Foundation Models (FM) - Revision history

BPeat at 15:17, 28 May 2025

2025-05-28T15:17:18Z

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

2024-03-16T15:31:03Z

BPeat at 01:17, 9 October 2023

2023-10-09T01:17:13Z

BPeat at 01:16, 9 October 2023

2023-10-09T01:16:41Z

BPeat at 15:57, 6 September 2023

2023-09-06T15:57:50Z

BPeat at 15:52, 6 September 2023

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BPeat at 01:48, 3 September 2023

2023-09-03T01:48:32Z

BPeat at 01:18, 17 August 2023

2023-08-17T01:18:50Z

BPeat at 22:23, 12 July 2023

2023-07-12T22:23:35Z

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-|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
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 [https://www.bing.com/news/search?q=ai+Foundation+Model&qft=interval%3d%228%22 ...Bing News]
-* [[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]]
 * [[Natural Language Processing (NLP)]] ... [[Natural Language Generation (NLG)|Generation (NLG)]] ... [[Natural Language Classification (NLC)|Classification (NLC)]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding (NLU)]] ... [[Language Translation|Translation]] ... [[Summarization]] ... [[Sentiment Analysis|Sentiment]] ... [[Natural Language Tools & Services|Tools]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]

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 * [[Natural Language Processing (NLP)]] ... [[Natural Language Generation (NLG)|Generation (NLG)]] ... [[Natural Language Classification (NLC)|Classification (NLC)]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding (NLU)]] ... [[Language Translation|Translation]] ... [[Summarization]] ... [[Sentiment Analysis|Sentiment]] ... [[Natural Language Tools & Services|Tools]]
 * [[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]]
 * [https://blogs.nvidia.com/blog/2023/03/13/what-are-foundation-models/ What Are Foundation Models?]  ... Foundation models are AI neural networks trained on massive unlabeled datasets to handle a wide variety of jobs from translating text to analyzing medical images.
 * [https://hai.stanford.edu/news/introducing-center-research-foundation-models-crfm Center for Research on Foundation Models (CRFM)]

← Older revision		Revision as of 01:17, 9 October 2023
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	* [https://hai.stanford.edu/news/introducing-center-research-foundation-models-crfm Center for Research on Foundation Models (CRFM)]		* [https://hai.stanford.edu/news/introducing-center-research-foundation-models-crfm Center for Research on Foundation Models (CRFM)]

−	The term Foundation Model (FM) was coined by [[Stanford]] researchers to introduce a new category of ML models. They defined FMs as models trained on broad data (generally using self-supervision at scale) that can be adapted to a wide range of downstream tasks. A foundation model is a large artificial intelligence model trained on a vast quantity of unlabeled data at scale (usually by self-supervised learning) resulting in a model that can be adapted to a wide range of downstream tasks. Foundation models have helped bring about a major transformation in how AI systems are built - [https://en.wikipedia.org/wiki/Foundation_models Wikipedia]	+	The term Foundation Model (FM) was coined by Stanford researchers to introduce a new category of ML models. They defined FMs as models trained on broad data (generally using self-supervision at scale) that can be adapted to a wide range of downstream tasks. A foundation model is a large artificial intelligence model trained on a vast quantity of unlabeled data at scale (usually by self-supervised learning) resulting in a model that can be adapted to a wide range of downstream tasks. Foundation models have helped bring about a major transformation in how AI systems are built - [https://en.wikipedia.org/wiki/Foundation_models Wikipedia]

← Older revision		Revision as of 01:16, 9 October 2023
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	* [https://hai.stanford.edu/news/introducing-center-research-foundation-models-crfm Center for Research on Foundation Models (CRFM)]		* [https://hai.stanford.edu/news/introducing-center-research-foundation-models-crfm Center for Research on Foundation Models (CRFM)]

−	A foundation model is a large artificial intelligence model trained on a vast quantity of unlabeled data at scale (usually by self-supervised learning) resulting in a model that can be adapted to a wide range of downstream tasks. Foundation models have helped bring about a major transformation in how AI systems are built - [https://en.wikipedia.org/wiki/Foundation_models Wikipedia]	+	The term Foundation Model (FM) was coined by [[Stanford]] researchers to introduce a new category of ML models. They defined FMs as models trained on broad data (generally using self-supervision at scale) that can be adapted to a wide range of downstream tasks. A foundation model is a large artificial intelligence model trained on a vast quantity of unlabeled data at scale (usually by self-supervised learning) resulting in a model that can be adapted to a wide range of downstream tasks. Foundation models have helped bring about a major transformation in how AI systems are built - [https://en.wikipedia.org/wiki/Foundation_models Wikipedia]

@@ Line 20: / Line 20: @@
 [https://www.bing.com/news/search?q=ai+Foundation+Model&qft=interval%3d%228%22 ...Bing News]
-* [[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]] ... [[GPT-4]] ... [[GPT-5]] ... [[Attention]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 * [[Natural Language Processing (NLP)]] ... [[Natural Language Generation (NLG)|Generation (NLG)]] ... [[Natural Language Classification (NLC)|Classification (NLC)]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding (NLU)]] ... [[Language Translation|Translation]] ... [[Summarization]] ... [[Sentiment Analysis|Sentiment]] ... [[Natural Language Tools & Services|Tools]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]

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 * [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]] ... [[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ...  [[Natural Language Tools & Services|Tools & Services]]
 * [[Large Language Model (LLM)#Multimodal|Multimodal Language Model]]s ... Generative Pre-trained Transformer ([[GPT-4]]) ... [[GPT-5]]
-* [[Generative AI]] ... [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [https://blogs.nvidia.com/blog/2023/03/13/what-are-foundation-models/ What Are Foundation Models?]  ... Foundation models are AI neural networks trained on massive unlabeled datasets to handle a wide variety of jobs from translating text to analyzing medical images.
 * [https://hai.stanford.edu/news/introducing-center-research-foundation-models-crfm Center for Research on Foundation Models (CRFM)]

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-Artificial intelligence (AI) has come a long way in recent years, with the development of increasingly sophisticated models that can perform a wide range of tasks. One of the most exciting developments in this field is the emergence of foundation models.  A foundation model is a “paradigm for building AI systems” in which a model trained on a large amount of unlabeled data can be adapted to many applications. This means that instead of building a separate AI model for each specific task, a single foundation model can be used for multiple tasks with minimal fine-tuning. Parameters are a machine learning term for the variables present in the model on which it was trained that can be used to infer new content.
+Artificial intelligence (AI) has come a long way in recent years, with the development of increasingly sophisticated models that can perform a wide range of tasks. One of the most exciting developments in this field is the emergence of foundation models.  A foundation model is a “paradigm for building AI systems” in which a model trained on a large amount of unlabeled data can be adapted to many applications. This means that instead of building a separate AI model for each specific task, a single foundation model can be used for multiple tasks with minimal [[fine-tuning]]. Parameters are a machine learning term for the variables present in the model on which it was trained that can be used to infer new content.
 = Why use Foundation Models? =
-One of the main advantages of using foundation models is their flexibility. Because they are designed to be adapted to various downstream cognitive tasks by pre-training on broad data at scale, they can be used for a wide range of applications. Another advantage of using foundation models is their reusability. Instead of having to build a new AI model from scratch for each new task, a single foundation model can be used for multiple tasks with minimal fine-tuning. This can save time and resources and make it easier to develop and deploy AI systems.
+One of the main advantages of using foundation models is their flexibility. Because they are designed to be adapted to various downstream cognitive tasks by pre-training on broad data at scale, they can be used for a wide range of applications. Another advantage of using foundation models is their reusability. Instead of having to build a new AI model from scratch for each new task, a single foundation model can be used for multiple tasks with minimal [[fine-tuning]]. This can save time and resources and make it easier to develop and deploy AI systems.
 * A foundation model is a type of deep learning algorithm that has been pre-trained with large data sets scraped from the public internet.
 * Foundation models are trained with a wide variety of data and can transfer knowledge from one task to another.
 * They can be fine-tuned to complete different types of tasks and are primarily used for natural language processing and generation.
-* Compared to standalone, task-oriented machine learning models, foundation models help create reliable AI solutions faster and cheaper, with less data involved and minimal fine-tuning.
+* Compared to standalone, task-oriented machine learning models, foundation models help create reliable AI solutions faster and cheaper, with less data involved and minimal [[fine-tuning]].
 = How to use Foundation Models?=
-Foundation models are trained on enormous quantities of unlabeled data through self-supervised learning. This means that the model learns by predicting missing information in the data, without the need for explicit labels. Once the foundation model has been trained, it can be used for various tasks through transfer learning. This involves adapting the model to a new task by fine-tuning it on a smaller amount of labeled data specific to that task. Some examples of foundation models include [[Generative Pre-trained Transformer (GPT)]]-3, [[Bidirectional Encoder Representations from Transformers (BERT)]], and [[Video/Image#DALL-E | DALL-E]]  2. These models have shown impressive capabilities in natural language processing and generation, as well as image generation. The use of foundation models has the potential to revolutionize many industries and applications. For example, they could be used to develop more sophisticated digital assistants, improve medical diagnosis, or generate new works of art.
+Foundation models are trained on enormous quantities of unlabeled data through self-supervised learning. This means that the model learns by predicting missing information in the data, without the need for explicit labels. Once the foundation model has been trained, it can be used for various tasks through transfer learning. This involves adapting the model to a new task by [[fine-tuning]] it on a smaller amount of labeled data specific to that task. Some examples of foundation models include [[Generative Pre-trained Transformer (GPT)]]-3, [[Bidirectional Encoder Representations from Transformers (BERT)]], and [[Video/Image#DALL-E | DALL-E]]  2. These models have shown impressive capabilities in natural language processing and generation, as well as image generation. The use of foundation models has the potential to revolutionize many industries and applications. For example, they could be used to develop more sophisticated digital assistants, improve medical diagnosis, or generate new works of art.
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