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Purpose: This paper aims to center the experiences of three cohorts (n = 40) of Black high school students who participated in a critical race technology course that exposed anti-blackness as the organizing logic and default setting of digital and artificially intelligent technology. This paper centers the voices, experiences and technological innovations of the students, and in doing so, introduces a new type of digital literacy: critical race algorithmic literacy. Design/methodology/approach: Data for this study include student interviews (called "talk backs"), journal reflections and final technology presentations. Findings: Broadly, the data suggests that critical race algorithmic literacies prepare Black students to critically read the algorithmic word (e.g. data, code, machine learning models, etc.) so that they can not only resist and survive, but also "rebuild" and "reimagine" the algorithmic world. Originality/value: While critical race media literacy draws upon critical race theory in education - a theorization of race, and a critique of white supremacy and multiculturalism in schools - critical race algorithmic literacy is rooted in critical race technology theory, which is a theorization of blackness as a technology and a critique of algorithmic anti-blackness as the organizing logic of schools and AI systems.

The following offers an overview of what it means for cities to be "smart." It draws the supporting definitions and critical insights into smart cities from a series of papers presented at the 2009 Trans-national Conference on Creating Smart(er) Cities. What the papers all have in common is their desire to overcome the all too often self-congratulatory nature of the claims cities make to be smart and their over-reliance on a distinctively entrepreneurial route to smart cities. Individually, they serve to highlight the major challenges cities face in their drive to become smart. Collectively they begin to uncover what it means for cities to be smart. Together the papers offer an alternative route to smart cities laid down by those advocating a more neo-liberal roadmap, rooted in a critically aware knowledge-base and more realistic understanding of what it means for cities to be smart(er). (Contains 1 note and 1 figure.)

Open online courses (OOC) with a massive number of students have represented an important development for online education in the past years. A course on artificial intelligence, CS221, at the University of Stanford was offered in the fall of 2011 free and online which attracted 160,000 registered students. It was one of three offered as an experiment by the Stanford computer science department to extend technology knowledge and skills to the entire world. The instructors were two of the best known experts in the subject of artificial intelligence. Although students would not get Stanford University grades or credit, 20,000 from 190 countries finished the course successfully receiving a "statement of accomplishment" from the tutors Sebastian Thrun and Peter Norvig. Udacity is a start-up from the authors of CS221 delivering similar massive free online courses. EdX, a joint partnership between The Massachusetts Institute of Technology (MIT) and Harvard University to offer online learning to millions of people around the world, is one of the most recent proposals in this realm. Massive open online courses known as connectivist MOOCs (c MOOCs) on the other hand have been delivered since 2008. They are based on the explicit principles of connectivism (autonomy, diversity, openness and interactivity) and on the activities of aggregation, remixing, repurposing and feeding forward the resources and learning. In the research literature, newspaper and magazine articles both types of OOCs, AI Stanford like courses (AI) and c-MOOCs, have been identified in many occasions as equivalent. Distance education (DE) pedagogy can be classified through the evolution of three categories: cognitive-behaviourist, social constructivist, and connectivist. These three current and future generations of DE pedagogy have an important place in a well-rounded educational experience. To a large extent, the generations have evolved in tandem with the technologies and all three models are very much in existence today and are categorized by a set of conditions. In this paper we study in detail representative courses from AI and c MOOC formats. We establish that although they share the use of distributed networks the format associated with c-MOOCs, which are defined by a participative pedagogical model, are unique and different from AI. We further assign to the AI to a cognitive-behaviourist (with some small contribution of social constructivist) and MOOCs to connectivist pedagogy. (Contains 2 tables and 4 figures.)

The increase in both the frequency and magnitude of natural disasters, coupled with recent advancements in artificial intelligence, has introduced prospects for investigating the potential of new technologies to facilitate disaster response processes. Preliminary Damage Assessment (PDA), a labor-intensive procedure necessitating manual examination of residential structures to ascertain post-disaster damage severity, stands to significantly benefit from the integration of computer vision-based classification algorithms, promising efficiency gains and heightened accuracy. Our paper proposes a Vision Transformer (ViT)-based model for classifying damage severity, achieving an accuracy rate of 95%. Notably, our model, trained on a repository of over 18,000 ground-level images of homes with damage severity annotated by damage assessment professionals during the 2020–2022 California wildfires, represents a novel application of ViT technology within this domain. Furthermore, we have open sourced this dataset—the first of its kind and scale—to be used by the research community. Additionally, we have developed a publicly accessible web application prototype built on this classification algorithm, which we have demonstrated to disaster management practitioners and received feedback on. Hence, our contribution to the literature encompasses the provision of a novel imagery dataset, an applied framework from field professionals, and a damage severity classification model with high accuracy. [ABSTRACT FROM AUTHOR]

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in P M 2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality ( O 3 and CO ) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O 3 and CO , respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly P M 2.5 and P M 10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for P M 2.5 and P M 10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily P M 2.5 and P M 10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978. [ABSTRACT FROM AUTHOR]

Not long after he completed his Ph.D. at the University of Michigan, Mike Van Lent used his interest in videogames and artificial intelligence to land a research professorship at the University of Southern California (USC). There he edits the Journal of Game Development and conducts studies for the Institute for Creative Technologies, a $45 million defense and entertainment industry collaboration "that advances the state-of-the-art in virtual reality and immersive environments" (http://www.ict.usc .edu). When Van Lent moved into his campus office, one of his first acts was to line the bookshelves with forty videogame boxes and wait for visitors so that he could ask them, with a twist on conventional academic one-upsmanship, how many of the games they had played. This document describes how video games are influencing higher education. The insights of five leading-edge thinkers in the field are presented in this document: James Paul Gee, J. C. Herz, Randy Hinrichs, Marc Prensky, and Ben Sawyer. All five had traveled to San Jose, California, in March 2004 for the Serious Games Summit at the annual Game Developers Conference. The following six topics are discussed: (1) The dysfunctions of conventional instruction; (2) The power of simulations; (3) The importance of game-based learning communities; (4) The reasons videogames promise a better learning future; (5) The changes necessary for the new paradigm to take hold; and (6) The practical steps that colleges/universities and influential academics can take to move institutions down the trail blazed by USC and others.

Artificial intelligence (AI) systems have become increasingly common and the trend will continue. Examples of AI systems include autonomous vehicles (AV), computer vision, natural language processing and AI medical experts. To allow for safe and effective deployment of AI systems, the reliability of such systems needs to be assessed. Traditionally, reliability assessment is based on reliability test data and the subsequent statistical modelling and analysis. The availability of reliability data for AI systems, however, is limited because such data are typically sensitive and proprietary. The California Department of Motor Vehicles (DMV) oversees and regulates an AV testing program, in which many AV manufacturers are conducting AV road tests. Manufacturers participating in the program are required to report recurrent disengagement events to California DMV. This information is being made available to the public. In this paper, we use recurrent disengagement events as a representation of the reliability of the AI system in AV, and propose a statistical framework for modelling and analysing the recurrent events data from AV driving tests. We use traditional parametric models in software reliability and propose a new non‐parametric model based on monotonic splines to describe the event process and to estimate the cumulative baseline intensity function of the event process. We develop inference procedures for selecting the best models, quantifying uncertainty and testing heterogeneity in the event process. We then analyse the recurrent events data from four AV manufacturers, and make inferences on the reliability of the AI systems in AV. We also describe how the proposed analysis can be applied to assess the reliability of other AI systems. This paper has online supplementary materials. [ABSTRACT FROM AUTHOR]

The increasing need for mental health support and a shortage of therapists have led to the development of the eXtended-reality Artificial Intelligence Assistant (XAIA). This platform combines spatial computing, virtual reality (VR), and artificial intelligence (AI) to provide immersive mental health support. Utilizing GPT-4 for AI-driven therapy, XAIA engaged participants with mild-to-moderate anxiety or depression in biophilic VR environments. Speaking with an AI therapy avatar in VR was considered acceptable, helpful, and safe, with participants observed to engage genuinely with the program. However, some still favored human interaction and identified shortcomings with using a digital VR therapist. The study provides initial evidence of the acceptability and safety of AI psychotherapy via spatial computing, warranting further research on technical enhancements and clinical impact. [ABSTRACT FROM AUTHOR]

Information about the 7th Association for the Advancement of Artificial Intelligence (AAI) Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) held at Stanford University in California from October 11 to 14, 2011 is presented. The conference reportedly featured a research track, industry track, a demonstration program, and three workshops. Lecturers include Michael Buro, Bob Fitch, and Akhil Madhani.

The Third International Conference on Data Engineering was held in Los Angeles, California on February 2 to 6, 1987. The conference was designed as an international forum for bringing together researchers, developers, managers, strategic planners and other users with an interest in the research, design and development of data engineering methodologies, strategies and systems. Its scope includes computer science, artificial intelligence, electrical engineering, and computer engineering. It featured papers from all major areas of data engineering, including database design and modeling, performance evaluation, algorithms, integrity, security, fault tolerance, query language, artificial intelligence approaches, knowledge bases, database machines, distributed databases and data engineering applications.

Recorded time series of relative humidity (RH) are modeled by using genetic expression programming (GEP) and artificial neural networks (ANNs) models. The data are noisy and contain missing datapoints. RH is modeled as a function of three meteorological variables: temperature, wind speed, and pressure. Various model structures of both of these models are investigated with the aim of testing the robustness of the predicted values in the presence of noise and missing data. Due to the presence of noise, a sophisticated treatment of missing data was not justifiable, and therefore, the strategy adopted was just to carry the datapoints backward, although this may induce bias in the time dimension and contaminate the predicted results. The results of this study indicate that through a careful selection of model structures both GEP and ANN can produce adequately reliable prediction of RH values 1 year into the future. The paper provides evidence that this model structure is feasible when the dependent variables include both the present and past values. [ABSTRACT FROM AUTHOR]

New forms of multimedia data (such as text, numbers, tags, networking, signals, geo-tagged information, graphs/relationships, three-dimensional/VR/AR and sensor data, etc.) have emerged in many applications in addition to traditional multimedia data (image, video, audio). Multimedia has become the biggest of big data as the foundation of today's data-driven discoveries. Almost all disciplines of science and engineering, as well as social sciences, involve multimedia data in some forms, such as recording experiments, driverless cars, unmanned aerial vehicles, smart communities, biomedical instruments, security surveillance. Some recent events demonstrate the power of real-time broadcast of unfolding events on social networks. Multimedia data is not just big in volume, but also multi-modal and mostly unstructured. Storing, indexing, searching, integrating, and recognizing from the vast amounts of data create unprecedented challenges. Even though significant progress has been made processing multimedia data, today's solutions are inadequate in handling data from millions of sources simultaneously. The IEEE International Conference on Multimedia Information Processing and Retrieval (IEEE-MIPR) aims to provide a forum for original research contributions and practical system design, implementation, and applications of multimedia information processing and retrieval for single modality or multiple modalities. The target audiences are university researchers, scientists, industry practitioners, software engineers, and graduate students who want to become acquainted with technologies for big data analytics, machine intelligence, information fusion in multimedia information processing and retrieval. Following a successful debut in Miami, Florida in 2018, the second MIPR, held in San Jose, California, attracted over 140 submissions and more than 100 attendees—a big growth from its inaugural event and an indication of its growing visibility and popularity in the multimedia research society. In addition to four keynote talks, MIPR'19 also featured six innovation multimedia forums organized by both academic and industrial researchers, with topics ranging from computer vision, self-driving, Natural Language Processing, next generation video display, to cutting edge video/audio coding techniques. [ABSTRACT FROM AUTHOR]

Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers’ performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California’s Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles. [ABSTRACT FROM AUTHOR]

Distribution shifts remain a problem for the safe application of regulated medical AI systems, and may impact their real-world performance if undetected. Postmarket shifts can occur for example if algorithms developed on data from various acquisition settings and a heterogeneous population are predominantly applied in hospitals with lower quality data acquisition or other centre-specific acquisition factors, or where some ethnicities are over-represented. Therefore, distribution shift detection could be important for monitoring AI-based medical products during postmarket surveillance. We implemented and evaluated three deep-learning based shift detection techniques (classifier-based, deep kernel, and multiple univariate kolmogorov-smirnov tests) on simulated shifts in a dataset of 130'486 retinal images. We trained a deep learning classifier for diabetic retinopathy grading. We then simulated population shifts by changing the prevalence of patients' sex, ethnicity, and co-morbidities, and example acquisition shifts by changes in image quality. We observed classification subgroup performance disparities w.r.t. image quality, patient sex, ethnicity and co-morbidity presence. The sensitivity at detecting referable diabetic retinopathy ranged from 0.50 to 0.79 for different ethnicities. This motivates the need for detecting shifts after deployment. Classifier-based tests performed best overall, with perfect detection rates for quality and co-morbidity subgroup shifts at a sample size of 1000. It was the only method to detect shifts in patient sex, but required large sample sizes ( > 3 0 ′ 000 ). All methods identified easier-to-detect out-of-distribution shifts with small (≤300) sample sizes. We conclude that effective tools exist for detecting clinically relevant distribution shifts. In particular classifier-based tests can be easily implemented components in the post-market surveillance strategy of medical device manufacturers. [ABSTRACT FROM AUTHOR]

Human inborn errors of immunity include rare disorders entailing functional and quantitative antibody deficiencies due to impaired B cells called the common variable immunodeficiency (CVID) phenotype. Patients with CVID face delayed diagnoses and treatments for 5 to 15 years after symptom onset because the disorders are rare (prevalence of ~1/25,000), and there is extensive heterogeneity in CVID phenotypes, ranging from infections to autoimmunity to inflammatory conditions, overlapping with other more common disorders. The prolonged diagnostic odyssey drives excessive system-wide costs before diagnosis. Because there is no single causal mechanism, there are no genetic tests to definitively diagnose CVID. Here, we present PheNet, a machine learning algorithm that identifies patients with CVID from their electronic health records (EHRs). PheNet learns phenotypic patterns from verified CVID cases and uses this knowledge to rank patients by likelihood of having CVID. PheNet could have diagnosed more than half of our patients with CVID 1 or more years earlier than they had been diagnosed. When applied to a large EHR dataset, followed by blinded chart review of the top 100 patients ranked by PheNet, we found that 74% were highly probable to have CVID. We externally validated PheNet using >6 million records from disparate medical systems in California and Tennessee. As artificial intelligence and machine learning make their way into health care, we show that algorithms such as PheNet can offer clinical benefits by expediting the diagnosis of rare diseases. Editor's summary: Common variable immunodeficiency (CVID) disease is an inborn error of immunity characterized by antibody deficiency and impaired B cell responses. This condition can be difficult to diagnose on account of its heterogenous presentation. Johnson et al. developed and validated a machine learning model designed to parse patient electronic health record data and rank individuals according to their likelihood of having CVID. Their retrospective analysis suggested that the method could help diagnose many individuals earlier than standard clinical methods, potentially hastening their referral for specialist treatment. —Catherine Charneski [ABSTRACT FROM AUTHOR]

Information about the Association for the Advancement of Artificial Intelligence (AAAI) workshop program held in San Francisco, California from August 7-18, 2011 is presented. It states that there were 15 workshops covering various topics related to artificial intelligence including microtext, applied adversarial reasoning, and game theory. Lecturers include Aristotelis Hadjakos, Boris Moltchanov, and Paul Lukowiz.

The generalization error bounds found by current error models using the number of effective parameters of a classifier and the number of training samples are usually very loose. These bounds are intended for the entire input space. However, support vector machine (SVM), radial basis function neural network (RBFNN), and multilayer perceptron neural network (MLPNN) are local learning machines for solving problems and treat unseen samples near the training samples to be more important. In this paper, we propose a localized generalization error model which bounds from above the generalization error within a neighborhood of the training samples using stochastic sensitivity measure. It is then used to develop an architecture selection technique for a classifier with maximal coverage of unseen samples by specifying a generalization error threshold. Experiments using 17 University of California at Irvine (UCI) data sets show that, in comparison with cross validation (CV), sequential learning, and two other ad hoc methods, our technique consistently yields the best testing classification accuracy with fewer hidden neurons and less training time. [ABSTRACT FROM AUTHOR]

The California bearing ratio (CBR) value of subgrade is the most used parameter for dimensioning flexible and rigid pavements. The test for determining the CBR value is typically conducted under soaked conditions and is costly, labour-intensive, and time-consuming. Machine learning (ML) techniques have been recently implemented in engineering practice to predict the CBR value from the soil index properties with satisfactory results. However, they provide only deterministic predictions, which do not account for the aleatoric uncertainty linked to input variables and the epistemic uncertainty inherent in the model itself. This work addresses this limitation by introducing an ML model based on the natural gradient boosting (NGBoost) algorithm, becoming the first study to estimate the soaked CBR value from this probabilistic perspective. A database of 2130 soaked CBR tests was compiled for this study. The NGBoost model showcased robust predictive performance, establishing itself as a reliable and effective algorithm for predicting the soaked CBR value. Furthermore, it produced probabilistic CBR predictions as probability density functions, facilitating the establishment of reliable confidence intervals, representing a notable improvement compared to conventional deterministic models. Finally, the Shapley additive explanations method was implemented to investigate the interpretability of the proposed model. [ABSTRACT FROM AUTHOR]

The article talks about evolution of the periodical "Intelligent Systems for Molecular Biology," (ISMB). It also discusses the contributions of its founder Lawrence Hunter, an artificial intelligence (AI) researcher. It adds that ISMB has celebrated its 20th annual international conference in 2012 in Long Beach, California.

Information about the International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS) 08 held in February 2008 in Los Angeles, California. Topics include the development and launch of manipulators for optical sensors and low-Earth-orbit applications in Canada, the progress in artificial intelligence (AI) and robotics in Europe, and improvements in rover technologies in the U.S. The symposium featured Gianfranco Visentin, Mitsushige Oda, and Paul Schenker.

The architectural landscape of high-performance computing stretches from superscalar uniprocessor to explicitly parallel systems to dedicated hardware implementations of algorithms. Single-purpose hardware can achieve the highest performance and uniprocessors can be the most programmable. Between these extremes, programmable and reconfigurable architectures provide a wide range of choice in flexibility, programmability, computational density, and performance. The UCSC Kestrel parallel processor strives to attain single-purpose performance while maintaining user programmability. Kestrel is a single-instruction stream, multiple- data stream (SIMD) parallel processor with a 512-element linear array of 8-bit processing elements. The system design focuses on efficient high-throughput DNA and protein sequence analysis, but its programmability enables high performance on computational chemistry, image processing, machine learning, and other applications. The Kestrel system has had unexpected longevity in its utility due to a careful design and analysis process. Experience with the system leads to the conclusion that programmable SIMD architectures can excel in both programmability and performance. This paper presents the architecture, implementation, applications, and observations of the Kestrel project at the University of California at Santa Cruz. [ABSTRACT FROM AUTHOR]

Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (R2 values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer. [ABSTRACT FROM AUTHOR]

Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels. [ABSTRACT FROM AUTHOR]

The Irvine School refers to an intellectual perspective on information technology in complex organizational settings that emerged at the University of California in Irvine, California (UCI), over the last three decades of the 20th century. In many ways, the rise of the Irvine School was synonymous with the rise of researcher Rob Kling's influence on the international community of scholars who would eventually form what Kling called social informatics. This article reflects on Kling's role in the creation of the Irvine School. Kenneth Kraemer, an architect and city planner by training, had joined the UCI Graduate School of Administration in 1967. He was one of the first scholars to begin careful empirical study of the effect of computerization in government agencies, and soon started a research program on information technology and local government at UCI's Public Policy Research Organization. In the early 1970s, researchers Jim Danziger and Rob Kling joined him in this endeavor. Danziger had been trained in political science and public administration. Kling had been trained in electrical engineering and computer science, and had done important work at the intersection of fuzzy logic and planning systems in the area of symbolic artificial intelligence.

The article offers information on the Al and Interactive Digital Entertainment (AIIDE) Conference which was held from October 11-13, 2010, at Stanford University in Palo Alto, California. The conference was hosted by the Association for the Advancement of Artificial Intelligence (AAAI) and featured 17 paper presentations, 18 posters, 5 demonstrations, 5 invited speakers, and a panel on teaching game Al in academe.

Recent progresses in artificial intelligence and machine learning make it possible to automatically identify seismic phases from exponentially growing seismic data. Despite some exciting successes in automatic picking of the first P‐ and S‐wave arrivals, auto‐identification of later seismic phases such as the Moho‐reflected PmP waves remains a significant challenge in matching the performance of experienced analysts. The main difficulty of machine‐identifying PmP waves is that the identifiable PmP waves are rare, making the problem of identifying the PmP waves from a massive seismic database inherently unbalanced. In this work, by utilizing a high‐quality PmP data set (10,192 manual picks) in southern California, we develop PmPNet, a deep‐neural‐network‐based algorithm to automatically identify PmP waves efficiently; by doing so, we accelerate the process of identifying the PmP waves. PmPNet applies similar techniques in the machine learning community to address the unbalancement of PmP datasets. The architecture of PmPNet is a residual neural network (ResNet)‐autoencoder with additional predictor block, where encoder, decoder, and predictor are equipped with ResNet connection. We conduct systematic research with field data, concluding that PmPNet can efficiently achieve high precision and high recall simultaneously to automatically identify PmP waves from a massive seismic database. Applying the pre‐trained PmPNet to the seismic database from January 1990 to December 1999 in southern California, we obtain nearly twice more PmP picks than the original PmP data set, providing valuable data for other studies such as mapping the topography of the Moho discontinuity and imaging the lower crust structures of southern California. Plain Language Summary: The success of machine learning in computer sciences, medical sciences, and many other fields has accelerated the implementation and development of machine learning techniques in seismology, making it possible to automatically identify seismic phases from the exponentially growing seismic data. At present, the auto‐identification of later seismic phases, such as the Moho‐reflected PmP waves, remains a significant challenge in matching the performance of experienced analysts. The main difficulty lies in the rare identifiable PmP waves, which makes the identification problem inherently unbalanced. In this work, by utilizing a high‐quality PmP data set in southern California, we develop a deep‐neural‐network‐based algorithm, PmPNet, to accelerate the process of identifying the PmP waves. We conduct systematic research with field data, and conclude that the PmPNet can efficiently achieve high precision and high recall simultaneously for automatically identifying the PmP waves from a massive seismic database. Applying the pre‐trained PmPNet to the seismic database from January 1990 to December 1999, we have tripled the PmP data set in southern California. Key Points: A deep‐neural‐network‐based algorithm, PmPNet, is developed to automatically pick the Moho‐reflected seismic waves PmPPmPNet efficiently achieves high precision and recall simultaneously to automatically identify PmP waves from a massive seismic databasePmPNet creates a large PmP database with a total of 28,093 high‐quality PmP picks in southern California [ABSTRACT FROM AUTHOR]

Application of principles of predicate calculus (PC) and artificial intelligence (AI) search methods to occupational medicine can meet several goals. First, they can improve understanding of the diagnostic process and recognition of the sources of uncertainty in knowledge and in case specific information. Second, PC provides a rational means of resolving differences in conclusion based upon the same premises. Third, understanding of these principles allows separation of knowledge (facts) from the process by which they are used and therefore facilitates development of AI-based expert systems. Application of PC to recognizing causation of pulmonary fibrosis is demonstrated in this paper, providing a method that can be generalized to other problems in occupational medicine. Application of PC and understanding of AI search routines may be particularly applicable to workers' compensation where explicit statement of rational and inferential process is necessary. This approach is useful in the diagnosis of occupational lung disease and may be particularly valuable in workers' compensation considerations, wherein explicit statement of rationale is needed.

The article offers information on several congresses related to the Association for the Advancement of Artificial Intelligence (AAAI) including the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI-11) and the Twenty-Third Conference on Innovative Applications of Artificial Intelligence (IAAI-11) to be held in San Francisco, California on August 7-11, 2011 and the 2011 AAAI Fall Symposium Series to be held at the Westin Arlington Gateway in Arlington, Virginia on November 4-6.

Information about the Association for the Advancement of Artificial Intelligence's (AAAI) 4th Artificial Intelligence for Interactive Digital Entertainment Conference workshops on intelligent narrative technologies and the game design process held in Stanford, California are presented. It discusses the highlights each workshop which include a lecture on generating narratives in real time based on player input. Lecturers include David Elson and Gillian Smith.

The article offers information on the IJCAI-09 Workshop on Learning Structural Knowledge from Observations (STRUCK-09), held as part of the International Joint Conference on Artificial Intelligence (IJCAI-09) in Pasadena, California on July 12, 2009. The workshop includes paper presentations, which were grouped into four sessions, a panel of discussion regarding the topics of each paper, and a group discussion. The workshop featured Susan Epstein, David J. Stracuzzi, and Prasad Tadepalli.

Information about the several issues discussed during the meeting of the Executive Council of the Association for the Advancement of Artificial Intelligence on March 25, 2007 in Palo Alto, California is presented. Ted Senator has reported that the return of investments has grossed 13.5 percent. Ron Brachman has informed that the Awards Committee is reviewing nominations for the Classic Paper Award, and Holly Yanco has reported that she is in the process of putting together a new committee.

Many law firms have done little to address the opportunities and threats presented by potentially disruptive technology (DT), such as artificial intelligence and machine learning. The purpose of this multiple-case study was to explore strategies that some law firm leaders use to address the potentially detrimental influences of DT on their organizations. The systems approach to management was employed as the conceptual framework. Data were collected from six participants at two international law firms with offices in California, using semistructured interviews and organizational artifacts. Data were analyzed using inductive and deductive coding and thematic analysis, resulting in four themes: (a) recognizing the legal ecosystem and legal firms are open systems, but organizational subsystems often function as semiclosed systems; (b) acknowledging that although DT represents the most significant potential challenge in the near future, the immediate challenge is improving technology, which requires organizational adjustments; (c) recognizing the need for firms to invest more heavily in innovation generation activities; and (d) realizing the need for increased utilization of augmenting technologies, such as artificial intelligence or machine learning, to streamline nonadvisory outputs. The findings of this study suggest that, while disruptive innovation may pose a moderate threat, there are also significant advantages to adopting disruptive innovation as a formal aspect of organizational strategy. The results of this study may contribute to social change by outlining ways in which firms can lower costs to clients while increasing access to legal services for those in underserved communities. [ABSTRACT FROM AUTHOR]

Highlights the AIENG 88 Third International Conference on Applications of Artificial Intelligence in Engineering in Stanford, California. Duration of the event; Topics discussed at the conference; Role of the Computational Mechanics International Ltd. as event organizer.

The Low Carbon Fuel Standards (LCFS) represents a new policy approach designed to reduce carbon dioxide emissions by applying standards to all stages of motor fuel production. We use the synthetic control and difference-in-differences econometric methods, and Lasso machine learning to analyze the effect of the LCFS on emissions in California’s transportation sector. The three different techniques provide robust evidence that the LCFS reduced carbon dioxide emissions in California’s transportation sector by around 10%. Furthermore, our calculations show that improved air quality, due to the application of the LCFS, may have benefited California in the magnitude of hundreds of millions of dollars through an increase in worker’s productivity. [ABSTRACT FROM AUTHOR]

Standard practice for prediction often relies on parametric regression methods. Interesting new methods from the machine learning literature have been introduced in epidemiologic studies, such as random forest and neural networks. However, a priori, an investigator will not know which algorithm to select and may wish to try several. Here I apply the super learner, an ensembling machine learning approach that combines multiple algorithms into a single algorithm and returns a prediction function with the best cross-validated mean squared error. Super learning is a generalization of stacking methods. I used super learning in the Study of Physical Performance and Age-Related Changes in Sonomans (SPPARCS) to predict death among 2,066 residents of Sonoma, California, aged 54 years or more during the period 1993–1999. The super learner for predicting death (risk score) improved upon all single algorithms in the collection of algorithms, although its performance was similar to that of several algorithms. Super learner outperformed the worst algorithm (neural networks) by 44% with respect to estimated cross-validated mean squared error and had an R2 value of 0.201. The improvement of super learner over random forest with respect to R2 was approximately 2-fold. Alternatives for risk score prediction include the super learner, which can provide improved performance. [ABSTRACT FROM PUBLISHER]

Information about the Association for the Advancement of Artificial Intelligence's (AAAI) 2011 Robotics Program held in San Francisco, California is presented. The event reportedly included a demonstration of research at the intersection of robotics and artificial intelligence. Lecturers include David Touretzky, Sonia Chernova, and Zachary Dodds. It notes that the participants convened 18 short seminars before the exhibition.

In many watersheds located in southern California, efforts are being focused on urban runoff because of its adverse impact on receiving water quality. The Sweetwater River watershed is a good example, where the drainage area is rapidly urbanizing and deteriorating reservoir water quality. Contaminated storm water is captured and diverted but as urbanization increases, additional runoff will be generated which will overload the existing infrastructure. To better manage the diversion systems and minimize future construction, storm-water volumes and pollutant loadings need to be estimated. Due to the lack of real-time storm-water runoff monitoring data, pollutant loadings must be estimated from land use information. We used satellite imagery to estimate selected storm-water pollutant loads and compared the results to predictions using land use information from public records. Satellite imagery was useful in estimating storm-water pollutant loads and identifying high loading areas. Satellite imagery with appropriate classification is a promising tool for watershed management and for prioritizing best management practices. [ABSTRACT FROM AUTHOR]

The article discusses on medical simulation to teach patient safety to the residents of Stanford University in Palo Alto, California. Medical simulation is used to teach things that cannot be learned readily in standard apprenticeship training. Using a simulator, a rare event can be created and then the participants can proceed to manage by making any mistakes without any direct harm to patients. The simulator in conjunction with the video debriefings can provide critical incidents on demand and provide valuable feedback to the participants on how they communicate and adhere to crisis management principles.