Welcome to StartupRed.io, your podcast and YouTube blog covering the German
startup scene with news, interviews and live events.
Music.
Hello and welcome everybody. This is Joe from StartupRed.io,
your startup podcast and YouTube blog from Germany, Austria and Switzerland.
And today, I would like to welcome Fred, who is an entrepreneur from Switzerland. Grüezi. Hello.
You are the CEO and co-founder of two different companies, AlpVision and FinalSpark.
To get this little bit sorted out and introduce yourself, I would just ask you
to introduce yourself. and then we can work on the story why we're definitely
here, a future technology called biocomputing.
But first, let's tell your story.
Well, you know, I'm a French physicist, actually, engineer.
And I went to Switzerland about 30 years ago to make a PhD in signal processing.
And then I met another guy doing PhD with me and then we create a first company called AlpVision.
And this company specialized in applications of steganography,
that means invisible hiding of information into digital image.
Okay and we use this technology actually to detect counterfeit products using smartphones,
so you take a shoe you want to know if it's authentic or
fake you use our app take a picture it's going to tell you if it's fake sorry
a stupid question if you're buying something on platforms that would be before
ebay and now like all the competitors does it also work if you just have a picture of the product?
Can you tell if it's fake or not from a picture?
Well, theoretically, yes.
But in practice, I would say 99% of the case, we ask our users to have the real product in hand.
Okay, I see. So this was,
you have to imagine, and when we started this first company with this friend
and I, we were basically in a garage, okay, trying to use the theoretical findings
that we made during the PhD and to make a living.
Okay. And the company went and is still going very well, actually.
We are protecting against counterfeiting more than 30 billion,
30 billion of products each year. Okay. So it's really huge.
You being in Switzerland, I would have an assumption that stuff you would protect
would include, of course, expensive wristwatches. Yes, they may indeed.
But, you know, confidentiality and discretion is a Swiss value.
So we don't talk about what we protect, but the technology is provided to the
consumers and nobody knows that it's us behind the scene.
So we work, we make money with the brand owners that pay us actually.
And so that was an interesting journey, okay? Trying to make money out of some
theoretical mathematical findings, okay?
And the company is still working well and we have offices now in Shanghai,
in New York. So it's really cool.
And in 2014, we said, okay, this company is going well. Let's make another one.
When we run a company, it's not enough. It's not enough stress, right?
Yeah, but the thing here is that we were lacking a little bit the R&D,
the fundamental research part, which we love, the co-founder and myself.
I said, OK, let's do something incredible in AI, artificial intelligence.
Let's follow a path that nobody has followed before or seriously followed before. Okay.
And that was in 2014. And we created FinalSpark. And this was,
again, two of us in parallel with the first company.
One question, Fred, doing a big shout out to your co-founder.
What's his name? So he's Martin Kutter.
And he's a Swiss German. And so we are actually fundamentally quite different
because I'm French, he's Swiss German.
But that makes actually a good team. So we've been working together for,
I don't know, 25 years now.
I totally assume you can both agree on fondue.
Yes, yes. But this is mandatory.
Otherwise, you know, federal state will take your passport.
Okay. Okay. And so, basically, 2014, you found what is today FinalSpark.
Can you give us a little bit introduction in the big picture and vision?
How on earth did you bump into biocomputing? Because my understanding is you like physics.
So, I would assume you fancy big machines, you like them.
How did you end up at neurons?
Honestly, this was not the plan.
Really not. As you said, our expertise was in signal processing,
physics, so mathematics, a lot of mathematics and programming and things like this.
And so we started to go into AI by exploring new paths, but purely digital,
like genetic programming.
So genetic programming is that you are basically creating random source code,
random source code, okay, that you compile and execute in the hope that it's
going to do what you want.
If you have any demand for that, I have a two-year-old here who's very enthusiastic
about hitting the keyboard.
For this to work, you have to create actually billions of programs of source
code and execute them. As I said, very enthusiastic.
And we used genetic algorithms approaches for this when you actually try to,
you said that my source code is an individual, I'm going to make some crossover
with another individual, which is another source code, and execute it.
And so, okay. It's a very strange way of doing AI, and this is what we loved, okay?
We were only looking at strange things.
And another strange thing, so we are testing a lot of things.
And something else that we started to test was spiking neurons.
So, I don't know, in the field of AI, normally people do not use so-called spiking neurons.
Normally, in AI, you use artificial neurons, which are much simpler than this.
You accumulate some values weighted by some other values, and you output another
values given a threshold function.
Basically, that's it. This is all, JGPD, everything is working with this. So simple.
And we said, okay, we're not going to use this model. We are going to use a
more sophisticated model, which is more realistic, still entirely digital. Okay.
And that means it's a very well-known model known for 30 years ago.
Okay. which basically is saying that this is a temple activation through time
that characterizes the activity of a single neuron. It's a spike, okay?
An action potential, actually. So you can also have some simulation of artificial
neurons which are spiking through time.
You can also create networks and do some learning. And we spent a number of
years on this kind of model, also because one of the leaders of this field are
actually in the Swiss Federal Institute of Technology, where we made our PhD.
So there was some cultural connection.
And after a few years, what we had was a few hundreds, hundreds of simulated
wonderful spiking neurons,
consuming about five kilowatts of power.
So 5 kilowatts for 100 neurons I don't know if you put this in perspective with
your brain it's 100 billion of neurons and it's not 5 kilowatts it's 20 watts.
So then we really realized that, at this point, we could not scale this.
Impossible, OK? If you already consume kilowatts with 100 neurons,
you're basically hitting the limit of your model.
Nearly no chance, OK? And then something else happened.
We had a third guy who joined us at this point.
And he was doing the military service, which you may know is mandatory in Switzerland.
At the Swiss Federal Institute of Technology. Okay.
And he was working in the lab of Professor Markram on the Human Brain Project.
And this project, it's all about living neurons, not simulation, okay?
But because it was out of our field, we were not looking at biology.
It wasn't anything about biology, but he had to work there, okay?
So he was working and measuring real living neurons.
And of course, we were chatting together. And at some point,
the idea came a bit spontaneously.
Like he said, they are consuming almost nothing, these neurons.
And on our side, it was the opposite. Our simulations are consuming way too much.
The idea came at this point. We said, OK, instead of trying to simulate neurons
on artificial networks, Next, let's try to use living neurons.
When you just speak about living neurons, what do people have to picture?
Because when you talk about living neurons as a chip, what I have in mind is
like a silicon waver with some gray matter on it.
Where do you get the neurons and how does it actually work? Well,
what you have in mind is not so far from the reality, surprisingly.
The thing is that most of the time, you cannot get neurons directly.
Particularly, if you talk about human neurons, it's almost impossible to get
what is called primary cells.
Primary cells are really cells extracting from a living body.
So you can get primary cells for mouse or rats, but for human, no.
So there is something else.
Another invention that came into play here is a Nobel Prize called Professor Yamanaka in Kyoto.
He invented a way to take some cells of your skin and convert them into stem cells.
Ah, and then from stem cells, you can go to neurons again? Yeah.
Oh, yeah. Okay, I see. And this is what we do in the lab.
Just five minutes before this interview, I was in the lab doing some experiments
using these stem cells, actually.
They are called induced pluripotent stem cells because we have induced the pluripotency
because if you take skin cells that are not pre-repotent at all,
the skin cell can only become a skin cell.
It will never become a neuron. It's dead for them, okay? It's over.
They have one thing to do.
But if you induce the pre-repotency, then you can again create whatever cells
you want, including neurons.
And this is what I was working on five minutes ago.
Ah, I see. And how can you put this biological matter into a working computer?
How do you work with the connection? Because I do assume you have them arranged them in some matter.
You cannot connect like every single neuron to its own very tiny electric circle or something.
Yes just before i answer i will make a remark to you as a human being yes go
ahead most of your neurons are not connected to your sensors,
Okay. Just to be clear on this, which means in practice that what we do is that
we are not first playing directly with neurons.
We are creating something a bit more sophisticated, which is called organoid.
So an organoid is a collection of living neurons connected together that creates like a small organ.
That is going to be, in our case, a small ball of half a millimeter.
And to answer your specific question, we are going to put it,
literally put it, deposit this ball on electrodes.
And then we can use the electrodes to receive and send information.
And as you correctly noticed, we are going to only discuss with a few neurons,
which will be the interface with the rest of the brain organoid.
I see. And the big advantage that you've already hinted in the beginning is
that it uses really much less energy than a normal silicon waiver would do.
Yes. Talk about a factor of one million.
If you want to see some publications on this, or the publication for me is the
one of Professor Artun, which I guess was done in the journal Fronties two years ago.
You can google Hartung Frontiers what you can do you can provide the link to
me after the recording and I'll link it in our blog post yeah perfect yeah because
it's available for free so okay so you can get this so and indeed you know,
Looking again at your brain, if I wanted to simulate your brain,
I would need, by today's standard, a small nuclear power.
Okay. Yeah, it sounds like a lot of energy. That explains why I eat such a lot of chocolate.
No, because with all the chocolate you eat, you are still consuming 20 watts
of power to run your incredible brain with 100 billion of neurons and 10,000
connections per neuron.
You know, nature has to be extremely efficient.
And nature optimized nervous tissues for 300 millions of years to be energy efficient.
So this is the benefit of and the good thing of using existing products of nature
is that it's already there and it's already really, really optimized with a
factor of 1 million better than what we can do in silicon.
And let me mention that I'm a physicist, okay?
So I love the integrated circuits and transistors and quantum mechanics.
This is the things I know, okay?
And I was not so happy to go in the biology direction because I didn't know anything about it.
So if I could have avoided, I would have avoided it because I had to learn everything from scratch.
So it's really as an engineer, you have, I would not say the responsibility. This is a too big word.
You have to have the rationality as an engineer to take the best solution on the market, I would say.
And the best solution here is not silicon, is living neurons, period.
Extrapolating a little bit out into the future, what do you think could.
Make, giving like all the big investments that we've seen just recently with
a 500 billion into infrastructure for AI.
What kind of difference could biocomputing make there?
Biocomputing is going to open an entirely new industry. Okay.
The most obvious application is that you talk about these investments,
but also all investments in servers and the power conception that they represent.
This power conception is converted into electricity, which is converted into $2.
If you come up with a server like the Amazon Web Services, for instance,
which consumes 100 times less,
knowing that this is the primary source of recurring cost of these servers,
the market is going to be huge. It's just obvious.
And the question is, I would assume since it is not like serial production level
you're working on right now,
I would assume the production costs are currently pretty high and the energy
consumption in the future is pretty low.
And when you get to the point where also the production costs kind of matches
the silicon costs, that's where it gets really, really competitive.
Yes but you have to realize that the production cost is going to be incredibly
quickly lower than any artificial device incredibly last week,
I mean, in the lab, we have created, I guess, a few millions of neurons. Only last week. Okay.
And the thing is that it's almost for free. I mean, when you have stem cells, they just multiply.
24-7, you get more and more and more. And you talk about production.
But it's natural. It's automatic. It's made for reproduction. production.
So the problem that we have sometimes is that we have too much.
So it's not going to be really a cost of production.
There are going to be other costs, but not on this side.
I see. So I do believe we now do have an understanding of what you guys are
doing to replace silicon wavers.
And just for my understanding and the understanding of the audience,
would your biocomputing those balls there, which I picture as kind of very small bioreactor,
would they in a computer completely replace the CPU or would you work alongside
the CPU and all the other conventional pieces of a computer?
Yes, you're right, alongside is the right keyword.
As I guess, quantum computers are going to work one day alongside biocomputers.
Like DSP, digital signal processors, work alongside CPU, and GPU work alongside CPU.
So this is going to get a little bit more complex.
But I can tell you what is not going to happen with biocomputers.
Bioprocessors are not going to run Windows 11.
They are not made for this.
Biocomputers are very well done to run any task which is done for AI today.
Because since AI is simulation of neurons, you can also use the real one.
In many applications, this works. Not in all applications, but in many applications,
it works. Particularly in generative AI.
I see. I see. And that's basically with the big models talking about JetGPT,
Gemini, DeepSeek, that is currently what the cutting edge of AI is.
Yes, absolutely. It's a very good example.
I see. I see. And now can you tell us a little bit more about what FinalSpark
does in this whole future potential industry?
Well, we want to be the first one to create a bioprocessor.
And now you have to consider that this is fundamental research at this point.
Let me tell you what is the main point of research. The main point of research is exactly the same.
Not surprisingly. Then the main point of research was 30 years ago with AI.
Do you know what was the main point of research 30 years ago in AI?
It was about training. We were able, because I've been working in artificial
neural networks 30 years ago, and we created networks of artificial neurons.
And the problem was, how do you tune the connections between the neurons so
that you get the output that you want for the given input?
This is called training.
If you have thousands or millions of connections to tune, it's impossible to do in practice.
But someone came with a solution, which is called the backpropagation which
is basically an algorithm or a mathematical approach which gives you the partial
derivative of the weight of the connection in respect of the error the network creates.
And with the help of this mathematical tool,
we are able now to know exactly how to connect how to change the connections
to have a learning platform.
So when you talk, when you hear about machine learning, it is this learning. Okay.
So this, finding the solution made a big difference, made the difference actually
between non-working AI and working AI. It's very schematic, of course.
But what is interesting is that it's the same problem today for us. We also have neurons.
We also need to rewire them in a network so that when you input something,
the output is meaningful.
How do we do this? We don't know. Okay.
No, no, but hey, let me tell you still a good news.
One thing interesting, which was not the case with artificial neurons 30 years
ago. We are sure it's possible.
You know why? Because you are able to learn.
So if a human brain can learn, the cells within the human brain should also be able to learn, right?
Correct. Particularly knowing that it's not only the human brain,
but bees and insects and very primitive animals are able to learn also.
So we know it's possible.
We just have to find how nature does it in order to reproduce it in vitro in a way which is reliable.
Oh, I see, see, see. Okay. And that's basically the part where you are.
My understanding is that there are only a handful of potential competitors out there.
You've been talking about like three companies globally with FinalSpark being one of them.
Yeah, yeah, it's great. It's an incredible era, actually.
You know, we live in a world where it's very difficult. When you wake up in
the morning as an engineer, I say, I'm going to work on something when nobody
thinks about it and it's going to make a revolution.
How many fields can you imagine this?
And I'm going to start by myself. Yes, and I can do this. And biocompeting is
one of these very, very rare fields that still exists today.
And indeed, you're right. We are three companies in the world.
And I'm not going to lie. I would prefer that there are more of them, not less of them.
Because this is going to be a revolution but we need a critical mass of companies
competing together so that venture capitalists and money starts to flow into this research.
I see do you have like a kind of idea when you would or you guess you would be able to.
Produce on an economic industrial scale biocomputing?
Just for simplification, I know it's not correct, but biocomputing CPUs that
we stick to a known frame of reference.
Yeah. Our business plan states that we should be able to have a first a bioserver
in about 12 to 13 years, one, three.
So what we target is basically cloud biocomputing. So you know about cloud computing.
And cloud biocomputing is the same, but powered by bioprocessors.
I see. Basically, here around Frankfurt, we have D6, the world's largest internet node.
So in the area I live around here, there's a lot of very big buildings with
a lot of air conditioning on top and that's basically like all the places where
all the calculations are done those data centers this may be replaced,
maybe one time in the future,
actually for me it's very clear biocomputing is not just a simple new technology
it's a new industry that is going to stay and change everything in the coming 10 to 20 years,
i see um before we get into challenges and ethics let us do a little ad break.
Hey guys welcome back i'm talking to fred jordan co-founder and ceo of final
spark one of just three companies in the world working on bio computing where
they are going to replace or work alongside the CPU,
a bio-CPU that we just talked about before.
I would now like to go a little bit into the challenges and ethics of biocomputing.
My question would be, what are the most immediate bottlenecks in biocomputing?
Is it scaling? Is it stability?
Is it reproductability?
Or is it just teaching?
I would say, teaching. First, teaching is the big thing, OK?
Really, you know, it's incredible.
Because in biology, you make a difference between in vivo and in vitro.
Teaching and learning, in general, has been studied in vivo for the past 50 years.
Hundreds of thousands of publications on this field.
However, comparatively to this, if you think about in vitro learning,
that means not in a living animal,
basically there is almost, I don't know, less than 10 publications.
So that means in vitro learning would be you basically teaching a baby,
an animal, something before it is born.
It's even not a baby. It's even not an animal.
When we create a brain organoid, it's just thousands of neurons connected together
in a small ball. That's what it is.
So it even doesn't look like a brain. It's a nervous tissue.
But a nervous tissue, we believe it's reasonable that it's possible to make it learn something.
I see, I see. Um, what is the upper limit to what biological neurons can compute efficiently?
Or do you expect exponential progress, um, similar to something like Moore's law in silicon chips?
Uh, yes and no. Um, in German, we do have a wonderful word for this.
Uh, we say ja and nein, it's called ja, exactly. Exactly.
No, because with computer chips, it's about artificial systems.
That's so it's a question of light diffraction and how you can compensate this
in order to engrave smaller and smaller circuits, OK?
But here, you have to realize that we are not making anything.
I am not controlling how well I am controlling very remotely,
OK, how these neurons are growing.
I cannot change the intrinsic, well, I can change to some extent,
but I'm very limited in the number of change I can do to these neurons.
For instance, the actual potential is going to propagate always more or less
at the same speed in all the neurons I do, whatever I do.
And I'm not going to be able to pack more neurons in the same space than they
would naturally accept.
A transistor is not accepting something. It's going to be a passive device,
which is controlled by a human being.
Here, you have to play with living things. So it's a bit different.
You have to behave differently.
But coming back to scalability, there is a big, big difference here.
I can grow nervous tissue. Like I said at the beginning, it's half a millimeter,
half a millimeter today.
But tomorrow, theoretically, I could do one centimeter.
I could do 10 centimeters. Actually, I could do 100 meters of nervous tissue.
I could culture it. there is no limit to this,
I mean a football field with a 5 cm nervous tissue is going to represent a serious
amount of computational power but I will have,
I will need to do nothing actually to get it's just more like when you do agriculture you know,
so you grow things you grow plants and here you grow neurons So,
it's more going to be in the scale of things, how big we can handle them.
I was wondering at the moment you were talking about this, usually we only talk
about electricity consumption.
Do you also need to provide sustainment for the cells?
I do assume they are not only living from electricity. Thank you.
Well, they're not living at all from electricity.
Not even remotely. OK? Electricity is not going to help them in any way.
So we are not using electricity for this.
For this, we use so-called culture medium. So these are medium is basically water, OK?
With many things inside. OK?
And the things that we put in the water to make cells live.
I mean this mixture has been invented in the 60s,
so it's not science fiction it's a very old school way of doing so we are not
inventing anything here so you take water you have all the vitamins all the
amino acids many salts, glucoses,
and you can put your cells inside that
are going to live for weeks and months mm-hmm talked
about the ethics here how do you personally wrestle
with the ethical implications of you using human derived neurons for computing
even though they're their your own so here is the the big change okay um i was
able to learn biology because this is still science okay,
I'm an engineer. It's okay.
But here, you talk about ethics. And now you hit the limit of what I can learn and my expertise, okay?
And I don't know how to answer to these questions, honestly.
It doesn't mean that these are not serious questions. I believe these are very
serious questions, okay?
And what we decided to do, actually, is to make connections with academics, okay?
And because there are people working in ethics this is their job they are competent
for this they are experts we are not I can tell you about many other things
but not this one and many other things I cannot tell you anything,
and for this academics what we did is that we are starting to come to philosophy
conference where we explain what is biocomputers to a teacher and we say guys
okay here is this thing We bring the science and bring the ethic.
When I've been thinking about this interview here,
I was curious, do you see a point where the conversation shifts from using neurons
to do dumb, stupid computing to really collaborate with neurons in a fundamentally new way?
First remark that strikes my mind when you ask this question,
is that, don't you think that in the past few months, we're starting to collaborate
with computers in an entirely new way, with ChatGPT and things like this?
Yes, and I can see definitely human traits in there when they're getting lazy.
So now the benefits and the consequence of the technology is always a vast debate, okay?
What can I can already tell you, You're better off living today than 1,000 years ago.
OK, so total benefit is clear.
Now, how can we interact with these nervous tissues? What is it going to change?
I start to wonder, you know, with this large language model interactions that
we are using more and more every day, like we have digital companion,
like general AI is like it's their occur actually.
It's actually less disruptive than we thought it would be so far.
And the fact that this interaction would be done with nervous tissues instead
of digital simulations of nervous
tissue, what difference does that make actually? Is it that important?
That is something that would take quite
a lot of philosophers quite some time to work
this out i see i see we're going to keep them busy for
some time with these questions i see um talking about the applications and real
world impact here um what do you think what industries will be the earliest
adopters of biocomputing and why?
So this one is quite simple, actually.
All industries who are using AI, therefore all industries.
And also those who use AI and care about their cost. All industries again.
Because what we are going to develop first is a server when the rental price
is going to be one-tenth to one-hundredth of of Amazon Web Services, period.
This is what we're going to do. I see. I see.
How soon could we see biocomputers solving real-world problems in fields like
cryptography, optimization, or even drug discovery? No.
For cryptography, I'm skeptical.
This one in particular.
It's not appropriate. I don't believe.
I'm skeptical for this. I would really bet on quantum computers,
not only because I love quantum effects and quantum mechanics,
but for hardcore raw computation and speed, this is not the appropriate approach.
Now, if you talk about drug discoveries, for instance, so here we can connect
again with actually machine learning and with traditional AI, OK?
And this can be used in the same way as AI for drug discoveries.
For instance, protein folding and prediction of protein effects, this would work.
Fundamentally, this could work the same way, actually.
The main difference is always how much power you need to use to get this result.
You know if you think about competitiveness in all countries,
it's always about how much energy do you have and the cost of your energy so
for the same energy you can have 100 times more it's like if you had 100 more energy,
Or the energy was 100 times less expensive.
I have to admit, I'm a big fan of the sci-fi author Peter Hamilton.
And therefore, the question, could biocomputing have a direct application in
human augmentation, such as interfacing with AI or even the human brain itself?
Because there are already companies out there who are putting silicon chips
into human brains. wouldn't it be easier to connect via a neuron by a computing-based chip?
So first of all, science fiction and Peter Hamilton and all the other science
fiction authors, I love them and I never go to bed without reading at least
15 minutes of science fiction every day. This is a rule.
Who's your favorite author before we get into the other stuff?
Well, I have to say Arthur C. Clarke. Arthur C.
Clarke, yeah. Yeah, so yes, you're right.
In this discussion, we've been talking about biocomputing.
But as soon as you start to look at neurons as small machines,
you are changing the way you look at things.
And when you change the way you look at things, this may lead,
actually, to new applications. And you're right.
Interfacing a human brain with a brain organoid.
Is that challenging? A number of research has been published on,
related fields, and you are first to consider that if I wanted to interface
a brain organoid with your brain, what I would do first is I would take your skin,
a different cells which have your DNA,
So they will not be rejected by your immune system. They will be exactly your cells.
And then I can tell you also, and you can sometimes see this online on our website,
when we have brain organoids that we put together like this,
we touch them, after two weeks, they're entirely fused.
So neurons love each other. They love to connect.
So it's absolutely not a challenge to connect. So the scenario that you described, of course, makes sense.
So now, if you get into science fiction, you could say, OK, I train a brain
organist to speak Chinese.
I put this in my head. I'm going to speak Chinese. I was a bit skeptical that
this could work as simple as this.
But of course, things are going to change a lot in this direction.
But not only this. I guess at some point you know today all the objects which
are around you where you're talking.
Well most of the objects I guess are not living you've got a mouse,
a microphone these are not living maybe you have a plant or a flower these are
living so you have two categories living, not living,
and not living most of the time more and more these are things which are created by human beings.
But in the future, you can imagine, if you talk about science fiction,
you could have objects which are just in between, hybrid, partly living.
For instance, a mouse that will have nervous tissue, that will recognize even
before you press, that you want to press.
So a way of interacting with objects which is totally different,
because they are just the interface between objects and living things,
that they integrate some living parts.
I like this question if you had unlimited
funding and no regulatory barriers
what would be the first moonshot experiment
you'd launch tomorrow so first this question is not as theoretical as you might
think since i'm i'm looking to raise 50 million of euros at this point of course
investor told exactly the same question.
And the answer is clear. We are going to hire. First, we need researchers here
because we have to test a number of things in part.
We've gone quickly on a number of things, but there are a number of challenges that we have to tackle.
And we have to create larger brain aganoids. We have to create a larger number
of electrodes, increase lifetime.
So the first thing I would do is that hire more people, increase the size of
the labs to have more and more experiments running in parallel.
I don't know what just sparked the idea, but just out of stupid curiosity.
We're talking about living cells here. They won't be affected by computer virus,
but could they be affected, meaning knocked
out by a traditional virus or
bacteria that gets somehow into the processor absolutely
yeah absolutely and so
what you describe is a very well known of anyone doing cell culture today and
for and since the past 50 years so in the lab just below here again so i can
tell you we are absolutely paranoid uh with this because uh they have no immune system,
And we are not using antibiotics most of the time.
So that means that if you have one single bacteria somewhere,
everything is dead in 24 hours.
So we have to be extremely careful about what we're doing.
I see. When I was brainstorming a little bit on questions, I was wondering,
traditionally, processors have been used to fulfill stupid,
repetitive tasks.
But could you see computers based on bioprocessors that can help in areas that
haven't traditionally been associated with computing, creativity,
art, helping handicapped people, talking about fusion of neurons,
or even emotionally based AI?
So your question covers a lot of things, okay? It does, yes.
And your question also is based on, you have voluntarily put aside chat GPT
in your question and LLM, okay?
Because, of course, nowadays, computers are not what they used to be anymore.
A little bit more.
And yes, definitely we can.
Well, the first thing is about the complexity of what you are generating.
When you use real neurons, they are going to have way more connections.
So it's one, okay? And they are in 3D. Our processors are not in 3D,
okay? They are in 2D, okay? We should.
Now, about interacting with you.
It's very, not easy, but in principle, people do it currently in research.
Easy to modify a neuron to detect molecules, for instance.
Like if you breath, for instance, it could detect that you have a cancer.
Just analyzing the molecules that you breathe. So talk about interaction between
a living object and a human being. Could be also in that direction.
So of course, the sensors are going to change a lot, because these are biosensors,
totally good to detect complex molecules, which is extremely challenging in an artificial manner.
So yes, the way we are going to interact, and the fact that it's living,
and also it's living at the same speed as us.
So it's interesting.
A computer is performing, I don't know, 1 billion computation per second.
This is not something that we can imagine. But the nervous tissue will work
exactly at the same speed as the brain ergonomic that we have.
OK, it's part of the, so everything is going to be in sync with us as human beings.
It's a totally different way to approach computing.
The last question of this section would be, what one unexpected but potentially
game-changing application of bio-computing that nobody is talking about yet can you see?
This is a very interesting question, you know, because the first thing I believe
that the most important application is going to be absolutely game-changer,
okay, of bio-computing.
Is not only more efficient servers.
This is already incredible, but it's not this. This is something else.
But I don't know it.
But, you know, there is a guy who was named Mr. Shockley.
Mr. Shockley invented about 80 years ago the solid state transistor. store.
I can promise and guarantee you, okay, that he had no clue that we would use
his invention to create a smartphone.
Okay, because that was 80 years ago. So if you ask me what are going to be used
by your computers, I'm not able to answer.
I know it's going to be big and way bigger than I think.
Mm-hmm. Okay. I see. We'll be back after second short ad break.
Okay, guys, we are back here with the last section interviewing Fred,
co-founder and CEO of FinalSpark, one of just three companies in the world working on biocomputing.
This is the very last section and I'm interested in like your personal take and legacy.
I'm curious, what motivates you to work on this every day and what keeps you awake at night?
You know, I co-created a first company that makes money, which is very good
and hires people. We're very good.
So now look at Earth from 100 million of kilometers, which you will see a small ball like this. Okay.
Imagine you are standing in space alone looking at Earth like this.
What can you do on Earth that is still meaningful when you look at this distance from Earth?
And making more money is not one of them. OK.
However, creating a new form of intelligence, yes, it makes sense.
I see. If somebody writes the definite history of biocomputing 50 years from now,
where do you hope your name appears in that story?
You know, if it appears only somewhere, I would be so delighted.
You know, first you have to imagine that 12 months ago, we were basically totally unknown. Okay.
And then we became viral with the publication we made in Frontiers in May.
Two weeks ago, I found there was a Wikipedia article, which is titled Science in 2025.
Where they list all the important things that happened in science in 2024.
And we are listed here.
That was talking about science fiction. This was really science fiction,
because I could not believe my eyes.
So I don't know what.
But right now, I'm not thinking about this. I'm just thinking about the next
experiment and what we can do better right now.
I see. Another theoretical here.
If you could have a conversation with Alan Turing, John von Neumann,
or any other computing pioneer, what questions would you ask them about biocomputing?
Well, von Neumann would love the idea, of course, because it is a von Neumann machine. Yes, yes, yes.
So we will have a good friend, okay?
There are many things about information theory, about spatiality, about movement.
I definitely would need to prepare this meeting.
And arrive with like a physical catalog of many, many pages just full of questions?
Maybe not only questions, but I would actually ask, what would you do if you
could program living neurons?
I would actually, I think, I'm sorry to say, or maybe it's obvious,
I think I would ask the same question that you asked me since the beginning of this.
Sorry to disappoint, but... That's totally fine. That's a big brace for me here. Totally good here.
What do you hope your biggest contribution to biocomputing will be,
not just scientifically, but also maybe philosophically?
I think there is something with technology, is that there are human beings and
technology, and more and more people start to say it's not good.
Technology is too far from human beings.
Actually, technology is almost against human beings, OK?
And as an engineer, I don't like it, OK?
I want to reunite technology and human beings, OK?
They should live in harmony, OK? And I believe biocomputers and synthetic biology can achieve this, OK?
We have done very good artifacts, which are called machines, artificial things,
We still need to have a good interface with us as human being.
This is the last piece that we should build. And we are going to do it, I'm sure.
Talking about going to build it, going to do it. You already talked about fundraising,
going to the very mundane pieces here again.
You guys are currently on the outlook for raising funds, right? Yes.
Yeah, indeed. So our mission is to raise 50 million of euros.
And so we are talking with a number of people worldwide, actually.
So we hope we can gather this maybe in three series, ABC, 10, 2020.
Honestly, I would prefer to avoid this because I don't want to be running after
money constantly. I want to work on the science here.
And this is the big challenge and the fact that we are standing in Europe.
Which doesn't help at all because I would already have this money in the US probably.
If people made it until here, that's almost an hour of listening.
Thank you very much. Clearly appreciate it. But those people are definitely
interested in you and biocomputing.
Are you open to talk to new people looking for talent?
Yes. So we handle actually a constant stream of people who want to work with us, several per week.
And these are only people who want to be hired. In addition to this,
we have internships who want to work here in the lab.
So we answer to everyone and we have some process to get people working with us.
Great. So everybody who's interested now, we link down here in the show notes
your website and the people can just basically reach out to you directly.
Yes, we can also do is that we have a Discord server.
So they can go to the Discord server and we will answer directly to the questions of everyone.
So first they can see all the questions which have already been asked and then
we are happy to exchange.
Great. So, Fred, thank you very much.
It was a pleasure to talk to you. It was more fun and much more extensive than I expected.
Thank you very much. It was a great pleasure to talk to you.
Hopefully, to have you back in a few years and talk about your successes there.
Yes, I would be happy to do so.
Great. Thank you very much. Have a good day. Bye-bye.
That's all, folks. Find more news, streams, events, and interviews at www.startuprad.io.
Remember, sharing is caring.
Music.