Explaining Valence Asymmetries in Value Learning: A Reinforcement Learning Account

To understand how acquired value impacts how we perceive and process stimuli, psychologists have developed the Value Learning Task (VLT; e.g., Raymond & O’Brien, 2009). The task consists of a series of trials in which participants attempt to maximize accumulated winnings as they make choices from a pair of presented images associated with probabilistic win, loss, or no-change outcomes. Despite the task having a symmetric outcome structure for win and loss pairs, people learn win associations better than loss associations (Lin, Cabrera-Haro, & Reuter-Lorenz, 2020). This asymmetry could lead to differences when the stimuli are probed in subsequent tasks, compromising inferences about how acquired value affects downstream processing. We investigate the nature of the asymmetry using a standard error-driven reinforcement learning model with a softmax choice rule. Despite having no special role for valence, the model yields the asymmetry observed in human behavior, whether the model parameters are set to maximize empirical fit, or task payoff. The asymmetry arises from an interaction between a neutral initial value estimate and a choice policy that exploits while exploring, leading to more poorly discriminated value estimates for loss stimuli. We also show how differences in estimated individual learning rates help to explain individual differences in the observed win-loss asymmetries, and how the final value estimates produced by the model provide a simple account of a post-learning explicit value categorization task.

Impact of Statistical Intelligent Analysis on Corona Virus Disease

Statistical intelligence formulates the analysis model and reveals the system that can be easily visible and understandable to mankind. On one hand, it will benefit the society to predict the nature or man-created virus environment, and on the other hand, it will solve the problems of intelligent agents' formation with their functionality. It's a well-known fact that the agents are visible and noticeable, and they perform their own assigned task, but their recognition process is delayed. The chapter will focus on the statistical intelligence analysis that includes the properties of the error tolerance, forecasting, and high reliability. The information is always the part of the memory, but the processing methodology that may lead to knowledge is lacking. This may include the logical induction, Bayesian statistics, functional decision theory, value learning, forecasting, etc. Statistics will assist in path selection to formulate the highly adaptive intelligent system with the said functionalities with reduction in the overall cost factors.

Provably Safe Artificial General Intelligence via Interactive Proofs

Methods are currently lacking to prove artificial general intelligence (AGI) safety. An AGI ‘hard takeoff’ is possible, in which first generation AGI1 rapidly triggers a succession of more powerful AGIn that differ dramatically in their computational capabilities (AGIn << AGIn+1). No proof exists that AGI will benefit humans or of a sound value-alignment method. Numerous paths toward human extinction or subjugation have been identified. We suggest that probabilistic proof methods are the fundamental paradigm for proving safety and value-alignment between disparately powerful autonomous agents. Interactive proof systems (IPS) describe mathematical communication protocols wherein a Verifier queries a computationally more powerful Prover and reduces the probability of the Prover deceiving the Verifier to any specified low probability (e.g., 2−100). IPS procedures can test AGI behavior control systems that incorporate hard-coded ethics or value-learning methods. Mapping the axioms and transformation rules of a behavior control system to a finite set of prime numbers allows validation of ‘safe’ behavior via IPS number-theoretic methods. Many other representations are needed for proving various AGI properties. Multi-prover IPS, program-checking IPS, and probabilistically checkable proofs further extend the paradigm. In toto, IPS provides a way to reduce AGIn ↔ AGIn+1 interaction hazards to an acceptably low level.

Provably Safe Artificial General Intelligence via Interactive Proofs

Methods are currently lacking to prove artificial general intelligence (AGI) safety. An AGI &lsquo;hard takeoff&rsquo; is possible, in which first generation AGI1 rapidly triggers a succession of more powerful AGIn that differ dramatically in their computational capabilities (AGIn≪AGIn+1). No proof exists that AGI will benefit humans or of a sound value-alignment method. Numerous paths toward human extinction or subjugation have been identified. We suggest that probabilistic proof methods are the fundamental paradigm for proving safety and value-alignment between disparately powerful autonomous agents. Interactive proof systems (IPS) describe mathematical communication protocols wherein a Verifier queries a computationally more powerful Prover and reduces the probability of the Prover deceiving the Verifier to any specified low probability (e.g., 2-100). IPS procedures can test AGI behavior control systems that incorporate hard-coded ethics or value-learning methods. Mapping the axioms and transformation rules of a behavior control system to a finite set of prime numbers allows validation of &lsquo;safe&rsquo; behavior via IPS number-theoretic methods. Many other representations are needed for proving various AGI properties. Multi-prover IPS, program-checking IPS, and probabilistically checkable proofs further extend the paradigm. In toto, IPS provides a way to reduce AGIn&harr;AGIn+1 interaction hazards to an acceptably low level.

Value Learning Model: Conception and Implementation (Model Study of Sekolah Perjumpaan Lombok)

Character, back to being a public issue lately, has even been disseminated countless amounts of implementation with a fairly high budget spent, as well as educational institutions have designed various attachments, strategies, and methodologies of learning but less successful because it still leaves many moral and ethical issues such as bullying, intolerance, corruption, hoax are increasing. This study aims to determine the empirical relevance of the theoretical conceptualization of the School of Encounters run by community Sekolah Perjumpaan Bangket Bilong and Sekolah Perjumpaan Pagutan Lombok Tengah. This research uses multiple strategies with rationalistic approach and qualitative approach. Data mining with observation techniques, in-depth interviews, and documentation studies. The results of this study show that the concept model Sekolah Perjumpaan (School of Encounter) substantially leads and demands the improvement of human life in the form of improvement of the model of attitudes and behaviors of life, as well as demanding learning based on good and dignified human values. Furthermore, it becomes relevant to the values of character learning that substantively empirically directs humanity to live with the values of the goodness of life continuously. The implications of implementing a value learning model have been able to make significant changes. This can be seen from: the spirit of new life, the awakening of learning motivation, stronger learning relationships, and the growth of caring attitudes of others, both in community life in Bangket Bilong and in the Community of Pagutan Central Lombok.

INFLUENCE OF JIGSAW MODEL WHICH COMBINED WITH PAPER OPERAND IDEA TECHNIQUE FOR SENIOR HIGH SCHOOL STUDENTS

The research is based on the problems in SMAN 4 Padang, the low learning competency of students. Learning competency is an ability that must be possessed by students to know what they have mastered during learning. In this study aims to observe the influence of the aplication of the jigsaw cooperative learning model combined with the paper operand technique of ideas on the learning competencies of students in class X MIA SMAN 4 Padang in the acedemic year 2019/2020. Forms of this study was Quasi Experimental Design with design Randomized Control Group Posttest Only Design. The population in this study were all students in grade X MIA SMAN 4 Padang. Sampling using purposive sampling. The data collection technique of learning outcomes using posttest and documentation. Learning outcomes data were analyzed by normality-test, f-test and t-test. The results showed that the average value learning competency experimental class is greater than the control class.

Primary School in the Time of Covid-19: Parents’ Engagement in Students’ Online Learning

This study aims to gather data from public and private primary school parents located in the urban, suburban, and rural areas that would contribute to understanding parental roles regarding fully online learning for their children during COVID-19. We also discussed several parents’ self-identified issues on their ability to participate in their children’s fully online learning experiences. A qualitative study was employed as the methodological basis for eliciting 12 parents whose children in 3rd, 4th, and 5th-grade attending conventional learning before school closure due to the pandemic. The semi-structured interview was conducted, and verbatim transcripts were analyzed using inductive and thematic analysis approaches. Four major themes were identified from parents’ role during home learning, including organizing and managing, mentoring and instructing, facilitating, and motivating children learning. Online learning relied on how active parents engage and maintain children’s interaction with learning content, teachers, and peers to familiarise their children with online educational settings successfully. However, personal issues arise, which can be stressful as teaching young learners virtually need special attention. Having significant responsibilities to their children learning, parents disclosed key issues and struggled, resulting in a rapid move from face-to-face to online instructions; learning value, learning efficiency, mental and physical health, accessibility.