One other thing that I started thinking of earlier. Suppose you have a Bayesian method that finds the most likely responses to a multiple choice event. If the likelihood of the range of responses (or event-actions) were constantly changing, the pure Bayesian method could not reliably predict the more likely responses. Interestingly, it could not predict it even if the likelihood was changing in a periodic manner. (It would require an additional abstraction of analysis.) If the number of possible event-actions were great enough the possible orderings could quickly go out of range. So the complexity argument is definitely relative to Bayesian methods as well. (That is not what I was thinking about before, I lost the previous thought as I thought about that. But it was in the same ballpark.)
And the mush-mellow methods like neural nets, excessive reliance on logic, and excessive reliance on weighted reasoning do not impress me. I am surprised by how much researchers have gotten out of them. I mean wow! If I am not a logic-based AGI yahoo then why am I so interested in SAT in p? Because the advance would be so powerful that it could be used to advance a range of complex computational methods including AGI. Jim Bromer On Wed, Dec 2, 2015 at 1:43 PM, Jim Bromer <[email protected]> wrote: > Stanley, > Thank you for asking me these questions! I have thought about them. I > want to respond over a few brief replies. The baby does not learn > language by trying every possibility and a baby does need good sources > to act as a guide in learning. (I mean I agree with those views.) But, > how do you get that in a computer program? One of the most significant > models of knowledge, (really the only one that I can imagine as > viable) is a component model of knowledge. These component parts can > be used in 'generating' different outputs. So we do not need to learn > a separate name and symbol for every number, we can use 'components' > to both represent them and to name them up to our limits of knowledge. > But we only need to remember maybe a hundred different labels and how > to attach them and we can thereby represent and talk about (samples) > of octodecillions of numbers (if you can find the right page on > Wikipedia to get the names of large numbers). > But, you still have to follow certain rules when using one of those > numbers. These rules are themselves expressed in the n-ary system as > compressions that are convenient to use with many large numbers. (It > is not convenient for us to do calculations on numbers in the > octodecillion range but it is convenient for a computer to do > calculations on numbers that large.) > > Some methods, like neural networks tend to combine the components 'of > knowledge' but the successes in the field seem to reflect somewhat > narrow AI applications. In other words the more interesting uses in AI > are when they are treated more like component methods. My argument > against them is that important knowledge (knowledge-stuff) is lost > when you methodically trash or mush the component representations of > that knowledge. > > However, there is no evidence -as of yet- that general knowledge can > be so conveniently broken down to a convenient number of components as > it has been done with the n-ary representations of numbers. So even > though our would be AGI programs would use component models of > knowledge it still would have to be able to go through heaps of stuff > in order to find the best responses for a given situation. > > In a simple world model an AGI program could do deep searches without > any problem. But as the world model becomes more and more nuanced the > deep searches would *themselves* become nuanced just because they too > are based on component models. The problem is that their are no > conveniently compressed component methods that can be used on general > AI. > > Imagination gives us the ability to think outside the box. It is at > the least an ingredient of the 'magic sauce'. Once you begin to think > deeply about reinforcement methods that are operating on an > imaginative AGI program you will find that the simplicity of the > method would start failing rather quickly at the least sign of > conceptual complexity. > Jim Bromer > > > On Wed, Dec 2, 2015 at 11:35 AM, Stanley Nilsen <[email protected]> wrote: >> Greetings Jim, >> Have some questions and comments about the recent email... >> (thread used to be: Scholarpedia article on AGI published) >> >> On generating possibilities... >> >> Complexity seems to be the issue that lies underneath much of your >> efforts. And, there is no doubt that life has it's complexity. I don't >> recall (didn't search past emails) any explanation from you, that ties this >> complexity to an AGI implementation. >> >> I personally have an issue with complexity and that is because it is >> over-rated. In many writings there is an implication that AGI type >> intelligence will emerge through the construction of a unit that will try >> lots of things and get feedback and thereby learn what creates the best >> reward. Sort of an evolutionary intelligence. Do we really believe that? >> I don't, do you? >> >> I've seen reference to "search space" and the implication is that the >> intelligent unit is going to find an efficient way to navigate this huge >> space of possible "stuff." Am I off track in assuming that this may be the >> connection with trying to discover the p=np solution? Thus enabling the >> traversing this search space more efficiently. Is this how you see a better >> intelligence created? >> >> Perhaps a simple example will suffice to see my contrasting view of the >> development of intelligence. Consider a new born baby, or imagine you >> create a computer baby that has a sound generating capability. One could >> program the computer to generate random sounds and then watch for feedback >> at the keyboard or through a listening device. Good luck with that. There >> are several ways one might alter the experiment and produce suggestive >> results. But, lets save time because we know that a human intelligence, the >> baby, does not "simply" try lots of sounds and thereby become a speaking >> person. Instead, the baby learns by lots of feedback and, importantly, by >> mimicking the sounds that it hears. What does this prove? >> >> First, it shows that the baby isn't developing an intelligence by randomly >> trying every combination. Rather, the baby is "adopting" behaviors that >> produce positive feedback from the adult who is nurturing the baby. Enough >> about babies... >> >> The computer intelligence will not evolve from nothing, far from it. It will >> need a core set of behaviors to work with. "Thousands of different >> approaches" could refer to the thousands of "cores" that are eventually >> developed by different researchers. The core will be a big factor in how >> the intelligent unit goes about acquiring behaviors to add to it's core. >> And, as you might suppose, the unit will be influenced by it's surroundings, >> the core isn't everything. >> >> To net it out... It isn't the "learning" by trial and error that is >> significant, it is the adoption process that dwarfs the "scientific" >> gathering of truth. The baby AGI doesn't need lots of experiments, it needs >> good sources to guide it's adoption process. >> >> Stan >> >> On 12/02/2015 07:36 AM, Jim Bromer wrote: >>> >>> ... >>> >>> It was useful to me only because it showed me how I might generate >>> variations on the abstractions underlying some purpose. So my methodology >>> might be used in an actual AI program to generate possibilities, or more >>> precisely, generate the basis's for the possibilities. It would not stand as >>> a basis for AI itself. Solomonoff's methods do not stand as a basis for AI. >>> (Years from now, when there are thousands of different approaches to AI and >>> AGI we might still disagree about this.) But it should be clear that I am >>> saying that Solomonoff's universal prediction methods are not actually >>> standards for viable AI. Compression methods are not a viable basis for AI >>> either. They could be used as part of the process but it is pretty far >>> fetched to say that any computational technique that could be used in an AI >>> or AGI program could be used as a basis for AI. >>> I just wanted to follow through since you wrote a reasonable reply to my >>> first reply. >>> >>> >> >> >> >> ------------------------------------------- >> AGI >> Archives: https://www.listbox.com/member/archive/303/=now >> RSS Feed: https://www.listbox.com/member/archive/rss/303/24379807-653794b5 >> 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/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
