The objective is to predict potential energy and forces.

Driving Automation Systems (DAS) are subject to complex road environments and vehicle behaviors and increasingly rely on sophisticated sensors and Artificial Intelligence (AI). These properties give rise to unique safety faults stemming from specification insufficiencies and technological performance limitations, where sensors and AI introduce errors that vary in magnitude and temporal patterns, posing potential safety risks. The Safety of the Intended Functionality (SOTIF) standard emerges as a promising framework for addressing these concerns, focusing on scenario-based analysis to identify hazardous behaviors and their causes. Although the current standard provides a basic cause-and-effect model and high-level process guidance, it lacks concepts required to identify and evaluate hazardous errors, especially within the context of AI. This paper introduces two key contributions to bridge this gap. First, it defines the SOTIF Temporal Error and Failure Model (STEAM) as a refinement of the SOTIF cause-and-effect model, offering a comprehensive system-design perspective. STEAM refines error definitions, introduces error sequences, and classifies them as error sequence patterns, providing particular relevance to systems employing advanced sensors and AI. Second, this paper proposes the Model-based SOTIF Analysis of Failures and Errors (MoSAFE) method, which allows instantiating STEAM based on system-design models by deriving hazardous error sequence patterns at module level from hazardous behaviors at vehicle level via weakest precondition reasoning. Finally, the paper presents a case study centered on an automated speed-control feature, illustrating the practical applicability of the refined model and the MoSAFE method in addressing complex safety challenges in DAS.

As with many fields, knowledge graphs boast a wide array of specialized terms. This guide provides a handy reference to these concepts. The Resource Description Framework (or RDF) is a conceptual framework established in the early 2000s by the World Wide Web Consortium for describing sets of interrelated assertions. RDF breaks down such assertions into underlying graph structures in which a subject node is connected to an object node via a predicate edge. The graph then is constructed by connecting the object nodes of one assertion to the subject nodes of another assertion, in a manner analogous to Tinker Toys (or molecular diagrams).

Machine learning algorithms, models, strategies, and other influential features are assisting us in unlocking a wide range of applications. These computer systems are capable of self-learning and making business decisions, as well as assisting research and improving technology. As machine learning finds new applications across various sectors, the demand for professionals in the field is growing. According to the US Bureau of Labor Statistics, the job outlook will rise 22 percent until 2030 for computer and information research scientists. Whichever area of machine learning interests you more, you must first familiarize yourself with machine learning terminology.

In a recent white paper, former Scholastic president of education Margery Mayer dubbed 2021 the "year of speech recognition" in education. And she may be right: A spike in adoption by edtech developers in the first half of this year reflects the recognition that technology holds the potential to not only create more engaging learning experiences for students, but to transform the very practice of early literacy instruction altogether. In prior years, such a vision may have seemed far fetched. But as EdSurge has previously noted, the science behind speech recognition for children has begun to come of age, enabling educational applications that have piqued the interest of edtech developers, educators and researchers alike. Part of what has enabled the growing use of speech recognition in education is the availability today of technology built specifically to cater to kids' voices and behaviors.

Glossary of common statistical, machine learning, data science terms used commonly in industry. Explanation has been provided in plain and simple English.

This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision. Also stochastic Hopfield network with hidden units. Also exhaustive search or generate and test. Also deep structured learning or hierarchical learning.

As business adoption of artificial intelligence (AI) expands rapidly, so does the vocabulary used to describe the technology and the myriad ways companies are putting it to work. While terms such as algorithm, machine learning and neural networks have become as familiar today as cloud, SaaS and IoT, dozens of new AI terms and trends are already entering the field or rising in importance. Here's a look at some of those--and why you should become familiar with each. A machine-learning training technique in which scientists intentionally expose algorithms to corrupted data to trick them into making faulty predictions or reach incorrect conclusions. The technique allows developers to uncover security vulnerabilities that could be exploited by hackers or to examine the results for hidden bias that could lead to flawed results.

As business adoption of artificial intelligence (AI) expands rapidly, so does the vocabulary used to describe the technology and the myriad ways companies are putting it to work. While terms such as algorithm, machine learning and neural networks have become as familiar today as cloud, SaaS and IoT, dozens of new AI terms and trends are already entering the field or rising in importance. Here's a look at some of those--and why you should become familiar with each. A machine-learning training technique in which scientists intentionally expose algorithms to corrupted data to trick them into making faulty predictions or reach incorrect conclusions. The technique allows developers to uncover security vulnerabilities that could be exploited by hackers or to examine the results for hidden bias that could lead to flawed results.

When an algorithm's decision-making process or output can't be easily explained by the computer or the researcher behind it. The field of A.I. concerned with teaching machines how to interpret the visual world -- a.k.a., how to see. ANNs that have multiple layers of connected neurons. This makes the process deep compared to earlier, more shallow networks. Most of the time, computer vision systems need to see hundreds or thousands (or even millions) of examples to figure out how to do something.