Bachelor's Degree In Intelligence Studies

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Bachelor's Degree In Intelligence Studies

Excellent articles represent cutting-edge research with significant potential for significant impact in the field. Distinctive articles are submitted by individual invitation or recommendation of scientific editors and are reviewed before publication.

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A feature article can be either an original research article, a substantial new scientific study, often involving multiple methods or approaches, or a comprehensive review with a concise and accurate update of recent advances in the field that systematically reviews the most interesting developments in the field of science. literature. This type of paper provides an overview of future research directions or possible applications.

Editor’s Choice articles are based on the recommendations of scientific journal editors from around the world. The editors select a small number of articles recently published in the journal that they believe will be of particular interest to readers or important in the relevant field of study. The aim is to provide a brief overview of some of the most interesting work published in the various research areas of the journal.

Lucas Fischer 1, * , Lisa Erlinger 1, 2 , Verena Geist 1 , Rudolf Ramler 1 , Florian Sobieski 1 , Werner Zellinger 1 , David Brunner 1 , Mohit Kumar 1 and Bernhard Moser 1

Received: 3 December 2020 / Revised: 23 December 2020 / Accepted: 25 December 2020 / Published: 31 December 2020

An Explainable Artificial Intelligence Approach For Predicting Cardiovascular Outcomes Using Electronic Health Records

Key issues are discussed along with past lessons and current research throughout the machine learning systems development cycle. This will be done by looking at the internals of current deep learning models, data and software quality issues, and human-centered artificial intelligence (AI), including reliability and ethical aspects. The analysis reveals a fundamental gap in theory and practice that challenges AI systems engineering at the levels of data quality assurance, modeling, software development, and deployment. The purpose of this article is to identify research topics for exploring approaches to solving these problems.

Many real-world tasks are characterized by uncertainty and probabilistic data that are difficult for humans to understand and process. Machine learning (ML) and knowledge extraction [1] help transform this data into useful information for a wide range of applications such as image recognition, scene understanding, decision support systems, etc., enabling new cases in a wide range of domains.

The success of various machine learning techniques, especially deep neural networks (DNNs), for computer vision and pattern recognition has led to a Cambrian explosion in the field of artificial intelligence (AI). In many application areas, AI researchers have turned to deep learning as the solution of choice [2, 3]. A hallmark of this development is the acceleration of progress in artificial intelligence over the past decade, which has resulted in artificial intelligence systems that are powerful enough to raise serious questions of ethical and societal acceptance. Another feature of this development is the method of building such systems.

First of all, there is a growing interconnection of traditionally separate disciplines such as data analysis, model building, and software development. As shown in Figure 1, AI systems development covers all stages of building AI systems, from problem understanding, problem specification, AI model selection, data collection and editing, to target deployment platforms and application environments.

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In particular, data-driven AI techniques such as DNNs allow data to shape models and the software systems that drive them. Accordingly, Herdel’s AI system development challenges can be divided into the following three categories:

The structure of this paper follows the structure of our previous conference paper [4], now improved and expanded with additional use cases, details and diagrams. This article is intended as a “Lessons Learned” article in which we recall our experience of past and current machine learning research projects for customers and research partners in various industries such as manufacturing, chemical, healthcare, and mobility . In contrast to recent reviews on the challenges of deploying machine learning systems [5], we also present current approaches from ongoing research projects. The paper is organized as part analysis and part illustrative examples to demonstrate new approaches from selected current research projects. The analysis section consists of three building blocks as shown in Figure 1: Block 1 — Barrier-to-order application requirements (see Section 2), Block 2 — Artificial intelligence simulation cycle (see Section 3), and Block 3 — System engineering software cycle (see Chapter 4). Selected approaches based on current research are presented in Chapter 5. An overview of the framework is provided below:

There are features of deep learning methods that affect the correct interpretation of system output and the transparency of system configuration.

The development and principles of machine learning testing are based on the theory of statistical learning and its fundamental theorems, such as Vapnik’s theorem [6]. Theoretical analysis is based on idealized assumptions, such as that the data are drawn independently and equally distributed from the same probability distribution. As stated in reference [7]; however, this assumption may be violated in typical applications such as natural language processing [8] and computer vision [9, 10].

General Intelligence Disentangled Via A Generality Metric For Natural And Artificial Intelligence

This dataset drift problem can arise from how the input features are used, how the training and test sets are chosen, from data sparsity, from changes in the data distribution due to non-stationary environments, and from changes in activation patterns in the layers of deep neural networks. Such drift in the data set can cause incorrect parameter settings when performing testing strategies such as cross-validation [11, 12].

This is why the development of machine learning systems relies heavily on the ability of a data scientist to investigate and solve such problems.

First, unlike traditional engineering, there is no uniqueness of internal configuration, which causes difficulties when comparing models. Machine learning-based systems, especially deep learning models, are generally considered black boxes. However, it is not only the complex nested nonlinear structure that is important, as often pointed out in the literature, see reference [13]. There are mathematical or physical systems that are also complex, nested, and nonlinear, but can be interpreted (eg, wavelets, statistical mechanics). A surprising, unexpected phenomenon is that such deep networks are easier to optimize (train) as the number of layers, hence the complexity, increases, see Reference [14, 15]. In particular, to find an acceptable suboptimal among many equally good possibilities. As a result, unlike classical engineering, we lose the uniqueness of the internal optimal state.

Another feature of the most modern deep learning methods is the absence of a confidence indicator. Unlike Bayesian approaches to machine learning, most deep learning models do not offer a justified confidence measure for model uncertainty. For example, in classification models, the probability vector obtained at the upper level (mostly the softmax result) is often interpreted as the confidence of the model, see e.g. reference [16] or reference [17]. However, functions such as softmax can lead to unreasonably high extrapolation confidence for points that are far from the training data, thus creating a false sense of security [18]. Therefore, it seems natural to try to introduce a Bayesian approach for DNN models as well. The resulting uncertainty measures (or, synonymously, confidence measures) rely on an approximation of the posterior distribution in terms of the weights given to the data. As a promising approach in this context, variational methods, such as those based on Monte Carlo dropout [19], allow to transform these Bayesian concepts into computational algorithms. The variational approach relies on the Kuhlbeck-Leibler dissimilarity to measure the difference between distributions. As a result, the resulting approximate distribution is concentrated around one mode, underestimating the uncertainty outside that mode. Thus, the resulting confidence measure for this example remains unsatisfactory, and there may still be areas of misinterpreted high confidence.

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In addition, there is still an unsolved problem of lack of control for high-dimensional effects. There are high dimensionality effects that are not yet fully understood in the context of deep learning, see Reference [20, 21]. Such high-dimensional effects can cause instabilities, as illustrated, for example, by the appearance of so-called competing cases, see, for example, Refs [22, 23].

The life cycle of an AI model refers to the stages of data-driven modeling development, from curating the data as a basis for training the model to finding a solution configuration for the proposed machine learning model for the task at hand. Typically, these steps aim to extract higher-level semantics and meaning from lower-level representations, as shown in Figure 2.

Instead of using raw data samples directly, it has become a standard machine learning technique to apply additional ones

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