Design of the Artificial: lessons from the biological roots of general intelligence

Our desire and fascination with intelligent machines dates back to the antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought (syllogism) and Heron of Alexandria's mechanical machines and automata. However, the quest for Artificial General Intelligence (AGI) is troubled with repeated failures of strategies and approaches throughout the history. This decade has seen a shift in interest towards bio-inspired software and hardware, with the assumption that such mimicry entails intelligence. Though these steps are fruitful in certain directions and have advanced automation, their singular design focus renders them highly inefficient in achieving AGI. Which set of requirements have to be met in the design of AGI? What are the limits in the design of the artificial? Here, a careful examination of computation in biological systems hints that evolutionary tinkering of contextual processing of information enabled by a hierarchical architecture is the key to build AGI.Comment: Theoretical perspective on AGI (Artificial General Intelligence

Quantum Artificial Life in an IBM Quantum Computer

We present the first experimental realization of a quantum artificial life algorithm in a quantum computer. The quantum biomimetic protocol encodes tailored quantum behaviors belonging to living systems, namely, self-replication, mutation, interaction between individuals, and death, into the cloud quantum computer IBM ibmqx4. In this experiment, entanglement spreads throughout generations of individuals, where genuine quantum information features are inherited through genealogical networks. As a pioneering proof-of-principle, experimental data fits the ideal model with accuracy. Thereafter, these and other models of quantum artificial life, for which no classical device may predict its quantum supremacy evolution, can be further explored in novel generations of quantum computers. Quantum biomimetics, quantum machine learning, and quantum artificial intelligence will move forward hand in hand through more elaborate levels of quantum complexity

ARTIFICIAL INTELLIGENCE AND MARKETING INTERSECTION POST-COVID-19: A CONCEPTUAL FRAMEWORK

As a result of mass digitization during the pandemic, businesses were able to automate business processes, giving people and brands a deeper connection. A proactive strategy, however, is the next step for organizations to implement AI during crisis situations by going one step further. In spite of this, most organizations still do not adequately address this growing problem. After Covid outbreaks, consumer behavior is unlikely to return to pre-pandemic levels. Consumers will increasingly buy goods and services online, and more people will work remotely. In the post-Covid-19 world, as economies slowly begin to open up again, artificial intelligence (AI) will be extremely valuable as companies begin to adapt to the new environment. Similar to other global crises, several major trends that were already underway before Covid are likely to accelerate as a result of the pandemic. Companies must continue to invest in artificial intelligence initiatives during the recovery phase. A conceptual framework for marketing and user engagement is presented in this paper that uses artificial intelligence and automation in ways that are user-centric, integrating traditional marketing practices into an overarching framework that can be implemented by structured artificial intelligence. Embedded technologies, artificial intelligence, and automation have had a significant impact on the four Ps of marketing and will continue to do so

Modeling and simulation with augmented reality

In applications such as airport operations, military simulations, and medical simulations, conducting simulations in accurate and realistic settings that are represented by real video imaging sequences becomes essential. This paper surveys recent work that enables visually realistic model constructions and the simulation of synthetic objects which are inserted in video sequences, and illustrates how synthetic objects can conduct intelligent behavior within a visual augmented reality

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). 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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. 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Emergence of collective behavior in a system of autonomous agents

We study the properties of emergent collective behavior in a system of interacting autonomous agents. The system evolves in time according to two rules of communication, one from interactions between agents and another one from interactions of the agents with an additional agent which evolves in time in a periodic and stable manner. The application of these two rules is decided by a prescribed probability distribution. We analyze the emergence and the efficiency (coordination) with which collective patterns are constructed in time as a function of the parameters of the system

Genetic learning of fuzzy reactive controllers

P. 33-41This paper concerns the learning of basic behaviors in an autonomous robot. It presents a method to adapt basic reactive behaviors using a genetic algorithm. Behaviors are implemented as fuzzy controllers and the genetic algorithm is used to evolve their rules. These rules will be formulated in a fuzzy way using prefixed linguistic labels. In order to test the rules obtained in each generation of the genetic evolution process, a real robot has been used. Numerical results from the evolution rate of the different experiments, as well as an example of the fuzzy rules obtained, are presented and discussedS