this domain if in this domain there is everything to solve > this problem. > > > > From this we can define a simple benchmark which is not sufficient for AGI > but which is **necessary** for a system to be an AGI system: > > > > Within the domain of chess there is everything to know about chess. So if > it comes up to be a good chess player > > learning chess from playing chess must be sufficient. Thus, an AGI which is > not able to enhance its abilities in chess from playing chess alone is no > AGI. > > > > Therefore, my first steps in the roadmap towards AGI would be the > following: > > 1. Make a concept for your architecture of your AGI > > 2. Implement the software for your AGI > > 3. Try if your AGI is able to become a good chess player from > learning in the domain of chess alone. > > 4. If your AGI can't even learn to play good chess then it is no AGI > and it would be a waste of time to make experiences with your system in more > complex domains. > > > > -Matthias > > > > > > > > > ------------------------------ > > *agi* | Archives <https://www.listbox.com/member/archive/303/=now> *Fehler! > Es wurde kein Dateiname > angegeben.*<https://www.listbox.com/member/archive/rss/303/>| > Modify <https://www.listbox.com/member/?&;> Your Subscription > > *Fehler! 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