Machine learning is a subset of artificial intelligence that enables computers to learn without being programmed.

Much anticipated and long awaited! The Defense Innovation Board released its final report "AI Principles: Recommendations on the Ethical Use of Artificial Intelligence by the United States Department of Defense What an exciting moment! Incredible leadership and a very thoughtful deliberative process that brought a multitude of stakeholders to the convening table.

How can AI tools--such as deep learning and neural networks--be used to predict unplanned hospital and skilled nursing facility admissions and adverse events? In the Artificial Intelligence (AI) Health Outcomes Challenge, the Centers for Medicare & Medicaid Services (CMS) is dangling up to $1.65 million in prize money to the innovators who can figure it out. Six hospitals and health systems are among the 25 participants who were selected from a field of more than 300 submissions. The ultimate goal is to harness AI solutions to predict health outcomes for healthcare providers and clinicians, as well as potential use in CMS Innovation Center innovative payment and service delivery models. "Artificial Intelligence is a vehicle that can help drive our system to value--proven to reduce out-of-pocket costs and improve quality," said CMS Administrator Seema Verma in a news release.

The Software Engineering Institute is creating a movement to mature the discipline of AI Engineering for Defense and National Security. As our government customers adopt artificial intelligence and machine learning to provide leap-ahead mission capabilities, we are working to provide the processes, practices, and tools to support operationalizing AI for robust and trustworthy mission capabilities. Our work includes scalable AI and ML; the tailoring of modern software practices for AI-enabled systems; architectures, design patterns, modeling, and representations; building tools and processes for trustworthy development of AI-enabled capabilities; and test, evaluation, verification, and validation of AI systems. Of course, building and maturing the discipline of AI Engineering also requires applying AI technologies to build real-world, mission-scale capabilities through envisioning creative solutions, solving practical engineering problems, and learning from those experiences. Are you creative, curious, energetic, collaborative, technology-focused, and hard-working?

Contxto – Did you know that Mexico was the first Latin American country to launch a national AI strategy? As of 2018, its implementation in both the public and private sectors is an ongoing reality. Some experts consider AI and its offshoots, machine learning and Big Data, a part of the "Fourth Industrial Revolution." With their growing importance in practically every industry, incentives to evolve have also followed suit. Of course, Latin America doesn't want to be left behind.

In addition to the traditional task of getting machines to answer questions, a major research question in question answering is to create interesting, challenging questions that can help systems learn how to answer questions and also reveal which systems are the best at answering questions. We argue that creating a question answering dataset---and the ubiquitous leaderboard that goes with it---closely resembles running a trivia tournament: you write questions, have agents (either humans or machines) answer the questions, and declare a winner. However, the research community has ignored the decades of hard-learned lessons from decades of the trivia community creating vibrant, fair, and effective question answering competitions. After detailing problems with existing QA datasets, we outline the key lessons---removing ambiguity, discriminating skill, and adjudicating disputes---that can transfer to QA research and how they might be implemented for the QA community.

We consider the fusion of two aerodynamic data sets originating from differing fidelity physical or computer experiments. We specifically address the fusion of: 1) noisy and in-complete fields from wind tunnel measurements and 2) deterministic but biased fields from numerical simulations. These two data sources are fused in order to estimate the \emph{true} field that best matches measured quantities that serves as the ground truth. For example, two sources of pressure fields about an aircraft are fused based on measured forces and moments from a wind-tunnel experiment. A fundamental challenge in this problem is that the true field is unknown and can not be estimated with 100\% certainty. We employ a Bayesian framework to infer the true fields conditioned on measured quantities of interest; essentially we perform a \emph{statistical correction} to the data. The fused data may then be used to construct more accurate surrogate models suitable for early stages of aerospace design. We also introduce an extension of the Proper Orthogonal Decomposition with constraints to solve the same problem. Both methods are demonstrated on fusing the pressure distributions for flow past the RAE2822 airfoil and the Common Research Model wing at transonic conditions. Comparison of both methods reveal that the Bayesian method is more robust when data is scarce while capable of also accounting for uncertainties in the data. Furthermore, given adequate data, the POD based and Bayesian approaches lead to \emph{similar} results.

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical data sets---collected with different scanners, protocols and disease populations---and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens' scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to data sets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment.

We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets. Exploiting tools from Bayesian nonparametrics, we develop a general meta-modeling framework that learns shared global latent structures by identifying correspondences among local model parameterizations. Our proposed framework is model-independent and is applicable to a wide range of model types. After verifying our approach on simulated data, we demonstrate its utility in aggregating Gaussian topic models, hierarchical Dirichlet process based hidden Markov models, and sparse Gaussian processes with applications spanning text summarization, motion capture analysis, and temperature forecasting.

Based on the online transaction data of COSCO group's centralized procurement platform, this paper studies the clustering method of time series type data. The different methods of similarity calculation, different clustering methods with different K values are analysed, and the best clustering method suitable for centralized purchasing data is determined. The company list under the corresponding cluster is obtained. The time series motif discovery algorithm is used to model the centroid of each cluster. Through ARIMA method, we also made 12 periods of prediction for the centroid of each category. This paper constructs a matrix of "Customer Lifecycle Theory - Five Elements of Marketing ", and puts forward corresponding marketing suggestions for customers at different life cycle stages.