Institute for Molecular Manufacturing

A new model for mechanical computing is demonstrated that requires only two basic parts, links, and rotary joints. These basic parts are combined into two main higher level structures, locks, and balances, and suffice to create all necessary combinatorial and sequential logic required for a Turing-complete computational system. While working systems have yet to be implemented using this new approach, the mechanical simplicity of the systems described may lend themselves better to, e.g., microfabrication, than previous mechanical computing designs. Additionally, simulations indicate that if molecular-scale implementations could be realized, they would be far more energy-efficient than conventional electronic computers.

Neuronanorobotics is the application of medical nanorobots to the human brain. This paper proposes three specific classes of neuronanorobots, named endoneurobots, gliabots and synaptobots, which together can non-destructively map and monitor the structural changes occurring on the 86 x 109 neurons and the 2.42 x 1014 synapses in the human brain, while also recording the synaptic-processed 4.31 x 1015 spikes/sec carrying electrical functional information processed in the neuronal and synaptic network.

Peer recommendation is a crowdsourcing task that leverages the opinions of many to identify interesting content online, such as news, images, or videos. Peer recommendation applications often use social signals, e.g., the number of prior recommendations, to guide people to the more interesting content. How people react to social signals, in combination with content quality and its presentation order, determines the outcomes of peer recommendation, i.e., item popularity. Using Amazon Mechanical Turk, we experimentally measure the effects of social signals in peer recommendation. Specifically, after controlling for variation due to item content and its position, we find that social signals affect item popularity about half as much as position and content do. These effects are somewhat correlated, so social signals exacerbate the ``rich get richer'' phenomenon, which results in a wider variance of popularity. Further, social signals change individual preferences, creating a ``herding'' effect that biases people's judgments about the content. Despite this, we find that social signals improve the efficiency of peer recommendation by reducing the effort devoted to evaluating content while maintaining recommendation quality.

This chapter describes the negative consequences of medical technology development and commercialization that is too slow, and makes the case for an immediate large scale investment in medical nanorobots to save 52 million lives a year. It also explains the essence of nanotechnology, its life-saving applications, the engineering challenges, and the possibility of 1000-fold improvement over our current human biological abilities. Every decade that we delay development and commercialization of medical nanorobotics, half a billion people perish who could have been saved.

Online social media provide multiple ways to find interesting content. One important method is highlighting content recommended by user’s friends. We examine this process on one such site, the news aggregator Digg. With a stochastic model of user behavior, we distinguish the effects of the content visibility and interestingness to users. We find a wide range of interest and distinguish stories primarily of interest to a users’ friends from those of interest to the entire user community. We show how this model predicts a story’s eventual popularity from users’ early reactions to it, and estimate the prediction reliability. This modeling framework can help evaluate alternative design choices for displaying content on the site.

The case is made that the path to AGI through cognitive and developmental robotics is compelling.Beyond the familiar argument that it keeps researchers honest by forcing their systems to cope with the real world, it encourages them to recapitulate the evolutionary developmental path which gave rise to intelligence in humans.Insights from this perspective are embodied in the Servo Stacks cognitive architecture with several salient features.The brain evolved as a body controller and thus is based largely on computational structures appropriate to physical process control.Evolution typically copies existing structure and modies it minimally to meet a new demand.We should therefore expect the higher cognitive functions to be performed by body controllers pressed into service as brain controllers.

This work has an innovative approach for the development of nanorobots withsensors for medicine. The nanorobots operate in a virtual environment comparingrandom, thermal and chemical control techniques. The nanorobot architecture modelhas nanobioelectronics as the basis for manufacturing integrated system deviceswith embedded nanobiosensors and actuators, which facilitates its application formedical target identification and drug delivery. The nanorobot interaction withthe described workspace shows how time actuation is improved based on sensorcapabilities. Therefore, our work addresses the control and the architecture design fordeveloping practical molecular machines. Advances in nanotechnology are enablingmanufacturing nanosensors and actuators through nanobioelectronics and biologicallyinspired devices. Analysis of integrated system modeling is one important aspectfor supporting nanotechnology in the fast development towards one of the mostchallenging new fields of science: molecular machines. The use of 3D simulation canprovide interactive tools for addressing nanorobot choices on sensing, hardwarearchitecture design, manufacturing approaches, and control methodology investigation.

A series of reactions of the type Y. + XH(4) --> YH + .XH(3) and Y'. + HX(CH(3))(3) --> Y'H + .X(CH(3))(3), where Y = H, CH(3); Y' = CH(3), C(CH(3))(3); and X = Si, Ge, Sn, Pb are studied using state-of-the-art ab initio electronic structure methods. Second-order Møller-Plesset perturbation theory; the coupled-cluster singles, doubles, and perturbative triples method; and density functional theory are used with correlation-consistent basis sets (cc-pVNZ, where N = D, T, Q) and their pseudopotential analogs (cc-pVNZ-PP) to determine the transition-state geometries, activation barriers, and thermodynamic properties of these reactions. Trends in the barrier heights as a function of the group IVA atom (Si, Ge, Sn, and Pb) are examined. With respect to kinetics and thermodynamics, the use of a hydrogen attached to a group IVA element as a possible hydrogen donation tool in the mechanosynthesis of diamondoids appears feasible.

Molecular nanotechnology will be physically capable of producing objects whose complexity exceeds that of any currently designed artifact by several orders of magnitude. Designs which make more than non-trivial use of this capability are beyond the capabilities of human designers, even using current CAD methods. Thus automatic design, or at least an improvement in automation over existing practice, will be a crucial component of molecular manufacturing. Central problems in automatic design include the allocation of scarce resources in the design (e.g. power and materials budgets), managing tradeoffs between conflicting design goals, and control of the overall design process itself and the simulations that it entails. The present effort is an investigation of a mixed-paradigm control model, drawing from evolution (the `genetic algorithm') and economics (`agoric algorithms'). We show that this model is a promising formulation for the general control and integration task. We present experimental results in which it performs certain desirable control tasks, including rational allocation of effort in stochastic methods, coordinating local expertise into an overall structure using the price mechanism, and driving the overall process towards global obtima.