I hae a question, have any of you worked with or toyed with OpenCog? On Sun, May 24, 2015 at 3:20 PM, colin hales <[email protected]> wrote:
> Hi Dorian et. al. > I am kind of blown away by what is happening here. Maybe this thing's time > really has come and this is what it looks like? Dunno. > > The personal crocodiles are keeping me too distracted to do much other > than cursory contact. And that'll keep going till the end of the week. > > Manuscript matters: > > All feedback gratefully accepted. It's a fair way from complete. If you > don't mind I'd like to keep going. When I have done a story with start > middle, end, refs then at that point I can release it into the wild of the > IGI and in particular Dorian ... > > It's already in .docx form. I use endnote for refs. I am assuming it will > be formatted to a journal's preferred layout in the end. > > The next section will cover practical instances so the reader sees the > hybrid and synth. And how it relates to analytic. I'll stick to the > analytic term for now. I can see the formal distinction working better when > in review because of the technical specificity. Perhaps a lighter term > might suit a broader audience. I will go with the various needs. > > The main thing I need is to learn when raw Colin starts to grate in the > eyes of potential investors/ funders, to whom the doc is likely to be > central. > > Off back to crocs and writing when I can. > > Regards, > Colin Hales > > > > > ------------------------------ > From: Dorian Aur <[email protected]> > Sent: 25/05/2015 4:37 AM > To: AGI <[email protected]> > Subject: Re: [agi] H-AGI towards S-AGI > > Colin, Ben et al > > Colin: Excellent start, I feel that anyone can get an idea about AI/AGI > goals (2016-1956 =60 years) > Ben: Indeed a careful selection of words e.g. synthetic/abstract may help > especially if the audience is picky. > Also very good questions.We should slightly alter Colin's text and > provide answers for every question at the end of the manuscript > -Discussion or Questions and Answers > > Also we need to be honest. IGI has an agenda to bring together everyone > and everything that works in AI,computer science, neuroscience, > electronics, nanotechnology to solve one problem - design a system that > generates human like intelligence or better. This part can be probably > written on the IGI webpage. > > We may like to include Potter and other similar labs > http://www.nature.com/srep/2014/140630/srep05489/full/srep05489.html on > our list of possible collaborators (list3) so I can't reveal the issue of > such approaches here. The robot rat, a nice attempt which may never work. > Remember everyone has followed the "mob opinion". If you read > http://www.researchgate.net/post/Place_cells_What_does_it_prove you may > be able to get at least a part of the problem. > > To fully write the paper, we may need a Word like environment, include, > keep corrections, references. > > > Dorian > > > > > > On Sun, May 24, 2015 at 6:10 AM, Benjamin Kapp <[email protected]> wrote: > >> When I read the ideas you have there Colin I don't feel like the ideas >> flow in a reasoned way. It feels contrived, like you have an agenda. It >> would be better if instead of assuming the conclusion we explored the issue >> without bias and let our empirical knowledge and rational faculties reign >> supreme. >> >> >> 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 definition of AGI is rather important, and it would be better to >> state what our definition of AGI is rather then just give examples of >> things that have 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. >> >> *I'm uncertain why this particular date is of great importance. The >> origins of AI predate 1956 (see Ada lovelace for an example). * >> >> >> 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. >> *The majority of AGI research yes, but not all research. (e.g. >> https://www.youtube.com/watch?v=1-0eZytv6Qk >> <https://www.youtube.com/watch?v=1-0eZytv6Qk>) * >> >> 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. >> *Syndrome? Seems rather harsh. Humans have always made analogies between >> the mind and the technology of their time. For Aristotle it was the mind >> being like a clay tablet, for others it was their mechanical clocks, and >> for us it is our computers. This isn't a syndrome, it is human nature. And >> this approach is being fruitful something you even admit later in this >> write up. And it is certainly something our personal experience can provide >> many examples of. To speak so harshly of this approach gives a strong >> negative impression in the mind of the reader that you aren't reasoning >> fairly and that you have an agenda to sell the reader on your approach.* >> >> >> >> 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. >> >> >> >> >> *Not sure if AGI is so small anymore. I think Google/deepmind/Kurzweil >> are in the process of creating AGI.And I think China is working on >> AGI.. China-Brain >> Projecthttp://www.igi-global.com/chapter/china-brain-project/46407 >> <http://www.igi-global.com/chapter/china-brain-project/46407>* >> >> >> >> 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. >> *Is this true? How do you know? Have you surveyed all current AGI >> research approaches?* >> >> 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. >> >> >> I think analytic/synthetic as you use them could be replaced by >> abstract/material, which are words that are of far more common usage and as >> such easier to understand. >> >> 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. >> *If human brains are instances of synthetic AGI then it would seem that >> ALL analytic AGI research would be checked against synthetic AGI since >> those doing the research are synthetic AGI and since they are those ones >> reasoning as to whether they're AGI is functioning as expected or not. As >> such the idea that the proposed approach is of great importance, or >> something that is under explored seems to be lacking.* >> >> 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 approac >> > > [The entire original message is not included.] > *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
