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 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?
*This approach requires physical things. And physical things require money
to acquire. This would create a barrier to entry for AGI research. As
such it would be better to use the analytic (abstract) approach over the
synthetic (material) approach as it would have less dependencies.*
*If we where to create brains in vats and hook them up to AGI interfaces
then perhaps we would be able to create minds. But would such research be
ethical? I'm fairly certain PETA might have a problem with research that
involved living animal brains. *
*Material AGI does have one advantage over Abstract AGI, namely we know
that it works. But the case needs to be made I believe for what exactly it
is that material AGI has that Abstract AGI lacks.*
*Material vs Abstract AGI*
*What if some essential part of human intelligence was bound up in the
material world and could not be represented in an abstract computer
simulation? If this was the case then the only way to create AGI would be
the material approach.*
*What would something that can't be represented abstractly in software look
like? Some people think that quantum physics is non-deterministic and as
such isn't something that you can model in a computer because computers are
deterministic. Brains are made of matter, and matter is subject to the
laws of quantum physics and so perhaps we could say then that brains are
made out of stuff that can't be represented in computers?*
*But certainly we can model probabilities in a computer. And probabilities
are the way we understand how things work in quantum physics. And as such
perhaps even something non-deterministic can be represented in a computer.
Perhaps we can even model quantum computers in classical computers
(http://www.quantumplayground.net/ <http://www.quantumplayground.net/>)*
*What then could we not represent? **There would have to be something
magical about the material world. Something that you just can't represent
in software simply because it isn't material. If this is true then perhaps
we are basing our approach to AGI on magic.*
On Sat, May 23, 2015 at 11:56 PM, Mark Seveland <[email protected]> wrote:
> my email is mseveland (at) gmail (dot) com. Please feel free to contact
> me if you would like:
>
> 1. Admin access to the website (must be ok with Bengimin Knapp (as he paid
> for domain).
> 2. Admin or moderator access to the forums.
> 3. Have suggestions or recommendation for the web site or anything
> contained within.
>
> I am an admin guy, not a web site designer. (I'm doing the best I know
> how), and would seriously appreciate feedback and input.
>
> Thank you, That is all, Please return to your previous program....
>
> On Sat, May 23, 2015 at 1:26 PM, Logan Streondj <[email protected]>
> wrote:
>
>> So what is this "inorganic tissue" made from? silicon?
>>
>>
>> On Fri, May 22, 2015 at 10:48:17AM +1000, Colin Hales wrote:
>> > Hi Dorian et. al.,
>> >
>> > I am going to have to piecemeal this. Up to armpits in crocs. Can't
>> commit
>> > to chat just yet.
>> >
>> > I have a suggestion for this bit
>> > ===============================
>> > Why H-AGI?
>> >
>> > - Present different forms of computation , ( particular forms of
>> > computation analog, digital -Turing machines )
>> > - Computations in the brain (examples of computations that are hardly
>> > replicated on digital computers) *I can do this (see below)*
>> > - H-AGI can include all forms of computations:
>> > algorithmic/non-algorithmic, analog/digital,*
>> > quantum/classical. Organic/Inorganic. *H-AGI incorporates whatever
>> level
>> > of natural biophysics is thought essential to AGI operation. It is
>> that
>> > biophysics that introduces some desired level of natural computation
>> into
>> > the H-AGI. The biophysics is literally incorporated in the
>> > processor/chipset. This could take the form of actual biological
>> substrate
>> > (cellular tissue) or it could be an inorganic version of some part
>> of the
>> > biophysics of the organic original.
>> > - The reason for beginning a program of H-AGI works is that by
>> virtue of
>> > its retention of the natural biophysics, it allows us to
>> scientifically
>> > determine the role of the natural biophysics in intelligence.
>> > Properties potentially lost the moment the boundary of any abstract
>> model
>> > of the biophysics is chosen.
>> > - H-AGI recognises the significance of the loss of natural biophysics
>> > has had essentially no attention in AI or AGI, both of which have
>> been
>> > confined to the 100% elimination of the natural biophysics. H-AGI
>> commences
>> > that investigation, not because of any particular knowledge, but
>> because
>> > the choice not to do it has had almost no attention since the
>> inception of
>> > AI.
>> >
>> > ===========================================
>> > Next, I would add another whole section to Dorian's wet version of
>> H-AGI:
>> > the 'dry' inorganic version. The wet version sounds fine to me. No
>> problem.
>> > I don't claim to have thought deeply about that. So I defer to Dorian.
>> >
>> > For the dry H-AGI I have already generated an example (in the paper I
>> have
>> > already written). I show the natural original, the modelled version
>> (C-AGI)
>> > and the inorganic version (H-AGI). It is not synthetic S-AGI because it
>> > doesn't have *all* the brain's biophysics. It only has that tiny
>> proportion
>> > of the natural physics being explored for its role in intelligence. It
>> is
>> > not C-AGI and it is not S-AGI. It is H-AGI. Somewhere in between. Where
>> the
>> > natural biophysics computation stops the abstract modelling (analog or
>> > digital) takes over in its more traditional guises (computer or
>> > neuromorphic chip). Dry H-AGI doesn't have *all* the brain's biophysics.
>> >
>> > Biological tissue usage is (wet) H-AGI because it introduces a
>> proportion
>> > of natural tissue (not the whole brain). All the biophysics, not all the
>> > brain. Inorganic replication of biological tissue biophysics is H-AGI
>> > because it retains part of the biophysics but is till not the whole
>> brain.
>> > Both wet and dry approaches are also H-AGI because they traditional
>> > modelling as a form of container for the biophysics.
>> >
>> > (Aside: The organic/wet H-AGI and the inorganic/dry H-AGI could be
>> classed
>> > as quantum-mechanical in nature by virtue of the biophysics ultimately
>> > being based on quantum processes. Classical physics is the only thing
>> > needed to describe it at the functional/construction level. The
>> properties
>> > conferred through the quantum substrate are something to be argued
>> *after
>> > you built it, IMO*)
>> >
>> > *Summary*
>> > Dorian wants to do H-AGI biologically. I want to do it inorganically.
>> Both
>> > are really hard. Neither are synthetic S-AGI because they also have
>> > abstract modelling of other brain processes. If Dorian made a brain
>> > entirely out of organics (no abstract computation/models) , that would
>> be
>> > synthetic. If I made a brain entirely out of inorganics (no abstract
>> > computation/models), that would be synthetic.
>> >
>> > Wet *and* Dry H-AGI are needed in an expanded conceptual basis for AGI
>> > future development. All that is required is to see that these
>> initiatives
>> > are currently unexplored and that the scientific knowledge needed to see
>> > which approach offers what future, and thereby put the pure abstract
>> > modelling approach on a scientific footing, is what the IGI is about.
>> >
>> > The Dry H-AGI section is what I want to add. I am hoping that we are on
>> the
>> > same page as regards the compartmentalisation of different approaches.
>> >
>> > Gnarly bits? Please advise.
>> >
>> > Gotta go ... panic prep for sale of my brother's house.
>> >
>> > regards
>> >
>> > Colin Hales
>> >
>> >
>> >
>> > -------------------------------------------
>> > AGI
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>
>
> --
> Regards,
> Mark Seveland
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