CyberMax-tools/superintelligence-search-questions
Keyfern: what people search about superintelligence (Sept 2026) Need it fresh, filtered or via API? This free file is a snapshot (search questions at the last refresh), last updated 2026-09-25. Keyfern on Apify ($0.001 per keyword idea): gets keyword ideas and questions for your own seeds from 5 search engines. Swellmeter on Apify ($0.003 per keyword analyzed): checks Google Trends direction for your own keywords with a plain rising/falling verdict, daily if scheduled.… See the full description on the dataset page: https://huggingface.co/datasets/CyberMax-tools/superintelligence-search-questions.
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1{2 "cells": [3 {4 "cell_type": "markdown",5 "metadata": {},6 "source": "# Quickstart: Keyfern: What People Search About Superintelligence (Sept 2026)\n\n\"Superintelligence\" moved from books and sci-fi into the news in the week of 22 Sep 2026: an AI remark in a UN General Assembly speech (22 Sep) and a proposed US bill to ban artificial superintelligence (reported by AP on 23 Sep; see the Policywren AI policy tracker for the verified events).\n\nDataset: [huggingface.co/datasets/CyberMax-tools/superintelligence-search-questions](https://huggingface.co/datasets/CyberMax-tools/superintelligence-search-questions) · file: `data/snapshot-2026-09-24.parquet` · by CyberMax.\n\nRuns anywhere with pandas (Colab, Kaggle, Jupyter, VS Code). The outputs below are from a real run of this notebook."7 },8 {9 "cell_type": "markdown",10 "metadata": {},11 "source": "## Load the data"12 },13 {14 "cell_type": "code",15 "execution_count": 1,16 "metadata": {},17 "outputs": [18 {19 "output_type": "stream",20 "name": "stdout",21 "text": "1,106 rows x 10 columns\n"22 },23 {24 "output_type": "execute_result",25 "execution_count": 1,26 "metadata": {},27 "data": {28 "text/plain": " snapshot_date seed ... best_position found_via\n0 2026-09-24 superintelligence ... 1 question:are\n1 2026-09-24 artificial superintelligence ... 1 seed\n2 2026-09-24 artificial superintelligence ... 2 seed\n3 2026-09-24 artificial superintelligence ... 2 seed\n4 2026-09-24 artificial superintelligence ... 3 seed\n5 2026-09-24 artificial superintelligence ... 2 seed\n6 2026-09-24 superintelligence ... 1 seed\n7 2026-09-24 superintelligence ... 1 seed\n8 2026-09-24 superintelligence ... 1 alphabet:c\n9 2026-09-24 superintelligence ... 1 seed\n\n[10 rows x 10 columns]",29 "text/html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>snapshot_date</th>\n <th>seed</th>\n <th>keyword</th>\n <th>intent</th>\n <th>cluster</th>\n <th>engines</th>\n <th>engine_count</th>\n <th>times_suggested</th>\n <th>best_position</th>\n <th>found_via</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2026-09-24</td>\n <td>superintelligence</td>\n <td>artificial superintelligence</td>\n <td>informational</td>\n <td>artificial</td>\n <td>[bing, google, youtube]</td>\n <td>3</td>\n <td>64</td>\n <td>1</td>\n <td>question:are</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2026-09-24</td>\n <td>artificial superintelligence</td>\n <td>artificial superintelligence alliance</td>\n <td>informational</td>\n <td>alliance</td>\n <td>[bing, google, youtube]</td>\n <td>3</td>\n <td>37</td>\n <td>1</td>\n <td>seed</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2026-09-24</td>\n <td>artificial superintelligence</td>\n <td>artificial superintelligence alliance fet</td>\n <td>informational</td>\n <td>alliance</td>\n <td>[bing, google, youtube]</td>\n <td>3</td>\n <td>35</td>\n <td>2</td>\n <td>seed</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2026-09-24</td>\n <td>artificial superintelligence</td>\n <td>artificial superintelligence asi</td>\n <td>informational</td>\n <td>asi</td>\n <td>[bing, google, youtube]</td>\n <td>3</td>\n <td>33</td>\n <td>2</td>\n <td>seed</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2026-09-24</td>\n <td>artificial superintelligence</td>\n <td>artificial superintelligence alliance coin</td>\n <td>informational</td>\n <td>alliance</td>\n <td>[bing, google, youtube]</td>\n <td>3</td>\n <td>32</td>\n <td>3</td>\n <td>seed</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2026-09-24</td>\n <td>artificial superintelligence</td>\n <td>artificial superintelligence alliance crypto</td>\n <td>informational</td>\n <td>alliance</td>\n <td>[bing, google, youtube]</td>\n <td>3</td>\n <td>26</td>\n <td>2</td>\n <td>seed</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2026-09-24</td>\n <td>superintelligence</td>\n <td>superintelligence ai</td>\n <td>informational</td>\n <td>ai</td>\n <td>[bing, google, youtube]</td>\n <td>3</td>\n <td>23</td>\n <td>1</td>\n <td>seed</td>\n </tr>\n <tr>\n <th>7</th>\n <td>2026-09-24</td>\n <td>superintelligence</td>\n <td>superintelligence nick bostrom</td>\n <td>informational</td>\n <td>nick</td>\n <td>[bing, google, youtube]</td>\n <td>3</td>\n <td>19</td>\n <td>1</td>\n <td>seed</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2026-09-24</td>\n <td>superintelligence</td>\n <td>artificial superintelligence crypto</td>\n <td>informational</td>\n <td>artificial</td>\n <td>[bing, google, youtube]</td>\n <td>3</td>\n <td>17</td>\n <td>1</td>\n <td>alphabet:c</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2026-09-24</td>\n <td>superintelligence</td>\n <td>superintelligence</td>\n <td>informational</td>\n <td>(seed)</td>\n <td>[bing, google, youtube]</td>\n <td>3</td>\n <td>17</td>\n <td>1</td>\n <td>seed</td>\n </tr>\n </tbody>\n</table>"30 }31 }32 ],33 "source": "import pandas as pd\n\nURL = \"https://huggingface.co/datasets/CyberMax-tools/superintelligence-search-questions/resolve/main/data/snapshot-2026-09-24.parquet\"\ndf = pd.read_parquet(URL)\nprint(f\"{len(df):,} rows x {len(df.columns)} columns\")\ndf.head(10)"34 },35 {36 "cell_type": "markdown",37 "metadata": {},38 "source": "Summary statistics for the numeric columns:"39 },40 {41 "cell_type": "code",42 "execution_count": 2,43 "metadata": {},44 "outputs": [45 {46 "output_type": "execute_result",47 "execution_count": 2,48 "metadata": {},49 "data": {50 "text/plain": " engine_count times_suggested best_position\ncount 1106.000 1106.000 1106.000\nmean 1.146 2.512 4.570\nstd 0.421 4.531 2.904\nmin 1.000 1.000 1.000\n25% 1.000 1.000 2.000\n50% 1.000 1.000 4.000\n75% 1.000 2.000 7.000\nmax 3.000 64.000 13.000",51 "text/html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>engine_count</th>\n <th>times_suggested</th>\n <th>best_position</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>1106.000</td>\n <td>1106.000</td>\n <td>1106.000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>1.146</td>\n <td>2.512</td>\n <td>4.570</td>\n </tr>\n <tr>\n <th>std</th>\n <td>0.421</td>\n <td>4.531</td>\n <td>2.904</td>\n </tr>\n <tr>\n <th>min</th>\n <td>1.000</td>\n <td>1.000</td>\n <td>1.000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>1.000</td>\n <td>1.000</td>\n <td>2.000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>1.000</td>\n <td>1.000</td>\n <td>4.000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>1.000</td>\n <td>2.000</td>\n <td>7.000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>3.000</td>\n <td>64.000</td>\n <td>13.000</td>\n </tr>\n </tbody>\n</table>"52 }53 }54 ],55 "source": "df[['engine_count', 'times_suggested', 'best_position']].describe().round(3)"56 },57 {58 "cell_type": "markdown",59 "metadata": {},60 "source": "## Go further\n- **Fresh data on demand:** [Keyfern — Long-Tail Keyword Ideas & Google Autocomplete Scraper](https://apify.com/cybermax/keyword-ideas) runs the same collection for your own list (API, schedules, MCP tool for AI agents).\n- **All CyberMax tools, datasets and free apps:** [https://cybermax-tools-cybermax.static.hf.space/](https://cybermax-tools-cybermax.static.hf.space/)"61 }62 ],63 "metadata": {64 "kernelspec": {65 "display_name": "Python 3",66 "language": "python",67 "name": "python3"68 },69 "language_info": {70 "name": "python"71 }72 },73 "nbformat": 4,74 "nbformat_minor": 575}