Hello, and welcome, everybody. This is Joe from Stella Bright. They're all
coming to you today again from a very, very hot Frankfurt
area. We are expecting to crash the 30 degrees
Celsius today. It's gonna be damn hot.
Nonetheless, I do have Danco here with me. Hey. Welcome.
Hello.
Totally. My pleasure to have you here because you're a very
interesting guy. You are a brain researcher
turned entrepreneur and working on machine Menninger.
And we get through this interview and, at the end, have a few
ideas how machine learning can profit from your
research and what your very interestingly
named Startupradio, robots Joe Manta,
actually does. But first, I would like to,
to thank our enabler. This recording is supported by
HDI, Essentrade and Invest, and Enterprise Europe Network.
These organizations have made tremendous contributions to helping start up
businesses succeed and thrive, providing a range of
services from helping to find grants to ongoing partnerships.
By taking advantage of these resources, startup companies can
network and develop innovative strategies for success on the
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in partnership with them called Tech Startups Germany.
Now that we do have this out of the way, as
always, I've been looking through your CV, and I found it
extremely interesting. You have been
teaching at the Technical University Darmstadt
statistics. That Yes. Nonetheless, it it it takes
it it takes quite a special person to really love
statistics and even teach that on university level. And we have to say,
Technical University is one of the top technical
universities here in Germany, maybe even in Europe. So, they
don't take anyone as statistics teacher. Then she
became a group leader, but the interesting twist here is
in brain research for the Max Planck Institute, can you take
us along your journey how you came from statistics
to brain research? Because then it kind of makes
sense how you ended up in machine learning and
artificial intelligence. Yeah. Yeah.
Very gladly. So thank you very much for having me here.
It's it's a pleasure to be here. So the story is a little
bit different. So I was teaching it at,
at only as a side gig when I
was already at the at the, Max Planck Institute. So
it was happening in parallel. Right?
But how I came to the brain research,
is is a story Okay. That's hard Let me get this straight. You
you've been, at the same time, a BRAID researcher and a
statistics teacher? Yeah. Yeah. Yeah. Yeah. Yeah.
Let's do a more. One can do that. I think I think about brain
research is always about biology. But, apparently, you can
also apply pretty much statistics. Sorry to interrupt you, but it's just
Oh, yeah. It's quite uncommon. There
is a lot of statistics in brain research. Trust me.
Like, when you get all this data, when you record all these brain signals,
you have to know what to do with that. Right? You have, like, bunch of
numbers in your computer, and you have to analyze them. You have to
understand them. There's lots of statistics. Believe me.
I was I was totally fine with all the statistics I've been taught
in Germany and the US. Menninger high five through professor Owen from Midwestern
State, by the way. He he was the only one who made it really
entertaining. Disclaimer, I've never attended your lessons.
Don't worry. And, also, statistics
is in in many ways, statistics is understood as
as discipline that preceded machine learning. Right? So
machine learning somehow built on top of statistical
research, statistical analysis, inventions in in
in statistics as well. Right? But that wasn't
really my story. Right? My story was
that I was first interested in artificial intelligence,
like, back back then. Realized
that artificial intelligence is struggling with doesn't have a
good solution. There is no good way of of
emulating brain, human brain in a computer.
And that's what triggered me to to try to understand the
brain. So I started with studying psychology, getting
PhD in cognitive psychology. So kind of first
understanding the brain at the level of behavior.
And then go then say, okay. Now I understand that, kind of.
Now let's go into the brain. Then I went to to to,
brain research. That's where I needed a lot of statistics.
And then after that, as
I don't know if you mentioned, but I then I went to industry after
some time at at the Max Planck Institute.
I I would be curious. How was the interview
with HR there? Because, for everybody, we
we do have a quite a vast audience in in Europe
where Max Planck Institution should be pretty well known.
But for for all the thousands of people listening from
abroad, Max Planck Institutes are one of the
premier basic research institutions in Germany.
So you simply can't can't tell. I go there. You have to do
your your undergraduate studies, your graduate studies, your
PhD, and then do some impressive work that they will consider you. I do believe
the vast majority of the
noble arts in Germany is associated with the Max Planck Institutes.
Oh, no. And then you you you go there to to to
study towards HR. And what did she ask
you? There is no HR in Max Planck. Yes. It's you.
Believe it or not. It's actually no way. When you went
into industry, I I was curious. How did the interviews go?
In the, in the industry? Yep.
Well, quite well. You know, it took having coming
from Max Planck Institute opens a lot of doors.
Right? And nobody asked any more question whether I'm
qualified to do statistics or machine learning.
This is kinda assumed that you know. The questions that HR
would in the industry ask was, like, how much do you know
about industry specific things? Like, do you have
experience with customers and and things like that, which I didn't. Right?
So I had to learn these type of things. But the
the the competence, they don't question. They just
respect. So that that's how I would
should also give we should
also give a mental shout out to, the the the place you
did your PhD because it's not too far away from where I
studied. They have a very well maintained rivalry with the
university. I attended its University of Oklahoma.
Did you, by the way, eat a lot of Oklahoma
onion burgers? Sorry, guys. The heat is really getting to me here.
No. Well, I did eat some burgers, but
I'm not great fan of burgers, I have
to admit. So, yeah, occasionally, but,
nah. Not too many. Not too
many. Great. So we've been at the place we
shifted into industry roles.
What did you do there? Because I do believe, they would
be they would be very happy to have you in any
team, working on machine learning.
Yeah. Exactly. As a machine learning is one
big topic, and the other big topic is statistics
in general, data analysis. This is what also what industry needs, what
companies need. And, this is
what I could help with, quite a bit. Right?
So, you know, any anything that has to do with lots of
data, trying to understand the data, systematize,
get insight into data. This is where I could help quite a bit. But also
then, of course, machine learning coming up with with, ways
of machine using the data, basically. That's what machine learning
is. Taking lots of data, compressing the
knowledge that this data contain into a machine so that the
machine could make some predictions
or decisions and things like that. Right? So these two things. Right?
I Startupradio, basically, as a data scientist.
That's what the job is, described
today.
I see. And then you got into
several positions. I see research fellow, head of AI,
senior AI consultant, a a lot of different positions
in start ups, in tech companies, also in research
institutes, for example, Frankfurt Institute for Advanced Studies,
all gearing toward,
different areas, but always,
kind of put together by either
either psychology, statistics, or both of
them. And then you started
robots Joe Menninger back in 2016 here in
lovely Frankfurt. But first, how did you get the name?
I was looking for for a cool name. And I there were a
couple of companies that that I I ran into,
and I thought, they have a cool name. I would like to have a company
with such a cool man. Right? One of them was what was
it called? Incredible Machines.
I was like, ah, that's a nice thing. And and,
couple of others, I can't remember now. So I was, like, thinking. I was I
switched on my creative mind for a while, and it took me
a month or so to come up with something
interesting. But, also, you know, the domain had to be available. That's
also another challenge that you have. Right?
And then when I came up with it, I was very happy.
Interestingly, the first person I I told
this idea to didn't get it. It's like, what? I don't get
it. So not everyone finds the name interesting. You do.
Many people do. Maybe, I don't know, 50%,
60%. But other people just some people
just don't get it. So the the the idea. Joe,
you know, it's a, it's a 5050
job. And what do you
guys are actually doing there?
So, we have
developed a new machine learning algorithm, which
was inspired by my understanding
of how the brain works. So if you
compare the neural networks in computers and and neural
networks in in in the brains, human or animal,
there's one big difference. Right? The brains in,
in computer, once they they are in
production, once you do once you train your neural network and you put
it in production, you don't train it anymore. It's not allowed.
And it's a really bad idea. Some people tried, It always
ended up with a disaster. And,
a famous example is when Google put one language model
out and allowed it to learn while people interact with
it. The model very quickly became racist and
had all sorts of bad behavior because people provoked
it. People knew it's gonna learn, so they actually intentionally
tried to gave gave it bad
bad, things because that's what human
do. And and then you Because you operate.
That's what's being human. You provoke everything. You try to
break things. And, and the
model was helpless against
mean humans.
And today, you don't do it. You don't do it anymore. Right? There's even
legally legally forbidden for
autonomous driving machines to have training
while the car is driving for similar reasons. Now
mathematically, there's I can explain to you statistically,
mathematically
Right? Because human brain is not like that. A
human brain can learn. We learn as we
go, and it's not a total disaster. We're not
perfect, but it's not such a disaster. May
I just say the bottom line is you
develop a new machine learning algorithm that
is training itself why it's already in production
without going mental, being
a total dick hat, or,
putting out all crate crazy kind of stuff and,
fantasizing, I do believe, is a call in some models. Right?
Yeah. Yeah. Yeah. Yeah. You you said it right. You
said it right. So, Okay. Can you tell us a little bit
about this? Because I do believe a lot of AI
and machine learning people, c level
executives in different companies, the their ears are now
really perking up here. Okay. Very good. Very
good. They should. So,
basically, the reason why human brain
is not, Joe
susceptible to manipulation and to random inputs
is that we have internal control of what is relevant,
what is not relevant. We can direct our attention towards learning.
We can make decisions. What do we pay attention? We we know what is
important, what is not important. We know where we wanna go. Right?
The classical AI algorithms are not
do not know anything. They are just completely open. Just teach me anything.
I'll take anything. Just give me data. That's practical. For some
purposes, it's a disaster if you have very small number of data
points and if these data points are
somehow not what you wanna learn. Right? So we have
developed a way to guide deep
learning, to guide neural networks. Right? So there is another layer
of parameters, another layer of knowledge, which which
knows what is allowed, what is not allowed, what could be done,
what what what shouldn't be done. Right?
Basically, each parameter in a neural network has its
own parameter telling telling it whether it should be
how much it should be allowed to learn. Right? And the made most
part of the network is not allowed to learn, only some parts.
Right? So
what we have in addition to that, we have an algorithm that
teaches this guiding algorithm. Right? So there are 2 levels of
learning, basically. 1st, you have a phase where you learn how to
learn, how to how to direct your
learning. And after that, you can go in production and
learn safely and know where you are going and being
being directed and not being open to any kind of
random input that you that you receive. Right?
So If if if I put it in my very,
very layman's term, you put a parent in there.
Because I do have a toddler here at home, and if
he's not supervised, he'll do all crazy
kind of stuff. Not to mention,
he has, like, a little shopping cart, in which he
sat backwards brushing his teeth or stuff like this, and this is
just the tip-off the iceberg. So, basically, you develop kind
of a a parental algorithms for
toddler AI. Is it something like that? It's it's
yeah. It's whatever is left in in us after we
have been parented. Right? So, like, you
know, you gotta get parented, but then after that,
you internalize these rules. And you're like, you don't do this. No. No. That's a
no no. Right? So these internalized things
we give we give to the network. Right? Yes.
That's that's correct. Right? I Another another
way before we proceed, would it be possible that you
because a lot of people this is, this is not necessarily
like a coding deep tech podcast per se. We try to
keep it on a very Bonnie level. Could you, before we
proceed, kind of split a little
bit for us what you mean by talk about AI,
machine learning, and deep learning because they're not necessarily all the
same even though people use them interchangeably.
Yeah. Okay. So machine learning
is a discipline on how to make machines learn
something. Right? It's a very general discipline. There's lots of different
algorithms, lots of different ways to make machine learning.
Deep learning is one subset. It's one set of algorithms.
There are many more that are not deep learning, but deep learning
is the most popular one. It everyone
talks about deep learning. And the reason is is
that deep learning is exactly open to learn anything.
And you can expand the number of parameters. You can expand the amount of
knowledge practically infinitely,
and it will still keep learning. It can't just take anything in
in itself. The price is that it requires lots computation,
lots of data, but it can learn anything. That's exactly the problem we needed
to solve. Right? Now AI is
an term where you that you refer
to in a popular way in the
resulting machine that you have built. Joe you you put
some deep learning, some other machine learning algorithms. You put some
other algorithms that are not machine learning and yet are AI
algorithms. You put everything together. You pack it. You make interface.
Now you have an AI. So
that's how I I, see it, how I understand it,
how I use it. Okay. Can you give us
some hints? Because you've you've been talking about,
you've been talking about let let me take one
step back. Let us because a lot of people who are out
there own companies, work for companies,
that would profit from a new AI
teaching methodology algorithm, like you said, when
when they can still learn, when they're in production. The thing is, let
us get a little bit through that by first telling
what you are allowed to learn, what not, and maybe
we drew your knowledge from to do that,
and then we could expand into where you think,
theoretically, this could be applied. Because my understanding
is you develop the algorithm, but it's
not, broadly applied yet.
Yeah. That's correct. Actually, you know, the
challenge we have right now is to find the right use
cases. So it's it's not the
same you know, one step is to create great technology,
and the other step is to find a great use case cases
for the great technology. And you can know really
be sure that you have a great technology, but still it takes time, and
it's still an effort to find the right use
cases, to find the right market fit. And that's our challenge
right now. And and, I think we
have, like, lots of examples in other technologies as well. Like, the first
computers people did not know how they will use it. They knew
they have some amazing machine that can do so many different things. But how
exactly it's gonna be used, they didn't know back then. Right? They didn't know that
that we are gonna use it for to play to to
for word processing or Excel sheets or or it took time
to invest the invent those. I know computer games. I don't know.
But similar happens here with large language models.
We are still searching the right use cases. Although we we have some
we know we have some amazingly powerful machine
there. So the same challenge
robots Go Menninger is facing right now. Find the right use case.
We have identified several potential ones that we are working on.
Right? For example, a car that is adjusting
itself, its visual system constantly while
it's driving. Like, for example, when a when a when you are
driving on a sunny day and suddenly you enter a tunnel. The
the the laws of visual,
shades, the lightning, and so on, it completely changes suddenly. Right?
And what our human brain does, it adjusts. It takes a few
seconds until you can see clearly when you enter it to
know. Right? The the
AI solutions that we have today, they don't adjust. They have no
capability of adjusting. Right? With our solution, it should
be possible. Right? Similar thing with when you when
you have, say, conversation. When machine has a conversation
with humans and has to listen to to to,
people's speech, different people have
different accents. Right? And we adjust to accents. Right? So when I
start talking to you in the morning, like, the first few sentences, you may
not be really it would be hard for you maybe to understand because I have
a unique accent, which comes from my Croatian
language. But after a few minutes, you get adjusted. Oh, doctor, don't
worry. Be before 10 AM, I barely under understand
anybody. I'm not a boring person.
Okay. So, you know, that's another use
case. Right? So we are we are we are
looking for those, and and the challenge is to find the right
market fit. Mhmm.
Can you talk about industries? We just
talked about self driving cars. I do
know there was a lot of AI involved, for example,
during the development of the COVID vaccines to end the
pandemic. Talk about health care, biotech.
Would you think your algorithm could also be
applied there, financial services and so on and so forth?
Yeah. I I I'm sure it can be.
But how exactly? I don't know yet. Right? But what what we
we have a very nice piece of evidence that it the algorithm
can reply there because we have, performed
one project together with NASA,
exactly in this field. And, what we
did, we trained the neural network to learn,
how to deal with RNA sequences.
Joe you basically one one general problem
that is relevant for for biotech industry
is to process RNA information. You have a sequence of
of nucleotides, and you need to work with
that as an AI solution. Now like
every other AI, you need a lot of
data to train anything. But NASA
had 6 mice only in space,
and they couldn't increase the data amount of data because it's just
prohibitively expensive. And they they just can't pay, I don't
know, 100,000,000 to send another
600 mice in space. So they had to find a solution how to train a
model with RNA coming from 6 month
month mice in space, and they had it at the same time as a control
6 mice on the ground. And they
had to train the model with it. So they discovered us.
We worked together, and and it worked. It
worked. So we could use the Earth, a lot
of massive Earth data to
train the model, to learn how to learn. And then after
that, it could deal effectively with just,
6 data points from space. And
and, we wrote this paper recently Joe one can find
it. Of of course. We link it down here in Joe
show notes on our blog post. You'll find
it at stethubreight.oforward/block. And and then I
I was curious. So your model was basically
your algorithm was first applied to teach the other
algorithm how to learn? Well, there's a
yeah. There are 2 algorithms. One algorithm
that, knows how to learn, but this
one has to learn first. So there's the first an algorithm that teaches the second
algorithm how to learn. Right? So there
there is a hierarchy of algorithms.
Like you said, the the parents and the there's a
parent and the The parent algorithm
algorithm.
I see. I see. I see. Very, very interesting.
Joe, everybody who'd like to
learn more about your algorithm, we'll link down here
in the show notes. First, your company's website. Secondly, your
LinkedIn profile. As always, we will be ending with a
few questions. First of them,
are you looking for talented people, HR, new
hires? I'm I'm
looking for talented people all the time. Right? There
is, it's
great pleasure to find talented machine
learning people. They are not easy to find, I have to say.
And every time I, discover someone,
it's a great pleasure to work with them. Right? And if there
are, any, say, PhD
students interested in a project, people who have
finished PhD interested in a great project,
please contact me. We have we
have really interesting problems to solve. Right?
That that are academically interesting, commercially
interesting. I would be interested. You're
likely not looking for the normal coders here.
Are you looking more for people with a statistics, computer science
background, or do you look more for people with a psychology
background? More statistics
slash machine learning,
which includes, of course, a lot of computer science understanding. Right?
That's the is
the the rarest breed.
These are the hardest to find.
That is already pretty good. Would you
guys be open to talk to new investors?
Yes. Definitely. Definitely. Yes. Yep. Yes.
And the last question, this is, supported by Hessen
Trade and Eves and the European Enterprise Network
Hessen. You would now have the chance to
address a question, a concern, an idea for improvement
for the state's Startupradio scene. If you
could address those decision makers, what would it be?
Well, one experience I had
by, you know, starting a company
was that in general in Germany and in Hessen,
it's very hard to start a deep tech company like we are. And deep
tech basically means that you have to
develop a technology before you have a use case,
before you have a market fit, before you have a customer. There's a
lots of work behind that.
And my feeling is that this is this is really hard to
get in Germany. The and that's
probably the reason why we see most of the companies that
come out with such successful
concepts are not coming from Germany, but from United States.
From UK is also much more,
effective in that way. And we somehow, it would
be really great if we found a way that works in
Germany for deep deep tech. Right? It's not just
there is a lots of talk about deep tech, but there's not lots of it's
not coming out really in a great way. We don't
have a list of great
German or Hessian companies
that have actually brought new AI
technologies to the world. Right? Everybody's trying to just pick up
whatever he's using and just go and make a make a business model, which
is great, which is, of course, useful, but it's not
how you become a leader. Right? We need to become a leader. And to become
a leader, you cannot do it with with the system the way how it
works right now. I don't know why what is broken. It's not
only the government. The governments can help a lot. But,
also, I think it's generally mindset. I don't know.
Also, investors are generally not don't have the
stomach for such risks.
They would like to go quickly, you know, have your customer first, and
then we'll talk. And,
fine. Reducing risk is fine. To become
a leader, the world leader, that's not possible that
Right? That's my that's my,
message. Find out a way to
become a leader. World leader help us become a world leader. We wanna
be world leader in the technology, in developing fundamental
technology for AI and help us.
And we don't we did get a small grant from Hassan. Thank you very
much. But we would need
also some some bigger support.
If if not, we'll fight. We'll keep fighting.
Awesome. Danco, it was a pleasure talking to you. Thank
you very much. I really enjoyed the interview. Yeah.
Me too. Thank you. Thank you. Have a good day.
Bye bye. Bye bye.