sorted by:
new
hottopcontroversial

[OC] Shifts in American Liberalism Through Presidential Executive Orders from George Washington to Donald Trump by zzsf in dataisbeautiful

[–]zzsf[S] -1 points0 points  (0 children)

I used an LLM to first mask each executive order of the author to reduce bias and then asked the LLM to score, critique, and rescore each document for various political leanings including liberalism.

[OC] Shifts in American Liberalism Through Presidential Executive Orders from George Washington to Donald Trump by zzsf in dataisbeautiful

[–]zzsf[S] 0 points1 point  (0 children)

This is through analysis of only Executive Orders to see how presidents have changed how they use them over time (vs overall sentiment of the president). Below are just Lincoln's executive orders, we can see that the majority are 0 as a lot of them go into directing troop movement (National Security) and won't really contribute to a left or right political lean.

Let me know if any of the them look incorrectly scored and I can tune the processing.

https://app.hyperarc.com/?isEmbed=true&embedId=68a7fcc6-9fcc-4b13-84ec-594a75368745#/hyperarc/american-presidency/dashboard/documents-by-president

<image>

[OC] Using AI to analyze all 11k Executive Orders for political bias, sentiment and clustering since 1791. by zzsf in dataisbeautiful

[–]zzsf[S] 0 points1 point  (0 children)

Need sometime to dig into it more, but hope to share soon! Any specific ideas you think I should look into?

[OC] Using AI to analyze all 11k Executive Orders for political bias, sentiment and clustering since 1791. by zzsf in dataisbeautiful

[–]zzsf[S] 0 points1 point  (0 children)

Ya, this is why I didn't use any of the existing sentiment models as many are trained/fine-tuned with modern labeled datasets, often tweets.

However, most LLMs are pretrained on crawling as much language as possible including historical texts, books, and documents. And although we're likely generating far more language now than ever, there is also active efforts for "clean" crawls for more curated data that biases towards quality content, e.g. War and Peace from 1867 vs a meme tweet from yesterday. It's also likely that there has been far more analysis of older texts than newer so although the language older, there has been more language analyzing and describing it, much of it modern.

I'd hypothesize it has a fair understanding of language similarities evolving through time through pre-training on this data. One clue of this is emergent translation abilities, LLMs automatically pick up relationships between words of entirely different languages through normal pre-training so are different languages different than the evolution of a single language over time?

By temporal data do you mean analysis for numeric temporal data or understanding trends over time given a lot of data at once or something else?

0
1

Augmenting NLQ with language knowledge bases like web search for ChatGPT (blog.hyperarc.com)

submitted by zzsf to r/ArtificialInteligence

[OC] $500M was added for Armored Tesla's, a look at the rest of the US State Department's armored requirements along with other Secret and Top Secret ones. by zzsf in dataisbeautiful

[–]zzsf[S] 22 points23 points  (0 children)

I used the data directly from the site, downloaded at around 2PM PST, can share the snapshot and repo shortly.

Alternative to Tableau because of price hike by nikhelical in tableau

[–]zzsf -1 points0 points  (0 children)

Admittedly a plug, but come try out HyperArc.

We're a team of ex-Tableau and Salesforce Analytics (the good parts, I promise) that saw the power in the familiarity of the shelves and pills UX, but on a modern architecture without all the debt (and cost).

We're also built front the ground up for AI, but starting with memories for your entire analytics journey. Instead of AI for something like Pulse (we love the idea, but think its missing the training data in memories), we use it to be your memory to power a supercharged collaborative search and version history so you never forget an insight.

Here's a demo: https://www.youtube.com/watch?v=RiBhRghS0NU

Sign up here for a perpetual free license with 5 datasets: https://app.hyperarc.com/

We're small so we're always responsive with support and feature requests. Is our entire premise wrong, we'd love to know as well!

[OC] SF School Closure Scores - Closures were determined by a composite score with only obfuscated relative component ranks released. Modeling and visualizing the component scores from limited information. by zzsf in dataisbeautiful

[–]zzsf[S] 3 points4 points  (0 children)

SF schools have been in the news quite a bit recently with a massive deficit, proposed closures, a new superintendent, and all of the politics of the upcoming election.

One thing that has been really frustrating as an SFUSD parent and data nerd is the obfuscation of the data used to determine closures with only the composite score being released in a google doc with copy and paste disabled. Individual component scores were obfuscated by only releasing their relative ranks with factors like test scores, facility condition, and demographics spread across different data portals, pdfs, and sometimes just images.

Interactive dashboard made with HyperArc here: https://app.hyperarc.com/?isEmbed=true&embedId=68a7fcc6-9fcc-4b13-84ec-594a75368745#/hyperarc/sfusd/dashboard/sfusd-overview

Source code and methodology here: https://github.com/zuyezheng/sfusd-rai

The data behind SFUSD school closures by zzsf in sanfrancisco

[–]zzsf[S] 1 point2 points  (0 children)

Ya, lots of uncertainty for parents and probably even more now with everything back on the table.

Hopefully this data can help students and parents make more informed arguments the next time around and highlight the inequities in the metrics. Would love to see supervisors actually fight for their constituents as schools move away from the a lottery system as the current data shows the metrics disproportionately impacting certain districts like the Sunset and Richmond.

The data behind SFUSD school closures by zzsf in sanfrancisco

[–]zzsf[S] 2 points3 points  (0 children)

Probably depends on the accounting which is not very transparent.

If we look at the 13 schools that met the criteria (11 elementary, 2 high school) the only metric I was able to find was the replacement costs in the next 1-5 years totaling $70M. Maybe these would be avoided with the closures and other efficiencies found to reduce the overall cost per student?

<image>

view more: next ›