Bard - Revision history

BPeat: Replaced content with "Gemini"

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BPeat at 01:04, 14 January 2024

2024-01-14T01:04:25Z

BPeat at 03:10, 6 December 2023

2023-12-06T03:10:44Z

BPeat at 02:44, 6 December 2023

2023-12-06T02:44:53Z

BPeat at 13:07, 9 October 2023

2023-10-09T13:07:55Z

BPeat: /* LaMDA */

2023-10-08T19:33:42Z

‎LaMDA

BPeat at 19:32, 8 October 2023

2023-10-08T19:32:01Z

BPeat at 19:28, 8 October 2023

2023-10-08T19:28:00Z

BPeat at 19:23, 8 October 2023

2023-10-08T19:23:10Z

BPeat at 19:14, 8 October 2023

2023-10-08T19:14:04Z

← Older revision		Revision as of 01:04, 14 January 2024
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	* [https://www.engadget.com/googles-bard-ai-chatbot-has-learned-to-talk-070111881.html Google's Bard AI chatbot has learned to talk \| Andrew Tarantola - Engadget] ... understanding 40 languages and can speak its responses.		* [https://www.engadget.com/googles-bard-ai-chatbot-has-learned-to-talk-070111881.html Google's Bard AI chatbot has learned to talk \| Andrew Tarantola - Engadget] ... understanding 40 languages and can speak its responses.
	* [https://www.neowin.net/news/google-bard-will-soon-switch-language-models-from-lamda-to-palm-to-compete-with-bing-chat/ Google Bard will soon switch langauage models from LaMDA to PaLM to compete with Bing Chat \| John Callaham - Neowin]		* [https://www.neowin.net/news/google-bard-will-soon-switch-language-models-from-lamda-to-palm-to-compete-with-bing-chat/ Google Bard will soon switch langauage models from LaMDA to PaLM to compete with Bing Chat \| John Callaham - Neowin]
		+	* [https://www.androidcentral.com/apps-software/google-assistant-bard-ui-spotted-again We now know how Google Assistant with Bard will look and work on Android \| Brady Snyder - Android Central]

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 * [[Development]]  ...[[Development#AI Pair Programming Tools|AI Pair Programming Tools]] ... [[Analytics]]  ... [[Visualization]]  ... [[Diagrams for Business Analysis]]
 * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]]
-* [https://techcrunch.com/2023/04/21/googles-bard-ai-chatbot-can-now-generate-and-debug-code/ Google’s Bard AI chatbot can now generate and debug code | Kirsten Korosec - TechCrunch] ... more than 20 programming languages including C++, Go, Java, [[Javascript|JavaScript]], [[Python]] and TypeScript. Users can export [[Python]] code to [[Google]] [[Colaboratory |Colab]].
+* [https://techcrunch.com/2023/04/21/googles-bard-ai-chatbot-can-now-generate-and-debug-code/ Google’s Bard AI chatbot can now generate and debug code | Kirsten Korosec - TechCrunch] ... more than 20 programming languages including C++, Go, Java, [[JavaScript]], [[Python]] and TypeScript. Users can export [[Python]] code to [[Google]] [[Colaboratory |Colab]].
 * [https://searchengineland.com/you-can-now-use-google-bard-to-help-you-code-395880 You can now use Google Bard to help you code | Barry Schwartz - Search Engine Land]

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 = Programming =
 * [https://blog.google/technology/ai/code-with-bard/ Bard now helps you code | ][[Google]]
-* [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[Javascript]] ... [[Generative AI with Javascript|GenAI w/ Javascript]] ... [[TensorFlow]] ... [[PyTorch]]
+* [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]]
 * [[Development]]  ...[[Development#AI Pair Programming Tools|AI Pair Programming Tools]] ... [[Analytics]]  ... [[Visualization]]  ... [[Diagrams for Business Analysis]]
 * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]]

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 = Programming =
 * [https://blog.google/technology/ai/code-with-bard/ Bard now helps you code | ][[Google]]
-* [[Python]]   ... [[Generative AI with Python]]  ... [[Javascript]]  ... [[Generative AI with Javascript]]
+* [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[Javascript]] ... [[Generative AI with Javascript|GenAI w/ Javascript]] ... [[TensorFlow]] ... [[PyTorch]]
 * [[Development]]  ...[[Development#AI Pair Programming Tools|AI Pair Programming Tools]] ... [[Analytics]]  ... [[Visualization]]  ... [[Diagrams for Business Analysis]]
 * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]]

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← Older revision		Revision as of 19:32, 8 October 2023
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−	Bard is a [[Large Language Model (LLM)]] that is trained on a massive dataset of text and code. This dataset includes everything from books and articles to code and code comments. Bard is able to learn from this data and generate text that is similar to the text that it has been trained on. Bard can generate spoken responses to your questions, which can be useful for people who are blind or have low vision. Bard can [[Development\|generate code in different programming languages, which can be useful for software developers]].	+	Bard is a [[Large Language Model (LLM)]] that is trained on a massive dataset of text and code. This dataset includes everything from books and articles to code and code comments. Bard is able to learn from this data and generate text that is similar to the text that it has been trained on. Bard can generate spoken responses to your questions, which can be useful for people who are blind or have low vision. Bard can [[Development\|generate code in different programming languages, which can be useful for software developers]]. Bard can generate a variety of creative text formats, such as poems, code, scripts, and musical pieces. Bard understands the meaning of text and can generate comprehensive and informative responses.

	The data that Bard is trained on is processed using a technique called [[Large Language Model (LLM)#Token\|Tokenization]]; the process of breaking down text into smaller units, called [[Large Language Model (LLM)#Token\|tokens]]. In the case of Bard, [[Large Language Model (LLM)#Token\|tokens]] are typically words or phrases. The model learns to associate these [[Large Language Model (LLM)#Token\|tokens]] with the meaning of the text that they are found in. The [[Large Language Model (LLM)#Token\|tokens]] that Bard is trained on are typically words or phrases. These [[Large Language Model (LLM)#Token\|tokens]] are learned from the massive dataset of text and code that Bard is trained on. The model learns to associate these tokens with the meaning of the text that they are found in. For example, the token "the" is typically associated with the beginning of a sentence, while the [[Large Language Model (LLM)#Token\|tokens]] "a" is typically associated with the beginning of an indefinite noun phrase. The model learns to associate these [[Large Language Model (LLM)#Token\|tokens]] with their meaning in the [[context]] of the text that they are found in. This allows Bard to generate text that is grammatically correct and that makes sense in the [[context]] of the user's query. The specific [[Large Language Model (LLM)#Token\|tokens]] that Bard is trained on are a trade secret. However, it is likely that the [[Large Language Model (LLM)#Token\|tokens]] include common words and phrases, as well as more specialized [[Large Language Model (LLM)#Token\|tokens]] that are related to specific domains of knowledge.		The data that Bard is trained on is processed using a technique called [[Large Language Model (LLM)#Token\|Tokenization]]; the process of breaking down text into smaller units, called [[Large Language Model (LLM)#Token\|tokens]]. In the case of Bard, [[Large Language Model (LLM)#Token\|tokens]] are typically words or phrases. The model learns to associate these [[Large Language Model (LLM)#Token\|tokens]] with the meaning of the text that they are found in. The [[Large Language Model (LLM)#Token\|tokens]] that Bard is trained on are typically words or phrases. These [[Large Language Model (LLM)#Token\|tokens]] are learned from the massive dataset of text and code that Bard is trained on. The model learns to associate these tokens with the meaning of the text that they are found in. For example, the token "the" is typically associated with the beginning of a sentence, while the [[Large Language Model (LLM)#Token\|tokens]] "a" is typically associated with the beginning of an indefinite noun phrase. The model learns to associate these [[Large Language Model (LLM)#Token\|tokens]] with their meaning in the [[context]] of the text that they are found in. This allows Bard to generate text that is grammatically correct and that makes sense in the [[context]] of the user's query. The specific [[Large Language Model (LLM)#Token\|tokens]] that Bard is trained on are a trade secret. However, it is likely that the [[Large Language Model (LLM)#Token\|tokens]] include common words and phrases, as well as more specialized [[Large Language Model (LLM)#Token\|tokens]] that are related to specific domains of knowledge.

← Older revision		Revision as of 19:28, 8 October 2023
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−	Bard is a [[Large Language Model (LLM)]] that is trained on a massive dataset of text and code. This dataset includes everything from books and articles to code and code comments. Bard is able to learn from this data and generate text that is similar to the text that it has been trained on. The data that Bard is trained on is processed using a technique called [[Large Language Model (LLM)#Token\|Tokenization]]; the process of breaking down text into smaller units, called [[Large Language Model (LLM)#Token\|tokens]]. In the case of Bard, [[Large Language Model (LLM)#Token\|tokens]] are typically words or phrases. The model learns to associate these [[Large Language Model (LLM)#Token\|tokens]] with the meaning of the text that they are found in. The [[Large Language Model (LLM)#Token\|tokens]] that Bard is trained on are typically words or phrases. These [[Large Language Model (LLM)#Token\|tokens]] are learned from the massive dataset of text and code that Bard is trained on. The model learns to associate these tokens with the meaning of the text that they are found in. For example, the token "the" is typically associated with the beginning of a sentence, while the [[Large Language Model (LLM)#Token\|tokens]] "a" is typically associated with the beginning of an indefinite noun phrase. The model learns to associate these [[Large Language Model (LLM)#Token\|tokens]] with their meaning in the [[context]] of the text that they are found in. This allows Bard to generate text that is grammatically correct and that makes sense in the [[context]] of the user's query. The specific [[Large Language Model (LLM)#Token\|tokens]] that Bard is trained on are a trade secret. However, it is likely that the [[Large Language Model (LLM)#Token\|tokens]] include common words and phrases, as well as more specialized [[Large Language Model (LLM)#Token\|tokens]] that are related to specific domains of knowledge.	+	Bard is a [[Large Language Model (LLM)]] that is trained on a massive dataset of text and code. This dataset includes everything from books and articles to code and code comments. Bard is able to learn from this data and generate text that is similar to the text that it has been trained on. Bard can generate spoken responses to your questions, which can be useful for people who are blind or have low vision. Bard can [[Development\|generate code in different programming languages, which can be useful for software developers]].
		+
		+	The data that Bard is trained on is processed using a technique called [[Large Language Model (LLM)#Token\|Tokenization]]; the process of breaking down text into smaller units, called [[Large Language Model (LLM)#Token\|tokens]]. In the case of Bard, [[Large Language Model (LLM)#Token\|tokens]] are typically words or phrases. The model learns to associate these [[Large Language Model (LLM)#Token\|tokens]] with the meaning of the text that they are found in. The [[Large Language Model (LLM)#Token\|tokens]] that Bard is trained on are typically words or phrases. These [[Large Language Model (LLM)#Token\|tokens]] are learned from the massive dataset of text and code that Bard is trained on. The model learns to associate these tokens with the meaning of the text that they are found in. For example, the token "the" is typically associated with the beginning of a sentence, while the [[Large Language Model (LLM)#Token\|tokens]] "a" is typically associated with the beginning of an indefinite noun phrase. The model learns to associate these [[Large Language Model (LLM)#Token\|tokens]] with their meaning in the [[context]] of the text that they are found in. This allows Bard to generate text that is grammatically correct and that makes sense in the [[context]] of the user's query. The specific [[Large Language Model (LLM)#Token\|tokens]] that Bard is trained on are a trade secret. However, it is likely that the [[Large Language Model (LLM)#Token\|tokens]] include common words and phrases, as well as more specialized [[Large Language Model (LLM)#Token\|tokens]] that are related to specific domains of knowledge.

← Older revision		Revision as of 19:23, 8 October 2023
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−	Bard is a [[Large Language Model (LLM)]] that is trained on a massive dataset of text and code. This dataset includes everything from books and articles to code and code comments. Bard is able to learn from this data and generate text that is similar to the text that it has been trained on. The data that Bard is trained on is processed using a technique called ~~tokenization~~.	+	Bard is a [[Large Language Model (LLM)]] that is trained on a massive dataset of text and code. This dataset includes everything from books and articles to code and code comments. Bard is able to learn from this data and generate text that is similar to the text that it has been trained on. The data that Bard is trained on is processed using a technique called [[Large Language Model (LLM)#Token\|Tokenization]]; the process of breaking down text into smaller units, called [[Large Language Model (LLM)#Token\|tokens]]. In the case of Bard, [[Large Language Model (LLM)#Token\|tokens]] are typically words or phrases. The model learns to associate these [[Large Language Model (LLM)#Token\|tokens]] with the meaning of the text that they are found in. The [[Large Language Model (LLM)#Token\|tokens]] that Bard is trained on are typically words or phrases. These [[Large Language Model (LLM)#Token\|tokens]] are learned from the massive dataset of text and code that Bard is trained on. The model learns to associate these tokens with the meaning of the text that they are found in. For example, the token "the" is typically associated with the beginning of a sentence, while the [[Large Language Model (LLM)#Token\|tokens]] "a" is typically associated with the beginning of an indefinite noun phrase. The model learns to associate these [[Large Language Model (LLM)#Token\|tokens]] with their meaning in the [[context]] of the text that they are found in. This allows Bard to generate text that is grammatically correct and that makes sense in the [[context]] of the user's query. The specific [[Large Language Model (LLM)#Token\|tokens]] that Bard is trained on are a trade secret. However, it is likely that the [[Large Language Model (LLM)#Token\|tokens]] include common words and phrases, as well as more specialized [[Large Language Model (LLM)#Token\|tokens]] that are related to specific domains of knowledge.

−	* Tokenization is the process of breaking down text into smaller units, called tokens. In the case of Bard, tokens are typically words or phrases. The model learns to associate these tokens with the meaning of the text that they are found in. The tokens that Bard is trained on are typically words or phrases. These tokens are learned from the massive dataset of text and code that Bard is trained on. The model learns to associate these tokens with the meaning of the text that they are found in. For example, the token "the" is typically associated with the beginning of a sentence, while the token "a" is typically associated with the beginning of an indefinite noun phrase. The model learns to associate these tokens with their meaning in the context of the text that they are found in.This allows Bard to generate text that is grammatically correct and that makes sense in the context of the user's query. The specific tokens that Bard is trained on are a trade secret. However, it is likely that the tokens include common words and phrases, as well as more specialized tokens that are related to specific domains of knowledge.
−
−	* The tokens that Bard is trained on are important because they allow the model to generate text that is relevant to the user's query. If the model is not trained on a specific token, it will not be able to generate text that is relevant to a query that uses that token. For example, if the model is not trained on the token "if", it will not be able to generate text that is relevant to a query that asks for a code snippet that uses the "if" statement. This allows Bard to generate text that is grammatically correct and that makes sense in the context of the user's query. The specific tokens that Bard is trained on are a trade secret. However, it is likely that the tokens include common words and phrases, as well as more specialized tokens that are related to specific domains of knowledge.
−
−	When a user prompts Bard with a question or request, the model uses the tokens that it has learned to generate text that is relevant to the user's query. The model also uses the tokens to generate text that is grammatically correct and that makes sense in the context of the user's query. The user interface for Bard is simple and easy to use. The user can type in a question or request, and the model will generate text that is relevant to the user's query. The model will generate text that is relevant to the user's query by using the tokens that it has learned to associate with the meaning of the user's query. The user can also ask the model to generate text in a specific style, such as a poem or a code snippet.

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 ** ... Open the Google app on your smartphone and tap on the [[Assistants#Chatbot | Chatbot]] icon, enter your prompt and hit enter
 ** ... help test Bard's latest version (experiment) in [https://labs.withgoogle.com/ Google Labs]
 * [[Gemini]] | [[Google]] DeepMind
 * [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]