Simple Artificial Brains to Govern Complex Tasks | Ramin Hasani | TEDxVienna

Simple Artificial Brains to Govern Complex Tasks | Ramin Hasani | TEDxVienna



today I'm here to tell you about what we can do with the brain of a little worm C elegans is a one millimetre long world with a rather simple brain it can exhibits remarkable behavioral plasticity that the current state state-of-the-art AI systems are yet to produce given the capacity its nervous system contains only three hundred and two cells that are hardwired by approximately around eight thousand neurons and with that simple brain it can still learn it it can mate it can complex it can process complex chemicals and complex sensory data and also it can adopt search mechanisms in the environment C elegans is amongst the world's best understood animals its entire nervous system was mapped in 1986 and it provided a substrate for research and asked the questions of how the brain gives rise to behavior so i malay rai scientists i'm fascinated by how much c elegans can get with such little computational resources especially given the capacity and given the AI systems that you're developing these days then when I look like closer to the architecture of the brain so there are certain features that I want to like discusses with you guys so the first thing is that I see a particular hierarchical architecture of the nervous system that has that is actually specific to C elegans and I'll tell you more about it then the network is not an all-too all connected network so every node is connect is not connected to all part of this nervous system it there are certain neuronal pathways so the network is a sparse and we know that synaptic transmission in nervous systems in general are much more complex than the way that we're currently treating AI systems and this is also to read how neuronal dynamics are realized and in actual nervous systems so I want to have these kind of features and ask myself can I have an AI system that can produce such similar behavior and be as expressive as the elegance given those features with together with our teams at Vienna University of Technology at Institute of Science and Technology in Institute of molecular pathology in Vienna and MIT we create these systems so we create systems that are similar and closer to the closer to the capacities of natural learning systems so I would like to also especially thank and acknowledge my colleagues contributions to this work that we are working together closely at all levels concept design and implementation we designed this system so we got those four features of the nervous system and we designed and we formalize them in in a form of an artificial and new artificial intelligence system that incorporates the hierarchical design so to process input data and to generate an output similar to what C elegans has and also we incorporated some rules that adapt the sparsity of the network within this platform we also got closer in mathematical expressions than that synaptic propagation have as well we actually have a richer synaptic propagation and richard synaptic connections so we for formulated actually a kind of dynamical system and now we want to solve AI tasks with such systems so here I'm showing you a control dashboard so on this dashboard on the right side you see one example circuit small circuit inspired by the brain the worm that wants to control an inverted pendulum problem so actually the environment on the Left I'm sure so basically the circuit wants to control and balance the pole on an upright position and on the rock on the top side I'm showing a learning curve during the learning process that the network was getting tuned in order to maximize the amount of reward in the system so that we can collect how much we can keep this pendulum actually upward so let's see so right now the red dot on the learning curve shows that where we are at the training so at the beginning of the process you see the circuit cannot realize the behavior so the pendulum Falls constantly but then the more the system is getting learned we see that we are trying to maximize actually the capacity of this system and now for example now we are in the middle of the curve Steele the circuit is trying to capture the behavior now I should work better because you're higher and we learned much more in this system and then once we reach on the right as part of the learning curve the system has learned how to control this pendulum like this scenario let me show you another example this is actually another control problem where we have an underpowered car that we want to create a momentum so this circuit is trying to create a momentum for the car to drive it uphill and now I'm showing you one successful episode of training in this system so as we see we are the circuit is interacting and interplay with each other in order to create that and now we have that so now let's see another episode of that where you can look at the circuit and dynamics of this system that is trying to create such momentum and to solve this problem and now so we also extended it in real-life applications okay that was simulated platforms and then we extended it to autonomous kind of parking of of some robots in our lab and as we see the task is done with the same same kind of circuit that I showed you before so what's the next step that we designed these platforms and that now what we do we try to quantify and we try to understand the behavior of these circuits that we have for example here I'm showing the neurons that that are involved in the in controlling those tasks and I'm projecting the activity of one node to the output in order to see what form of dynamics this network is creating and with some quantitative methods that we developed we want to understand what happens inside the systems so after and then this actually increases also the expressivity of the network so what what why do we have to do this like you have artificial neural networks that already can handle these problems so what are the advantages wider why you're doing this so we actually try to compare this to other artificial intelligence systems that are available and I want to count three advantages of this system the first one is in environment such as full autonomous parking scenarios so actually at vu here okay so this car is actually moving in the environment trying to find a parking spot and then performing a part in trajectory and also it can avoid collisions so with with obstacles on this way then in those kind of tasks then with the simplest artificial neural network we could do for this specific task beach 943 trainable parameters we could solve this problem and then with our systems we could do that with only 49 tradable parameters so our networks are smaller in size than current artificial intelligence systems we know that environment is always comes with uncertainty and there are so many interaction and disturbances on the world like noise like for example an autonomous car that is moving in the street so all of a sudden environment can cause rain and you know sensors of the robot can get distracted so noise is the parameters that always attack a learning system so then if you try to have a curve where we are increasing on the horizontal axis we are increasing the amount of noise of the environment and we measure the output disturbances on the system we want to compare this between artificial intelligence methods and our methods and this is how our system is reacting so these systems are highly resilient to noise so as as we compared to their equivalent artificial intelligence in tasks such as autonomous parking and the third advantage that I want to count is the most important one these systems are more interpretable they are expressive we can understand then an internal their internal dynamics so on the left side I am showing you a simple artificial neural network that handle these formal problems and as we see this is such a complex kind of architecture in order to understand its behavior so on the right I'm showing one of our networks that actually can solve the similar problem with much less parameters and also like having it's more expressive kind of neuronal dynamics so it networks with such advantages we are getting closer to have control systems that are safer such as autonomous cars that you're working on right now and we know that autonomous cars and environments with high uncertainty are gonna come are gonna soon become the everyday norm for artificial intelligence agents thank you [Applause]

One thought on “Simple Artificial Brains to Govern Complex Tasks | Ramin Hasani | TEDxVienna

Leave a Reply

Your email address will not be published. Required fields are marked *