Why and How We Are Not Zombies

A robot that is functionally indistinguishable from us may or may not be a mindless Zombie. There will never be any way to know, yet its functional principles will be as close as we can ever get to explaining the mind

Classification

In Classification learning, an algorithm is presented with a set of classified examples or ‘‘instances’’ from which it is expected to infer a way of classifying unseen instances into one of several ‘‘classes’’. Instances have a set of features or ‘‘attributes’’ whose values define that particular instance. Numeric prediction, or ‘‘regression,’’ is a variant of classification learning in which the class attribute is numeric rather than categorical. Classification learning is sometimes called supervised because the method operates under supervision by being provided with the actual outcome for each of the training instances. This contrasts with Data clustering (see entry Data Clustering), where the classes are not given, and with Association learning (see entry Association Learning), which seeks any association – not just one that predicts the class

From Parallel Sequence Representations to Calligraphic Control: A Conspiracy of Neural Circuits

Calligraphic writing presents a rich set of challenges to the human movement control system. These challenges include: initial learning, and recall from memory, of prescribed stroke sequences; critical timing of stroke onsets and durations; fine control of grip and contact forces; and letter-form invariance under voluntary size scaling, which entails fine control of stroke direction and amplitude during recruitment and derecruitment of musculoskeletal degrees of freedom. Experimental and computational studies in behavioral neuroscience have made rapid progress toward explaining the learning, planning and contTOl exercised in tasks that share features with calligraphic writing and drawing. This article summarizes computational neuroscience models and related neurobiological data that reveal critical operations spanning from parallel sequence representations to fine force control. Part one addresses stroke sequencing. It treats competitive queuing (CQ) models of sequence representation, performance, learning, and recall. Part two addresses letter size scaling and motor equivalence. It treats cursive handwriting models together with models in which sensory-motor tmnsformations are performed by circuits that learn inverse differential kinematic mappings. Part three addresses fine-grained control of timing and transient forces, by treating circuit models that learn to solve inverse dynamics problems.National Institutes of Health (R01 DC02852

Designing and Teaching Adaptive+Active Learning Effectively

To fulfill the promise of providing all learners with access to education, institutions of higher education are exploring personalized learning for individuals with different skills, abilities, and interests. These universities have turned to an instructional model that combines adaptive courseware and learner-centered instruction. This is often referred to as active learning. Despite growth in adaptive courseware and generous support through national organizations, successful implementation of adaptive systems is mixed (SRI Education, 2016). This article highlights the need for a systems approach and illustrates this approach through design and pedagogy decisions that have contributed to the success of adaptive learning at Arizona State University (ASU)

Ideological Misalignment in the Discourse(s) of Higher Education: Comparing University Mission Statements with Texts from Commercial Learning Analytics Providers

This study analyzes, interprets, and compares texts from different educational discourses. Using the Critical Discourse Analysis method, I reveal how texts from university mission statements and from commercial learning analytics providers communicate and construct different ideologies. To support this analysis, I explore literature strands related to public higher education in America and the emerging field of study and practice called learning analytics. Learning analytics is the administrative, research, and instructional use of large sets of digital data that are associated with and generated by students. The data in question may be generated by incidental online activity, and it may be correlated with a host of other data related to student demographics or academic performance. The intention behind educational data systems is to find ways to use data to “optimize” instructional materials and practices by tailoring them to perceived student needs and behaviors, and to trigger “interventions” ranging from warning messages to prescribed courses of study. The use of data in this way raises questions about how such practices relate to the goals and ideals of higher education, especially as these data systems employ similar theories and techniques as those used by corporate juggernauts such as Facebook and Google. Questions not only related to privacy and ownership but also related to how learning, education, and the purpose of higher education are characterized, discussed, and defined in various discourses are explored in this study