Design and Intelligent Machines

Many are interested in the design of intelligent machines. I am interested in design and how intelligent machines can transform this human endeavor in positive ways. I would like us to renew our consideration of some of the fundamental questions from the field of artificial intelligence, questions about human creativity and agency, in the context of design.

Design and Intelligent Machines

Many are interested in the design of intelligent machines.  I am interested in design and how intelligent machines can transform this human endeavor in positive ways.  I would like us to renew our consideration of some of the fundamental questions from the field of artificial intelligence, questions about human creativity and agency, in the context of design.

Semantics for Digital Engineering Archives Supporting Engineering Design Education

This article introduces the challenge of digital preservation in the area of engineering design and manufacturing and presents a methodology to apply knowledge representation and semantic techniques to develop Digital Engineering Archives. This work is part of an ongoing, multiuniversity, effort to create cyber infrastructure-based engineering repositories for undergraduates (CIBER-U) to support engineering design education. The technical approach is to use knowledge representation techniques to create formal models of engineering data elements, workflows and processes. With these formal engineering knowledge and processes can be captured and preserved with some guarantee of long-term interpretability. The article presents examples of how the techniques can be used to encode specific engineering information packages and workflows. These techniques are being integrated into a semantic wiki that supports the CIBER-U engineering education activities across nine universities and involving over 3500 students since 2006.

A study of shape distributions for estimating histologic grade

Breast cancers can be histologically categorized (graded) based upon their architectural patterns and cellular types. Inaccurate histologic grading can result in inappropriate treatment for a given patient. Computational analysis of breast cancers offers an operator-independent method for histologic grading that should enhance grading reliability. We present the initial efforts to develop computational technologies that may be used to automatically and objectively estimate the histologic grade of breast cancer tumors. The approach utilizes image processing and shape analysis of imaged histologic sections. Our work is based on the hypothesis that cellular structures found in breast cancer tumors can be transformed into distinct high-resolution shape distributions using geometric measures from stochastic geometry. The resulting shape distributions define well-populated regions of the associated high-dimensional space. Mapping an unknown breast cancer sample into this high-D space and determining to which region it belongs will allow for the automatic estimation of its histologic grade.