A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over- capacity, where the associative-memory behavior of the mod(cid:173) el breaks down, the processing performed by the model can be inter(cid:173) preted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical view(cid:173) point of sparse distributed memory and for which the standard for(cid:173) mulation of SDM is a special case. This viewpoint suggests possi(cid:173) ble enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with'Genetic Algorithms', and a method for improving the capacity of SDM even when used as an associative memory.

With this paper, we aim to put an issue on the agenda of AI ethics that in our view is overlooked in the current discourse. The current discussions are dominated by topics suchas trustworthiness and bias, whereas the issue we like to focuson is counter to the debate on trustworthiness. We fear that the overuse of currently dominant AI systems that are driven by short-term objectives and optimized for avoiding error leads to a society that loses its diversity and flexibility needed for true progress. We couch our concerns in the discourse around the term anti-fragility and show with some examples what threats current methods used for decision making pose for society.

Despite the relative precision of this computer tool, however, Broussard notes that "our statistical prediction of who survived and who died on the Titanic will never be 100 percent accurate--no statistical prediction can or will ever be 100 percent accurate--because human beings are not and never will be statistics." For example, she recounts the actions of two passengers whose fates had nothing to do with gender, age, or passenger fare, but, rather, how far they jumped when fleeing the sinking vessel.

The Blogs dataset is a subset of Recent years have resulted in several well motivated the Blogs06 [16] collection of blog posts from 06/12/2005 and carefully described studies coping with the problem to 21/02/2006. Only posts attracting more than 100 of opinion formation and its spreading [1]. This kind of comments were extracted, as these apparently initialised research usually aimed at qualitative descriptions of some non-trivial discussions. Both datasets have similar structures.

ABSTRACT A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near-or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with'Genetic Algorithms', and a method for improving the capacity of SDM even when used as an associative memory. OVERVIEW This work is the result of studies involving two seemingly separate topics that proved to share a common framework. The fIrst topic, statistical prediction, is the task of associating extremely large perceptual state vectors with future events.

David Rogers Research Institute for Advanced Computer Science MS 230-5, NASA Ames Research Center Moffett Field, CA 94035 ABSTRACT A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near-or over-capacity, where the associative-memory behavior of the model breaksdown, the processing performed by the model can be interpreted asthat of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint ofsparse distributed memory and for which the standard formulation ofSDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with'Genetic Algorithms', and a method for improving the capacity of SDM even when used as an associative memory. OVERVIEW This work is the result of studies involving two seemingly separate topics that proved to share a common framework. The fIrst topic, statistical prediction, is the task of associating extremely large perceptual state vectors with future events.