Our contextual inquiry into the practices of oral historians unearthed a curious incongruity. While oral historians consider interview recordings a central historical artifact, these recordings sit unused after a written transcript is produced. We hypothesized that this is largely because books are more usable than recordings. Therefore, we created Books with Voices: bar-code augmented paper transcripts enabling fast, random access to digital video interviews on a PDA. We present quantitative results of an evaluation of this tangible interface with 13 participants. They found this lightweight, structured access to original recordings to offer substantial benefits with minimal overhead. Oral historians found a level of emotion in the video not available in the printed transcript. The video also helped readers clarify the text and observe nonverbal cues.

Photographs play a central role in many types of informal storytelling. This paper describes an easy-to-use device that enables digital photos to be used in a manner similar to print photos for sharing personal stories. A portable form factor combined with a novel interface supports local sharing like a conventional photo album as well as recording of stories that can be sent to distant friends and relatives. User tests validate the design and reveal that people alternate between “photo-driven” and “story-driven” strategies when telling stories about their photos.

"Error-Confidence" measures the probability that the proportion of errors made by a classifier will be within epsilon of EB, the optimal (Bayes) error. Probably Almost Bayes (PAB) theory attempts to quantify how this confidence increases with the number of training samples. We investigate the relationship empirically by comparing average error versus number of training patterns (m) for linear and neural network classifiers. On Gaussian problems, the resulting EC curves demonstrate that the PAB bounds are extremely conservative. Asymptotic statistics predicts a linear relationship between the logarithms of the average error and the number of training patterns. For low Bayes error rates we found excellent agreement between the prediction and the linear discriminant performance. At higher Bayes error rates we still found a linear relationship, but with a shallower slope than the predicted-1. When the underlying true model is a three-layer network, the EC curves show a greater dependence on classifier capacity, and the linear predictions no longer seem to hold.

Describes a special purpose, very high speed, digital deterministic Boltzmann neural network VLSI chip. Each chip has 32 physical neural processors, which can be apportioned into an arbitrary topology (input, multiple hidden and output layers) of up to 160 virtual neurons total. Under typical conditions, the chip learns at approximately 5*10/sup 8/ connection updates/second (CUPS). Through relatively minor (subsequent) modifications, the authors' chips can be 'tiled' in multi-chip modules, to make multi-layer networks of arbitrary size suffering only slight communications delays and overhead. In this way, the number of CUPS can be made arbitrarily large, limited only by the number of chips tiled. The chip's high speed is due to massively parallel array computation of the inner products of connection weights and neural activations, limited (but adequate) precision for weights and activations (5 bits), high clock rate (180 MHz), as well as several algorithmic and design insights.>