npub123…w3f8g on Nostr: npub16pgsu…jhalg imagine a huge matrix of every English word on the x and y. Now ...
npub16pgsua5f879059jk0pnyq9lven75puenhy6mfc5zu8vrdrvlpsxqzjhalg (npub16pg…halg) imagine a huge matrix of every English word on the x and y. Now take the 2 words bouncy and castle. Castle comes after bouncy ~99% and castle comes before bouncy 0%. The ‘model’ learns this from reading a billion web pages. It doesn’t keep the list of pages it learned that from. It’s more complex than that but. It’s also learned that phrase is found on pages about kid’s parties. So if you ask “what’s good for kids party?” It knows the context of your ? and replies “bouncy castles”.
Published at
2023-10-09 13:23:08Event JSON
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"pubkey": "5467462eea496c91fcebf35fc06315a3a64e31623118b1b9a206f8f9f4c36871",
"created_at": 1696857788,
"kind": 1,
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[
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"content": "nostr:npub16pgsua5f879059jk0pnyq9lven75puenhy6mfc5zu8vrdrvlpsxqzjhalg imagine a huge matrix of every English word on the x and y. Now take the 2 words bouncy and castle. Castle comes after bouncy ~99% and castle comes before bouncy 0%. The ‘model’ learns this from reading a billion web pages. It doesn’t keep the list of pages it learned that from. It’s more complex than that but. It’s also learned that phrase is found on pages about kid’s parties. So if you ask “what’s good for kids party?” It knows the context of your ? and replies “bouncy castles”.",
"sig": "4242b2b3195def1a974fc17c5a373e8ae6aa0b75375f9ed57828da1e9874c18fcb08d7bec4dd1b60857bc4bbf31287a7cfbbbcc765bd70950b1a9578aac3a8fb"
}