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Strategy to build Beneficial Artificial General Intelligence inspired by the Brain

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The WBA Session presentation slides at the 28th Annual Conference of the Japanese Neural Network Society (JNNS2018) http://jnns.org/conference/2018/

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Strategy to build Beneficial Artificial General Intelligence inspired by the Brain

  1. 1. Kkkj Hiroshi Yamakawa, Yutaka Matsuo, Koichi Takahashi and Naoya Arakawa Strategy to build Beneficial Artificial General Intelligence inspired by the Brain Symposium 2: Whole-Brain Architecture 14:00-15:30, Oct. 26th The 28th Annual Conference of the Japanese Neural Network Society (JNNS2018)
  2. 2. Abstract Since 2013, we have been advocating the idea of whole brain architecture. The Whole Brain Architecture Initiative (a specified NPO) promotes ‘to create (engineer) a human-like artificial general intelligence (AGI) by learning from the architecture of the entire brain, ’ and explores the way to develop brain-inspired AGI through activities such as hackathons . In recent years, AI researches are energized by deep learning technology and neuroscience researches are heading toward elucidation of high-level cognitive functions. Despite such boosts, obstacles remains on the way to brain-inspired AGI. Here we present our strategy to construct brain-inspired AGI. As AGI's impact on society will be enormous, we should make AI development beneficial to humanity by learning from the brain. Symposium 2: Whole-Brain Architecture@JNNS2018
  3. 3. History of WBA Symposium 2: Whole-Brain Architecture@JNNS2018 2013 20152014 20172016 2018 20302025 Goal:CompletionofWholeBrainArchitecture Whole Brain Architecture Seminars WBA Future Leaders The Whole Brain Architecture Initiative 2ndWBAsymposium 3rdWBAhackathon 3rdWBAsymposium 4thWBAhackathon 1stWBAsymposium 2ndWBAhackathon 1stWBAhackathon BasicIdeas(Vision) WBAcorehypothesis VisitGatsbywithKenji ICONIPSession BiCASession DiscussionstartedbyYutakaMatsuo,YujiIchisugiandHiroshiYamakawa KyusyuBranch Branchof workingpeople Kanto/Kansai branch JNNSsymposium
  4. 4. Whole Brain Architecture approach Symposium 2: Whole-Brain Architecture@JNNS2018 ‘to create a human-like AGI by learning from the architecture of the entire brain’ Artificial General Intelligence
  5. 5. Priority is to seek AGI-specific technology X Symposium 2: Whole-Brain Architecture@JNNS2018 BS- AI Knowledge Knowledge Knowledge Knowledge Task Task Task Task Big switch statement AI Switch Task Task Task Task AGI AGI Know ledge Know ledge Know ledge Know ledgeKnow ledge Ability to generate valid hypotheses with less data Technology X: Inference mechanism by combining existing knowledge Technology X= AGI − 𝒊 Specialized AI 𝒊 <Gap <Gap <Gap
  6. 6. How to build "Technology X" Symposium 2: Whole-Brain Architecture@JNNS2018 Essential technology specific to AGI Technology X: Inference mechanism by combining existing knowledge ① Acquisition of highly reusable knowledge (machine learning) • Extension of mapping scope • Disentanglement • Correspondence between distributed expressions and symbols • Standardization of representation ② Building Architecture: Connect reusable ML modules mimicking the whole brain. (All parts are aligned. ) ③ Theory for exploring hypotheses A theory for efficiently searching promising combinations of knowledge as hypotheses out of a large number of possibilities. Find combinations of knowledge according to the task Utilization of knowledge: Architecture that can combine knowledge in various ways Task Task Task Task Know ledge Know ledge Know ledge Know ledgeKnow ledge
  7. 7. Whole Brain Architecture approach Symposium 2: Whole-Brain Architecture@JNNS2018 ‘to create a human-like AGI by learning from the architecture of the entire brain’ To reach AGI, we start from mimicking high-level architectures of the brain, and gradually introduce details as needed Re- cognition Executive Reward Utility Evaluation State Transition Perception Action Intelligent Agent Intention Goal State Utility State State Reward Generation Environment Hippo- campus BG Neocortex Amygdala AI (1) Develop machine learning modules for parts of the brain (2) Integrate the modules to create cogni ve architecture Basal Ganglia Neocortex Amygdala Hippocampus Brain
  8. 8. GeneralNarrow High Computationalcost Low Super AGI human brain Task diversity Mouse brain Insects brain WBA WBA as a pass point for ‘super AGI’ Symposium 2: Whole-Brain Architecture@JNNS2018
  9. 9. Obstacles & prescriptions in WBA development Symposium 2: Whole-Brain Architecture@JNNS2018 “Road Map Construction” Problem: How can we define the capability set required for AGI and plan overall development? “Trap of Specialization” Problem: How can we construct an integrated AGI, not a collection of narrow AIs from different development projects? "Engineers are not Neuroscientists": How can AI/ML engineers without full understanding of the brain mechanism build brain-inspired AGI? Knowledge base: • Brain organs framework • Connectome architecture PrescriptionsObstacles Stub-driven development: • Brain-constrained refactoring • Merge development Function map: • Termination condition • Iterative development Knowledge base: • Brain organs frameworks • Connectome architecture
  10. 10. Agent Knowledge base: Symposium 2: Whole-Brain Architecture@JNNS2018 St. ML St. St. Environment : stub : ML(WBA Development) : Brain organ's I/F St. ML Tasks (test) Brain Organ Frameworks Brain organs I/F (Information processing semantics) WBCA (Connectome) Functions of brain organs and circuits Creating Specifications for engineers to develop WBA ※ Stub : a piece of code used to stand in for some other programming functionality Proto- typing
  11. 11. Complete WBA 追加開発 プロト開発 プロト開発 マージ開発 プロト開発 Stub-driven development to avoid the Big-switch Symposium 2: Whole-Brain Architecture@JNNS2018 ML St. St. St. Environment St. ML St. St. Environment St. St. ML St. Environment ML ML St. St. Environment St. St. ML ML Environment 追加開発 改良開発 ML ML St. St. Environment St. ML ML ML Environment マージ開発 ML ML ML ML Environment Replacing with ML : Expanding inductive reasoning Generality of Brain-inspired Architecture ① Brain organ Frameworks (I/F, functions) ②Brain- constrained refactoring Expantion ML ML ML ML Environment ③Meta-level mechanism for exploring hypotheses :Stub :ML [WBA Development] :Brain organs I/F St. ML Entire Architecture Add Prototype Prototype Merge Prototype ML St. St. St. Environment St. ML St. St. Environment St. St. ML St. Environment ML ML St. St. Environment St. St. ML ML Environment Add Improvement ML ML St. St. Environment St. ML ML ML Environment Merge ML ML ML ML Environment
  12. 12. 山川宏 おわりにあたってOct.6th-8th , 2018
  13. 13. History of WBA hackathons Symposium 2: Whole-Brain Architecture@JNNS2018 Key Concepts Hackathon themes 2015201620172018 The Whole Brain Architecture Core Hypothesis Combining ML Cognitive Architecture Open platform strategy Learning from the Brain • Tactile • Hippocampus GPS criteria Gaze control
  14. 14. The GPS criteria Symposium 2: Whole-Brain Architecture@JNNS2018 • Functionally General • Biologically Plausible • Computationally Simple
  15. 15. Winner Susumu Ota built a system that accomplished 5/6 tasks Hackathon 2018 Point of arrival If only partially, a system that can be called WBA that meets the GPS criteria was realized for the first time ! Symposium 2: Whole-Brain Architecture@JNNS2018
  16. 16. Six tasks in the Hackathon Symposium 2: Whole-Brain Architecture@JNNS2018 Point To Target Random Dot Motion Discrimination Multiple Object Tracking Visual Search Change Detection Odd One Out
  17. 17. Function allocation in the winner’s model From https://github.com/wbap/oculomotor/wiki/en:Architecture-Summary Template Opt. Flow Accumulator Phase Working Memory Allocentric Image Subsumption Architecture Task Saliency Map Actor-CriticReLU Retina Image Retina + Angle Reward Action Symposium 2: Whole-Brain Architecture@JNNS2018
  18. 18. Functions requirement and results for each task Function Search Saliency Map Template Matching Working Memory Optical Flow Results Tasks Point To Target ○ ○ ○ ◯ Change Detection ◎ ◯ Odd One Out ◎ ◎ ○ ◯ Visual Search ○ ○ ○ ◯ Multiple Object Tracking ○ ○ × Random Dot Motion Discrimination ◎ ◯ Symposium 2: Whole-Brain Architecture@JNNS2018
  19. 19. Representations used in the system Symposium 2: Whole-Brain Architecture@JNNS2018 Opt. Flow 4 kinds of accumulators Phase Working Memory Task Saliency Map Retinal Image Template The basal ganglia select which accumulator to use Video • Point to Target: Input • Point to Target: Inspector • All Task: Input • All Task: Inspector
  20. 20. Historical impact of AGI Symposium 2: Whole-Brain Architecture@JNNS2018 Collaborative efforts by the AGI development community and other key stakeholders are necessary to harmonize the welfare of AGI with the benefits of all humanity. (IEEE, Ethically Aligned Design – Version II, 2017) The main factor affecting human welfare is intelligence. AGI automates science, technology, and economic innovation In reality, a global AGI development race is in progress. We want to prevent technological monopoly/oligopoly. The greatest impact since the Industrial Revolution Benefits: Unprecedented prosperity Risks: • Economic inequality • Malefic uses (e.g., weapons and crimes) • Effects on people's occupation, dignity, and values. • Uncontrollablilty
  21. 21. Nonprofit organizations Global AI development trend from the viewpoint of AGI Enterprises Brain- inspired AI Standard narrow AI development AGI development (& promotion) AGI development promotion AGI development Non Brain- inspired AI Symposium 2: Whole-Brain Architecture@JNNS2018
  22. 22. Open Platforms • Guidelines • Specifications • Software platforms • Knowledge • Advices Spreading collaborative AGI development Lead / promote democratic development of Beneficial brain-inspired AGI • Direction • Technical support Neuroscientists interested in functional models AI/ML Experts Building Brain-inspired AGI Future Humanity In harmony with AI Committed to promote AGI development for humanity Non-profit organization 2018〜2019 2020〜2021 2022〜2023 2024〜2029 FeedbackBenefits Primaryactivities - Become co-authors of AI / ML papers - Refer to models in experimental data analysis Symposium 2: Whole-Brain Architecture@JNNS2018
  23. 23. A community to support developing brain-inspired AI is necessary. Open Platforms • Guidelines • Specifications • Software platforms • Knowledge • Advices Spreading collaborative AGI development Lead / promote democratic development of Beneficial brain-inspired AGI • Direction • Technical support Neuroscientists interested in functional models AI/ML Experts Building Brain-inspired AGI Future Humanity In harmony with AI Committed to promote AGI development for humanity Non-profit organization 2018〜2019 2020〜2021 2022〜2023 2024〜2029 FeedbackBenefits Primaryactivities - Become co-authors of AI / ML papers - Refer to models in experimental data analysis Symposium 2: Whole-Brain Architecture@JNNS2018
  24. 24. Positions of 4 presentations in this symposium Kosuke Miyoshi: Do top-down predictions of time series lead to sparse disentanglement? Seiya Sato: Visualization of morphism tuples of equivalence structures Kotone Itaya: BriCA Kernel: Cognitive Computing Platform for Large- scale Distributed Memory Environments Masahiko Osawa: Development of biologically inspired artificial general intelligence navigated by circuits associated with tasks Symposium 2: Whole-Brain Architecture@JNNS2018 Today’s POSTER Today’s POSTER
  25. 25. Basic idea of WBAI Vision: Create a world in which AI exists in harmony with humanity. Values: • Study: Deepen and spread our expertise. • Imagine: Broaden our views through public dialogue. • Build: Create AGI through open collaboration. http://wba-initiative.org/en/2171/ Mission: Promote the open development of Whole Brain Architecture ‘to create a human-like AGI by learning from the architecture of the entire brain’ Symposium 2: Whole-Brain Architecture@JNNS2018
  26. 26. Summary • The combinatory space of machine learning to realize AGI is huge. – Using brain as reference architecture prevents divergence of development and makes AGI reachable. • Three main obstacles for WBA development : “Road Map Construction, ” “Trap of Specialization” and “ Engineers are not Neuroscientists. ” – WBAI is constructing open platforms to overcome these. • The winner of the latest hackathon implemented a model that fairly fulfilled the GPS criteria. • To prevent monopoly/oligopoly of powerful AGI, the promotion of democratic development by non-profit position could be valuable. Symposium 2: Whole-Brain Architecture@JNNS2018
  27. 27. Symposium 2: Whole-Brain Architecture@JNNS2018 Thank you for your attention!

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