Chief Justice Robots

Say an AI program someday passes a Turing test, because it can con-verse in a way indistinguishable from a human. And say that its develop-ers can then teach it to converse—and even present an extended persua-sive argument—in a way indistinguishable from the sort of human we call a “lawyer.” The program could thus become an AI brief-writer, ca-pable of regularly winning brief-writing competitions against human lawyers. Once that happens (if it ever happens), this Essay argues, the same technology can be used to create AI judges, judges that we should accept as no less reliable (and more cost-effective) than human judges. If the software can create persuasive opinions, capable of regularly winning opinion-writing competitions against human judges—and if it can be adequately protected against hacking and similar attacks—we should in principle accept it as a judge, even if the opinions do not stem from human judgment

Head Start since the War on Poverty: Taking on New Challenges to Address Persistent School Readiness Gaps

This article explores Head Start’s overall effectiveness in improving school readiness outcomes and its potential to reduce gaps in these outcomes in light of changing program goals, resource and funding capacity, and the demographic changes in the low-income child population it serves. Although not an explicit goal of the Head Start program, we assess whether and how the program can address reducing school readiness gaps between children of different racial and ethnic backgrounds and income groups. Because of changing policy priorities and targeting vulnerable groups of children with diverse needs, meeting Head Start goals within funding constraints can be challenging. Yet, as we will show in this paper, the program has successfully adapted to its changing environment, and despite the evolving nature of its goals and populations served, it has managed to demonstrate a favorable impact on children. Future research on tailored programming, program implementation and impacts on specific groups of children is needed to help Head Start further improve its ability to address persistent school readiness gaps

Software-in-the-loop applications for improved physical model tests of ocean renewable energy devices using artificial intelligence

Experimental research in laboratory is a necessary and useful method to explore the full potential of a device. Because it does not only require much less money than the prototype at sea test, it also provides more reliable results compared to numerical simulations. Hence, it is significantly vital to make accurate model tests of the concerned ocean renewable energy (ORE) devices possible. For this reason, this study for a PhD degree has been finished and a thesis, therefore, is produced. There is a need for a method to provide linear or nonlinear real-time power-take-off forces to the wave energy converting mechanism in the water during the experiment. More urgently, it is essential to overcome the discrepancy caused by following Froude-scaling law and Reynold-scaling law in the test of a model-scaled FOWT. Two applications for WECs and FOWTs are proposed separately, to meet the challenges.;Following the conceptual design of the software-in-the-loop (SIL) application for a WEC, an innovative generic platform, which can explicitly provide a real-time PTO damping force in terms of either linear or non-linear (at different scales) is developed and characterised by 1349 drop tests. Subsequent physical model tests of a OWSC WEC device are carried out. The power efficiency of the OWSC WEC device under different PTO strategies is then estimated based on the analysis of experimental results. The best linear damping in regular waves is driven by gaining 80 in the control function, while 160 for nonlinear PTO damping. Furthermore, it is revealed that nonlinear PTOs have no distinct advantage in the amount of electricity output, but can lead to better stability and broader damping range. Following the conceptual design of an AI-based hybrid testing application for a FOWT system, a prediction module of the rotor thrust is needed to be estimated and optimised in the first place. For this reason, a considerable amount of simulations under various conditions are carried out by fully-coupled computation software, and the results obtained are used to train an artificial intelligence structure. Then a prediction module which depends on five inputs, and gives one output rotor thrust, is estimated mathematically. The mathematical module is converted to the control function in the program in a controller to execute it in real-time tests. Therefore, the AI machine is sometimes referred to as the SIL application for FOWTs, which consists of a prediction module obtained by AI training, a controller, and the program in the controller. The AI machine is the key component to implement the AI-based real-time hybrid model (AIReaTHM) testing methodology.;As one of the highlights in the present study, the AIReaTHM testing rig is developed, and bench tests are carried out with a manoeuvrable motion simulator. The comprehensive testing results are analysed for three purposes: 1, validating the AIReaTHM testing methodology. 2, assessing the influences of wind speed, wind turbulence intensity, wave spectrum, input hydrodynamic motions on rotor thrust are reflected by the SIL application.3, evaluating the systematic uncertainty in the testing rig, which is to be compensated by further improving the testing system. The effect of the surge frequency, wave spectrum and wind models have on the targeted thrust is discussed. The time delay in the testing system is identified as within 0.1s, and the overall uncertainty from the testing rig is 5-15KN (the minimum rotor thrust is 508KN, hence the uncertainty is 0.98%-2.95% in percentage) when compared to the AI prediction.;The testing rig developed is further applied to a 1:73 model of a Hywind floating wind turbine. 4 testing campaigns are carried out, and 303 independent tests are conducted. Testing results with the real-time rotor thrust provided by the AI-based software-in-the-loop application are compared with the other three comparative testing patterns. They are tests with a constant rotor thrust, without any rotor thrust, with AI predicted rotor thrust but without wave inputs, and in only wave conditions respectively. The performance of the rotor thrust obtained by the AI prediction agrees well with the benchmark testing results. Then, the hydrodynamic responses of the model are compared among those four testing patterns, for both regular wave tests and irregular wave tests in terms of time histories, RAOs, statistical analysis, and spectral analysis. The RAOs of the model under three testing patterns are given for regular wave tests. The hydrodynamic response revealed that the AIReaTHM is better than applying a constant rotor thrust atop of the model, though further improvement is required to meet realistic response. In the final chapter, conclusions are drawn and original contribution of this PhD study is outlined. Besides, a few points concerning future work are addressed.Experimental research in laboratory is a necessary and useful method to explore the full potential of a device. Because it does not only require much less money than the prototype at sea test, it also provides more reliable results compared to numerical simulations. Hence, it is significantly vital to make accurate model tests of the concerned ocean renewable energy (ORE) devices possible. For this reason, this study for a PhD degree has been finished and a thesis, therefore, is produced. There is a need for a method to provide linear or nonlinear real-time power-take-off forces to the wave energy converting mechanism in the water during the experiment. More urgently, it is essential to overcome the discrepancy caused by following Froude-scaling law and Reynold-scaling law in the test of a model-scaled FOWT. Two applications for WECs and FOWTs are proposed separately, to meet the challenges.;Following the conceptual design of the software-in-the-loop (SIL) application for a WEC, an innovative generic platform, which can explicitly provide a real-time PTO damping force in terms of either linear or non-linear (at different scales) is developed and characterised by 1349 drop tests. Subsequent physical model tests of a OWSC WEC device are carried out. The power efficiency of the OWSC WEC device under different PTO strategies is then estimated based on the analysis of experimental results. The best linear damping in regular waves is driven by gaining 80 in the control function, while 160 for nonlinear PTO damping. Furthermore, it is revealed that nonlinear PTOs have no distinct advantage in the amount of electricity output, but can lead to better stability and broader damping range. Following the conceptual design of an AI-based hybrid testing application for a FOWT system, a prediction module of the rotor thrust is needed to be estimated and optimised in the first place. For this reason, a considerable amount of simulations under various conditions are carried out by fully-coupled computation software, and the results obtained are used to train an artificial intelligence structure. Then a prediction module which depends on five inputs, and gives one output rotor thrust, is estimated mathematically. The mathematical module is converted to the control function in the program in a controller to execute it in real-time tests. Therefore, the AI machine is sometimes referred to as the SIL application for FOWTs, which consists of a prediction module obtained by AI training, a controller, and the program in the controller. The AI machine is the key component to implement the AI-based real-time hybrid model (AIReaTHM) testing methodology.;As one of the highlights in the present study, the AIReaTHM testing rig is developed, and bench tests are carried out with a manoeuvrable motion simulator. The comprehensive testing results are analysed for three purposes: 1, validating the AIReaTHM testing methodology. 2, assessing the influences of wind speed, wind turbulence intensity, wave spectrum, input hydrodynamic motions on rotor thrust are reflected by the SIL application.3, evaluating the systematic uncertainty in the testing rig, which is to be compensated by further improving the testing system. The effect of the surge frequency, wave spectrum and wind models have on the targeted thrust is discussed. The time delay in the testing system is identified as within 0.1s, and the overall uncertainty from the testing rig is 5-15KN (the minimum rotor thrust is 508KN, hence the uncertainty is 0.98%-2.95% in percentage) when compared to the AI prediction.;The testing rig developed is further applied to a 1:73 model of a Hywind floating wind turbine. 4 testing campaigns are carried out, and 303 independent tests are conducted. Testing results with the real-time rotor thrust provided by the AI-based software-in-the-loop application are compared with the other three comparative testing patterns. They are tests with a constant rotor thrust, without any rotor thrust, with AI predicted rotor thrust but without wave inputs, and in only wave conditions respectively. The performance of the rotor thrust obtained by the AI prediction agrees well with the benchmark testing results. Then, the hydrodynamic responses of the model are compared among those four testing patterns, for both regular wave tests and irregular wave tests in terms of time histories, RAOs, statistical analysis, and spectral analysis. The RAOs of the model under three testing patterns are given for regular wave tests. The hydrodynamic response revealed that the AIReaTHM is better than applying a constant rotor thrust atop of the model, though further improvement is required to meet realistic response. In the final chapter, conclusions are drawn and original contribution of this PhD study is outlined. Besides, a few points concerning future work are addressed

A Domain Specific Approach to High Performance Heterogeneous Computing

Users of heterogeneous computing systems face two problems: firstly, in understanding the trade-off relationships between the observable characteristics of their applications, such as latency and quality of the result, and secondly, how to exploit knowledge of these characteristics to allocate work to distributed computing platforms efficiently. A domain specific approach addresses both of these problems. By considering a subset of operations or functions, models of the observable characteristics or domain metrics may be formulated in advance, and populated at run-time for task instances. These metric models can then be used to express the allocation of work as a constrained integer program, which can be solved using heuristics, machine learning or Mixed Integer Linear Programming (MILP) frameworks. These claims are illustrated using the example domain of derivatives pricing in computational finance, with the domain metrics of workload latency or makespan and pricing accuracy. For a large, varied workload of 128 Black-Scholes and Heston model-based option pricing tasks, running upon a diverse array of 16 Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both the makespan and accuracy are generally within 10% of the run-time performance. When these models are used as inputs to machine learning and MILP-based workload allocation approaches, a latency improvement of up to 24 and 270 times over the heuristic approach is seen.Comment: 14 pages, preprint draft, minor revisio