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Industrial Artificial Intelligence
Industrial artificial intelligence, or industrial AI, usually refers to the application of artificial intelligence to industry. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis and insight discovery. Although in a dystopian vision of AI applications, intelligent machines may take away jobs of humans and cause social and ethical issues, industry in general holds a more positive view of AI and sees this transformation of economy unstoppable and expects huge business opportunities in this process. History The concept of artificial intelligence was initially proposed in the 1940s, and the idea of improving productivity and gaining insights through smart analytics ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. The ''Oxford English Dictionary'' of Oxford University Press defines artificial intelligence as: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Discovery
Knowledge extraction is the creation of Knowledge representation and reasoning, knowledge from structured (relational databases, XML) and unstructured (text (literary theory), text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must Knowledge representation and reasoning, represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (Natural language processing, NLP) and extract, transform, load, ETL (data warehouse), the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a Database schema, relational schema. It requires either the reuse of existing Knowledge representation and reasoning, formal knowledge (reusing identifiers or ontologies) or the generation of a schema based on the source data. The RDB2RDF W3C group is currently standardizing a language for extraction of Re ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Operational Artificial Intelligence
Operational artificial intelligence, or operational AI, is a type of intelligent system designed for real-world applications, particularly at commercial scale. The term is used to distinguish accessible artificially intelligent (AI) systems from fundamental research, fundamental AI research and from industrial AI applications which are not integrated with the routine usage of a business. The definition of operational AI differs throughout the IT Industry (economics), industry, where vendors and individual organizations often create their own custom definitions of such processes and services for the purpose of marketing their own products. Applications include text analytics, advanced analytics, facial recognition system, facial and image recognition, machine learning, and natural language generation. Definitions According to a white paper by software company Tupl Inc, continuous machine learning model training and results extraction in the telecom industry requires a large number ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Domain Know-how
Domain knowledge is knowledge of a specific, specialized discipline or field, in contrast to general (or domain-independent) knowledge. The term is often used in reference to a more general discipline—for example, in describing a software engineer who has general knowledge of computer programming as well as domain knowledge about developing programs for a particular industry. People with domain knowledge are often regarded as specialists or experts in their field. Knowledge capture In software engineering, ''domain knowledge'' is knowledge about the environment in which the target system operates, for example, software agents. Domain knowledge usually must be learned from software users in the domain (as domain specialists/experts), rather than from software developers. It may include user workflows, data pipelines, business policies, configurations and constraints and is crucial in the development of a software application. Expert's domain knowledge (frequently informal and il ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Big Data
Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe Big data is the one associated with large body of information that we could not comprehend when used only in smaller amounts. In it primary definition though, Big data refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: ''volume'', ''variety'', and ''velocity''. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Prognostics And Health Management
Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. This lack of performance is most often a failure beyond which the system can no longer be used to meet desired performance. The predicted time then becomes the remaining useful life (RUL), which is an important concept in decision making for contingency mitigation. Prognostics predicts the future performance of a component by assessing the extent of deviation or degradation of a system from its expected normal operating conditions. The science of prognostics is based on the analysis of failure modes, detection of early signs of wear and aging, and fault conditions. An effective prognostics solution is implemented when there is sound knowledge of the failure mechanisms that are likely to cause the degradations leading to eventual failures in the system. It is therefore necessary to have initial information on the possible failures (incl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Prognostics
Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. This lack of performance is most often a failure beyond which the system can no longer be used to meet desired performance. The predicted time then becomes the remaining useful life (RUL), which is an important concept in decision making for contingency mitigation. Prognostics predicts the future performance of a component by assessing the extent of deviation or degradation of a system from its expected normal operating conditions. The science of prognostics is based on the analysis of failure modes, detection of early signs of wear and aging, and fault conditions. An effective prognostics solution is implemented when there is sound knowledge of the failure mechanisms that are likely to cause the degradations leading to eventual failures in the system. It is therefore necessary to have initial information on the possible failures (incl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Preventive Maintenance
The technical meaning of maintenance involves functional checks, servicing, repairing or replacing of necessary devices, equipment, machinery, building infrastructure, and supporting utilities in industrial, business, and residential installations. Over time, this has come to include multiple wordings that describe various cost-effective practices to keep equipment operational; these activities occur either before or after a failure. Definitions Maintenance functions can defined as maintenance, repair and overhaul (MRO), and MRO is also used for maintenance, repair and operations. Over time, the terminology of maintenance and MRO has begun to become standardized. The United States Department of Defense uses the following definitions:Federal Standard 1037C and from MIL-STD-188 and from the Department of Defense Dictionary of Military and Associated Terms * Any activity—such as tests, measurements, replacements, adjustments, and repairs—intended to retain or restore a func ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Predictive Maintenance
Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted. Thus, it is regarded as condition-based maintenance carried out as suggested by estimations of the degradation state of an item. The main promise of predictive maintenance is to allow convenient scheduling of corrective maintenance, and to prevent unexpected equipment failures. The key is "the right infor equipment lifetime, increased plant safety, fewer accidents with negative impact on environment, and optimized spare parts handling. Predictive maintenance differs from preventive maintenance because it relies on the actual condition of equipment, rather than average or expected life statistics, to predict when maintenance will be required. Typically, Machine Learning approaches are ado ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Deep Learning
Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks and Transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, Climatology, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, artificial ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
NARA
The National Archives and Records Administration (NARA) is an " independent federal agency of the United States government within the executive branch", charged with the preservation and documentation of government and historical records. It is also tasked with increasing public access to those documents which make up the National Archive. NARA is officially responsible for maintaining and publishing the legally authentic and authoritative copies of acts of Congress, presidential directives, and federal regulations. NARA also transmits votes of the Electoral College to Congress. It also examines Electoral College and Constitutional amendment ratification documents for prima facie legal sufficiency and an authenticating signature. The National Archives, and its publicly exhibited Charters of Freedom, which include the original United States Declaration of Independence, United States Constitution, United States Bill of Rights, and many other historical documents, is headquart ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
