Research Issues and Challenges on Brain Informatics Towards Computing & Intelligence in the Big Data Era Ning Zhong
I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted. Alan M. Turing ACM Turing Centenary Celebration June 15-16, 2012 Palace Hotel, San Francisco turing100.acm.org 2
What is the basic model of data representation in the big data era? 3
Web Intelligence (WI 12) Intelligent Agent Technology (IAT 12) Active Media Technology (AMT 12) Brain Informatics (BI 12) Methodologies for Intelligent Systems (ISMIS 12)
Human Intelligence -- An Important Scientific Issue in the Future Three Important Scientific Issues in the Future: Universe Molecular Biology Human Intelligence Edward Feigenbaum Stanford University, USA 1994 Turing Award Winner WIC Advisor Turing Keynote Talk at the 2012 World Intelligence Congress (WIC 2012) December 4-7, 2012 Macau, China
Memoriam of John McCarthy 1927-09-4 2011-10-24 (1971 Turing Award Winner) Roads to Human Level AI September 2004 at BJUT WIC Advisor, WI IAT 04 Keynote Speaker 6
Human Level Capabilities We need to better understand - How human being does complex adaptive, distributed problem solving and reasoning. - How intelligence evolves for individuals and societies, over time and place. Ignoring what goes on in human brain and focusing instead on behavior has been a large impediment to understand how human being does complex adaptive, distributed problem solving and reasoning.
Brain: Big Science, Big Data, Big Innovation, Big Brain Big Data Computing Brain Structure Educational Psychology Gene Neuron Artificial Intelligence Cognitive psychology Forensics Neuroinformatics Psychiatry Clinical Medicine Brain Informatics NeuroImage Neuroeconomics Geriatrics Intelligent Information Technology Web Intelligence Cognitive Neuroscience Behavior Brain Imaging EEG Human Brain Project (EU) (2013 2023) Obama's Brain Project (USA) (2013 -)
Developing brain inspired intelligent technologies to provide (human-level) wisdom services Intelligent Diagnosis and Treatment Technologies of Brain and Mental Disorders Wisdom Marketing Smart City/ Smart Health New Brain-Computer Interfaces Neuron Brain Informatics Gene Brain Structure Intelligent Information Technologies and Services Neuroimaging EEG/ERP Cognitive Psychology New Industry Chain
Computer Science (AI + ICT) Brain Informatics (BI) Cognitive Science Neuroscience
AI + ICT Brain Informatics (BI) Cognitive Science Neuroscience
Brain (Big) Data Computing AI + ICT Brain Informatics (BI) Cognitive Science Neuroscience fmri, EEG, MEG, PET, Eye-Tracking,
Brain Data Computing AI + ICT brain research from informatics perspective brain research supported by information technology BI = Brain Big Data Science Cognitive Science Neuroscience
Brain Data Computing - 1 AI + ICT Human brain = Information Processing System (HIPS) with big data brain research from informatics perspective brain research supported by information technology BI = Brain Big Data Science Cognitive Science Neuroscience
Brain Data Computing - 2 AI + ICT brain research from informatics perspective brain research supported by information technology Curation, mining, use of brain big data Cognitive Science BI = Brain Big Data Science Neuroscience
Brain Informatics Initiatives IEEE-CIS Task Force on Brain Informatics (http://braininformatics.org) N. Zhong et al. Brain Informatics, Springer, 2014 N. Zhong, J.M. Bradshaw, J.M. Liu, J.G. Taylor (eds.) Special Issue on Brain Informatics, IEEE Intelligent Systems, 26(5) (2011) N. Zhong, J. Liu, Y.Y. Yao (eds.) Special Issue on Brain Informatics, Cognitive Systems Research, Elsevier, 11(1) (2010) B. Hu, J. Liu, L. Chen, N. Zhong (eds.) Brain Informatics, LNAI 6889, Springer (2011) Y.Y. Yao, R. Sun, T. Poggio, J. Liu, N. Zhong, J. Huang (eds.) Brain Informatics, LNAI 6334, Springer (2010) N. Zhong, K. Li, S. Lu, L. Chen (eds.) Brain Informatics, LNAI 5819, Springer (2009) N. Zhong et al (eds.) Web Intelligence Meets Brain Informatics, LNCS 4845, Sate-of-the-Art Survey, Springer (2007)
2006 International Workshop on Web Intelligence meets Brain Informatics WI meets BI December 2006, Beijing, China Beijing Key Lab of MRI & Brain Informatics
International Journal, Conference and Collaboration Beijing International Collaboration Base on Brain Informatics and Wisdom Services
WIC 2014: Web Intelligence Congress 11 14 August 2014, Warsaw Poland Web Intelligence (WI 14) Intelligent Agent Technology (IAT 14) Brain Informatics and Health (BIH 14) Active Media University Technology of Warsaw (AMT 14) Warsaw, Poland wic2014.mimuw.edu.pl
IEEE Intelligent Systems Special Issue on Brain Informatics Vol 26, No 5, 2011 20
Three Aspects of BI Research Systematic investigations for complex brain science problems New information technologies for systematic brain science studies BI studies based on WI (W2T) research needs
Systematic investigations for complex brain science problems Human thinking centric cognitive functions (e.g. reasoning, problemsolving, decisionmaking, learning, attention, and emotion) Clinical diagnosis and pathology of human brain, mind and mental related diseases (e.g. MCI: mild cognitive impairment, epilepsy, AD: alzheimer disease, depression)
Core Issues in BI Methodology Human brain is regarded as an information processing system (HIPS) with big data systematic studies Systematic investigation of complex brain science problems Systematic design of cognitive experiments Systematic data management the Data Brain Systematic data analysis/simulation multi-aspect approach
Systematic investigations for complex brain science problems Human thinking centric cognitive functions (e.g. reasoning, problemsolving, decisionmaking, learning, attention, and emotion) Clinical diagnosis and pathology of human brain, mind and mental related diseases (e.g. MCI: mild cognitive impairment, epilepsy, AD: alzheimer disease, depression)
Core Issues in BI Methodology Human brain is regarded as an information processing system (HIPS) with big data systematic studies Systematic investigation of human thinking centric high cognitive functions Systematic design of cognitive experiments Systematic data management the Data Brain Systematic data analysis/simulation multi-aspect approach
Human Intelligence = Thinking + Perception Thinking ----------------- Perception computation learning discovery creativity Reasoning Language Memory Attention Vision decision-making audition planning tactile problem-solving
Reasoning Centric, Thinking Oriented Functions and Their Inter-relationships A Conceptual View Decision-Making emotion Problem-Solving memory Planning deduction granularity uncertainty Learning stability Discovery Reasoning (commonsense) Creativity search Computation induction abduction autonomy attention Language
Reasoning Centric, Thinking Oriented Functions and Their Inter-relationships A Conceptual View Problem-Solving Decision-Making memory Planning emotion granularity deduction uncertainty Learning stability Discovery Reasoning (commonsense) Creativity search Computation induction abduction autonomy attention Language
Reasoning Centric, Thinking Oriented Functions and Their Inter-relationships A Conceptual View Problem-Solving Decision-Making memory Planning emotion deduction granularity uncertainty Reasoning search (commonsense) Learning induction abduction Computation stability autonomy Discovery attention Creativity Language
Investigation Paradigm in MRI Functional MRI: Subsequent States (time) Pre-taskresting state Task-on state (active, passive) Post-taskresting state Represent Representation Represent Disturbance Represent Graph model Graph model Graph model
Systematic investigations for complex brain science problems X.Q. Jia, P.P. Liang, J. Lu, Y.H. Yang, N. Zhong, K.C. Li. Common and Dissociable Neural Correlates Associated Functional MRI: with Subsequent Component States (time) Processes of Inductive Reasoning. NeuroImage, Elsevier, 56(4):2292-2299, 2011. Z. Wang, J. Liu, N. Zhong, Y. Qin, H. Zhou, K.C. Li. Changes in the Brain Intrinsic Organization in Both On-Task State and Post-Task Resting State. NeuroImage, Elsevier, 62:394-407, 2012. G. Jin, K.C. Li, Y. Hu, Y. Qin, X. Wang, J. Xiang, Y. Yang, J. Lu, N. Zhong. Amnestic Mild Cognitive Impairment: Functional MR Imaging Study of Response in Posterior Cingulate Cortex and Adjacent Precuneus during Problem-solving Tasks. Radiology, RSNA, 261(2): 525-533, 2011. M. Li, N. Zhong, K. Li, S. Lu. Functional Activation of the Parahippocampal Cortex and Amygdala During Social Statistical Information Processing. Cognitive Systems Research, Elsevier, 17-18:25-33, 2012.
Systematic investigations for complex brain science problems Human thinking centric cognitive functions (e.g. reasoning, problemsolving, decisionmaking, learning, attention, and emotion) Clinical diagnosis and pathology of human brain, mind and mental related diseases (e.g. MCI: mild cognitive impairment, epilepsy, AD: alzheimer disease, depression)
Systematic Study on Depression Macro BI Methodology Multi-modal Systematic Meso Symptoms & Behaviors Structure & Function of Brain To understand depression Macro Micro Neuron Synapse & gene Micro
Systematic Study on Depression Diagnostic Criteria Depressive Patients High Risk People Healthy People Target Groups
Unifying Studies of Cognition, Emotion and Depression Study Human Information Processing System (HIPS) Influence Study Emotion Impaired Depression (MDD)
Multi-modal Experiments
Three Aspects of BI Research Systematic investigations for complex brain science problems New information technologies for systematic brain science studies BI studies based on WI (W2T) research needs
New information technologies for systematic brain science studies N. Zhong, J. Chen. Constructing a New-style Conceptual Model of Brain Data for Systematic Brain Informatics. IEEE Transactions on Knowledge and Data Engineering, 24(12): 2127-2142, 2012. J. Chen, N. Zhong. Towards the Data-Brain Driven Systematic Brain Data Analysis. IEEE Transactions on Systems, Man and Cybernetics (Part A). 43(1): 222-228, 2013. J. Chen, N. Zhong. P. Liang. Data-Brain Driven Systematic Human Brain Data Analysis: A Case Study in Numerical Inductive Reasoning Centric Investigation. Cognitive Systems Research, Elsevier, 15-16:17-32, 2012. N. Zhong, S. Motomura. Agent-Enriched Data Mining: A Case Study in Brain Informatics. IEEE Intelligent Systems, 24(3):38-45, 2009. N. Zhong, J.M. Bradshaw, J.M. Liu, J.G. Taylor. Brain Informatics. IEEE Intelligent Systems, 26 (5):16-21, 2011.
The shortcomings of existing fmri and EEG databases FSPEEG fmridc They cannot describe cognitive functions from multiple aspects because of only storing a single type of data, such as EEG or fmri data. They cannot effectively identify the data relationships among different experiments because of only providing descriptions for each dataset. They cannot help users, who aren t familiar with recent database structures and terms of special domains because of only providing the keyword-based query or term dictionary-based query expansion They cannot effectively support cloud-based big data mining because of not describing data analysis and not storing analytical results. It is an urgent work to develop a multi-functional brain data center for various requirements from education, research, clinical diagnosis, health care, etc.
Curation, Mining & Use of Brain Big Data on the Wisdom Web of Things (W2T) A brain data centre needs to be constructed on the W2T for effectively utilizing the data wealth as services (i.e. WaaS Wisdom as a Service) Curate BI big data, which can be characterized by five parameters: volume, velocity, variety, veracity and value, in order to support data sharing and reuse among different BI experimental and computational studies for generating and testing hypotheses about human and computational intelligence.
Brain Big Data 5V Big Amount of Data Big Data Value Volume Value Big Speed of Data In and Out Velocity Variety Big Range of Data Veracity Big Data Accuracy and Truthfulness Types and Sources How to support the whole process of BI methodology and integrate brain big data for systematic studies? 41
WaaS Standard and Service Platform WaaS Content Schedule Standards Intelligent Application Standards WaaS Standard System WaaS Application Portal Data/Information/Knowledge Buses WaaS Platform Data Accessing Standards Data Content and Format Standards Data Transmission Protocols Data Collection Interface Standards DaaS Standard System Data Query Data Management Data Cleaning Data Collection DaaS Platform DaaS Information Accessing Standards Metadata Standards Data-Mining-Related Standards Information- Retrieval-Related Standards InaaS Standard System Data Curation Data Mining Information Retrieval InaaS Platform InaaS Knowledge Accessing Standards Knowledge Representation and Organization Standards Knowledge Retrieval Standards KaaS Standard System Knowledge Query Knowledge Management Knowledge Development Knowledge Retrieval KaaS Platform KaaS Private Cloud Brain and Intelligence Big Data Center Information Assurance Framework Large Knowledge Collider
LarKC - Large Knowledge Collider The aim of the EU FP 7 Large-Scale Integrating Project LarKC is to develop a platform for massive distributed incomplete reasoning that will remove the scalability barriers of currently existing reasoning systems for the Semantic Web. Fourteen member units, a total investment of 10 millions Euros. Three use cases: urban computing and intelligent traffic, semanticsbased medical literature retrieval, and the relationships between cancer and gene. 10M Large Knowledge Collider 43
Data-Brain, BI Provenances and the LarKC How to model the whole process of BI methodology? Conceptual View Structural View Experiment Dimension Function Dimension Analysis Dimension Data Dimension Data-Brain How to extract and represent brain big data related information and knowledge? Data Provenances Analysis Provenances How to integrate brain big data related information and knowledge into a global framework? Large Knowledge Collider
Data-Brain The Data-Brain is a conceptual model of brain data which models brain data from four aspects, i.e., cognitive functions, experiments, data themselves and analytical processes. N. Zhong and J. H. Chen. Constructing a new-style conceptual model of brain data for systematic Brain Informatics. IEEE Transactions on Knowledge and Data Engineering, 24(12), pp. 2127-2142, 2012. J. H. Chen and N. Zhong. Toward the Data-Brain driven systematic brain data analysis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(1), pp. 222-228, 2013.
BI Provenances BI Provenances are the metadata describing the origin and subsequent processing of various human brain data in systematic BI studies, including data provenances and analysis provenances. Data Provenances J. H. Chen, N. Zhong, and P. P. Liang. Data-Brain driven systematic human brain data analysis: A case study in numerical inductive reasoning centric investigation. Cognitive Systems Research, Elsevier, vol. 15-16, pp. 17-32, 2012. Analysis Provenances
A Data-Brain Based Brain Data Center owl:thing Concept Instance Brain Data fd:cognitive- Function ed:experimental -Groupta dd:bi-da dd: ad:analytic- Process ad: Function Dimension Experiment Dimension Relations between dimensions Data Dimension Data-Brain Analysis Dimension Relations in dimension BI Provenances Brain Data
A Data-Brain Based Brain Data Center owl:thing Concept Instance Brain Data fd:cognitive- Function ed:experimental -Groupta dd:bi-da dd: ad:analytic- Process ad: Function Dimension Experiment Dimension Relations between dimensions Data Dimension Data-Brain Analysis Dimension Relations in dimension BI Provenances Open Data Brain Data
A Data Brain Based Brain Data Center owl:thing Concept Instance Brain Data fd:cognitive- Function ed:experimental -Group dd:bi-data ed: ad:analytic- Process A brain data and knowledge base Function Dimension Experiment Dimension ad: Relations between dimensions Data Dimension Data-Brain Analysis Dimension Relations in dimension BI Provenances Brain Data
Wisdom Web of Things (W2T) Social World Hyper World Cyber World Individuals Companies/Societies Service Service Service ServiceServiceService Developing Transparent Services Internet /WWW Hypw-TSBus Hypw-DKServer Wisdom Intelligent KNOWLEDGE/ INFORMATION/DATA Utilization SEA-net Knowledge Intelligent INFORMATION Analysis Information (from Web, SEA-net) Web-Information Sensor-Information Intelligent DATA Pre-processing Data Physical World TSBus Transparent Service Bus Data Service 50
Wisdom Web of Things (W2T) Social World Hyper World The Wisdom Web of Things (W2T) is an extension of the Wisdom Web in the hyper-world with big data. The Wisdom means that each of things in the IoT/WoT can be aware of both itself and others to provide the right service for the right object at a right time and context. Physical World Cyber World Research challenges and perspectives on wisdom web of things (W2T). Journal of Supercomputing, Springer, 64(3):862-882, 2013.
From the Wisdom Web to W2T N. Zhong, J. Ma, R. Huang, J. Liu, Y.Y. Yao, Y. Zhang, J. Chen. Research Challenges and Perspectives on Wisdom Web of Things (W2T), Journal of Supercomputing, Springer. 2013, 64(3):862-882 (2010, DOI 10.1007/s11227-010-0518-8) N. Zhong, J. Liu, Y.Y. Yao. Web Intelligence (WI), Encyclopedia of Computer Science and Engineering, Vol. 5, Wiley (2009) 3062-3072. N. Zhong, J. Liu, Y.Y. Yao. Envisioning Intelligent Information Technologies Through the Prism of Web Intelligence. Communications of the ACM, 50(3), 89-94, 2007. N. Zhong. Impending Brain Informatics Research from Web Intelligence Perspective, International Journal of Information Technology and Decision Making, Vol. 5, No. 4, World Scientific (2006) 713-727. N. Zhong, J. Liu, Y.Y. Yao. Web intelligence, Springer, 2003. N. Zhong, J. Liu, Y.Y. Yao. In Search of the Wisdom Web. IEEE Computer, 35 (11), 27-31, 2002. N. Zhong, J. Liu, Y.Y. Yao, S. Ohsuga. Web Intelligence (WI), Proc. 24th IEEE COMPSAC, IEEE-CS Press (2000) 469-470. 52
W2T Initiatives N. Zhong et al. Wisdom Web of Things, Springer, 2014 J.H. Chen, J.H. Ma, N. Zhong, Y.Y. Yao, J.M. Liu, R.H. Huang, W.B. Li, Z.S. Huang, Y. Gao, and J.P. Cao. WaaS Wisdom as a Service. IEEE Intelligent Systems (in press) WIC 2012 Turing-Centenary Panel on Top 10 Questions in Intelligent Informatics/Computing. wi consortium.org/blog/top10qi/index.html Special Issue on Wisdom Web of Things (W2T) World Wide Web Journal, Springer, 16(4) 2013 WI-IAT 2011 Panel on Wisdom Web of Things (W2T) : Fundamental Issues, Challenges and Potential Applications, wi-iat-2011.org N. Zhong, J. Ma, R. Huang, J. Liu, Y.Y. Yao, Y. Zhang, J. Chen. Research Challenges and Perspectives on Wisdom Web of Things (W2T), Journal of Supercomputing, Springer. 2013, 64(3):862-882 (2010, DOI 10.1007/s11227-010-0518-8) 53
W2T Big Data Cycle 54
BI Methodology vs BI Data Cycle Guided by such a BI methodology, the whole process of BI research can be regarded as a BI data cycle Implemented by measuring, collecting, modeling, transforming, managing, mining, interpreting, and explaining multiple forms of brain big data obtained from various cognitive experiments by using powerful equipments, such as fmri and EEG.
WaaS - Wisdom as a Service A content architecture of the W2T cycle A perspective of W2T in services IEEE Intelligent Systems (in press) Providing services based on both already-created and will-created raw data, information, knowledge and wisdom. DIKW)Hierarchy Wisdom Knowledge Information Data WaaS Architecture Wisdom-as-a-Service (WaaS) Knowledge-as-a-Service (KaaS) Information-as-a-Service (InaaS) Data-as-a-Service (DaaS) Knowledge Query Services Knowledge Retrieval Services Development and Management Services of Knowledge Bases Information Retrieval Services Data Mining Services Data Curation Services Data Collection Service Data Production Service Data Sharing Services
WaaS Wisdom as a Service Providing the right service, including infrastructure, platform, software, as well as data, information, and knowledge, for the right object at a right time and context. Wisdom-as-a-Service (WaaS) Interfaces Software-as-a-Service (SaaS) (Applications/Workflows) Knowledge-as-a-Service (KaaS) (Ontology/Models/Cases) Platform-as-a-Service (PaaS) (Middleware-AS/WS) Information-as-a-Service (InaaS) (Meta Data/Data Features) Infrastructure-as-a-Service (IaaS) (Servers/Storage Devices/Internet/IoT/MI) Data-as-a-Service (DaaS) (Big Data-Web Pages/Video/Audio/Text/Images) Cyber World 5V: Volume-Velocity-Variety-Veracity-Value
WaaS Wisdom as a Service individuals Agents Companies/Societies Socio-culture & organizational components Software-as-a-Service (SaaS) (Applications/Workflows) Platform-as-a-Service (PaaS) (Middleware-AS/WS) Wisdom-as-a-Service (WaaS) interaction, personalization, Knowledge-as-a-Service (KaaS) context-aware, active-service, (Ontology/Models/Cases) affective/emotion, auto-perception Information-as-a-Service (InaaS) (Meta Data/Data Features) Infrastructure-as-a-Service (IaaS) (Servers/Storage Devices/Internet/IoT/MI) Data-as-a-Service (DaaS) (Big Data-Web Pages/Video/Audio/Text/Images) 5V: Volume-Velocity-Variety-Veracity-Value Cyber World
Brain Data Center and BI Research Platform BI Research Community BI Portal W2T Brain Data Center GLS-BI Internet /WWW SEA-net BI Experimental Studies Hypw-TSBus Hypw-DKServer Domain Ontologies BI Provenances Data-Brain Analysis Agents Data Agents Data-Brain Based Data/Analysis/Knowledge Description, Integration and Publishing GLS-BI Global Learning Scheme for BI Data Service 59
Depression Data Center and Service Platform Physician Health-care Pervasive Service Community Depression victims Ambulance Hospital Nurse SEA-net Internet /WWW Mental Health Research Community Hypw-TSBus Hypw-DKServer W2T Depression Transparent Service Platform Depression Unified Data Center 60
Portable Brain/Mental Health Monitoring System
Data Brain Based Multi-Aspect Human Brain Data Analysis Various analysis agents (association, classification, clustering, manifold, peculiarity-oriented, SPM etc) are deployed on the data brain for multi-aspect analysis in multiple data sources. Studying functional relationships and neural structures of the activated areas, and trying to understand - how a peculiar part of the brain operates - how they are linked functionally and intrinsically - how they work cooperatively to implement a whole information processing. Changing the perspective of cognitive scientists from a single type of experimental data towards a long-term, holistic vision.
An Investigation Flow Based on BI Methodology Experimental Design Part Data Mining Part Experiments (Multiple Difficulty Levels) Data Collection Data Extraction Model Transformation Determination of tasks Perception : Vision : Audition : Tactile etc. Memory, Attention, etc. Visualization & Integration of Results (Multi-aspect Data Analysis) Data Mining Data Mining Data Mining EEG/ ERP data Spectrum data fmri Image Thinking : Computation : Reasoning : Learning etc. Explanation/Synthesis Knowledge Discovery ACT-R Simulation
Three Aspects of BI Research Systematic investigations for complex brain science problems New information technologies for systematic brain science studies BI studies based on WI (W2T) research needs
Ways for Studying Intelligence AI Computer Science: 4 types of definition (cognitive, Turing test, logic, agents), computational models of intelligence, computer based technologies; Cognitive Science Psychology: Mind & Behavior based cognitive models of intelligence; Neuroscience Medicine: Brain & Biological models of intelligence.
Memoriam of Herbert A. Simon 1916-06-15 2001-02-09 Notable awards Turing Award 1975 Nobel Prize in Economics 1978 National Medal of Science 1986 von Neumann Theory Prize 1988 Known for Logic Theory Machine General Problem Solver Bounded Rationality Fields: Artificial Intelligence Cognitive psychology Computer science Economics Political science 66
Related to Simon s Contributions These fundamental issues of the W2T are related Simon's contributions, including bounded rationality & decision making etc. Human problem solving and GPS as a cognition inspired computer program; Human scientific discovery process and Bacon series as a cognition inspired computer program. 67
Extension of Simon s Works Exploring the neural basis of human problem solving and scientific discovery by using Brain Informatics means; Developing Web based problem solving and knowledge discovery systems with human level capabilities for meeting WI (W2T) real world needs. 68
What Are Problems? The traditional AI research has not produced major breakthrough recently due to a lack of understanding of human brains and natural intelligence. Most of the AI models and systems will not work well when dealing with dynamically changing, open and distributed big data at Web scale and in a ubiquitous environment.
A New Perspective of WI: WI Meets Brain Informatics (BI) New instrumentation (fmri etc) and advanced IT are causing an impending revolution in WI and brain sciences (BS). BI for WI: New understanding and discovery of human intelligence models in BS will yield a new generation of WI research/development. WI for BI: WI based technologies will provide a new powerful platform for BS.
WI = AI + IT brain = information processing system Web = information processing system Unifying Studies Cognitive Science Neuroscience
Unifying Study of Human & the Web Brain = information processing system (IPS) Human-Human question-answering Human reasoning/ problem solving/ decision making/ learning The Web = information processing system (IPS) Human-Web question-answering Web reasoning/ problem solving/ decision making/ e-learning Human+the Web = two aspects of IPS
Unifying Study on Human and the Web Multi granule/multi source/?? networks Rule based/case based/?? reasoning Variable Precision Granule reasoning Common-sense Common-sense reasoning Granule-based cognitive model Granule reasoning Common-sense reasoning Human commonsense model Common-sense reasoning Distributed & personalized problem-solving Personalization Distributed network based reasoning Brain distributed cooperation mechanism PSML: Problem Solver Markup Language
Human Level Web Intelligence (WI) Combining the three intelligence related areas Understanding intelligence in depth AI Human Intelligence Machine Intelligence Brain Sciences Habitu ation Social Intelligence
AI WI Brain Sciences Constraint satisfaction Probabilistic reasoning Planning Multi-agent Knowledge based methods Spatial representations Learning Reasoning Vision Language Hebbian learning Problem-solving Decision-making Population codes Motivation Multi-perception Emotion Attention Memory and forgetting Habituation
AI WI Brain Sciences Hebbian Constraint Spatial learning satisfaction representations Motivation Social networks Population Probabilistic reasoning Community Learning Small world codes Habituation discovery theory Multi-agent Reasoning Multi-perception Social brain Planning Social Vision Social Emotion Knowledge agents Language Attention Memory dynamics and based methods Groupwareforgetting Social media Social Intelligence
Emotion Machine vs Logic Machine Human (Brain): Emotion Machine Computer (Web): Logic Machine How to develop Intelligent Web systems? How to develop Web based Emotion Machine? WI meets BI
Basic Philosophy Considering both scalability and personalization in the same importance. Organizing and using granular networks of data-information-knowledge as a way to achieve such a goal both scalability and personalization. Developing a human-like natural reasoning and problem-solving system that can be carried out on the granular networks.
Natural Reasoning/Problem Solving (Human vs the Web) Retrieval and Reasoning for Problem-Solving/Decision-Making rationality context-aware affective/emotion variable precision common-sense personalization Organization and Use of Multiple Information Granule Networks
Both the Web and Brain are huge complex networks with big data [From Google Image] 80
Linked Data Cloud 31 billion RDF triples 81
Complex system, brain and complex network?? WImBI Research Object? Dynamic information processing system with fast emergence computing Dynamic information processing system with fast emergent computing Complex networks with the organization of small-world and scale-free or truncated power-law degree distribution Case New Tool/Model Mechanisms 82
Sequential States (Time) Pre-taskresting state Task-on state Semantic-matching task stimuli Post-taskresting state Representation Represent Whole brain (large-scale), over states Represent Represent Disturbance Network model Network model Network model
General Processing of fmri Data Analysis - Preprocess - Parcellation - Filtering (0.01~0.08Hz) - Regression - Correlation - The AAL-based DMN 15 Subjects across the 3 states - Sparsity threshold 3-Layer Perspectives L1: Globally small-world topology L2: The global/full DMN topography L3: The DMN nodal properties
L1: Globally small-world topology Post-task resting state vs. Pre-task resting state On-task state vs. Pre-task resting state On-task state vs. Post-task resting state
L2: The global/full DMN topography
L3: The DMN nodal properties DMN DMN DMN DMN
Eight Examples of WIC Brain Informatics Research 1. Common and Dissociable Neural Correlates Associated with Component Processes of Inductive Reasoning 2. Different Brain Networks Revealed by Solving Sudoku Puzzles 3. BI Based Study on Depression Mechanisms and Diagnosis 4. Curation, Mining and Use of BI Big Data on the Wisdom Web of Things 5. Agent Enriched Data Mining: A Case Study in Brain Informatics 6. Intrinsic Neural Connectivity of ACT R ROIs 7. Changes in the Brain Intrinsic Organization in Both On Task State and Post Task Resting State 8. Effects of Visual Information Forms on Human Information Processing 88
The Brain Informatics studies will yield profound advances in our analyzing and understanding of the mechanism of data, information, knowledge and wisdom, as well as their inter-relationships, organization and creation process. Informatics-enabled brain studies are transforming various brain sciences, as new methodologies enhance human interpretive powers when dealing with big data increasingly derived from advanced neuro-imaging technologies, as well as from other sources like eyetracking and from wearable, portable devices. It will fundamentally change the nature of IT in general and AI in particular Towards Computing & Intelligence in the big data era
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