have you ever taught
what would be our future with the
existence of artificial intelligence in
our daily life current understanding of
artificial intelligence is intelligent
machine that has the capacity of
communication with humans and of course
sometimes a little bit scary from my
understanding artificial intelligence is
a decision-making system that has the
capacity of finding optimal solution in
interaction with its environmental
signal or in response to the specific
signal as an example an artificial
intelligence can be a decision-making
system a cognitive system that help us
to make the optimal decision even by
direct connection to our brain or it can
be a smart carrier of an anti-cancer
drug that specifically targets cancer
cell with the lowest effect on the
normal cells from my background in
veterinary medicine I was always
fascinated by their variety in the
morphology and behavior of different
species of animals and I was so curious
about the programs and codes behind all
of these varieties and this motivated me
to continue my studies in genetics
biochemistry and then later in stem cell
biology biological systems are highly
cognitive systems in different forms
from their very primitive to the most
developed forms and they are able to
make a smart decision in intra in their
interaction with their environmental
factors
just as chameleon that is able to make
decision over its own ask in color and
can synchronize its skin color with the
environment just as a hiding mechanism
or the self navigation capacity of birds
over the entire of their migration
flight in another example the
engineering is key enough honey
is for making highly specific structures
and architectures of honey honeycomb and
more importantly unlike any other
human-made computers biological systems
are able to make decision in every
single of their subunits for example in
a growing embryo
every single stem cell is able to make
its own decision in communication with
other cells to find its own position
collectively they are able to
communicate each other making highly
specific geometries which is growing in
size and complexity by time this is
called
self-organization self-organization
capacity of SM cells in a growing embryo
pretty much is like the formation of a
castle in a sandwich though during the
embryonic development in addition to the
geometries thousands of different
machineries are forming by time in
different tissues and organs now the big
question is that how we can learn from
biological system in designing and
improving our decision-making system
currently the main challenge in
artificial intelligence is about the
designing decision making algorithms
which could find the optimal solution in
interaction with the environmental
signals especially when the system is
interacting with several factors this
class of problems in mathematics are
called NP problems because by their
complexity they cannot be solved by
logical time and they are considered as
unsolvable problems in contrast to the
other class of problems that we call P
problems or that means they can be so
of easily biological time such as
multiplication problem as an example of
NP problems consider travelling salesman
problem travelling salesman problem ask
in a given number of cities what is the
shortest possible path that with it
every cities and comes back to the
original city the problem statement
sounds easy but as a number of cities
increase the number of scenarios and the
operation times grows exponentially
therefore solution will be impossible to
be fine in a logical time even by
current supercomputers I’m not a
travelling cells woman and I’m not a
mathematician but I saw this problem
somewhere else in differentiating
pathways of stem cells during the early
stage of embryonic development
differentiating a stem cells are able to
make thousands of decision by finding
the optimal combination of gene
regulatory factors toward different
destinations for example different types
of tissues and cells in our body and
they are able to make thousands of
decision by by solving NP problems in a
network as large as the entire of their
genome now the question is that how as
themselves are able to make thousands of
decisions in a highly efficient way
while still we have several problems in
solving similar NP problems applying our
supercomputers this question engage my
mind for a long time since I was a PhD
student I was trying to find an
algorithm in computer science that could
be matched with the decision-making
capacities of a stem cells and later I
was trying to understand the mechanisms
of coding and computing
in a stem cells by getting deep in the
chemistry of cells but unfortunately
after a long time research I didn’t find
a solution but then later I was able to
connect the dots and find this the
solution in chemistry but in a special
form of chemistry that information are
embedded in the molecules and in the
materials so I call this platform
cognitive chemistry cognitive chemistry
is a chemical based coding system that
information are embedded in the
molecules and materials Nina is a simple
example of for cognitive materials and
we can apply DNA in the synthetic form
as a matrix for information storage but
cognitive chemistry is not limited to
DNA and we can apply different forms of
synthetic materials for information and
storage in addition in a cognitive
chemistry system the system has the
recognition capacity over its
environment applying its molecular
sensors therefore by combining these two
properties we understand that in
cognitive chemistry the software and
hardware are integrated together in
addition unlike current computers that
are limited to single layer of coating
which is binary coding in cognitive
chemistry we have multiple layers of
coding for example DNA coding language
with for coding subunits with different
with four different nucleotides or
protein coding language with 20
different coding subunits therefore by a
multi-layer coding capacity of cognitive
system the system will be impoverished
two parallel operation which enhance the
operation rate of system incredibly now
the question is that can be solved NP
problems such as Traveling Salesman
problem applying cognitive chemistry
algorithm let’s see as we discussed
earlier we know that the main limitation
in our current computers for solving NP
problems is about the sequential
operation the system needs to check all
the possible scenarios before finding
the optimal solution however in
cognitive chemistry applying the
parallel operation capacity of the
system we can pass this problem in this
limitation and the system is able to
find the solution immediately in this
animation we can see how a current
complex method is trying to find the
near optimal solution for travelling
salesman problem applying a semi
sequential method as you see here the
system is trying to define the problem
and then checking all the possible
scenarios before finding the optimal
solution however in cognitive chemistry
applying the parallel operation capacity
of the system the algorithm is able to
check all scenarios in parallel and at
the same time there would be no gap
between the problem and the solution
therefore cognitive chemistry algorithm
has been able to move traveling salesman
problem from the category of unsolvable
problems into solvable problems the
importance of this solution is
definitely in designing of decision
making algorithms in artificial
intelligence but more important
this solution can be applied in
biomedical science for treatment of
cancer by analysis of large datasets
from cancer patients and a small
targeting of cancer-related genes also
it can be applied for treatment of
protein misfolding diseases such as
Alzheimer’s Parkinson’s and prior
related diseases in my opinion and it
disease is that coding disorder and if
we know the appropriate code we can we
can fix the problem during the entire of
my career I was so curious about the
power of life and I try to understand
the connection between chemistry physics
and mathematics in biological system and
I believe if we delve deeper we will
find that there is just one rule
governing the entire of nature and
universe and that’s the universal
science thank you
you
what would be our future with the
existence of artificial intelligence in
our daily life current understanding of
artificial intelligence is intelligent
machine that has the capacity of
communication with humans and of course
sometimes a little bit scary from my
understanding artificial intelligence is
a decision-making system that has the
capacity of finding optimal solution in
interaction with its environmental
signal or in response to the specific
signal as an example an artificial
intelligence can be a decision-making
system a cognitive system that help us
to make the optimal decision even by
direct connection to our brain or it can
be a smart carrier of an anti-cancer
drug that specifically targets cancer
cell with the lowest effect on the
normal cells from my background in
veterinary medicine I was always
fascinated by their variety in the
morphology and behavior of different
species of animals and I was so curious
about the programs and codes behind all
of these varieties and this motivated me
to continue my studies in genetics
biochemistry and then later in stem cell
biology biological systems are highly
cognitive systems in different forms
from their very primitive to the most
developed forms and they are able to
make a smart decision in intra in their
interaction with their environmental
factors
just as chameleon that is able to make
decision over its own ask in color and
can synchronize its skin color with the
environment just as a hiding mechanism
or the self navigation capacity of birds
over the entire of their migration
flight in another example the
engineering is key enough honey
is for making highly specific structures
and architectures of honey honeycomb and
more importantly unlike any other
human-made computers biological systems
are able to make decision in every
single of their subunits for example in
a growing embryo
every single stem cell is able to make
its own decision in communication with
other cells to find its own position
collectively they are able to
communicate each other making highly
specific geometries which is growing in
size and complexity by time this is
called
self-organization self-organization
capacity of SM cells in a growing embryo
pretty much is like the formation of a
castle in a sandwich though during the
embryonic development in addition to the
geometries thousands of different
machineries are forming by time in
different tissues and organs now the big
question is that how we can learn from
biological system in designing and
improving our decision-making system
currently the main challenge in
artificial intelligence is about the
designing decision making algorithms
which could find the optimal solution in
interaction with the environmental
signals especially when the system is
interacting with several factors this
class of problems in mathematics are
called NP problems because by their
complexity they cannot be solved by
logical time and they are considered as
unsolvable problems in contrast to the
other class of problems that we call P
problems or that means they can be so
of easily biological time such as
multiplication problem as an example of
NP problems consider travelling salesman
problem travelling salesman problem ask
in a given number of cities what is the
shortest possible path that with it
every cities and comes back to the
original city the problem statement
sounds easy but as a number of cities
increase the number of scenarios and the
operation times grows exponentially
therefore solution will be impossible to
be fine in a logical time even by
current supercomputers I’m not a
travelling cells woman and I’m not a
mathematician but I saw this problem
somewhere else in differentiating
pathways of stem cells during the early
stage of embryonic development
differentiating a stem cells are able to
make thousands of decision by finding
the optimal combination of gene
regulatory factors toward different
destinations for example different types
of tissues and cells in our body and
they are able to make thousands of
decision by by solving NP problems in a
network as large as the entire of their
genome now the question is that how as
themselves are able to make thousands of
decisions in a highly efficient way
while still we have several problems in
solving similar NP problems applying our
supercomputers this question engage my
mind for a long time since I was a PhD
student I was trying to find an
algorithm in computer science that could
be matched with the decision-making
capacities of a stem cells and later I
was trying to understand the mechanisms
of coding and computing
in a stem cells by getting deep in the
chemistry of cells but unfortunately
after a long time research I didn’t find
a solution but then later I was able to
connect the dots and find this the
solution in chemistry but in a special
form of chemistry that information are
embedded in the molecules and in the
materials so I call this platform
cognitive chemistry cognitive chemistry
is a chemical based coding system that
information are embedded in the
molecules and materials Nina is a simple
example of for cognitive materials and
we can apply DNA in the synthetic form
as a matrix for information storage but
cognitive chemistry is not limited to
DNA and we can apply different forms of
synthetic materials for information and
storage in addition in a cognitive
chemistry system the system has the
recognition capacity over its
environment applying its molecular
sensors therefore by combining these two
properties we understand that in
cognitive chemistry the software and
hardware are integrated together in
addition unlike current computers that
are limited to single layer of coating
which is binary coding in cognitive
chemistry we have multiple layers of
coding for example DNA coding language
with for coding subunits with different
with four different nucleotides or
protein coding language with 20
different coding subunits therefore by a
multi-layer coding capacity of cognitive
system the system will be impoverished
two parallel operation which enhance the
operation rate of system incredibly now
the question is that can be solved NP
problems such as Traveling Salesman
problem applying cognitive chemistry
algorithm let’s see as we discussed
earlier we know that the main limitation
in our current computers for solving NP
problems is about the sequential
operation the system needs to check all
the possible scenarios before finding
the optimal solution however in
cognitive chemistry applying the
parallel operation capacity of the
system we can pass this problem in this
limitation and the system is able to
find the solution immediately in this
animation we can see how a current
complex method is trying to find the
near optimal solution for travelling
salesman problem applying a semi
sequential method as you see here the
system is trying to define the problem
and then checking all the possible
scenarios before finding the optimal
solution however in cognitive chemistry
applying the parallel operation capacity
of the system the algorithm is able to
check all scenarios in parallel and at
the same time there would be no gap
between the problem and the solution
therefore cognitive chemistry algorithm
has been able to move traveling salesman
problem from the category of unsolvable
problems into solvable problems the
importance of this solution is
definitely in designing of decision
making algorithms in artificial
intelligence but more important
this solution can be applied in
biomedical science for treatment of
cancer by analysis of large datasets
from cancer patients and a small
targeting of cancer-related genes also
it can be applied for treatment of
protein misfolding diseases such as
Alzheimer’s Parkinson’s and prior
related diseases in my opinion and it
disease is that coding disorder and if
we know the appropriate code we can we
can fix the problem during the entire of
my career I was so curious about the
power of life and I try to understand
the connection between chemistry physics
and mathematics in biological system and
I believe if we delve deeper we will
find that there is just one rule
governing the entire of nature and
universe and that’s the universal
science thank you
you
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