Context - Revision history

BPeat at 15:18, 28 May 2025

2025-05-28T15:18:05Z

BPeat: /* Challenges */

2025-01-14T13:55:18Z

‎Challenges

BPeat at 13:04, 14 January 2025

2025-01-14T13:04:09Z

BPeat: /* Challenges */

2025-01-14T12:57:42Z

‎Challenges

BPeat: /* Challenges */

2025-01-14T12:57:12Z

‎Challenges

BPeat at 12:32, 14 January 2025

2025-01-14T12:32:18Z

BPeat at 21:59, 28 April 2024

2024-04-28T21:59:32Z

BPeat at 21:07, 28 April 2024

2024-04-28T21:07:25Z

BPeat at 20:20, 28 April 2024

2024-04-28T20:20:52Z

BPeat: Text replacement - "* Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu" to "* Conversational AI ... [[C...

2024-03-16T15:49:51Z

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 |title=PRIMO.ai
 |titlemode=append
-|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
+|keywords=ChatGPT, artificial, intelligence, machine, learning, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 <!-- Google tag (gtag.js) -->
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 = How Do Chatbots Use Context? =
-* [[Large Language Model (LLM)]] ... [[Large Language Model (LLM)#Multimodal|Multimodal]] ... [[Foundation Models (FM)]] ... [[Generative Pre-trained Transformer (GPT)|Generative Pre-trained]] ... [[Transformer]] ... [[GPT-4]] ... [[GPT-5]] ... [[Attention]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
+* [[Large Language Model (LLM)]] ... [[Large Language Model (LLM)#Multimodal|Multimodal]] ... [[Foundation Models (FM)]] ... [[Generative Pre-trained Transformer (GPT)|Generative Pre-trained]] ... [[Transformer]] ... [[Attention]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 * [https://blog.gopenai.com/how-to-speed-up-llms-and-use-100k-context-window-all-tricks-in-one-place-ffd40577b4c The Secret Sauce behind 100K context window in LLMs: all tricks in one place | Galina Alperovich - GoPenAI]

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 * <b>Model complexity</b>: Context can make AI models more complex, as they need to be able to understand and process a wider range of information.
 * <b>Security</b>: Context can make AI systems more vulnerable to attacks, as they need to be able to access and process sensitive information.
 <youtube>Y4JpqH-Sqzw</youtube>
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 </center><hr>
 = How Do Chatbots Use Context? =

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 <youtube>Y4JpqH-Sqzw</youtube>
 [https://sproutsschools.com/ Sprouts]
 = How Do Chatbots Use Context? =

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-[https://sproutsschools.com/ Situative Learning: How Context Matters | Sprouts]
 = How Do Chatbots Use Context? =

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 <youtube>Y4JpqH-Sqzw</youtube>
+[https://sproutsschools.com/ Situative Learning: How Context Matters | Sprouts]
 = How Do Chatbots Use Context? =

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 ||
 <youtube>BQOEGMzgJSM</youtube>
-<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/
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 <youtube>pEmVn2uRn-I</youtube>
 <b>Creating Context Aware Artificial Intelligence (AI) in Marketing
-</b><br>eContext – Creating Context Aware Artificial Intelligence (AI) in Marketing eople are underestimating the importance of the data set that you are training against to provide these anomalies. Where I believe that we differentiated at eContext as we have this capacity in real-time to take third party unstructured data sets and create a structure knowledge based which they can do their own machine learning or deep learning. There's also a big distinction from machine learning, whether you were able to do AI based on AGI (Artificial General Intelligence) or ANI (Artificial Narrow Intelligence). So we hear a lot about artificial narrow intelligence versus artificial general intelligence. ANI is where you are asking your learning algorithm to do narrow, specific tasks. Some of these things they're doing very well. For example, machine translation work is using deep learning very effectively as well as image recognition. But when it comes to AGI, this is an area that the marketing world needs to embrace because we're taking a holistic view or your audience, and their preferences, and their interests in order to clearly understand those interests across all verticals and all categories. But that's very hard for AI to do that because we're many miracles away from understanding, in the correct context human conversations from an AI perspective. There are a number of things they are doing to try and understand patterns and recognize these anomalies but to really be effective you have to have an understanding across all verticals. So we believe that the approach of lending curated taxonomy work with Artificial Intelligence is the way forward and probably will be for many years to come. Despite what people tell you in Silicon Valley or in newspapers. So what does that mean from a perspective of a marketer. Well. If you're able to take unstructured text and structure that across all verticals then that general understanding is what's known as a general index.
+</b><br>eContext – Creating Context Aware Artificial Intelligence (AI) in Marketing eople are underestimating the importance of the data set that you are training against to provide these anomalies. Where I believe that we differentiated at eContext as we have this capacity in real-time to take third party unstructured data sets and create a structure knowledge based which they can do their own machine learning or deep learning. There's also a big distinction from machine learning, whether you were able to do AI based on AGI (Artificial General Intelligence) or ANI (Artificial Narrow Intelligence). So we hear a lot about artificial narrow intelligence versus artificial general intelligence. ANI is where you are asking your learning algorithm to do narrow, specific tasks. Some of these things they're doing very well. For example, machine translation work is using deep learning very effectively as well as image recognition. But when it comes to AGI, this is an area that the marketing world needs to embrace because we're taking a holistic view or your audience, and their preferences, and their interests in order to clearly understand those interests across all verticals and all categories. But that's very hard for AI to do that because we're many miracles away from understanding, in the correct context human conversations from an AI [[perspective]]. There are a number of things they are doing to try and understand patterns and recognize these anomalies but to really be effective you have to have an understanding across all verticals. So we believe that the approach of lending curated taxonomy work with Artificial Intelligence is the way forward and probably will be for many years to come. Despite what people tell you in Silicon Valley or in newspapers. So what does that mean from a [[perspective]] of a marketer. Well. If you're able to take unstructured text and structure that across all verticals then that general understanding is what's known as a general index.
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 <youtube>BQOEGMzgJSM</youtube>
 <b>It's All About Context | 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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 [https://www.bing.com/news/search?q=ai+context+~Contextual+Awareness+SOS&qft=interval%3d%228%22 ...Bing News]
-* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
+* [[Perspective]] ... [[Context]] ... [[In-Context Learning (ICL)]] ... [[Transfer Learning]] ... [[Out-of-Distribution (OOD) Generalization]]
 * [[Cybersecurity Frameworks, Architectures & Roadmaps#Zero Trust|Zero Trust]]
 * [[Memory]]

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 * [https://en.wikipedia.org/wiki/Special:PrefixIndex/Context Context | Wikipedia listing]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
-* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 * [[Loop#Triple Golden OODA|Triple Golden OODA loop]]
 * [[Architectures]] for AI ... [[Generative AI Stack]] ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]