Artificial Intelligence: Difference between revisions

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ChatGPT is all the rage; stock market valuations of major companies like Alphabet (Google) fluctuate billions of dollars overnight due to perceived strength or weakness of the product's AI-powered features.

See [[wp:Artificial intelligence]] on Wikipedia

This page will capture some of the interesting points about AI and its use or relevance in Knowledge Management, MediaWiki, and probably some other tangents like deep fakes or politics.

<s>Open AI's [https://chatgpt.com/ ChatGPT], Anthropic's [https://claude.ai Claude], Meta's LLaMA</s> China's [https://www.deepseek.com/ DeepSeek] is all the rage<ref>ed. note: I crossed out the prior models to illustrate the litany of new models that are hyped in rapid succession. Of course ChatGPT and OpenAI are still relevant - and release updates to their prior technology in the never-ending 'arms race' of AI.</ref>; stock market valuations of major companies like [https://www.marketwatch.com/investing/stock/goog Alphabet] (Google) or [https://www.marketwatch.com/investing/stock/nvda NVidia] fluctuate billions of dollars overnight due to perceived strength or weakness in the new technological arms race. Companies like [[wp:Mistral_AI|Mistral AI]], founded only in 2023, are worth billions of dollars.

This page will capture some of the interesting points about AI and its use or relevance in Knowledge Management, [[MediaWiki]], and probably some other tangents like deep fakes or politics.

One interesting essay I read was "''[https://adam.harvey.studio/creative-commons Creative Commons and the Face Recognition Problem]''" by Adam Harvey. He describes how 100 million images from Flickr were used to train facial recognition systems using peoples wedding and vacation photos.

== Understanding AI ==

An excellent introduction to Artificial Intelligence and Large Language Models (LLMs) is an article [https://www.understandingai.org/p/large-language-models-explained-with Large language models, explained with a minimum of math and jargon] by Timothy Lee and Sean Trott - July 27, 2023<~~blockquote~~>''Tim Lee is a journalist with a master’s degree in computer science. The article is the result of two months of in-depth research. Co-author Sean Trott is a cognitive scientist at the University of California, San Diego.''</~~blockquote~~>

An excellent introduction to Artificial Intelligence and Large Language Models (LLMs) is an article [https://www.understandingai.org/p/large-language-models-explained-with Large language models, explained with a minimum of math and jargon] by Timothy Lee and Sean Trott - July 27, 2023<ref>''Tim Lee is a journalist with a master’s degree in computer science. The article is the result of two months of in-depth research. Co-author Sean Trott is a cognitive scientist at the University of California, San Diego.''</ref>

[[wp:Graphics_processing_unit|GPU]]<nowiki/>s - specialized electronic circuits initially developed for computer graphics - are an important aspect of AI (and other forms of computing such as neural networks, cryptocurrency).

==Llama confusion==

[[File:18-08-25-Åland RRK6596a.jpg|thumb|right|llama from disc golf island]]

[[wp:llama.cpp|llama.cpp]], is a library written in C++ that performs inference on various LLMs (including [[wp:Llama_(language_model)|Llama]]) by implementing tensor algebra so systems without GPUs can do "AI". [[Ollama]] is a LLM you can run on your own hardware, off-net.

== Vectors are not just for graphics ==

[[Svg|SVG]] is cool for graphics. But vectors aren't just for graphics.

[[Svg|SVG]] is cool for graphics. But [[vectors]] aren't just for graphics.

The [https://vectors.nlpl.eu/ Nordic Language Processing Laboratory], [https://www.mn.uio.no/ifi/english/research/groups/ltg/ Language Technology Group] at the University of Oslo, Norway publishes their research tools which help visualize how word vectors work in LLMs For instance, here's the [https://vectors.nlpl.eu/explore/embeddings/en/MOD_enwiki_upos_skipgram_300_2_2021/cat_NOUN/ vector for cat]. (Click the link that says "Show the raw vector").

== Biases in the Hive Mind ==

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At Google, they have two series of LLMs: [https://deepmind.google/technologies/gemini/ Gemini] ([https://github.com/google-gemini/cookbook cookbook]) and [https://ai.google.dev/gemma Gemma]

== Dive Deeper ==

* [https://huggingface.co/ Hugging Face]

** [https://huggingface.co/docs/transformers.js/index transformers.js - run transformers directly in your browser]

* [https://www.kaggle.com/ Kaggle] - competitions, open datasets, models and notebooks like [https://www.kaggle.com/code/markishere/day-1-prompting day-1-prompting]

* [https://temporal.io/resources Temporal] - reliable, scalable AI orchestrator

[[Category:Artificial Intelligence]]

@@ Line 1: / Line 1: @@
-ChatGPT is all the rage; stock market valuations of major companies like Alphabet (Google) fluctuate billions of dollars overnight due to perceived strength or weakness of the product's AI-powered features.
+See [[wp:Artificial intelligence]] on Wikipedia
-This page will capture some of the interesting points about AI and its use or relevance in Knowledge Management, MediaWiki, and probably some other tangents like deep fakes or politics.
+<s>Open AI's [https://chatgpt.com/ ChatGPT], Anthropic's [https://claude.ai Claude], Meta's LLaMA</s> China's [https://www.deepseek.com/ DeepSeek] is all the rage<ref>ed. note: I crossed out the prior models to illustrate the litany of new models that are hyped in rapid succession. Of course ChatGPT and OpenAI are still relevant - and release updates to their prior technology in the never-ending 'arms race' of AI.</ref>; stock market valuations of major companies like [https://www.marketwatch.com/investing/stock/goog Alphabet] (Google) or [https://www.marketwatch.com/investing/stock/nvda NVidia] fluctuate billions of dollars overnight due to perceived strength or weakness in the new technological arms race. Companies like [[wp:Mistral_AI|Mistral AI]], founded only in 2023, are worth billions of dollars.
+This page will capture some of the interesting points about AI and its use or relevance in Knowledge Management, [[MediaWiki]], and probably some other tangents like deep fakes or politics.
 One interesting essay I read was "''[https://adam.harvey.studio/creative-commons Creative Commons and the Face Recognition Problem]''" by Adam Harvey. He describes how 100 million images from Flickr were used to train facial recognition systems using peoples wedding and vacation photos.
 == Understanding AI ==
-An excellent introduction to Artificial Intelligence and Large Language Models (LLMs) is  an article [https://www.understandingai.org/p/large-language-models-explained-with Large language models, explained with a minimum of math and jargon]  by Timothy Lee and Sean Trott - July 27, 2023<blockquote>''Tim Lee is a journalist with a master’s degree in computer science. The article is the result of two months of in-depth research. Co-author Sean Trott is a cognitive scientist at the University of California, San Diego.''</blockquote>
+An excellent introduction to Artificial Intelligence and Large Language Models (LLMs) is  an article [https://www.understandingai.org/p/large-language-models-explained-with Large language models, explained with a minimum of math and jargon]  by Timothy Lee and Sean Trott - July 27, 2023<ref>''Tim Lee is a journalist with a master’s degree in computer science. The article is the result of two months of in-depth research. Co-author Sean Trott is a cognitive scientist at the University of California, San Diego.''</ref>
+[[wp:Graphics_processing_unit|GPU]]<nowiki/>s - specialized electronic circuits initially developed for computer graphics - are an important aspect of AI (and other forms of computing such as neural networks, cryptocurrency).
+==Llama confusion==
+[[File:18-08-25-Åland RRK6596a.jpg|thumb|right|llama from disc golf island]]
+[[wp:llama.cpp|llama.cpp]], is a library written in C++ that performs inference on various LLMs (including [[wp:Llama_(language_model)|Llama]]) by implementing tensor algebra so systems without GPUs can do "AI". [[Ollama]] is a LLM you can run on your own hardware, off-net.
 == Vectors are not just for graphics ==
-[[Svg|SVG]] is cool for graphics. But vectors aren't just for graphics.
+[[Svg|SVG]] is cool for graphics. But [[vectors]] aren't just for graphics.
-The [https://vectors.nlpl.eu/ Nordic Language Processing Laboratory], [https://www.mn.uio.no/ifi/english/research/groups/ltg/ Language Technology Group] at the University of Oslo, Norway publishes their research tools which help visualize how word vectors work in LLMs For instance, here's the [https://vectors.nlpl.eu/explore/embeddings/en/MOD_enwiki_upos_skipgram_300_2_2021/cat_NOUN/ vector for cat]. (Click the link that says "Show the raw vector").
 == Biases in the Hive Mind ==
@@ Line 24: / Line 30: @@
 At Google, they have two series of LLMs: [https://deepmind.google/technologies/gemini/ Gemini] ([https://github.com/google-gemini/cookbook cookbook]) and [https://ai.google.dev/gemma Gemma]
+== Dive Deeper ==
+* [https://huggingface.co/ Hugging Face]
+** [https://huggingface.co/docs/transformers.js/index transformers.js - run transformers directly in your browser]
+* [https://www.kaggle.com/ Kaggle] - competitions, open datasets, models and notebooks like [https://www.kaggle.com/code/markishere/day-1-prompting day-1-prompting]
+* [https://temporal.io/resources Temporal] - reliable, scalable AI orchestrator
+{{#ev:youtube|https://www.youtube.com/watch?v=n__rXmGjwYY}}
 [[Category:Artificial Intelligence]]