Row 15532

Row ID: 15532 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 15532 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

The research behind GPT1 and GPT2 laid the foundation for generalized pretraining. What that means is that we can model discrete data sets as a language.

For video games, instead of using letters from an alphabet or words from a vocabulary, we can create our own vocabulary that isn’t tied to English or Spanish or any traditional language. For an MMORPG this would equate to using “SpellID 5029” as a *character* in our language like the letter B. Words are now combos with combinations of spells.

Since GPT 1 and 2 laid the ground work for generalized pretraining (Generalized Pretrained Transformer) we don’t need a ton of gold standard data to properly represent the language. Instead we can just throw all the data at it and get better results than if we had a good gold standard data set.

Going further you could think of PvP as a translation problem. Bot detection as a clustering problem where Variational AutoEncoders excel and our pretrained base model is a powerful VAE.

The hardest part is encoding game states though we can get around this by feeding in game state data alongside our “language” similar to how we would feed in image data in document models.

FieldValue
text The research behind GPT1 and GPT2 laid the foundation for generalized pretraining. What that means is that we can model discrete data sets as a language. For video games, instead of using letters from an alphabet or words from a vocabulary, we can create our own vocabulary that isn’t tied to English or Spanish or any traditional language. For an MMORPG this would equate to using “SpellID 5029” as a *character* in our language like the letter B. Words are now combos with combinations of spe…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-20
username_encoded Z0FBQUFBQm5Lakw4REF0LU1yQ3FBWmt4LTl1ZmpGU1ZXa255UU5PajhGR0JTY0l6RzZiN3drT0JQX2pJcEJDb2hjdUF1a2Z3M0lteHRSdkJOSXNEalU3bldmWFBDRk93ZlE9PQ==
url_encoded Z0FBQUFBQm5Lak9MWEFPWmIxeko1WU1EYjBHclNwVTZVTUpkd3U3LVQzQ0tWS3FPV05mNHd5Vmh5cnZydWozNmpQbDN1TVhOUHN4LXg3TkJtaTU0TVkzQnpiNnJoNC1YdWxMaXRMdVBhVUdPMkRrUXRGTi1PQjdzQ19KM2ZKNnRGdjAxTFowQkhYVy1pYUNUbTZPTDF4a0dYeC1McFhuR1lZY3lGZVk4SUpZcm5PNkVrZG1kamdRT01SdU1TR0dxX2dfRTAxV0ZqZ3NEVTBLS2VRQTYtYVNLM3BrT3h5N1VZQ0VoRFd3dHlWZ25sVjdqWndZQUwtST0=

Raw Record

{
  "text": "The research behind GPT1 and GPT2 laid the foundation for generalized pretraining.  What that means is that we can model discrete data sets as a language.  \n\nFor video games, instead of using letters from an alphabet or words from a vocabulary, we can create our own vocabulary that isn’t tied to English or Spanish or any traditional language.  For an MMORPG this would equate to using “SpellID 5029” as a *character* in our language like the letter B.  Words are now combos with combinations of spells.  \n\nSince GPT 1 and 2 laid the ground work for generalized pretraining (Generalized Pretrained Transformer) we don’t need a ton of gold standard data to properly represent the language.  Instead we can just throw all the data at it and get better results than if we had a good gold standard data set.  \n\nGoing further you could think of PvP as a translation problem.  Bot detection as a clustering problem where Variational AutoEncoders excel and our pretrained base model is a powerful VAE.  \n\nThe hardest part is encoding game states though we can get around this by feeding in game state data alongside our “language” similar to how we would feed in image data in document models.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-20",
  "username_encoded": "Z0FBQUFBQm5Lakw4REF0LU1yQ3FBWmt4LTl1ZmpGU1ZXa255UU5PajhGR0JTY0l6RzZiN3drT0JQX2pJcEJDb2hjdUF1a2Z3M0lteHRSdkJOSXNEalU3bldmWFBDRk93ZlE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9MWEFPWmIxeko1WU1EYjBHclNwVTZVTUpkd3U3LVQzQ0tWS3FPV05mNHd5Vmh5cnZydWozNmpQbDN1TVhOUHN4LXg3TkJtaTU0TVkzQnpiNnJoNC1YdWxMaXRMdVBhVUdPMkRrUXRGTi1PQjdzQ19KM2ZKNnRGdjAxTFowQkhYVy1pYUNUbTZPTDF4a0dYeC1McFhuR1lZY3lGZVk4SUpZcm5PNkVrZG1kamdRT01SdU1TR0dxX2dfRTAxV0ZqZ3NEVTBLS2VRQTYtYVNLM3BrT3h5N1VZQ0VoRFd3dHlWZ25sVjdqWndZQUwtST0="
}
Explore Dataset Explore Row

Entry Information