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- 15:41, 23 July 2024 Argument Mining Techniques (hist | edit) [4,252 bytes] Wikinimda@home (talk | contribs) (Created page with "Argument mining is a subfield of natural language processing (NLP) and computational linguistics that focuses on extracting and analyzing the structure of arguments within text. The goal is to identify the components of arguments, such as claims, premises, and conclusions, and to understand how these components relate to one another. Here are some key techniques used in argument mining: # '''Argument Component Detection''': #* '''Claim Detection''': Identifying statemen...") Tag: Visual edit
- 13:38, 14 July 2024 Cognitive Dissonance (hist | edit) [1,766 bytes] Wikinimda@home (talk | contribs) (Created page with "thumb|496x496px|Lying to ourselves is a way we avoid cognitive dissonance Cognitive dissonance is a psychological theory proposed by Leon Festinger in 1957. It refers to the mental discomfort or tension that arises when a person holds two or more contradictory beliefs, values, or attitudes simultaneously, or when their behavior conflicts with their beliefs or values. This discomfort often leads individuals to try to red...") Tag: Visual edit
- 10:59, 9 July 2024 Adaptability Quotient (AQ) (hist | edit) [3,186 bytes] Wikinimda@home (talk | contribs) (Created page with "Adaptability Quotient (AQ) is a measure of an individual's capacity to adapt to and thrive in rapidly changing environments, particularly in the context of accelerating artificial intelligence (AI) advancements. Unlike traditional metrics such as IQ, AQ focuses on cognitive flexibility, emotional resilience, and the ability to integrate AI into one's thought processes. Importance in the AI Era: As AI reshapes the cognitive landscape, AQ has emerged as a critical factor...") Tag: Visual edit
- 10:21, 9 July 2024 Cognitive biases (hist | edit) [4,819 bytes] Wikinimda@home (talk | contribs) (Created page with "Cognitive biases are systematic patterns of deviation from norm or rationality in judgment. These biases often result from the brain's attempt to simplify information processing, leading to perceptual distortion, inaccurate judgment, or illogical interpretation. Common examples include confirmation bias, where people favor information that confirms their preexisting beliefs, and availability heuristic, where people overestimate the importance of information that is readi...") Tag: Visual edit
- 10:04, 9 July 2024 Neural plasticity (hist | edit) [1,455 bytes] Wikinimda@home (talk | contribs) (Created page with "Neural plasticity, also known as neuroplasticity, is the brain's ability to reorganize itself by forming new neural connections throughout life. This process allows the neurons (nerve cells) in the brain to compensate for injury and disease and to adjust their activities in response to new situations or changes in their environment. Key aspects of neural plasticity include: # '''Synaptic Plasticity''': This involves changes in the strength of connections between neuron...") Tag: Visual edit
- 17:41, 8 July 2024 Rule-Based Tasks (hist | edit) [3,113 bytes] Wikinimda@home (talk | contribs) (Created page with "A rule-based task is an activity or process that follows a set of predefined rules or instructions to achieve a specific outcome. These tasks are typically structured, repetitive, and predictable, making them ideal for automation through rule-based systems or algorithms. Here are some key characteristics and examples of rule-based tasks: === Characteristics of Rule-Based Tasks === # '''Structured and Repetitive''': The task involves repetitive steps that follow a speci...") Tag: Visual edit
- 19:26, 7 July 2024 AI Analytics Modes (hist | edit) [2,584 bytes] Wikinimda@home (talk | contribs) (Created page with "thumb|626x626px|5 of the 8 Modes of Analytics AI analytics encompasses a variety of modes, each suited to different types of data and analysis needs. Here are some key modes of AI analytics: # '''Descriptive Analytics:''' #* '''Purpose:''' To describe what has happened in the past. #* '''Techniques:''' Data aggregation, data mining, and data visualization. #* '''Examples:''' Reports, dashboards, and data summaries. # '''Diagnostic Analy...") Tag: Visual edit
- 15:24, 7 July 2024 Google's DeepMind (hist | edit) [3,184 bytes] Wikinimda@home (talk | contribs) (Created page with "Google's DeepMind is an artificial intelligence (AI) research lab known for its pioneering work in developing advanced AI technologies. Founded in 2010 by Demis Hassabis, Shane Legg, and Mustafa Suleyman, DeepMind was acquired by Google in 2015 and is now a subsidiary of Alphabet Inc., Google's parent company. Here are some key aspects of DeepMind: === Mission and Vision === * '''Mission:''' DeepMind aims to solve intelligence and then use that to solve everything else...") Tag: Visual edit
- 03:18, 7 July 2024 Key AI Technology (hist | edit) [2,432 bytes] Wikinimda@home (talk | contribs) (Created page with "thumb|671x671px|Key AI Technologies === Summary === The page provides an overview of various AI technologies, including Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Computer Vision, Predictive Analytics, Optimization Algorithms, Robotics, Generative AI, Expert Systems, and Ensemble Methods. Each technology is associated with specific applications and examples, showcasing the diverse capabilities of AI in areas...") Tag: Visual edit
- 12:18, 5 July 2024 AI Ecosystem (hist | edit) [23,354 bytes] Wikinimda@home (talk | contribs) (Created page with "The AI Ecosystem consists of 7 layers === AI Core === The AI Core refers to a central, foundational component or set of components in artificial intelligence systems. It typically encompasses the fundamental technologies, algorithms, and models that enable AI capabilities. These core elements are essential for building and deploying AI applications. Here are some key aspects that the AI Core might include: # '''Machine Learning Models''': Algorithms and models that all...") Tag: Visual edit
- 08:51, 4 July 2024 AI Driven Adaptive Learning Systems (hist | edit) [7,836 bytes] Wikinimda@home (talk | contribs) (Created page with "AI-driven adaptive learning systems leverage artificial intelligence to enhance the personalization and effectiveness of educational experiences. These systems use machine learning algorithms, natural language processing, and other AI techniques to analyze data and make real-time adjustments to the learning environment. Here are some key aspects of AI-driven adaptive learning systems: === Key Components === # '''Machine Learning Algorithms:''' #* Analyze vast amounts o...") Tag: Visual edit
- 07:16, 4 July 2024 AI Models (hist | edit) [1,656 bytes] Wikinimda@home (talk | contribs) (Created page with "In artificial intelligence (AI), a model is a mathematical representation or algorithm that is trained on data to make predictions or decisions without being explicitly programmed to perform the task. Here are some key points about AI models: # '''Training Data''': AI models learn from data. The quality and quantity of this data can significantly affect the model's performance. # '''Algorithms''': The model uses algorithms to process the data and identify patterns or re...") Tag: Visual edit originally created as "Model"
- 16:16, 2 July 2024 Algorithm (hist | edit) [4,735 bytes] Wikinimda@home (talk | contribs) (Created page with "An algorithm is a well-defined set of instructions or rules designed to perform a specific task or solve a particular problem. Algorithms can be simple or complex and are used in various fields, including computer science, mathematics, and everyday life. Key characteristics of an algorithm include: # '''Finite Steps:''' An algorithm must have a finite number of steps, which means it should terminate after a certain number of steps. # '''Clear Instructions:''' Each step...") Tag: Visual edit
- 10:19, 1 July 2024 Anchoring Bias (hist | edit) [1,157 bytes] Wikinimda@home (talk | contribs) (Created page with "Anchoring bias is a cognitive bias that describes the common human tendency to rely too heavily on the first piece of information offered (the "anchor") when making decisions. During decision-making, this initial anchor influences subsequent judgments and evaluations. For example, if you first see a shirt that costs $1,000 and then see a second one that costs $100, you might perceive the second shirt as cheap, even if it’s actually quite expensive. The initial price o...") Tag: Visual edit
- 08:05, 1 July 2024 Data Bias (hist | edit) [4,155 bytes] Wikinimda@home (talk | contribs) (Created page with "'''Data bias''' in AI refers to biases that originate from the data used to train and test AI models. This bias can result from various factors related to how the data is collected, processed, and utilized, leading to unfair or inaccurate outcomes when the AI system is deployed. Here are key aspects of data bias: # '''Sampling Bias''': Occurs when the data collected is not representative of the entire population. For example, if an AI model for medical diagnosis is trai...") Tag: Visual edit
- 08:00, 1 July 2024 Algorithmic Bias (hist | edit) [3,887 bytes] Wikinimda@home (talk | contribs) (Created page with "'''Algorithmic bias''' in AI refers to systematic and repeatable errors in an AI system that result in unfair outcomes, such as privileging one group over another. This type of bias originates from the algorithms used to process data and make decisions. It can occur at various stages of AI development and deployment, including data collection, model training, and application. Here are some key aspects of algorithmic bias: # '''Bias in Training Data''': If the data used...") Tag: Visual edit
- 07:52, 1 July 2024 Interaction Bias (hist | edit) [3,499 bytes] Wikinimda@home (talk | contribs) (Created page with "'''Interaction bias''' in AI refers to biases that arise from the ways in which users interact with AI systems. This type of bias can occur during the training phase when AI systems learn from user interactions or during deployment when users engage with the AI in various ways. Here are key aspects of interaction bias: # '''User Input''': The data provided by users can be biased. For example, if an AI system relies on user-generated content (like search queries or socia...") Tag: Visual edit
- 07:48, 1 July 2024 Deployment Bias (hist | edit) [2,935 bytes] Wikinimda@home (talk | contribs) (Created page with "'''Deployment bias''' in AI refers to the introduction of biases and unintended consequences that can occur when AI systems are put into real-world use. This type of bias arises not from the AI's design or training data but from how the AI system is integrated, used, and interacts with the environment and users. Here are some key aspects of deployment bias: # '''Contextual Misalignment''': When an AI system is deployed in a context different from the one it was trained...") Tag: Visual edit
- 14:51, 30 June 2024 Deepfake (hist | edit) [5,066 bytes] Wikinimda@home (talk | contribs) (Created page with "A deepfake is a synthetic media technology that uses artificial intelligence, particularly deep learning, to create realistic but fake audio, video, or images. This technology can manipulate or fabricate audio, visual, or textual content in a way that makes it appear as if someone said or did something they did not. Deepfakes are often used to create convincing impersonations of people, making it difficult to distinguish between real and manipulated content. While they h...") Tag: Visual edit
- 10:10, 29 June 2024 Confirmation Bias (hist | edit) [2,046 bytes] Wikinimda@home (talk | contribs) (Created page with "thumb Confirmation bias refers to the tendency of people to search for, interpret, favor, and recall information in a way that confirms their preexisting beliefs or hypotheses. This bias can lead individuals to selectively gather evidence that supports their views while dismissing or ignoring contradictory evidence. It can affect decision-making, reasoning processes, and even the way people interpret new information, often...") Tag: Visual edit
- 09:33, 29 June 2024 Vertical AI (hist | edit) [2,503 bytes] Wikinimda@home (talk | contribs) (Created page with "Vertical AI refers to artificial intelligence systems and solutions that are specifically designed and optimized for a particular industry or domain. Unlike horizontal AI, which is more general-purpose and applicable across various industries, vertical AI focuses on the unique needs, challenges, and requirements of a specific sector. Key characteristics of vertical AI include: # '''Industry-Specific Applications''': Vertical AI solutions are tailored to address the spe...") Tag: Visual edit
- 12:19, 25 June 2024 Superintelligent AI (hist | edit) [2,941 bytes] Wikinimda@home (talk | contribs) (Created page with "Superintelligent AI, often referred to as superintelligence or artificial superintelligence (ASI), is a hypothetical form of artificial intelligence that surpasses human intelligence across all fields, including scientific creativity, general wisdom, and social skills. A superintelligent AI would outperform the brightest human minds in every respect: it would be able to solve problems, make decisions, and understand complex concepts far better than any human. Key charac...") Tag: Visual edit
- 12:17, 25 June 2024 General AI (hist | edit) [2,244 bytes] Wikinimda@home (talk | contribs) (Created page with "General AI, also known as Artificial General Intelligence (AGI) or strong AI, refers to a type of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks and domains, much like a human being. AGI aims to replicate human cognitive abilities and can perform any intellectual task that a human can, including reasoning, problem-solving, understanding natural language, and exhibiting creativity. Key characteris...") Tag: Visual edit
- 12:15, 25 June 2024 Narrow AI (hist | edit) [1,480 bytes] Wikinimda@home (talk | contribs) (Created page with "Narrow AI, also known as weak AI, refers to artificial intelligence systems that are designed and trained for a specific task or a limited range of tasks. Unlike general AI, which aims to perform any intellectual task that a human can, narrow AI focuses on performing a single function or a set of related functions. These systems do not possess generalized intelligence or consciousness and are not capable of performing tasks outside their designated domain. Examples of n...") Tag: Visual edit
- 09:23, 20 June 2024 CTO: AI Leadership and Strategy (hist | edit) [2,830 bytes] Wikinimda@home (talk | contribs) (Created page with "==== '''Introduction''' ==== Artificial Intelligence (AI) is a critical driver of technological innovation. For CTOs, leveraging AI is essential for advancing technology capabilities and maintaining a competitive edge. ==== '''The Importance of AI for CTOs''' ==== AI can accelerate product development, enhance IT infrastructure, and drive innovation. CTOs must understand AI's potential to transform technology strategy and operations. ==== '''Strategic Implementation of...") Tag: Visual edit
- 09:20, 20 June 2024 CIO: AI Leadership and Strategy (hist | edit) [2,751 bytes] Wikinimda@home (talk | contribs) (Created page with "'''Introduction''' Artificial Intelligence (AI) is a transformative force in IT and business operations. For CIOs, leveraging AI is crucial for driving technological innovation and optimizing IT infrastructure. '''The Importance of AI for CIOs''' AI enhances IT operations, improves cybersecurity, and supports data-driven decision-making. CIOs must harness AI to ensure technological leadership and operational efficiency. '''Strategic Implementation of AI''' Integr...") Tag: Visual edit
- 09:01, 20 June 2024 CHRO: AI Leadership and Strategy (hist | edit) [2,780 bytes] Wikinimda@home (talk | contribs) (Created page with "==== '''Introduction''' ==== Artificial Intelligence (AI) is reshaping human resources (HR) and talent management. For CHROs, leveraging AI is essential for optimizing HR processes and enhancing employee experience. '''The Importance of AI for CHROs''' AI can streamline recruitment, improve employee engagement, and enhance workforce planning. CHROs must harness AI to drive HR efficiency and strategic talent management. ==== '''Strategic Implementation of AI''' ==== I...") Tag: Visual edit
- 08:58, 20 June 2024 CMO: AI Leadership and Strategy (hist | edit) [2,805 bytes] Wikinimda@home (talk | contribs) (Created page with "==== '''Introduction''' ==== Artificial Intelligence (AI) is transforming marketing strategies and customer engagement. For CMOs, leveraging AI is crucial for enhancing marketing effectiveness and driving innovation. ==== '''The Importance of AI for CMOs''' ==== AI can enhance customer insights, personalize marketing campaigns, and optimize advertising spend. CMOs must harness AI to improve marketing efficiency and effectiveness. ==== '''Strategic Implementation of AI'...") Tag: Visual edit
- 08:53, 20 June 2024 COO: AI Leadership and Strategy (hist | edit) [2,932 bytes] Wikinimda@home (talk | contribs) (Created page with "==== '''Introduction''' ==== Artificial Intelligence (AI) is revolutionizing operational processes across industries. For COOs, leveraging AI is essential for enhancing operational efficiency and driving innovation. ==== '''The Importance of AI for COOs''' ==== AI can streamline operations, optimize resource allocation, and improve supply chain management. COOs must understand AI's potential to enhance productivity and operational excellence. ==== '''Strategic Implemen...") Tag: Visual edit
- 08:49, 20 June 2024 CFO: AI Leadership and Strategy (hist | edit) [2,738 bytes] Wikinimda@home (talk | contribs) (Created page with "==== '''Introduction''' ==== Artificial Intelligence (AI) is transforming financial operations and strategies. For CFOs, understanding and leveraging AI is crucial for optimizing financial performance and decision-making. ==== '''The Importance of AI for CFOs''' ==== AI enhances financial analysis, forecasting, and risk management. CFOs must harness AI to improve accuracy, efficiency, and strategic insights. ==== '''Strategic Implementation of AI''' ==== Integrating AI...") Tag: Visual edit
- 21:17, 19 June 2024 CEO: AI Leadership and Strategy (hist | edit) [2,960 bytes] Wikinimda@home (talk | contribs) (Created page with "==== '''Introduction''' ==== Artificial Intelligence (AI) is revolutionizing business operations across industries. For CEOs, understanding and leading AI initiatives is critical to maintaining competitive advantage and driving innovation. ==== '''The Importance of AI for CEOs''' ==== AI can significantly enhance operational efficiency, customer experiences, and decision-making processes. CEOs must recognize AI's potential to transform business models and create new rev...") Tag: Visual edit
- 02:35, 19 June 2024 Data Preprocessing (hist | edit) [3,895 bytes] Wikinimda@home (talk | contribs) (Created page with "Data preprocessing is a crucial step in the machine learning and artificial intelligence (AI) pipeline, involving the transformation and cleaning of raw data before it is used to train models. Effective data preprocessing enhances the quality of the data, which in turn can improve the performance and accuracy of AI models. Here are the key components and steps involved in data preprocessing: # '''Data Cleaning''': #* '''Handling Missing Values''': Addressing missing dat...") Tag: Visual edit
- 02:28, 19 June 2024 Cloud Computing (hist | edit) [4,181 bytes] Wikinimda@home (talk | contribs) (Created page with "Cloud computing is a technology that provides on-demand computing resources over the internet. These resources include servers, storage, databases, networking, software, and analytics, which are delivered on a pay-as-you-go basis. Cloud computing allows businesses and individuals to access and manage these resources without the need for physical infrastructure, offering flexibility, scalability, and cost efficiency. Here are the main aspects of cloud computing: # '''Ser...") Tag: Visual edit
- 02:22, 19 June 2024 Edge Computing (hist | edit) [3,556 bytes] Wikinimda@home (talk | contribs) (Created page with "Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, improving response times and saving bandwidth. This approach reduces the latency associated with sending data to centralized cloud data centers and is particularly useful for applications that require real-time processing and low-latency communication. Here are the key components and concepts of edge computing: # '''Proximity to Data Sou...") Tag: Visual edit
- 02:20, 19 June 2024 Internet of Things (IoT) (hist | edit) [3,355 bytes] Wikinimda@home (talk | contribs) (Created page with "The Internet of Things (IoT) refers to a network of interconnected physical devices that communicate and exchange data with each other and with other systems over the internet. These devices, often embedded with sensors, software, and other technologies, can range from everyday household objects to sophisticated industrial tools. Here are the key components and concepts of IoT: # '''Devices and Sensors''': IoT devices include a wide variety of objects such as smart home...") Tag: Visual edit
- 02:04, 19 June 2024 Big Data (hist | edit) [4,557 bytes] Wikinimda@home (talk | contribs) (Created page with "Big Data refers to extremely large and complex datasets that traditional data processing software cannot adequately handle. These datasets can be so vast and intricate that they require specialized tools and techniques to store, process, analyze, and visualize. Big Data is characterized by the "Five Vs": === 1. '''Volume:''' === * '''Scale of Data:''' Big Data involves massive volumes of data generated from various sources such as social media, sensors, transactions, l...") Tag: Visual edit
- 01:59, 19 June 2024 Data Storage (hist | edit) [3,896 bytes] Wikinimda@home (talk | contribs) (Created page with "Data storage in the context of artificial intelligence (AI) refers to the methods and systems used to save and manage the data that AI systems require for training, inference, and continuous learning. The data stored can include raw data, processed data, intermediate results, model parameters, and outputs. Effective data storage is crucial for AI applications as it impacts the performance, scalability, and reliability of AI systems. Here are some key aspects of data stor...") Tag: Visual edit
- 01:45, 19 June 2024 Machine Learning (hist | edit) [5,247 bytes] Wikinimda@home (talk | contribs) (Created page with "Machine learning is a branch of artificial intelligence (AI) that focuses on building systems that can learn from and make decisions based on data. Instead of being explicitly programmed to perform a task, these systems use algorithms to identify patterns, make predictions, or determine actions based on data inputs. Here are some key concepts and components of machine learning: # '''Data''': The foundation of machine learning. Data can come in various forms, such as tex...") Tag: Visual edit
- 07:42, 14 June 2024 START Success with AI (hist | edit) [1,746 bytes] Wikinimda@home (talk | contribs) (Created page with "'''START Success with AI''' is a structured methodology designed to help businesses effectively harness the power of artificial intelligence to achieve real-world results. Here's how it breaks out: '''S''' – '''Strategy''': Develop a clear plan for how AI will be used in your business. This involves understanding your goals, identifying the areas where AI can have the most impact, and setting a roadmap for implementation. '''T''' – '''Tactics''': Identify the speci...") Tag: Visual edit
- 11:49, 7 June 2024 Natural Language Processing (NLP) (hist | edit) [5,454 bytes] Wikinimda@home (talk | contribs) (Created page with "Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) and linguistics that focuses on the interaction between computers and human language. It involves enabling computers to understand, interpret, and generate human language in a way that is both meaningful and useful. NLP combines computational linguistics, machine learning, and deep learning techniques to process and analyze large amounts of natural language data. === Key Components of NLP: =...") Tag: Visual edit
- 11:47, 7 June 2024 Deep Learning (hist | edit) [5,464 bytes] Wikinimda@home (talk | contribs) (Created page with "Deep learning is a subset of machine learning that focuses on using neural networks with many layers (hence "deep") to model and understand complex patterns in data. It is particularly effective for tasks involving large amounts of unstructured data, such as images, audio, and text. Deep learning has been instrumental in advancing artificial intelligence (AI) and has led to significant breakthroughs in various applications. === Key Concepts of Deep Learning: === # '''N...") Tag: Visual edit
- 11:46, 7 June 2024 Neural Networks (hist | edit) [5,810 bytes] Wikinimda@home (talk | contribs) (Created page with "Neural networks are a class of machine learning models inspired by the structure and function of the human brain. They consist of interconnected layers of nodes, or neurons, which process and transmit information. Neural networks are particularly powerful for tasks involving pattern recognition, such as image and speech recognition, natural language processing, and many other applications in artificial intelligence (AI). === Key Components of Neural Networks: === # '''...") Tag: Visual edit
- 11:44, 7 June 2024 Recommendation Systems (hist | edit) [5,246 bytes] Wikinimda@home (talk | contribs) (Created page with "Recommendation systems, also known as recommender systems, are a subclass of information filtering systems that seek to predict the preferences or interests of users and make suggestions for items or content that are likely to be of interest to them. These systems are widely used in various applications, such as e-commerce, social media, online streaming services, and content-based websites, to enhance user experience and increase engagement. === Key Concepts of Recomme...") Tag: Visual edit
- 11:41, 7 June 2024 Speech Recognition (hist | edit) [5,037 bytes] Wikinimda@home (talk | contribs) (Created page with "Speech recognition is a technology that enables computers and devices to understand and process human speech. This technology converts spoken language into text, making it possible for machines to respond to voice commands, transcribe spoken words, and interact with users through natural language. Speech recognition systems leverage a combination of acoustic models, language models, and various algorithms to interpret and understand speech accurately. === Key Components...") Tag: Visual edit
- 11:38, 7 June 2024 Computer Vision (hist | edit) [5,207 bytes] Wikinimda@home (talk | contribs) (Created page with "Computer vision is a field of artificial intelligence (AI) that enables computers to interpret and understand the visual world. By using digital images from cameras, videos, and deep learning models, computer vision seeks to automate tasks that the human visual system can perform. The goal is to give machines the ability to see, process, and analyze visual data in a way that mimics human vision, ultimately enabling them to make decisions based on this data. === Key Comp...") Tag: Visual edit
- 11:32, 7 June 2024 Predictive Analytics (hist | edit) [4,908 bytes] Wikinimda@home (talk | contribs) (Created page with "Predictive analytics is a branch of advanced analytics that uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. The primary goal of predictive analytics is to forecast future events and trends, helping organizations make more informed decisions. === Key Components of Predictive Analytics: === # '''Data Collection''': Gathering relevant historical data from various sources,...") Tag: Visual edit
- 11:25, 7 June 2024 Quantum Computing (hist | edit) [4,533 bytes] Wikinimda@home (talk | contribs) (initial entry) Tag: Visual edit
- 11:10, 7 June 2024 Explainable AI (XAI) (hist | edit) [4,185 bytes] Wikinimda@home (talk | contribs) (initial entry) Tag: Visual edit
- 11:06, 7 June 2024 Edge AI (hist | edit) [3,465 bytes] Wikinimda@home (talk | contribs) (initial entry) Tag: Visual edit originally created as "Edge ai"
- 11:02, 7 June 2024 Federated Learning (hist | edit) [3,058 bytes] Wikinimda@home (talk | contribs) (initial entry) Tag: Visual edit
