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

Decision making and risk management in adventure sports coaching

Adventure sport coaches practice in environments that are dynamic and high in risk, both perceived and actual. The inherent risks associated with these activities, individuals’ responses and the optimal exploitation of both combine to make the processes of risk management more complex and hazardous than the traditional sports where risk management is focused almost exclusively on minimization. Pivotal to this process is the adventure sports coaches’ ability to make effective judgments regarding levels of risk, potential benefits and possible consequences. The exact nature of this decision making process should form the basis of coaching practice and coach education in this complex and dynamic field. This positional paper examines decision making by the adventure sports coach in these complex, challenging environments and seeks to stimulate debate whilst offering a basis for future research into this topic

Quantifying dimensionality: Bayesian cosmological model complexities

We demonstrate a measure for the effective number of parameters constrained by a posterior distribution in the context of cosmology. In the same way that the mean of the Shannon information (i.e. the Kullback-Leibler divergence) provides a measure of the strength of constraint between prior and posterior, we show that the variance of the Shannon information gives a measure of dimensionality of constraint. We examine this quantity in a cosmological context, applying it to likelihoods derived from Cosmic Microwave Background, large scale structure and supernovae data. We show that this measure of Bayesian model dimensionality compares favourably both analytically and numerically in a cosmological context with the existing measure of model complexity used in the literature.Comment: 14 pages, 9 figures. v2: updates post peer-review. v3: typographical correction to equation 3

Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment

This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs. online discussion) in computer-supported collaborative learning (CSCL) on student knowledge construction. Students (N = 87) were first randomly assigned to the two information presentation conditions to work individually on a case-based assignment in IDLM. Students who received information during learning task clusters tended to show better results on knowledge construction than those who received information only before each cluster. The students within the two separate information presentation conditions were then randomly assigned to pairs to discuss the outcomes of their assignments under either the personal discussion or online discussion condition in CSCL. When supportive information had been presented before each learning task cluster, online discussion led to better results than personal discussion. When supportive information had been presented during the learning task clusters, however, the online and personal discussion conditions had no differential effect on knowledge construction. Online discussion in CSCL appeared to compensate for suboptimal timing of presentation of supportive information before the learning task clusters in IDLM

Discussion Analytics: Identifying Conversations and Social Learners in FutureLearn MOOCs

Discussion among learners in MOOCs has been hailed as beneficial for social constructive learning. To understand the pedagogical value of MOOC discussion forums, several researchers have utilized content analysis techniques to associate individual postings with differing levels of cognitive activity. However, this analysis typically ignores the turn taking among discussion postings, such as learners responding to others’ replies to their posts, learners receiving no reply for their posts, or learners just posting without conversing with others. This information is particularly important in understanding patterns of conversations that occur in MOOCs, and learners’ commenting behaviors. Therefore, in this paper we categorize comments in a FutureLearn MOOC based on their nature (post vs. reply to others’ post), classify learners based on their contributions for each type of post-ing, and identify conversations based on the types of comments composing them. This categorization quantifies the dynamics of conversations in the discussion activities, allowing monitoring of on-going discussion activities in FutureLearn and further analysis of identified conversations, social learners, and course steps with an unusually high number of a particular type of comment

Learning from the Anthropocene: Adaptive Epistemology and Complexity in Strategic Managerial Thinking

open access articleTurbulence experienced in the business and social realms resonates with turbulence unfolding throughout the biosphere, as a process of accelerating change at the stratigraphic scale termed the Anthropocene. The Anthropocene is understood as a multi‐dimensional limit point, one dimension of which concerns the limits to the lineal epistemology prevalent since the Age of the Enlightenment. This paper argues that future conditions necessitate the updating of a lineal epistemology through a transition towards resilience thinking that is both adaptive and ecosystemic. A management paradigm informed by the recognition of multiple equilibria states distinguished by thresholds, and incorporating adaptive and resilience thinking is considered. This paradigm is thought to enhance flexibility and the capacity to absorb influences without crossing thresholds into alternate stable, but less desirable, states. One consequence is that evaluations of success may change, and these changes are considered and explored as likely on‐going challenges businesses must grapple with into the future

THE USEFULNESS OF ANALYTICAL TOOLS FOR SUSTAINABLE FUTURES

The aim of this study is to assess the usefulness of analytical tools for policy evaluation. The study focuses on a multi-method integrated toolkit, the so-called SMILE toolkit. This toolkit consist of the integration of three evaluation frameworks developed within an EU-funded consortium called Development and Comparison of Sustainability (DECOIN) and further applied within the consortium Synergies in Multi-Scale Inter-Linkages of Eco-social systems (SMILE). This toolkit is developed to provide reporting features that are required for monitoring policy-making. The sustainable development perspective is rather difficult to attempt due to its dynamism and its multi-dimensionality. Therefore, in this study, we aim to assess the usefulness of the SMILE toolkit to sustainable development issues on the basis of the critical factors of sustainable development. In other words, here, we will prove the usefulness of the toolkit to help policymakers to think about and work on sustainable developments in the future.