Done On Sat, May 23, 2015 at 7:19 PM, Piaget Modeler <[email protected]> wrote:
> Shouldn't it go on the FAQ page as well (in the interim)? > > ~PM > > ------------------------------ > Date: Sat, 23 May 2015 18:49:57 -0700 > Subject: Re: [agi] H-AGI towards S-AGI > From: [email protected] > To: [email protected] > > > I'll post that to the IGI site forum for discussion and further refinement. > > On Sat, May 23, 2015 at 6:36 PM, Colin Hales <[email protected]> wrote: > > Dear IGI enthusiasts, > Here's a stab at an intro to a paper that I hope begins to capture the > essence of what is proposed. > I don't claim it as perfect or the final product. > What I need to know is if it speaks in a way that might lead to the change > we are looking for. > > *=========================================* > *AGI Directions: towards Hybrid (H) and Synthetic (S) Forms.* > By > Dorian Aur (see previous posts) > > (blame for this bit is accepted by Colin Hales > others? TBA. > 1 Introduction > > Here we seek to instigate a broadening of approaches to artificial general > intelligence (AGI). Be it an artificial brain the size of a worm, ant, bee, > dog or human, such an artificial intelligence is recognized here as a kind > of AGI. The original science program coined ‘artificial intelligence’ (AI) > in 1956 {refs} set sail, at the birth of computing, with a goal to create > machines that potentially have human level intelligence or better. What has > actually happened since then is the application of computers to a vast > array of technical challenges within which great successes have occurred > and are ongoing. However, in practice AI successes fell, and continue to > fall, within a now well recognized category called ‘narrow’ or > ‘domain-bound’ AI. Within the atmosphere of its successes, however, the > original goal of human-level intelligence has, at least so far, evaded the > energies of a huge investment. Such has been the prevalence of this pattern > it can now be called a kind of syndrome and in recognition of that syndrome > in recent years the attainment of the original goal of human level AI has > taken on two main forms. > > > > The first approach to human level AI one of simple assumption that by > attending to the AI ‘parts’ that the route to the AGI ‘whole’ will become > apparent or emerge naturally. This activity, now industrialised, forms the > backbone of AI investment at this present time. Its successes emerge almost > weekly now. The second approach is one of a concerted direct attack on > human-level AI. This is a recent phenomenon manifest in a comparatively > small community of investigators, with commensurate levels of investment, > who have explicitly coined the name of the goal: AGI. In doing so the > target is explicitly recognised as being of a nature deserved of an > integrated, holistic approach. This, too, is having its successes, but once > again the syndrome of narrow-AI outcomes tends to be what the practice > achieves. > > > > Throughout all this history one thing has been invariant: The use of the > computer or more generally the use of models of intelligence as an instance > of machine intelligence. This document signals the beginning of another > approach: where the computer (model) approach is joined (to an extent to be > determined) by its natural counterpart. This new approach, for whatever > reason, is essentially untried and invisible to the AI community. It was > always an option. All we do here is get it off the shelf and dust it off as > an AGI option. This paper is a vehicle for the clear expression of an > untried approach. As such it is hoped that AI and AGI acquire a suite of > ideas and new scientific assessment techniques that will improve AI > generally as a science discipline based on a new kind of empirical testing. > Investment in the approach has been zero since day one of AI. We seek here > to make a case that if investment in this new approach was non-zero, a > cost-effective dramatic shift may occur in our understanding of the > potential kinds of machine intelligence. Specifically we seek to introduce > the concept of synthetic and hybrid AGI. > 2 Computation and AGI – a perspective on practice > > To understand what follows we need to carefully compare and contrast two > fundamentally different forms of computation. Formally their difference is > best captured by the words analytic computation and synthetic computation. > The first kind, analytic, is easily recognised as model-based computation. > This is where, by whatever means chosen, an abstract model is explored by > its designers. Its usefulness is inherent in what the computation tells us > upon interpretation. Within the model are representations of > characteristics that are being studied. A voltage in model may be used, for > example, to represent the actual voltage of what is being modelled. That > *representation* of something is not an *instance of* the original thing. > Recognizable forms of analytic computation include that of the analog or > digital computer (Turing machines). Its distinguishing feature is that > however the computation is carried out, its meaning is ultimately inherent > in the mental processes of a designer or in some explicit, separate > document such as software or a circuit diagram of a model. However, complex > the model is, it is best thought of as a description of something. The > description itself is the analytic form. Clearly the analytic form is > responsible for a dramatic change and technological advances in science > over decades. The computer revolution itself. > > > > The second kind of computation, synthetic, is best understood as simply > the regularity of nature itself. Synthetic computation occurs when nature > itself is simply regarded as computation. Synthetic computation, too, may > have a designer. That is, the distinction between analytic and synthetic > computation is not held up as the distinction between ‘human-made’ and > ‘naturally occurring’. Synthetic computation is when the regularity of > nature itself accepted as, or configured to be the computation. There may > be documents needed to establish the initial conditions of the > ‘computation’. For example, an engineer builds and configures the initial > conditions of natural material as an automobile. The result is a synthetic > computation called ‘the automobile’ or ‘transport’. No documents are needed > to further interpret the meaning of the result of the computation. Nature > itself is the outcome of synthetic computation. Another simple example of > such computation may be seen in the concept of flight. A bird ‘computes’ > those aspects of the physics of flight suited to the needs of a bird. > Humans have used those same synthetic computations (manifest in > air/fight-surface interactions) to create artificial flight. The result is > a regularity in nature accepted as a form of computation. Physically the > result is flight. That being the case, what is ‘analytic flight’? We all > recognise this: the flight simulator. > > > > The program of future directions proposed here is one that recognises the > two different kinds of computation in the very specialized science of the > brain where the analytic/synthetic distinction can be shown to be > under-developed and potentially confused. The brain is unique in that it is > a synthetic object with a specialised role to become the natural regularity > that forms the control system of natural organisms. It embodies the > intellect of whatever creature it inhabits. The kinds of tasks such a > control system does can and have been modelled to great effect in analytic > approaches. The question is: *“What is the difference, application to the > brain, between the analytic and the synthetic approach?”* Asking that > question, and expecting a scientific answer, is what this paper is seeking. > > > > For over half a century, approaches to creating an artificial brain have > been entirely confined to analytic forms. These analytic approaches are > explorations of models of the brain made by humans. That being the case, > then the hyper-critical issue is in understanding the conditions under > which the analytic is indistinguishable from the synthetic. If there is a > difference, then how does that difference manifest in the capability of an > AGI. For the brain, for these many decades, the synthetic half of the route > to AGI has simply been neglected for a variety of reasons. The actual > reasons for the absence of synthetic approaches to AGI is something > historians can evaluate. The practical restoration of the synthetic > approach is the goal here. The restoration of the synthetic approach is > necessary to scientifically test the difference between the analytic and > synthetic AGI. Whatever that difference is, the whole AGI enterprise has > been living within a realm of that difference for reasons that are > essentially unexplored. *Scientifically *evaluating the > analytic/synthetic difference (or the lack of it) is the goal of the > proposed shift in methodology. > > > > In summary: The prospect of restoration of a synthetic approach to AGI is > our topic. We look at a potential change in the direction of AGI science, > and therefore the investment profile, where the analytic, the synthetic and > their hybrid are formally recognised as separate and where scientific > testing is then applied to compare and contrast their scope and > effectiveness in application to the science of the artificial brain as AGI. > In the creation of such a brain the approach can be > > 1. > > Nil% synthetic computation (entirely analytic) > > or > > 1. > > 100% synthetic computation > > or > > 1. > > H% synthetic. A hybrid form of both. > > > > That is, the inclusion of synthetic computation to some desired level > becomes an experimental parameter. Natural brain tissue can be regarded as > naturally occurring object based on (2) synthetic computation. In > application to artificial brain tissue (AGI) so far, option (1) has been > the only approach. This has achieved all of the progress in artificial > intelligence to date. Here we suggest that the success of analytic > approaches be joined by synthetic approaches to AGI. If indeed the time has > arrived for the formal introduction of (2) synthetic AGI and (3) hybrid AGI > as viable prospects, then we need to open a discourse. What would the new > AGI science look like? What does it tell us about the scope, nature and > expectations inherent in the purely analytic approach? What does it add to > the nearly 70 year-old AGI program? > (end of section) > ============================ > > This is offered up for discussion as the possible first part of the > document Dorian started. I have a lot more to add. > > regards > > Colin Hales > > > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/27079473-66e47b26> | > Modify <https://www.listbox.com/member/?&;> Your Subscription > <http://www.listbox.com> > > > > > -- > Regards, > Mark Seveland > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/19999924-4a978ccc> | > Modify <https://www.listbox.com/member/?&;> Your Subscription > <http://www.listbox.com> > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/27079473-66e47b26> | > Modify > <https://www.listbox.com/member/?&;> > Your Subscription <http://www.listbox.com> > -- Regards, Mark Seveland ------------------------------------------- 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
