Intelligence without Representation: A Historical Perspective

This paper reflects on a seminal work in the history of AI and representation: Rodney Brooks’ 1991 paper Intelligence without Representation. Brooks advocated the removal of explicit representations and engineered environments from the domain of his robotic intelligence experimentation, in favour of an evolutionary-inspired approach using layers of reactive behaviour that operated independently of each other. Brooks criticised the current progress in AI research and believed that removing complex representation from AI would help address problematic areas in modelling the mind. His belief was that we should develop artificial intelligence by being guided by evolutionary development of our own intelligence, and that his approach mirrored how our own intelligence functions. Thus the field of behaviour-based robotics emerged. This paper offers a historical analysis of Brooks’ behaviour-based robotics approach and its impact in artificial intelligence and cognitive theory at the time, as well as in modern-day approaches to AI

Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems

In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line of research aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems

Accompanying technology development in the Human Brain Project:From foresight to ethics management

This paper addresses the question of managing the existential risk potential of general Artificial Intelligence (AI), as well as the more near-term yet hazardous and disruptive implications of specialised AI, from the perspective of a particular research project that could make a significant contribution to the development of Artificial Intelligence (AI): the Human Brain Project (HBP), a ten-year Future and Emerging Technologies Flagship of the European Commission. The HBP aims to create an ICT-based scientific research infrastructure for brain research, cognitive neuroscience, and brain-inspired computing. This paper builds on work undertaken in the HBP’s Ethics and Society subproject (SP12). Collaborators from two activities in SP12, Foresight and Researcher Awareness on the one hand, and Ethics Management on the other, use the case of machine intelligence to illustrate key aspects of the dynamic processes through which questions of ethics and society, including existential risks, are approached in the organisational context of the HBP. The overall aim of the paper is to provide practice-based evidence, enriched by self-reflexive assessment of the approach used and its limitations, for guiding policy makers and communities who are, and will be, engaging with such questions

Organizational Decision to Adopt Chatbot Technology: The Role of Organizing Vision and Technological Frame

This research project aims to explore the socio-cognitive determinants of the organizational adoption of artificial intelligence based chatbot by insurance companies. Technological frame and reception of organizing vision are used as conceptual foundations. A mixed method approach consisting of qualitative interviews and a quantitative questionnaire will serve as input to a Fuzzy set Qualitative Comparative Analysis. Main expected contribution is an understanding of the combined effect of technological frame and reception of organizing vision on organizational adoption of information technology

PHYRON: cognitive computing for the creation of an innovative Intelligence Experience Center

[EN] This research presents the results of a project called “PHYRON: Cognitive Computing for the creation of an innovative Intelligence Experience Center”, funded by the Basque Government (Economic Development, Sustainability and Environment Department). The project started in April 2019 and it will end in December 2021. Its main objective was to arrange an industrial research about cognitive computing. The main aim was the application of these systems for the development of an Intelligent Experience Center (IExC) to facilitate:  i) enrichment of processes, products and services, in general client experiences, ii) automatic generation of technical predictions related to the product and the client behaviour through the exploitation of acquired knowledge, and iii) rationalization and automation of the processes that are involved in the after sale services both at technical and management level. The technological outcome presented in this paper is built using cognitive engines to enable learning from the client experience, and predictive models to anticipate client necessities.We would like to thank the Basque Government for their support in the development of this project. Special thanks to the Economic Development, Sustainability and Environment Department.Ruiz, M.; Rodriguez, JJ.; Erlaiz, G.; Olibares, I. (2021). PHYRON: cognitive computing for the creation of an innovative Intelligence Experience Center. International Journal of Production Management and Engineering. 9(2):103-112. https://doi.org/10.4995/ijpme.2021.15300OJS10311292Agrawal, A., Gans, J., & Goldfarb, A. (2017). What to expect from artificial intelligence. MIT Sloan Management Review. https://doi.org/10.3386/w24690Biecek, P. (2018). DALEX: explainers for complex predictive models in R. The Journal of Machine Learning Re-search, 19(1), 3245-3249.Bond, A. H., & Gasser, L. (Eds.). (2014). Readings in distributed artificial intelligence. Morgan Kaufmann.Bringsjord, S., & Schimanski, B. (2003, August). What is artificial intelligence? Psychometric AI as an answer. In IJCAI (pp. 887-893).Engelmore, R. S. (1987). Artificial intelligence and knowledge based systems: origins, methods and opportunities for NDE. In Review of Progress in Quantitative Nondestructive Evaluation (pp. 1-20). Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1893-4_1Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning.Hagberg, J., Sundstrom, M., & Egels-Zandén, N. (2016). The digitalization of retailing: an exploratory frame-work. International Journal of Retail & Distribution Management. https://doi.org/10.1108/IJRDM-09-2015-0140High, R. (2012). The era of cognitive systems: An inside look at IBM Watson and how it works. IBM Corporation, Redbooks, 1-16.Hollnagel, E., & Woods, D. D. (1983). Cognitive systems engineering: New wine in new bottles. International journal of man-machine studies, 18(6), 583-600. https://doi.org/10.1016/S0020-7373(83)80034-0Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data research. Big Data Research, 2(2), 59-64. https://doi.org/10.1016/j.bdr.2015.01.006Katal, A., Wazid, M., & Goudar, R. H. (2013, August). Big data: issues, challenges, tools and good practices. In 2013 Sixth international conference on contemporary computing (IC3) (pp. 404-409). IEEE. https://doi.org/10.1109/IC3.2013.6612229Kuhn, M. (2008). Building predictive models in R using the caret package. J Stat Softw, 28(5), 1-26. https://doi.org/10.18637/jss.v028.i05Forrester (2017): "Predictions 2018: The Honeymoon For AI is Over".LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539Mousannif, H., Sabah, H., Douiji, Y., & Sayad, Y. O. (2014, August). From big data to big projects: a step-by-step roadmap. In 2014 International Conference on Future Internet of Things and Cloud (pp. 373-378). IEEE. https://doi.org/10.1109/FiCloud.2014.66PwC (2017). UK Economic Outlook. Recuperado de http:// www.pwc.co.uk/economicsPWc, (2018): "Bots, Machine Learning, Servicios Cognitivos. Realidad y perspectivas de la Inteligencia Artificial en España, 2018".Rusk, N. (2016). Deep learning. Nature Methods, 13(1), 35-35. https://doi.org/10.1038/nmeth.3707Steels, L. (1993). The artificial life roots of artificial intelligence. Artificial life, 1(1_2), 75-110. https://doi.org/10.1162/artl.1993.1.1_2.75Steyerberg, E. W., Harrell Jr, F. E., Borsboom, G. J., Eijkemans, M. J. C., Vergouwe, Y., & Habbema, J. D. F. (2001). Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. Journal of clinical epidemiology, 54(8), 774-781. https://doi.org/10.1016/S0895-4356(01)00341-9Strong, A. I. (2016). Applications of artificial intelligence & associated technologies. Science [ETEBMS-2016], 5(6).Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2013). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107. https://doi.org/10.1109/TKDE.2013.109Zhang, X. D. (2020). Machine learning. In A Matrix Algebra Approach to Artificial Intelligence (pp. 223-440). Springer, Singapore. https://doi.org/10.1007/978-981-15-2770-8_