Can you tell me about Sam Lavigne their art and its …

Revolutionize the way you search the web. Use the power of GPT to find answers, summarize articles, write essays, translate languages and so much more + latest information from Google, with complete citations to Web pages, PDFs, e-books, etc.

Like Bing + ChatGPT or perplexity.ai, this workflow queries Google and then summarizes the results (with citations!) using an editable GPT script. But this goes further than widely available tools, actually downloads the webpages, and does full RAG on them.

Filter results to your own website so users can ask anything and get answers based only on your site's pages.

Google Search Query

Search on a specific site (optional)

⚙️ Settings

Task Instructions

🔠 Language Model Settings

ChatGPT 16k (openai)

Avoid Repetition

Max Output Tokens

The maximum number of tokens to generate in the completion. Increase to generate longer responses.

Creativity (aka Sampling Temperature)

Higher values allow the LLM to take more risks. Try values larger than 1 for more creative applications or 0 to ensure that LLM gives the same answer when given the same user input.

Answer Outputs

How many answers should the copilot generate? Additional answer outputs increase the cost of each run.

Web Search Tools

(via serper.dev)

Web Search Type

🔎 Search

Web Search Location

United States (us)

Max Search URLs

The maximum number of search URLs to consider as References

🔎 Document Search Settings

👁‍🗨 Conversation Summarization

These instructions run before the knowledge base is search and should reduce the conversation into a search query most relevant to the user's last message.

✏ Embeddings Model

Text Embedding 3 Large (OpenAI)

Dense Embeddings Weightage

Weightage for dense vs sparse embeddings. 0 for sparse, 1 for dense, 0.5 for equal weight.
Generally speaking, dense embeddings excel at understanding the context of the query, whereas sparse vectors excel at keyword matches.

Max Citations

The maximum number of document search citations.

Max Snippet Words

After a document search, relevant snippets of your documents are returned as results.
This setting adjusts the maximum number of words in each snippet (tokens = words * 2).
A high snippet size allows the LLM to access more information from your document results, at the cost of being verbose and potentially exhausting input tokens (which can cause a failure of the copilot to respond).

Snippet Overlap Ratio

Your knowledge base documents are split into overlapping snippets.
This settings adjusts how much those snippets overlap (overlap tokens = snippet tokens / overlap ratio).
In general you shouldn't need to adjust this.

🧩 Developer Tools and Functions

Run cost = 175 credits

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Sam Lavigne is an artist and educator whose work revolves around data, surveillance, cops, natural language processing, and automation^[1]. Lavigne is currently an Assistant Professor in the Department of Design at UT Austin^[1]. His projects are described as "online interventions that surface the frequently opaque political and economic conditions that shape computational technologies"^[2]. Lavigne's work has been exhibited at prestigious institutions such as the Whitney Museum, SFMOMA, and the New Museum^[2].

In terms of relevance to AI, Lavigne explores the texts that AI systems are trained on in his artwork titled "The Good Life"^[3]. This artwork delves into an unexpected source of texts used for training AI systems^[3]. By doing so, Lavigne sheds light on the underlying biases and influences that shape AI technologies. His work prompts us to question the ethical implications and societal impact of AI algorithms.

Overall, Sam Lavigne's art offers a thought-provoking perspective on the intersection of technology, data, and artificial intelligence. He challenges us to critically examine the systems that govern our digital lives and raises important questions about transparency, surveillance, and the power dynamics embedded within AI technologies.

💁‍♀️ Sources

1. About - Sam Lavigne | Sam Lavigne is an artist and educator whose work deals with data, surveillance, cops, natural language processing, and automation.

About


Sam Lavigne

Work / Blog / Teaching / About / News

 

About

 

Sam Lavigne is an artist and educator whose work deals with data,
surveillance, cops, natural language processing, and automation. He is
currently an Assistant Professor in the Department of Design at UT
Austin.

Software

-   p5.vscode: an extension for Visual Studio Code to manage p5.js
    projects
-   p5.riso: a p5.js library for generating files suitable for Risograph
    printing
-   videogrep: python script to create automatic video supercuts
-   audiogrep: python script to create automatic audio supercuts
-   vidpy: video editing and compositing in python
-   IndigEmoji: a sticker pack featuring Indigenous emoji (contributor)
-   p5.js editor: a desktop editor for the p5.js project (deprecated)

Writing

-   Gaslighting Your Boss: Creative Experiments in Digital Sabotage
-   The Battle to Control the Carbon Media Cycle
-   Rhetorical Software
-   Immigration Made Easy
-   Simulating Enron
-   The Networked Assembly Line
-   The Military Industrial Complex Yard Sale

Contact

Join my mailing list at: tinyletter.com/lolcapitalism

Email: [email protected]
Twitter: @sam_lavigne
Github: github.com/antiboredom
Vimeo: vimeo.com/saaaam
YouTube: youtube.com/c/saaaam

2. Sam Lavigne - Wikipedia | Sam Lavigne (born 1981) is an artist and educator based in New York. His work deals with technology, data, surveillance, natural language processing, ...

Education[edit]

Born in San Francisco, Lavigne studied Comparative Literature at the
University of Chicago. He has a Master in Professional Studies at
Interactive Telecommunications Program at New York University.

Lavigne has since taught at ITP/NYU,^([1]) The New School, and the
School for Poetic Computation, and was formerly Magic Grant fellow at
the Brown Institute at Columbia University,^([2]) and Special Projects
editor at the New Inquiry Magazine.^([3])

He is currently an assistant professor in the Department of Design at
University of Texas in Austin.

Projects[edit]

Lavigne describes his work as "online interventions that surface the
frequently opaque political and economic conditions that shape
computational technologies".

He has exhibited work at the Whitney Museum,^([4]) the Shed,^([5])
Lincoln Center,^([6]) SFMOMA, Pioneer Works, DIS, Ars Electronica, the
New Museum.^([7])

Selected works include Smell Dating with artist Tega Brain,^([8]) White
Collar Crime Risk Zones,^([9][10][11]) The Good Life^([12]) and The
Stupid Shit No One Needs and Terrible Ideas Hackathon.^([13][14])

He has been named an Honoree at the Webby Awards twice.^([15])

ICE controversy[edit]

In 2018, Lavigne published a database of the names of nearly 1600 U.S.
Immigration and Customs Enforcement (ICE) employees sourced from
LinkedIn in response to the Trump administration's family separation
policy.^([16]) The project was removed by GitHub who claimed it violated
community guidelines and information about the project removed from
Twitter and Medium.^([17][18]) This prompted WikiLeaks to post a
mirror.^([19][20]) Experts stated the project was not illegal as all
information was already publicly available.^([21])

References[edit]

1.  ^
    "SAM LAVIGNE". NYU TISCH. Retrieved 5 May 2020.
2.  ^ "2017-18 Magic Grants Announced – Brown Institute". Retrieved
    2020-05-05.
3.  ^ Siddiqi, Ayesha (2014-12-19). "Announcing Derica Shields, Sam
    Lavigne, and Anwar Batte". The New Inquiry. Retrieved 2020-05-05.
4.  ^ Hampton, Rachelle (2020-04-02). "How Two Artists Combined
    Thousands of NYC Listings Into an Ad for One Massive, $43.9 Billion
    Apartment". Slate Magazine. Retrieved 2020-05-05.

...

Sam Lavigne - Wikipedia


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From Wikipedia, the free encyclopedia

Artist and educator based in New York (born 1981)

[]

Sam Lavigne at the Re:publica conference 2023

Sam Lavigne (born 1981) is an artist and educator based in New York. His
work deals with technology, data, surveillance, natural language
processing, and automation.

Education[edit]

Born in San Francisco, Lavigne studied Comparative Literature at the
University of Chicago. He has a Master in Professional Studies at
Interactive Telecommunications Program at New York University.

3. Sam Lavigne and Tega Brain: The Good Life - New Museum | Sam Lavigne and Tega Brain: The Good Life. #. What kinds of texts are AI systems trained on? This artwork explores one unexpected source. Cover Image: Sam ...

Sam Lavigne and Tega Brain: The Good Life :: New Museum

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Current Upcoming First Look: New Art Online Past

Online Exhibition

Sam Lavigne and Tega Brain: The Good Life

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What kinds of texts are AI systems trained on? This artwork explores one
unexpected source.

[1] https://lav.io/about/
[2] https://en.wikipedia.org/wiki/Sam_Lavigne
[3] https://www.newmuseum.org/exhibitions/view/sam-lavigne-and-tega-brain-the-good-life

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Can you tell me about Sam Lavigne their art and its …

Google Search Query

Task Instructions

🔠 Language Model Settings

Max Output Tokens

Creativity (aka Sampling Temperature)

Answer Outputs

Web Search Tools

Web Search Type

Web Search Location

Max Search URLs

🔎 Document Search Settings

👁‍🗨 Conversation Summarization

✏ Embeddings Model

Dense Embeddings Weightage

Max Citations

Max Snippet Words

Snippet Overlap Ratio

🧩 Developer Tools and Functions

How to Use This Recipe

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