Improving AI systems' dependability by utilizing historical knowledge

A Turing Test is a promising way to validate AI systems which usually have no way to proof correctness. However, human experts (validators) are often too busy to participate in it and sometimes have different opinions per person as well as per validation session. To cope with these and increase the validation dependability, a Validation Knowledge Base (VKB) in Turing Test - like validation is proposed. The VKB is constructed and maintained across various validation sessions. Primary benefits are (1) decreasing validators' workload, (2) refining the methodology itself, e.g. selecting dependable validators using V KB, and (3) increasing AI systems' dependabilities through dependable validation, e.g. support to identify optimal solutions. Finally, Validation Experts Software Agents (VESA) are introduced to further break limitations of human validator's dependability. Each VESA is a software agent corresponding to a particular human validator. This suggests the ability to systematically "construct" human-like validators by keeping personal validation knowledge per corresponding validator. This will bring a new dimension towards dependable AI systems

A pilot study to assess the feasibility and accuracy of using haptic technology to occlude digital dental models.

Objectives: The use of haptic technology as an adjunct to clinical teaching is well documented in medicine and dentistry. However its application in clinical patient care is less well documented. The aim of this pilot study was determine the feasibility and accuracy of using a haptic device to determine the occlusion of virtual dental models. Methods: The non-occluded digital models of 20 pre-treatment individuals were chosen from the database of Faculty of Dentistry, The University of Hong Kong. Following minimal training with the haptic device (Geomagic® TouchTM), the upper model was occluded with the lower model until a stable occlusion was achieved. Seven landmarks were placed on each of the corners of the original and haptically aligned upper model bases. The absolute distance between the landmarks was calculated. Intra- and inter-operator errors were assessed. Results: The absolute distance between the 7 landmarks for each original and corresponding haptically aligned model was 0.54 ± 0.40mm in the x-direction (lateral), 0.73 ± 0.63mm in the y-direction (anterior-posterior) and 0.55 ± 0.48mm in the z-direction (inferior-superior). Conclusion: Based on initial collision detection to prevent interpenetration of the upper and lower digital model surfaces, and contact form resistance during contact, it is possible to use a haptic device to occlude digital study models

Improving Ai Systems\u27 Dependability By Utilizing Historical Knowledge

A TURING Test is a promising way to validate Al systems which usually have no way to proof correctness. However, human experts (validators) are often too busy to participate in it and sometimes have different opinions per person as well as per validation session. To cope with these and increase the validation dependability, a Validation Knowledge Base (VKB) in Turing Test - like validation is proposed. The VKB is constructed and maintained across various validation sessions. Primary benefits are (I) decreasing validators\u27 workload, (2) refining the methodology itself, e.g. selecting dependable validators using VKB, and (3) increasing AI systems\u27 dependabilities through dependable validation, e.g. support to identify optimal solutions. Finally, Validation Experts Software Agents (VESA) are introduced to further break limitations of human validator\u27s dependability. Each VESA is a software agent corresponding to a particular human validator. This suggests the ability to systematically construct human-like validators by keeping personal validation knowledge per corresponding validator. This will bring a new dimension towards dependable Al systems

Predictability of Microbial Adhesion to Dental Materials by Roughness Parameters

Microbial adhesion to intraoral biomaterials is associated with surface roughness. For the prevention of oral pathologies, smooth surfaces with little biofilm formation are required. Ideally, appropriate roughness parameters make microbial adhesion predictable. Although a multitude of parameters are available, surface roughness is commonly described by the arithmetical mean roughness value (Ra). The present study investigates whether Ra is the most appropriate roughness parameter in terms of prediction for microbial adhesion to dental biomaterials. After four surface roughness modifications using standardized polishing protocols, zirconia, polymethylmethacrylate, polyetheretherketone, and titanium alloy specimens were characterized by Ra as well as 17 other parameters using confocal microscopy. Specimens of the tested materials were colonized by C. albicans or S. sanguinis for 2 h; the adhesion was measured via luminescence assays and correlated with the roughness parameters. The adhesion of C. albicans showed a tendency to increase with increasing the surface roughness—the adhesion of S. sanguinis showed no such tendency. Although Sa, that is, the arithmetical mean deviation of surface roughness, and Rdc, that is, the profile section height between two material ratios, showed higher correlations with the microbial adhesion than Ra, these differences were not significant. Within the limitations of this in-vitro study, we conclude that Ra is a sufficient roughness parameter in terms of prediction for initial microbial adhesion to dental biomaterials with polished surfaces