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Computer programming and associated Computational Thinking (CT) skills are essential to thriving in today's academic and professional world. There has been a growing focus globally on fostering CT skills as well as on introducing computer programming concepts and languages beginning as early as kindergarten and pre-primary school. Tools, curriculum, and frameworks to promote CT in the early years must be designed and implemented in ways that engage children who cannot yet read and write, who learn through play, and who have a short attention span and limited working memory but also strong natural curiosity. This review summarises empirical and theoretical literature on the state of the field of CT as it relates to early learning and development, a time when young children are being introduced to foundational skills, such as literacy and numeracy, which can carefully be complemented by an exploration of CT.

The ethical issues concerning the AI-based exoskeletons used in healthcare have already been studied literally rather than technically. How the ethical guidelines can be integrated into the development process has not been widely studied. However, this is one of the most important topics which should be studied more in real-life applications. Therefore, in this paper we highlight one ethical concern in the context of an exoskeleton used to train a user to perform a gesture: during the interaction between the exoskeleton, patient and therapist, how is the responsibility for decision making distributed? Based on the outcome of this, we will discuss how to integrate ethical guidelines into the development process of an AI-based exoskeleton. The discussion is based on a case study: AiBle. The different technical factors affecting the rehabilitation results and the human-machine interaction for AI-based exoskeletons are identified and discussed in this paper in order to better apply the ethical guidelines during the development of AI-based exoskeletons., Comment: Robot Trust for Symbiotic Societies (RTSS) at IROS 2022

We study the computational complexity of finding a competitive equilibrium (CE) with chores when agents have linear preferences. CE is one of the most preferred mechanisms for allocating a set of items among agents. CE with equal incomes (CEEI), Fisher, and Arrow-Debreu (exchange) are the fundamental economic models to study allocation problems, where CEEI is a special case of Fisher and Fisher is a special case of exchange. When the items are goods (giving utility), the CE set is convex even in the exchange model, facilitating several combinatorial polynomial-time algorithms (starting with the seminal work of Devanur, Papadimitriou, Saberi and Vazirani) for all of these models. In sharp contrast, when the items are chores (giving disutility), the CE set is known to be non-convex and disconnected even in the CEEI model. Further, no combinatorial algorithms or hardness results are known for these models. In this paper, we give two main results for CE with chores: 1) A combinatorial algorithm to compute a $(1-\varepsilon)$-approximate CEEI in time $\tilde{\mathcal{O}}(n^4m^2 / \varepsilon^2)$, where $n$ is the number of agents and $m$ is the number of chores. 2) PPAD-hardness of finding a $(1-1/\mathit{poly}(n))$-approximate CE in the exchange model under a sufficient condition. To the best of our knowledge, these results show the first separation between the CEEI and exchange models when agents have linear preferences, assuming PPAD $\neq $ P. Finally, we show that our new insight implies a straightforward proof of the existence of an allocation that is both envy-free up to one chore (EF1) and Pareto optimal (PO) in the discrete setting when agents have factored bivalued preferences., Comment: Accepted in EC 2022. The PPAD hardness section also appeared in arXiv:2008.00285

I show that a computer cannot have unambiguous thoughts, not even about a number. What we believe computers do is our own convention. It may seem objective because we anchor it in the user interface. But many other conventions are possible, and they yield different computations, equally valid according to the principles of Computer Science. I prove that the alternative computations equally happen when a single computation is carried out, and in principle they can be accessed. I exemplify this with a program that computes the result for a given input, and then decodes it into the results for all other possible inputs. If thinking would be a computation, a computer would have different, possibly opposite thoughts, corresponding to many alternative computations it implements at the same time. I show probabilistically that the human mind does not have this ambiguity. Therefore, even if the human mind can be simulated by a computer, it cannot be reduced to computation. [ABSTRACT FROM AUTHOR]