0:00:13so the person methods
0:00:15um this paper
0:00:19okay and
0:00:20assume the presence of so
0:00:24and the lab as comedies and
0:00:27uh_huh of a
0:00:29speech two systems where
0:00:34a this just that the sequence of presence an to assume a present and number some the and so on
0:00:40so should just a
0:00:41but most work two
0:00:43a is a web i'm um uh uh if
0:00:47become a member of members are bones were exposed was in like to as or have
0:00:53has been to do so
0:00:55um uh if you want to become a member can the to and of the members of of a spectrum
0:00:59to some
0:01:01and two
0:01:05we have a
0:01:08as shows but that's prince
0:01:10and uh
0:01:11the fruit uh well as a
0:01:13perhaps or i'm so it from us and uh
0:01:18but a man to talk about the
0:01:21process the mission
0:01:25this session
0:01:26as the presentations of them after each presentation of the few is for questions
0:01:32and and the um
0:01:33more to have come to ask questions to a sparse image
0:01:39okay thank you very much on the hawk and that would like to thank of few for coming
0:01:43have have coming here
0:01:44unfortunately i have one of them right of the this so i apologise i that in that's if i'm of
0:01:49a little bit
0:01:50fast than usual
0:01:51no as you can imagine a a a a speech M is very but are we cover lots of a
0:01:55has lots of interesting at as
0:01:57uh uh in the general of statistical signal processing adaptation and learn in compressive sensing and so forth
0:02:04and and and be a question was
0:02:06you know i
0:02:07should be but a but but are or should be a big some topic what want topic or two topics
0:02:12and but that can be deeper
0:02:14and um
0:02:15for consultations with of a common with the of we decided let's pick a topic "'em" at least
0:02:20expose at the m-th at least to what's going on in that at and many fat interested of course you
0:02:24can go
0:02:25uh i i i and check the reference and a to us and would be not to be you more
0:02:28and more
0:02:30input put into that
0:02:32no i would be talking and or so of a come would be thought of it becomes a possible compliment
0:02:37essentially that are two uh a general approach is that and women each other in this area yeah and very
0:02:42excited about the same as that going to see he takes adaptation and learning to new levels it adds a
0:02:47i have to this
0:02:48a to this very exciting a year i usually like to to start my by showing this fit you're
0:02:54a piece i have a video here
0:02:57i a look about to i one to see how used to something
0:03:01i not sing anything okay
0:03:03so maybe somebody can help because this was playing a or level
0:03:11see something
0:03:17much showing anything
0:03:20to just so i
0:03:24that see if we have a thousand about
0:03:26okay okay
0:03:28that meant to see here is
0:03:30for a lot of them but it's a i'm down to the left them to the right now you tell
0:03:35that is not a but tell them what to do that is not but i think these that the words
0:03:39and what direction we should go
0:03:40we have a thousand people in a state will they hit each other or not do you tell be find
0:03:45a lot the people that want to on or but each other
0:03:48how but does do that
0:03:49that's incredible behave i will go two algorithms they you were processing that you like learning that you was how
0:03:56they had job
0:03:57okay a to a two that was i um them at this is a highly sophisticated be here but if
0:04:02you if you look at the dock in system each but
0:04:05is a the processing information just a it to make decisions about how to go on to to go
0:04:10and you have seen patterns of intelligent behaviour like this H nature would probably just
0:04:15go to
0:04:16but the me when it just a look at the just start time to understand but yeah going to see
0:04:20how D this behave at is how difficult actually is to produce that kind of behaviour okay
0:04:26sampling to this is a you to video about the up my video
0:04:38but some the example of intelligent him and that you see that
0:04:42we have a very very smart man and here you have an example of this is a picture
0:04:46a a and joe graphic just look are they
0:04:49a a a a all that have the colour last same most and parallel some
0:04:54such they see an obstacle that not
0:04:57and a nice i that the shape and then they can call to them should now you tell me how
0:05:02to achieve that kind of behaviour
0:05:04is that in the fish that and key a position yourself at this location and you position yourself at that
0:05:10that's not what's happening and this kind of smile in go be behave that that just to that is the
0:05:16that of highly localized information process
0:05:20i come information processing can do you know that to and that kind of sophisticated behave but this out of
0:05:26N one questions and a very interesting question
0:05:29i and if you look at the literature of course people have we study this or many many using the
0:05:34fun disciplines them by would you complete the science
0:05:37and uh in a colour G
0:05:39and and i think now
0:05:41a seven is an hour in our field uh uh a include this because we had also time to to
0:05:46to understand as best as we can that kind of behaviour and to to produce it not that we want
0:05:51and and make and my behaviour but i think that is something come from them to back to make a
0:05:56algorithm as the smart a robust morning just a
0:06:00a of and so for okay
0:06:01yeah a just but its of information
0:06:04but also by the a from that the for that i had but also a fly in your formation
0:06:08the have sense for that okay
0:06:10and for the call reasons for that but see it
0:06:13before the
0:06:14ration to this kind of that this i that time to be the first one and then a end and
0:06:19the for to the the bound to flight that a and like this is that is out of highly localised
0:06:26in information processing that is and this kind of
0:06:31a rational behave and how do they do it that interest be have yeah right
0:06:35a more to look into it get
0:06:37i you get excited at that this is another example of some which are P about a to use that
0:06:43well if some some press i have a it video to show you here
0:06:46but that like to see
0:06:49so if you were
0:06:50this time
0:06:52but that somebody to i mean lots of that you have a this sub time
0:06:56and and it was that i
0:06:57in in a dish
0:06:59where is i believe was some oxygen and like where the made and then the but this time
0:07:03and there is at least i was size devices you can hardly see them
0:07:09that's that that's you to left on the item and them on a motion
0:07:13now when you all the of the and that i score my micro quite this is what to see this
0:07:17kind of but
0:07:18you see these time of devices is or and thus the in
0:07:22a the set that action
0:07:23so that's a here a K i like T V "'cause" i don't know i'm just asking questions that a
0:07:29back to you
0:07:31oh this kind of high
0:07:33gram may behave at a notation in a single direction that that's an interesting kind of behaviour
0:07:38so that's happening at me
0:07:40a and we had a eyes level of so this seven
0:07:43we this thing questions that come up when you stop observing
0:07:47this kind of behaviour in nature
0:07:51so if we look at these examples
0:07:54but to be okay
0:07:56a much of to see is that what each individual H and in a i is not sophisticated
0:08:02but them together
0:08:04a the interaction action at that highly localised level needs to
0:08:10oh patterns of behaviour or people have of this for years of course
0:08:14look at the question now for us
0:08:17am and just uh a show it on the next the slide but this is the convolution of this examples
0:08:20that to see here examples are
0:08:23actions and
0:08:24the agents
0:08:25i to sophisticated complex behaviour of course on S
0:08:30as i like good as that can do nothing i'd these it to have some
0:08:34some cognition in them okay know
0:08:37okay but when they put them to to have that it leads to that kind of interesting behavior so can
0:08:42and our a little is
0:08:43but i in statistic signal processing an adaptation and learning in cognition in machine learning can i
0:08:49and make that kind of intelligence
0:08:52that lots from that the
0:08:53but but that's in a question that will like
0:08:56for example an important questions that to make a so that
0:08:59i'm i i behave i i i is is
0:09:02i i love for a kind of that action is you to have to generate a data set kind of
0:09:05sophisticated be here the might be interested in a big a level
0:09:09a of G
0:09:10should exist between a and so that they show an information in such a way that that is and be
0:09:15kind of carbonated behave but that you would like to result
0:09:19how much should information be quantized
0:09:21should be used at and share information at the very high presentation level is
0:09:26low quantization level enough i'm sure that the fish number see a shop but they can shared information like this
0:09:32P of the shop but the colour of the shot that action but just a segment and that's that's that
0:09:38just a shot
0:09:40i i and everybody gets i about the shark
0:09:43but this is one example of quantization how much information relation position
0:09:48such a that that kind of behaviour
0:09:50yeah appears and the last question is then a of them you know if you think about it
0:09:55at least example that i sure that have a static networks
0:09:59and is at a time
0:10:02and may was five minutes from now the connections that change of the time
0:10:06this is that high networks and a T had
0:10:09and for is learning that inference is mission okay and the other way and i also be a a a
0:10:14a a to five and that may also in france to an environment "'cause" of this call this questions are
0:10:20tied together and i decide
0:10:24a from the perspective of a a a a two a from the perspective of signal processing in general are
0:10:28there are many many techniques you come up i
0:10:31that this problem like this to the calm is going to but also about some additional techniques which are should
0:10:36have listed yeah
0:10:37so a you have this to the process can of course is at the heart of a lot of this
0:10:41for the estimation adaptation and and the assume game phonetic a and methods
0:10:47uh i i a statistical as
0:10:50that you can use to try to but that kind of behaviour
0:10:53i like i said
0:10:54people problem uh that uh that's have this these behaviours as and they have exploited that
0:10:59and a different ways you even see and nations of naked behaviours in movies
0:11:04but i have a computer that science have generated an issues
0:11:08that you see are going to a uh uh i
0:11:11a to produce this kind of behaviour yeah about insist of course
0:11:14and the the different level of processing the different kinds of questions how the whole algorithm
0:11:19that can generate
0:11:21that kind of high level intelligence
0:11:23right from high localised it's set
0:11:26actions at the local level look at this is a high level question we have mine
0:11:30no of course i don't have time have to gossip algorithms that
0:11:35i as we have a we have a a a a a large family of
0:11:38i a
0:11:40can can and i guess
0:11:42adaptation i'm of such conditions you have more networks
0:11:46and that that actions that local i with them up to discuss the algorithm
0:11:51this the of everything but i would like to show some simulations examples to example
0:11:56are the things that are able to do that
0:11:59and interesting thing this of a lot of them to come up i to my be B here for a
0:12:04in the back to the that behaviour fish a a
0:12:06and have that as so that that is something that are okay for example a to to see in the
0:12:10first example
0:12:11a a fish
0:12:13i don't know
0:12:15we to find a different is that want to to gather have to make to have a fine but the
0:12:19for this and in that direction
0:12:22and that's not a shock peers
0:12:24so they have to be a bit of that environment they have to also to that where the shock
0:12:28so for that is the first
0:12:30object of shop as a moving object that they have to track it and this problem
0:12:35so i went to see the simulation this is not a kind of application i would like to
0:12:40sort of consensus
0:12:42because if fish that's scores
0:12:44as to behave in a different manner than a fish that's found away from the shock and the before
0:12:49this is a kind of example that the as we to have something of age
0:12:53i have like to to add that are but in addition to apply to them from the but it's not
0:12:58enough to for a to teach my senses would you but you have to be a a a a a
0:13:03a a and and that's that's the situation
0:13:05but on your on nice estimate
0:13:06so he had to have
0:13:08and and this is just
0:13:10the of an adaptive a cognitive networks are i i-th that to get that in the and to which are
0:13:15with each other and learning from each other and but skip that
0:13:18and this is an example you see that for a is that that i
0:13:21on that i i a that you have before and the fish like to have as before
0:13:25no this is a a and that if network okay it's
0:13:30went to see it from the stop down here
0:13:32that that was the for for a i think that actions and then that but that is present in the
0:13:37environment at two
0:13:39and then go
0:13:41this is using a a an adaptive algorithm on the kind that should be for nothing or
0:13:45yeah a highly localized that action
0:13:48and is out and to make the kind of used in H okay
0:13:53this is what zap of the next example of to see two networks working dance each other and also X
0:13:59the the kind of be to find in the true that that's the group of shots
0:14:02time time back a of fish
0:14:06in of the so of the fish
0:14:08a so you have a network of shocks score than a with each other
0:14:12so that they
0:14:13the fish and the network of fish score than a with each other that so that they know if it
0:14:17is where the shots i don't not they should do or that's what are able to see next example
0:14:22so he it is
0:14:23a function of mm the shots B
0:14:27also also that have the kind of a but this is a adaptation and
0:14:32and this is a kind of thing that i like i said
0:14:34you can expect this fish to check check limit of everybody else that i
0:14:39to say of themselves are K they do that best like an a show then neighbours but not it comes
0:14:44time to decide for your life then you have to take some decisions on your
0:14:48or or or okay so we start to examples that i not to show a about how patient and let
0:14:54as in high and i make a bad environments of every multiple agents so give some going to stop you
0:14:59a from this gives an overview
0:15:00okay i five
0:15:02yeah yeah is okay
0:15:09uh okay so if there questions of you have any questions as a consequence
0:15:19and and uh i i thank you thank you about sally things
0:15:23okay can i like to uh
0:15:25i ask become "'cause" number of T are also
0:15:29can has i a um about
0:15:34signal process and thank you very much
0:15:36it is going to talk about uh
0:15:39as a
0:15:41a like to to that this that's per session is not a summary of what's happening at time "'cause" mom
0:15:48i'm sure
0:15:49or we find the uh that is that the that uh put to but we were try to to focus
0:15:57a a a a a as and we can't cover everything so opens the pressure
0:16:03we can uh
0:16:04oh have an appreciation of what the
0:16:06where the we thank you and thank you are so uh uh a a a i only have two slides
0:16:11a in the first i'd i just want to give a incomplete list of several
0:16:15oh important areas
0:16:16based on submissions and i guess this year and i guess a extrapolation of what might be interesting
0:16:21so this is an incomplete list is always these for like popping to some few leagues and i apologise to
0:16:27any of that
0:16:30these are six
0:16:32here is we've listed here
0:16:33the first two signal
0:16:35processing a signal dynamic rows and as many of you know that is actually a fairly important here at the
0:16:40we but looking at consensus formation information flows games
0:16:44so that
0:16:46and a
0:16:46quite multi disciplinary as well you you think about it
0:16:49not only does it affect signal processing but i guess
0:16:52if you looking at things like social networks you point to model house a siding functions in terms of some
0:16:56of these
0:16:57a dallas sings out with
0:16:59then another area which is also
0:17:02uh of
0:17:03in not most that our T in in
0:17:04take are are uh S P D and society is
0:17:07a dimensional signal processing get sparsity
0:17:10mean uh if you look at this year's i guess is been
0:17:12uh enormous number of people in that area
0:17:14oh from a mathematical point if few many of these results to you with
0:17:18this very uh seminal idea of concentration of measure your room
0:17:22and and
0:17:23compressed sensing is one such example
0:17:25oh many of you would known that there are
0:17:27special sessions uh in i cast on that
0:17:30the transaction a signal processing which is you must papers in that area
0:17:33i so the to special use of journal selected topic
0:17:36the processing
0:17:37again it's multi disciplinary
0:17:39in people and computer science machine learning and also by a medical imaging
0:17:43the uses that
0:17:45another area which is also a
0:17:47significant importance
0:17:48coming from signal processing this financial time-series
0:17:52traditionally a people in mathematical finance do things it continuous time
0:17:56the use things like stochastic was and so on but
0:17:58there is also a because we did you do things a discrete time and and those was are
0:18:02people do financial time to
0:18:04and of course is a smart grid and and most of us know that that's something with a lot of
0:18:09a a potential
0:18:10as distributed sensing decision making a control and that's really what i'll was talking about it in this part of
0:18:15the top my next slide i wanna give a few more ideas behind that
0:18:19so you can think of these as called a systems the uh ability of a system to use feedback
0:18:24to we can figure its behaviour and on top of bit more about that later
0:18:28and and think this is very multi disciplinary
0:18:31or people in economics of done some very serious work in this area or uh and and also with other
0:18:37and finding the signal processing in the life sciences
0:18:40so we
0:18:41comes a a laid down from
0:18:44oh molecules how you can simulate the behaviour of small molecules
0:18:48and how drop spine to small molecules
0:18:51that really up to a large scale by medical imaging and instrumentation
0:18:55i just found as little uh snippet but from looking at the nature but side nature or column nature one
0:19:00of the highest in that turtles and by
0:19:02uh and as a top here
0:19:04so that actually companies like pfizer or and no part is which are drug companies
0:19:08uh investing
0:19:09billions of dollars
0:19:11in high speed stochastic simulation
0:19:14at the molecular level to see how drugs
0:19:16buying buying to Q
0:19:19and we use
0:19:20mark of T the column
0:19:21so so that's
0:19:22something which i guess people in signal processing to quite a bit of
0:19:24and uh you might be interested in the things that but that map lab is also developing a
0:19:29so i all the G two which allows
0:19:31no no uh people to
0:19:33and i
0:19:34chemical part ways
0:19:36and also estimate parameters and sensitivity of these part we so so it's be a very active area
0:19:41as the normal significance to signal processing
0:19:44that so that's
0:19:44a but incomplete list of some areas of importance
0:19:48i and i one a in my last slide simply focus on the area of distributed sensing which
0:19:53which i guess uh what
0:19:54or something which are ads on to what all these said in his still
0:19:59so that's just kind of a a look at the big picture your once again
0:20:02so it's standard signal processing we do things open loop we have a sensor measuring
0:20:07a a signal and noise
0:20:09and then we want a process that noisy measurement to estimate the the like signal
0:20:14no you can think of a philosophical that bounce in that were use some form of feedback where
0:20:18the estimate
0:20:19a if signal
0:20:20this fed back to the sensor
0:20:22so that the sensor can reconfigure its peak a pure in time
0:20:26one such example could be
0:20:28a distributed system where
0:20:30the the picture of the right hand side
0:20:32but you have multiple agents
0:20:34sensors or agents
0:20:36and each should are one individual control unit
0:20:39i each guy wants to we figure its behaviour to do something
0:20:43so i mean one can think of a that experiment where perhaps
0:20:46you sensor so is more drink some sort of target
0:20:49too many sensors to and all the the wasting battery life
0:20:52so they don't all all to at all
0:20:54to a few sensors to not if too few measurements
0:20:57and then if you do what day of data fusion and you have a very high variance
0:21:02all the the sense decide
0:21:04without a thing to all the sensor should turn on a roll
0:21:08keeping in mind that
0:21:09it's not completely capitalistic
0:21:11was company capitalistic it would say to have with everybody else i what sleep but not to at because that
0:21:15present about battery life
0:21:17so a company socialistic goes that it would simply be to all the time but out to die
0:21:21and uh wouldn't be very useful
0:21:23so without talking to other people
0:21:25by using rationality the ability to predict what other senses of there to do
0:21:29how does the decide when it which is on a is it's which off
0:21:32now what economists do uh and really a talking in this slide from economics point
0:21:38an economist would do to add a lie such a problem is they would introduce the assumption that each agent
0:21:43is rational
0:21:45a we we say to devour rationality that of actually a very similar in some sense
0:21:49so by shall only needs the following that if i get a measurement
0:21:53i i can put big from that
0:21:56are the agents a gonna do because all the had estimation
0:21:59so i are use by measurement to predict what other people are gonna do and what i a
0:22:03based on that
0:22:04and i don't people know that i'm gonna react to a measure it in the way i'm gonna do
0:22:08so that you know what i'm doing
0:22:10but you know that i i know that the you know what i'm doing and so on ad infinitum that
0:22:14really results is something the called equilibrium
0:22:17a types of put the breed a because if you're
0:22:19nash equilibrium correlated equilibrium so on
0:22:22but the main idea being that here as a system on the right hand side where eventually if things were
0:22:27done correctly
0:22:28individual agents like fig
0:22:30or shots or whatever
0:22:32can we can figure the because you would minimal communication
0:22:35to achieve a common goal
0:22:40one can play was your at all three are really
0:22:42which people and micro economics of studied in great detail of the last ten fifteen years
0:22:47the first question can be
0:22:49how can agents autonomously
0:22:51man the behavior
0:22:53and that's really the i guess the question i poles
0:22:56you can think of it off sure of that as
0:22:58if each image and that something simple
0:23:01how can you ensure that it a little your is complex
0:23:05or ration
0:23:07and again a uh i i just what a point out to one it'll paper we didn't by so your
0:23:12use who's a really famous economist
0:23:14i to look at it can a metric or two thousand and five
0:23:16yeah the fifty page people which gives you an amazing perspective of how
0:23:21individual a in a can we do something very simple
0:23:24like grand a simple adaptive filter
0:23:28the performance is remarkable
0:23:30but eventually every agent at a user consensus
0:23:34not just good as an estimation
0:23:36but can as an action that is for all is eventually
0:23:40this same decision policy C
0:23:42so they have some remarkable to that in micro economics how
0:23:45to come up with some
0:23:46and the to the user is really game theory as an analysis but that all
0:23:51also so into reading the learning out with them
0:23:54which further refine how agents can act
0:23:57a second question one could pose
0:24:00the standard power in signal processing
0:24:02typically typical one is looking at agents and sensors
0:24:05each sensor has measurements
0:24:08and it up estimates based of those measurements
0:24:12now you can think of a further the refinement of that where agents not only get measurements
0:24:17but look at
0:24:19other each
0:24:20so you look and see what of the people done
0:24:22how could i learn from what other people have done
0:24:25if you think about that
0:24:27and i really is some sense
0:24:29quantization an observation because the agent gets and observation
0:24:32the agent processes that maybe no rational way by bayes rule and then picks an action
0:24:37if you simply look at the action
0:24:38it's in some sense a plant ties version of the observation
0:24:42you but some information
0:24:43so how can be agents learned from other agents that if you do this
0:24:48do things actually work out
0:24:49well there is this really classical
0:24:52uh for example which has been studied in great detail in economics at i just one quickly mention that
0:24:57results in something called a rational for
0:25:00so just to give you the example of that
0:25:02in nineteen ninety five
0:25:03to manage but gurus
0:25:06C and mum wrote a book
0:25:08the market
0:25:10for reviews so what they did was
0:25:12we secretly for fifty thousand copies of the old ball
0:25:15and i got the best seller a new york times
0:25:18people so this is a best or it must be good T
0:25:20everybody started by it
0:25:22so it rational people
0:25:24i know
0:25:25because close following the actions of other regions
0:25:29see if of the observation as opposed to the action
0:25:32the observation would be who bought the book
0:25:35and then you what was the all as buying the book they want to port so that do you act
0:25:39purely only based
0:25:40all all the actions of other and
0:25:43you can show in many cases eventually or agents
0:25:46and up sampling something called the rational for
0:25:49the old eventually make everybody else
0:25:51and they throw away the actual observations
0:25:53so that means a very interesting work done in this area in economics and if you look at
0:25:58for example the book by we
0:25:59published by cambridge university press
0:26:01and rational words
0:26:02to to you a very interesting perspective on how you can model
0:26:06complicated system
0:26:08and how they before
0:26:09now this also that obviously applications in sensors where you might put them but the she's agent and
0:26:14which to look at the at a particular way
0:26:16i it makes every the each would eventually act in that way
0:26:19so you can think of a applications in in several areas
0:26:23but one question which one it again can pose again it's and by people that micro economics as
0:26:28how to local decisions
0:26:31it will bill decision making
0:26:34so uh in in a little picture or the better of the slide have uh a on the left hand
0:26:38side a bunch of red and green dots that it's actually just a a a a a very straight for
0:26:42example of a sequential change detection problem
0:26:44so that as you me have a bunch of agents
0:26:47each agent gets an observation of a particular thing
0:26:50uh i and
0:26:51we can't really the observation to the next guy
0:26:54a can relate is you do they detect a change so it's change or it's not change to maybe green
0:26:59means it hasn't changed
0:27:00but means it's changed thing so
0:27:02the first i it's maybe it's change a second that is not change and so on
0:27:05each each make making a local decision
0:27:08if you could or take these local decisions
0:27:10how how a global decision maker decide
0:27:13to say that there was a change in stop that's a standard quick as time sequential detection problem
0:27:19turns out that the more you raise your
0:27:22global decision on local actions
0:27:24take scroll catastrophic leave wrong
0:27:27so you can show that the optimal policy in this case
0:27:30as a double threshold and to look at it's
0:27:32hi country to it so that's of the right hand side of the ball
0:27:35so on the X axis
0:27:37as a probability
0:27:39posterior probabilities a patient detection problem of change
0:27:42and the S
0:27:43S uh and the vertical axis is the decision each agent takes
0:27:47so you can see that i as the belief
0:27:49as a posterior distribution that the changes happened goes
0:27:53you declare a change
0:27:54amazingly but it gets even larger you signal does not change
0:27:58the reason is you have a discontinue reading a bayesian update because you basing it based on actions of the
0:28:02previous agents
0:28:03and that of course but the around you say there is a change so
0:28:06these multiple threshold policies are
0:28:08cut very contrary to because why would you
0:28:11she and decision if you get more sure about what you measure
0:28:14and it's it's something again which is big study
0:28:16uh in great detail uh by people in economics who really try to
0:28:20more complex
0:28:23in terms of local and global decision
0:28:25and how
0:28:26decisions affect each of
0:28:27so those are three things are just a i'd mention in terms of
0:28:30applications of some of the methodologies all these side pose
0:28:34a unifying theme behind all of this is how does not behavior affect global will behaviour
0:28:40a if you like the by just you can view to say
0:28:42if i have the structure
0:28:44which is little cool
0:28:45how could predict the function which is global
0:28:47that's or by all the just do they have
0:28:49the structural of different small molecules
0:28:52a wanna see if these modules
0:28:54function in the cell
0:28:55how to predict what the actually do
0:28:57given the structure and it's really really
0:29:00that's all i wanted say thank you
0:29:06X and you much read from so
0:29:09can can see to it that to this you two has become a no research to i
0:29:14and the
0:29:15this is a uh one came out of these two do so
0:29:19a the comments or uh
0:29:21questions from the is i'm so this session is very short but uh
0:29:25with the
0:29:26a lot some questions is then
0:29:31i as one when with had an example of to the case of the model of my malicious uh a
0:29:36up lot of i yeah
0:29:39so uh so i i i could do another example was i do quite a bit of mathematical finance and
0:29:43many if you would do to do that all forty percent of all treating is done by computers
0:29:49where street runs now add second time scale
0:29:52and and wants all buy and sell it a millisecond time see little quick enough also
0:29:56a lot of the buying and selling is done by computer programs
0:29:59no many people know that so if you want to design a computer program which deliberately for example started buying
0:30:05your role
0:30:06a the the the value for you would go well
0:30:09if if you are a computer program was running from a reputable agency like woman sex a hope will be
0:30:14go but set as you but anyway you would make
0:30:17but we because of a reputation that the your is really going up
0:30:20are these are the computer programs a custom trained to for a will once as doing they was start by
0:30:26and then you would suddenly cell it's called a speculative can see attack
0:30:30a big liens
0:30:31and the for the other that's can react
0:30:33that's a you're all of the market then you made a lot of money so this of things are widely
0:30:36use an economics the call speculative currency C tax
0:30:39and so if you look at for example how paper
0:30:42she talks about those in great detail
0:30:45to about but you shouldn't and not be should versions of such games
0:30:51these are all of dynamical systems so not not none of this is static we talking but dynamical systems that
0:30:56how you can control K
0:30:59be another question
0:31:00we four set session goes only till twelve fifteen
0:31:04so round question or comment
0:31:08if this is not the case are like to thank you again
0:31:11a a to have a with the session and the C or not