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- 00:00, 19 March 2024 Fermi Paradox (hist | edit) [3,585 bytes] Gmelliapi (talk | contribs) (Created page with " A Fermi Paradox is an Extraterrestrial Life that ... * <B>See:</B> #Original Conversations, Rare Earth Hypothesis, Extraterrestrial Life, Insider Inc, The Washington Post, Italian-American, Physicist, Enrico Fermi, Edward Teller, Herbert York, Emil Konopinski, UFO. ---- ---- ==References== === 2024 === * (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Fermi_paradox Retrieved:2024-3-19. ** The '''Fe...")
- 23:19, 18 March 2024 Mass Hysteria (hist | edit) [1,230 bytes] Gmelliapi (talk | contribs) (Created page with " A Mass Hysteria is a Nervous System that ... * <B>AKA:</B> Mass Psychogenic Illness. * <B>See:</B> Nervous System, Pieter Brueghel The Younger, Dancing Mania, Middle Ages, Psychiatry, Clinical Psychology, Somatic Symptom Disorder. ---- ---- ==References== === 2024 === * (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Mass_psychogenic_illness Retrieved:2024-3-18. ** '''Mass psychogenic illness''' ('''MPI'''), also...")
- 23:02, 18 March 2024 Brainstorming with an AI Chatbot (hist | edit) [2,048 bytes] Gmelli (talk | contribs) (Created page with "A Brainstorming with an AI Chatbot is a brainstorming session that incorporates an AI chatbot to facilitate or enhance the generation of ideas. * <B>Context:</B> ** It can (typically) leverage the AI chatbot's ability to provide instant information, suggestions, and relevant data, thus enriching the brainstorming process. ** It can (typically) allow participants to interact with the AI chatbot to explore diverse perspectives and stimulate creative thinking. *...")
- 22:59, 18 March 2024 Brainstorming Session (hist | edit) [1,943 bytes] Gmelli (talk | contribs) (Created page with "A Brainstorming Session is a creative human session that aims to generate a large number of ideas for the solution of a problem. * <B>Context:</B> ** It can (typically) encourage participants to build on each other's ideas. ** It can (typically) apply a Brainstorming Technique. ** It can (often) involve a facilitator to guide the session and ensure that all participants have the opportunity to contribute. ** It can (often) include a follow-up phase where idea...")
- 22:50, 18 March 2024 AI Organization Entity (hist | edit) [4,044 bytes] Gmelli (talk | contribs) (Created page with "An AI Organization Entity is an organization that primarily focuses on the research, development, application, and promotion of artificial intelligence technologies. * <B>Context:</B> ** It can (typically) engage in a wide range of activities, including AI research, AI product development, AI ethics and governance, and AI policy advocacy. ** It can (often) consist of a multidisciplinary team that includes AI researchers, software enginee...")
- 22:27, 18 March 2024 Organization Governance Structure (hist | edit) [1,296 bytes] Gmelli (talk | contribs) (Created page with "A Organization Governance Structure is a governance structure that involves the processes, customs, policies, laws, and institutions affecting the way an organization is directed, administered, or controlled. * <B>Context:</B> ** It can typically encompass the mechanisms by which organizations ensure accountability, fairness, and transparency in their relationships with stakeholders. ** It can often include guidelines for board of directors composition,...")
- 22:06, 18 March 2024 Collective Intelligent Agent (hist | edit) [1,904 bytes] Gmelli (talk | contribs) (Created page with "A Collective Intelligent Agent is an entity that exhibits intelligence and decision-making capabilities through the integrated actions of multiple agents, whether those agents are Human Agents, Artificial Agents, or a combination thereof. * <B>AKA:</B> Collaborative Intelligence. * <B>Context:</B> ** It can leverage the diverse expertise and perspectives of its components, such as Ant Colonies, Research Teams, or Decentralized Autonomous...")
- 20:48, 18 March 2024 Multi-Modal Large Language Model (MLLM) (hist | edit) [7,770 bytes] Gmelli (talk | contribs) (Created page with "A Multi-Modal Large Language Model (MLLM) is a large language model that is a text-to-* model. * <B>Context:</B> ** It can (typically) be developed using advanced ML Techniques and Deep Learning Techniques. ** It can (often) surpass traditional text-only LLMs by integrating and processing information from various modalities, enhancing its understanding and response generation capabilities. ** It can (often) be a Fine-Tuned Multi-Modal LLM. **...")
- 20:29, 18 March 2024 Decoder-Only Neural Model Architecture (hist | edit) [1,214 bytes] Gmelli (talk | contribs) (Redirected page to Decoder-only Neural Model Architecture) Tag: New redirect
- 20:28, 18 March 2024 KOSMOS-1 Architecture (hist | edit) [3,386 bytes] Gmelli (talk | contribs) (Created page with "A KOSMOS-1 Architecture is a decoder-only architecture (that processes both text and visual tokens) based on the foundational approach utilized in the Kosmos-1 model. * <B>Context:</B> ** It can typically be used to build performant Multimodal Large Language Models (MLLMs). ** It can often serve as a base for integrating visual capabilities into Large Language Models, enhancing their ability to understand and generate content that combines language an...")
- 20:25, 18 March 2024 Deep Neural Model Instruction-Tuning Task (hist | edit) [7,455 bytes] Gmelli (talk | contribs) (Created page with "A Deep Neural Model Instruction-Tuning Task is a model fine-tuning task that involves refining a Large Language Model's ability to follow instructions more accurately by training it on a dataset of instructions and their desired outcomes. * <B>Context:</B> ** It can (typically) involve Model Fine Turning Data (of instructions and the corresponding correct responses or actions). ** It can (often) leverage existing model predictions and human feedback to ge...")
- 20:21, 18 March 2024 Deep Neural Model Fine-Tuning Task (hist | edit) [1,650 bytes] Gmelli (talk | contribs) (Created page with "A Deep Neural Model Fine-Tuning Task is a deep learning model transfer learning task that allows the adjustment of the parameters of a pre-trained deep learning model to improve its performance on a specific task. * <B>Context:</B> ** It can (typically) be solved by a Deep Neural Model Fine-Tuning System (that implements an model fine-tuning algorithm). ** ... * <B>Example(s):</B> ** Image Model Fine-Tuning Task...")
- 20:17, 18 March 2024 Deep Neural Model Fine-Tuning Algorithm (hist | edit) [3,596 bytes] Gmelli (talk | contribs) (Created page with "A Deep Neural Model Fine-Tuning Algorithm is a deep-learning transfer learning algorithm that adjusts the parameters of a pre-trained deep learning model to improve its performance on a specific task. * <B>AKA:</B> Fine-Tuning (Deep Learning). * <B>Context:</B> ** It can (typically) be implemented by a System (to solve a neural fine-tuning...")
- 19:57, 18 March 2024 Zhe Gan (hist | edit) [3,208 bytes] Gmelli (talk | contribs) (Created page with "Zhe Gan is a person. * <B>See:</B> [[]]. ---- ---- == References == * https://scholar.google.com/citations?user=E64XWyMAAAAJ&hl=en ---- __NOTOC__")
- 19:54, 18 March 2024 Visual Instruction Tuning Task (hist | edit) [1,837 bytes] Gmelli (talk | contribs) (Created page with "A Visual Instruction Tuning Task is a instruction fine-tuning task that enhances Large Language Models with visual capabilities through instruction-based learning. * <B>Context:</B> ** It can typically involve training a pre-trained language model on a dataset containing instructions or prompts paired with visual data, aiming to improve the model's performance on multimodal tasks. ** It can often leverage datasets with paired image-text data or struct...")
- 23:50, 17 March 2024 AI Prompt Implementation System (hist | edit) [3,927 bytes] Gmelli (talk | contribs) (Created page with "An AI Prompt Implementation System is a software implementation system that supports AI prompt implementations. * <B>Context:</B> ** It can (typically) provide a development environment tailored to the creation and testing of AI prompts, incorporating tools and features that streamline the implementation process. ** It can (often) include Integrated Development Environment (IDE) features such as syntax highlighting, code completion, and debugging tools sp...")
- 23:46, 17 March 2024 AI Prompt Implementation Task (hist | edit) [2,371 bytes] Gmelli (talk | contribs) (Created page with "A AI Prompt Implementation Task is an AI Prompt Development Process that involves the coding or scripting of AI prompts into a format understandable by AI models. * <B>Context:</B> ** It can (typically) involve translating the designed prompt concepts into executable code or scripts that can be processed by AI systems. ** It can (typically) require knowledge of the specific programming languages or development environments used by the...")
- 23:28, 17 March 2024 A Midsummer Night's Dream (hist | edit) [1,297 bytes] Gmelli (talk | contribs) (Created page with "A A Midsummer Night's Dream is a comedy drama that ... * <B>Example(s):</B> ** ... * <B>See:</B> Hippolyta, William Shakespeare, Theseus, Oberon, Titania (A Midsummer Night's Dream), Hermia, Lysander (A Midsummer Night's Dream), Helena (A Midsummer Night's Dream), Demetrius (A Midsummer Night's Dream). ---- ---- == References == === 2024 === * (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/A_Midsummer_Night's_Dream R...")
- 23:24, 17 March 2024 Drama (hist | edit) [5,992 bytes] Gmelliapi (talk | contribs) (Created page with " A Drama is a Mode (Literature) that ... * <B>See:</B> Opera, Viola Spolin, Mode (Literature), Fiction, Mimesis, Performance, Play (Theatre), Mime, Ballet, Theatre, Radio Drama, Television. ---- ---- ==References== === 2024 === * (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Drama Retrieved:2024-3-17. ** '''Drama''' is the specific mode of fiction represented...")
- 23:24, 17 March 2024 Comedy Drama (hist | edit) [2,985 bytes] Gmelli (talk | contribs) (Created page with "A Comedy Drama is a drama that is a comedy. * <B>Example(s):</B> ** ... * <B>See:</B> Romantic Comedy, Stand-up Comedy, Comedy Album, Comedy Film, Radio Comedy, Television Comedy, Black Comedy, Blue Comedy, Character Comedy. ---- ---- == References == === 2024 === * (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Comedy_(drama) Retrieved:2024-3-17. ** '''Comedy''' is a genre of dramatic performance having a...")
- 22:51, 17 March 2024 Neural NER Algorithm (hist | edit) [2,231 bytes] Gmelli (talk | contribs) (Created page with "A Neural NER Algorithm is an Entity Mention Recognition Algorithm that employs Neural Networks, specifically designed to solve Named Entity Recognition Tasks. * <B>Context:</B> ** It can (typically) utilize various types of neural architectures, such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformer Models, to process and analyze text data for entity recognition. ** It can (often) involve training on la...")
- 22:50, 17 March 2024 Bidirectional Transformer Encoder-based NER Algorithm (hist | edit) [4,467 bytes] Gmelli (talk | contribs) (Created page with "A Bidirectional Transformer Encoder-based NER Algorithm is a neural NER algorithm that employs a Bidirectional Transformer Encoder (to solve am NER task). * <B>Context:</B> ** It can (typically) analyze text data in both directions (left-to-right and right-to-left), enabling the extraction of rich contextual embeddings. ** It can be based on pre-training text corpora and fine-tuned for specific NER tasks. ** It can handle languages with complex syntac...")
- 22:30, 17 March 2024 Contract Review Chatbot Development Task (hist | edit) [1,409 bytes] Gmelli (talk | contribs) (Created page with "A Contract Review Chatbot Development Task is a chatbot development task aimed at creating a contract review chatbot system. * <B>Context:</B> ** It can (often) require collaboration between legal experts, AI specialists, and software developers to ensure the chatbot accurately reflects legal standards and practices. ** It can utilize advanced AI and machine learning techniques to enhance the chatbot's ability to analyze and interpret complex contract docume...")
- 22:29, 17 March 2024 Chatbot Containment Rate (hist | edit) [4,610 bytes] Omoreira (talk | contribs) (Created page with "A Chatbot Containment Rate is a Chatbot Performance Metric that measures the percentage of customer interactions successfully managed by a chatbot without human intervention. * <B>Context:</B> *** It is a crucial metric in assessing a chatbot's effectiveness in autonomously handling customer inquiries and providing relevant responses within its operational scope. ** It can indicate how well a chatbot is performing in delivering instant responses and resolving...")
- 22:21, 17 March 2024 Chatbot Development Task (hist | edit) [3,054 bytes] Gmelli (talk | contribs) (Created page with "A Chatbot Development Task is a software system development task for creating, programming, and deploying a chatbot system. * <B>Context:</B> ** It can (typically) include phases such as: *** Chatbot Conceptualization, where the purpose, personality, and target audience of the chatbot are defined. *** Chatbot Design, focusing on the conversation flow, user interaction design, and the chatbot's persona. *** Natural Language Processing Integration,...")
- 22:06, 17 March 2024 Chatbot Fallback Rate (hist | edit) [4,465 bytes] Omoreira (talk | contribs) (Created page with "A Chatbot Fallback Rate is a Chatbot Performance Metric that quantifies the percentage of interactions in which a Chatbot fails to understand or appropriately respond to the user's input. * <B>Context:</B> ** It is a critical metric for evaluating a chatbot's ability to handle interactions autonomously without escalating to human agents. ** It can reflect the effectiveness of the chatbot's Natural Language Processing capabilities and training. ** It o...")
- 21:53, 17 March 2024 AI Prompt Development Process (hist | edit) [2,777 bytes] Gmelli (talk | contribs) (Created page with "An AI Prompt Development Process is a software-system development process designed for developing and refining AI prompts. * <B>Context:</B> ** It can (typically) include stages such as: *** Prompt Requirements Gathering, to identify the objectives and constraints of the prompts based on user needs and AI capabilities. *** Prompt Design Development, where the structure, format, and content of prompts are conceptualized. *** Prompt Implementation,...")
- 21:42, 17 March 2024 AI Prompt Engineering Technique (hist | edit) [754 bytes] Gmelli (talk | contribs) (Created page with "An AI Prompt Engineering Technique is an software engineering method for prompt engineering tasks. * <B>Example(s):</B> ** MAPS (Multi-Aspect Prompting and Selection) Prompting. ** Chain-of-Thought Prompting. ** ... * <B>See:</B> Text-to-Image Prompt Engineering Method. ---- ---- == References == === 2023 === * chat ** There are several types of prompt engineering methods. </s> Some of them include template-based prompts, conversation...")
- 20:27, 17 March 2024 Chatbot Training Duration (hist | edit) [3,081 bytes] Omoreira (talk | contribs) (Created page with "A Chatbot Training Duration is a chatbot performance metric that quantifies the time spent to train a chatbot model on relevant data before it's proficient in handling tasks autonomously. * <B>Context:</B> ** It can vary significantly based on several factors, including the chatbot's complexity, the quality and quantity of training data, and the specific goals set for the chatbot's performance. ** It can range from a few weeks to several months or ev...")
- 17:37, 16 March 2024 Médecins Sans Frontières (MSF) NGO (hist | edit) [5,452 bytes] Gmelli (talk | contribs) (Created page with "A Médecins Sans Frontières (MSF) NGO is a humanitarian-aid mission-driven NGO. * <B>AKA:</B> Doctors Without Borders. * <B>Example(s):</B> ** Médecins Sans Frontières of 1971. ** Médecins Sans Frontières of 2016. ** … * <B>Counter-Example(s):</B> ** UNICEF. ** Peta. ** Amnesty International. * <B>See:</B> Médecins du Monde, Humanita...")
- 00:49, 16 March 2024 Clean Room (hist | edit) [1,805 bytes] Gmelliapi (talk | contribs) (Created page with " A Clean Room is an Engineered Space, that ... * <B>AKA:</B> Cleanroom. * <B>See:</B> Virology, Particulates, Contamination, Semiconductor, Biological Engineering, Nuclear Engineering, Pharmaceutical Industry. ---- ---- ==References== === 2024 === * (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Cleanroom Retrieved:2024-3-16. ** A '''cleanroom''' or '''clean room''' is an engineered space, which maintains a very...")
- 00:49, 16 March 2024 Feed-Forward Neural Network Layer (hist | edit) [2,151 bytes] Gmelli (talk | contribs) (Redirected page to Feed-Forward Layer) Tag: New redirect
- 21:01, 15 March 2024 Transformer Encoder Layer (hist | edit) [2,289 bytes] Gmelli (talk | contribs) (Created page with "A Transformer Encoder Layer is a feedforward layer in a Transformer-based Neural Network Architecture that performs sequence encoding by utilizing self-attention and position-wise feed-forward operations. * <B>Context:</B> ** It can (typically) be a part of a Transformer Encoder which comprises multiple such layers stacked together. ** It can process input sequences by assigning varyin...")
- 00:12, 15 March 2024 Basic Complexity Legal Task (hist | edit) [1,425 bytes] Gmelli (talk | contribs) (Created page with "A Basic Complexity Legal Task is a legal task that is basic complexity task (which represents a foundational level of complexity in legal matters). * <B>Context:</B> ** It can (typically) be characterized by straightforward legal principles, clear regulations, and minimal interpretative ambiguity. ** It is (often) encountered in everyday legal transactions, such as drafting simple contracts, understanding basic legal rights, and adhering to well-establis...")
- 23:37, 14 March 2024 Karthik Narasimhan (hist | edit) [2,296 bytes] Gmelli (talk | contribs) (Created page with "Karthik Narasimhan is a person. * <B>See:</B> SWE-bench. ---- ---- == References == * https://scholar.google.com/citations?user=euc0GX4AAAAJ&hl=en === 2024 === * (Shinn et al., 2024) ⇒ N. Shinn, F. Cassano, A. Gopinath, K. Narasimhan, and S. Yao. (2024). "Reflexion: Language Agents with Verbal Reinforcement Learning.” In: Advances in Neural Information Processing Systems 36. === 2023 === * (Yao et al., 2023) ⇒ S. Ya...")
- 23:03, 14 March 2024 SWE-bench (hist | edit) [7,162 bytes] Gmelli (talk | contribs) (Created page with "A SWE-bench is a ____ benchmark of real-world GitHub issues. * <B>Context:</B> ** It can (typically) involve providing a language model with a codebase and an issue, where the model must generate a patch that resolves the described problem. ** It can (often) include a set of Issue-Pull Request Pairs from Python repositories. ** Itcan perform evaluations by unit test verification using post-PR behavior as the reference solution. **...")
- 22:55, 14 March 2024 Product Issue State (hist | edit) [1,814 bytes] Gmelli (talk | contribs) (Created page with "A Product Issue State is a defect state in an organizational product. * <B>Context:</B> ** It can range from being an Observed Product Issue to being a Hypothesized Product Issue. ** It can range from being a Customer Reported Product Issue to being a Internally Discovered Product Issue. ** It can have a Product Issue Root Cause, such as: Manufacturing Defects, Design Flaws, Material Quality Issues, Usability Challenges, or...")
- 22:29, 14 March 2024 Hypothesized Product Issue State (hist | edit) [2,358 bytes] Gmelli (talk | contribs) (Created page with "A Hypothesized Product Issue State is a hypothesized state of a product issue (that is based on preliminary evidence, observations, or assumptions). * <B>Context:</B> ** It can originate from customer feedback, employee observations, analytical predictions, or automated monitoring systems before concrete evidence is available. ** It represents a stage in the problem identification process where there is a suspicion or concern that may lead to an actual Pro...")
- 18:50, 14 March 2024 European Union (EU) Regulation (hist | edit) [3,353 bytes] Gmelli (talk | contribs) (Created page with "An European Union (EU) Regulation is a European Union legal act that becomes immediately enforceable as law in all member states simultaneously. * <B>Context:</B> ** It can (typically) be designed to ensure uniformity and consistency in the application of laws across the European Union. ** It can (typically) be adopted by the European Commission, the Council of the European Union, or the European Parliament. ** It can (typically) address various p...")
- 09:02, 14 March 2024 Contract-Related Request (hist | edit) [5,834 bytes] Gmelli (talk | contribs) (Created page with "A Contract-Related Request is a legal request that pertains to the interpretation, analysis, creation, or modification of legal contracts. * <B>Context:</B> ** It can (typically) be initiated by individuals or entities seeking legal assistance or clarification regarding contracts. ** It can (typically) be addressed through various means, including legal consultation, use of contract management software, or contract-related chatbots. ** It can (typically) requ...")
- 08:59, 14 March 2024 Contract-Related Chatbot Request (hist | edit) [1,183 bytes] Gmelli (talk | contribs) (Created page with "A Contract-Related Chatbot Request is a contract-related request that is a domain-specific chatbot request. * <B>Context:</B> ** It can (typically) originate from users seeking assistance with understanding, analyzing, or drafting legal contracts through an automated chat interface. ** It can (often) involve requests for specific information extraction, clarification of terms, risk assessment, clause analysis, and document quality review. ** It can (typically...")
- 06:52, 14 March 2024 Multi-Head Attention Mechanism (hist | edit) [3,469 bytes] Gmelli (talk | contribs) (Created page with "A Multi-Head Attention Mechanism is an attention mechanism that includes simultaneous attention to information from different representation subspaces at different positions. * <B>Context:</B> ** It can allow models to capture a richer understanding of the input by attending to it in multiple "ways" or "aspects" simultaneously, significantly improving the model's ability to handle complex tasks such as language translation, document summarization, and question-a...")
- 06:45, 14 March 2024 Block Sparse Attention Mechanism (hist | edit) [3,579 bytes] Gmelli (talk | contribs) (Created page with "A Block Sparse Attention Mechanism is an attention mechanism that improves efficiency by computing attention weights within or between predefined blocks of the input sequence (rather than across the entire sequence). * <B>Context:</B> ** It can (typically) allow deep learning models, especially those based on the Transformer architecture, to process longer sequences than would be feasible with standard, full attention mechanisms. ** It can (often) employ var...")
- 05:27, 14 March 2024 Behavior-based Job Interview Question (hist | edit) [3,247 bytes] Gmelli (talk | contribs) (Created page with "A Behavior-based Job Interview Question is a job interview question that aims to assess a candidate's past behavior in specific situations, which is indicative of their future behavior in similar circumstances. * <B>Context:</B> ** It can (often) focus on: Teamwork Skills, Problem-Solving Skills, Leadership Skills, and Conflict Resolution Skills. ** It can range form being a [[ ]] to being a [[]]. ** It can be based on the premise that past behav...")
- 00:43, 14 March 2024 Contract Understanding Atticus Dataset (CUAD) Clause Label (hist | edit) [10,110 bytes] Gmelli (talk | contribs) (Created page with "A Contract Understanding Atticus Dataset (CUAD) Clause Label is a classification label used to identify and categorize specific contract clauses within contract agreements in Contract Understanding Atticus Dataset (CUAD). * <B>Context:</B> ** It can (typically) be used in for training machine learning models to recognize and understand various aspects of legal documents. ** It can (often) cover a wide range of contractual elements, including but n...")
- 21:44, 13 March 2024 MAUD (Merger Agreement Dataset) (hist | edit) [3,364 bytes] Gmelli (talk | contribs) (Created page with "A MAUD (Merger Agreement Dataset) is a legal dataset that consists of a collection of merger agreements with detailed expert annotations. * <B>Context:</B> ** It can (typically) provide over 47,000 labels across 152 merger agreements. ** It can (typically) be utilized to identify 92 questions in each agreement, which are aligned with the standards of the 2021 American Bar Association (ABA) Public Target Deal Points Study. ** It can (often) support Natural Langu...")
- 20:54, 13 March 2024 Tree of Thought (ToT) Method (hist | edit) [729 bytes] Gmelli (talk | contribs) (Created page with "A Tree of Thought (ToT) Method is a prompt engineering method that ... * <B>Example(s):</B> ** ... * <B>See:</B> Chain-of-Thought, Beam Search, Breadth-First Search. ---- ---- ==References== === 2024 === * (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Prompt_engineering#Tree-of-thought Retrieved:2024-3-13. ** ''Tree-of-thought prompting''<ref name="LongTreeofThought"></ref> generalizes chain-of-thought by prompting the model to gener...")
- 19:48, 13 March 2024 General Cloud-Computing Platform (hist | edit) [2,301 bytes] Gmelli (talk | contribs) (Created page with "A General Cloud-Computing Platform is a Cloud Computing Platform. * <B>Context:</B> ** It can (typically) offer a range of Cloud Services, including: *** Infrastructure as a Service (IaaS), providing virtualized computing resources over the internet. *** Platform as a Service (PaaS), offering hardware and software tools over the internet, usually for application development. *** Software as a Service (SaaS), delivering software applications over t...")
- 19:42, 13 March 2024 Alibaba Cloud Platform (hist | edit) [3,742 bytes] Gmelli (talk | contribs) (Created page with "An Alibaba Cloud Platform is a Cloud Computing Platform. * <B>Context:</B> ** It can (typically) be operated by Alibaba Cloud Corp. (a subsidiary of Alibaba Group). ** It can (typically) Alibaba Cloud Services, such as: *** Alibaba Cloud Elastic Compute Service (ECS), providing scalable cloud servers. *** Alibaba Cloud Object Storage Service (OSS), offering secure, cost-effective cloud storage solutions. *** Alibaba Cloud Relational Database...")
- 19:26, 13 March 2024 Qwen Family LLM (hist | edit) [3,738 bytes] Gmelli (talk | contribs) (Created page with "A Qwen Family LLM is a LLM developed by Alibaba Corp.. * <B>Example(s):</B> ** Qwen Family LLM, 2023-12. ** Qwen Family LLM, 2024-01. ** Qwen Family LLM, 2024-01. ** ... * <B>Counter-Example(s):</B> ** ... * <B>See:</B> [[]]. ---- ---- == References == * https://github.com/QwenLM/Qwen === 2024 === * https://huggingface.co/Qwen ** QUOTE: This is the organization of Qwen (abbr. for Tongyi Qianwen 通义千问), which refers to the large...")