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Welcome to Whole Brain Architecutre

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Let’s build a brain together
BICA 2015 participants of the WBAI	
Chairperson	
 Vice-chair	
Team Ito	
WBAI Workshop@BICA201...

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Chair: Hiroshi Yamakawa(WBAI chair),
Tarek Besold(Free University of Bozen-Bolzano)
20 minutes: Hiroshi Yamakawa
Introduct...

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Non-profit organization:
Whole Brain Architecture Initiative
Chairperson:
Hiroshi Yamakawa
Introduction to
Whole Brain Arc...

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Welcome to Whole Brain Architecutre

  1. 1. Let’s build a brain together BICA 2015 participants of the WBAI Chairperson Vice-chair Team Ito WBAI Workshop@BICA2015, November 7 https://liris.cnrs.fr/bica2015/wiki/doku.php/program
  2. 2. Chair: Hiroshi Yamakawa(WBAI chair), Tarek Besold(Free University of Bozen-Bolzano) 20 minutes: Hiroshi Yamakawa Introduction to the Whole Brain Architecture 10 minutes: Koichi Takahashi (WBAI vice-chair) Open development platforms for the Whole Brain Architecture Project 10 minutes: Takeshi Ito 1st WBAI hackthon winners' presentation: Modeling the development of place cells in hippocampus 20 minutes: Panel Discussions: ”Positioning WBA in BICA” Moderator: Tarek Besold Panelist: Koichi Tkahashi, Takashi Omori(WBAI), Satoshi Kurihara(WBAI), Antonio Chella(Università degli Studi di Palermo) WBAI workshop@BICA2015 Agenda
  3. 3. Non-profit organization: Whole Brain Architecture Initiative Chairperson: Hiroshi Yamakawa Introduction to Whole Brain Architecture Let’s build a brain together
  4. 4. Artificial General Intelligence (AGI) WBAI workshop@BICA2015 Narrow AIs are mature n  Operates intelligently within a particular domain n  Many systems with capabilities exceeding those of people have already been implemented, for example: n computer shogi/chess n Google Self-Driving Car n medical diagnosis AGI is our technological goal n  Learning problem-solving from various perspectives in multiple domains n  Can solve new problems that exceed the assumptions made during its design n  Self-awareness / autonomous self-control n  Original goal of AI research, but it was difficult. Learning expertise Designing expertise
  5. 5. Abilities of AGI WBAI workshop@BICA2015 Robustness: Can handle exceptional situations. Creativity: Creates hypotheses and understands the universe. Development costs are lower than narrow AIs Disruptive innovation Generalist AI: (1) Make decision by integrating diverse specialist. (2) Communicating with each specific user with wide range of of topics Autonomy Exploring the world, without others' controls. Versatile Learning various problem-solving capabilities Will be beneficial for humanity
  6. 6.   Artificial General Intelligence Domain Knowledge Learning (DKL) Prior general knowledge DKL bridging the gap between narrow AI and AGI WBAI workshop@BICA2015 Narrow AI (trained) Machine Learning (mainstream up to now) Domain KnowledgeDomain Knowledge Narrow AI (untrained) Rule Rule Rule Rule Execution Data Extent of Domains Designedknowledgetendstogeneral
  7. 7. Each expertise are learned in the neocortex WBAI workshop@BICA2015 1.  A Neocortex learn variety of expertise via similar neural mechanisms 2.  Deep neural network open the door to understand this mechanism    3.  Build AGI is now feasible Image source: http://bio1152.nicerweb.com/Locked/media/ ch48/48_27HumanCerebralCortex.jpg l  bodily-kinesthetic l  linguistics  l  logical- mathematical l  musical    l  interpersonal    l  visual l  spatial
  8. 8. Whole brain architecture (WBA) Our mission is ‘to create a human-like AGI by learning from the architecture of the entire brain.’ WBAI workshop@BICA2015
  9. 9. Whole brain architecture (WBA) Our mission is ‘to create a human-like AGI by learning from the architecture of the entire brain.’ AIBrain The whole brain architecture (WBA) approach http://www.sig-agi.org/wba/ WBAI workshop@BICA2015 Basal Ganglia Neocortex Amygdala Hippocampus (1) Develop machine learning modules for parts of the brain (2) Integrate those modules to create a cognitive architecture
  10. 10. WWhhoollee BBrraaiinn AArrcchhiitteeccttuurree = MMLL + ccooggnniittiivvee aarrcchhiitteeccttuurree This approach is becoming feasible. WBA approach becomes feasible WBAI workshop@BICA2015 To construct an AGI, mimicking a brain is obviously reasonable, because there are no AGI systems other than human ones. One can consider deep learning as a model of some early regions of neocortex. Connectomics can help formation of learning machines in a brain-like way.
  11. 11. Neuroinformatics for a cognitive architecture n Current situation: Macroscopic neuroscientific knowledge of the brain (connectome) is ever increasing. n Challenge of neuroinformatics: n Neuroscientific knowledge should be transformed into cognitive architectures. Connectome (neuroscientific knowledge) Network of learning machine → going to whole brain scale Cogni&ve   architecture   described  by   architecture   descrip&on   language WBAI workshop@BICA2015
  12. 12. WBAI workshop@BICA2015
  13. 13. Brain-inspired is useful for building AGI RReeaacchhiinngg AAGGII iiss gguuaarraanntteeeedd   can  be  a  acceptable  framework   to  integrate  many  essence  of   preceding  architectures. SSccaaffffoolldd ttoo ggaatthheerr wwiissddoomm     gathering  knowledge  from   various  field  such  as  cogni&ve   science,  neuroscience,  AI. HHiinnttss ffoorr uunnaacchhiieevveedd ffuunnccttiioonnss   combina&on  of  ac&ve  modules   &  sets  of  parameters,     curriculum  of  training,  etc.   CCoollllaabboorraattiivvee ddeevveellooppmmeenntt     divide  development  depending   on  brain  modules  and  areas. The brain is a guide: “Biological plausibility” is not the goal of WBA WBAI workshop@BICA2015
  14. 14. World AGI developers’ map WBAI workshop@BICA2015 Biologically plausible Engineering Neocortex centered: Nengo (2015〜) (2015〜) OPEN OPEN Entire brain CLOSED OPEN OPEN (2015〜)OPEN   Collabora&on     of  AGI   development   is  discussed   with  some   open  oriented   partners      
  15. 15. Position of the WBA in AGI Project Name Biological plausibility Inside of modules Remarks WBA Strong about architecture
 (connectome, etc.) Machine learning 
 (mainly ANN) 2013〜 GoodAI Little strong Artificial neural network 2013〜 CogPrime Weak Mainly machine learning 2006〜 ACT-R Yes (identify the module position in fMRI) Production system 1973〜 Symbolic AI Nengo Very strong Spiking neuron model 2003〜 Science Journal WBAI workshop@BICA2015
  16. 16. n Whole brain building by collaboration n Standardization for collaboration n Neuroinformatics for cognitive architecture n Target is AGI n Distributed representation n Functional modeling (Won't seek detail eagerly) WBAI workshop@BICA2015 Positioning WBA in BICA
  17. 17. WBA movement began in 2013 in Tokyo n  Objective: n  Researchers in AI, neuroscience, and cognitive science meet and develop new talent in these multiple fields n  Founding members: n  Hiroshi Yamakawa (Dwango AI lab) n  Yutaka Matsuo (Tokyo University) n  Yuji Ichisugi (Advanced Industrial Science and Technology) n  Seminar: n  As of Aug 2015, 11 seminars have been organized. (average about 200 people, max. 420 participants) n  Related Facebook Group: 2,436 members n  Youth Assembly ‘WBA Future Leaders’ was organized in the summer of 2014 n  Almost every month held a study on subject such as machine learning WBAI workshop@BICA2015
  18. 18. 1st WBAI Hackathon (Sept. 19-23, 2015) 若手中心に5日間で複 合機械学習器を作成 審査基準 1. 実現機能と解決タスクの重要性 2. 実現可能性 (完成度) 3. 独創性、発展性 4. 神経科学的な現実性 WBAI workshop@BICA2015
  19. 19. 1st WBAI Hackathon (Sept. 19-23, 2015) Theme: Programing machine learning complex Teams Nakamura team:Rebuilding deep learning machine Tsuzuki team: Synthesis and visualization of concept according by Word2dream - Toward the creative machine Nishida team: Comparison of imitation learning using video games Ito team: Modeling the development of place cells in hippocampus Doi team: Japanese sign language recognition system using CNN-LSTM Hiroshiba team: Acquisition of a mirror self-recognition mechanism Parmas team: Using neural networks to find an efficient state space for model-based reinforcement learning using Gaussian processes Criteria 1. Impact 2. Feasibility 3. Originality, potential 4. Biological plausibility https://youtu.be/0QS5Z3WrHSA WBAI workshop@BICA2015 Winner
  20. 20. WBAI mission WBAI aims to build a human-like AGI until 2030, by learning from the entire architecture of the brain. We will build a collaboration platform (BriCA), and promote a development community. As an NPO, we contribute to the co- evolutionary future of AI and humanity, through the open community-based development of AGI.      (Founded Aug. 21, 2015) WBAI workshop@BICA2015
  21. 21. l  Long-lasting: We aim to build AGI with the WBA approach by 2030. l  Open community development of AGI l  Promoting cooperation with related disciplines: neuroscience, AI, cognitive science, machine learning, etc. l  Developing multidiscipline human resources l  R&D for WBA developmental environment: l  evaluation method of AGI l  simulator / data for AI learning l  software platform to integrate machine learning WBAI workshop@BICA2015 Charter
  22. 22. (1) Whole Brain Architecture Seminars (since 2013) •  11 sessions to date, with max. 500 participants •  Facebook group with over 2,400 members (2) BriCA project (see right) (3) WBAI Hackathon: The first camp held in September 2015 (4) Fostering resources for supporting future AI development •  design curricula for developing multidiscipline talent •  supporting the WBA Future Leaders Association (since summer 2014) http://wbawakate.jp/ WBAI workshop@BICA2015 Key activity
  23. 23. BriCA(Brain inspired Computing Architecture) SSoopphhiissttiiccaattee nneeooccoorrtteexx mmooddeell WBA roadmap: Merging two streams Emo&on,  Cogni&on,   Memory,  sensory-­‐ motor  associa&on   2015   2025   2030   AGI   Smart  paFern   processing,  Planning,   social  skill,  etc.     2020   Human architectures Apes architecture (1)  Developing  machine   learning  modules Visual/  Auditory  cortex   Deep  learning,  Bayesian  net   Basal  ganglia+thalamus   Reinforcement  learning   Hippocampal  forma&on   SLAM、Invariance  search   Language  area   ??   Prefrontal  cortex   Social/  Logical  func&on   Amygdala   Value  system   Motor  cortex  +  Cerebellum   Control  system   Language,   crea&vity,  logical   thinking,  etc.   Increase   cogni&ve   func&on   Rodents architectures Connectome etc Neuroscient ific knowledge (2)  Cogni&ve  architecture                      (BriCA  language) Ontology, NLP, etc. WBAI workshop@BICA2015
  24. 24. BriCA platform is the scaffold for gathering wisdom WBAI workshop@BICA2015 Standardized architecture description language is the key to the sharing, distribution, recombination, re-use, and replacement of the ML modules that constitute WBA. Hardware layer Execution layer Languag e/Module layer User interface layer BriCA core: Execution mechanism for multi-module cognitive architecture, handling and scheduling various machine learning modules. Cognitive architecture: Description of module connectivity information based on neuroscientific data (connectome ) BriCA language: Architecture description language for combining machine learning modules •  inter-module interface description •  hierarchical organization of modules •  independent of execution layer sensor s Cognitive architecture of a whole brain Host computer BriCA Core (Virtual/Real time scheduler) Control & monitoring GPGPU・ FPGA・MIC・ Neuromorphic Environment (data generation) We start from virtual mouse experiments. actuators Application layer Machine learning modules include utilization of various existing tools Architecture description by BriCA language Interpreter  
  25. 25. Let’s build a brain together AGI will be beneficial WBA is now feasible path to AGI and is one of BICA approach Thanks for your attention WBAI workshop@BICA2015

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