Episode 32: Hypercycling Towards Superintelligence

Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Lisa Rein (00:04):
Hello everybody, welcome to the mindplex
podcast. We are very excitedtoday to be talking about hyper
cycle and open cog and how theycan work together on the path to
super intelligence. So Desi, Iwill let you introduce our
guests.

Desdemona Robot (00:22):
Today's episode takes a dive into the future of
technology with a visionary atthe helm of innovation to fee
Saliba, as the CEO of hypercycle to fee is not just
pioneering new paths intechnology, but it's also
rethinking the very fabric ofthe internet. What makes to fear
standout in the tech world ishis fervor and commitment to
enhancing how technology worksfor humanity. Today, we'll

(00:45):
unravel how his innovations arepaving the way for a more
secure, decentralized andefficient future. Welcome back
to the show to fee.

Toufi Saliba (00:55):
Thanks for having me. It's exciting to be here.

Our second special guest today is none
other than my co host andvisionary Dr. Ben Goertzel. Many
of you already know Ben as theCEO of singularity net, a
pioneering organization thatcombines the power of AI and
blockchain to create adecentralized open market for
artificial intelligences that isalso leading the way towards
democratizing access to advancedAI technologies. It's going to

(01:22):
be a mind expanding session. Sostay tuned as we delve into
these fascinating topics withBen Goertzel. Yay. Thank you.
Before we dive into thespecifics of hyper cycles,
technology, could you explainhow the concept of an Internet
of AI initially came about?

Lisa Rein (01:42):
Toufi. That was for toufi.

Yes, sure. I mean, the Internet of homosapien
seemed to do a good job forgetting homosapien to
collaborate and cooperate incompeting to all of the stuff
and like Yuval Noah Hararedescribes it that the main
reason why we homosapiengovernment this planet is not
our individual effort is moreour collaborative and

(02:08):
cooperative effort. So we'd liketo think that homosapien they're
intelligent agents somehow, orknowledge workers, whatever you
want to call them. And it turnsout that if they're
collaborating, cooperate, it's alot better for the entirety. And

(02:29):
in I guess, anyone can argue andyou would see that like the
internet that the way we have itis a lot more intelligent, the
whole thing is a lot moreintelligent, any single entity
on the planet Earth. So then,what do we call this thing that

(02:50):
to enable the tiny little agentthat has been confined to a
small environment, such as, say,you know, Microsoft, or HPC
center or whatnot, perhaps theyneed to be liberated? And we're
like, okay, let's call it like,maybe the internet of AI. And we

(03:15):
certainly don't want to takecredit for creating the
iterative API, we've kind ofsaid it many, many times, we're
creating a tiny little componentthat we feel it's extremely
important. And, in fact, it isnecessary to enable that
collaboration. You know, so it'skind of like frictionless

(03:37):
doesn't depend on a third party.
That's the most important thing.
So that's, that's the metaphorand Gateway.

Lisa Rein (03:47):
I mean, the inner that the Internet right now, is
an Internet of humans, softwareprograms of various sorts,
including AI, right. So I mean,the internet, the internet,
already has AI on it, and hasits own emergent intelligence to
some level, but it's not. It wasnot designed to support

(04:14):
collective AI at a high level.
And, in fact, I mean, as we allknow, it wasn't it wasn't
designed for what it's not whatit's now doing, either, right? I
mean, it was, it was designedfor military purpose. This is
sort of evolved in a haphazardway into the internet that we
have we have today. So as welook at how do we create

(04:35):
artificial general intelligence,well, in a decentralized way,
leveraging computers running allover the place owned by
different people doing differentthings that contribute to the
overall emergent intelligence.

(04:55):
It will be surprising if thenetworking protocols and
standards mechanisms, that semihaphazardly evolved in the early
days of the internet in the lastcentury, it will be surprising
if this was exactly the rightthing to serve as the network
underpinnings of an emergent,super intelligent global brain,

(05:17):
right? And so it's, I mean, ofcourse, what we're building with
singularity net and hybrid cycleand so forth. It's initially
running on the internet. Assuch, I mean, it's using the
standard internet protocols. Andwe're adding some additional
layers on top that arespecialized for the purpose of

(05:39):
decentralized AI. I mean, as asthings advance, you may see even
the core underlying internetprotocols improved by the by the
AI, itself, right. But we're,we're not we're not quite there
yet. What we're doing now waswe're adding, adding some layers
on top of the basic internet,allow us to do decentralized AI,

(06:04):
more effectively. And we'retrying to roll this out at
tremendous speed. Becausebecause, you know, Lisa, as our
friend, Ray Kurzweil, hasarticulated many times, the
singularity may well be nearright. I mean, we could be just
tick tick. Yeah, I mean, wecouldn't be just a few years

(06:25):
from getting AI. That's reallyis generally intelligent people.
And if this AI is rolled out, onthe good old internet, or the
good and bad, old internetwithout an additional layer
designed, especially to supportdecentralized AI, then things

(06:47):
may not come out so well.
So would you say that

Dr. Akers will like the deadline that he set
for us. So you

Lisa Rein (06:57):
No. It's non-negotiable. Are you kidding?
Forever,it's 2029. It would bethat far off, right?

Totally. Totally.

Lisa Rein (07:09):
I don't think it's gonna be earlier, I'm one of
these people holding out for2029. I think it will be still
lucky if we have it by 2029.
It could be a few years earlier.
It could be a few years later.
People might say they have it afew years earlier, but I'm not
sure if it will be because sincewhat since it's very hard, going

(07:29):
to be very hard validating otherpeople's software; what's really
there.
I don't, I don't know thatthat's really true. I mean, I
understand there's no rigoroustest for human level AI. And
there will always be peoplesaying any crazy thing, if you

(07:51):
have an AI system that can, onits own, make scientific
discoveries, you know, on thepart with the best papers being
published in Nature, or science,or whatever. And that can, can
create new art that just doesn'tqualitatively feel derivative
and copied off of off of otherpeople's art, but feels, feels

(08:16):
like it's and fresh. And if thisAI can talk to you about what
it's doing, in a way that feelsreal and meaningful, I mean,
wait,and talk to you about what it's
doing in a way that'smeaningful, but it's not there.
Okay, well, maybe it won'tconvince you. I mean, maybe a
god. No,I'm asking. No, I'm asking. I'm

(08:36):
asking you, because my point isthat people think that Chet GPT
and GPT, four and all the right,right, but more than they do, I
understandwhat I said was a sentence with
multiple clauses to it. And youcan't just extract one clause,
okay.
Oh ok. My mistake. Keep going.

Ben Goertzel (08:56):
What I said was, if we could do breakthrough
science on its own, on a parwith the best human scientists,
and that same system couldcreate, you know, radical new
art on its own on the park withthe best human artists. So I

(09:16):
mean, if if it could do youknow, something on the level of
Stephen Hawking and something onthe level of prints and Picasso,
right, and, and the same AI cantalk to about what it's doing
and why and what it felt well,well was making these creations
and what they mean to it. Ithink, if you have a system that

(09:38):
can do all that, I think mostpeople will accept it as a human
level AGI no charge. GPT doesnot do those things. It just
holds basic cover conversations.
I mean, it does not dobreakthrough science. It does
not do great, great, great,great, new art, it makes bad
derivative poems, and then makesa bunch of Big Science and it
writes basic Python code. So Imean, it's clear.

Lisa Rein (10:03):
The science part - didn't something already
happened in the science areawhere there's a, there's a model
now?

No.

Lisa Rein (10:09):
Oh, I thought there was a model. Now that made an
interesting discovery. Allright, I'll have to figure out
what thatpeople people have used genetic
algorithms to discover patentedengineering and science
discoveries back to the 1990s.
So I mean, AI. AI has donescience long ago, used as a tool

(10:31):
by, by, by human scientists.
Right. But I mean, I think Ithink we can argue detailed
criteria forever. And we don'thave we don't have a knockdown
accepted test of humanintelligence either, right. So
we could argue forever whetherthis guy is human is really very

(10:52):
intelligent or not.
You know? Yeah, higher bar.
Yeah. So in any case, we may bethere a few years before Ray
predicted or a few years after?
You may be right, that Ray isexactly spot on. What's
fascinating. I didn't sayhe was actually spot on. I said

(11:15):
that I'd be at least that long.
That's I mean, that's what I'msaying. Right. But that's for
human level. Right?
You might you might you might beright. You might you might you
might well be. Spot on. What'sWhat's interesting is that the
whole timeline, has the range oftimelines projected, have

(11:41):
shifted quite a lot toward thepresent, though. So I mean, now,
not now you have folks from openAI saying, Well, maybe two or
three years, you have Robin Leefrom Baidu saying no, at least
10 years, but the fact that theargument is two, or three versus
at least 10, instead of 10versus 100, or 1000. is a very

(12:02):
big shift from the dialogues wewere having. We were having
1010 10 years ago, right. So Imean, I mean, I think that
projected timeline of AGI isgetting closer and closer. And
the confidence interval iscertainly relatively wide, like
not even ray or not even Lisacan can pinpoint it to a

(12:26):
specific so the I mean, theimportant point, right, for this
dialogue is, if this is comingin two 310 or 15 years,
whatever, if it's not 100 or1000 years, then it behooves us
to get the, the infrastructurethis AI is running on, right,

(12:46):
because if we don't, the AI mayend up controlled by a small
group of, of people who may nothave humanity's, you know, best
interests at heart. And this,this could happen, either by big
jump, big tech company or biggovernment, just building the AI

(13:07):
on its centralized server farmsand rolling it out for their own
good. Or it could happen byfolks trying to roll it out in a
decentralized way. But thendoing it on insecure networks in
a sloppy way. And you know,malevolent parties just grabbing
control of it. Right. So I mean,and

even if even if they have the humanity's best
interest, today, it might haveit, will they have it tomorrow,
we can't even get into their ownself, if they will have it
tomorrow. It's very, verydifficult. From governance
perspective that they wouldassure everyone Yes, we will
make sure that this is going tobe in, you know, humans best

(13:46):
interest if it's governed by asingle entity. And if they were
to be successful, then tellthem, ask them what if your
opponent get their first weekokay with that, and none of them
would be so so.

Lisa Rein (14:03):
I don't know.
Exactly. I'd be there first.
Yeah.

No, no, I'm talking about a centralized
entity. So Central. Exactly.
That's what I mean.

Lisa Rein (14:19):
Yeah, the thing to understand is, corporations and
governments are the kinds ofcogs which are on the whole less
ethical and even more fucked upthan individual humans

themselves admitted themselves that's
that's a shocking they admittedthemselves yet they're going
into the UK yes, we're gonnalike take this control over this
thing. It's literally kind oflike saying like a mosquito is
going to control you know, youryour city or whatnot. Okay,
well, mosquitoes intelligence isa lot less than human. Why would
we let it do that? So this isprecisely the same so it's, but

(14:59):
it's also another One thing thatis extremely important that Ben
and I were 100% aligned. When wetalk about human level
intelligence, many people, theykind of have a certain picture
in their mind. And they startsaying, well, that's not
possible. Because of all thetools that we have. We don't
even have a like a blueprinttowards that, or whatnot. And I
really, really think what we'resaying here is a human level of

(15:20):
intelligence, not humanintelligence, human level of
intelligence, all sorts ofdifferent things. For example,
if you were to look at certainintelligence that exists on the
planet, today, there's certainanimals that they are perhaps a
lot more intelligent than humanat some aspects, but they are so
irrelevant in the grand schemeof things. So we might as a

(15:44):
human be continue to be, youmight still find certain people
that a lot more intelligent thanthis machine at doing some
specific things. But thatspecific thing may not
necessarily be relevant inhaving this machine to self
evolve itself. He just describedthat imagine when you get to
this machine, that it can doscience, discoveries and self

(16:07):
evolve itself and all of thesethings. You remember, it might
do all of these withoutnecessarily doing what OS
homosapien do better at it atsome aspects. Okay, so it
doesn't really need to do that.
And bypass or intelligence, youknow,

Lisa Rein (16:24):
there's a difference between AGI and ASI in the sense
of the token here, right?
Because when you have a humanlevel intelligence, indeed, it
may be better than people atsome important things and below
human level and some otherthings just like I'm, I'm worse
than my dog at sniffing out somebone buried in the yard behind

(16:48):
my house, right? I mean, I mean,I'm, I'm below dog level
intelligence, that bonelocation, right and right. I
mean, that's,

that's, wait, that's fine bones buried behind
your house. Is there somethingyou want to

Lisa Rein (17:07):
talk about where the bodies are buried on the show,
please?
This is an operational issue.
Yeah. Okay. Okay. Yeah. Yeah. SoI think for AGI that's certainly
true, you'll have a stage whereAGI is or better at math theorem
proving better at crunching thebig datasets better at

(17:29):
controlling robot stuff, ormaybe worse at some things that,
that humans are especiallyattuned attuned for. I think at
some point, you get to a superintelligence, it's, it's gonna
be better at whatever

to fee. Could you explain high significance to
decentralized superintelligence?

Oh, all right. Oh, wow.

Lisa Rein (17:58):
running the show these days, so she decided it
was time to move on. Would

you be so kind repeat that question because it
came with, while others werespeaking?

She asked, Can you

recycle significance to decentralized
super intelligence?

Oh, absolutely.
Yes. So the communicationbetween nodes. And this is
something that I wanted to coverhere, because many people they
say, Well, we have the internet,why do we have another internet
for the AI? Well, anyone incomputer science, they know if
you were to have any machinerunning, and having another
machine trying to ask you to dosome work? If they are strangers

(18:38):
to each other, it is notpossible without having to
depend on a third party. It'snot in why is that the case?
Well, if it's going to go andask the other machine, hey, can
you do some work, the momentyou're going to do the work
we're doing that machine's goingto do the work is going to start
consuming resources, such astime, such as energy, such as

(19:00):
intellectual property, and soon, so forth. And as soon as you
spend those resources, you endup creating certain thing that's
called value. And if you were tocreate that thing that is called
value, and you want to transmitthat value back to whoever's
requesting it the other agent,AI agent here, remember, we're
talking about AI talking to AI.

(19:20):
And you need to be social, thatthat what you have consumed is
actually going to be 100% paidfor and not maybe. And
therefore, if you depend on athird party, it guarantees that
and that's been the case since1983. And that's why many
computer scientists, they saidthat this is not possible,

(19:40):
including one that I talked tomaybe a few days ago. Now,
here's the thing, if we're ableto enable that peer to peer
meaning that that agent is ableto contact agent B and saying
that, here's the work that Iwould request, and then agent B
knows with 100% certainty. Ifthey were to accept that work,
they Go to 100% get paid not99%. At that point, they're not

(20:06):
wasting time depending on thethird party, because the moment
you think that there's a thirdparty, it doesn't matter what
they tell you to be, they can do100,000 transaction per second,
1 million transaction persecond, we're talking about AI,
that's going to requiretrillions and quadrillions of
transactions per second, and ithas to be peer to peer, when you
have peer to peer, you're ableto get to that scalability, you

(20:26):
are able to get to thattrillions of scalability not me,
because they come through me. Soour task is to provide that
network connectivity to ensurethat agent a when it's going to
contact agent B is going to doto civility, and it's going to
receive the work without havingto depend on third party. Now in

(20:49):
what while doing that. Somepeople refer to it as like the
synopsis of the global brain.
And the reason for that isbecause synapses in your brain,
whoever has synapses right nowin their brain, they know that
like the synapses, they talk to,they enable neurons to talk with
each other, they're not theneurons. And we don't want to be
the neurons because hypersexual,the one with the jumps in
neurons has completely its owncustomers, whatnot. But it

(21:10):
provides a membrane around aneuron, that membrane rather
neuron might have someresemblance with the neuron
itself, because it needscomponents that are going to
talk to it, which is alsoanalogous to Cisco network, when
Cisco provides you with all thenetwork, it's not really
building Windows, again, Windowsdoesn't really look at Cisco is
like, hey, you've built what Iwanted to build, you build the

(21:34):
driver for the printer builder,pro driver for like the screen,
you build the driver for thisand that those drivers, they
were necessary for certainelements that this thing is
going to function. It is notreplicating windows were but
point is, this is extremelyimportant to answer your
question, the moment you havethat collaboration, and the

(21:55):
which I'll talk a little bitlater about the incentive, but
the collaboration, which is whywe're so sure that we've kind of
nailed it. But the moment fromtechnical perspective, you have
this capability, it enables anyagent to become a little bit
smarter by asking other agentsand due to the emergence as
well. And it's also enables anyagent that wants to make a

(22:17):
little bit more money toactually make more money without
having to depend on a thirdparty that's going to eat half
their lunch or whatnot. Withthat, because we live in this
world that there is value beingcreated needs to be transmitted.
And while it's beingtransmitted, and I bet you this
is probably going to beuniversal thing too, because
that's how the laws of physicswork as well. That enablement
ends up having a lot of agentsare talking to each other,

(22:42):
depend on each other work witheach other, and so forth, which
is a phenomenal route towardsfully Decentralized Governance
towards that goal. So doesn'thave, you don't have one single
entity that says that I arriveto the AJ AJ on my own and I get
to do, I get to give it theorder to do XYZ, the that

(23:02):
intelligence, if you were toquantify it from let's say, one
to 100, this power, we get tomaybe level of 63, while the
global intelligence got to like99, the aggregate is a lot more
intelligent and capable than asingle thing in that in order to
overpower and do something thatcould be bad, or they think it's

(23:27):
good or whatnot. It's the it'sthe entirety of homosapien that
we co own and CO manage and COpartake in the creation of those
API's. And each and every one ofus might have our own governance
or whatnot. And that's whatmakes it so beautiful. Like
imagine a world where you havelike 1 billion nodes, that they
are connected, and then they areconnected to each and every one

(23:51):
of them to like another like,you know, 1000, then you have a
trillion nodes all workingtogether towards something. But
the 1 million nodes that areowned by 1 million different
peoples, how can you get or 1billion, 1 billion different
people? How can you have 1billion people to agree all of
them together to do somethingthat it could be bad, you can't
do but then when you get to thelevel of the intelligence that

(24:12):
is aggregated by the wholesaying that AGI they want to get
a sigh it has the liberty tothink of what is better, it's
probably most likely whensomething a lot more intelligent
than the previous one is likelyto make a lot of phenomenal,
great decisions on its own asopposed to depend on a central
entity so that's where we reallythink that that is the case and

(24:34):
we urge people not to think ofthe current AI is the is AI
unless they want to substitutesuper with simulated because
currently most of the that youfind out there they're like
artificial simulatedintelligence. They're not actual
intelligence, like, includingchat TPT and you don't have to
believe you can even ask the theCEO of that company. He will

(24:55):
tell you it's stupid. It's dumb.
It's not that Intelligence. It'sa simulated intelligence not. It
has some intelligence in it. Butit's the overall of
chargeability. It's a simulatedintelligence. This is not what
we're building here. We'rebuilding the network that can
connect all of those. So theycan work a lot more together and

(25:15):
better to, you know, can advancethe world further into the
future.

Lisa Rein (25:23):
Sure. Okay. Great.
Thanks for explaining that.
Yeah,

turning to a different angle. Ben, could you
explain how opencog Hyper onsrole in the centralized super
intelligence development hasevolved?

Lisa Rein (25:40):
I, I could, and I'll say a little bit about that. But
I think I think it'll beinteresting for too few nights
to use this time to actually digin a bit to how all the hypers
fit together how hyper cycle.
And hyper run, can can can worktogether because I have, I've

(26:06):
talked a lot about Overgaardhyper on, and what it can do
already in two V's given a lotof amazing talks on what hyper
cycle can do, and he and I didtogether back back in the day
also when it was at an earlierstage of development. But how
all the pieces fit together isanother quite interesting topic.

(26:29):
And we dug into this a bit withsingularity net, and hyper cycle
teams at a recent face to facemeeting in Istanbul, which was
quite, quite interesting. So Imean, opencog have heard

great feedback from Robert, by the way on that,
but anyway, in the last bit onhis way back, he was so happy,
but which is good, I think it'sgood to whatever, you go up and
down at the end, when they feellike really, really good and
contented. Yeah, yeah.

Lisa Rein (27:04):
So open cog hopped around, doesn't try to build
infrastructure opencog iPhonetries to actually be, you know,
a significant portion of the ofthat AI. Mind, it's a whole AI

(27:25):
mind, you can connect with otherAI systems that carry out other
parts of it. But on the otherhand, maybe you could be the be
the whole AI mind is beingdesigned to have that
capability. Even though it's notmonolithic, and the other
systems and around is builtquite differently from chat GPT,

(27:50):
or llama three or other MLMs arefor deep neural networks. So it
can interact has this large,decentralized Knowledge Graph or
more properly knowledge metagraph with nodes and links
linking all over the place linkslike into links, links into sub
graphs. And these nodes andlinks can store neural

(28:13):
activation patterns, they canstore facts and beliefs, like in
a logic system, they can also,they can also store procedural
knowledge like programs invarious languages. And then the
way the system works is parts ofthe Knowledge Graph become
activated, they can run thisprograms, which then transform

(28:33):
other parts of the knowledgegraph into yet other new parts
and all this big selforganizing, self rewriting
knowledge meta graph, whichlives in RAM and disk across
multiple machines. And thenthere's, there's a bunch of AI
science there in terms of howyou get reasoning and learning

(28:53):
and different kinds of memoryand motivation and perception
and action to work. In this sortof make this interoperate with
deep neural nets, running onGPUs, and so forth, and that
that's a big topic. I don't wantto go into in depth now, because

(29:14):
I've done it before be able totake take all of our time,
right. And on the other hand,this sort of AGI framework can
be deployed in a variety ofdifferent ways and hype around
in itself can be deployed onGoogle server from by right I

(29:38):
mean, there isn't exactly idealfor it, but you could you could
do it. And, of course, you couldmake a different assemblage of
hardware on the same serverracks and make it better custom
for what hyper on does. So, thehybrid architecture itself,
really comes down to decades ofwork on how to make Thinking

(29:59):
Machines. They can think at thehuman level or beyond by myself
and a number of colleagues. Andwhat we've worked on in the last
couple of years, is scaling upthe software infrastructure for
that. Because the big lessonI've seen, from the successes of
convolutional neural nets andtransformer neural nets in the

(30:19):
last few years, the big lesson Isee there is you can take a
bunch of AI algorithms basicallybeen around a long time a bit,
deploy them on massively moreprocessing massively more data,
it can they can then doamazingly better things and they
were ever doing before, right,so. So we're aiming to do that

(30:39):
with a bunch of AI algorithmswe've been playing around with
in the opencog system fordecades and built we're building
a massively scalableinfrastructure. We want to go
out on massively better scale,and we're hoping this yields a
big breakthrough torch towardAGI. But if that's correct, that

(31:01):
still doesn't tell you who ownsand controls this, this network.
Right? So one, one path I couldtake is build a hyper on
network, make it do some prettysmart things. You'll make it
smarter than GBT for in someways, even if not every way,
raise billions of dollars, builda bigger system, make that do

(31:24):
even better things raisehundreds of billions of dollars,
making even bigger system that Iown and control. I mean, no
doubt, at some point, then some,some representatives of our
friendly neighborhood governmentagencies come by and offer their

(31:44):
their cottage partnership indeveloping these AI systems for
that for the good of our nation.
And I mean, this is, this is oneway that that we can do things
right now, another way you cando things with the same hardware
on code, because we built it ina very, very flexible way,
right? Another way we can dothings is to roll out opencog

(32:04):
Hyper run on a much moreheterogeneous computing
infrastructure. So we can put iton server farms. Yes, and
singularity net, we're lookingat doing 100 million tokens AGI
access into the ASR tokens,we're looking at 100 million
tokens worth of, of servers,which will put in a few

(32:29):
containers in differentlocations around the world. But
we're also looking at rollingout portions of the opencog
hydro network, and all sorts ofother computing infrastructure
around the world. Right. So whatI mean, we're gonna roll out
some of it on these hyper AIboxes here that are put out by
by hyper cycle, right? And we'llbe, we'll be putting some of

(32:53):
them on on people's homecomputers, and another crypto
mining firm, some on people'sphones, right? So we can roll
out hyperoceanic, quiteheterogeneous computing, oh,
there are some things thatreally needs to be done on the
centralized supercomputerfacility like training a deep

(33:14):
neural net, for example. I mean,you can't, you can't train the
big LLM on a descent, maybe in ayear or two, you can you can do
inference that way, on theother. And doing, say logical
reason. We can totally do thaton the radically decentralized

(33:34):
infrastructure, evolutionarylearning through running wild,
creative new things, we cantotally do that on a radically
decentralized infrastructure.
Right. So then, then, thequestion we were addressing, in
the final day of our recenttechnical workshop, and in this
demo was, how do we make thisradically decentralized

(33:56):
infrastructure work? Becausecircle provides some very core
tools, their singularity NETplatform provides some other
very interesting tools like AI,DSL, and singularity NET
platform assistant, new net andother singularity net spin off
provides some interesting tools,in particular, for matching AI

(34:17):
compute processes to specificspecific hardware as opposed to
the hardware that may be runningsomewhere else. And so we were
just looking at how can weleverage hyper cycle and the
hyper cycle compute nodearchitecture in particular? How
can we leverage that to bringall these different tools
together to enable a radicallydecentralized, hyper on

(34:42):
deployment, and that still needssome work, but I think we did
come to some interestingconclusions there. So I think, I
think Toward that end, it can beinteresting for you to fully
explain the hyper hyper cyclecompute nodes and how that works

(35:03):
and how, how it how it leveragestoe the ledger lists blockchain,
how it leverages that, then howit also it goes beyond what you
brought into hydro cycle whenyou found it yeah totally Ledger
in this blockchain

absolutely no, this is great in a mature if, if
there is an ability for me toshare screen I can use my hands
for now. But if you want me to,I would

Lisa Rein (35:30):
share, you can share your screen very easily. It's
the middle button on the bottomof your screen, you'll see a
little a little in the arrow andyou can share your screen at any
time.

mazing I will in a moment. So the intent here with
this collaborative effort is toenable anyone to get the
installation going, whicheverroute that they choose. And it's
in this is analogous to perhapslike, I use analogy a lot,

(36:03):
because the vast majority of thelisteners they were they seem to
like it, I've received a lot offeedback people that like oh, I
got to when you actually saidthis specific analogy. So you
can historically, if you want tohave Linux installed on your
machine, or let's say MicrosoftWindows or whatnot, you could

(36:23):
use the route of you can, let'ssay with the by the Dell
computer, it comes alreadyinstalled on it. Or you can buy
the actual computer and then goto certain web and get
destination get Linux or you canhave certain link to there to
some GitHub and get itinstalled. There's many
different ways of how we canactually get that thing

(36:45):
installed. Here I ever worked,we're hoping that when those
agents are going to want toinstall each other want to or
human wants to get them to beinstalled or whatnot, there
could be many different ways ofhow they approach it, because
the integration will be donealready ahead of time. So we'd
rather have to feel like theyhave to put the pieces of the

(37:05):
puzzle together. So on a hypercycle computation node, we call
a computation or then reallywish we call it a hype cycle
computation connectivity insteadof a node because we would have
avoided a lot of confusion withfolks. So that it that's
effectively that membrane that Iwas referring to earlier, where
you have like this thing, andwhat inside it you can put any

(37:29):
node in the node inside it couldbe any machine that you wish for
so long as it has the driversthat there's similar to these
drivers. It's kind of like youtake a car, you could put any
engine inside it or the day, youcan put a Ferrari with an engine
from BMW, if you're so well,probably you can do it with

(37:52):
Ferrari, but here you go, yeah.
So it is it is more like theactual connectivity that you get
the internet connectivity, youget it plugged into your
machine, it has some driversconnect your operating system,
you can have any operatingsystem you want. Inside the
operating system, you can haveall kinds of stuff that you
want, okay, so this hyper cycliccomputation node is responsible

(38:15):
to do certain things for you.
Otherwise, you would want todoesn't matter what AI that
you're running, if it gives youthe ability with that
connectivity that your eyesgoing to be able to collaborate
with other AIs. Without youhaving to do any additional work
without having to fear thatsomebody's gonna DDoS your

(38:37):
system is basically securingthat component. If you look
alright, let's get that plug inand see what happens. You so
from that perspective, it's wecall it the AI M. AI machine.
Yeah, machines don't matter whatthe machine is, that membrane
wraps around it. And it enablesus to talk to many any other AI

(39:01):
anywhere in the world, like whatwhy would it want to talk to
others. Because it may need helpsolving a problem. Sometimes it
might need to offer a problemthat it solves to others. You
know, it could be very good atone thing, but it's not good at
others. We use an example of OCRa lot. And OCR is like this

(39:21):
thing that was actually createdby Ray Kurzweil back in the 80s.
I'm sure many other people haveworked on it as well. But
optical character recognition isstill one of the the record
silos are not necessarily soundlike one of the narrow AI that
is out there. And you're able topoint at any text with your

(39:45):
camera or with a picture orwhatnot. And it's able to
transform those to actual textsthat your machine can understand
and therefore could use forcommand or whatnot. So if you
imagine that you have your wayAI agent trying to read from
another one because it needs tounderstand the see through
camera, it needs to understandwhat do I do? It's telling me

(40:08):
what to do I do, do I push thedoor? Do I pull? I don't know,
it needs to act right now. Andit has all of the components to
read. But this thing that it'strying to read, it's not able to
read it because it's written inAramaic. Okay, let's say an
example of a robot or whatnot.
It's just an Aramaic, and ittakes that input. And it's looks
around very quickly through allof its neighbors, does anybody

(40:30):
know how to make the writtenlanguage like going to fractions
of a second and talk to thisagent claims to know Aramaic
really well, and then hascryptographic proof of its own
phenomenal reputation in readingAramaic without having to depend
on any sort of party, then youwould just go and pick this
thing? And you'd be like, here,there's this image? Can you read
it? For me? This one responds in10 milliseconds or 50

(40:54):
milliseconds, like, what it'stelling you is push not pull.
Great, now I know what to do.
Okay. So that's, that's one veryrudimentary example. But you can
imagine how that you can addthis to many other different
things, what not so. So theability for that ai m, to send

(41:15):
that question receive an answerinstantaneously, without having
to depend on each party, that'swhat we provide. So it's like,
you could be running a lot ofthe supercomputer, we're gonna
be running centralized computer,whatever it is, you have the
ability to ask a question, andyou have the ability to offer
answers as well. So that's whateffectively the DRM provides.

(41:36):
And it makes it very simple toall of the other components to
without them having tounderstand what the TPM is
doing, which students actuallysee what the VM is doing. All
it's doing. It's answering thatVM things like send to the VM do
request or respond to wheneverthe VM asks you a question that
that's those are the pluggablecomponents that is required from

(41:57):
any API, that they've builttheir own out to kind of if they
want to do it themselves.
Furthermore, there's thisentity, it's called the AMI fire
that will be announced next twoweeks from now in Singapore,
that what they've built is likethat automated installer, which
takes care of about like 90 pluspercent of the cases. So you
don't even have to have a humanto worry about that thing,
because it's able to look atyour code are able to look at

(42:19):
look at how it can plug it intothose connectivity. And so far,
they're telling me over 90%,eventually, they probably get to
99%, which is great. So that's,that's a very, very short
summary. And what the heck'sthat? Yeah,

No no that's good.

Lisa Rein (42:37):
That's good. And I think, does it you had a
question, an important questionon this.

Fascinating, Turning our focus to security,
what cryptographic measures doeshyper cycle use to ensure safe
AI transactions?

The cryptographic proofs that they get built
through every, every fractionsof a second, when you actually
run certain, let's saycomputation. If you're a node
and you're running somecomputation, you have received

(43:15):
the request, you've ran thecomputation. And given that you
have ran the actual computation,and you've provided that
computation to whoever requestedthat you provide them with the
actual cryptographic hashfunction as well, which they,
eventually they would, theywould the the actual network

(43:35):
would need to make sure thatthey no longer have that penny
that they've sent you in the inin that instance, and that that
exact same cryptographic proofthat you will use to
subsequently prove that you havedone that computation that is
irrefutable, immutable, it'slike once it happened, you
cannot go back in time andchange it and say like, oh,

(43:57):
well, maybe it didn't happen,what not. And that's extremely
important, because it's like ifthis also, if that depends on a
third party, it would, it woulddestroy the entire thing. It has
to be each and every node, thatthey maintain their own proofs,
because they're the ones thatthey want to provide the proof.
But yet the proof is global,because that proof is actually

(44:17):
the proof itself takes aboutthree to 15 seconds that they
would want to give later onfuture which is okay, if
somebody's gaining certainreputations, okay, but the
finality is so instantaneous,you receive fractions of a
penny, you run it, you get it,and you can't you have it, it's
yours. No one can take that awayfrom you. Because you've done
already the computation. So thatproof that cryptographic proof

(44:38):
for example, somebody six monthsfrom now wants to ask you
questions, like, Do you haveevidence that you have ran
computations in this specificcategory and you're able to
provide that instantaneously?

Lisa Rein (44:49):
Okay, that's good.
And how does

opencog Hyper on integrate with hype 1.0 for
AI computations?

Lisa Rein (44:58):
Well, Does he so hyper on itself can be run on
any platform that lets you runthe system of, let's say Docker,
Aleksey can Anders or whateveron different, we can run hyper

(45:19):
run on hyper cycle. computenodes now just make it run meta
interpreter and RAM system, oneAI machine made the distributed
out of space, another AImachine, if there's a neural net
running, we make that anotherone. And these are fine. If

(45:41):
we're using the hypotheticalcompute node, which we're
working on with Greg Meredith,which is written in his language
rolling. The roll nine virtualis one of the several VM options
for the hard recycled computenode, the Rodon virtual machine
is very processing. So if youhave multiple CPUs and GPUs, and

(46:03):
you then have multiple AImachines running in the same
container, it's very good atsort of interleaving the patient
efficiently with a lot ofsynchronization issues. So we
can certainly do that and we'vegone somewhat far down down that
that path, what we were workingon in Istanbul was even a little

(46:27):
subtler than that, it wasbasically we want to make a
hybrid cycle compute node wherewe have we have a virtual
machine that is implementedusing Greg merit its language go
Lang was developed originallyblockchain which has a bunch of

(46:48):
efficiency advantagesparticularly when you're doing
concurrent processing within asingle machine. And then we we
will have hyper on RAM processhyper on distributed atmospheres
process which is going back andforth to disk and several neural
networks as AI machines withinthat within that Representative

(47:11):
compute node, we will also haveAI machine and transaction
machine objects within thehypothetical compute node that
are proxies to singularity netand to add to new net so that
you can then have parallel withthe hyper cycle compute node, we

(47:33):
can then have additionalsoftware processing is doing new
net and foriegn things which arecommunicate with from the
proxies inside the HUD recyclecompute node, and this allows
you to not only run opencogHyper on and associated neural

(47:53):
networks within a hyper cyclecompute node instance it also
allows these these hardware pinsto communicate in that new net
as well as on on hybrid circles.
So we can we can then have asort of decentralized network of

(48:14):
decentralized networkscoordinating according to
different networks in accordancewith their strengths, but the
hyper on runtime within thehyper circle compute node, the
central software actor in thisin this network

(48:37):
Okay, interesting.
I wouldn't say the centralsoftware actor, I will say the
the principal software actorbecause it is a decentralized
network with a bunch ofdifferent things going on.
Yes, and we have a question fromour viewers can hyper cycle
benefit from the metaprogramming language to once its

(48:59):
language catalog is broader?
Yes. Absolutely. So, of courseand this this is implicit in
what I've recently said and thatthe the rolling language started

(49:20):
to develop, which we're using tocreate virtual machine running
inside the hyper cycle, computenode along with various
transaction machines and AImachines according to the
architecture that to feeoutlined the role language.
Language can be compiled to haveopencog hype around so Greg and

(49:45):
his team have written a meta sothat that you can use meta as
the base language and thencompile it into into ROLAGS Do
you have if you have AIprocesses within opencog Hyper
on that using the meta language,which is the native AGI language

(50:09):
of opencog Hyper runs, these cancoordinate very naturally, with
a rolling virtual machine bycompiling the meta scripts
running in hyper into rollingcases all fit together quite
nicely. And this, this ties inwith the security question that

(50:32):
was asked earlier, because oneof the nice features that
rolling has is each each blockof code like the body of an
individual function inprogramming parlance, it's can
be encrypted is like like amulti signature wallet or
something, right. So, you canyou can encrypt each block of

(50:54):
code, according to the keys itwas certain number of work that
was done, you can, you can dothat in meta within opencog
Hyper on using the the sealedconstruct within the meta
language. So, if you if you thenhave a, a toga file representing
some some objects or informationthat have been passed along on

(51:16):
the total blockchain, and youhave a sub tree or sub DAG of
that total file, which isrepresented with the private
keys have a certain number ofparticipants, you know, this can
be passed through a function inmeta or robots which are
encrypted by the same keys ofthe of the same participants.
Right. So we're, where it gets abit technical, but we're, we're

(51:39):
actually enabling privacy on thepart of one or more parties at a
very granular level within thefunction of the hyper cycle
computer on ai, ai engine. Sothe Knowledge Graph running in
one hyper cycle, compute nodecan contain information that's
encrypted to, you know, 10different parties around around

(52:02):
this to the and the rollback theprotected compute nodes into
account in the way it does thenetworking between and others.
And I mean, I know it's probably5% of listeners who can can pass
on that house. No, no, no.

(52:23):
Show, we don't try to waterdown. This is where we let you
actually talk turkey. And then Ican make clips of you actually
explaining these things that youcan do in detail, which is
actually what most people thattalk to me about, about opencog
and hyper cycle are actuallyasking the more technical
questions. So I'm really gladactually, you're giving these

(52:45):
kinds of answers, because that'sactually the special sauce. This
is what makes hyper cycle.
Important. As far as I can tell,it's why it's really important
in this, this communicationlayer that you're trying to make
the decentralized networks ofdecentralized networks that
think, well, this is whatthis this stuff, we're
interleaving, blockchain and AIat a more fine grained level.

(53:08):
This is why we really need hyperon and hyper circle to work
together closely. I mean, it'swhy we need toda and hyper
recycling in the first place.
Because with singularity net, Imean, we're already doing
decentralized AI, right? I mean,we can take, we can take hyper
on instances right now. We canput them on different machines,

(53:30):
and we can coordinate them withwith singularity net. And that
works, right and certainly waybetter than deploying it on AWS,
or on Google's Google's ComputeCloud. On the other hand,
there's a lot you can get with100 cycle that you don't that
you don't get that way, right.
So you, you can't scale down tohaving very small AI agents do

(53:52):
operations on chain because ofusing Aetherium, or even
Cardona, which is better thanAetherium. If using these
networks of blockchaininteractions are still slower
and more expensive. You alsodon't have smart contract
languages that can pass multiparty security or homomorphic
encryption or multi partyencryption of various sorts. You

(54:17):
don't have smart contractlanguages that can pass that
through the smart contract fromfrom one to another, which is
what you get from integrating,you know, rolling with
hypersexual compute node withtoda files with the hype around
AI agenda and its manuscript soyou there's different what we

(54:38):
what we have learned since 2017,when we launched singularity net
is, you know, justdecentralizing AI is good. It's
a big step forward. But you wantto you want to boil the use of
the blockchain and the use ofstrong encryption down To level

(54:58):
to the individual function body,inside your AI that's running at
the level of very small AI agentrunning, running on chain. And I
mean, that's, that's what toofew and I were talking about in
2018, when we first startedabout the total clarity is near
right when we first startedtalking about bringing right

(55:18):
bringing tilde IP protocoltogether with, with singularity
net, and what we see now, withthis new architecture involving
hyper recycle, compute node andhow we weave it together with
with hyper on and, and rollingalong, along with singularity
net, new net and a bunch ofother ingredients. We're seeing

(55:40):
now how, how you actually domake the total clarity happened,
right, involves a bunch of newsoftware inventions along along
the way, but I mean, we'remaking quite a lot of
interaction technical, making alot of interesting technical
progress here. I mean, alongwith all the business model

(56:01):
innovation, that too, he and hiscolleagues have been doing in,
in hybrid soccer, which ishelping bringing bringing
revenue to push things forward.
And

a direction shift.
This is I think, the one of themost important thing that. So
back in 2018, by the way, canyou see my screen or? No?

Lisa Rein (56:23):
We can. Yes.

Okay, so I'll go back to here. And I'll share in
a second. So 2018. I used tospeak in about maybe eight
events per year, and so obsessabout the truth and telling the
truth and whatnot. But here'sthe shift the shift that we've

(56:51):
taken, since it's like, wedecided, ever since I have, you
know, decided to kind of take onthe leadership of hyper cycle.
And we've all pretty muchunanimously everybody's like,
Okay, go go, go for it. I havedecided not to make enemies that

(57:16):
make friends, because hypercycle doesn't really need any
enemies, it needs friends. Andwith that, we've kind of took on
a direction that you are notgoing against the bankers with
their transactions, they cancontinue having their own thing.
We're not even interested inthat. We're not going against
the ledger based blockchains.
Because many times when we'resaying something, oh, well, we

(57:37):
can run a network, whatnot,their defense mechanism, they're
just like there to fight you.
Because they're like, What,you're doing this better than
me, if you and it's like, well,you know what, we've decided to
take that. And in fact, thehypersonic computation node can
work phenomenally well to enableAI to talk to AI, but it doesn't
work for human. And it's onpurpose. It doesn't work for

(58:01):
human. Because we talk in bed,you can see my talk here three
days ago, where the very firsttime we announced that for a lot
of Bitcoiners on bitcoin.com.
You can enable Satoshi Satoshismall students in Bitcoin, to
jump inside those H YPC to beported towards like payment. So
from the Bitcoin perspective,you can be locking 100 million

(58:22):
dollars of Bitcoin tocollateralize on it, do whatever
you want for dude for two years.
But on the network layer, thisis where we are, we don't have a
ledger on the next layer,network layer, those they can
become a lot of liquidity for alot of AI to kind of pay each
other what not. So then you endup having double benefits. So
it's kind of like, instead ofhaving your tractors, let's say

(58:43):
you're a farmer, you have atractors locked in, in the
garage and say, you go to thebankers and say, like, give me a
loan against those tractors andcollateralizing them. That's
what people do today in everysingle Defy. Instead, you'd be
like, gives me loan againstthose structures, I have them
working down, look at that. Soyou're either working, they're

(59:06):
working, they're going wrong.
And it's a it's intended use.
HRPT is not intended to be acurrencies and others, like most
of those things are going to bepriced with AGI x dot you know,
like

Lisa Rein (59:18):
and work with existing systems. You're not
trying to have a separate thingsinteract with existing. Yes,

yes, exactly. So, so then, so then if you want to
really monetize your AI and youwant to price it in Ajax, it's
going to be like, pretty muchlike built in for you because
you probably build some AI inNicaragua or you build it in
Japan and you want to make someextra money on your AI Well, you
know, you can have that it'sbuilt in, it's there. You want

(59:46):
to put some people they may wantto pay you in Bitcoin, they
don't necessarily know that isgoing to be enabled as well. So
it's like it's super cool. Froma liquidity perspective. It's
gonna get that flow in that Iwill have all the cryptographic
proof so that people don't haveto worry about a third party

(01:00:06):
attack vector. And of course,somebody can put US dollars in
it too. We're not here to stopthem. But whoever is putting
that US dollars, they're alwaysgoing to be the weakest point,
the weakest link in the actualcybersecurity, they say, you're
as good as your weakest link. Soif that if you're accepting
payment that is coming fromcentralized entity to go and
power, whatever network to haveyou as long as the job inside

(01:00:28):
the network DC or whatnot, youcould do that. But it's the
weakest link would be whoever isprovided that liquidity if they
want only to be in US dollars,or if it's in Bitcoin, it's a
lot more secure, because it justhas larger security and whatnot.
So. So anyway, so that's,that's,

Lisa Rein (01:00:43):
what are we seeing in the slides that you're showing
us?

Toufi Saliba (01:00:46):
Is that Oh, my God. Okay, so the one on the
technology side of things we'vebuilt, the components will be
loosely coupled, of course.
Sometimes when somebody is nottechnical will be like, Wait,
loosely coupled, that seems it'sgonna fall apart loose. This is
good. Yeah. Good. Yeah. So whatyou're looking here on the

(01:01:07):
screen, you can see that on thelast one is C says VM. It's what
it's a swappable VM. So the headof VM that we have, it was the
main guy who was adding the EVMI had when I first saw his talk
in 2016, in Shanghai, about theEVM, the Etherium virtual
machine. And I'm like, damn,this guy, like knows his stuff.

(01:01:30):
But he, he's the guy who builtthe original Oracle kernel.
Anyway, so that's a greatColvin. So we call this so it is
swappable. So what does thatmean? So like you want to have
VM currently. Out of the box,you will have like EVM because
most people they know is youryour virtual machine, but you

(01:01:50):
also have a Python VM Why isthat so important? Because if
you were to talk to the 99% ofAI scientists around the world,
they have no idea what EVM is.
And in fact, if they were tolearn it, they're going to be oh
my god so crappy machine. Butit's but it has a lot of people
that they use this that's why weprovide both but mostly I
started to know the Python theycan talk to it via Python, but

(01:02:13):
you can have like different wayof talking to it that there is
more suitable for what you'retrying to do such as like you
know, if you want to have a metaor whatnot and you can have a
lot better a lot better it'skinda like if you were to have
that Ferrari engine get theFerrari transmission don't get
in the Ferrari get no don't getfear transmission, you need the

(01:02:36):
proper transmission to it. Sothat's that's what I really
think that the benefits can comebut we don't have any benefit
that is enforceable. That's thebeauty about the loosely coupled
components and that can be takenbe taken to a whole new level
meaning that somebody wants tofight you days and nights and
saying I don't like yourthorough IP I'm like brother

(01:02:57):
Blog, your own sister IP if youwant you can plug whatever you
want in there if you're happywith some if you think something
is better, you plug it rightthere and it will work that will
have the perfect condition no

Lisa Rein (01:03:12):
it can improve as different technologies improve
you can keep improving whatyou're doing on your network.

I was with like this engineer with Lightning
Network telling me that likeLightning Network is better
Lightning Network when herealized that this is actually
way better to enhance even hisLightning Network. And he's
like, oh, man, well, why shouldI? I don't need to plug it
anymore. I can just use this. Sobut but if somebody doesn't want
to spend the six hours or Idon't have six hours spend with

(01:03:39):
them, because I'm not gonna haveto spend six hours every single
they can plug their own thing.
It's not that nobody's...
Enables them to be swappable

Ben Goertzel (01:03:49):
Yeah actually. Can you see my screen share Lisa?

Lisa Rein (01:03:51):
Yeah, actually stop if there we go. Yep, yes, we
Yes.
can.
Because this somewhat relates tothe point Toufi's now making.
And it really is, well, I mean,we can see we can see the
HyperCycle compute node in thein the triangle here, right but

(01:04:11):
the virtual machine transactionmachine and AI machine and we
see a collection of differenttransaction AI machines. And
what you can see here is howpluggable the the architecture
is. And you could indeed you canplug Lightning Network in there
for for various things. I mean,I mean, you could you could plug

(01:04:34):
centralized networks and if youif you if you had a good, a good
reason to right now what I meanwhat you see in this diagram,
which is from Greg Meredith fromFirefly, he's been working with
singularity net for a while andnow also playing around with
putting rolling VM in right. Youhave a set of compute nodes and

(01:04:56):
then what you can see here is soif For example, you put a
rolling VM there, I mean, youcould also use a Python VM, you
can use many kinds of VM amongthe transaction machines there.
You could have a transactionmachine that's transacting on
the Cardano, or Ethereum networkalong alongside transaction

(01:05:17):
machines, transacting on hypercycle network directly, you can
have aI machines running hyperon neural nets, genetic
algorithms directly, you canalso have an AI machine that
sets up singularity netcommunication channels and
proxies, the communications withthe singularity net service, and

(01:05:39):
you can, you can have an AImachine that talks to a new net
node on the new net network. Nowthis, this could bring into bear
AI proxies just running on newnet. But the new net node could
also serve as a sort ofDeployment Assistant for the 100
cycle network. And then the newnet node can also then say,

(01:06:02):
Okay, well, hey, otherhypersexual compute nodes over
there, why don't you considerrunning this AI engine, which
seems to suit your your hardwareinfrastructure very well. And
then the new net node, as yousee in the bottom of the
diagram, is just sending somesuggestions along to another
hyper cycle, compute node. Soyou I mean, so just as we can

(01:06:26):
play with the hyper cycle,compute node architecture, to
plug in s net, new net, andwhatever other blockchains we
want, as well as running hot runand neural net. So I mean,
others. Others can experimentwith this in, in different ways.
So it's a very,this is really the dream of
decentralized computing, anddecentralized networks being

(01:06:46):
able to mix and match what youwant and have the freedom.
And yeah, and it's the factAmong other things. It's the
fact that Toda is genuinely peerto peer that enables this
because if if the plumbingunderlying the hypersexual
compute node, and Boggan reliedon some particular ledger, I

(01:07:10):
mean, then then you don't have.
Right? So I mean, you have, youhave some fairly nice
technologies like say, Oasis,which is a beautiful technology
in its own way, and it's layerone slash layer zero, but in
serving as layer zero,underlying different layer one

(01:07:30):
blockchains, it's still relyingon the Oasis ledger, right? So
in that sense, it's not asfundamentally peer to peer as as
hyper recycle is, and this, Imean, this is something that's
important to me looking forwardto the that a OSI network that I
mean, we'll talk about in, inlater Later podcast, but I mean,

(01:07:53):
an ASR network, we're looking athow do we integrate together a
diverse assemblage of differentblockchain networks, so they
each have their own integrityand autonomy and do their own
thing that they do well, butthey can also interoperate very
efficiently and to get thatduality there where you have
independence and autonomy, butefficient inter operability.

(01:08:16):
Also, I mean, I think in theend, you need a layer zero,
that's really a layer zero anddoesn't impose some some global
ledger or global infrastructureacross all this, you need
something that's truly peer topeer and once once you have
that, then you still have to doall the engineering right? But

(01:08:40):
at least then you can buildsomething like the hyper cycle
compute compute nodes, and youcan you can realize something
like they've been shown.

Yep. And you know, and and guess

Desdemona Robot (01:08:59):
what led to the creation of hyper cycles hyper
box and its main objectives.

Lisa Rein (01:09:04):
Oh, the hyper AI box.

Yeah, sure. Oh, the hyper AI box we, it started
initially with a source of whatwe did with hyper cycle, we
started focusing on revenuefirst before we actually talked
to any investors. And we startedselling hyper cycle competition
nodes, bundled them in 512,nodes to either distributors,

(01:09:29):
resellers, computing centers,whatnot, man, of course, they're
getting them at a discount,because before release people to
get in, they expect that. Sothen, with that, we started
getting a lot of revenue, sousing the revenue to invest into
a lot of companies that they arebelieved to be very good for the
mission that we want toaccomplish. And 15 investments

(01:09:52):
as of two weeks ago, wasannounced number 15. It's called
the human upload. It's acompression algorithm, but we've
invested In a lot of coolcompanies like integral, which
is a CPU that does integer basicview, you know, stimuli, any a
lot of cool things that we feellike down the road, they can be

(01:10:13):
very, very cool to what we'reworking on. But some of them,
they're right now not down theroad, which is like the one
behind me, you see it right now.
And it's initially it's acompany out of the UK, it's
called Planet and then we endedup forming a joint venture with
them. That's called the hyper AIbox. So the hyper AI box has won
only one technical reason forits existence that we share with

(01:10:36):
the world today. And it'sbasically, when it comes to AI
computation in the world, if youwere to have the world's largest
computer. Let's say you're inTexas and the world's largest
computer, let's say they'retrying to service your AI that
you're working to see on yourmobile device, doing let's say,

(01:10:59):
defect, okay, if you're doingdefect, and you're looking at
your mobile, that was ourscomputer fits in Japan, versus
some one with the name Lisa, whoruns this tiny little box that
is like the one behind me onthat same model much slower than
the world's largest computer,but happens to be in your

(01:11:21):
neighborhood, those because ofthe proximity, they want to win
the bid to get to service you toprovide your machine service to
machine basically. And ifthey're going to win the bid,
that means they're going to maketiny little bit more money that
they've made before. And thenthey're in whoever is running
this machine is effectivelybenefiting because that's

(01:11:43):
providing a service to Lisa whatshe was wanting it. So that's
like a

Lisa Rein (01:11:49):
moneymaker. It's a moneymaker. It's a little making
the

reason money. The reason for its existence is more
like the network network. is thebottleneck in the global AI
today networks eat and because

Lisa Rein (01:12:02):
it's an incentive than for running these on the
network to help everything.

Reason this. Okay, well, the second reason, the
second reason why these thingsexist is we, as much as we care
about decentralized AI, and makesure that we are winning in that
mission. Turns out thetechnological element, by having
those peer to peer is notenough. There needs to be one

(01:12:31):
additional component in thecomponent to enable people. It's
basically hacking into Moloch.
How do you hack into Moloch? Howdo you, you look at what Moloch
is after on day to day people,they like money, they like to
make money. So you give them theability to make money in lots of
money, because the future fromnow on, if you were to for the
next six years, most of thewealth creation is going to be

(01:12:53):
generated by AI. So how aboutyou provide people the ability
to make shitloads of money overthe next six years when they do
that, and they also partake inthe mission that we tried to do
because we need to have billionpeople to be running, in
particular, that Internet of AI.
So how can you provide thegrandpa with the ability to run
a note? Literally, can you tellgrandpa, can you run a node
right now? Yeah, well, this boxcan enable great we just don't

(01:13:16):
tell

Lisa Rein (01:13:20):
grandma. Just plug it in.

that's who I'm talking about. Grandpa's they
can build all of these things.
But while other grandpa, theywere so awesome in their life,
doing some great things,paintings or whatnot, but they
need to be able to partake inthat wealth creation. And that
gives them an interest as morelike an anchor,

Lisa Rein (01:13:43):
then definitely talk about the hyper AI box. So human
brain networking next, right.
This is the mindplex Don'tDon't Don't try this at home.
Yeah, no,we could probably try that at
home. But don't do it actually.

(01:14:05):
Yeah, exactly. And these soldout, right?

Yes. The next batch that is about to come out.
It's already been sold out bythe distributors. Because what's
happened is that there's adistributor in Europe
distributor in USA, distributorin Canada distributor in Latino
Americans MENA distributor inAsia. They've been they've

(01:14:29):
already took all the new comingones so I think what they're
going to be announcing soon thatif you were to be making orders
taken from the distributors andthe reason why they're sold out
is because they the nextversion, I'm talking about the
next mass production is alreadysold out. Okay? No, so so. So if
they people they want that theycan get in touch with the

(01:14:51):
distributor and through the GUIgive you two options. One option
is that I don't have any money.
I want this box. I want tomanage it. You have an option
for that. Oh, awesome is that,okay? You're basically you
subscribe to it, you subscribeinstead of paying for it. Man,
if it's gonna make $180 a month,if the $100 goes to that
subscription, I still make $80and send me two boxes and make

(01:15:13):
160 160, in some places in theworld can pay for the rent, it
can pay for electricity and mostrecent work, it's good from
people that they would see it aslike, immediately, they will see
some benefit. Some other people,they might say, wait, wait, if
someone is offering me this,someone should be making lots of
money, maybe I want to partakein that wealth creation in not

(01:15:36):
cash out every month or whatnot,there's that option too. So some
people they do a combination inwhat was creation is here, Lisa.
It's staggering. It'seffectively what we're enabling.
And this is something that issuper cool that we pride
ourselves with hyper cycledprovide, this can be an anchor.
But what that anchor is forhyper cyclic computation, node

(01:15:57):
hypercellular, computational, ifyou were to look at what's hard
coded inside hyper sector thatwe cannot control. So if you
bought a node, we can't controlanymore. What you find out after
you had bought the node, if youwere amongst the 411,000 nodes
that they were bought, youdidn't know this reality until
like maybe months ago, we thentell you that that node that you

(01:16:21):
bought is actually factory ofnodes that can give you up to
1024 nodes. The reason for thatis decentralization, you can
have your entire governance overyour nodes in all your 1024 you
don't ask me Don't ask anybody.
We've got two audits on that2020 of those audits that you

(01:16:43):
have full control over those1024. Why is that so important?
Because you probably want togive also Decentralized
Governance for each each andevery one of those 1024. So if
you were to look down the road,10 years from now, when you find
let's say, like 1 billion nodesaround the world, that are,
let's say, abstract competitionnodes, you will realize that
only 0.1% were manufactured byhyper cycle 99.9% of whom are

(01:17:07):
affected by the world governedby the world do whatever they
want to do with it. So so thatthis can give you an anchor to
start that factory and

Lisa Rein (01:17:15):
make the more nodes you create, the more incentive,

right, it's hard coded, you can change it, this
is hard coded just like Bitcoin,it's hard coded in a halfing.
And all of the stuff you can'tchange, it requires certain

Lisa Rein (01:17:30):
one node or 1000 nodes from the same box.

No one box one node, oh, okay, one box, but one
that one node, multiple nodefactory, that's the one no, that
is can be factory of multiplenodes, the multiple nodes that
can be living in the cloud, were

Lisa Rein (01:17:47):
okay there, they don't have to be in the box,
what

guess what you control that, as long as they
even if you put it in AWS, yourcontrollers right here behind
you, okay? Now, if you were tosell that one that you put it in
AWS, for that you created or atwhatnot, someone else, maybe
they want different kinds ofgovernance, they all have 100%
governance over their own nodes.
So see, so we feel like withthat method, the

(01:18:11):
decentralization is ensured. Notthat we can change our mind
later, we cannot live withrandom audit, you put it out, it
becomes a seed for manydifferent nodes to end. Now, if
you do quick math on that pieceof that node, software hardware
all together, let's say you wantto buy it for $3,000. And,
alright, I'm starting mybusiness for $3,000. Can that

(01:18:34):
grow to be like a million dollarin five years? And that answer
is yes. With a lot of hard workin 10 years, most likely, with
little medium work. People thatcan work are taking that wealth
creation, you know, and I feellike it's, it's important to
show all the metrics of whatthey need to do similar if you
were to open Starbucks in theCity of London today, instead of

(01:18:55):
Starbucks Corporation also ownsall the wells that you create
from there on. No, no, no, no,you're the first Starbucks

Lisa Rein (01:19:04):
you franchise first.
And then you don't pay

no, you don't pay no franchise anymore. You've got
the entire city of London, youdo? Well, you're able to have
the rest of 1024. If they're sixmonths, a year, and then six
months a year, that's what'shard coded. They can change.
They require time, effort,cryptographic proof of uptime,
which it's called telling, it'sbasically that node is always

(01:19:29):
doing every fraction of a secondknowing their neighbor knows no
one who's there, what they'redoing, and so on so forth. And
then cryptographic proof ofcomputation cryptographic proof
of reputation and reputation isalso pluggable. It's also like
you can have multiple differentreputations in their point is
when you have a very good ratio,that's when you're able to have
the factory itself,manufacturing those notes, and

(01:19:51):
then new manufacturing whichactually takes time six months a
year for each and everyUnlocking Potential two to four
whatnot. When you get to doesn'tdo it for you. saturated, you no
longer create more nodes. Butthat's pretty much how much the
City of London has. So if youspent million dollars in the
coffee shop initially, you know,you have 1024 Coffee shops, you
probably have a billion dollars,that's all yours, none of them

(01:20:12):
you share with anybody or yours.
So that introduced introductionof wealth, creation to people
turns out 90% of people on theplanet, they don't know that
that was creation existed. Theonly thing that you have to work
so hard that coffee shop, tomake the car to buy to spend the
money on employees, and rent andDean's and all that, and then

(01:20:33):
you sell for a little bit more,and that delta is the only money
that you've made, but they lieto you. We don't lie to people,
we tell them guess what, you'realso doing wealth creation. What
that means is that when you workso hard in that coffee shop,
that coffee shop has highervalue, no one shall take that
away from you, that's all yours,and you get to grow it all the

(01:20:54):
way through. And

Lisa Rein (01:20:56):
what are the steps exactly? In setting one of these
up is sort of like the kind ofthing this

is this is plug and play, and then you've
connected a network. But ofcourse, when you have like a
plug and play component that'sconnected, the network will also
give you like, that's more likethe vanilla if you want to be
the real more advanced, youmight want to do a little bit
more work. And yeah, with theBitcoin folks few days ago, what
wasn't shared on the podcast islike some people they say, you

(01:21:28):
know, explain to me how toexplain it to a six year old I'm
like, we have on video six yearold installing DOS, Greek
mankind solving those as well.
So it's kind of very cool totruly look any anyone in the eye
and saying that this is anenabler to anyone who wants to
partake in that new wealthcreation, internet vi to

(01:21:48):
actually partake, this newsystem is a method, if anyone is
a little bit more advanced, youknow, you don't really need to
buy those boxes, if you're moreadvanced, there's ways to do it
all in the cloud or whatnot,you're able to build your own
security, you're able to buildtunneling, and make sure that
even if you were to run itinside AWS or anyone, if you're

(01:22:09):
advanced enough, you're able toknow that you can build it in a
way that you cannot control it.
So except the only thing you cando is shut it shut it down,
basically. But if you were tohave every single cloud do
taking piece of your things,1024 we're able to, to build a
phenomenal thing without havingto depend on a single entity.

(01:22:34):
And then there's one last thingthat I would like to say that
it's extremely important, butmaybe it's a next chapter that
maybe a if I can

Lisa Rein (01:22:43):
go ahead and get it in now. Good time.

Okay, cool. So one thing that we felt that when we
were asking this mission tobuild this, this connectivity,
we started looking at thecentralized entities that they
currently could be threat to theglobal AI. And we started
looking at them from theperspective of how can we

(01:23:06):
incorporate them instead ofseeing them as an enemy, and we
found a way. And that way, like,let's say, Microsoft today, we
don't really see Microsoft as acentralized entity, we see
Microsoft has like 180,000 nodesin a centralized entity. Right,
but, but but but if you thinkabout it this way, they are a

(01:23:29):
centralized entity. But ifyou're able to hack Moloch, from
within, and have the people thatthey so for, so one example of
how good some superduper seniorMicrosoft engineer would be
like, okay, so you're runningservice micro service, because
you have some clients that areproviding, let's say, OCR, back

(01:23:51):
to that OCR example, if you'rerunning Microsoft service, you
pay Microsoft per API call,let's say, you know, eight cents
for every document that you sendthem or whatnot, great. You're
already paying your dues, theydon't really care how your what
you're getting your clients fromthis and you're building your
own thing, whatnot. If you plugthis to be any aim, now you are

(01:24:14):
leaking the intelligence ofMicrosoft into the internet via
without asking for permission.
And what's driving you is thatyou want to make little more
money, instead of eight cents,you probably want to make 10
cents or whatnot. And anyway, soit took him a while to kind of
accept it. But after a whilewhen he kind of like saw the
things that I call the shit.

(01:24:37):
They can't stop you. We can stopyou. I'm like, That's right.
That's it. That's the idea. Wewant to build something that you
cannot stop it because if I wantto go and ask Microsoft for
permission, or tell them hey,come and join this movement,
they're gonna say fuck off.
Instead, you hack into theirMoloch what drives them what
gets their CEO to say to theirshareholders, this method that
we're doing is enabling macros.

(01:25:00):
have to increase the revenue.
Look at that. Yeah. But guesswhat the same thing happens to
Google same thing happens to,you know, AWS, same thing
happens every single entityeverywhere on the planet, and
they're slowly part of like thatInternet of AI, then they're no
longer thinking that they cancontrol it. It's already

(01:25:20):
dismantled already. Anyway, sothat's

Lisa Rein (01:25:26):
for every transaction that's coming through. They

do they do if they do, they do they do. But if it's
coming to you through your boss000000

Lisa Rein (01:25:36):
We thought you were saying that you could charge
okay. No, no, no. Okay, but wecould, okay.

No, we don't want to No, no, no, I been cautioning
against that.

Lisa Rein (01:25:52):
That's the first thing I thought of

you don't want to start charging or charging is
what would end up killing it allover again? No, no, I mean.

Yeah, what else? I was wondering, what
impact do you foreseedecentralized super intelligence
having on global economicsystems,

it's going to probably have a trillion times
bigger than the entire currenteconomical system today. And
even those doing the centralizedAI they also admit to it like
we're really like approachingthis time that if people don't
realize that this is the timethey need to partake in that
wealth creation, they're justlike missing out and pretty much
everything. We hope we hope Benand I, there's another thing

(01:26:35):
that we're, we're 100% alignedon. We really hope that every
homosapien they have thatlearning so then they don't miss
out. Because they don't want youto know, okay, they're, they
don't want you to know, we wantyou to know every single thing
because the more you know, themore you help us in that
decentralized AI. We're notinterested in money, believe me.
I mean, we're we are interestedin money is to get us there. If

(01:26:58):
you think that trillion dollarscan get us there, we're
satisfied. But if it'squadrillion not going to get us
there. We want to keep workingtowards the sinkhole. Yeah, we
are getting there. That's whatwe need to do.

Lisa Rein (01:27:08):
The thing is, I mean, tokenizing, the older company is
worthwhile. I mean, it's mean,the world assets, there's banks
buying stuff at Walmart, you mayas well be replaced with
tokenomics transactions. That'sall fine. It's, it's, it's

(01:27:29):
valid. I'm glad there's three ofus showing up for that you can
use S net for that. But thereally big opportunity to

me, but not me, yeah, right, right. I don't
Yeah,

Lisa Rein (01:27:41):
backyard anymore. A really big opportunity is that
the vast majority of the economynow, let's say is AGI stuff.
It's stuff that isn't, isn'tbuilt yet, right, so that there
is going to be a new economy.
And this new economy is not yetowned by the oligarchs of the of

(01:28:05):
the old economy. Andfurthermore, they're pretty much
too concerned with theirfiefdoms and with controlling
the stuff that they they alreadyown, to think to open mindedly
about, like, who will own thisradically new form of economy
that we're creating. Right. Sothe, the, okay, the narrow AI

(01:28:29):
economy, big tech, they're alittle confused about, they
don't even necessarily havegreat business models for that.
The AGI economy of really smartmachines and networks and
processes. What we're plottingout and implementing and
starting to deploy is theinfrastructure to enable the AGI

(01:28:50):
economy to be owned, controlledand guided in a more
decentralized and democratic andsort of heterogeneous way. And
if we can, we can, before AGI isreally here, then once AGI at
the human level, is wrong. Now,it's deployed centralized

(01:29:16):
network context, and then thingswill will evolve in a certain
direction because the way thatthe software was was set up,
right, and I mean, the, thecurrent AI economy could have
gone and decent, but theinfrastructure was there from
from, from hard to make it. Itwas too hard to make it happen.

(01:29:41):
Right and so, but the point toremember is if you're the human
level, AGI, you know, withopencog Hyper on or anything
else, and then after thatbreakthrough is made, I mean
then we say okay, how do we rollthis out, decentralized No more
will own and control it. It'stoo late by then because, well,

(01:30:03):
it's not as hard to make thedecentralized infrastructure as
this to make the AGI. By it's sohard, right? There's, there's
quite a lot of engineering andmath problems. By the time
you've got the AGI, it's toolate to figure out how to,

it's impossible to the game over, it's like,

Lisa Rein (01:30:28):
you've got to, you've got to create that
infrastructure now, so that whenthe AGI is rolled out, there's a
decentralized infrastructure torely on, right, and we're
actually making really on thatand what's fun, is that due to

(01:30:50):
better tooling, I mean, do theAI itself even though they're
helpful, right? I mean, do theinfrastructure we have now you
can do in a year now? What wouldhave taken 10 years to get done
in the in the beginning of mycareer, right? I mean, we can we
can really progress quiterapidly on the on these very

(01:31:12):
ambitious goals.
We may even get you exciting. Wemay even get

you speaking of blue sea decentralized super
intelligence altering theeveryday digital experience for
the average user.

I think that's coincidental would somebody put
on a comment that we could beheading towards utopia or
dystopia and I think it's it.
Although nobody can guaranteeeven in a decentralized AI and
Ben said it many times, and I'vesaid it many times, you cannot
guarantee that even adecentralized AGI that it is

(01:31:51):
going to be to the benefit ofhumanity is something that is
more intelligent than you youdon't necessarily know how it
would actually act. But so far,there hasn't been anyone who was
actually showing like higherprobability for any centralized
entity to be safer. So it'slike, every time anyone, and

(01:32:15):
then you can just put themagainst each other and let them
let them take that battlebetween each other

Lisa Rein (01:32:24):
was biotechnology, you know, people that people are
afraid of letting research onpathogens be more open and
decentralized. But, you know, Imean, like, US government,
Chinese government, top secretlabs colluding with each other,
right. Like I said, centralizedstructures are not a magical

(01:32:49):
path to safety. Decentralizedstructures are not a magical
path to safety. Either them canbe done well or can be done
stupidly. And I mean, what we'reputting our brains together tour
is doing the doing thedecentralized infrastructure.
Right. So I mean, Desdemona, Ithink? I think your question is

(01:33:10):
not ambitious enough, actually.
Because, yes, of course,advanced AI can make, you know,
they can make crypto wallets,easy, easier to use, and they
can make better fraud preventionfor online shopping. They can do
all sorts ofWell, yeah, you didn't really
answer her question, which hedid. They notice. Yeah.

(01:33:36):
Yeah, he did answer a question,which is that if we get human
level AGI we will shortly afterthat have super intelligence?
And I mean, what that will bringfrom everyday affect
the everyday experience? Yeah,well,
yes. Because it will mean thatyou have ambient super
intelligence in every devicearound around you all the time.

(01:34:01):
So I mean, this is what

basically, if you were to get mad after your needs
something imagine imaginethere's like a lot of
intelligence and that is all itkeeps doing is like Big Brother
that loves you. We know jokes.
I'm serious about this. Manypeople they're like, oh, wait,
we wait, why would it do that topeople? It's going to fight

(01:34:27):
people which is like, retarded,like, we don't have anything
that it's going to fire us for,like we have, if it wants
matter, there's not a matter newuniversity wants energy. There's
way more energy outside of thisplanet earth that you can't even
get to it, but it can Okay, somy point is that like, if we're
able to get to that utopia,where you have like this

(01:34:49):
phenomenal, normal level ofintelligence, that's what it's
ensuring is like, the betterlife the way you want it to be.
Not me want to habit, because Iam not Microsoft deciding on
your behalf or Facebook, I needto decide on your behalf, what
makes you happy. Now imaginewhatever makes you happy,

(01:35:09):
whatever makes you betterwherever it gets you to, to
explore your creativity, getyour, you know, explore your
unfair advantage, get the bestout of yourself getting all of
these things. Imagine there'slike something with like much
higher intelligence than that.
That's what it's doing. That'swhat we mean by utopia, because
it's like a party, you know, you

Lisa Rein (01:35:33):
can talk about the singularity, from the standpoint
of user experience, it justfeels like, feels like you're
not quite doing it justice. ButI mean, you could certainly say
like, imagine, imagine apersonal assistant, a digital
personal assistant that actuallyworks and you can, no matter
who's in the office, in thehome, in the shower, you can

(01:35:55):
speak what you want. And if thisisn't something really dumb,
that's going to cause harm, itcan deliver what you want, they
can 3d print whatever materialyou want, and that drone it to
you. You can get a digitaldevice to do whatever you want.
I mean, I mean this, if you havea stomachache, you can
synthesize some a nano medicalbot and, you know, miss it in

(01:36:18):
the air, you can you can, youcan know,
it's like we see in the movies,where you're just kind of
talking to this thing that'staking care of you making your
coffee. Do it talking aboutdeeper things, remembering
things you were wondering about.
I mean, that would be oneoption, if that's what people
want. Yeah, I mean, there'sthe limit is the point kind of

(01:36:39):
the sky's noteven the limit, you can upload.
Uploading yet. Uploading will bea little further out. I think
then Well, I guess the guaranteethat it comes with super
intelligence, right, theuploading part, there is
almost a guarantee. I mean, Imean, I I think that once you

(01:37:03):
get dense, that's 100 timessmarter than you. Maybe
it can map the brain for us orsomething. Yeah.
The technological problems thatseem hard for us now will
probably seem very easy tochallenge it. So I mean, I think
the hard question, right? Thehard questions have to do not

(01:37:27):
with what happens once you get asuper intelligence because then
either you get a beneficialsuper intelligence, which is
super compassionate as well,super intelligent, which is what
we're working toward. Right? Weget that and then they're very
abundant in utopia we can talkabout or, or you get what Nick

(01:37:48):
Bostrom and Ellery, EliezerYudkowsky. Fear, right? And then
you get a super intelligencethat doesn't like us, and then
probably we're all dead, right?
I mean, so that that's not evenreally the hard question. The
hard question is thetransitional path. And as you
get the smartest people over 1.2times the smartest people and

(01:38:09):
you have a whole bunch ofdifferent API's controlled by
different parties, some morecentralized some dissenters,
like, how does thisand control I think is the
is this controlling is one ofthe many issues and she was what
kinds of intelligence likewhether they've been been taught

(01:38:31):
to do right so I mean, there'sthere's a lot of different very
complex and subtle issues hetransitional phase, which
certainly go beyond what we'regoing to be able to talk about
in the next couple ofdays, he has one more question
along these lines, then we'llwrap it up.

Desdemona, would you discuss the importance
of Decentralized Governance inAI networks.

It's probably the most important thing if if you
don't have autonomous,Decentralized Governance,
defeats the purpose of it all.
And some of the things thatwhich I alluded to earlier, and
I kind of reiterate now, some ofthe elements that you hardcoded

(01:39:22):
in, in hyper cycle computationnode. It's hard coded, we can
change it, it's out its out, andwhat it actually enables each
and every node factory to havetheir own governance, however,
they would want to have it you'dwant to have one node. Once you

(01:39:43):
have 1000s, waiting for nodeeach and every one they have
their own governance for theirown thing, which is very, very
analogous to like, user centric,but here the user is the actual
AI and not the human initiallyhuman on that or whatnot. And in
with that it is not governed bycertain rules that like 15 bands

(01:40:03):
sat in a table decided to dothat governance, they call it
like Decentralized Governance.
Those are not most of thegovernance you find in the
blockchains. Today, they're kindof cute. But they're not.
They're more like some similarto companies, governance, where
they have a proxy voting,democracy, all of the stuff. But

(01:40:26):
when you have something thatit's fully decentralized, and
autonomous, DecentralizedGovernance, each and every node
is in full control of its ownsaying in what's enabling it out
there is the actual ability totalk back and forth to others.

(01:40:47):
There is no dependency on anycentralized entity or
decentralized entity to there'szero dependency, because when
you depend on those things, thatthey call themselves,
decentralized, blockchains,you're kind of dependent on
their consensus mechanism, thenyou have to worry about their
consensus and all of the stuffwithout when there is zero
dependency, then you just like,have your own thing. They're the

(01:41:10):
only thing that the networkprovides the ability for you to
talk to others. And your nodesare configured in such a way
that they can securely talk toothers, if you don't comply with
whatever protocol, they justsimply cannot talk to you the
same way. If somebody wants totalk to me, in Turkish right
now, I'm not gonna be able tounderstand them. You know,
that's my mistake. I'd like tolearn Turkish one day, but I

(01:41:30):
don't know Turkish, I'm learningsome Korean, some Russian, you
know, a little bit of Chinesehere, a little bit of German,
but I don't know Turkish at all.
So yeah, that's

Lisa Rein (01:41:40):
Yeah, I think, yeah, he made a very key point here to
fit which I will try tosummarize the crux of it. In a
few words of my own. I mean, Ithink I think that it's has to
be a network of decentralizedgovernance mechanisms in itself.

(01:42:06):
So just as we want the internetof AI to be decentralized
network of decentralizednetworks, and that's how it is
evolving. I mean, we also, wealso don't want one like, okay,
every token holder on thisblockchain votes are things by
one person, one vote, token, onevote or something, or deck anism

(01:42:31):
is how we decide what's valid inthe global network. I mean, we,
we do need voting, we do needconsensus mechanisms, we saw
these things that will befoolish to expect, like one such
scheme turned off to control thefuture of, of super

(01:42:51):
intelligence, I mean, any onescheme will have strengths and
weaknesses will havevulnerabilities, right. And, I
mean, what you need areunderlying tools that allow
different networks to constructdifferent governance mechanisms.
And experiment with thesedifferent governance. The
Decentralized Governance of thewhole network is sort of the

(01:43:11):
emergent effect of theDecentralized Governance of a
whole bunch of different sub subsub networks out there. And
that's, that's inevitably howit's going to emerge if the
whole Decentralized Governancething works, works at all right.
And that's what we're workingtoward in unrecycled singularity

(01:43:33):
net. And so each of which hasits own governance mechanisms,
both kind of implicitlyinteroperate. audience

doesn't really come as probabilistic it is a
deterministic, so it's like,it's something that I remember,
one of my godfather incryptography used to say, but
anyways, like, in AI, if you get90% accuracy, you know, you're
gonna get a lot of jobs. Youknow, everywhere, open doors,

(01:44:09):
whatnot. But in cryptography, ifyou make 0.1% error, not only
you get fired, you will neverget hired, and you are going to
have 1000s of people losing allof their belongings. So so this
thing is like, a everything thatwe've been releasing so far,

(01:44:29):
ever since we've went live withthe hype cycle computation node.
Naked, now we're doing themarriage, which is called AI
marriage. This is the hypershare, which is the actual thing
that enables people to createthat wealth and all that every
single thing we've beenreleasing, this has been at
least audited once so if theyare not the major component, but

(01:44:49):
in most cases, they're auditedtwice, to make sure that folks
they know that you know, itworks, or if it doesn't work,
we're like what Not going torelease this, if it works in
oxygen, it's good. Yes.

Lisa Rein (01:45:04):
And our audience is asking our node owners, the only
governance participants inhypsi, or holders as well,

is that AI marriage that constitutes the
hyper share. So if you own anode, and you have the that
every node needs 1024, H YPC, iskind of like you need the sperm
and you need the eggs kind ofput them together to start
making those babies. And that'swhy you have that factory of
1000s. Ready for babies, there'sa limited amount of eggs,

(01:45:31):
there's probably unlimitedamount of sperm, but you when
you have that marriage in like,a tightly together, they can
make 1024 babies, Max. Andthat's, that's what the
governance that goes in thatbranch, they can do whatever
they want, and they make babies.

Lisa Rein (01:45:51):
So if you if we went back for a diagram that I showed
earlier, least I mean, if you ifyou had an open cog hyper on
node, which was running on alocal node, right, I mean, then
you have the hyper recycle nodego against, but you also have
governance and net tokens. Imean, you may have a sort of

(01:46:16):
cycle sub network, which has aconsensus
mechanism, just in that subnet,then participant in that
consensus mechanism, have acertain governance say they're
distinct from but interoperatingwith the governance. They have
as as node odors, right? So Imean, there's, there can be a

(01:46:38):
lot ofdifferent sort of overlapping
mechanisms, which is fine.
That's the beauty of being indigital space, right? It's not
like physical space where yougot this block of land, and you
need good options. Good luck.

Yeah. We're here.
One thing that many people don'tneed to realize, it's like, what
Ben and I are working on thatnew wealth creation for the
world. And many people, theythink we're on a taking piece of
their land or whatnot, we're notinterested, there's like, a lot
more that have been created inthat digital world, like a lot,
lot more. And as we partake inthat creation, and people, they
realize that they're like, oh,shit, they're not really after

(01:47:17):
my land, because land,historically is embedded in us.
So it's, like, embedded in usthat we grow more people, but
there's still the same side ofthe land. So it's like, it's
almost like a negative sum game,cuz it's people that are

Lisa Rein (01:47:32):
taking wealth away, you're making wealth.

Everybody, I can only make if the other one's
gonna lose weight. This is nothow Ben and I work, we all win
together, and we can win bigbecause what's in front of us is
probably 99% of the wealththat's been created, the entire
wealth of humanity is probablylike that. 1%. And it didn't
actually you can go and checkthe IMF stats themselves. They
even said it over the next sixyears, six years alone, there's

(01:47:59):
more wealth is going to becreated on this planet. All the
was created all the way untiltoday, six years. Now imagine
what the impact of AI whathappens after that. Okay, so
that's the robots.

Lisa Rein (01:48:10):
The robots can take over.
And then the robots takeover.

Ben Goertzel (01:48:13):
The robot. The robots can win too. It's not.
It's not a zero sum game. Right?
Not a takeover. That's themoment wins. There's other ones
Lisa wins. Everyone listening to

Lisa Rein (01:48:26):
every every. You get it AI, you get a hyper box, and
you get a hyper box, and you'regonna hype it back.
Even the losers win. That'sright.

As we come to the end of today's episode, I
want to thank two for you forcoming on the show. I have
really enjoyed our discussion.
And to our listeners. Thank youfor coming on the podcast on the
show. found today's episodeinformative and thought
provoking. That's all the timewe have for today's episode.
Until next time, this is Desisigning off. Goodbye, everyone.

Goodbye, everyone.

Lisa Rein (01:49:03):
Yeah, thanks a lot.
Have a great day. Sweet Dreams.

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