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Sadi Evren Seker
Sadi Evren Seker is a Turkish computer scientist and data scientist. He is the first person to introduce the concept of "Computerized Argument Delphi Technique" in the literature, which is an enhancement to the Delphi method originally developed on Project RAND. Career and research After obtaining a Ph.D. degree in computer engineering from Yildiz Technical University in 2010, Seker joined the faculty at Istanbul University in 2011 as an assistant professor. He also served as a visiting scholar at the Data Mining Lab at the University of Texas at Dallas from 2012 to 2014. In addition, he held a visiting professorship at Smith College in Massachusetts, within the Department of Computer Science from 2016 to 2017 In recognition of his achievements, he was promoted to the rank of professor in the Department of Computer Engineering in 2020. He is the inventor of several data science methods and holds patents for: * A method and system for clustering performance evaluation and i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Turkish Language
Turkish ( , , also known as 'Turkish of Turkey') is the most widely spoken of the Turkic languages, a member of Oghuz languages, Oghuz branch with around 90 million speakers. It is the national language of Turkey and one of two official languages of Cyprus. Significant smaller groups of Turkish speakers also exist in Germany, Austria, Bulgaria, North Macedonia, Greece, other parts of Europe, the South Caucasus, and some parts of Central Asia, Iraqi Turkmen, Iraq, and Syrian Turkmen, Syria. Turkish is the List of languages by total number of speakers, 18th-most spoken language in the world. To the west, the influence of Ottoman Turkish language, Ottoman Turkish—the variety of the Turkish language that was used as the administrative and literary language of the Ottoman Empire—spread as the Ottoman Empire expanded. In 1928, as one of Atatürk's reforms in the early years of the Republic of Turkey, the Persian alphabet, Perso-Arabic script-based Ottoman Turkish alphabet was repl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Visiting Scholar
In academia, a visiting scholar, visiting scientist, visiting researcher, visiting fellow, visiting lecturer, or visiting professor is a scholar from an institution who visits a host university to teach, lecture, or perform research on a topic for which the visitor is valued. In many cases, the position is not salaried because the visitor is salaried by their home institution (or partially salaried, as in some cases of sabbatical leave from US universities). Some visiting positions are salaried. Typically, a visiting scholar may stay for a couple of months or even a year,UT"Visiting Scholar". The University of Texas at Austin. though the stay can be extended. A visiting scholar is usually invited by the host institution, and it is not out of the ordinary for them to provide accommodation. Such an invitation is often regarded as recognizing the scholar's prominence in the field. Attracting prominent visiting scholars often allows the permanent faculty and graduate students t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Explainable Artificial Intelligence
Explainable AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), is a field of research within artificial intelligence (AI) that explores methods that provide humans with the ability of ''intellectual oversight'' over AI algorithms. The main focus is on the reasoning behind the decisions or predictions made by the AI algorithms, to make them more understandable and transparent. This addresses users' requirement to assess safety and scrutinize the automated decision making in applications. XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision. XAI hopes to help users of AI-powered systems perform more effectively by improving their understanding of how those systems reason. XAI may be an implementation of the social right to explanation. Even if there is no such legal right or regulatory requirement, XAI can improve the user experience of a product or servi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Automated Machine Learning
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning. The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. Common techniques used in AutoML include hyperparameter optimization, meta-learning and neural architecture search. Comparison to the standard approach In a ty ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Regulation Of Artificial Intelligence
Regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI). It is part of the broader regulation of algorithms. The regulatory and policy landscape for AI is an emerging issue in jurisdictions worldwide, including for international organizations without direct enforcement power like the IEEE or the OECD. Since 2016, numerous AI ethics guidelines have been published in order to maintain social control over the technology. Regulation is deemed necessary to both foster AI innovation and manage associated risks. Furthermore, organizations deploying AI have a central role to play in creating and implementing trustworthy AI, adhering to established principles, and taking accountability for mitigating risks. Regulating AI through mechanisms such as review boards can also be seen as social means to approach the AI control problem. Background According to Stanford University's 2025 AI Index, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Big Data Analytics
Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing software. Data with many entries (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. Thus a fourth concept, ''veracity,'' refers to the quality or insightfulness of the data. Without sufficient investment in expertise for big data veracity, the volume and variety of data can produce costs and risks that exceed an organization's capacity to create and capture ''value'' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
