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

Walking Paths to and from a Goal Differ: On the Role of Bearing Angle in the Formation of Human Locomotion Paths

The path that humans take while walking to a goal is the result of a cognitive process modulated by the perception of the environment and physiological constraints. The path shape and timing implicitly embeds aspects of the architecture behind this process. Here, locomotion paths were investigated during a simple task of walking to and from a goal, by looking at the evolution of the position of the human on a horizontal (x,y) plane. We found that the path while walking to a goal was not the same as that while returning from it. Forward-return paths were systematically separated by 0.5-1.9m, or about 5% of the goal distance. We show that this path separation occurs as a consequence of anticipating the desired body orientation at the goal while keeping the target in view. The magnitude of this separation was strongly influenced by the bearing angle (difference between body orientation and angle to goal) and the final orientation imposed at the goal. This phenomenon highlights the impact of a trade-off between a directional perceptual apparatus-eyes in the head on the shoulders-and and physiological limitations, in the formation of human locomotion paths. Our results give an insight into the influence of environmental and perceptual variables on human locomotion and provide a basis for further mathematical study of these mechanisms

The relationship between the use of artificial intelligence and the customer experience in the power industry

The literature on customer experience and artificial intelligence is extensive, but there is a gap in the power industry regarding these issues. This thesis aims to assess if there is a connection between artificial intelligence usage and the customer experience in the industry. An index that indicates several technologies 'existence or absence in each of the forty companies examined was developed. There we reno significant effects of AIan dCX on the power industry, but the null hypothesis is not accepted since it was not proved that there is no relationship. It might exist, but the current study missed it

Observation and imitation of actions performed by humans, androids, and robots : an EMG study

Understanding others’ actions is essential for functioning in the physical and social world. In the past two decades research has shown that action perception involves the motor system, supporting theories that we understand others’ behavior via embodied motor simulation. Recently, empirical approach to action perception has been facilitated by using well-controlled artificial stimuli, such as robots. One broad question this approach can address is what aspects of similarity between the observer and the observed agent facilitate motor simulation. Since humans have evolved among other humans and animals, using artificial stimuli such as robots allows us to probe whether our social perceptual systems are specifically tuned to process other biological entities. In this study, we used humanoid robots with different degrees of human-likeness in appearance and motion along with electromyography (EMG) to measure muscle activity in participants’ arms while they either observed or imitated videos of three agents produce actions with their right arm. The agents were a Human (biological appearance and motion), a Robot (mechanical appearance and motion), and an Android (biological appearance and mechanical motion). Right arm muscle activity increased when participants imitated all agents. Increased muscle activation was found also in the stationary arm both during imitation and observation. Furthermore, muscle activity was sensitive to motion dynamics: activity was significantly stronger for imitation of the human than both mechanical agents. There was also a relationship between the dynamics of the muscle activity and motion dynamics in stimuli. Overall our data indicate that motor simulation is not limited to observation and imitation of agents with a biological appearance, but is also found for robots. However we also found sensitivity to human motion in the EMG responses. Combining data from multiple methods allows us to obtain a more complete picture of action understanding and the underlying neural computations

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given.Comment: Submitted to Image and Vision Computing, 44 pages, 7 figure

A petri-net based methodology for modeling, simulation, and control of flexible manufacturing systems

Global competition has made it necessary for manufacturers to introduce such advanced technologies as flexible and agile manufacturing, intelligent automation, and computer-integrated manufacturing. However, the application extent of these technologies varies from industry to industry and has met various degrees of success. One critical barrier leading to successful implementation of advanced manufacturing systems is the ever-increasing complexity in their modeling, analysis, simulation, and control. The purpose of this work is to introduce a set of Petri net-based tools and methods to address a variety of problems associated with the design and implementation of flexible manufacturing systems (FMSs). More specifically, this work proposes Petri nets as an integrated tool for modeling, simulation, and control of flexible manufacturing systems (FMSs). The contributions of this work are multifold. First, it demonstrates a new application of PNs for simulation by evaluating the performance of pull and push diagrams in manufacturing systems. Second, it introduces a class of PNs, Augmented-timed Petri nets (ATPNs) in order to increase the power of PNs to simulate and control flexible systems with breakdowns. Third, it proposes a new class of PNs called Realtime Petri nets (RTPNs) for discrete event control of FMS s. The detailed comparison between RTPNs and traditional discrete event methods such as ladder logic diagrams is presented to answer the basic question \u27Why is a PN better tool than ladder logic diagram?\u27 and to justify the PN method. Also, a conversion procedure that automatically generates PN models from a given class of logic control specifications is presented. Finally, a methodology that uses PNs for the development of object-oriented control software is proposed. The present work extends the PN state-of-the-art in two ways. First, it offers a wide scope for engineers and managers who are responsible for the design and the implementation of modem manufacturing systems to evaluate Petri nets for applications in their work. Second, it further develops Petri net-based methods for discrete event control of manufacturing systems

The Tiger Vol. 83 Issue 23 1990-04-20

https://tigerprints.clemson.edu/tiger_newspaper/3154/thumbnail.jp

A simulation-enhanced intraoperative planning tool for robotic-assisted total knee arthroplasty

The purpose of the present study was to investigate current methods of surgical planning used in conjunction with robotics-assisted total knee arthroplasty (raTKA) to determine if improvements could be made using advanced computational techniques. Thus, through the use of musculoskeletal multi-body dynamic simulations, an enhanced surgical planning tool was developed, which provides insight on active postoperative joint mechanics. Development of the tool relied on patient-specific simulations using single-leg and full-body models. These simulations were constructed using two publicly-available datasets (Orthoload and SimTK); in particular, joint loading data obtained from subjects during various activities. Simulation parameters were optimized using a design-of experiments (DOE) methodology and validation of each of the models was conducted by calculating the root mean square error (RMSE) between joint loading calculated using the model and the corresponding results given in the appropriate dataset. Optimized and validated variants of each of the models were used in conjunction with the results of DOE studies that characterized the influence of a number of surgical planning variables on various biomechanical responses and linear regression analysis to derive knee performance equations (KPEs). In literature studies, some of the aforementioned responses have been strongly correlated with two outcomes commonly reported by dissatisfied TKA patients, namely, anterior knee pain and poor proprioception. In a proof-of-concept study, KPEs were used to calculate optimal positions and orientations of the femoral and tibial components in the case of one subject featured in the SimTK dataset. These results differed from corresponding ones reportedly achieved for the implant components in the subject. This trend suggests there is potential to improve robotic surgical planning for current-generation raTKA systems through the use of musculoskeletal simulation. Use of the proposed surgical planning tool does not require computational resources beyond what are used with a specified current-generation raTKA system (Navio Surgical System). Furthermore, there are only minimal differences between the workflow involving the proposed planning tool and that when Navio Surgical System is used. A number of recommendations for future studies are made, such as larger scale simulation validation work and use of more complex regression techniques when deriving the KPEs

Cognitive Reasoning for Compliant Robot Manipulation

Physically compliant contact is a major element for many tasks in everyday environments. A universal service robot that is utilized to collect leaves in a park, polish a workpiece, or clean solar panels requires the cognition and manipulation capabilities to facilitate such compliant interaction. Evolution equipped humans with advanced mental abilities to envision physical contact situations and their resulting outcome, dexterous motor skills to perform the actions accordingly, as well as a sense of quality to rate the outcome of the task. In order to achieve human-like performance, a robot must provide the necessary methods to represent, plan, execute, and interpret compliant manipulation tasks. This dissertation covers those four steps of reasoning in the concept of intelligent physical compliance. The contributions advance the capabilities of service robots by combining artificial intelligence reasoning methods and control strategies for compliant manipulation. A classification of manipulation tasks is conducted to identify the central research questions of the addressed topic. Novel representations are derived to describe the properties of physical interaction. Special attention is given to wiping tasks which are predominant in everyday environments. It is investigated how symbolic task descriptions can be translated into meaningful robot commands. A particle distribution model is used to plan goal-oriented wiping actions and predict the quality according to the anticipated result. The planned tool motions are converted into the joint space of the humanoid robot Rollin' Justin to perform the tasks in the real world. In order to execute the motions in a physically compliant fashion, a hierarchical whole-body impedance controller is integrated into the framework. The controller is automatically parameterized with respect to the requirements of the particular task. Haptic feedback is utilized to infer contact and interpret the performance semantically. Finally, the robot is able to compensate for possible disturbances as it plans additional recovery motions while effectively closing the cognitive control loop. Among others, the developed concept is applied in an actual space robotics mission, in which an astronaut aboard the International Space Station (ISS) commands Rollin' Justin to maintain a Martian solar panel farm in a mock-up environment. This application demonstrates the far-reaching impact of the proposed approach and the associated opportunities that emerge with the availability of cognition-enabled service robots

Fourth NASA Goddard Conference on Mass Storage Systems and Technologies

This report contains copies of all those technical papers received in time for publication just prior to the Fourth Goddard Conference on Mass Storage and Technologies, held March 28-30, 1995, at the University of Maryland, University College Conference Center, in College Park, Maryland. This series of conferences continues to serve as a unique medium for the exchange of information on topics relating to the ingestion and management of substantial amounts of data and the attendant problems involved. This year's discussion topics include new storage technology, stability of recorded media, performance studies, storage system solutions, the National Information infrastructure (Infobahn), the future for storage technology, and lessons learned from various projects. There also will be an update on the IEEE Mass Storage System Reference Model Version 5, on which the final vote was taken in July 1994