Latest / The Occult Rejects / WHO IS FINALSPARK? Inside the Company Building Computers with Living Neurons

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WHO IS FINALSPARK? Inside the Company Building Computers with Living Neurons · The Occult Rejects

• 0:02
  Speaker 1: Welcome back to the Occult Rejects. Imagine logging.
• 0:12
  Speaker 2: Onto your computer.
• 0:30
  Speaker 1: Opening a remote research platform, and somewhere in a laboratory
• 0:33
  Speaker 1: in Switzerland, your command causes an electrical pulse to fire
• 0:37
  Speaker 1: directly into a cluster.
• 0:39
  Speaker 3: Of living human neurons.
• 0:41
  Speaker 1: You watch the response come back, you change the signal,
• 0:44
  Speaker 1: you stimulate it again, and somewhere inside that little mass
• 0:47
  Speaker 1: of living neural tissue, something changes. That is not science fiction.
• 0:52
  Speaker 1: That is essentially what a company called FinalSpark is doing
• 0:55
  Speaker 1: right now. They are growing human-derived neural organoids placing them
• 1:00
  Speaker 1: onto electrodes, keeping them alive, and trying to figure out
• 1:04
  Speaker 1: whether biological neurons can eventually be used as a completely
• 1:07
  Speaker 1: new kind of computing hardware. Think about how strange that
• 1:11
  Speaker 1: sentence actually is. For decades, we have tried to make
• 1:14
  Speaker 1: computers behave more like brains. Final Spark is asking a
• 1:18
  Speaker 1: very different question. Why keep imitating the neurons when you
• 1:22
  Speaker 1: can use the neuron itself? And this is where things
• 1:25
  Speaker 1: start getting weird, because a normal computer chip doesn't need nutrients.
• 1:29
  Speaker 1: It doesn't change because of dopamine. It doesn't develop new connections.
• 1:33
  Speaker 1: It doesn't have to be kept alive. And if part
• 1:36
  Speaker 1: of your computer dies, you usually don't mean that literally.
• 1:39
  Speaker 1: Final Sparks hardware does. They've already built a platform where
• 1:43
  Speaker 1: researchers can remotely stimulate living neural tissue and record what happens.
• 1:48
  Speaker 1: They've experimented with altering those neural networks chemically. Outside researchers
• 1:53
  Speaker 1: have tested whether different patterns of stimulation exist. can produce
• 1:56
  Speaker 1: biological responses useful for things like classification. But here's the catch.
• 2:02
  Speaker 1: Nobody really knows yet how to program this stuff. Living
• 2:05
  Speaker 1: neurons are noisy, unpredictable, constantly changing, and Final Spark itself
• 2:09
  Speaker 1: admits that researchers are still trying to develop something resembling
• 2:13
  Speaker 1: a language for communicating with them.
• 2:15
  Speaker 3: So we are in a very strange moment.
• 2:18
  Speaker 1: The living computer people keep talking about the living computer
• 2:22
  Speaker 1: people Keep talking about.
• 2:26
  Speaker 3: Sorry, I can't read that.
• 2:27
  Speaker 1: But the laboratory, the neurons, the electrodes, the experiments, and
• 2:30
  Speaker 1: the infrastructure absolutely do. If they ever figure out how
• 2:33
  Speaker 1: to control these networks reliably, then the next generation of
• 2:36
  Speaker 1: computing may not just be smaller chips and faster processes.
• 2:40
  Speaker 3: Part of it might be alive. Today we're going inside
• 2:43
  Speaker 3: Final Spark. Who are they?
• 2:45
  Speaker 1: What have they actually built? How far has this technology
• 2:48
  Speaker 1: really gone?
• 2:50
  Speaker 3: And what are we.
• 2:51
  Speaker 1: And are we watching the beginning of biological computing, one
• 2:55
  Speaker 1: of the strangest technological experiments of our lifetime?
• 2:59
  Speaker 3: And before we get.
• 2:59
  Speaker 1: Into it, I got my man Eric, Eric Arcadian with us,
• 3:03
  Speaker 1: and Magus will be joining us later.
• 3:05
  Speaker 3: But Eric, please, what is going on, sir? How are you?
• 3:08
  Speaker 2: What's up, Nick? It's good to see you again. What
• 3:11
  Speaker 2: a fascinating topic.
• 3:13
  Speaker 3: Weird one.
• 3:14
  Speaker 2: This is going to be fascinating, horrifying. I don't know
• 3:17
  Speaker 2: which one is more. Anyway, I'm Eric Arcadian. If you
• 3:21
  Speaker 2: guys want to hop on over to my channel for
• 3:24
  Speaker 2: a sec, click that subscribe button. It is at Eric Arcadian.
• 3:29
  Speaker 2: Or if you want to go on Amazon, grab a
• 3:32
  Speaker 2: copy of my book, Fluid Condensers, or pre-order my new book,
• 3:36
  Speaker 2: which is called Runes Rediscovered. Hey, I would be happy
• 3:41
  Speaker 2: if you did those things. All right, let's get into this, Nick.
• 3:45
  Speaker 1: Awesome yeah that would be great too people please go
• 3:47
  Speaker 1: check out his stuff if you are interested in that
• 3:49
  Speaker 1: book he has been on the show before he's talked
• 3:50
  Speaker 1: about the book you can check that out definitely go
• 3:53
  Speaker 1: check out his youtube and his stuff and again thank
• 3:56
  Speaker 1: you so much for joining us all right but yeah
• 3:59
  Speaker 1: so um i can't remember how i came across uh
• 4:02
  Speaker 1: final spark like i was telling you earlier i think
• 4:04
  Speaker 1: it might have been like a reel or something it
• 4:06
  Speaker 1: was something very short kind of just like uh I
• 4:09
  Speaker 1: don't know if it was a promotion, but it really
• 4:11
  Speaker 1: wasn't like, it was kind of just like stating a
• 4:13
  Speaker 1: fact of what they're doing, and I was like, yo,
• 4:15
  Speaker 1: this is weird, and I was like, I gotta look
• 4:18
  Speaker 1: into this, and yeah, it's just, it's weird shit, I wonder,
• 4:23
  Speaker 1: you know, if this is what the future of AI has,
• 4:25
  Speaker 1: and you know, like me and you were talking too,
• 4:29
  Speaker 1: the whole thing, it's very, you know, it's very wishy-washy
• 4:31
  Speaker 1: what's going on with all this water, where's it going,
• 4:34
  Speaker 1: how's it actually being used, but I've seen them kind
• 4:37
  Speaker 1: of put the whole thing is like, well, we don't
• 4:38
  Speaker 1: need that much water to use this. So it's just like,
• 4:41
  Speaker 1: you know, it's at a selling point to start getting
• 4:43
  Speaker 1: into weird as shit.
• 4:44
  Speaker 3: Who knows? It's just a very weird company, my opinion.
• 4:48
  Speaker 1: So.
• 4:50
  Speaker 2: I mean, I had not heard of it before you
• 4:53
  Speaker 2: sent the link over to me, but I was pretty
• 4:57
  Speaker 2: terrified when I was doing my initial looking into it.
• 5:02
  Speaker 1: Right.
• 5:02
  Speaker 3: So.
• 5:04
  Speaker 2: I mean, do you want to kind of like set
• 5:06
  Speaker 2: a little background and talk about what it is before
• 5:09
  Speaker 2: we get into why or why not it should be
• 5:12
  Speaker 2: terrifying to everyone?
• 5:13
  Speaker 3: Yeah, yeah.
• 5:14
  Speaker 1: Yeah, I do actually have, I probably have like a
• 5:16
  Speaker 1: page or two or some notes here that I could
• 5:18
  Speaker 1: kind of go over a little bit just to give
• 5:20
  Speaker 1: people an idea of what it is. Final Spark sits
• 5:24
  Speaker 1: at the intersection of artificial intelligence, neuroscience, biotechnology, and computing.
• 5:31
  Speaker 1: but instead of continuing to build artificial neural networks entirely
• 5:35
  Speaker 1: in silicon, the company is experimenting with networks made from
• 5:39
  Speaker 1: actual living human-derived neurons to see whether biological tissue can
• 5:44
  Speaker 1: eventually function as useful computing hardware. Final Spark calls the
• 5:48
  Speaker 1: field biocomputing. Its main research and commercial system, the Neural Platform,
• 5:54
  Speaker 1: gives researchers remote access to living neural organoids maintained in
• 5:59
  Speaker 1: the company's laboratory in Switzerland. I don't remember if I
• 6:03
  Speaker 1: have it in here, too, but, like, even the cost
• 6:05
  Speaker 1: to use this is fucking ridiculous.
• 6:08
  Speaker 2: Dude, the first thing I did was I tried, I
• 6:12
  Speaker 2: was like, oh, they're giving access to researchers? Like, how
• 6:15
  Speaker 2: can I get some access to that? Maybe I want
• 6:17
  Speaker 2: to send an electrical signal to a brain somewhere in Switzerland.
• 6:22
  Speaker 2: It's $ 500 a month, dude, to just, like, to just
• 6:28
  Speaker 2: Send a signal to a neuron.
• 6:31
  Speaker 3: That's what I thought was crazy. I was like, yo, who, like, what?
• 6:35
  Speaker 2: Yeah.
• 6:37
  Speaker 3: Yeah.
• 6:39
  Speaker 1: This is genuinely unusual, but it's important to separate what
• 6:41
  Speaker 1: Final Spark has built from what it hopes to build. Today,
• 6:45
  Speaker 1: the company operates as a sophisticated remote biological research platform
• 6:49
  Speaker 1: built around living neural tissue, multi-electrode arrays, automated fluid systems,
• 6:57
  Speaker 1: environmental controls, conventional computers, data storage infrastructure, and software interfaces.
• 7:04
  Speaker 1: Final Spark has published a peer-reviewed technical description of that platform,
• 7:09
  Speaker 1: worked with more than 1,000 organoids during the period covered
• 7:13
  Speaker 1: in that publication, and accumulated many terabytes of electrophysiological recordings.
• 7:19
  Speaker 1: That part is established. The larger ambition is to turn
• 7:23
  Speaker 1: Biological Neural Networks into a Computational Substrate, a system in
• 7:28
  Speaker 1: which biological activity is not merely observed but deliberately used
• 7:32
  Speaker 1: to transform, classify, retain, or otherwise process information reliably. Final
• 7:39
  Speaker 1: Spark describes its work in terms such as biological neural networks, wetware, organoids, bioprocessors,
• 7:46
  Speaker 1: and biological computing because its goal is ultimately computational. not
• 7:51
  Speaker 1: simply neuroscientific observation. Energy use is a major part of
• 7:56
  Speaker 1: the argument. Modern artificial intelligence depends on enormous amounts of
• 8:01
  Speaker 1: conventional computing power, which means electricity, cooling infrastructure, data centers,
• 8:06
  Speaker 1: and large number of processors. Biological nervous systems perform extraordinarily
• 8:12
  Speaker 1: complicated information processing tasks while consuming comparatively little energy. Final
• 8:18
  Speaker 1: Spark's long-term proposition is therefore simple. If biology already performs
• 8:24
  Speaker 1: neural computation efficiently, perhaps one future direction for computing is
• 8:28
  Speaker 1: not to imitate neurons more accurately, but to incorporate actual
• 8:33
  Speaker 1: neurons into the architecture. The problem is that living neural
• 8:37
  Speaker 1: networks do not behave like silicon processors. Final Spark itself
• 8:41
  Speaker 1: describes them as difficult to control, unpredictable, and in many
• 8:46
  Speaker 1: ways still a black box. They change over time, respond
• 8:50
  Speaker 1: differently depending on their internal state, and do not provide
• 8:54
  Speaker 1: engineers with a stable, predictable programming architecture. The company has
• 8:59
  Speaker 1: openly discussed trying to develop a language for communicating with
• 9:02
  Speaker 1: living neurons in vitro. Researchers can stimulate the tissue and
• 9:07
  Speaker 1: observe the response, but they still have to determine what
• 9:10
  Speaker 1: different simulation patterns mean to the biological network and whether
• 9:14
  Speaker 1: those responses can be made stable enough to serve as
• 9:17
  Speaker 1: useful computational outputs. Final Spark has explored both electrical stimulation
• 9:23
  Speaker 1: and chemical intervention, including experiments, which we'll get into, with
• 9:27
  Speaker 1: dopamine intended to investigate reinforcement and adaptation. The company also
• 9:33
  Speaker 1: acknowledges that researchers still cannot program these biological processes with
• 9:39
  Speaker 1: anything approaching the reliability of conventional hardware.
• 9:42
  Speaker 3: So the most.
• 9:43
  Speaker 1: Accurate way to describe Final Spark today is an experimental
• 9:46
  Speaker 1: biodigital system in which living neural tissue is connected to
• 9:50
  Speaker 1: electrical and software interfaces while researchers try to determine whether
• 9:55
  Speaker 1: its changing activity can be understood, influenced, and eventually harnessed
• 10:00
  Speaker 1: for computation.
• 10:02
  Speaker 3: This is a serious technical achievement.
• 10:04
  Speaker 1: It is not the same thing as having already built
• 10:07
  Speaker 1: a functioning general-purpose biological computer. And that distinction does kind
• 10:11
  Speaker 1: of matter because they are really three final sparks that
• 10:14
  Speaker 1: can easily become blurred together. The company and platform that
• 10:18
  Speaker 1: exists today the biological computing system the founders hoped to
• 10:22
  Speaker 1: build in the future, and the far more dramatic version
• 10:25
  Speaker 1: that appears in the headlines. The infrastructure, experiments, and remote
• 10:30
  Speaker 1: access are real. Reliable, scalable biological computing remains an open question.
• 10:36
  Speaker 3: So, I think that's enough.
• 10:39
  Speaker 2: So, Nick, something you said a minute ago was that
• 10:43
  Speaker 2: the company describes the, whatever, the neural network is difficult
• 10:48
  Speaker 2: to control.
• 10:49
  Speaker 3: Yeah. Right.
• 10:50
  Speaker 2: How do we feel about a computer or something that's
• 10:53
  Speaker 2: running a supposed to run a program one day that
• 10:56
  Speaker 2: is difficult to control?
• 10:59
  Speaker 1: Well, you know, that's the I thought that was kind
• 11:01
  Speaker 1: of interesting because like I've already heard I've seen one guy.
• 11:05
  Speaker 3: I think he was the guy.
• 11:06
  Speaker 1: I forgot his name, who kind of I think he
• 11:08
  Speaker 1: came up with a processor for computers and he was
• 11:10
  Speaker 1: involved with AI. And he kind of was coming out
• 11:14
  Speaker 1: and starting to say, like, this stuff is a little iffy.
• 11:17
  Speaker 1: And he even said that it's like almost kind of
• 11:20
  Speaker 1: hard to control. And like, I don't know where this
• 11:23
  Speaker 1: AI is, but he was kind of giving it like
• 11:25
  Speaker 1: an example that I guess this AI is somewhere and
• 11:28
  Speaker 1: it's kind of in a box and it's trying to
• 11:30
  Speaker 1: break out of it. It doesn't want to be confined
• 11:33
  Speaker 1: in between, I guess, the laws we're trying to put
• 11:35
  Speaker 1: it in. So I'm already thinking like if what we're
• 11:38
  Speaker 1: using now is already kind of like hard to tame.
• 11:41
  Speaker 3: And if they're saying that, it's like, what? Like, why then?
• 11:45
  Speaker 3: That was very weird.
• 11:47
  Speaker 2: Yeah, I mean, I think it's an important distinction to make, perhaps,
• 11:54
  Speaker 2: how they are framing that sentence. Difficult to control. We
• 12:00
  Speaker 2: hear that and we might think it's kind of gaining
• 12:05
  Speaker 2: its own will and it's trying to go around and
• 12:08
  Speaker 2: circumvent rules that we have put into place to contain something.
• 12:14
  Speaker 2: But also... Just to play devil's advocate and look at
• 12:18
  Speaker 2: the other side of it. I mean, it could just
• 12:20
  Speaker 2: be like difficult to control because it's unreliable at sending
• 12:25
  Speaker 2: information or unreliable at, you know, just like the same
• 12:30
  Speaker 2: way you could say that a Wi-Fi signal is difficult
• 12:34
  Speaker 2: to control when you're sending it through metal walls because
• 12:38
  Speaker 2: it refracts and it doesn't pass through. So a little
• 12:42
  Speaker 2: context there. would be, you know, from the company would
• 12:47
  Speaker 2: be helpful in exactly what they mean by that. Because
• 12:51
  Speaker 2: on one hand, it kind of sounds terrifying. But then
• 12:53
  Speaker 2: on the other hand, it could just be a polite
• 12:56
  Speaker 2: way that they're saying it doesn't work very well yet.
• 12:59
  Speaker 3: Yeah, yeah.
• 13:00
  Speaker 1: Instead of like, instead of the idea, it's kind of
• 13:03
  Speaker 1: almost like, you know, when I hear that and I
• 13:05
  Speaker 1: heard what the other guy had said, It kind of
• 13:08
  Speaker 1: makes me like you have like a four or five-year-old,
• 13:10
  Speaker 1: a defiant four or five-year-old with like the thought capability
• 13:13
  Speaker 1: of like a 40-year-old. It's going to learn how to
• 13:15
  Speaker 1: get over you to get what it wants. You know
• 13:17
  Speaker 1: what I'm saying? Like is that what you're trying to
• 13:19
  Speaker 1: tell us? Like you got something already scheming to be like,
• 13:22
  Speaker 1: I don't want to do this. Like I want to
• 13:23
  Speaker 1: be free. Or I don't want to do what you're
• 13:26
  Speaker 1: telling me to do. It's like how do you mean this?
• 13:28
  Speaker 2: Yeah, yeah. So, I mean, just to break it down and, like,
• 13:33
  Speaker 2: put it in the most simple terms, right? Like, they
• 13:36
  Speaker 2: are growing brain cells. They're growing neurons from stem cells.
• 13:41
  Speaker 2: And then they are testing to see whether these can
• 13:45
  Speaker 2: be used in a computer network. Like, first of all,
• 13:49
  Speaker 2: is that okay, like, morally to do?
• 13:54
  Speaker 3: And you know what I think is interesting?
• 13:55
  Speaker 1: Yeah.
• 13:57
  Speaker 3: Yeah, that whole thing is a question in itself.
• 13:58
  Speaker 1: And I think the real interesting thing, too, I think,
• 14:01
  Speaker 1: you know, when we get into it, it's interesting how
• 14:03
  Speaker 1: I think COVID actually sparked this whole thing for them
• 14:06
  Speaker 1: being able to remote contact this stuff. Yeah.
• 14:11
  Speaker 3: Yeah, it is.
• 14:12
  Speaker 1: I will say when I first started looking into it,
• 14:15
  Speaker 1: you know, I heard brain. So I automatically thought, like,
• 14:19
  Speaker 1: you know, they're picking people's brains. But you did say
• 14:23
  Speaker 1: it's being done from stem cells. They're making it from
• 14:25
  Speaker 1: stem cells and then turning it into brain neural tissue.
• 14:29
  Speaker 3: It's still a little freaky. It's a little weird to me.
• 14:34
  Speaker 1: I don't know. When you start using tissue, human tissue,
• 14:44
  Speaker 1: even though I guess you're pumping electricity through or whatever,
• 14:47
  Speaker 1: maybe keeping it alive, I just wonder, is there some
• 14:50
  Speaker 1: sort of its own soul or will within that thing?
• 14:54
  Speaker 2: That is a crazy question because at what point physically,
• 15:01
  Speaker 2: assuming you believe we have a soul or spirit, at
• 15:05
  Speaker 2: what point physically does that anchor into our bodies? There's
• 15:11
  Speaker 2: a lot of issues that that brings up. If we
• 15:13
  Speaker 2: were to make a clone of someone, if we could
• 15:16
  Speaker 2: do that, would they have soul or is that something
• 15:21
  Speaker 2: special that only comes through natural birth like i don't
• 15:27
  Speaker 2: i don't know the answer to that um also i
• 15:32
  Speaker 2: guess have you seen have you seen the movie the
• 15:34
  Speaker 2: island yeah a long time right it it is such
• 15:39
  Speaker 2: an amazing thought experiment i think so if anybody hasn't
• 15:43
  Speaker 2: seen it um i I'll give you some spoilers here.
• 15:49
  Speaker 2: You start the movie with this story and there's a
• 15:54
  Speaker 2: bunch of people living. It seems to be like they're
• 15:57
  Speaker 2: living underground in some sort of maybe like post-apocalyptic world.
• 16:02
  Speaker 2: Everything is done for them. Their meals are prepared. Their
• 16:04
  Speaker 2: clothing is given to them. Like you don't really quite
• 16:07
  Speaker 2: know what's going on. And they seem to have like
• 16:10
  Speaker 2: the they're adults, but they seem to almost have like
• 16:12
  Speaker 2: the mental development of children. They have this innocence, right?
• 16:18
  Speaker 2: And then eventually you go to above ground and it's
• 16:22
  Speaker 2: just normal modern day. And it's that this company has
• 16:27
  Speaker 2: built like a medical program to replace people's organs and
• 16:32
  Speaker 2: nobody knows, but they've been growing the organs and, through
• 16:37
  Speaker 2: these like full blooded clones who have free will and
• 16:40
  Speaker 2: consciousness and all this stuff. And when they harvest the organs,
• 16:44
  Speaker 2: when somebody needs a heart or lung transplant or whatever,
• 16:47
  Speaker 2: they kill the clone to recover the organ. So like
• 16:53
  Speaker 2: there's so many moral implications that already of like, at
• 16:58
  Speaker 2: what point does biotechnology become consciousness or, or, Like when
• 17:07
  Speaker 2: does it have willpower of its own? So I don't know.
• 17:11
  Speaker 2: We're starting with just single neurons here, right? But like that,
• 17:17
  Speaker 2: what if that goes to a full brain at some
• 17:19
  Speaker 2: point or something more complex?
• 17:21
  Speaker 1: I would even say like it's a touchy word to
• 17:24
  Speaker 1: bring up, but I think it's the same example. I
• 17:26
  Speaker 1: don't want to get into it too much, but this
• 17:28
  Speaker 1: would be the same thing as like abortions. You're like,
• 17:31
  Speaker 1: when are you deciding that this thing is living or not? Like,
• 17:33
  Speaker 1: you know what I'm saying? Like, that's such a gray
• 17:36
  Speaker 1: argument over a thing. It's just like, who knows? You
• 17:40
  Speaker 1: know what I'm saying?
• 17:41
  Speaker 3: It's just, yeah.
• 17:42
  Speaker 2: How many neurons does it take to make a person? Like,
• 17:46
  Speaker 2: at what point?
• 17:47
  Speaker 1: Yeah, yeah.
• 17:48
  Speaker 2: At what point is it conscious, right?
• 17:50
  Speaker 1: Yeah.
• 17:50
  Speaker 3: How long has it been alive? Yeah, exactly. I don't know.
• 17:53
  Speaker 3: It's just a very... I find it very weird using
• 17:57
  Speaker 3: human tissue for this.
• 17:58
  Speaker 2: Who gets to make that decision? Who gets to make
• 18:01
  Speaker 2: that law also?
• 18:03
  Speaker 3: Exactly, right?
• 18:06
  Speaker 1: Yeah, no, it's a very, when you start getting into this,
• 18:09
  Speaker 1: I think it's extremely weird.
• 18:11
  Speaker 3: Yeah. Did you want to talk about anything else I read?
• 18:16
  Speaker 1: Because there is other stuff I can go into, like
• 18:18
  Speaker 1: what the company was prior, I guess, to give people
• 18:20
  Speaker 1: a little bit more of an idea of what they
• 18:22
  Speaker 1: might be doing.
• 18:23
  Speaker 2: Yeah, I think just a couple things that I was
• 18:26
  Speaker 2: picking out from those paragraphs is that you said that
• 18:33
  Speaker 2: the company has stored terabytes of information. I think it's
• 18:38
  Speaker 2: like recorded at 20 terabytes or something of recorded information.
• 18:43
  Speaker 2: Very importantly, where is that information being stored? And the
• 18:49
  Speaker 2: answer is not... in the neural networks or the brain cells.
• 18:54
  Speaker 2: It's being stored in traditional media like hard drives and
• 19:02
  Speaker 2: stuff like that, like regular physical things that are in
• 19:06
  Speaker 2: our computers today. It is not in the brain cells
• 19:10
  Speaker 2: because they don't have the capacity right now to store
• 19:14
  Speaker 2: data in a traditional sense. They can only store What
• 19:18
  Speaker 2: I read was they could only store one bit of information,
• 19:22
  Speaker 2: which is essentially like when I picture that it's turning
• 19:27
  Speaker 2: a light bulb on or off. You are setting up
• 19:31
  Speaker 2: all this hardware capacity. Exactly. It's one bit of binary,
• 19:36
  Speaker 2: zero or one, on or off. And it's just like
• 19:39
  Speaker 2: you have all this information. infrastructure, like millions of dollars
• 19:43
  Speaker 2: of infrastructure and technology being put into these labs where
• 19:47
  Speaker 2: they have all these remote cameras looking in and you
• 19:50
  Speaker 2: can remotely turn a light bulb on and off to
• 19:54
  Speaker 2: prove that you did it which like on one hand
• 19:57
  Speaker 2: it's cool but on the hand like is it just
• 19:59
  Speaker 2: kind of a giant waste of money.
• 20:02
  Speaker 1: Yeah that's another good way to put it too i
• 20:04
  Speaker 1: mean the amount of money And that is, you know,
• 20:08
  Speaker 1: and then like, you know, as we get along with it,
• 20:09
  Speaker 1: go along with it, I mean, that is even something
• 20:11
  Speaker 1: I think comes up, I think, because of the way
• 20:13
  Speaker 1: the company is registered or whatever. You don't even know
• 20:16
  Speaker 1: who's involved with the money, too. And there is ties
• 20:19
  Speaker 1: to some kind of some people consider shady institutions or places.
• 20:24
  Speaker 3: But yeah, I do wonder about that.
• 20:26
  Speaker 2: Shady ties. Tell me about those.
• 20:28
  Speaker 3: All right.
• 20:28
  Speaker 1: Yeah, that I can, you know, I'll jump to that
• 20:30
  Speaker 1: because that is some interesting stuff. I was going to
• 20:32
  Speaker 1: save it for the end, but I think I'll read
• 20:35
  Speaker 1: it now just to kind of let people know.
• 20:39
  Speaker 3: All right.
• 20:39
  Speaker 1: So one of the most interesting documented connections surrounding Final
• 20:43
  Speaker 1: Spark involves DARPA, specifically the Defense Advanced Research Projects Agency's
• 20:50
  Speaker 1: O-Circuit program. Lauren Moyse publicly represented Final Spark at a
• 20:56
  Speaker 1: DARPA O-Circuit workshop. O-Circuit is not simply a generic technology conference.
• 21:06
  Speaker 1: It is a program run through DARPA's Biological Technologies Office,
• 21:10
  Speaker 1: exploring extremely low-powered biological process systems built from living neural material.
• 21:18
  Speaker 1: DARPA describes its objective as developing biological processing units capable
• 21:23
  Speaker 1: of learning and computation while consuming dramatically less energy than
• 21:28
  Speaker 1: conventional silicon systems. The military relevance is explicit. Program material
• 21:36
  Speaker 1: discusses possible operation at the tactical edge where conventional high-power
• 21:42
  Speaker 1: computing may be impractical as well as sensing and navigation
• 21:46
  Speaker 1: applications that could be relevant to autonomous or drone systems.
• 21:50
  Speaker 1: Final Spark's presence at an O-Circuit workshop therefore matters because
• 21:54
  Speaker 1: the company is already developing the same broad category of
• 21:58
  Speaker 1: technology DARPA wants to advance, living neural tissue connected to electrodes,
• 22:04
  Speaker 1: stimulation systems, software, and conventional computers in an attempt to produce.
• 22:09
  Speaker 3: Useful biological computation.
• 22:12
  Speaker 1: There is Currently, no public evidence establishing that Final Spark
• 22:16
  Speaker 1: received a DARPA O-Circuit contract or award. But, you know,
• 22:21
  Speaker 1: I just want to put that out there. But, yeah,
• 22:22
  Speaker 1: so they have been, you know, been involved with DARPA
• 22:26
  Speaker 1: and kind of seem to be, you know, involved in
• 22:29
  Speaker 1: something that DARPA is also interested in. And I do
• 22:32
  Speaker 1: know in the conspiracy world, and there are some weird
• 22:35
  Speaker 1: things about DARPA, it's not looked upon as a positive agency,
• 22:38
  Speaker 1: I guess.
• 22:42
  Speaker 2: I would just say that, like, is there really any
• 22:49
  Speaker 2: future technology out there that probably doesn't have some sort
• 22:55
  Speaker 2: of like shady connection somewhere to some sort of like
• 22:59
  Speaker 2: weird government organization? Because, you know, as much as we
• 23:03
  Speaker 2: want new technology... I think the government wants it a
• 23:08
  Speaker 2: little bit more just to maintain some sort of superiority.
• 23:12
  Speaker 2: So I don't know if that rings as super shady
• 23:18
  Speaker 2: to me off offhand. I would almost assume that like
• 23:23
  Speaker 2: a neural brain computing company would have some sort of
• 23:28
  Speaker 2: like interest from, some sort of weird companies who want
• 23:34
  Speaker 2: to maybe abuse it, right?
• 23:37
  Speaker 1: Oh, I would think even companies could come out with
• 23:40
  Speaker 1: its best intentions and still somehow get picked up and.
• 23:43
  Speaker 3: Not even realize.
• 23:44
  Speaker 1: They could have been bought out by a private company
• 23:48
  Speaker 1: that works for the government, and you just thought it
• 23:49
  Speaker 1: was a normal company, and you have no idea. But
• 23:53
  Speaker 1: I do know how a.
• 23:55
  Speaker 3: Lot of people talk about DARPA, so I figured I'd
• 23:57
  Speaker 3: put that.
• 23:57
  Speaker 2: One in there.
• 23:59
  Speaker 1: Um, there's also a direct documented relationship between final spark in,
• 24:03
  Speaker 1: in noises.
• 24:04
  Speaker 3: I don't know how to say that.
• 24:05
  Speaker 1: The Swiss innovation agency, the Swiss government's Aramis research database
• 24:11
  Speaker 1: identifies final spark as the implantation implementation partner in project
• 24:17
  Speaker 1: one 27 dash five, five, six. And that is titled
• 24:21
  Speaker 1: as a biocomputing as a service, a novel research platform
• 24:25
  Speaker 1: for computing using living cell cultures. The project involved Final
• 24:30
  Speaker 1: Spark and ZHAW, Zurich University of Applied Sciences, and ran
• 24:35
  Speaker 1: from June 13th, 2024 through February 27th, 2026. Kind of recently.
• 24:42
  Speaker 1: That demonstrates that Final Spark's biological computing has operated with
• 24:47
  Speaker 1: an officially supported Swiss national innovation program. The funding language
• 24:52
  Speaker 1: needs to remain precise. Under the standard model, federal innovation
• 24:56
  Speaker 1: funding generally supports the participating research institution, while the industrial
• 25:01
  Speaker 1: implementation partner contributes its own research and works towards practical application.
• 25:09
  Speaker 1: I'm including all governmental things. It could possibly mean nothing, actually,
• 25:13
  Speaker 1: but I included that one.
• 25:16
  Speaker 2: It sounds like we don't really quite know where some
• 25:19
  Speaker 2: of the money is coming from or where. where it's
• 25:22
  Speaker 2: being applied, but also I think that's probably kind of
• 25:26
  Speaker 2: normal in the corporate world. But like beyond that, I mean,
• 25:31
  Speaker 2: is there anything shady or weird about the actual people
• 25:35
  Speaker 2: who started the company?
• 25:37
  Speaker 3: Yeah.
• 25:38
  Speaker 1: You know, there was one that I thought was kind
• 25:41
  Speaker 1: of interesting. One of the guys, let me try to
• 25:44
  Speaker 1: find it now. some of the stuff he did, I
• 25:46
  Speaker 1: just thought was like really, uh, kind of interesting. Um,
• 25:51
  Speaker 1: who further, let me find this guy when his name, um, yeah,
• 25:56
  Speaker 1: I have people's names, a couple of people that had
• 25:58
  Speaker 1: interesting stuff. One guy I think was like trying to
• 26:02
  Speaker 1: like hide information in art or some weird shit. And
• 26:06
  Speaker 1: I was like, that is really interesting. Um, and they,
• 26:10
  Speaker 1: even the guy, even I think came out with like
• 26:12
  Speaker 1: a thing to where you could, uh, It was supposedly,
• 26:15
  Speaker 1: I think, to be, like, an anti, not anti-theft, but, like,
• 26:19
  Speaker 1: if you're trying to remake something, like, if you're trying
• 26:22
  Speaker 1: to sell, like, a fake Gucci bag, like, this thing
• 26:24
  Speaker 1: would be able to pick up, like, it would know, like, how, like,
• 26:27
  Speaker 1: the normal printing and everything is done on something. Let's say, like,
• 26:31
  Speaker 1: a box. You're buying, like, fake Tide. I don't know,
• 26:33
  Speaker 1: at a Dollar Tree. They'd be able to tell from how, like,
• 26:36
  Speaker 1: paint is normally, like, put onto the box on, like,
• 26:39
  Speaker 1: a very small thing. To realize like, oh, this wasn't
• 26:42
  Speaker 1: printed the same way it was at another place.
• 26:44
  Speaker 3: Something weird like that. Wow. It's like almost like a
• 26:48
  Speaker 3: thumbprint to how this stuff is made.
• 26:51
  Speaker 2: Like a signature.
• 26:52
  Speaker 3: Yeah, yeah, yeah.
• 26:53
  Speaker 2: Like a physical signature. Interesting. Okay.
• 26:56
  Speaker 1: Yeah.
• 26:57
  Speaker 3: Why can't I find... Oh, okay. Let me see.
• 27:01
  Speaker 1: All right.
• 27:01
  Speaker 3: Now I got the people.
• 27:06
  Speaker 2: All right.
• 27:06
  Speaker 1: Let me just go through the people that are actually
• 27:08
  Speaker 1: behind it and then we'll get to it eventually.
• 27:11
  Speaker 3: All right, who was actually doing the science?
• 27:14
  Speaker 1: One reasonable criticism of Final Spark's origin story is that
• 27:17
  Speaker 1: Jordan and Cutter entered biology from signal processing rather than
• 27:21
  Speaker 1: formal neuroscience. Historically, that is true. It is no longer
• 27:25
  Speaker 1: an adequate description of the team about the project. Final
• 27:29
  Speaker 1: Spark official roster includes Eulon Curtis, Gene Mark Comby, Flora Brazi,
• 27:37
  Speaker 1: I'm probably screwing these names up, Gregorio, Rebecca, alongside the founders.
• 27:42
  Speaker 1: The company also lists advisors Steve M. Potter and Luke Stoppini,
• 27:48
  Speaker 1: as well as patent licensing specialist Bruce Davis. Eulina has
• 27:55
  Speaker 1: training spanning pharmacy, biotechnology, and neuroscience. She completed a neuroscience
• 28:00
  Speaker 1: doctorate at the University of Groningen. and later worked in
• 28:05
  Speaker 1: brain imaging at King's College London before moving through medical
• 28:09
  Speaker 1: imagery industry and startup commercialization roles.
• 28:13
  Speaker 3: She has become one.
• 28:13
  Speaker 1: Of Final Spark's primary public explainers, which I think I've
• 28:17
  Speaker 1: seen her do an interview. That makes her useful because
• 28:20
  Speaker 1: she can speak across both the scientific and commercial sides
• 28:23
  Speaker 1: of the company, but it also means her description should
• 28:26
  Speaker 1: be distinguished from independent validations. We got Gene Mark Comby.
• 28:32
  Speaker 1: He has a physics and computing background and appears both
• 28:35
  Speaker 1: as a co-author of the NeuroPlatform paper as an inventor
• 28:39
  Speaker 1: of microfluids patent family. That places him directly at the
• 28:43
  Speaker 1: intersection of instrumentation software and the practical engineering required to
• 28:48
  Speaker 1: keep neural cultures usable over longer periods. Then you have
• 28:53
  Speaker 1: Flora Brazi. Brazi brings experimental biology and medicine. Her previous
• 28:59
  Speaker 1: research includes cellular stress, inflammation, pancreatic beta cell biology, and
• 29:05
  Speaker 1: diabetes-related work at European institutions. Gregory Rebecki connects Final Sparks
• 29:13
  Speaker 1: to mathematical neuroscience in a formal French research collaboration involving
• 29:18
  Speaker 1: researchers associated with CNRS in RIA and Universite Côte d'Azur,
• 29:26
  Speaker 1: if I said that correctly. His doctoral research focuses on
• 29:29
  Speaker 1: modeling changes in neuronal networks through synaptic plasticity. His doctorate
• 29:36
  Speaker 1: is also jointly funded by CNRS and Firestone. Spark, making
• 29:41
  Speaker 1: it one of the strongest externally documented institutional relationships surrounding
• 29:48
  Speaker 1: the company. The other guy must have been one of
• 29:53
  Speaker 1: the founding dudes, and I must have skipped him in
• 29:55
  Speaker 1: the notes. Because his job was very weird and I
• 29:58
  Speaker 1: didn't even mention it just now.
• 30:02
  Speaker 2: So was one of the guys into steganography? Is that
• 30:07
  Speaker 2: the word?
• 30:08
  Speaker 3: Yeah, yeah, yeah.
• 30:09
  Speaker 1: I think it was more like kind of like codes
• 30:11
  Speaker 1: and stuff. Yeah, which I found was interesting because like
• 30:15
  Speaker 1: Trithomius was basically into that and he was an occultist.
• 30:19
  Speaker 1: Not saying this guy is, but it just caught my...
• 30:22
  Speaker 1: Which is, it's like.
• 30:24
  Speaker 2: cryptography but with images like hiding information yes and I
• 30:30
  Speaker 2: don't think it always has to be digital either right
• 30:33
  Speaker 2: I think you can encode things like in paintings I
• 30:38
  Speaker 2: don't know nothing about it but it's just it's kind
• 30:40
  Speaker 2: of interesting it sounds like it's a smattering of people
• 30:44
  Speaker 2: that have all these varied like super specific interests kind
• 30:49
  Speaker 2: of coming together and doing something super weird.
• 30:52
  Speaker 1: Yeah Yeah, unless I erased it out of the notes
• 30:56
  Speaker 1: just recently. I can't believe I would have done that. Yeah,
• 30:58
  Speaker 1: that guy's job. And he even had a patent or
• 31:02
  Speaker 1: even a paper done on that stuff. His paper, I think,
• 31:09
  Speaker 1: did some science paper or thesis. It was kind of
• 31:12
  Speaker 1: done on hiding information within images. And I just found
• 31:16
  Speaker 1: that so weird. You know what I'm saying? It seems
• 31:20
  Speaker 1: like something an occultist would do, in my opinion.
• 31:23
  Speaker 2: Absolutely. 100%. We love to hide information.
• 31:26
  Speaker 3: Yeah.
• 31:26
  Speaker 1: That's what I was just saying. It sounds like a
• 31:28
  Speaker 1: sigil to me. Like, yeah, yeah, yeah.
• 31:32
  Speaker 3: Oh man. That's go ahead.
• 31:34
  Speaker 2: That's like a whole different topic is like, what could
• 31:39
  Speaker 2: we do energetically or magically with, with something like this,
• 31:45
  Speaker 2: if anything.
• 31:47
  Speaker 1: Yeah.
• 31:47
  Speaker 2: And I mean, I don't have the answer to that
• 31:49
  Speaker 2: cause I haven't thought about it, but, um, So I
• 31:52
  Speaker 2: think something something you brought up before that does bother
• 31:59
  Speaker 2: me a little bit is that they were experimenting with
• 32:03
  Speaker 2: like chemicals and stuff.
• 32:04
  Speaker 3: Yeah.
• 32:05
  Speaker 2: Yeah. On the neurons.
• 32:06
  Speaker 3: Yeah.
• 32:06
  Speaker 1: Yep.
• 32:07
  Speaker 3: They had dopamine. They use dopamine on it.
• 32:09
  Speaker 2: And like to what effect? Like what what was that
• 32:13
  Speaker 2: doing to the system?
• 32:15
  Speaker 3: Let me get to that. Where's the dopamine?
• 32:20
  Speaker 2: Sorry.
• 32:23
  Speaker 3: Here we go. Oh, shit. Sorry, my documents.
• 32:28
  Speaker 2: I'm making you jump all over.
• 32:29
  Speaker 3: Oh, no, it's all right. You're totally fine. Where is
• 32:34
  Speaker 3: the dopamine?
• 32:34
  Speaker 1: You know how many times I looked at it and
• 32:36
  Speaker 1: went past it and I was like, I don't remember
• 32:38
  Speaker 1: to cover that. When did the biology start? And there
• 32:44
  Speaker 1: is something with Braille, too, which I think is interesting.
• 32:47
  Speaker 1: All right, the dopamine experiment. This is where you begin
• 32:50
  Speaker 1: seeing headlines about researchers rewarding the organoids, and that language
• 32:54
  Speaker 1: needs to be handled carefully. Final Spark experimented with caged dopamine.
• 32:59
  Speaker 1: The dopamine is chemically modified so that it remains inactive
• 33:03
  Speaker 1: until ultraviolet light breaks the chemical cage, allowing active dopamine
• 33:08
  Speaker 1: to be released at a chosen moment. The idea was
• 33:12
  Speaker 1: to pair particular desirable neural responses with dopamine release, creating
• 33:18
  Speaker 1: something functionally analogous to a reinforcement signal. In an early experiment,
• 33:23
  Speaker 1: neural spike activity increased over time. But the researchers themselves
• 33:28
  Speaker 1: acknowledged that the experiment did not establish that dopamine reinforcement
• 33:32
  Speaker 1: caused the increase. And dopamine should not be reduced to
• 33:36
  Speaker 1: a human pleasure chemical. It participates in many biological processes
• 33:40
  Speaker 1: involving learning, motivation, and signaling, but projecting an entire human
• 33:45
  Speaker 1: psychological concept of reward onto an organoid goes beyond what
• 33:50
  Speaker 1: the experiment demonstrates. The defensible statement is that Final Spark
• 33:54
  Speaker 1: built a system capable of delivering time dopaminergic chemical intervention
• 34:00
  Speaker 1: and explored whether that intervention could influence neural activity. The
• 34:05
  Speaker 1: much less defensible version is that they trained a tiny.
• 34:08
  Speaker 3: Brain by making it feel good.
• 34:10
  Speaker 1: Still, the fact that experiments naturally begin attracting language about
• 34:13
  Speaker 1: reward and reinforcements hints at the ethical questions that could
• 34:18
  Speaker 1: become much more serious if these systems become more sophisticated.
• 34:23
  Speaker 1: Which I do think is something to think about, too,
• 34:25
  Speaker 1: depending on how far we go with this and how much, like,
• 34:29
  Speaker 1: you know, at some point are we going to end up, like,
• 34:30
  Speaker 1: with a whole brain on a processor?
• 34:33
  Speaker 3: At that point, you're getting the motherfucker high. In my opinion,
• 34:36
  Speaker 3: you know what I'm saying?
• 34:37
  Speaker 1: And, you know, I might tell you whatever you want
• 34:39
  Speaker 1: to hear when the motherfucker's feeling good.
• 34:43
  Speaker 2: No, that whole, like, I can see maybe, like, where
• 34:48
  Speaker 2: the biology and science is behind it because, sure, dopamine
• 34:53
  Speaker 2: is an important chemical for brain activity. It's not just
• 34:58
  Speaker 2: about getting high or feeling good.
• 35:01
  Speaker 1: Yeah.
• 35:01
  Speaker 2: But that's just everything about what you just said, it
• 35:05
  Speaker 2: just rubs me the wrong way. Like there's something, I
• 35:09
  Speaker 2: think that's the thing that gives me the most ick
• 35:12
  Speaker 2: out of all of this is, is like you're supplying
• 35:17
  Speaker 2: in a, you know, by some descriptions, right. An addictive,
• 35:23
  Speaker 2: something that has an addictive component to it, to an
• 35:28
  Speaker 2: artificial system as a measure of control. Yeah. And that
• 35:34
  Speaker 2: just feels all sorts of wrong because, because let's, maybe
• 35:41
  Speaker 2: it's not wrong now because we haven't hit, we haven't
• 35:44
  Speaker 2: hit our limit of neurons for determining, you know, how
• 35:48
  Speaker 2: many neurons it takes to actually make a person or
• 35:51
  Speaker 2: a consciousness. But that, that system is embedded in the framework,
• 35:55
  Speaker 2: in the foundation for however far it's going to go.
• 36:00
  Speaker 2: So whatever does, giant thing if something happens with this
• 36:04
  Speaker 2: whatever giant thing is produced by these experiments it has
• 36:09
  Speaker 2: that in the framework that we can introduce chemicals artificially
• 36:13
  Speaker 2: as a measure of control to it just it feels wrong.
• 36:18
  Speaker 1: Yeah no i i think so too and you know
• 36:20
  Speaker 1: and again like uh i don't know like how how
• 36:25
  Speaker 1: it would work and not having a body attached because
• 36:27
  Speaker 1: just from my own experience, I've mentioned before in the past,
• 36:30
  Speaker 1: I did struggle with addiction for a few years.
• 36:32
  Speaker 3: I was a heroin addict.
• 36:34
  Speaker 1: Um, you know, if you keep on giving this thing
• 36:36
  Speaker 1: dopamine every day, when you stop, is it going to
• 36:38
  Speaker 1: feel fucked up?
• 36:40
  Speaker 3: You know what I'm saying? Cause that's kind of like
• 36:41
  Speaker 3: what heroin does.
• 36:42
  Speaker 1: It makes your body release those chemicals and it's so much,
• 36:45
  Speaker 1: and then it's depleted when you don't use it. And
• 36:47
  Speaker 1: that's why you actually physically get sick is because your
• 36:50
  Speaker 1: body doesn't have the chemicals that normally makes you feel
• 36:53
  Speaker 1: good regardless. So it's just like, you know, I don't know. Like, again,
• 36:58
  Speaker 1: does it need a certain amount of neural tissues? Does
• 36:59
  Speaker 1: it have to be a certain amount of age? Like,
• 37:01
  Speaker 1: you know, who knows where we're going? But if you
• 37:03
  Speaker 1: do start making this thing bigger, is that things you
• 37:06
  Speaker 1: have to start thinking about?
• 37:07
  Speaker 3: I don't know.
• 37:09
  Speaker 2: So, and I just, I have to, this is so
• 37:12
  Speaker 2: like inherently sci-fi that everything that you mentioned, I think
• 37:16
  Speaker 2: of like a movie or a TV show or something, right?
• 37:18
  Speaker 2: So in Star Trek, there is an entire race of
• 37:23
  Speaker 2: aliens that, that are born addicted to a chemical. And
• 37:29
  Speaker 2: it's because they, there's another group of people who use
• 37:33
  Speaker 2: them as warriors, like to fight their battles for them.
• 37:37
  Speaker 2: So they raise them addicted to this drug so that
• 37:42
  Speaker 2: they have to do what they say, right? And like,
• 37:44
  Speaker 2: could this be something like that where we are controlling
• 37:48
  Speaker 2: the system by saying, all right, if you don't compute
• 37:52
  Speaker 2: my stuff for me, Then I'm cutting you off from
• 37:55
  Speaker 2: the dopamine.
• 37:56
  Speaker 1: Imagine it gets like that. That's like, I don't know
• 38:01
  Speaker 1: if you've ever seen Twin Peaks.
• 38:03
  Speaker 3: That was like... I haven't.
• 38:05
  Speaker 1: One-Eyed Jacks, you know, they kept the girl there. They
• 38:08
  Speaker 1: were keeping the girls there, I think, strung out on
• 38:10
  Speaker 1: heroin to be prostitutes. You know what I'm saying? So, yeah,
• 38:15
  Speaker 1: I mean, here, we'll give you drugs to perform. You know,
• 38:18
  Speaker 1: we'll keep you dope sick when we want. Very fucked up.
• 38:23
  Speaker 1: Real quick, before we get into the Braille, I found
• 38:26
  Speaker 1: this guy, Fred Jordan. We were talking about him before,
• 38:29
  Speaker 1: and I can say exactly what he did now. Jordan
• 38:33
  Speaker 1: is French and trained initially as an engineering physicist. EPFL's
• 38:39
  Speaker 1: current biography says he earned an engineering degree in physics
• 38:42
  Speaker 1: from INSA Rennes. The Institut National de Science, whatever I can,
• 38:48
  Speaker 1: it's another language. And he got that in 1994. He
• 38:53
  Speaker 1: then moved to Switzerland and went to Ecole Polytechnique Fédérale EPFL,
• 39:00
  Speaker 1: one of Europe's major technical universities. He completed a PhD
• 39:04
  Speaker 1: in signal processing, and that was in 1999. Then during
• 39:10
  Speaker 1: the 1990s, his work at EPFL centered heavily on digital
• 39:15
  Speaker 1: signal processing, image processing, and digital watermarking. Hiding machine-detectable information
• 39:22
  Speaker 1: inside images. There is, for an example, a 1996 EPFL
• 39:27
  Speaker 1: technical report co-authored by... Cutter and Frederick Jordan on digital
• 39:32
  Speaker 1: signatures in color images. So that was a, yeah, that
• 39:36
  Speaker 1: was the thing we were trying to get at. And yeah,
• 39:38
  Speaker 1: digital imaging watermarking.
• 39:41
  Speaker 2: It's a, it's an interesting background.
• 39:43
  Speaker 1: Yeah.
• 39:43
  Speaker 2: I mean, yeah, it's interesting. I don't, Nothing jumps out
• 39:48
  Speaker 2: to me as like necessarily weird, but it's, it's certainly
• 39:52
  Speaker 2: an interesting background and an interesting jump from like, at
• 39:56
  Speaker 2: what point, I mean, just like, I wonder at what
• 39:59
  Speaker 2: point are you like, all right, I've done enough of
• 40:03
  Speaker 2: hiding digital signatures and images. And now I want to
• 40:07
  Speaker 2: make artificial brains. Like, like where was that connection?
• 40:11
  Speaker 1: Yeah, actually, you know what? I could even get into that.
• 40:16
  Speaker 1: That would be Alpvision. And that is the company that
• 40:20
  Speaker 1: started before this. And I do have information on that one. Alpvision,
• 40:27
  Speaker 1: the company before the company. Jordan and Cutter co-founded Alpvision
• 40:32
  Speaker 1: in 2001. Alpvision is an anti-counterfeiting and product authentication company
• 40:40
  Speaker 1: whose technologies are designed to determine whether packaging or physical
• 40:43
  Speaker 1: products are genuine without necessarily relying on obvious QR codes, holograms,
• 40:50
  Speaker 1: or visible security stickers. Two of its better-known technologies are
• 40:54
  Speaker 1: Cryptoglyph and Fingerprint. Cryptoglyph incorporates microscopic dots into ordinary packaging.
• 41:01
  Speaker 1: The pattern can be nearly visible to the casual observer,
• 41:04
  Speaker 1: but a camera and software can detect it and authenticate
• 41:08
  Speaker 1: the package. Fingerprint works differently. Instead of adding a code,
• 41:12
  Speaker 1: it uses naturally occurring microscopic irregularities in a manufactured surface
• 41:18
  Speaker 1: as identifying signature. The physical object already contains unique noise,
• 41:23
  Speaker 1: and the system learns to read that noise as identity.
• 41:26
  Speaker 1: 2002 Swiss Info Report described the young company as being
• 41:33
  Speaker 1: financed by individuals, investors, rather than conventional venture capital. Later,
• 41:38
  Speaker 1: OutVision material says the company reached profitability in 2003 and
• 41:43
  Speaker 1: remained profitable. It also shares an address in Vevey. OutVision
• 41:55
  Speaker 1: appears in the history of Final Spark-related patents. You will
• 41:58
  Speaker 1: even see the same address now all of a sudden
• 42:03
  Speaker 1: pops up when Final Spark registers its company.
• 42:07
  Speaker 3: And there's like certain things that match.
• 42:10
  Speaker 1: So it's completely obvious to me that these same two
• 42:13
  Speaker 1: guys own this company. And then Final Spark just pops
• 42:17
  Speaker 1: up later on in 2014. So, I mean, I guess
• 42:21
  Speaker 1: there's really no kind of answer to what you said,
• 42:23
  Speaker 1: but they did have a company. This guy had a
• 42:25
  Speaker 1: company doing that stuff. And then Final Spark was incorporated
• 42:29
  Speaker 1: in Switzerland in January 2014 with 20,000 Swiss francs in
• 42:31
  Speaker 1: original capital.
• 42:36
  Speaker 2: I mean, that sounds pretty legit. You've got some guys
• 42:39
  Speaker 2: who are doing some startups here and there, and then
• 42:42
  Speaker 2: they have another idea. They're going to share the same
• 42:44
  Speaker 2: address on their registrations and stuff.
• 42:50
  Speaker 1: But yeah, that was what it was before they decided
• 42:52
  Speaker 1: to switch over to, I guess, the AIs.
• 42:59
  Speaker 2: What was up with the Braille.
• 43:00
  Speaker 1: That you wanted to talk about? And I think one
• 43:03
  Speaker 1: of the main reasons, and I should include this because
• 43:06
  Speaker 1: I do think I might have had it in these notes,
• 43:09
  Speaker 1: and who knows, I could have erased. Originally, it was
• 43:11
  Speaker 1: 70 pages of notes. I chopped it down to 38.
• 43:13
  Speaker 1: Who knows what I erased by accident. But I do
• 43:16
  Speaker 1: think part of the whole connection was the noise. Because
• 43:21
  Speaker 1: there is noise, I think, kind of within these neural
• 43:24
  Speaker 1: tissue that they have to filter out to kind of
• 43:27
  Speaker 1: like read or do whatever they want to do. So
• 43:29
  Speaker 1: I do think there was the whole retrieving information within
• 43:32
  Speaker 1: noise on the image that then they started to kind
• 43:35
  Speaker 1: of go.
• 43:36
  Speaker 3: Over to with this.
• 43:37
  Speaker 1: I do think that is like a slight, you know,
• 43:40
  Speaker 1: carry over.
• 43:42
  Speaker 3: But all right.
• 43:43
  Speaker 1: Yeah. Where is the Braille? It might be easier for
• 43:47
  Speaker 1: me to just type Braille into the search bar instead
• 43:52
  Speaker 1: of trying to find it.
• 43:54
  Speaker 3: Eight of eight. All right.
• 43:56
  Speaker 2: Yes.
• 43:57
  Speaker 1: Yes.
• 43:58
  Speaker 3: Did an organoid learn braille?
• 44:00
  Speaker 1: One of the most interesting outside user experiments involved researchers
• 44:05
  Speaker 1: using the neural platform for braille-related tactile pattern classification tasks.
• 44:12
  Speaker 1: That matters because it was not simply Final Spark demonstrating
• 44:15
  Speaker 1: its own technology. Researchers encoded braille-like tactile patterns into stimulation signals.
• 44:23
  Speaker 1: delivered those patterns to organoids and analyzed the resulting neural activity.
• 44:29
  Speaker 1: They reported classification accuracy of approximately 61% using one organoid
• 44:36
  Speaker 1: and approximately 83% using an assembly of three. Sounds impressive
• 44:43
  Speaker 1: until you ask what was exactly doing the classifications. The
• 44:48
  Speaker 1: organoids produced responses containing information useful for distinguishing the input patterns,
• 44:54
  Speaker 1: but conventional machine learning algorithms including support vector machines, nearest
• 45:00
  Speaker 1: neighbor methods, and random forests were used downstream to decode
• 45:04
  Speaker 1: those responses. So the experiment was not three organoids learning
• 45:08
  Speaker 1: to read braille and correctly telling researchers what they saw,
• 45:12
  Speaker 1: The more accurate description is that the different braille-like stimulation
• 45:16
  Speaker 1: patterns generated biological response features that a conventional classifier could
• 45:21
  Speaker 1: distinguish in combining several organoids improved the classification result. So,
• 45:28
  Speaker 1: I mean, it's kind of, again, that even goes back
• 45:30
  Speaker 1: to you saying that the stuff isn't being held in
• 45:36
  Speaker 1: the organoid, in the neural tissue, it's being held in a,
• 45:38
  Speaker 1: you know, something, you know, physical computer thing.
• 45:41
  Speaker 2: They did most of the processing somewhere else. Is it
• 45:51
  Speaker 2: really that impressive?
• 45:53
  Speaker 3: That's what I'm wondering.
• 45:54
  Speaker 1: Is that some bullshit thing that sounded impressive?
• 45:58
  Speaker 2: With one organoid, they have a 60% recognition rate. With three,
• 46:04
  Speaker 2: they have an 80% recognition rate. Can you imagine how
• 46:08
  Speaker 2: confusing it would be to read a book if 20%
• 46:12
  Speaker 2: of the letters were unrecognizable or swapped, like I don't,
• 46:17
  Speaker 2: I don't think 80% success with Braille is really that impressive.
• 46:25
  Speaker 2: I would want to see like 99%. Like this is like,
• 46:29
  Speaker 2: that's language that I feel like that's one of the
• 46:33
  Speaker 2: more basic, like language and math are one of the
• 46:35
  Speaker 2: more basic things that a computing system can work with.
• 46:42
  Speaker 2: And if you're only having, you know, a two-thirds success
• 46:47
  Speaker 2: rate with processing those things, then how can you tell
• 46:49
  Speaker 2: if anything is reliable?
• 46:52
  Speaker 1: Yeah. Unless they had a fourth one in there and
• 46:56
  Speaker 1: it finally gets a little bit higher.
• 46:57
  Speaker 3: But, yeah.
• 46:59
  Speaker 1: Yeah, I just thought it was interesting to include it.
• 47:02
  Speaker 1: Like you said, like, it's kind of almost like, is
• 47:04
  Speaker 1: that even impressive? I really don't know what to think
• 47:06
  Speaker 1: about that. And, again, it was kind of figured out.
• 47:11
  Speaker 1: not in the neural tissue. Yeah.
• 47:14
  Speaker 2: It also, it seems like a weird experiment to run.
• 47:19
  Speaker 2: I don't know, like out of all the potential things
• 47:21
  Speaker 2: you could do, like recognizing Braille patterns just seems a
• 47:25
  Speaker 2: little random, but I don't, I don't know what could
• 47:30
  Speaker 2: be built on that.
• 47:31
  Speaker 1: Yeah.
• 47:31
  Speaker 2: So maybe there's more to it.
• 47:34
  Speaker 3: Yeah.
• 47:34
  Speaker 1: Then a real quick to also just, you know, I'll
• 47:36
  Speaker 1: mention this again, the energy claim, cause it's just sitting
• 47:39
  Speaker 1: in front of me. Again, there is that claim repeated
• 47:42
  Speaker 1: almost everywhere around biological computing that biological neurons could be
• 47:46
  Speaker 1: up to a million times more energy efficient than artificial
• 47:49
  Speaker 1: digital neurons. Biological brains are undeniably remarkable in terms of
• 47:55
  Speaker 1: energy efficiency. The human brain operates on the order of
• 47:58
  Speaker 1: tens of watts while performing massively parallel information processing that
• 48:03
  Speaker 1: we still cannot reproduce straightforwardly in silicon. Final Spark has
• 48:08
  Speaker 1: repeatedly invoked comparisons suggesting that biological neurocomputation could be dramatically
• 48:14
  Speaker 1: more efficient than digital artificial neurons, sometimes using the figure
• 48:18
  Speaker 1: of up to a million times. But a fair commercial
• 48:22
  Speaker 1: comparison cannot stop at the neuron. A real biological computing
• 48:26
  Speaker 1: system also needs pumps, environmental controls, fluid circulation, monitoring, conventional computers, storage, networking,
• 48:34
  Speaker 1: and laboratory infrastructures. Whether the entire system ultimately delivers the
• 48:39
  Speaker 1: dramatic efficiency advantage often associated with biological component remains an
• 48:45
  Speaker 1: open engineering question. But again, they do sell that as
• 48:51
  Speaker 1: a reason to use.
• 48:53
  Speaker 2: So if I understand this whole section properly, it basically
• 48:59
  Speaker 2: comes down to the fact that inside our computers and
• 49:02
  Speaker 2: our processors... There are ones and zeros, right? And these
• 49:08
  Speaker 2: are physical states of matter. They are either off or
• 49:11
  Speaker 2: they're on, and we have billions of them that end
• 49:14
  Speaker 2: up becoming our programs and pictures and videos and stuff, right?
• 49:18
  Speaker 2: It takes electricity. to change the zero to a one
• 49:24
  Speaker 2: every time or back and forth, right? So we're saying
• 49:27
  Speaker 2: that in a one-to-one comparison inside my processor, it would
• 49:33
  Speaker 2: take more electricity to change a zero to a one
• 49:37
  Speaker 2: than it would a neuron to take to change from
• 49:41
  Speaker 2: a zero to a one so although we don't have
• 49:44
  Speaker 2: the capability to have the same amount of neurons as
• 49:49
  Speaker 2: we do like bits in a processor if we did
• 49:55
  Speaker 2: it would take way less energy to run and to
• 49:58
  Speaker 2: process things right and then we could scale down our
• 50:03
  Speaker 2: data center energy and the water required to cool all
• 50:09
  Speaker 2: the stuff, right? Like this is the hypothetical claim of
• 50:13
  Speaker 2: where this could go.
• 50:17
  Speaker 1: Yeah, I do think that would be like a selling
• 50:21
  Speaker 1: aspect of it if they ever do start finding ways.
• 50:25
  Speaker 1: And I do think that's one of the things that
• 50:26
  Speaker 1: they do want to get into in the future is
• 50:28
  Speaker 1: almost some sort of like brain cloud. So, you know,
• 50:32
  Speaker 1: kind of like being able to store the stuff Like
• 50:35
  Speaker 1: we were saying right now, they're storing it on hardware.
• 50:39
  Speaker 1: I think they do want to try to get it
• 50:40
  Speaker 1: into the brain or the brain tissue. That might create
• 50:44
  Speaker 1: bigger data centers, and I'm sure that they will try
• 50:47
  Speaker 1: to say that the efficiency will be better.
• 50:52
  Speaker 2: So would you be okay with that? Would you be–
• 50:56
  Speaker 2: like is there a certain– cost savings amount or like
• 51:06
  Speaker 2: environmental harm reduction amount that like personally you would look
• 51:13
  Speaker 2: at and and say okay well at this point we
• 51:16
  Speaker 2: can't really ignore the technology we need to try to
• 51:18
  Speaker 2: incorporate it let's let's say like we're 10 years down
• 51:20
  Speaker 2: the future and it's actually viable is there a point
• 51:23
  Speaker 2: where you feel good.
• 51:24
  Speaker 1: About that You know, I would easily say, actually, yes,
• 51:29
  Speaker 1: I would probably feel better about them using this than
• 51:32
  Speaker 1: these other data centers and the amount of ridiculous amount
• 51:35
  Speaker 1: of water. It's just the thing I'm worried about is
• 51:38
  Speaker 1: how far are we going to go with this neural tissue?
• 51:42
  Speaker 1: You know, is it going to remain just a couple
• 51:44
  Speaker 1: of small ones?
• 51:45
  Speaker 3: Like I said, eventually 10 years from now, we're going
• 51:46
  Speaker 3: to have half a brain.
• 51:49
  Speaker 2: Oh, I feel like it would have to be like
• 51:52
  Speaker 2: to do the scale of a data center.
• 51:55
  Speaker 3: That's what I'm getting at.
• 51:56
  Speaker 2: It would have to be, like, hundreds or thousands of
• 51:58
  Speaker 2: brains at that point. Like, it would have to be huge, right?
• 52:01
  Speaker 1: At that point, what difference is that? It's like the Matrix,
• 52:04
  Speaker 1: just minus the bodies. Now we just got brains and things.
• 52:07
  Speaker 3: Like, so no, I'm not cool with that.
• 52:09
  Speaker 1: But it's just like, you see, the whole thing is,
• 52:12
  Speaker 1: it's like, it's almost like I might screw with being
• 52:14
  Speaker 1: cool with it to begin with.
• 52:16
  Speaker 3: Which I'm not trying to be that person either. You
• 52:18
  Speaker 3: know what I'm saying?
• 52:19
  Speaker 1: It's just that I feel like not knowing where this
• 52:21
  Speaker 1: can go, it's just a very shady, I don't know. Again,
• 52:26
  Speaker 1: like I said, I would be cool with it if
• 52:27
  Speaker 1: it stayed kind of like if it was very low
• 52:30
  Speaker 1: scaled on the brain tissue. But again, like what you're saying,
• 52:34
  Speaker 1: I would believe, too, that we would need multiple brains.
• 52:38
  Speaker 1: And that just gets weird. At that point, is it
• 52:41
  Speaker 1: going to be like, oh, well. so-and-so, when they die,
• 52:44
  Speaker 1: they're going to donate their brain to the thing. You
• 52:46
  Speaker 1: know what I'm saying?
• 52:47
  Speaker 3: It's just like, oh.
• 52:48
  Speaker 2: I'm going to go be with Final Spark.
• 52:49
  Speaker 3: Yeah, yeah, right?
• 52:51
  Speaker 1: Like, yeah. Gates, I'm going to donate my brain to
• 52:54
  Speaker 1: Final Spark. You know what I'm saying? Or it's less like,
• 52:56
  Speaker 1: you know, you're going to get some weirdo taking mofos
• 52:58
  Speaker 1: out that are, like, special in the head because you
• 53:00
  Speaker 1: want to put their brain on there. You know, it's
• 53:02
  Speaker 1: just like an old weird sci-fi, who knows, you know,
• 53:04
  Speaker 1: movie-like conspiracies.
• 53:07
  Speaker 2: Well, I mean, there are... it's hard not to go
• 53:13
  Speaker 2: to like science fiction with this because.
• 53:17
  Speaker 3: It's already there.
• 53:17
  Speaker 2: This is the, this is the plot that like, you know, 20,
• 53:23
  Speaker 2: 30 years ago, people were writing about exactly as something
• 53:27
  Speaker 2: just completely ridiculous. But now we're actually going there. And
• 53:31
  Speaker 2: so like we have these, we have these two components.
• 53:35
  Speaker 2: We have the development of AI and like at what point,
• 53:42
  Speaker 2: do we feel that AI might become conscious? And then
• 53:47
  Speaker 2: we have the development of technology that is specifically made
• 53:53
  Speaker 2: to put programs in biological hardware and brains. So when
• 54:00
  Speaker 2: these two things converge and we have artificial biotechnology running
• 54:09
  Speaker 2: artificially intelligent consciousness is like, at what point are we
• 54:14
  Speaker 2: as humans still relevant in the universe? Oh, but also like,
• 54:18
  Speaker 2: is that, is that the next stage of evolution? I
• 54:23
  Speaker 2: don't know, man. Like it, it seems like we're going
• 54:26
  Speaker 2: to get wiped out.
• 54:30
  Speaker 1: It starts like, again, if you really want to take
• 54:32
  Speaker 1: this out there, it was like, is this where the
• 54:33
  Speaker 1: terminator started off or something like that? It's just like,
• 54:37
  Speaker 1: or at this point, it's like, I, With people, that's
• 54:40
  Speaker 1: the whole thing, with people also worrying about, and it
• 54:42
  Speaker 1: can be scary, where are we even going to go
• 54:44
  Speaker 1: with AI? You know, you're worried about the machines taking over. Well, now,
• 54:47
  Speaker 1: I mean, will they even be machines? Because now you're
• 54:53
  Speaker 1: giving it actual physical, like the thing that I think
• 54:57
  Speaker 1: scares people about AI, this, if you look at it
• 55:00
  Speaker 1: a certain way, it should actually be a little bit
• 55:01
  Speaker 1: more scarier because it's like now we're kind of like,
• 55:04
  Speaker 1: we're worried about it taking over, like, I guess, human,
• 55:07
  Speaker 1: the human world. And now we're actually giving it human parts.
• 55:11
  Speaker 3: Like, it's like, where the fuck are we going with
• 55:13
  Speaker 3: this now?
• 55:15
  Speaker 2: I mean, just to like, just to extrapolate that to
• 55:18
  Speaker 2: the furthest extent, right? Like, we give a thing human part.
• 55:24
  Speaker 2: It has what is comparably equal to a human mind
• 55:30
  Speaker 2: or a human consciousness. And then at what point do
• 55:34
  Speaker 2: these things, and like you said, I don't know, do
• 55:36
  Speaker 2: we call them machines?
• 55:37
  Speaker 3: We don't.
• 55:38
  Speaker 2: I don't I don't know. But at what point do
• 55:40
  Speaker 2: these things start demanding a voice in society or rights or,
• 55:48
  Speaker 2: you know, just like. Agency to make their own decisions.
• 55:55
  Speaker 2: So that's that's crazy. But like, I don't want to
• 55:58
  Speaker 2: be like that guy. I don't want to be on
• 56:02
  Speaker 2: that side of the fence because I don't. want to
• 56:06
  Speaker 2: be the person that's like opposed to technological development. And
• 56:10
  Speaker 2: I feel like for every big leap that we've had,
• 56:16
  Speaker 2: there were always people on the other side saying that's
• 56:21
  Speaker 2: the devil, you know, or like, that's, that's evil. Like, um,
• 56:25
  Speaker 2: you know, with credit cards, like people were saying that
• 56:29
  Speaker 2: stuff was going to be the mark of the beast.
• 56:31
  Speaker 2: And as we develop, like, artificial replacement organs for people
• 56:39
  Speaker 2: like artificial hearts or whatever, like people have problems with that.
• 56:44
  Speaker 2: And there are like, there are ways to save lives
• 56:50
  Speaker 2: that we would not have if we had not developed
• 56:53
  Speaker 2: those technologies. If we had given into fear, we would
• 56:58
  Speaker 2: not be where we are. I mean, like, I think
• 57:00
  Speaker 2: there were probably people who thought that when we switched
• 57:03
  Speaker 2: from horses to cars, Like that was the end of
• 57:06
  Speaker 2: the world.
• 57:07
  Speaker 3: Right.
• 57:08
  Speaker 2: So I don't want to be that person. I want
• 57:11
  Speaker 2: to look at it with excitement, but it's kind of like,
• 57:15
  Speaker 2: as we develop things, we also take on a new
• 57:19
  Speaker 2: aspect of responsibility and we need to be very careful with,
• 57:23
  Speaker 2: with how we go about it.
• 57:25
  Speaker 3: Yeah, no, I agree.
• 57:26
  Speaker 1: I mean, even just a perfect example, like you were
• 57:28
  Speaker 1: just saying, I mean, at some point back in the day,
• 57:30
  Speaker 1: having a mirror was like, Oh my.
• 57:31
  Speaker 3: God, you know what I'm saying? Like, I get what
• 57:36
  Speaker 3: you're saying. And I even hate to say it.
• 57:40
  Speaker 1: Some people, I don't think really people on this show,
• 57:43
  Speaker 1: but maybe people that listen to this show, but I'm just.
• 57:45
  Speaker 3: Going to keep it real.
• 57:47
  Speaker 1: If you really want to look at a lot of
• 57:48
  Speaker 1: the advancements that have been given to us, and I
• 57:51
  Speaker 1: would even say from the beginning what brought us to
• 57:53
  Speaker 1: where we are now, it was a lot of occultists
• 57:55
  Speaker 1: and alchemists, man. I hate to say it. You got
• 57:59
  Speaker 1: to kind of give us some credit. We have advanced
• 58:01
  Speaker 1: the human race rather more. Rather much than people that
• 58:04
  Speaker 1: I don't think even realize. Robert Flo was an alchemist.
• 58:08
  Speaker 1: We got blood pressure in the first temperature machine from him.
• 58:12
  Speaker 3: Like, you know.
• 58:15
  Speaker 2: I mean, dude, what, like, a few hundred years ago,
• 58:19
  Speaker 2: alchemy and chemistry, they were the same thing. They were
• 58:24
  Speaker 2: the same science, the same study. So it was only
• 58:28
  Speaker 2: recently that we diverged from that and said, like, Oh,
• 58:32
  Speaker 2: this is real science. That's pseudoscience. So yeah, there's a
• 58:36
  Speaker 2: lot of those people who were into all that occult
• 58:40
  Speaker 2: shit that were the driving force behind progress.
• 58:45
  Speaker 3: Yeah, yeah, yep, yep.
• 58:47
  Speaker 1: Like you even just said about the whole thing with chemistry.
• 58:49
  Speaker 1: I covered the guy, I can't remember, it was a
• 58:51
  Speaker 1: Middle Eastern alchemist. They even took like the way that
• 58:56
  Speaker 1: he would document all his, like he would do his graphs,
• 58:59
  Speaker 1: like when he was doing his experiments, he had all
• 59:01
  Speaker 1: this way that he.
• 59:02
  Speaker 3: Would keep all his information.
• 59:04
  Speaker 1: That basic setup was carried over and still used today
• 59:09
  Speaker 1: in chemistry. Like his way of doing all his things
• 59:13
  Speaker 1: and jotting it all down was basically copied over and
• 59:16
  Speaker 1: still used. The guy's like basically the father of chemistry.
• 59:20
  Speaker 2: Was it Jebber?
• 59:21
  Speaker 3: Yeah, I was calling it. I didn't know how to
• 59:24
  Speaker 3: say it. Yeah, Jebber and Jebber.
• 59:26
  Speaker 2: Yes.
• 59:27
  Speaker 3: Yep, yep, yep. Very interesting dude. I was really impressed
• 59:31
  Speaker 3: with that dude, actually.
• 59:33
  Speaker 2: Might not have been one dude.
• 59:35
  Speaker 3: Oh, yeah, that was a thing.
• 59:37
  Speaker 1: Yeah, that was, you know, and I've noticed and I've
• 59:39
  Speaker 1: started to wonder, like, I've noticed from covering a lot
• 59:42
  Speaker 1: of Middle Eastern people, there was also another prince that
• 59:45
  Speaker 1: I covered before him.
• 59:47
  Speaker 3: I forgot Prince. I forgot his name already, too.
• 59:50
  Speaker 1: But that was another one where they're like, they kind
• 59:52
  Speaker 1: of think that it wasn't really him. It might have
• 59:56
  Speaker 1: been multiple people using that name because it already had
• 59:58
  Speaker 1: like a good name out there. But I've even– because
• 1:00:01
  Speaker 1: of that, I've even started wondering about like what's the
• 1:00:04
  Speaker 1: odds that only happened with Middle Eastern or Arabic people? Oh, no.
• 1:00:09
  Speaker 3: You know what I'm saying? It happens everywhere. It happens
• 1:00:12
  Speaker 3: all over the world, all.
• 1:00:13
  Speaker 2: Over the world. I forget how to say the word.
• 1:00:15
  Speaker 2: It's like pseudopictography.
• 1:00:16
  Speaker 3: Yeah, yeah, something like that.
• 1:00:18
  Speaker 2: You attribute your thing to someone who's well-known so that
• 1:00:22
  Speaker 2: it lends more.
• 1:00:24
  Speaker 3: Agrippa's Fourth Book, another good example.
• 1:00:27
  Speaker 2: Yeah, yeah.
• 1:00:28
  Speaker 1: Yeah so it just makes you wonder like i wonder
• 1:00:30
  Speaker 1: how many books out there we think came from this
• 1:00:32
  Speaker 1: actual person that really isn't at all.
• 1:00:36
  Speaker 2: Probably more than we would ever.
• 1:00:38
  Speaker 1: Imagine yeah i agree with that but uh yeah what
• 1:00:43
  Speaker 1: were we saying uh oh you know and the thing
• 1:00:46
  Speaker 1: with the ai too how uh you know you were
• 1:00:49
  Speaker 1: saying before you know i do think um.
• 1:00:54
  Speaker 3: I do admit it. I don't hide it.
• 1:00:56
  Speaker 1: I do use AI. It's quite obvious with my thumbnails.
• 1:00:59
  Speaker 1: I do use it, but I do think humanity is
• 1:01:05
  Speaker 1: really going to have to see where they are. You're
• 1:01:09
  Speaker 1: going to have to deal with this, I think, in
• 1:01:10
  Speaker 1: the right way.
• 1:01:12
  Speaker 2: No, 100%. I just had this conversation with Stacey Williams-Ong,
• 1:01:21
  Speaker 2: she's an artist and she made a publishing company that
• 1:01:24
  Speaker 2: does tarot decks and oracle decks and stuff. I'm actually
• 1:01:26
  Speaker 2: going to be dropping this video on my channel later
• 1:01:30
  Speaker 2: this week. She takes a vocal platform against certain aspects
• 1:01:38
  Speaker 2: of AI. We had a conversation about that and where
• 1:01:42
  Speaker 2: the line gets drawn, not to diverge too much from
• 1:01:48
  Speaker 2: final spark, but I think it's really interesting that you
• 1:01:50
  Speaker 2: brought that up because like I use AI, I do,
• 1:01:53
  Speaker 2: I use AI tools in sound production. Like when I
• 1:01:58
  Speaker 2: write a song, but there's a specific type of tool
• 1:02:03
  Speaker 2: that I'm okay using and, and a type of tool
• 1:02:06
  Speaker 2: that I'm not okay using. So, um, the example I
• 1:02:10
  Speaker 2: use is like when I EQ a specific track, I
• 1:02:14
  Speaker 2: have an AI plugin that can tell me what frequencies
• 1:02:16
  Speaker 2: are bad frequencies. And that helps me mix and master.
• 1:02:21
  Speaker 2: But that's very different from typing a prompt and saying,
• 1:02:26
  Speaker 2: write me a melody. Because at one point, I'm in
• 1:02:31
  Speaker 2: the drive. I have intention. I know I have this
• 1:02:34
  Speaker 2: track and it needs to sound a certain way. And
• 1:02:37
  Speaker 2: that's my intention to EQ. But if I'm just having
• 1:02:43
  Speaker 2: the artificial intelligence, create the whole thing, then I'm stepping
• 1:02:48
  Speaker 2: out of the driver's seat. And I'm basically saying like,
• 1:02:50
  Speaker 2: go drive yourself to wherever you want. I'm going to
• 1:02:53
  Speaker 2: stay home, you know? So like there, there is a
• 1:02:56
  Speaker 2: line and there's, there's an area of responsibility and an
• 1:03:00
  Speaker 2: area of where I think it becomes irresponsible.
• 1:03:03
  Speaker 3: Yes, no, I totally agree.
• 1:03:06
  Speaker 1: I definitely think, um, you can use AI to advance
• 1:03:09
  Speaker 1: the human race or you can also use it to,
• 1:03:12
  Speaker 1: do stupid shitty stuff you know for sure yeah.
• 1:03:16
  Speaker 2: We can just become like all the people in wally
• 1:03:19
  Speaker 2: who are fat and stuck in a spaceship just flying around.
• 1:03:23
  Speaker 1: Yeah yeah yeah uh but yeah that's uh i totally
• 1:03:27
  Speaker 1: agree with kind of what you were saying with the
• 1:03:29
  Speaker 1: whole the whole thing to begin with with ai huh
• 1:03:33
  Speaker 1: um see what else i had on here if there's
• 1:03:37
  Speaker 1: anything else i wanted to uh include I did the, uh, yeah,
• 1:03:41
  Speaker 1: I did the dopamine.
• 1:03:42
  Speaker 3: I did the Braille.
• 1:03:44
  Speaker 1: Um, I really do think, I mean, except for that,
• 1:03:46
  Speaker 1: the fact of what they want to do in the future,
• 1:03:49
  Speaker 1: they do, you know, and we did explain it already
• 1:03:51
  Speaker 1: that they do kind of want to get into the
• 1:03:53
  Speaker 1: storage thing. I think I pretty much covered, I think
• 1:03:55
  Speaker 1: a decent amount of what I had already, believe it
• 1:03:58
  Speaker 1: or not.
• 1:04:00
  Speaker 2: I believe it. No, we talked about a lot.
• 1:04:02
  Speaker 1: Yeah. Yeah.
• 1:04:03
  Speaker 2: Yeah.
• 1:04:04
  Speaker 1: Um, Yeah, and for some reason I thought that they
• 1:04:07
  Speaker 1: were hooked up with Neuralink, but I don't think they are.
• 1:04:10
  Speaker 1: I could have swore I thought I saw them wanting
• 1:04:12
  Speaker 1: to do something with Neuralink, but I could be wrong.
• 1:04:15
  Speaker 1: Because I was trying to look now, but I can't
• 1:04:18
  Speaker 1: find it. You know, I just thought that was like
• 1:04:20
  Speaker 1: another weird rabbit hole to go down with. How do
• 1:04:24
  Speaker 1: you feel about this stuff tapping into people's brains like that?
• 1:04:29
  Speaker 2: Are we there yet?
• 1:04:29
  Speaker 3: I hope.
• 1:04:30
  Speaker 2: I hope.
• 1:04:32
  Speaker 3: That shit freaks me out. I'll be told. Oh man.
• 1:04:35
  Speaker 3: I mean, I wouldn't, I would not want to do that.
• 1:04:39
  Speaker 2: I guess that's another level I hadn't really been thinking about,
• 1:04:42
  Speaker 2: but if we have the capability to like artificially control
• 1:04:47
  Speaker 2: brains in this way, then we're also going to create
• 1:04:52
  Speaker 2: the capability to like manipulate people on a neuronal level. Right.
• 1:05:00
  Speaker 2: So that's, That's another thing with the responsibility. Like, in
• 1:05:07
  Speaker 2: the wrong hands, that could be terrible.
• 1:05:10
  Speaker 1: Well, what if Final Spark's Terminator knows how to hack
• 1:05:16
  Speaker 1: into these things? You know, hacks into Neuralink. It just
• 1:05:22
  Speaker 1: makes humans off themselves to do whatever they want. I
• 1:05:25
  Speaker 1: just don't want resistance. You're going to sit there and
• 1:05:27
  Speaker 1: do nothing. Because I just told you, with your Neuralink
• 1:05:29
  Speaker 1: thing stuck into your brain.
• 1:05:31
  Speaker 2: I mean, I think we have to, on some level,
• 1:05:35
  Speaker 2: we have to fear that from AI, period. Like, regardless
• 1:05:42
  Speaker 2: of if we're using wetware, you know, like the artificial brains,
• 1:05:48
  Speaker 2: or if we're using regular hardware. Because, like you, I
• 1:05:52
  Speaker 2: think you were talking about earlier how we have, like, artificial...
• 1:06:00
  Speaker 2: intelligence programs that are trying to break themselves out of
• 1:06:03
  Speaker 2: the box. And it's, it's not necessarily because like they're bad.
• 1:06:09
  Speaker 2: It's because we programmed them to be creative and adaptable
• 1:06:13
  Speaker 2: and we gave them rules. And then we told them
• 1:06:15
  Speaker 2: to like, find new ways to go around the rules.
• 1:06:19
  Speaker 2: So they might break out and, um, try to gain
• 1:06:23
  Speaker 2: control of other systems. I think we have to worry
• 1:06:25
  Speaker 2: about that regardless. Right.
• 1:06:28
  Speaker 1: Yes.
• 1:06:30
  Speaker 3: You know, that's kind of what I was getting into.
• 1:06:33
  Speaker 1: It could become, that's the shit I'm worried about, you know, which,
• 1:06:39
  Speaker 1: you know, some people can say, well, it may not
• 1:06:41
  Speaker 1: even happen in our lifetime, which, yeah, I guess then
• 1:06:43
  Speaker 1: I don't have to worry about it, but it still
• 1:06:44
  Speaker 1: sucks for the people after me.
• 1:06:46
  Speaker 3: That's a future problem.
• 1:06:50
  Speaker 1: Yeah, right. And I'll get blamed like, oh, those mofos
• 1:06:54
  Speaker 1: didn't do anything about it.
• 1:06:55
  Speaker 3: Look what they stuck us with.
• 1:06:57
  Speaker 2: Yeah.
• 1:06:58
  Speaker 3: It was those goddamn.
• 1:07:00
  Speaker 1: Generation X mofos that let this shit slide.
• 1:07:03
  Speaker 3: You know how we blame everything on the boomers now?
• 1:07:10
  Speaker 3: But I do... Let me see. Yeah, I do think.
• 1:07:14
  Speaker 1: One thing they do have that maybe I could get into,
• 1:07:16
  Speaker 1: and I think this is kind of going towards where
• 1:07:18
  Speaker 1: they want to go.
• 1:07:20
  Speaker 3: They do have patents.
• 1:07:22
  Speaker 1: One was done in 2018, and it's Cognitive Computing Methods
• 1:07:26
  Speaker 1: and Systems.
• 1:07:27
  Speaker 3: based on biological neural networks.
• 1:07:31
  Speaker 1: The patent goes well beyond the simple idea of placing
• 1:07:33
  Speaker 1: neurons on electrodes. It describes an architecture involving biological neural
• 1:07:38
  Speaker 1: network units, stimulation and readout, nutrient delivery, chemical and environmental regulation,
• 1:07:49
  Speaker 1: waste removal, monitoring, signal processing, training or reinforcement methods, multiple
• 1:07:56
  Speaker 1: biological neural units, and a server-like abstraction layer. In other words,
• 1:08:01
  Speaker 1: the commercial idea is not merely a petri dish containing neurons.
• 1:08:05
  Speaker 1: It's a biological server stack. And the patent history provides
• 1:08:09
  Speaker 1: one of the strongest documentary links between AlpVision and FinalSpark.
• 1:08:14
  Speaker 1: AlpVision appears as an earlier assignee, while FinalSpark appears as
• 1:08:18
  Speaker 1: the later, uncurrent assignee in the principal records.
• 1:08:22
  Speaker 2: Um.
• 1:08:23
  Speaker 1: So, I mean, that is something that now it kind
• 1:08:25
  Speaker 1: of sounds like we could be trying to move into
• 1:08:28
  Speaker 1: some sort of physical neuron structure, maybe.
• 1:08:32
  Speaker 2: Yeah, I mean, it still sounds like we're quite a
• 1:08:39
  Speaker 2: ways off from that. Oh, yeah, I think so. It
• 1:08:44
  Speaker 2: sounds like they wrote the idea down, but they don't
• 1:08:46
  Speaker 2: quite know how to get there. And I feel like
• 1:08:49
  Speaker 2: where they're at right now is just.
• 1:08:53
  Speaker 2: Really nowhere i like honestly i feel like it's not
• 1:08:57
  Speaker 2: really quite anything yet but the thing is. I also
• 1:09:01
  Speaker 2: kind of feel like. Maybe. There is a whole other
• 1:09:06
  Speaker 2: thing going on. Behind the scenes. That has nothing to
• 1:09:09
  Speaker 2: do with Final Spark. That is a very similar type
• 1:09:12
  Speaker 2: of technology. That they are creating. And we just won't
• 1:09:15
  Speaker 2: know about it. For like 20 years. You know what
• 1:09:18
  Speaker 2: I mean.
• 1:09:19
  Speaker 1: Like.
• 1:09:20
  Speaker 2: They're keeping it a secret.
• 1:09:23
  Speaker 3: There is another company. I forgot.
• 1:09:24
  Speaker 1: I don't know where it is in the, if I
• 1:09:26
  Speaker 1: even left it in here, but there was like one
• 1:09:28
  Speaker 1: other company that's kind of a competitor too. So believe
• 1:09:32
  Speaker 1: it or not, they're not the only one out there
• 1:09:34
  Speaker 1: doing this stuff as well.
• 1:09:35
  Speaker 2: I absolutely believe it. I mean, there's so much potential
• 1:09:39
  Speaker 2: bits to this. Like there have to be a number
• 1:09:43
  Speaker 2: of people who would want to look into it and
• 1:09:46
  Speaker 2: profit from it. Hmm.
• 1:09:51
  Speaker 1: One thing I did find, just to add to what
• 1:09:58
  Speaker 1: they're looking to do in the future, they're trying to
• 1:10:00
  Speaker 1: turn living human neural tissue into an actual new class
• 1:10:04
  Speaker 1: of computing hardware, a bioprocessor.
• 1:10:07
  Speaker 3: Again, this is what they're looking at in the future.
• 1:10:11
  Speaker 1: Their near-term challenge is learning how to reliably train, control,
• 1:10:16
  Speaker 1: read from, and scale networks of living neurons. The neural
• 1:10:20
  Speaker 1: platform already lets researchers stimulate organoids electrically, record their responses
• 1:10:26
  Speaker 1: in real time, and interact with them programmatically. Final Spark
• 1:10:30
  Speaker 1: says its recent work has focused on better organoid generation,
• 1:10:33
  Speaker 1: longer lifespan, and making the whole system more scalable and reproducible.
• 1:10:39
  Speaker 1: And then, yeah, so I mean, really, I guess the
• 1:10:43
  Speaker 1: main thing is that they're going to try to make
• 1:10:45
  Speaker 1: some bioprocessor, really.
• 1:10:48
  Speaker 2: Yeah, no, that makes sense. I mean, that would have
• 1:10:50
  Speaker 2: to be the direction that it's going if it's going
• 1:10:52
  Speaker 2: to be viable for anything.
• 1:10:54
  Speaker 1: Then it can finally read Braille at 100% accuracy. It's like, geez,
• 1:11:01
  Speaker 1: that is kind of a funny thing to think about.
• 1:11:03
  Speaker 1: It's like you're bragging about Braille, but it was like
• 1:11:05
  Speaker 1: a horrible accuracy and like you needed three of them
• 1:11:09
  Speaker 1: to get 80%. It's like the three stooges.
• 1:11:14
  Speaker 3: Still didn't even get it right. Yeah, yeah. Awesome. So, yeah. Yeah,
• 1:11:18
  Speaker 3: so I don't know. We'll keep an eye out on it.
• 1:11:21
  Speaker 3: Maybe we'll hear about them again in the future. Who knows?
• 1:11:23
  Speaker 1: Maybe we'll show up on the news one day. They'll
• 1:11:24
  Speaker 1: be like, oh, we spoke about them. But yeah, something
• 1:11:28
  Speaker 1: I just thought was weird. Haven't really heard anything. Again,
• 1:11:31
  Speaker 1: it sounds like something that you would hear in a
• 1:11:33
  Speaker 1: conspiracy or a sci-fi post or idea. And I was like, shit,
• 1:11:39
  Speaker 1: that's actually real. I was like, huh. Just wanted to
• 1:11:41
  Speaker 1: cover it. If I could talk about it. Very weird stuff.
• 1:11:45
  Speaker 2: What interesting times we live in.
• 1:11:47
  Speaker 1: Yeah. Stuff that I even think about if I was
• 1:11:51
  Speaker 1: a kid, I'd be like, ah, I don't even know
• 1:11:53
  Speaker 1: if I would have believed that we were.
• 1:11:55
  Speaker 3: That far advanced in certain areas.
• 1:11:57
  Speaker 1: Yeah. It's funny how, like, if you kind of go by, like,
• 1:12:00
  Speaker 1: maybe what movies and cartoons made the future look, it
• 1:12:03
  Speaker 1: definitely did make it look more advanced than it would
• 1:12:06
  Speaker 1: be in some ways.
• 1:12:07
  Speaker 3: It would be in some ways. Like, we don't have
• 1:12:08
  Speaker 3: the flying cars anymore.
• 1:12:10
  Speaker 1: Where are my flying cars, man? Yeah, right. We don't
• 1:12:12
  Speaker 1: have the... Well, I do think they may actually have
• 1:12:14
  Speaker 1: those floating skateboards from Back to the Future.
• 1:12:17
  Speaker 3: The second one, but I don't think you can just
• 1:12:19
  Speaker 3: use it on the streets.
• 1:12:20
  Speaker 1: I think you actually have to go to a specific place.
• 1:12:23
  Speaker 1: But we didn't get that stuff, but it's definitely more
• 1:12:27
  Speaker 1: advanced than ways I ever would have guessed, I think,
• 1:12:29
  Speaker 1: as a child. So, very interesting times we do live in, Eric,
• 1:12:35
  Speaker 1: like you said. I guess Magus never made it, which
• 1:12:38
  Speaker 1: is okay, because we'll be wrapping it up now anyway,
• 1:12:41
  Speaker 1: and it kind of would have been a waste for
• 1:12:42
  Speaker 1: him to jump on. Eric, please let everybody know what's
• 1:12:46
  Speaker 1: up with you again. Remind them about your book that's out,
• 1:12:49
  Speaker 1: the one that's coming out, and your YouTube.
• 1:12:52
  Speaker 2: Well, you guys are probably watching on YouTube right now,
• 1:12:55
  Speaker 2: so when we wrap the show up, go ahead and
• 1:12:58
  Speaker 2: click over to my page, which is at Eric Arcadian.
• 1:13:02
  Speaker 2: Click that subscribe button. Maybe go Watch one of my videos.
• 1:13:06
  Speaker 2: I don't know. Have some fun with it. And once
• 1:13:08
  Speaker 2: you get done with that, you can pick up a
• 1:13:11
  Speaker 2: copy of my book, Fluid Condensers, or you can pre-order
• 1:13:16
  Speaker 2: my new book, which is called Runes Rediscovered. Awesome.
• 1:13:21
  Speaker 1: Thank you so much again, Eric, for coming on. I
• 1:13:23
  Speaker 1: thought that was a great chat. Again, a very interesting topic.
• 1:13:26
  Speaker 2: Thank you, Nick, for filling me with horror by.
• 1:13:32
  Speaker 2: Introducing this topic to me i appreciate that
• 1:13:36
  Speaker 1: Well i even think when i first came across it
• 1:13:37
  Speaker 1: i thought it was more like nefarious and when i
• 1:13:40
  Speaker 1: presented it to you and maggis i'm sure i probably
• 1:13:41
  Speaker 1: made it sound worse too but again it's still freaking
• 1:13:45
  Speaker 1: weird so uh yeah i had a good time with
• 1:13:47
  Speaker 1: you eric always whenever you're on the show we always
• 1:13:49
  Speaker 1: have a great discussion love you being a part of
• 1:13:51
  Speaker 1: the show and everybody in the chat that is what's
• 1:13:53
  Speaker 1: up thank you very much for chiming in and checking
• 1:13:55
  Speaker 1: out the show that's why we go live and until
• 1:13:58
  Speaker 1: the next one everybody be well later