Developmental Bootstrapping of AIs

Although some current AIs surpass human abilities in closed artificial worlds such as board games, their abilities in the real world are limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Mainstream approaches for creating AIs are the traditional manually-constructed symbolic AI approach and generative and deep learning AI approaches including large language models (LLMs). These systems are not well suited for creating robust and trustworthy AIs. Although it is outside of the mainstream, the developmental bootstrapping approach has more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. They interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. They acquire the competences they need through bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before their speech is fluent. They also do not bridge the Reading Barrier, to skillfully and skeptically draw on the socially developed information resources that power current LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to acquire further competences and create robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure

Machine Medical Ethics

In medical settings, machines are in close proximity with human beings: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. Machines in these contexts are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for empathy and emotion detection necessary? What about consciousness? The essays in this collection by researchers from both humanities and science describe various theoretical and experimental approaches to adding medical ethics to a machine, what design features are necessary in order to achieve this, philosophical and practical questions concerning justice, rights, decision-making and responsibility, and accurately modeling essential physician-machine-patient relationships. This collection is the first book to address these 21st-century concerns

Knowledge strategies for managers in a networked world

Thesis (S.M.M.O.T.)--Massachusetts Institute of Technology, Sloan School of Management, Management of Technology Program, 2002.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 116-121).As our world becomes more complex and information-rich, the effort needed to share and create knowledge is increasing greatly. Transformation from Industrial Age to Information Age organizations is not simple. But there are strategies managers can use and emulate, to make their organizations more successful in sharing and creating new knowledge, to achieve better performance. Knowledge loss is a significant issue. Demographics may cause the "first-of-type" implementation pioneers to retire, or events such as those of Fall 2001 may cause people to be no longer available -- or no longer able to reach their knowledge support systems, as seen when anthrax attacks closed Congressional offices for weeks. Strategies can be implemented for the different kinds of knowledge -- explicit knowledge, meta knowledge, and tacit knowledge. Processes can be used to enhance knowledge sharing, extending the number of people who know and reducing the risk of loss. The US Army is a learning organization which has spent the past decade becoming "knowledge centric and network centric." Techniques, processes and knowledge lessons learned are presented, including a case study of the Project Management Office for Bradley Fighting Vehicle Systems, as it transformed its people, organization, and vehicles being developed from Industrial Age to internet-work Information Age systems. Rather than focusing on knowledge management, which has become synonymous with archiving what is already known into digital databases, I am focused on the strategies real-world managers can use for knowledge. The goal is to help the organization achieve better performance by sharing knowledge. Technology can help, when supporting instead of driving the goals. Networking, both in person and virtually, can overcome the isolation of knowledge. Many of my examples tap into the experiences I had or observed in the US Army product development community -- but I believe they are valuable and generalizable to other high- performance organizations. "Hope is not a method" -- knowledge sharing is a better technique.by Lisa M. Shaler-Clark.S.M.M.O.T

Inverse Kinematic Analysis of Robot Manipulators

An important part of industrial robot manipulators is to achieve desired position and orientation of end effector or tool so as to complete the pre-specified task. To achieve the above stated goal one should have the sound knowledge of inverse kinematic problem. The problem of getting inverse kinematic solution has been on the outline of various researchers and is deliberated as thorough researched and mature problem. There are many fields of applications of robot manipulators to execute the given tasks such as material handling, pick-n-place, planetary and undersea explorations, space manipulation, and hazardous field etc. Moreover, medical field robotics catches applications in rehabilitation and surgery that involve kinematic, dynamic and control operations. Therefore, industrial robot manipulators are required to have proper knowledge of its joint variables as well as understanding of kinematic parameters. The motion of the end effector or manipulator is controlled by their joint actuator and this produces the required motion in each joints. Therefore, the controller should always supply an accurate value of joint variables analogous to the end effector position. Even though industrial robots are in the advanced stage, some of the basic problems in kinematics are still unsolved and constitute an active focus for research. Among these unsolved problems, the direct kinematics problem for parallel mechanism and inverse kinematics for serial chains constitute a decent share of research domain. The forward kinematics of robot manipulator is simpler problem and it has unique or closed form solution. The forward kinematics can be given by the conversion of joint space to Cartesian space of the manipulator. On the other hand inverse kinematics can be determined by the conversion of Cartesian space to joint space. The inverse kinematic of the robot manipulator does not provide the closed form solution. Hence, industrial manipulator can achieve a desired task or end effector position in more than one configuration. Therefore, to achieve exact solution of the joint variables has been the main concern to the researchers. A brief introduction of industrial robot manipulators, evolution and classification is presented. The basic configurations of robot manipulator are demonstrated and their benefits and drawbacks are deliberated along with the applications. The difficulties to solve forward and inverse kinematics of robot manipulator are discussed and solution of inverse kinematic is introduced through conventional methods. In order to accomplish the desired objective of the work and attain the solution of inverse kinematic problem an efficient study of the existing tools and techniques has been done. A review of literature survey and various tools used to solve inverse kinematic problem on different aspects is discussed. The various approaches of inverse kinematic solution is categorized in four sections namely structural analysis of mechanism, conventional approaches, intelligence or soft computing approaches and optimization based approaches. A portion of important and more significant literatures are thoroughly discussed and brief investigation is made on conclusions and gaps with respect to the inverse kinematic solution of industrial robot manipulators. Based on the survey of tools and techniques used for the kinematic analysis the broad objective of the present research work is presented as; to carry out the kinematic analyses of different configurations of industrial robot manipulators. The mathematical modelling of selected robot manipulator using existing tools and techniques has to be made for the comparative study of proposed method. On the other hand, development of new algorithm and their mathematical modelling for the solution of inverse kinematic problem has to be made for the analysis of quality and efficiency of the obtained solutions. Therefore, the study of appropriate tools and techniques used for the solution of inverse kinematic problems and comparison with proposed method is considered. Moreover, recommendation of the appropriate method for the solution of inverse kinematic problem is presented in the work. Apart from the forward kinematic analysis, the inverse kinematic analysis is quite complex, due to its non-linear formulations and having multiple solutions. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network (ANN) can be gainfully used to yield the desired results. Therefore, in the present work several models of artificial neural network (ANN) are used for the solution of the inverse kinematic problem. This model of ANN does not rely on higher mathematical formulations and are adept to solve NP-hard, non-linear and higher degree of polynomial equations. Although intelligent approaches are not new in this field but some selected models of ANN and their hybridization has been presented for the comparative evaluation of inverse kinematic. The hybridization scheme of ANN and an investigation has been made on accuracies of adopted algorithms. On the other hand, any Optimization algorithms which are capable of solving various multimodal functions can be implemented to solve the inverse kinematic problem. To overcome the problem of conventional tool and intelligent based method the optimization based approach can be implemented. In general, the optimization based approaches are more stable and often converge to the global solution. The major problem of ANN based approaches are its slow convergence and often stuck in local optimum point. Therefore, in present work different optimization based approaches are considered. The formulation of the objective function and associated constrained are discussed thoroughly. The comparison of all adopted algorithms on the basis of number of solutions, mathematical operations and computational time has been presented. The thesis concludes the summary with contributions and scope of the future research work

Fuzzy Computational Model for Emotion Regulation Based on Affect Control Theory

Emotion modeling is a multi-disciplinary problem that has managed to attract a great deal of research work spanned to a wide spectrum of scholarly areas starting at humanistic science fields passing through applied sciences and engineering and arriving at health care and wellbeing. Emotion research under the umbrella of IT and Computer Science was extensively successful with a handful of achievements especially in the last two decades. Affective Computing is an IT originated systematic research area that strives to best model emotions in a way that fits the needs for computer applications enriched with affective component. A comprehensive Affective Computing based system is made of three major components: a component for emotion detection, a component for emotion modeling, and finally a component to generating affective responses in artificial agents. The major focus of this dissertation is on developing efficient computational models for emotions. In fact most of the research works presented in this dissertation were focused on a sub problem of emotion modeling known as emotion regulation at which we strive to model the dynamics of changes in the emotional response levels of individuals as a result of the overt or covert situational changes. In this dissertation, several emotion related problems were addressed. Modeling the dynamics for emotion elicitation from a pure appraisal approach, investigating individualistic differences in emotional processes, and modeling emotion contagion as a type of social contagion phenomena are a few to name from those conducted research works. The main contribution of this dissertation was to propose a new computational model for the problem of emotion regulation that is based on Affect Control Theory. The new approach utilized a hybrid appraisal-dimensional architecture. By using a fuzzy modeling approach, the natural fuzziness in perceiving, representing and expressing emotions was effectively and efficiently addressed. Furthermore, the combination of automata framework with the concept of bipolar emotional channels used at the heart of the modeling processes of the proposed model has further contributed to promote the behavior of the model in order to exhibit an accepted degree of human-like affective behavior

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202