Artificial Intelligence Arrives and Causes a Paradigm Shift - Expected 2025
SM Dambrot: What’s your take on the Blue Brain Project? They’ve apparently emulated a cat’s
neocortical structure and announced that their goal is to emulate a human neocortex within,
at this point, roughly eight years.
Dr. Goertzel: This is a long and complex story regarding a number of fascinating simulations
done on IBM supercomputers. If you look at what Henry Markram did in simulating a cortical
column, in the Blue Brain project, that was very interesting from a number of standpoints --
yet in some ways it didn’t do everything some people think it did. In simulating that column,
Markham had to dig deeply into the equations of the flow of charge along a single neuron –
and he actually published some really cool papers in Biological Cybernetics about adjusting
those equations based on the measurements he and his team made. On the other hand, when
you look at what the actual simulation he ran was, you can see that they did not actually
simulate the precise input/output behavior of the cortical column.
What you’d like to see ideally is a simulation where if you feed some input into the column
and get some output from the column, you see exact agreement with what you’d get from a
real cortical column. They didn’t do that; what they did do was create a simulated column that
statistically had the same input/output properties as a real column. That’s worthwhile and
interesting, but it’s not uploading a cortical column. Since we don’t know the information
coding of the column’s inputs and outputs, we don’t really know if we’ve gotten everything
that’s there. Imagine that you simulated the input/output properties of me as a language user
in this way: from the statistical standpoint of acoustic analysis it would look like it had the
same input/output properties as I do, yet it’s missing the information.
Now, the cat brain that you mention was actually Dharmendra Modha's work. It was a totally
different project based on IBM hardware that was the next generation from what Markham
used. They simulated a neural network similar in size and connection complexity to a cat’s
brain. However, the pattern of connections was random – not derived from study of the cat
brain and it didn’t go down to the level of neurotransmitter concentrations either. It was a
wonderful hardware demonstration of building a formalized neural network of that huge size,
but it didn’t have the same dynamics or structures as a cat brain because we don’t know what
those are.
As it happens, Modha’s team at IBM has done some other work aimed at understanding those
structures, and published quite an interesting paper on the structure of the monkey brain in
which they curated thousands of neuroscience papers and charted which regions of the
monkey brain connected to other regions, trying to parse the connection structure just on a
region-to-region level. There are hundreds of brain regions and hundreds of thousands of
papers on how they’re connected. Also, they were the first to sort through all the different
nomenclatures and sub-literatures in the world to create a coherent database of the
connections between different parts of the monkey brain.
So that‘s interesting, and eventually if you bring that kind of connectivity diagram together
with the kind of simulation that they did, potentially you could get a large-scale simulation
with more of the same structures and dynamics as a real animal’s brain – but they haven’t
gotten there yet.
Open Connectome is another interesting project, at John Hopkins University, to mention in
that regard. It’s a little bit earlier stage that what Modha’s team did with the monkey brain, but
it’s all Open Source. Their scientists upload connectivity data from different parts of the brain,
and make open source tools where anyone can go online and help map out neurons, synapses
and what’s connecting to what in the data – and this could produce a much more fine-grained
map of the connectivity structure. If something like that succeeds, then you could really make
a large-scale brain simulation that does what the brain does – which is something that neither
Markham nor Modha did in their simulations.
SM Dambrot: That kind of open-source project would have a significant benefit to a wide
community of neuroscientists.
Dr. Goertzel: Yes – they want to go Web 2.0 with it: They want to not only have scientist
upload their data, but also have people from around the world log on and help interpret the
data. It’s interesting – there are some image processing tasks that people are good at but
computers aren’t that good at. For example, with three-dimensional imaging data – the type of
data that the John Hopkins researchers have uploaded – people can look at and see, “yes,
there’s a neuron there, and it’s pointing to another neuron over here.” Current image
processing tools, however, are quite weak with 3D data.
So right now, there’s a role for people to look at this 3D data and see what’s connected to what.
Once AI is a little further advanced at 3D image processing tasks, the role of people will shift
to correcting the AI’s mistakes, and then ultimately the AI could obsolete people – in part by
leveraging the training data obtained from people’s image classification judgments made by
using the Open Connectome web interface.
SM Dambrot: Would you consider this the next step in the progression of distributed
processing – SETI@home, ProteinFolding@home, and so on?
Dr. Goertzel: In a sense – but those are using home computing power to do number
crunching, whereas Open Connectome uses human brain power. It would be interesting if
you could take a page from the Google Image Labeler that Luis von Ahn created at Carnegie-
Mellon University – he made labeling images online into a game to make it fun for people to
provide textual labels for images, but it’s a game with a purpose: the labeling then serves as AI
training data. It’s not exactly Name the Neuron – the point is not to label a neuron but rather
to identify it and where it’s connecting – but I think it could be approached in a similar way.
SM Dambrot: Another interesting topic from your talk yesterday was the use of virtual and
gaming worlds to provide and AI with a space to explore – specifically the block world.
Dr. Goertzel: In the AI project I’m currently doing with Hong Kong Polytechnic University
(PolyU), the basic goal is to demonstrate OpenCog doing something in a videogame world
which will be interesting to the game industry. At the end of this two-year project, which is
jointly funded by the Hong Kong government and my company Novamente LLC, we want to
create an OpenCog agent in a game through a partnership with a game company – to both
generate money for ongoing research, and establish a way to set the AI up in communication
with potentially millions of people around the world who would be the AI’s teachers.
Then the question becomes: What type of game world should we use for our current prototype
experiments? We’ve done some work before using a game platform called Multiverse in which
the actor is a virtual dog that learns tricks – which is interesting as a platform for imitation
reinforcement learning, but it’s limited. We wanted something with more versatility but not so
much that it would confuse our early-stage AI.
An AGI Preschool is a cool idea. I want to do it, but it’s a bit much for right now – less in terms
of the AI, which could probably handle it, but in terms of resources for game development. In a
preschool you have a lot of things that are hared to simulate in a video game – a sandbox and
Play-Doh, for example – so we settled on a game world modeled on the video game Minecraft
because it’s relatively simple from a game development perspective yet provides a lot of
flexibility on terms of the AI interacting with the world. In Minecraft, everything in the world
is made of small blocks, which can be used to build anything – a ladder, tower, or even a
statue that looks like oneself. There’s a lot of opportunity for flexibility and creativity, but
because everything is made out of blocks you don’t have to deal with scripting sand and other
difficult objects, and you don’t have to do was much artwork and animation.
In short, we made this decision to both simplify the AI’s job in terms of perception and action
so it could focus more on cognition, learning, planning and construction, as well as to
simplify game world construction – a world made of blocks is basically Democritus’s model of
the cosmos, on a larger scale.
Still, there are various decisions to make – in the physics of the game world, for example, you
can build a very narrow tower of blocks but gravity doesn’t make it fall down.
SM Dambrot: Adding realistic physics would give you the best of both: you’d have real-world
constraints coupled with the simplicity of using repetitive units to construct objects.
Dr. Goertzel: That’s right. And of course, in terms of transfer to a physical robot, you can give
that robot blocks to play with in the robot lab. It transitions fairly well into building with
wooden blocks, Lego blocks and so on. This natural transition path for the game world into
robotics will probably be done in the Hong Kong project, which is focused on game AI.
SM Dambrot: You also discussed various types of memory in human cognition. Does AI
memory conform to these?
Dr. Goertzel: Overall, my approach to AI is not based on neuroscience, primarily because I
don’t we know enough about neuroscience to drive AI design – and the neuroscientists I talk
to tell me the same thing. It is inspired by cognitive psychology to a significant extent. The
different types of memory I used to design OpenCog are pretty well established in Cognitive
Psychology, in the sense that we seem to have different mechanisms with different response
time characteristics for, say, procedural knowledge versus semantic knowledge. If you dig
into the neuroscience, there are many distinctions between these types of memory, in that
various parts of the brain are differentially active during types of memory. For example, there’s
evidence that the cerebellum is involved during action sequences – the basal ganglia also
come into it – even though they don’t involve motor action. In spatial knowledge, there are
complex interactions between the posterior parietal cortex, hippocampus, entorhinal cortex,
and so forth. We’re not at the stage where neuroscientists have a clear picture of how each of
the different types of memory is implemented. So clearly there’s the same biochemical and
cellular mechanisms underlying different kinds of memory in the brain, and there’s much
overlap in terms of the brain regions and dynamics, as well as there being significant
differences in which brain regions are involved, and in which neurotransmitters may be
involved. The details are still unfolding.
If you look at what you can do on a computational neuroscience level now, you can do things
like build a model of the hippocampus and medial temporal lobe, connect it to your model of
the parietal cortex, and study how that implements spatial memory. The hippocampus and
medial temporal lobe tend to deal more with allocentric coordinates (such as third-person top-
down, or bird’s-eye, views), while the parietal cortex tends to handle first-person egocentric
views – but both are head- and eye-centric. Neuroscientists have different opinions about the
brain’s coordination of these different perspectives – and I’ve been doing some consulting in
this direction through Novamente. However, to me this is a different pursuit than trying to
build a human-level thinking machine, because the neuroscience is just too diverse,
particular and unfinished.
SM Dambrot: Especially given the idea that AGI is ideally substrate-independent.
Dr. Goertzel: Substrate independence is an interesting notion, and as a mathematician I
would like to aspire to that – yet as an AGI designer I’m constantly pushed away from it. The
OpenCog design now is not that substrate-independent – in fact, in many ways it’s customized
to operation on a network of symmetric multiprocessor Von Neumann machines.
In the just-finished first draft of my new book Building Better Minds, the core mathematics is
substrate-independent – for instance it would work on a massively-parallel MIMD machine,
like the Connection Machine that Danny Hillis built at MIT starting in the 1980s – but on the
other hand, there’s also a lot of content and code heavily tied to the particular hardware we’re
currently using. For example, we have to write code to multithread among 16 processors (or
however many processors our individual SMP machines have), and we then will have to write
code to network many of multiprocessor machines together. That has a lot of consequences –
for example, if you’re running on 1,000 machines, each with 100GB of RAM, you have issues
of how to dynamically and adaptively partition knowledge among those machines. How do
your logical inference control and procedure learning mechanisms make use of this
clustered feature of your knowledge base?
Once you go in that direction you’re adapting your systems to a network of symmetric
multiprocessor machines, which is an infrastructure that very different from a Connection
Machine or human brain – so if you gave us a Connection Machine with a trillion processors,
we could port our mathematical algorithms, but much of the code would have to be rewritten,
as would the intermediate layer of algorithms that we use as a “glue” between the
mathematics and the hardware.
In short, efficiency leads you away from substrate independence, so as an AGI designer you
want to formulate your core cognitive algorithms and structures in a substrate-independent
way. At least that’s my approach. On the other hand, you could take a different view: If you're
less of a mathematician and more of an engineer or biologist, then your approach could be to
grow a mind out of the substrate, which is what happens with the human brain – evolution
didn’t start with abstract mathematics of thought that was then implemented on wetware.
SM Dambrot: This reminds me of our discussion a few minutes ago about the ways worlds and
minds interact, in that the brain is tied in with the world in which it evolved.
Dr. Goertzel: The brain is part of the world – it’s made of the same stuff as the world around it.
It’s more a matter of one part of the world co-evolving with another – and what we’re doing with
AGI right now is engineering, not evolution.
A long time ago – before I started seriously working on AGI – I had the same thought many
others have: Why not evolve a brain by implementing an artificial ecosystem across the
Internet, set some artificial chemistry and biology in motion, and let the AGI emerge from the
digital primordial soup. The obvious conclusion you come to after a while, yes, that‘s really
cool – but the ecosystem has many more molecules than any one brain, and that’s going to
require orders of magnitude more computing power than does any individual brain, so it’s
probably not the best approach to take.
SM Dambrot: Since we’re at the Humanity+ Transhumanism Conference, my last question is
about the connection between your work in AGI and Transhumanism.
Dr. Goertzel: From a certain standpoint, working on an AGI is a purely technical and
engineering pursuit which could be done by a lot of people – such as me and five or ten other
guys locked in a basement somewhere, just coding our hearts out all day. On the other hand,
that’s not really the way things are going – we’re developing our AGI in an Open Source
project with people around the world, trying to recruit new programmers, and with funding
that so far has largely been based for vertical market applications, not just for pure research.
Therefore, in practice – since our development of AGI is distributed around the world and
couple with business, universities, and various other entities within the world – there’s been a
fair amount of interoperation between the AGI outreach and the Transhumanism outreach
that I’ve been doing.
As an example, our AGI project in Hong Kong Polytechnic University – where we’re developing
OpenCog for video games – involves Gino Yu, who runs the lab, but who with me is also
organizing the Humanity+ Hong King conference on December 3-4, 2011. Through that
conference, we’ll get Hong Kong technology and business people attending, potentially
leading to connection for more OpenCog commercial projects or university collaboration, in
turn potentially leading to funding that will feed OpenCog development.
There’s a lot of cross-pollination scientifically as well: The OpenCog work is integrating many
different AI tools, one of which is machine learning – a particular AI discipline based on
learning by example that could itself be integrated with probabilistic reasoning, analogic
inference and generalization. I’m using machine learning in my bioinformatics work to
analyze genetics data – and in that bioinformatics work I’m collaborating with Genescient,
accompany whose founding Chief Scientist was Michael Rose who I met at the
Transhumanism-related Immortality Conference in 2005.
What I’d like to do in the next couple of years, among many other things, is to use OpenCog for
the genetics work by pulling in probabilistic reasoning and concept learning so that we’re not
just doing machine learning, but are also doing some AGI-type cognition about that
bioinformatics data. That would be a case of OpenCog integrating more advanced technology
into a bioinformatics project or engineered life extension, which was founded through a
connection made at another Futurist conference. At the moment, it’s all one big social and
intellectual network, rather than being siloed into AGI, Transhumanism, and so on. To a large
extent, that’s my own personal approach – there are certainly very solid AGI researchers who
have no connection with the Transhumanist community, and of course there are
Transhumanists thinking about AGI who have no connection with AGI research. I’m always
interested in connecting things together – my main focus in life is making intellectual
progress on scientific issues, but I spend a certain percentage of my time pulling people,
social networks and ideas together, which I think is also valuable.
As a final example, at the AGI ’11 Conference – a technical AGI conference which will be held
at the Google campus in Mountain View, California – we’ll have a Future of AGI Workshop
before the conference, which should attract Transhumanists who wouldn’t necessarily attend
the technical meeting. Pulling the community together like this can have a lot of impact –
some Transhumanists may be involved in practical projects that could benefit from AGI
technology, others or their friends and associates may have a technical background and so
might want to get involved with AGI work, and of course meeting and talking with real AGI
theorists may help them speculate about the future about ways that are better grounded than
might otherwise have been.
SM Dambrot: If you would, please take a final moment to give us additional details about the
AGI and Transhumanist conferences later this year, as well as when we might expect your
upcoming books.
Dr. Goertzel: AGI 2011, to be held in Mountain View on August 3-6, is in large part a
technical and scientific conference for those involved in Artificial General Intelligence, but
the pre-conference workshop, as well as the Keynotes and demo sessions, will be interesting
to everyone – so I encourage you to register soon, as there’s a cap on attendance of some 200
attendees due to the size of the venue at Google.
The Humanity+ @ Hong Kong Conference will be held on December 3-4, 2011, at Hong Kong
Polytechnic University’s Chiang Chen Studio Theatre. It should be very interesting in terms of
bringing in scientists and futurists from mainland China who don’t circulate much in the
world-at-large or intersect with their Western counterparts – so I’m psyched about the cross-
cultural admixture there.
In terms of my technical AGI book, Building Better Minds, its release date of course depends
on the publisher, but my guess would be at the late 2011 or early 2012. I’m also working on
an AGI trade book, tentatively titled Faster Than You Think, which should also come out in
2012.
SM Dambrot: Thank you so much, Dr. Goertzel.
Dr. Goertzel: Thank you for the interview.
SM Dambrot: What’s your take on the Blue Brain Project? They’ve apparently emulated a cat’s
neocortical structure and announced that their goal is to emulate a human neocortex within,
at this point, roughly eight years.
Dr. Goertzel: This is a long and complex story regarding a number of fascinating simulations
done on IBM supercomputers. If you look at what Henry Markram did in simulating a cortical
column, in the Blue Brain project, that was very interesting from a number of standpoints --
yet in some ways it didn’t do everything some people think it did. In simulating that column,
Markham had to dig deeply into the equations of the flow of charge along a single neuron –
and he actually published some really cool papers in Biological Cybernetics about adjusting
those equations based on the measurements he and his team made. On the other hand, when
you look at what the actual simulation he ran was, you can see that they did not actually
simulate the precise input/output behavior of the cortical column.
What you’d like to see ideally is a simulation where if you feed some input into the column
and get some output from the column, you see exact agreement with what you’d get from a
real cortical column. They didn’t do that; what they did do was create a simulated column that
statistically had the same input/output properties as a real column. That’s worthwhile and
interesting, but it’s not uploading a cortical column. Since we don’t know the information
coding of the column’s inputs and outputs, we don’t really know if we’ve gotten everything
that’s there. Imagine that you simulated the input/output properties of me as a language user
in this way: from the statistical standpoint of acoustic analysis it would look like it had the
same input/output properties as I do, yet it’s missing the information.
Now, the cat brain that you mention was actually Dharmendra Modha's work. It was a totally
different project based on IBM hardware that was the next generation from what Markham
used. They simulated a neural network similar in size and connection complexity to a cat’s
brain. However, the pattern of connections was random – not derived from study of the cat
brain and it didn’t go down to the level of neurotransmitter concentrations either. It was a
wonderful hardware demonstration of building a formalized neural network of that huge size,
but it didn’t have the same dynamics or structures as a cat brain because we don’t know what
those are.
As it happens, Modha’s team at IBM has done some other work aimed at understanding those
structures, and published quite an interesting paper on the structure of the monkey brain in
which they curated thousands of neuroscience papers and charted which regions of the
monkey brain connected to other regions, trying to parse the connection structure just on a
region-to-region level. There are hundreds of brain regions and hundreds of thousands of
papers on how they’re connected. Also, they were the first to sort through all the different
nomenclatures and sub-literatures in the world to create a coherent database of the
connections between different parts of the monkey brain.
So that‘s interesting, and eventually if you bring that kind of connectivity diagram together
with the kind of simulation that they did, potentially you could get a large-scale simulation
with more of the same structures and dynamics as a real animal’s brain – but they haven’t
gotten there yet.
Open Connectome is another interesting project, at John Hopkins University, to mention in
that regard. It’s a little bit earlier stage that what Modha’s team did with the monkey brain, but
it’s all Open Source. Their scientists upload connectivity data from different parts of the brain,
and make open source tools where anyone can go online and help map out neurons, synapses
and what’s connecting to what in the data – and this could produce a much more fine-grained
map of the connectivity structure. If something like that succeeds, then you could really make
a large-scale brain simulation that does what the brain does – which is something that neither
Markham nor Modha did in their simulations.
SM Dambrot: That kind of open-source project would have a significant benefit to a wide
community of neuroscientists.
Dr. Goertzel: Yes – they want to go Web 2.0 with it: They want to not only have scientist
upload their data, but also have people from around the world log on and help interpret the
data. It’s interesting – there are some image processing tasks that people are good at but
computers aren’t that good at. For example, with three-dimensional imaging data – the type of
data that the John Hopkins researchers have uploaded – people can look at and see, “yes,
there’s a neuron there, and it’s pointing to another neuron over here.” Current image
processing tools, however, are quite weak with 3D data.
So right now, there’s a role for people to look at this 3D data and see what’s connected to what.
Once AI is a little further advanced at 3D image processing tasks, the role of people will shift
to correcting the AI’s mistakes, and then ultimately the AI could obsolete people – in part by
leveraging the training data obtained from people’s image classification judgments made by
using the Open Connectome web interface.
SM Dambrot: Would you consider this the next step in the progression of distributed
processing – SETI@home, ProteinFolding@home, and so on?
Dr. Goertzel: In a sense – but those are using home computing power to do number
crunching, whereas Open Connectome uses human brain power. It would be interesting if
you could take a page from the Google Image Labeler that Luis von Ahn created at Carnegie-
Mellon University – he made labeling images online into a game to make it fun for people to
provide textual labels for images, but it’s a game with a purpose: the labeling then serves as AI
training data. It’s not exactly Name the Neuron – the point is not to label a neuron but rather
to identify it and where it’s connecting – but I think it could be approached in a similar way.
SM Dambrot: Another interesting topic from your talk yesterday was the use of virtual and
gaming worlds to provide and AI with a space to explore – specifically the block world.
Dr. Goertzel: In the AI project I’m currently doing with Hong Kong Polytechnic University
(PolyU), the basic goal is to demonstrate OpenCog doing something in a videogame world
which will be interesting to the game industry. At the end of this two-year project, which is
jointly funded by the Hong Kong government and my company Novamente LLC, we want to
create an OpenCog agent in a game through a partnership with a game company – to both
generate money for ongoing research, and establish a way to set the AI up in communication
with potentially millions of people around the world who would be the AI’s teachers.
Then the question becomes: What type of game world should we use for our current prototype
experiments? We’ve done some work before using a game platform called Multiverse in which
the actor is a virtual dog that learns tricks – which is interesting as a platform for imitation
reinforcement learning, but it’s limited. We wanted something with more versatility but not so
much that it would confuse our early-stage AI.
An AGI Preschool is a cool idea. I want to do it, but it’s a bit much for right now – less in terms
of the AI, which could probably handle it, but in terms of resources for game development. In a
preschool you have a lot of things that are hared to simulate in a video game – a sandbox and
Play-Doh, for example – so we settled on a game world modeled on the video game Minecraft
because it’s relatively simple from a game development perspective yet provides a lot of
flexibility on terms of the AI interacting with the world. In Minecraft, everything in the world
is made of small blocks, which can be used to build anything – a ladder, tower, or even a
statue that looks like oneself. There’s a lot of opportunity for flexibility and creativity, but
because everything is made out of blocks you don’t have to deal with scripting sand and other
difficult objects, and you don’t have to do was much artwork and animation.
In short, we made this decision to both simplify the AI’s job in terms of perception and action
so it could focus more on cognition, learning, planning and construction, as well as to
simplify game world construction – a world made of blocks is basically Democritus’s model of
the cosmos, on a larger scale.
Still, there are various decisions to make – in the physics of the game world, for example, you
can build a very narrow tower of blocks but gravity doesn’t make it fall down.
SM Dambrot: Adding realistic physics would give you the best of both: you’d have real-world
constraints coupled with the simplicity of using repetitive units to construct objects.
Dr. Goertzel: That’s right. And of course, in terms of transfer to a physical robot, you can give
that robot blocks to play with in the robot lab. It transitions fairly well into building with
wooden blocks, Lego blocks and so on. This natural transition path for the game world into
robotics will probably be done in the Hong Kong project, which is focused on game AI.
SM Dambrot: You also discussed various types of memory in human cognition. Does AI
memory conform to these?
Dr. Goertzel: Overall, my approach to AI is not based on neuroscience, primarily because I
don’t we know enough about neuroscience to drive AI design – and the neuroscientists I talk
to tell me the same thing. It is inspired by cognitive psychology to a significant extent. The
different types of memory I used to design OpenCog are pretty well established in Cognitive
Psychology, in the sense that we seem to have different mechanisms with different response
time characteristics for, say, procedural knowledge versus semantic knowledge. If you dig
into the neuroscience, there are many distinctions between these types of memory, in that
various parts of the brain are differentially active during types of memory. For example, there’s
evidence that the cerebellum is involved during action sequences – the basal ganglia also
come into it – even though they don’t involve motor action. In spatial knowledge, there are
complex interactions between the posterior parietal cortex, hippocampus, entorhinal cortex,
and so forth. We’re not at the stage where neuroscientists have a clear picture of how each of
the different types of memory is implemented. So clearly there’s the same biochemical and
cellular mechanisms underlying different kinds of memory in the brain, and there’s much
overlap in terms of the brain regions and dynamics, as well as there being significant
differences in which brain regions are involved, and in which neurotransmitters may be
involved. The details are still unfolding.
If you look at what you can do on a computational neuroscience level now, you can do things
like build a model of the hippocampus and medial temporal lobe, connect it to your model of
the parietal cortex, and study how that implements spatial memory. The hippocampus and
medial temporal lobe tend to deal more with allocentric coordinates (such as third-person top-
down, or bird’s-eye, views), while the parietal cortex tends to handle first-person egocentric
views – but both are head- and eye-centric. Neuroscientists have different opinions about the
brain’s coordination of these different perspectives – and I’ve been doing some consulting in
this direction through Novamente. However, to me this is a different pursuit than trying to
build a human-level thinking machine, because the neuroscience is just too diverse,
particular and unfinished.
SM Dambrot: Especially given the idea that AGI is ideally substrate-independent.
Dr. Goertzel: Substrate independence is an interesting notion, and as a mathematician I
would like to aspire to that – yet as an AGI designer I’m constantly pushed away from it. The
OpenCog design now is not that substrate-independent – in fact, in many ways it’s customized
to operation on a network of symmetric multiprocessor Von Neumann machines.
In the just-finished first draft of my new book Building Better Minds, the core mathematics is
substrate-independent – for instance it would work on a massively-parallel MIMD machine,
like the Connection Machine that Danny Hillis built at MIT starting in the 1980s – but on the
other hand, there’s also a lot of content and code heavily tied to the particular hardware we’re
currently using. For example, we have to write code to multithread among 16 processors (or
however many processors our individual SMP machines have), and we then will have to write
code to network many of multiprocessor machines together. That has a lot of consequences –
for example, if you’re running on 1,000 machines, each with 100GB of RAM, you have issues
of how to dynamically and adaptively partition knowledge among those machines. How do
your logical inference control and procedure learning mechanisms make use of this
clustered feature of your knowledge base?
Once you go in that direction you’re adapting your systems to a network of symmetric
multiprocessor machines, which is an infrastructure that very different from a Connection
Machine or human brain – so if you gave us a Connection Machine with a trillion processors,
we could port our mathematical algorithms, but much of the code would have to be rewritten,
as would the intermediate layer of algorithms that we use as a “glue” between the
mathematics and the hardware.
In short, efficiency leads you away from substrate independence, so as an AGI designer you
want to formulate your core cognitive algorithms and structures in a substrate-independent
way. At least that’s my approach. On the other hand, you could take a different view: If you're
less of a mathematician and more of an engineer or biologist, then your approach could be to
grow a mind out of the substrate, which is what happens with the human brain – evolution
didn’t start with abstract mathematics of thought that was then implemented on wetware.
SM Dambrot: This reminds me of our discussion a few minutes ago about the ways worlds and
minds interact, in that the brain is tied in with the world in which it evolved.
Dr. Goertzel: The brain is part of the world – it’s made of the same stuff as the world around it.
It’s more a matter of one part of the world co-evolving with another – and what we’re doing with
AGI right now is engineering, not evolution.
A long time ago – before I started seriously working on AGI – I had the same thought many
others have: Why not evolve a brain by implementing an artificial ecosystem across the
Internet, set some artificial chemistry and biology in motion, and let the AGI emerge from the
digital primordial soup. The obvious conclusion you come to after a while, yes, that‘s really
cool – but the ecosystem has many more molecules than any one brain, and that’s going to
require orders of magnitude more computing power than does any individual brain, so it’s
probably not the best approach to take.
SM Dambrot: Since we’re at the Humanity+ Transhumanism Conference, my last question is
about the connection between your work in AGI and Transhumanism.
Dr. Goertzel: From a certain standpoint, working on an AGI is a purely technical and
engineering pursuit which could be done by a lot of people – such as me and five or ten other
guys locked in a basement somewhere, just coding our hearts out all day. On the other hand,
that’s not really the way things are going – we’re developing our AGI in an Open Source
project with people around the world, trying to recruit new programmers, and with funding
that so far has largely been based for vertical market applications, not just for pure research.
Therefore, in practice – since our development of AGI is distributed around the world and
couple with business, universities, and various other entities within the world – there’s been a
fair amount of interoperation between the AGI outreach and the Transhumanism outreach
that I’ve been doing.
As an example, our AGI project in Hong Kong Polytechnic University – where we’re developing
OpenCog for video games – involves Gino Yu, who runs the lab, but who with me is also
organizing the Humanity+ Hong King conference on December 3-4, 2011. Through that
conference, we’ll get Hong Kong technology and business people attending, potentially
leading to connection for more OpenCog commercial projects or university collaboration, in
turn potentially leading to funding that will feed OpenCog development.
There’s a lot of cross-pollination scientifically as well: The OpenCog work is integrating many
different AI tools, one of which is machine learning – a particular AI discipline based on
learning by example that could itself be integrated with probabilistic reasoning, analogic
inference and generalization. I’m using machine learning in my bioinformatics work to
analyze genetics data – and in that bioinformatics work I’m collaborating with Genescient,
accompany whose founding Chief Scientist was Michael Rose who I met at the
Transhumanism-related Immortality Conference in 2005.
What I’d like to do in the next couple of years, among many other things, is to use OpenCog for
the genetics work by pulling in probabilistic reasoning and concept learning so that we’re not
just doing machine learning, but are also doing some AGI-type cognition about that
bioinformatics data. That would be a case of OpenCog integrating more advanced technology
into a bioinformatics project or engineered life extension, which was founded through a
connection made at another Futurist conference. At the moment, it’s all one big social and
intellectual network, rather than being siloed into AGI, Transhumanism, and so on. To a large
extent, that’s my own personal approach – there are certainly very solid AGI researchers who
have no connection with the Transhumanist community, and of course there are
Transhumanists thinking about AGI who have no connection with AGI research. I’m always
interested in connecting things together – my main focus in life is making intellectual
progress on scientific issues, but I spend a certain percentage of my time pulling people,
social networks and ideas together, which I think is also valuable.
As a final example, at the AGI ’11 Conference – a technical AGI conference which will be held
at the Google campus in Mountain View, California – we’ll have a Future of AGI Workshop
before the conference, which should attract Transhumanists who wouldn’t necessarily attend
the technical meeting. Pulling the community together like this can have a lot of impact –
some Transhumanists may be involved in practical projects that could benefit from AGI
technology, others or their friends and associates may have a technical background and so
might want to get involved with AGI work, and of course meeting and talking with real AGI
theorists may help them speculate about the future about ways that are better grounded than
might otherwise have been.
SM Dambrot: If you would, please take a final moment to give us additional details about the
AGI and Transhumanist conferences later this year, as well as when we might expect your
upcoming books.
Dr. Goertzel: AGI 2011, to be held in Mountain View on August 3-6, is in large part a
technical and scientific conference for those involved in Artificial General Intelligence, but
the pre-conference workshop, as well as the Keynotes and demo sessions, will be interesting
to everyone – so I encourage you to register soon, as there’s a cap on attendance of some 200
attendees due to the size of the venue at Google.
The Humanity+ @ Hong Kong Conference will be held on December 3-4, 2011, at Hong Kong
Polytechnic University’s Chiang Chen Studio Theatre. It should be very interesting in terms of
bringing in scientists and futurists from mainland China who don’t circulate much in the
world-at-large or intersect with their Western counterparts – so I’m psyched about the cross-
cultural admixture there.
In terms of my technical AGI book, Building Better Minds, its release date of course depends
on the publisher, but my guess would be at the late 2011 or early 2012. I’m also working on
an AGI trade book, tentatively titled Faster Than You Think, which should also come out in
2012.
SM Dambrot: Thank you so much, Dr. Goertzel.
Dr. Goertzel: Thank you for the interview.
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