Public
Edited
May 6, 2024
Importers
navio(zeroShotArray, {height: 500, attribWidth: 16})
// viewof zeroShot = navio(zeroShotArray, {height: 500, attribWidth: 16})
// navio(ToTFewShotsArray, {height: 500, attribWidth: 16})
// viewof zeroShotWithRules = navio(zeroShotWithRulesArray,{height: 500, attribWidth: 16})
// {
// return html`
// <div style="display: flex;">
// <div style="flex-basis:35%">${viewof zeroShot} </div>
// <div style="flex-basis:35%">${viewof CoTFewShots}</div>
// <div style="flex-basis:35%">${viewof zeroShotWithRules} </div>
// </div>`}
{
const treeSvg = Tree(hierarchyZeroShot, {
label: (d) => {
return d[0];
},
width: 1100,
fontSize: 14
});
return htl.html`<h2>Class/Subclass/Subsubclass Taxonomy Zero Shot</h2>
<div>${treeSvg}</div>`;
}
{
const treeSvg = Tree(hierarchyfewShotCOT, {
label: (d) => {
return d[0];
},
width: 1100,
fontSize: 14
});
return htl.html`<h2>Class/Subclass/Subsubclass Taxonomy Few-Shot Chain-of-Thought Prompting</h2>
<div>${treeSvg}</div>`;
}
{
const treeSvg = Tree(hierarcyToT, {
label: (d) => {
return d[0];
},
width: 1100,
fontSize: 14
});
return htl.html`<h2>Class/Subclass/Subsubclass Taxonomy Tree-of-Thoughts Few-Shot Prompting</h2>
<div>${treeSvg}</div>`;
}
myPanel = panel
import { panel } with {
zeroShotArray as data,
defaultGroupBy,
defaultColorBy,
defaultLayout
} from "a578473b724795d5"
defaultLayout = "pack"
defaultColorBy = "Country"
defaultGroupBy = "Country"
import {Tree} from "@john-guerra/tidy-tree-component"
// hireachyFewShot = hierarchy(fewShotsProcessed)
hierarchyZeroShot = hierarchy(zeroShotArray,"latest_prompt")
hierarchyfewShotCOT = hierarchy(fewShotCOTArray,"latest_prompt")
hierarcyToT = hierarchy(ToTFewShotsArray,"latest_prompt")
function hierarchy(items, type) {
const hierarchy = new Map();
items.forEach(item => {
// Ensure item['Classes of irresponsible AI use'] is treated as an array
const classes = item ? Array.isArray(item['Classes of irresponsible AI use'])
? item['Classes of irresponsible AI use']
: [item['Classes of irresponsible AI use']] : [];
classes.forEach(classKey => {
// Initialize a Map for this classKey if it doesn't exist
if (!hierarchy.has(classKey)) {
hierarchy.set(classKey, new Map());
}
const classMap = hierarchy.get(classKey);
// Process subclasses if this classKey is a key in the subclass object
if (item['Subclasses'] && item['Subclasses'][classKey]) {
const subclassArray = item['Subclasses'][classKey];
subclassArray.forEach(subclassItem => {
// Initialize a Map for this subclassItem under classKey
if (!classMap.has(subclassItem)) {
classMap.set(subclassItem, new Map());
}
const subclassMap = classMap.get(subclassItem);
// Process sub-subclasses based on the type
if (type === 'zeroShot' && item['Sub-subclasses'] && item['Sub-subclasses'][subclassItem]) {
const subsubclassArray = item['Sub-subclasses'][subclassItem];
subsubclassArray.forEach(subsubclassItem => {
// Initialize an array for this subsubclassItem under subclassItem if it doesn't exist
if (!subclassMap.has(subsubclassItem)) {
subclassMap.set(subsubclassItem, []);
}
});
} else if (type === 'zeroShotRules' && item['Sub-subclass'] && item['Sub-subclass'][classKey] && item['Sub-subclass'][classKey][subclassItem]) {
const subsubclassArray = item['Sub-subclass'][classKey][subclassItem];
subsubclassArray.forEach(subsubclassItem => {
// Initialize an array for this subsubclassItem under subclassItem if it doesn't exist
if (!subclassMap.has(subsubclassItem)) {
subclassMap.set(subsubclassItem, []);
}
});
} else if (type === 'latest_prompt' && item['Sub-subclass'] && item['Sub-subclass'][subclassItem]) {
const subsubclassArray = item['Sub-subclass'][subclassItem];
subsubclassArray.forEach(subsubclassItem => {
// Initialize an array for this subsubclassItem under subclassItem if it doesn't exist
if (!subclassMap.has(subsubclassItem)) {
subclassMap.set(subsubclassItem, []);
}
});
}
});
}
});
});
return hierarchy;
}
viewof zeroShotUnrolled = {
// Generates an object where each groupBy attribute is a field and the value is a function spliting by that attribute
const objForDerive = Object.fromEntries(
groupByZeroShot.map((a) => [a, aq.escape((d) => op.split(d[a], ","))])
);
let df = aq
.from(zeroShotArray)
.derive(objForDerive);
for (let a of groupByZeroShot) {
df = df.unroll(a);
}
return df
.view();
}
viewof zeroShotResultsTable = {
let df = aq.from(zeroShotArray);
return df.view();
}
attribs_zeroShot = Object.keys(zeroShotArray[0])
selectedIncidents = {
const ids = [0,1,2,3,4,5,6,9,10,11,13,14,451,382,505,167,39]
const id_set = new Set(ids)
const selectedFieldsArray = mergedIncidentsArr.map(item => {
return {
title: item.title,
'incident id': item['incident id'],
description: item.description,
'date occurrence': item.date,
'date publicly known': item['date published'],
};
});
const selectedIncidentsArr = selectedFieldsArray.filter(item => id_set.has(+item['incident id']))
return selectedIncidentsArr
}
zeroShotArray
zeroShotArray
zeroShotArray = {
//zeroShotFile consists of 16 incidents, including the awful AI incident
const zeroShotFile = await FileAttachment("zero_shot_1_processed_results_2024_03_26_17_08.json").json();
//zeroShotFile2 consists of incident 39
const zeroShotFile2 = await FileAttachment("zero_shot_39_processed_results_2024_03_26_17_16.json").json();
const zeroShotArray = Object.entries(zeroShotFile).map(([key,value]) => {
return {id : key,
...value};
})
const obj_39 = Object.entries(zeroShotFile2).map(([key,value]) => {
return {id: key,
...value}
})
//merging the incidents together to one in zeroShotArray
zeroShotArray.push(obj_39[0])
// post-processing here to merge the rest of the fields from incident AI database, such as date, title, description, to the zeroShotArray results
let zeroShotMergedArray = zeroShotArray.map((item,index)=>{
const incident = selectedIncidents.find(i=> +i["incident id"] === +item.id)
if(incident){
return {
...item,
title: incident.title,
description: incident.description,
"date occurrence": incident["date occurrence"],
"date publicly known": incident["date publicly known"]
}
}
}
)
return zeroShotMergedArray
}
fewShotCOTArray = {
//fewShotFile consists of 15 incidents
const fewShotFile = await FileAttachment("cot_1_processed_results_2024_03_26_16_21.json").json();
//fewShotFile2 consists of incident 0 (from awful ai database)
const fewShotFile2 = await FileAttachment("cot_0_processed_results_2024_03_26_16_40.json").json();
//fewShotFile3 consists of incident 39
const fewShotFile3 = await FileAttachment("cot_39_processed_results_2024_03_26_17_19.json").json();
const fewShotArray = Object.entries(fewShotFile).map(([key,value]) => {
return {id : key,
...value};
})
const obj_39 = Object.entries(fewShotFile3).map(([key,value]) => {
return {id: key,
...value}
})
const obj_0 = Object.entries(fewShotFile2).map(([key,value]) => {
return {id: key,
...value}
})
//merging the incidents together to one in fewShotArray
fewShotArray.push(obj_39[0])
fewShotArray.push(obj_0[0])
// post-processing here to merge the rest of the fields from incident AI database, such as date, title, description, to the fewShotArray results
let fewShotMergedArray = fewShotArray.map((item,index)=>{
const incident = selectedIncidents.find(i=> +i["incident id"] === +item.id)
if(incident){
return {
...item,
title: incident.title,
description: incident.description,
"date occurrence": incident["date occurrence"],
"date publicly known": incident["date publicly known"]
}
}
}
)
return fewShotMergedArray
}
ToTFewShotsArray = {
//ToTFile consists of 16 incidents including one awful AI data (incident id 0)
const ToTFile = await FileAttachment("tot_1_processed_results_2024_03_26_16_58.json").json();
//ToTFile2 consists of incident 39 (from awful ai database)
const ToTFile2 = await FileAttachment("tot_39_processed_results_2024_03_26_17_26.json").json();
const ToTArray = Object.entries(ToTFile).map(([key,value]) => {
return {id : key,
...value};
})
const obj_39 = Object.entries(ToTFile2).map(([key,value]) => {
return {id: key,
...value}
})
//merging the incidents together to one in ToTArray
ToTArray.push(obj_39[0])
// post-processing here to merge the rest of the fields from incident AI database, such as date, title, description, to the ToTArray results
let ToTMergedArray = ToTArray.map((item,index)=>{
const incident = selectedIncidents.find(i=> +i["incident id"] === +item.id)
if(incident){
return {
...item,
title: incident.title,
description: incident.description,
"date occurrence": incident["date occurrence"],
"date publicly known": incident["date publicly known"]
}
}
}
)
return ToTMergedArray
}
selectedIncidents
// mergedIncidentsArray = [...mergedIncidents.values()]
mergedIncidentsArray = incidents
mergedIncidentsArr = {
const obj_0 = {"title":"AI's Environmental Footprint","incident id":0,"url":"https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/","news":null,"description":"The environmental footprint of AI, particularly in training large models, is significant. According to a study by researchers at the University of Massachusetts, the energy used in training certain popular large AI models can produce about 626,000 pounds of carbon dioxide. This amount is equivalent to roughly 300 round-trip flights between New York and San Francisco, highlighting the substantial carbon footprint associated with advanced AI technologies. This data underscores the need for more sustainable practices in the field of AI to mitigate its impact on climate change.","awful-ai md":null,"date":"2023-07-18","date published":"2023-07-18","Affected Population":"General Human","Actual Impact":"unknown","Potential Impact":"unknown","Application":"AI training","Area":"tech","class":"environmental impact","subclass":null,"subsubclass":null,"city":null,"state":null,"country":"United States of America","continent":"North America","company ":null,"company city":null,"company state":null,"company country":null,"Online":"Not online","source":"awful-ai","thumb":null,"tags":null,"curated":null,"Researcher":null,"Comments":null,"News Outlet":null}
// mergedIncidentsArray.push(obj_0)
let mergedIncidentsArr = []
mergedIncidentsArr = [...mergedIncidentsArray,obj_0]
const i = mergedIncidentsArray.find(i=> +i["incident id"]===0)
return mergedIncidentsArr
}
import {mergedIncidents,incidents} from "126c228131c034d7"
import {aq, op} from "@uwdata/arquero"
// import { BubbleChart } from "@d3/bubble-chart-component"
import {Pack } from "@d3/pack-component"
import {Icicle} from "@d3/icicle-component"
import {multiAutoSelect} from "@john-guerra/multi-auto-select"
import {SpaceTree} from "@john-guerra/spacetree"
// d3 = require("d3@7")
import {navio} from "@john-guerra/navio"
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
