We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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Stargazing Live! aims to capture the imagination of learners with a combination of live and interactive planetarium lessons, real astronomical data, and lessons built around interactive knowledge representations. The lessons were created using a co-creation model and tackle concepts in the pre-university (astro)physics which students find difficult to grasp with traditional interventions. An evaluation study in 9 Dutch classrooms showed that learners are inspired and engaged by the planetarium lessons but are not always able to link the content to the classroom. Pre- and post-tests showed that the accompanying star properties activity significantly increased learners’ understanding of the causal relationships between mass and other properties (such as luminosity, gravity, and temperature) in a main sequence star.
Data, the raw material from which information is derived, is stored, copied, moved and modified more easily than ever. This quantum leap reaches levels outside our imagination. Surrounded by sensors, recommendation systems, invisible algorithms, spreadsheets and blockchains, the ‘difference that makes a difference’ can no longer be identified. Big Data is a More Data ideology, driven by old school hypergrowth premisses. As Nathan Jurgenson once observed: “Big Data always stands in the shadow of the bigger data to come. The assumption is that there is more data today and there will necessarily be even more tomorrow, an expansion that will bring us ever closer to the inevitable pure ‘data totality.” (2) Nothing symbolizes the current hypergrowth obsession better than Big Data. Let’s investigate what happens when we apply degrowth to data and reserve datafication–as a decolonial project, a collective act of refusal, an ultimate sign of boredom. We’re done with you, data system, stand out of my light.
MULTIFILE
