{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## Load the data"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1,106 rows x 10 columns\n"
    },
    {
     "output_type": "execute_result",
     "execution_count": 1,
     "metadata": {},
     "data": {
      "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]",
      "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>"
     }
    }
   ],
   "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)"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "Summary statistics for the numeric columns:"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 2,
     "metadata": {},
     "data": {
      "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",
      "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>"
     }
    }
   ],
   "source": "df[['engine_count', 'times_suggested', 'best_position']].describe().round(3)"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## Go further\n- **Need it fresh, filtered or via API?** [Keyfern on Apify](https://data.cybermaxtools.com/buy/keyfern?s=hf-nb-superintelligence-search-questions)\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/)"
  }
 ],
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