Chapter 1. Introduction in Creating Brain-Like Intelligence, Sendhoff et al. Course: Robots Learning from Humans Jo, HwiYeol Biointelligence Laboratory School of Computer Science and Engineering Seoul National University

Contents The World Inside the Brain –The Brain Inside the World The Evolved Brain The Benchmarked Brain Summary and Conclusion 2

The World Inside the Brain The Brain Inside the World Cognitive architectures Brain inside the world Adaptive control and planning system an autonomously behaving system Vijayakumar et al. “Planning and moving in dynamic environments : a statistical machine learning approach” Goerick “Toward cognitive robotics” PISA (Practical Intelligence Systems Architecture) ex> ASIMO © 2015, SNU CSE Biointelligence Lab., 3

The World Inside the Brain The Brain Inside the World Cognitive architectures World inside the brain Focus on the conceptual role of control process Eggert and Wersing “Approaches and challenges for cognitive vision system” Ex> Image segmentation, multicue tracking, object online learning Sloman : study the features of the environment Else Tsujino et al. “Basal ganglia models for autonomous behavior learning” The system-level model : reinforcement learning framework The neuron-level model : employs a spiking NN © 2015, SNU CSE Biointelligence Lab., 4

Evolved Brain Selected intelligence -> Evolve Evolvability heavily constraints … The process is brittle to any evolutionary change The genetic representation : structural and temporal A lot of advantages To the optimization of complex structures ex> The field of evolutionary robots demonstrates this © 2015, SNU CSE Biointelligence Lab., 5

Evolved Brain Examples Elfing et al. “Co-evolution of rewards and meta-parameters in embodied evolution” Cyber rodent project Successful self-preservation, self-reproduction Suzuki et al. “Active vision for goal- oriented humanoid robot walking” Co-evolve active vision and feature selection in a neural architecture Without explicit fitness assignment © 2015, SNU CSE Biointelligence Lab., 6

The Benchmarked Brain Benchmark © 2015, SNU CSE Biointelligence Lab., 7 As ScienceAs Technology Neurophysiological or Psychological Experimental Data Accomplishes its intended requirements cognitive adequacy Requirements + competition Ex> reaction times, error rates, perception measures Ex> ImageNet, Robocup, Darpa Urban Challenge

The Benchmarked Brain Solution…? In our opinion, the truth will be somewhere between the scientific and the technological standpoint Function = Technology + Experimental Observation © 2015, SNU CSE Biointelligence Lab., 8

Summary and Conclusion The overall picture remains fuzzy a fuzzy target using brittle approaches The right direction More open, more flexible, more adaptive Build systems to operate (X) Just in the environment (O) With the environment (O) Because of the environment © 2015, SNU CSE Biointelligence Lab., 9

Summary and Conclusion The gap Neuroscience is too much focused on the details Research in brain-like are in the grasp of technology Research in intelligent systems has proceeded by incorporating more and more biological principles Cognitive Architecture Cognitive Vision Etc.. © 2015, SNU CSE Biointelligence Lab., 10

Summary and Conclusion Why the brain-like intelligence? Turing “Computing machinery and intelligence” “We can only see a short distance ahead, but we can see plenty there that needs to be done” © 2015, SNU CSE Biointelligence Lab., 11

Discussion Topic The World Inside the Brain – The Brain Inside the World ? How can we bench marked the brain ? Difference between cells in an organic system and gates in a silicon system ? Is Human-like Intelligence really needed ? © 2015, SNU CSE Biointelligence Lab., 12