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    Nencki321 day2 Nencki321 day2 Presentation Transcript

    • Cognitive Computing From Brain Modelling to Large Scale Machine Learning Dariusz Plewczynski, PhD ICM, University of Warsaw D.Plewczynski@icm.edu.pl
    • How the brain works? P. Latham P. Dayan
    • Simulating the Brain neuron introduced by Heinrich von Waldeyer-Hartz 1891 http://en.wikipedia.org/wiki/Neuron
    • Simulating the Brain synapse introduced by Charles Sherrington 1897 http://en.wikipedia.org/wiki/Synapse
    • How the brain works? Cortex numbers: 1 mm^2 1 mm3 of cortex: 50,000 neurons 10,000 connections/neuron (=> 500 million connections) 4 km of axons whole brain (2 kg): 1011 neurons 1015 connections 8 million km of axons P. Latham P. Dayan
    • How the brain really learns? Time & Learning: • You have about 1015 synapses. • If it takes 1 bit of information to set a synapse, you need 1015 bits to set all of them. • 30 years ≈ 109 seconds. • To set 1/10 of your synapses in 30 years, you must absorb 100,000 bits/second. Learning in the brain is almost completely unsupervised! P. Latham P. Dayan
    • Neurons: Models E. Izhikevich
    • Levels of details E. Izhikevich
    • Something Smaller ... Mammalian thalamo-cortical System by E. Izhikevich: The simulation of a model that has the size of the human brain: a detailed large-scale thalamocortical model based on experimental measures in several mammalian species. The model exhibits behavioral regimes of normal brain activity that were not explicitly built-in but emerged spontaneously as the result of interactions among anatomical and dynamic processes. It describes spontaneous activity, sensitivity to changes in individual neurons, emergence of waves and rhythms, and functional connectivity on different scales. E. Izhikevich
    • ...and Less Complicated E. Izhikevich
    • ... and Even More ... E. Izhikevich
    • Smaller? Large-Scale Model of Mammalian Thalamocortical System The model has 1011 neurons and almost 1015 synapses. It represents 300x300 mm2 of mammalian thalamo-cortical surface, and reticular thalamic nuclei, and spiking neurons with firing properties corresponding to those recorded in the mammalian brain. The model simulates one million multicompartmental spiking neurons calibrated to reproduce known types of responses recorded in vitro in rats. It has almost half a billion synapses with appropriate receptor kinetics, short-term plasticity, and long-term dendritic spike-timing-dependent synaptic plasticity (dendritic STDP). E. Izhikevich
    • Why? <<Why did I do that?>> “Indeed, no significant contribution to neuroscience could be made by simulating one second of a model, even if it has the size of the human brain. However, I learned what it takes to simulate such a large-scale system. Implementation challenges: Since 2^32 < 10^11, a standard integer number cannot even encode the indices of all neurons. To store all synaptic weights, one needs 10,000 terabytes. How was the simulation done? Instead of saving synaptic connections, I regenerated the anatomy every time step (1 ms).” E. Izhikevich
    • Time ... <<Why did I do that?>> “Question: When can we simulate the human brain in real time? Answer: The computational power to handle such a simulation will be available sooner than you think. “ His benchmark: 1 sec = 50 days on 27 3GHz processors “However, many essential details of the anatomy and dynamics of the mammalian nervous system would probably be still unknown.” Size doesn't matter; it's what you put into your model and how you embed it into the environment (to close the loop). E. Izhikevich
    • ... and Space Spiking activity of the human brain model Connects three drastically different scales: o It is based on global (white-matter) thalamocortical anatomy obtained by means of diffusion tensor imaging (DTI) of a human brain. o It includes multiple thalamic nuclei and six-layered cortical microcircuitry based on in vitro labeling and three-dimensional reconstruction of single neurons of cat visual cortex. o It has 22 basic types of neurons with appropriate laminar distribution of their branching dendritic trees. E. Izhikevich
    • Less Complicated? Spiking activity of the human brain model Connects three drastically different scales: o Single neurons with branching dendritic morphology (pyramidal, stellate, basket, non-basket, etc.); synaptic dynamics with GABA, AMPA, and NMDA kinetics, short-term and long-term synaptic plasticity (in the form of dendritic STDP); neuromodulation of plasticity by dopamine; firing patterns representing 21 basic types found in the rat cortex and thalamus (includes Regular Spiking, Intrinsically Bursting, Chattering, Fast Spiking, Late Spiking, Low-Threshold Spiking, Thamamic Bursting, etc., types); o 6-Layer thalamo-cortical microcircuitry based on quantitative anatomical studies of cat cortex (area 17) and on anatomical studies of thalamic circuitry in mammals; o Large-scale white-matter anatomy using human DTI (diffusion tensor imaging) and fibertraking methods. E. Izhikevich
    • so - is it less complicated? E. Izhikevich
    • Less Complicated? E. Izhikevich
    • Goal Large-Scale Computer model of the whole human brain “This research has a more ambitious goal than the the Blue Brain Project conducted by IBM and EPFL (Lausanne, Switzerland). The Blue Brain Project builds a small part of the brain that represents a cortical column, though to a much greater detail than the models I develop. In contrast, my goal is a large-scale biologically acurate computer model of the whole human brain. At present, it has only cortex and thalamus; other subcortical structures, including hippocampus, cerebellum, basal ganglia, etc., will be added later. Spiking models of neurons in these structures have already being developed and fine-tuned.” E. Izhikevich
    • Another Approach: Cortical Simulator C2 Simulator IBM Research D. Modha
    • Cortical Simulator: C2 simulator C2 Simulator incorporates: 1.Phenomenological spiking neurons by Izhikevich, 2004; 2.Phenomenological STDP synapses model of spike-timing dependent plasticity by Song, Miller, Abbot, 2000; 3.axonal conductance delays 1-20 ms; 4.80% excitatory neurons and 20% inhibitory neurons; 5.a certain random graph of neuronal interconnectivity, like Mouse-scale by Braitenberg & Schuz, 1998:  16x106 neurons  8x103 synapses per neuron  0.09 local probability of connection D. Modha
    • Cortical Simulator: Connectivity Micro Anatomy Gray matter, short-distance, statistical/random; • Binzeggar, Douglas, Martin, 2004 (cat anatomy); • 13% I + 87% E with 77% E->E; 10% E->I; 11%I->E; 2% I->I Macro Anatomy White matter, long-distance, specific; • CoCoMac www.cocomac.org (Collations of Connectivity data on the Macaque brain); • 413 papers; • two relationships: subset (20,000) and connectivity (40,000) • roughly 1,000 areas with 10,000 connections: The first neuro-anatomical graph of this size D. Modha Emerging dynamics, small world phenomena
    • Cortical Simulator Cortical network puts together neurons connected via an interconnections. The goal: to understand the information processing capability of such networks. 1. For every neuron: a. For every clock step (say 1 ms): i. Update the state of each neuron ii. If the neuron fires, generate an event for each synapse that the neuron is post-synaptic to and pre-synaptic to. 2. For every synapse: When it receives a pre- or post-synaptic event, update its state and, if necessary, the state of the post-synaptic neuron D. Modha
    • Cortical Simulator in action D. Modha
    • Cortical Simulator in action C2 Simulator is demonstrating how information percolates and propagates. It is NOT learning! D. Modha
    • yet, Cortical Simulator is not Brain The cortex is an analog, asynchronous, parallel, biophysical, fault-tolerant, and distributed memory machine. Therefore, a biophysically-realistic simulation is NOT the focus of C2! The goal is to simulate only those details that lead us towards insights into brain's high-level computational principles. C2 represents one logical abstraction of the cortex that is suitable for Its simulation on modern distributed memory multiprocessors. Simulated high-level principles will hopefully guide us to novel cognitive systems, computing architectures, programming paradigms, and numerous practical applications. D. Modha
    • Brain: Physical Structure P. Latham P. Dayan
    • Brain: Connectivity Red - Interhemispheric fibers projecting between the corpus callosum and frontal cortex. Green - Interhemispheric fibers projecting between primary visual cortex and the corpus callosum. Yellow - Interhemispheric fibers projecting from corpus callosum and not Red or Green. Brown - Fibers of the superior longitudinal fasciculus, connecting regions critical for language processing. Orange - Fibers of inferior longitudinal fasciculus and uncinate fasciculus, connecting regions to cortex responsible for memory. Purple - Projections between parietal lobe and lateral cortex Blue - Fibers connecting local regions of the frontal cortex D. Modha
    • but what about Brain in Action? P. Latham P. Dayan
    • Very Long Story: Artificial Intelligence Q. What is artificial intelligence? A. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. Q. Yes, but what is intelligence? A. Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines. Q. Isn't there a solid definition of intelligence that doesn't depend on relating it to human intelligence? A. Not yet. John McCarthy http://en.wikiversity.org/wiki/Artificial_Intelligence
    • Less Ambitious: Machine Learning The goal of machine learning is to build computer systems that can adapt and learn from their experience. Different learning techniques have been developed for different performance tasks. The primary tasks that have been investigated are SUPERVISED LEARNING for discrete decision-making, supervised learning for continuous prediction, REINFORCEMENT LEARNING for sequential decision making, and UNSUPERVISED LEARNING T. Dietterich
    • Machine Learning Problems: • High dimensionality of data; • Complex rules; • Amount of data; • Automated data processing; • Statistical analysis; • Pattern construction; Examples: • Support Vector Machines • Artificial Neural Networks • Boosting • Hidden Markov Models • Random Forest • Trend Vectors ...
    • Machine Learning Tasks Learning Task • Given: Set of cases verified by experiments to be positives and the given set of negatives • Compute: A model distinguishing if an item has prefered characteristic or not Classification Task • Given: calculated characteristics of a new case + a learned model • Determine: If new case is a positive or not. black box Predictive Training data (learning machine) Model Model
    • Machine Learning Tasks Training data • GivenCases with known class membership (Positives) + background preferences (Negatives) Model • The model which can distinguish between active and non- active cases. Can be used for prediction. Knowledge • Rules and additional information (databases and ontologies) Training data Acquired Discovery Model Process Knowledge Existing Knowledge Emad, El-Sebakhy: Ensemble Learning Learning
    • A Single Model Goal is always to minimize the probability of model errors on future data! Motivation - build a single good model. • Models that don’t adhere to Occam’s razor: o Minimax Probability Machine (MPM) o Trees o Neural Networks o Nearest Neighbor o Radial Basis Functions • Occam’s razor models: The best model is the simplest one! o Support Vector Machines o Bayesian Methods G. Grudic o Other kernel based methods (Kernel Maching Pursuit ...)
    • An Ensemble of Models Motivation - a good single model is difficult to compute (impossible?), so build many and combine them. Combining many uncorrelated models produces better predictors... • don’t use randomness or use directed randomness: o Boosting o Specific cost function o Gradient Boosting o Derive a boosting algorithm for any cost function • Models that incorporate randomness: o Bagging: Bootstrap Sample by uniform random sampling o Stochastic Gradient Boosting: Bootstrap Sample: Uniform random sampling (with replacement) o Random Forests uniform random sampling & randomize Grudic G. inputs
    • Machine Learning Software • WEKA  University of Waikato, New Zealand • YALE • RAPIDMINER (->YALE)  University of Dortmund, Germany  Rapid-I GmbH • MiningMart • SCRIPTELLA ETL  University of Dortmund, Germany  ETL (Extract-Transform-Load) • Orange • JAVA MACHINE LEARNING LIBRARY  University of Ljubljana, Slovenia  machine learning library • Rattle • IBM Intelligent MINER  Togaware  IBM • AlphaMiner • MALIBU  University of Hong Kong  University of Illinois • Databionic ESOM Tools • KNime  University of Marburg, Germany  University of Konstanz • MLC++ • SHOGUN  SGI, USA  Friedrich Miescher Laboratory • MLJ • ELEFANT  Kansas State University  National ICT Australia • BORGELT • PLearn  University of Magdeburg, Germany  University of Montreal • GNOME DATA MINE o TORCH  Togaware  Institut de Recherche Idiap • TANAGRA • PyML/MLPy  University of Lyon 2  Colorado State University /Fondazione Bruno Kessler • XELOPES  Prudsys • SPAGOBI  ObjectWeb, Italy • JASPERINTELLIGENCE  JasperSoft http://mloss.org/ • PENTAHO  Pentaho
    • Consensus Learning Consensus Approaches in Machine Learning Consensus: A general agreement about a matter of opinion. Learning: Knowledge obtained by study. Machine Learning: Discover the relationships between the variables of a system (input, output and hidden) from direct samples of the system. Brainstorming: A method of solving problems in which all the members of a group suggest ideas and then discuss them (a brainstorming session). Oxford Advanced Learner’s Dictionary
    • Consensus Learning Motivation Distributed artificial intelligence (DAI): the strong need to equip multi-agent systems with a learning mechanism so as to adapt them to a complex environment; Goal: improve the problem-solving abilities of individual agents and the overall system in complex multi-agent systems. Using learning approach, agents can exchange their ideas with each other and enrich their knowledge about the domain problem; Solution: the consensus learning approach. Oxford Advanced Learner’s Dictionary
    • Consensus Learning diferent data representations + ensemble of ML algorithms Build the expert system using various input data representations & different learning algorithms: • Support Vector Machines • Artificial Neural Networks • Boosting • Hidden Markov Models • Decision Trees • Random Forest • ….
    • Brainstorming INPUT: objects & features Representations annotations generation Similarity Text External External External Structure Properties PCA/ICA Mining Tools Annotations Databases … SQL Training Database feature decomposition Scores, Structural Features Thresholds Similarity Similarity OUTPUT: Support Model Neural Random Decision Vector Features Networks Forest Trees Machine Decision Reliability Score machine learning MLcons consensus
    • Brainstorming Advantages • Statistical If training data is very small, there are many hypotheses which describe it. Consensus reduces the chance of selecting a bad classifier. • Computational Each ML might get stuck in local minima of training errors. Consensus reduces the chance of getting stuck in wrong minima. • Representational Consensus may represent a complex classifier which was not possible to formulate in the original set of hypotheses.
    • Brainstorming Uwe Koch & Stephane Spieser Pawel G Sadowski, Tom Dariusz Plewczyński Kathryn S Lilley darman@icm.edu.pl Marcin von Grotthuss Krzysztof Ginalski kginal@icm.edu.pl Leszek Rychlewski Adrian Tkacz Jan Komorowski & Marcin Kierczak Lucjan Wyrwicz
    • Back to the Brain Cortical simulator puts together neurons connected via an interconnections. The goal: to understand the information processing capability of such networks. 1. For every neuron: a. For every clock step (say 1 ms): i. Update the state of each neuron ii. If the neuron fires, generate an event for each synapse that the neuron is post-synaptic to and pre-synaptic to. 2. For every synapse: When it receives a pre- or post-synaptic event, update its state and, if necessary, the state of the post-synaptic neuron D. Modha
    • Cognitive Network Cognitive network CN similarly puts together single learning units connected via an interconnections. The goal: to understand the learning as the spatiotemporal information processing and storing capability of such networks. 1. For every LU (learning unit): a. For every clock step: i. Update the state of each LU if data is changing ii. If the LU retraining was performed with success, generate an event for each coupling that the LU is post-event coupled to and pre-event coupled to. 2. For every TC (time coupling): When it receives a pre- or post-event, update its state and, if necessary, the state of the post-event LUs
    • Spatial Cognitive Networks Cellular automata has only two states and do not retrain the individual LUs Each cell in cellular automata static model represent single machine learning algorithm, or certain combination of parameters, and optimization conditions affecting the classification output of this particular method.
    • Spatial Cognitive Networks I call this single learner by the term “learning agent”, each characterized for example by its prediction quality on a selected training dataset. The coupling of individual learners is described by short- , medium- or long-range interaction strength, so called learning coupling. The actual structure, or topology of coupling between various learners is described using term “learning space”, and can have different representations, such as Cartesian space, fully connected or hierarchical geometry. The result of the evolution, dynamics of such system given by its stationary state is defined here as the consensus equilibration. The majority of learners define, in the stationary limit, the “learning consensus” outcome of the meta-learning procedure.
    • Spatial Cognitive Networks 1) Binary Logic I assume the binary logic of individual learners, i.e. we deal with cellular automata consisting of N agents, each holding one of two opposite states (“NO” or “YES”). These states are binary , similarly to Ising model of ferromagnet. In most cases the machine learning algorithms that can model those agents, such as support vector machines, decision trees, trend vectors, artificial neural networks, random forest, predict two classes for incoming data, based on previous experience in the form of trained models. The prediction of an agent answers single question: is a query data contained in class A (“YES”), or it is different from items gathered in this class (“NO”).
    • Spatial Cognitive Networks 2) Disorder and random strength parameter Each learner is characterized by two random parameters: persuasiveness and supportiveness that describe how individual agent interact with others. Persuasiveness describes how effectively the individual state of agent is propagated to neighboring agents, whereas supportiveness represent self-supportiveness of single agent.
    • Spatial Cognitive Networks 3) Learning space and learning metric Each agent is characterized by a location in the learning space, therefore one can calculate the abstract learning distance of two learners i and j. The strength of coupling between two agents tend to decrease with the learning distance between them. Determination of the learning metric is a separate problem, and the particular form of the metric and the learning distance function should be empirically determined, and in principle can be a very peculiar geometry. For example: the fully connected learning space, where all distances between agents are equal usefull in the case of simple consensus between different, yet not organized machine learning algorithms.
    • Spatial Cognitive Networks 4) Learning coupling Agents exchange their opinions by biasing others toward their own classification outcome. This influence can be described by the total learning impact I that ith agent is experiencing from all other learners. Within the cellular automata approach this impact is the difference between positive coupling of those agents that hold identical classification outcome, relative to negative influence of those who share opposite state, and can be formalized as: where Ip and Is are the functions of persuasiveness and supportiveness impact of the other agents on the i-th agent.
    • Spatial Cognitive Networks 5) Meta-Learning equations The equation of dynamics of the learning model defines the state of ith individual at the next time step as follows: with rescaled learning influence: I assume a synchronous dynamics, i.e. states of all agents are updated in parallel. In comparison to standard Monte Carlo methods the synchronous dynamics takes shorter time to equilibrate than serial methods, yet it can be trapped into periodic asymptotic states with oscillations between neighboring agents.
    • Spatial Cognitive Networks 6) Presence of noise The randomness of state change (phenomenological modeling of various random elements in the learning system, and training data) is given by introducing noise into dynamics: where h is the site-dependent white noise, or a uniform white noise. In the first case h are random variables independent for different agents and time instants, whereas in the second case h are independent for different time instants.
    • Back to AI Cognitive Networks Cognitive networks CN are inspired by brain structure and performed cognitive functions CN put together single machine learning units connected via an interconnections. The goal is to understand the learning as the spatiotemporal information processing and storing capability of such networks (Meta-Learning!). 1. Space: for every LU (learning unit): a. For every time step: i. Update the state of each LU using changed training data ii. If the LU learning was performed with success, generate an event for each coupling that the LU is post-event coupled to and pre-event coupled to. 2. Time: For every TC (time coupling): When it receives a pre- or post-event,
    • and Cortical Simulator Cortical simulator puts together neurons connected via an interconnections. The goal: to understand the information processing capability of such networks. 1. For every neuron: a. For every clock step (say 1 ms): i. Update the state of each neuron ii. If the neuron fires, generate an event for each synapse that the neuron is post-synaptic to and pre-synaptic to. 2. For every synapse: When it receives a pre- or post-synaptic event, update its state and, if necessary, the state of the post-synaptic neuron D. Modha
    • both are Information Processing NETWORKS Network puts together single units connected via an interconnections. The goal is to understand the information processing of such network, as well as the spatiotemporal characteristics and ability to store information in such networks (Learning).
    • Network: technical challenges Memory constrains to achieve near real-time simulation times, the state of all learning units LUs and time couplings TCs must fit in the random access memory of the system. In real brain synapses far outnumber the neurons, therefore the total available memory divided by the number of bytes per synapse limits the number of synapses that can be modeled. The rat-scale model (~55 million neurons, ~450 billion synapses) needs to store state for all synapses and neurons where later being negligible in comparison to the former. D. Modha
    • Network: technical challenges Communication constrains that is on an average, each LU fires once a second. Each neuron connects to roughly 8,000 other neurons, and, therefore each neuron would generate 8,000 spikes (“messages" or so called neurobits) per second. This amounts to a total of 448 billion messages/neurobits per second. D. Modha
    • Network: technical challenges Computation constrains: on an average, each LU fires once a second. In brain modelling, on an average, each synapse would be activated twice: once when its pre-synaptic neuron fires and once when its post-synaptic neuron fires. This amounts to 896 billion synaptic updates per second. Let us assume that the state of each neuron is updated every millisecond. This amounts to 55 billion neuronal updates per second. Synapses seem to dominate the computational cost. D. Modha
    • Network: hardware Hardware solution: a state-of-the-art supercomputer BlueGene/L with 32,768 CPUs, 256MB of memory per processor (8 TB in total), and 1.05GB/sec of in/out communication bandwidth per node. To meet the above three constraints, one has to design data structure and algorithms that require no more than 16 bytes of storage per TC, 175 Flops per TC per second, and 66 bytes per neurobit (spike message). A rat-scale brain, near real-time learning network! D. Modha
    • Network: software Software solution: a massively parallel learning network simulator, LN, that runs on distributed memory multiprocessors. Algorithmic enhancements: 1. a computationally efficient way to simulate learning units in a clock-driven ("synchronous") and couplings in an event- driven ("asynchronous") fashion; 2. a memory efficient representation to compactly represent the state of the simulation; 3. a communication efficient way to minimize the number of messages sent by aggregating them in several ways and by mapping message exchanges between processors onto judiciously chosen MPI primitives for synchronization. D. Modha
    • Computational Size of the problem BlueGene/L can simulate the 1 sec of model in 10 sec at 1Hz firing rate and 1ms simulation resolution using random stimulus. MOUSE HUMAN BlueGene/L Neurons 2x8x106 2x50x109 3x104 CPUs Synapses 128x109 1015 109 CPUs pairs Communication 128x109 1015 1.05 GB/sec (66 B/spike) Spikes/sec Spikes/sec 64x109 b/sec in/out Computation 2x128x109 2x1015 45 TF (350 synapse/sec synapse/sec 4x1013F F/synapse/sec) 8,192 CPUs Memory O(128x109) O(1015) 4 TB (32 B/synapse) O(64x1012) (D. Modha: assuming neurons fire at 1Hz)
    • D. Modha Progress in large-scale cortical simulations
    • IBM BlueGene D. Modha
    • D. Modha Future of cortical simulations
    • D. Modha C2 Simulator vs Human Brain: The human cortex has about 22 billion neurons which is roughly a factor of 400 larger than the rat-scale model which has 55 million neurons. They used a BlueGene/L with 92 TF and 8 TB to carry out rat-scale simulations in near real-time [one tenth speed]. So, by naïve extrapolation, one would require at least a machine with a computation capacity of 36.8 PF and a memory capacity of 3.2 PB. Furthermore, assuming that there are 8,000 synapses per neuron, that neurons fire at an average rate of 1 Hz, and that each spike message can be communicated in, say, 66 Bytes. One would need an aggregate communication bandwidth of ~ 2 PBps. D. Modha
    • SyNAPSE The goal is to create new electronics hardware and architecture that can understand, adapt and respond to an informative environment in ways that extend traditional computation to include fundamentally different capabilities found in biological brains. Stanford University: Brian A. Wandell, H.-S. Philip Wong Cornell University: Rajit Manohar Columbia University Medical Center: Stefano Fusi University of Wisconsin-Madison: Giulio Tononi University of California-Merced: Christopher Kello IBM Research: Rajagopal Ananthanarayanan, Leland Chang, Daniel Friedman, Christoph Hagleitner, Bulent Kurdi, Chung Lam, Paul Maglio, Stuart Parkin, Bipin Rajendran, Raghavendra Singh
    • NeuralEnsemble.org NeuralEnsemble is a multilateral effort to coordinate and organize Neuroscience software development efforts into a larger meta-simulator software system, a natural and alternate approach to incrementally address what is known as the complexity bottleneck, presently a major roadblock for neural modelling. "Increasingly, the real limit on what computational scientists can accomplish is how quickly and reliably they can translate their ideas into working code." Gregory V. Wilson, Where's the Real Bottleneck in Scientific Computing?
    • Emergent A comprehensive, full-featured neural network simulator that allows for the creation and analysis of complex, sophisticated models of the brain. It has high level drag-and-drop programming interface, built on top of a scripting language that has full introspective access to all aspects of networks and the software itself, allows one to write programs that seamlessly weave together the training of a network and evolution of its environment without ever typing out a line of code. Networks and all of their state variables are visually inspected in 3D, allowing for a quick "visual regression" of network dynamics and robot behavior.
    • Cognitive Computers The Future Today algorithms and computers deals with structured data (age, salary, etc.) and semi-structured data (text and web pages). No mechanisms exists that is able to act in a context- dependent fashion while integrating ambiguous information across different senses (sight, hearing, touch, taste, and smell) and coordinating multiple motor modalities. Cognitive computing targets the boundary between digital and physical worlds where raw sensory information abounds. For example, while instrumenting the outside world’s with some sensors, and streaming this information in the real-time manner to a cognitive computer that may be able to detect spatio-temporal correlations.
    • Niels Bohr Predicting is very difficult especially about the future…
    • Brainstorming Uwe Koch & Stephane Spieser Pawel G Sadowski, Tom Dariusz Plewczyński Kathryn S Lilley darman@icm.edu.pl Marcin von Grotthuss Krzysztof Ginalski kginal@icm.edu.pl Leszek Rychlewski Adrian Tkacz Jan Komorowski & Marcin Kierczak Lucjan Wyrwicz