Evaluation of Robot Professor Technology in Teaching and Business

In this paper the Robot technology development will be evaluated in teaching and business. Impacts hypothesis of robot’s applications are discussed in addition to the types of the robots with time line. The results of this study show the advantages and disadvantages of using the Robot teacher in classroom and business and it shows that the Robot teacher performance is not like the human teacher due to the senses that owned by human being. Robot impacts minimizes the employment worldwide and it is predicted that around 800 million of global workers will lose their jobs in 2030. The results show that impacts hypothesis is minimized through improving the performance of robot technology by using AI Artificial Intelligence, also the Robot applications in the global health system have contributed effectively and successfully to prevention of the spread of the coronavirus pandemic. The Sophia Robot with AI improvements, behaves with good feelings, senses and debating friendly and it is approved by UNDP on 2018. In this study, it is concluded that the robot is not like human being, although developing human-like robot is still out of reach. Also, AI is the best tool for enhancing the Robot technology and increase the employment involvement rate in near future, which is one of the future challenges and studies proposed in this research, industry 4.

A condição humana e a condição docente: das ilusões de onipotência ao reconhecimento do desamparo

A condição humana e a condição docente: das ilusões de onipotência ao reconhecimento do desamparo Resumo: Partindo do princípio de que todo processo educacional é aberta ou veladamente guiado por uma determinada maneira de se compreender o ser humano, procuramos nesse artigo colocar em questão os ideais de “humano”, e consequentemente, de humano-professor, que a tradição ocidental nos legou. Em seguida, tentamos indicar alguns caminhos para repensarmos contemporaneamente o que significa ser humano e, por conseguinte, o que significa ser professor. Enquanto os antigos ideais insistem em projeções de poder e invulnerabilidade ao assumir uma essência imaterial para o humano, apostamos numa concepção de humano encarnado, afetivo e desejante que envolve, em contrapartida, a disposição para assumirmos o irremediável desamparo e a incontornável vulnerabilidade que são intrínsecos à condição humana. Palavras-chave: condição humana; ilusões de onipotência; desamparo The human condition and the teacher condition: from the illusions of omnipotence to the recognition of helplessness Abstract: Assuming that every educational process is openly or veiled guided by a certain way of understanding the human being, we seek in this article to question the ideals of "human," and consequently of human-teacher, that the Western tradition bequeathed to us. Next, we try to indicate some ways to rethink contemporaneously what it means to be human and, therefore, what it means to be a teacher. While the old ideals insist on projections of power and invulnerability by assuming an immaterial essence for the human, we bet on an incarnate, affective and desiring human conception that involves, on the other hand, the disposition to assume the irremediable helplessness and the unavoidable vulnerability that are intrinsic to the human condition. Keywords: human condition; omnipotence illusions; helplessness La condición humana y la condición docente: de las ilusiones de omnipotencia al reconocimiento del desamparo Resumen: A partir del principio de que todo proceso educativo es abierto o veladamente guiado por una determinada manera de comprenderse el ser humano, buscamos en ese artículo plantear en cuestión los ideales de "humano", y consecuentemente, de humano-profesor, que la tradición occidental en legó. A continuación, intentamos indicar algunos caminos para repensar contemporáneamente lo que significa ser humano y, por lo tanto, lo que significa ser profesor. Mientras los antiguos ideales insisten en proyecciones de poder e invulnerabilidad al asumir una esencia inmaterial para lo humano, apostamos en una concepción de humano encarnado, afectivo y deseante que envuelve, en contrapartida, la disposición para asumir el irremediable desamparo y la ineludible vulnerabilidad que son intrínsecos a la condición humana. Palabras clave: condición humana; ilusiones de omnipotencia; desamparo Data de registro: 20/05/2020Data de aceite: 02/10/2020

Beyond Cross-Validation—Accuracy Estimation for Incremental and Active Learning Models

For incremental machine-learning applications it is often important to robustly estimate the system accuracy during training, especially if humans perform the supervised teaching. Cross-validation and interleaved test/train error are here the standard supervised approaches. We propose a novel semi-supervised accuracy estimation approach that clearly outperforms these two methods. We introduce the Configram Estimation (CGEM) approach to predict the accuracy of any classifier that delivers confidences. By calculating classification confidences for unseen samples, it is possible to train an offline regression model, capable of predicting the classifier’s accuracy on novel data in a semi-supervised fashion. We evaluate our method with several diverse classifiers and on analytical and real-world benchmark data sets for both incremental and active learning. The results show that our novel method improves accuracy estimation over standard methods and requires less supervised training data after deployment of the model. We demonstrate the application of our approach to a challenging robot object recognition task, where the human teacher can use our method to judge sufficient training.

Do You Feel Me?

In working towards accomplishing a human-level acquisition and understanding of language, a robot must meet two requirements: the ability to learn words from interactions with its physical environment, and the ability to learn language from people in settings for language use, such as spoken dialogue. The second requirement poses a problem: If a robot is capable of asking a human teacher well-formed questions, it will lead the teacher to provide responses that are too advanced for a robot, which requires simple inputs and feedback to build word-level comprehension. In a live interactive study, we tested the hypothesis that emotional displays are a viable solution to this problem of how to communicate without relying on language the robot doesn't--indeed, cannot--actually know. Emotional displays can relate the robot's state of understanding to its human teacher, and are developmentally appropriate for the most common language acquisition setting: an adult interacting with a child. For our study, we programmed a robot to independently explore the world and elicit relevant word references and feedback from the participants who are confronted with two robot settings: a setting in which the robot displays emotions, and a second setting where the robot focuses on the task without displaying emotions, which also tests if emotional displays lead a participant to make incorrect assumptions regarding the robot's understanding. Analyzing the results from the surveys and the Grounded Semantics classifiers, we discovered that the use of emotional displays increases the number of inputs provided to the robot, an effect that's modulated by the ratio of positive to negative emotions that were displayed.

Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System

The idea of clustering students according to their online learning behavior has the potential of providing more adaptive scaffolding by the intelligent tutoring system itself or by a human teacher. With the aim of identifying student groups who would benefit from the same intervention in AC-ware Tutor, this research examined online learning behavior using 8 tracking variables: the total number of content pages seen in the learning process; the total number of concepts; the total online score; the total time spent online; the total number of logins; the stereotype after the initial test, the final stereotype, and the mean stereotype variability. The previous measures were used in a four-step analysis that consisted of data preprocessing, dimensionality reduction, the clustering, and the analysis of a posttest performance on a content proficiency exam. The results were also used to construct the decision tree in order to get a human-readable description of student clusters.

Learning System for Japanese Kanji Calligraphy with Computerized Supervision

The most popular way of learning oriental calligraphy has been by practicing the calligraphy under the supervision of a human teacher, but finding a good instructor can be difficult. There are a number of studies in the literature that have evaluated calligraphic characters in holistic ways, but such systems do not support detailed supervision of scripting errors. This study proposes a Kanji calligraphy learning system with computerized supervision and analyzes the learning efficiency of the system, where the supervision includes symmetries between strokes. The proposed system compares a written calligraphic character of a user to the model of a human expert, and indicates error spots with explanations. An experiment with 22 participants proved that this system was more efficient at reducing the number of scripting errors in comparison to the traditional manner of a human expert. The main contribution of this paper was to identify and reveal the efficacy of computerized supervision in comparison to a human supervisor. The proposed system decreased the writing-error-rates of learners from 32.7% to 3.4%, whereas the traditional practice reduced the error rates from 31.0% to 6.8%. This result shows that computerized supervision is more efficient than human supervision for learning calligraphy.