Row 40676

Row ID: 40676 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 40676 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

It's not really trained for this sort of thing. The image recognition isn't great, it can tell what a picture is, but as for detail it's mostly based on context and guesses. It knows it's looking at beads, but it doesn't really recognise the individual ones the same way a human does. It assumed the 'R' was in the lower right because in the list of letters it gave it was near the end (going from top left, to bottom right). Even though there aren't any 'R's in the picture.

Another example might be giving a picture of a rose, and asking what the "black spots" are - it would recognise a rose, but probably wouldn't have even known it had black spots. But since you mentioned it, it would then present information about fungus as though it knew all along what's in the image. It's really good at giving you the impression it's smarter than it is.

It definitely would be possible to create a model to recognise where letters are on the picture though (it might not even need to be a ML model).

FieldValue
text It's not really trained for this sort of thing. The image recognition isn't great, it can tell what a picture is, but as for detail it's mostly based on context and guesses. It knows it's looking at beads, but it doesn't really recognise the individual ones the same way a human does. It assumed the 'R' was in the lower right because in the list of letters it gave it was near the end (going from top left, to bottom right). Even though there aren't any 'R's in the picture. Another example might …
label r/gpt4
dataType comment
communityName r/GPT4
datetime 2024-05-22
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url_encoded Z0FBQUFBQm5Lak9iNXdycGFhNHozblRUSkJNWWl6MUx6LXhsa3gyT2hCemx4LV9VcjRXa3pfVnFjRFlINWxSM3hkeXh2UEVoVEtMeFZmUmJXc1lPbTVXeDRlcTV6UW5Ka1JIY05YSXIzSTFQTWJvSHlLdDJjMEFPTVpja0pzYTFsZklPeXFsTno2Z3A5bXN1eFppM01zNmhlTjJWRGU4N1BYMW1ZRThQN3R6MjlxSWNzSDNidWRNPQ==

Raw Record

{
  "text": "It's not really trained for this sort of thing. The image recognition isn't great, it can tell what a picture is, but as for detail it's mostly based on context and guesses. It knows it's looking at beads, but it doesn't really recognise the individual ones the same way a human does. It assumed the 'R' was in the lower right because in the list of letters it gave it was near the end (going from top left, to bottom right). Even though there aren't any 'R's in the picture. \n\nAnother example might be giving a picture of a rose, and asking what the \"black spots\" are - it would recognise a rose, but probably wouldn't have even known it had black spots. But since you mentioned it, it would then present information about fungus as though it knew all along what's in the image. It's really good at giving you the impression it's smarter than it is.\n\nIt definitely would be possible to create a model to recognise where letters are on the picture though (it might not even need to be a ML model).",
  "label": "r/gpt4",
  "dataType": "comment",
  "communityName": "r/GPT4",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1NQW93Mm9Cekk0NkpPZWhmTTlLMlR0X3pBakFHTnVqc0UxYWVxdDNHMHotRFptRVNsQlllYmljbGl4VkVlbkVoU1UyaGtsRXJpQXB5N3R3QU5wOWJscGc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9iNXdycGFhNHozblRUSkJNWWl6MUx6LXhsa3gyT2hCemx4LV9VcjRXa3pfVnFjRFlINWxSM3hkeXh2UEVoVEtMeFZmUmJXc1lPbTVXeDRlcTV6UW5Ka1JIY05YSXIzSTFQTWJvSHlLdDJjMEFPTVpja0pzYTFsZklPeXFsTno2Z3A5bXN1eFppM01zNmhlTjJWRGU4N1BYMW1ZRThQN3R6MjlxSWNzSDNidWRNPQ=="
}
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