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Opentech AI - Architecture, Ecosystem and Roadmap


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Sides of a talk at the Cognitive Systems Institute Speaker Series February 8th 2018.

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Opentech AI - Architecture, Ecosystem and Roadmap

  1. 1. VTT TECHNICAL RESEARCH CENTRE OF FINLAND LTD Opentech AI - Architecture, Ecosystem and Roadmap: Drafting the big picture and future directions of open artificial intelligence technology Cognitive Systems Institute Speaker Series, 2/8/2018 Daniel Pakkala, IBM Distinguished Visiting Researcher - IBM Research Almaden. Principal Scientist - VTT Technical Research Centre of Finland Ltd. E-mail: LinkedIn:
  2. 2. 207/02/2018 2 Outline  Introduction  Drafting the big picture of AI  Is AI more than ML/DL?  AI amongst sciences  Takeaways from the AI related sciences jungle  AI systems today and in the future  A big picture of AI – “AI Cube”  Towards architecture framework for AI systems  Catalysing and measuring progress of AI
  3. 3. 307/02/2018 3 Introduction (1/2)  Research exchange co-operation between VTT Technical Research Centre of Finland and IBM Research – Almaden.  Co-created blog: 
  4. 4. 407/02/2018 4 Introduction (2/2)  Open Source  Tools  Platforms  Code  Open Datasets  Open Models  Open Challenges & Leaderboards Science:  Reproducability & Progress Industries:  Applications & Impact Why?
  5. 5. 507/02/2018 5 Is AI more than Machine/Deep Learning?  Mainstream of AI R&D today: ML/DL  Narrow AI applications are reaching or outperforming human capabilities:  Jeopardy! (IBM Watson, 2011)  Go (Google AlphaGo, 2016)  ”Reading comprehension”/Searching wikipedia for answers (SQuAD, 2018)  What more is needed?:  Address the real (open) world problems and tasks!  common sense & world model  learning concepts (in addition to categorizing & mapping),  multiple/adaptive learning strategies and  context-awareness, adaptation and natural interaction blog:
  6. 6. 607/02/2018 6 AI amongst other sciences  Core: Systems science, Computer science and Mathematics  Interfaces: 1. AI implementing cognitive architectures within intelligent agents, multi-agent systems and other intelligent systems for various applications. 1.1 Artificial neurons and artificial neural networks 1.2 Natural language processing, understanding and generation. Word vectors. 2. Intelligence is a natural (biological) phenomena, which AI aims to mimic. Potential new findings related to evolution, cells, neurons and brain. 3. Availability and applicability of physical materials and energy for building and operating any construct. Physical environment and physical laws highly relevant for AI applications in the physical word. 4. Acceptance, impact, need and use of AI applications by individuals and organizations. Value of the AI applications is defined by humans and human organizations in social context. blog:
  7. 7. 707/02/2018 7 Takeaways from the AI related sciences jungle 1. Progress of AI exploitation is dependent on the progress of R&D on the related fields  still active fields of research with potential for new discoveries relevant for AI R&D. 2. Multiple parallel tracks of AI research and development are proceeding in parallel  not necessarily mutually inter-operable from the viewpoint of building new AI systems and applications.  ML/DL - Artificial Neural Networks  “data classification/segmentation engines”  Cognitive Science - Cognitive Architectures  “data based processes”  Symbolic, Subsymbolic, Hybrid approaches 84 / 49 active… FF, RNNs, Spiking,…~25 ~1042…complexity
  8. 8. 807/02/2018 8 AI systems today and in future  AI systems today: Mainly evolution of smart features of existing systems  enabled by machine learning, increasing computing power and availability of data.  AI systems in future?: Systems autonomously maintaining themselves, operating, learning and interacting over extended periods as part of society and culture  Forbus, K. D. (2016). Software social organisms: implications for measuring AI progress. AI Magazine, 37(1), 85-90. ~10∞
  9. 9. 907/02/2018 9 A big picture of AI – “AI Cube” ~10∞ ~1042 ~109 see:
  10. 10. 1007/02/2018 10 Towards architecture framework for AI systems (1/2)  The transition from current AI systems towards future AI systems deserves special attention from the viewpoint of systems architecture:  Complex autonomic behavior of an system  safety, security, trust, governance and institutional control  Incremental learning and adaptive behavior  learning from data and past interactions, capabilities/knowledge  System life-cycle and maintainability  applicable methodologies and processes, quality  Social, cultural, ethical and environmental compliance  acceptance and value  Interaction and value  collaborating vs. using Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017). A Standard Model of the Mind: Toward a Common Computational Framework Across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics. AI Magazine, 38(4). Kotseruba, I., Tsotsos, J. K. (2016). A Review of 40 Years of Cognitive Architecture Research: Core Cognitive Abilities and Practical Applications, eprint arXiv:1610.08602. blog:
  11. 11. 1107/02/2018 11 Towards architecture framework for AI systems (2/2) Stakeholders:  R&D Community, System stakeholders and System Collaborators (~users)  Environment as a stakeholder – env.&soc. responsibility Subsystems:  Embodiment – physical material structure enabling implementation of other subsystems and bounding interfaces with the natural environment.  Perception and Actuation – all mechanisms related to sensing and actuation of the system with the environment  Memory – all different memory mechanisms  Behavior – all behavior related mechanisms  Cognition –all higher cognition related mechanisms accumulating the internal world model of the system, and possibly adapting the behavior of the AI System over time.  Internal world model – The natural environment including human culture as the AI system comprehends it internally. All knowledge contained by the AI System as a result of training and learning while in operation.  Internal data processing –The internal continuous data processing and analysis pipeline of the AI System defining the mechanism converting data flow from perception subsystem into information, knowledge and wisdom as modifications of the internal world model and behavior. Hybrid model driven approach combining agent-orientation and holistic two- dimensional system orientation, where system thinking is applied both to the external environment and to the internal organization of the AI system.
  12. 12. 1207/02/2018 12 Catalysing and measuring progress of AI  Architecture framework might catalyse AI systems development  Breaking down the complexity involved in AI systems engineering  Analysis and design framework for new AI systems  Enabling hybrid/mash-up approaches in applications, when identifying synergies between different AI systems becomes easier  Measuring progress of AI  AI Challenges and Leaderboards  see:  More holistic view to AI progress, performance also in terms of:  Energy  Matching human energy consumption and environmental footprint in a task (in addition to just task outcome performance)  Brain simulation with current supercomputers: ~20 GW  Human brain: ~20 W  New metrics to be considered  E.g. grams of CO2/task – flops/task – Wh/task - …
  13. 13. 1307/02/2018 13 Thank you! Questions or Comments?