Artificial Intelligence and Robotics

The recent successes of AI have captured the wildest imagination of both the scientific communities and the general public. Robotics and AI amplify human potentials, increase productivity and are moving from simple reasoning towards human-like cognitive abilities. Current AI technologies are used in a set area of applications, ranging from healthcare, manufacturing, transport, energy, to financial services, banking, advertising, management consulting and government agencies. The global AI market is around 260 billion USD in 2016 and it is estimated to exceed 3 trillion by 2024. To understand the impact of AI, it is important to draw lessons from it's past successes and failures and this white paper provides a comprehensive explanation of the evolution of AI, its current status and future directions.

[1]  Julie Adams,et al.  Artificial Intelligence ( AI ) and VU / VUMC , 2018 .

[2]  Nikolaos Mavridis,et al.  A review of verbal and non-verbal human-robot interactive communication , 2014, Robotics Auton. Syst..

[3]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[4]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[5]  John McCarthy,et al.  Programs with common sense , 1960 .

[6]  C. Qiang,et al.  Economic Impacts of Broadband , 2009 .

[7]  Matteo Colombo,et al.  Why build a virtual brain? Large-scale neural simulations as jump start for cognitive computing , 2017, J. Exp. Theor. Artif. Intell..

[8]  Avelino J. Gonzalez,et al.  Validation and verification of intelligent systems - what are they and how are they different? , 2000, J. Exp. Theor. Artif. Intell..

[9]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[10]  Nick Bostrom,et al.  Whole Brain Emulation , 2008 .

[11]  Eric Horvitz One Hundred Year Study on Artificial Intelligence: Reflections and Framing , 2016 .

[12]  Lotfi A. Zadeh,et al.  Fuzzy logic - a personal perspective , 2015, Fuzzy Sets Syst..

[13]  Bruce A. MacDonald,et al.  Acceptance of Healthcare Robots for the Older Population: Review and Future Directions , 2009, Int. J. Soc. Robotics.

[14]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[15]  Philip Alston Lethal robotic technologies: The implications for human rights and international humanitarian law , 2012 .

[16]  T. Kretschmer,et al.  Broadband Infrastructure and Economic Growth , 2009, SSRN Electronic Journal.

[17]  Yasuo Kuniyoshi,et al.  Humanoid robot which can lift a 30kg box by whole body contact and tactile feedback , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Li Zhang,et al.  Intelligent facial emotion recognition and semantic-based topic detection for a humanoid robot , 2013, Expert Syst. Appl..

[19]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[20]  Norman Spinrad Mr Singularity , 2017, Nature.

[21]  Kazuhito Yokoi,et al.  Dynamic lifting by whole body motion of humanoid robots , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  R. Needham,et al.  Artificial Intelligence : A General Survey , 2012 .

[23]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[24]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[25]  Ck Cheng,et al.  The Age of Big Data , 2015 .

[26]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[27]  Kevin Waugh,et al.  DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker , 2017, ArXiv.

[28]  Carmen C. Y. Poon,et al.  Big Data for Health , 2015, IEEE Journal of Biomedical and Health Informatics.

[29]  Kevin Waugh,et al.  DeepStack: Expert-level artificial intelligence in heads-up no-limit poker , 2017, Science.

[30]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[31]  Simon Haykin,et al.  GradientBased Learning Applied to Document Recognition , 2001 .

[32]  Stephen Senn,et al.  (www.interscience.wiley.com) DOI: 10.1002/sim.2639 Trying to be precise about vagueness , 2022 .

[33]  Guang-Zhong Yang,et al.  Disparity in Frontal Lobe Connectivity on a Complex Bimanual Motor Task Aids in Classification of Operator Skill Level , 2016, Brain Connect..

[34]  Christopher Krügel,et al.  BareCloud: Bare-metal Analysis-based Evasive Malware Detection , 2014, USENIX Security Symposium.

[35]  John Tait,et al.  Future Patent Search , 2011, Current Challenges in Patent Information Retrieval.

[36]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[37]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[38]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[39]  David Hémous,et al.  The Rise of the Machines: Automation, Horizontal Innovation and Income Inequality , 2014, American Economic Journal: Macroeconomics.

[40]  Ignacio Rojas,et al.  Neural networks: An overview of early research, current frameworks and new challenges , 2016, Neurocomputing.

[41]  K. Gallagher,et al.  Global Economic Impacts Associated with Artificial Intelligence , 2016 .

[42]  R. O’Reilly,et al.  Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain , 2000 .

[43]  Dana Kulic,et al.  Curiosity-Based Learning Algorithm for distributed interactive sculptural systems , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[44]  Steffen Staab,et al.  Web Science , 2013, Informatik-Spektrum.

[45]  Robert J. Wood,et al.  Science, technology and the future of small autonomous drones , 2015, Nature.

[46]  Jenay M. Beer,et al.  Older Adults’ Preferences for and Acceptance of Robot Assistance for Everyday Living Tasks , 2012, Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual Meeting.

[47]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[48]  Ulf Ahlstrom,et al.  Cognitive Workload and Learning Assessment During the Implementation of a Next-Generation Air Traffic Control Technology Using Functional Near-Infrared Spectroscopy , 2014, IEEE Transactions on Human-Machine Systems.

[49]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[50]  Tetsuya Ogata,et al.  Developmental Human-Robot Imitation Learning of Drawing with a Neuro Dynamical System , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[51]  Pierre-Yves Oudeyer,et al.  Socially guided intrinsic motivation for robot learning of motor skills , 2014, Auton. Robots.

[52]  Mingjun Zhang,et al.  A bio-inspired swimming robot , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[53]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .

[54]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[55]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[56]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[57]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[58]  Rachid Alami,et al.  Human-aware robot navigation: A survey , 2013, Robotics Auton. Syst..

[59]  Stefan Ratschan,et al.  Safety Verification of Hybrid Systems by Constraint Propagation Based Abstraction Refinement , 2005, HSCC.

[60]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[61]  Martin Ford,et al.  Rise of the robots : technology and the threat of a jobless future , 2015 .

[62]  Moti Yung,et al.  Deniable password snatching: on the possibility of evasive electronic espionage , 1997, Proceedings. 1997 IEEE Symposium on Security and Privacy (Cat. No.97CB36097).

[63]  Véronique Perdereau,et al.  Tactile sensing in dexterous robot hands - Review , 2015, Robotics Auton. Syst..

[64]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[65]  Minoru Asada,et al.  Towards Artificial Empathy , 2015, Int. J. Soc. Robotics.

[66]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[67]  Guang-Zhong Yang,et al.  Deep Learning for Health Informatics , 2017, IEEE Journal of Biomedical and Health Informatics.

[68]  Grupo Scimago,et al.  SCImago journal & country rank: un nuevo portal, dos nuevos rankings , 2007 .

[69]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[70]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[71]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[72]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..