Difference between revisions of "Context"
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− | <b>It's All About Context | Simon Sinek | + | <b>It's All About Context | [[Creatives#Simon Sinek|Simon Sinek]] |
</b><br>One of the great things about the infinite mindset is that it forces you to take a wider [[perspective]]. In order to have a true understanding of something, you have to look at it within its context. Nothing is quite what it seems. Simon is an unshakable optimist who believes in a bright future and our ability to build it together. Described as “a visionary thinker with a rare intellect,” Simon teaches leaders and organizations how to inspire people. With a bold goal to help build a world in which the vast majority of people wake up every single day feeling inspired, feel safe at work, and feel fulfilled at the end of the day, Simon is leading a movement to inspire people to do the things that inspire them. Simon is the author of multiple best-selling books including Start With Why, Leaders Eat Last, Together is Better, and Find Your Why. His new book, The Infinite Game, will be released in 2019. Simon’s WHY: To inspire people to do the things that inspire them so that, together, each of us can change our world for the better. Website: https://simonsinek.com/ | </b><br>One of the great things about the infinite mindset is that it forces you to take a wider [[perspective]]. In order to have a true understanding of something, you have to look at it within its context. Nothing is quite what it seems. Simon is an unshakable optimist who believes in a bright future and our ability to build it together. Described as “a visionary thinker with a rare intellect,” Simon teaches leaders and organizations how to inspire people. With a bold goal to help build a world in which the vast majority of people wake up every single day feeling inspired, feel safe at work, and feel fulfilled at the end of the day, Simon is leading a movement to inspire people to do the things that inspire them. Simon is the author of multiple best-selling books including Start With Why, Leaders Eat Last, Together is Better, and Find Your Why. His new book, The Infinite Game, will be released in 2019. Simon’s WHY: To inspire people to do the things that inspire them so that, together, each of us can change our world for the better. Website: https://simonsinek.com/ | ||
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Latest revision as of 16:59, 28 April 2024
YouTube ... Quora ...Google search ...Google News ...Bing News
- Perspective ... Context ... In-Context Learning (ICL) ... Transfer Learning ... Out-of-Distribution (OOD) Generalization
- Causation vs. Correlation ... Autocorrelation ...Convolution vs. Cross-Correlation (Autocorrelation)
- Zero Trust
- Memory
- Contextual Literature-Based Discovery (C-LBD)
- Assistants - Capability Requirements
- Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
- Context-Conditional Generative Adversarial Network (CC-GAN)
- Context | Wikipedia listing
- Artificial Intelligence (AI) ... Generative AI ... Machine Learning (ML) ... Deep Learning ... Neural Network ... Reinforcement ... Learning Techniques
- Conversational AI ... ChatGPT | OpenAI ... Bing/Copilot | Microsoft ... Gemini | Google ... Claude | Anthropic ... Perplexity ... You ... phind ... Ernie | Baidu
- Triple Golden OODA loop
- Architectures for AI ... Generative AI Stack ... Enterprise Architecture (EA) ... Enterprise Portfolio Management (EPM) ... Architecture and Interior Design
- Excel ... Documents ... Database; Vector & Relational ... Graph ... LlamaIndex
- Microsoft’s AI rewrites sentences based on context | Kyle Wiggers - VentureBeat
- Adobe Sensei | Adobe ...drawing a direct link from customer intelligence to personalised experiences that are valuable and relevant.
- Toward AI Standards: Context Makes AI More Robust | Amy E. Hodler - Neo4j
- Real Examples of Why We Need Context for Responsible AI | Amy E. Hodler - Neo4j
- Capability Cases - A Solution Envisioning Approach | Irene Polikoff, Robert Coyne, and Ralph Hodgson - TopQuadrant
- The Verge: OpenAI’s ChatGPT chatbot now has ‘custom instructions’ to store your preferences
- Stanford study challenges assumptions about language models: Larger context doesn’t mean better understanding | Matt Marshall - VentureBeat ... Large Language Model (LLM) often fail to access and use relevant information given to them in longer context windows.
- The Secret Sauce behind 100K context window in LLMs: all tricks in one place | Galina Alperovich - GoPenAI
Context is important in AI because it allows AI systems to understand the meaning of information in a way that is relevant to the situation; in which data is accessed, used, and shared. This includes managing access controls, data classification, and data usage policies. Without context, AI systems would be unable to make accurate predictions or decisions; effective context management is critical for ensuring data security and privacy, as it helps to prevent unauthorized access, misuse, and data breaches. Context management in computing involves identifying, gathering, and utilizing relevant information to enable effective decision-making and action. There are various research and standards on managing context in the computing field. Here are some of them:
- Context Modeling: One of the key research areas in context management is context modeling. The aim is to create a framework for representing context data in a structured and meaningful way that can be easily understood and utilized by various applications. The Context Modeling Language (CML) is an example of such a framework.
- Context Awareness: Another research area is context awareness. Context-aware systems can automatically adapt their behavior based on the current context. For instance, a smartphone that adjusts its settings (such as screen brightness and volume) depending on the user's location, time of day, and other contextual factors.
- Context Integration: Context integration refers to the process of combining context data from multiple sources to enable a more comprehensive understanding of the current situation. Research in this area aims to develop techniques for integrating context data from different sources, such as sensors, social media, and the web.
- Context Standards: Standards are essential for ensuring interoperability and compatibility between context-aware systems. The Open Geospatial Consortium (OGC) has developed several standards for context management, including the Sensor Observation Service (SOS) and the Web Processing Service (WPS).
- Privacy and Security: Managing context data raises several privacy and security concerns. Research in this area aims to develop techniques for protecting personal information while still enabling effective context management.
Contextual AI: The Next Frontier of Artificial Intelligence - Oliver Brdiczka
For example, consider the sentence "The cat is on the mat."
This sentence can have different meanings depending on the context...
- If the sentence is spoken in a pet store, it is likely referring to a real cat that is sitting on a real mat.
- However, if the sentence is spoken in a metaphor, it could be referring to something else entirely, such as a person who is always lazy or someone who is always getting into trouble.
AI systems that are able to understand context can make more accurate predictions and decisions. For example, an AI system that is used to recommend products to customers could use context to determine which products are most likely to be of interest to a particular customer. The AI system could take into account factors such as the customer's location, past purchases, and interests.
Context is also important for AI systems that are used to generate text. For example, an AI system that is used to write news articles could use context to determine the tone and style of the article. The AI system could take into account factors such as the target audience, the subject matter, and the overall tone of the publication. Overall, context is an important concept in AI. By understanding context, AI systems can make more accurate predictions and decisions, and they can generate text that is more relevant to the situation.
Here are some of the benefits of using context in AI:
- Improved accuracy: AI systems that can understand context can make more accurate predictions and decisions.
- Increased relevance: AI systems that can understand context can generate text that is more relevant to the situation.
- Enhanced user experience: AI systems that can understand context can provide a more personalized and engaging user experience.
- Reduced bias: AI systems that can understand context can be less likely to make biased decisions.
Contents
Challenges
There are a number of challenges associated with using context in AI, including:
- Data collection: Context can be difficult to collect, as it can be spread across multiple sources.
- Data labeling: Context can be difficult to label, as it can be subjective and open to interpretation.
- Model complexity: Context can make AI models more complex, as they need to be able to understand and process a wider range of information.
- Security: Context can make AI systems more vulnerable to attacks, as they need to be able to access and process sensitive information.
How Do Chatbots Use Context?
- Large Language Model (LLM) ... Multimodal ... Foundation Models (FM) ... Generative Pre-trained ... Transformer ... GPT-4 ... GPT-5 ... Attention ... GAN ... BERT
- The Secret Sauce behind 100K context window in LLMs: all tricks in one place | Galina Alperovich - GoPenAI
Chatbots employ context in various ways to enhance their capacity to comprehend and address user inquiries. The ability to grasp context is the differentiating factor between an AI chatbot and a search engine. Nevertheless, there is a growing convergence between these two domains. For instance, Bing AI functions as an AI search engine while also incorporating an AI chatbot feature. The degree of contextual comprehension is contingent upon the underlying language model and its training. Perplexity exhibits greater speed, whereas ChatGPT Plus excelled in terms of contextual understanding. This is why ChatGPT Plus surpasses the majority of other chatbots in providing detailed and insightful responses. When an AI chatbot grapples with contextual comprehension challenges, it may resort to producing hallucinatory and irrational responses. It is worth noting that these chatbots have the capability to access real-time web information and maintain a reasonably respectable level of contextual understanding. Chatbots use context to:
- Identify the intent of the user's query. Chatbots use the context of the query to determine what the user is trying to achieve. For example, if the user asks "What is the capital of France?", Chatbots know that the user is trying to find the answer to a factual question. However, if the user asks "What is your favorite city in France?", Chatbots know that the user is trying to get a personal opinion.
- Identify the meaning of words and phrases. The meaning of words and phrases can change depending on the context in which they are used. For example, the word "bank" can refer to a financial institution or to the side of a river. Chatbots use the context of the query to determine the correct meaning of words and phrases.
- Resolve ambiguity. Many queries can be interpreted in multiple ways. For example, the query "What is the meaning of life?" can be interpreted as a philosophical question or as a request for a definition. Chatbots use the context of the query to resolve ambiguity and provide the most relevant response.
- Generate creative text formats. Chatbots can generate different creative text formats of text content, like poems, code, scripts, musical pieces, email, letters, etc., based on the context of the query. For example, if the user asks a Chatbot to write a poem about love, Chatbots will generate a poem that is relevant to the topic of love.
Overall, Chatbots use context to improve its ability to understand and respond to user queries. By understanding the context of a query, Chatbots can provide more accurate, relevant, and creative responses.
Pillars
In the realm of contextual AI, the concept of "intelligible" stands as a foundational pillar. It represents the critical capacity of AI systems to not only possess knowledge and perform tasks but also to transparently and comprehensibly communicate their thought processes. Research in this field is rapidly advancing, aiming to make AI systems more accountable and user-friendly. Scientists and engineers are actively developing algorithms and techniques that enable AI to Explainable AI|explain not only what it knows but also how it acquired that knowledge, and the rationale behind its actions. This pursuit is essential for building trust in AI systems, as it allows users to better understand the decisions and recommendations provided by these intelligent agents. Moreover, this research not only has implications for improving user interactions with AI but also holds promise in fields like healthcare, finance, and law, where transparency and accountability are of paramount importance. As the development of intelligible AI continues, we move closer to AI systems that not only perform tasks effectively but also empower users with meaningful insights into their inner workings.
In the realm of contextual AI, the pillar of "adaptive" plays a pivotal role, emphasizing the AI's ability to meet users' expectations and deliver optimal performance in diverse and dynamic environments. Scientists and engineers are focused on developing AI models that can autonomously adapt their behavior, preferences, and decision-making processes based on the context in which they operate. This involves exploring various machine learning techniques, including reinforcement learning and continual learning, to ensure that AI systems can seamlessly transition from one environment to another without compromising their effectiveness. Additionally, researchers are investigating methods to make AI systems more aware of the context they're in, allowing them to fine-tune their responses and actions to align with user expectations. As AI adaptivity continues to evolve, we can expect more intelligent, context-aware systems that consistently deliver relevant and valuable experiences to users across a wide array of situations and scenarios. This research has profound implications not only in improving user satisfaction but also in enhancing the utility of AI across industries such as autonomous vehicles, healthcare, and customer service.
In the domain of contextual AI, the pillar of "customizabile" stands as a vital component, emphasizing the AI's capacity to be tailored and controlled to a significant degree by the user. Scientists and engineers are exploring innovative techniques and interfaces that allow users to customize various aspects of AI behavior, from fine-tuning its responses to defining its ethical guidelines and privacy parameters. This research not only aims to provide users with a sense of agency and trust in AI but also addresses concerns related to bias and fairness by allowing users to influence the AI's decision-making processes. Moreover, customization research is expanding into areas like personalization, where AI systems can adapt and learn from user feedback to continuously improve their performance. As this field evolves, we can anticipate AI systems that are not only powerful but also highly adaptable and responsive to individual user requirements, ensuring that AI serves as a valuable tool that aligns seamlessly with user objectives and values.
In the realm of contextual AI, "context-aware" stands as a pivotal pillar, signifying the AI's capability to perceive its surroundings and understand them at a level akin to human comprehension. Research in this area is a frontier where scientists and engineers are pushing the boundaries of AI's sensory and contextual awareness. This entails developing AI systems that can not only interpret data and information but also perceive the subtle nuances, emotions, and environmental cues that humans naturally pick up on. Cutting-edge research explores advancements in computer vision, natural language processing, and sensor integration to enable AI systems to process and contextualize information much like a human would. The goal is to create AI that can adapt to ever-changing situations, understand social dynamics, and respond with a level of empathy and nuance that enhances user interactions. As context-aware AI research progresses, it holds the potential to revolutionize various fields, from healthcare to customer service, by enabling AI to function as more than just a tool but as a perceptive, empathetic, and contextually astute partner in our daily lives.
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Context for Communication
- Telecommunications ... Computer Networks ... 5G ... Satellite Communications ... Quantum Communications ... Agents ... AI Broadcast; Radio, Stream, TV
- What is the importance of context in communication? | The News Independent
- Context in Communication | Importance, Types & Examples - Video | Jo Amy Rollo - Study.com
Context is critical in communication because it helps the receiver understand the importance of something, what assumptions to draw about what is being communicated, and most importantly, it puts meaning into the message. Understanding, blending, and tailoring your content to the context will increase the likelihood of being heard and understood and create an action-driven behavior upon your receptors.
AI systems use context to exchange information in a variety of ways. For example, contextual AI enables systems to interpret information the same way a human would. From analyzing wording and sentiments to recognizing cultural and environmental contexts, this “intuitive” understanding allows AI systems to produce more in-depth, relevant, and accurate outputs². Some examples of how AI uses context to exchange information include chatbots and virtual assistants that have a real-world interpretation of language, audio, video, and images so they can behave less like traditional computers and more like humans². Is there anything else you would like to know?
Contexts of communication are:
- Cultural: how the culture impacts communication
- Temporal: the expectations people have for the communication based on past behaviors
- Social-psychological: the feelings and relationships present
- Physical: the area and physical aspects as communication takes place
- Analyzing wording
- Sentiments
- Environmental
IDEAS
IDEAS is an acronym that stands for Inference, Definition, Example, Antonym, and Synonym. It is a method used to understand new words by looking at the words around a new word to guess its meaning. Context clues are hints or suggestions that help readers figure out the meaning of unfamiliar words by looking at the words and sentences around them. By using the IDEAS method, readers can use these different types of context clues to figure out the meaning of new words and improve their reading comprehension.
Using context clues...
- Inference - The meaning is not given so you must use text clues to infer what the unknown word means.
- Definition - The author will include the actual definition of the word. Look at the sentence after the unknown word to see if the author explains what the word means.
- Example - There might be an example of what the unknown word looks like in action. Do the sentences before or after give you an example?]
- Antonym - Sometimes we can figure out unknown words because the author will provide the antonym nearby
- Synonym - You might be able to figure out the unknown word by looking for synonyms. Are there other words that are listed that are similar to the unknown word?]
'Context' | Wikipedia
• Context • Context-Based Access Control • Context-Based Sustainability • Context-Driven School • Context-Free Language • Context-Free languages • Context-Sensitive Help • Context-adaptive binary arithmetic coding • Context-adaptive variable-length coding • Context-aware • Context-aware collaborative filtering • Context-aware pervasive systems • Context-aware services • Context-awareness • Context-based access control • Context-based adaptive binary arithmetic coding • Context-based adaptive variable-length coding • Context-based learning • Context-based model of minimal counterintuitiveness • Context-based model of minimal counterintuiveness • Context-dependent • Context-dependent grammar • Context-dependent memory • Context-driven • Context-free • Context-free grammar generation algorithms • Context-free grammars • Context-free languages • Context-mixing • Context-sensitive • Context-sensitive (disambiguation) • Context-sensitive L-system • Context-sensitive definite clause grammars • Context-sensitive grammar • Context-sensitive half-life • Context-sensitive help • Context-sensitive languages • Context-sensitive shaping • Context-sensitive solutions • Context-sensitive solutions (transport) • Context-sensitive spell checker • Context-sensitive user interface • ContextLogic • ContextLogic Inc • ContextObjects in Spans • ContextPlus • Context (Computing) • Context (archaeology) • Context (computing) • Context (disambiguation) • Context (festival) • Context (language use) • Context (literary) • Context (mycology) • Context Art • Context Books • Context Change Potential • Context Development • Context Effects • Context Filtering • Context Group • Context Is for Kings • Context Labs • Context MBA • Context Management • Context Menus • Context Reframing • Context Relevant • Context Sensitive Solutions • Context Switch • Context Tree Weighting Method • Context adaptation • Context analysis • Context and Dependency Injection • Context art • Context as Other Minds • Context awareness • Context books • Context change potential • Context completion • Context diagram • Context driven • Context effects • Context filtering • Context format • Context free • Context key • Context management • Context menu • Context mixing • Context model • Context modeling • Context of computational complexity • Context of situation • Context of utterance • Context principle • Context sensitive • Context sensitive analysis • Context sensitive design • Context sensitive grammar • Context sensitive help • Context sensitive language • Context sensitive user interface • Context speaking budgies • Context switch • Context switching • Context theory • Context tree weighting • Context tree weighting method • Contextomy • Contexts • Contexts (journal) • Contexts and Dependency Injection • Contextual • Contextual (disambiguation) • Contextual Advertising • Contextual Integrity • Contextual Ligatures • Contextual Modernism • Contextual Query Language • Contextual Searching • Contextual Theatre • Contextual Theology • Contextual Value Added • Contextual ad • Contextual advertisement • Contextual advertising • Contextual application design • Contextual archaeology • Contextual architecture • Contextual bandit algorithm • Contextual biases • Contextual computing • Contextual cueing effect • Contextual deep link • Contextual deep linking • Contextual design • Contextual documentation • Contextual empiricism • Contextual equivalence • Contextual frame • Contextual help • Contextual image classification • Contextual inquiry • Contextual intelligence • Contextual learning • Contextual learning object • Contextual lie • Contextual menu • Contextual method • Contextual neutralisation • Contextual neutralization • Contextual objectivity • Contextual performance • Contextual query language • Contextual search • Contextual searching • Contextual targeting • Contextual theatre • Contextual theology • Contextual therapy • Contextual value added • Contextual variables • Contextual web search • Contextualisation • Contextualism • Contextualist • Contextuality • Contextualization • Contextualization (computer science) • Contextualization (disambiguation) • Contextualization (missiology) • Contextualization (sociolinguistics) • Contextualize