Row 30726

Row ID: 30726 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 30726 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

By Steven Levy

Maybe you’ve seen neuroscience studies that interpret MRI scans to identify whether a human brain is [entertaining thoughts](https://www.science.org/content/article/ai-re-creates-what-people-see-reading-their-brain-scans) of a plane, a teddy bear, or a clock tower. Similarly, Anthropic has plunged into the digital tangle of the neural net of its LLM Claude, and pinpointed which combinations of its crude artificial neurons evoke specific concepts, or “features.”

The company’s researchers have identified the combination of artificial neurons that signify features as disparate as burritos, semicolons in programming code, and—very much to the larger goal of the research—deadly biological weapons. Work like this has potentially huge implications for AI safety: If you can figure out where danger lurks inside an LLM, you are presumably better equipped to stop it.

Read the full story: [https://www.wired.com/story/anthropic-black-box-ai-research-neurons-features/](https://www.wired.com/story/anthropic-black-box-ai-research-neurons-features/)

FieldValue
text By Steven Levy Maybe you’ve seen neuroscience studies that interpret MRI scans to identify whether a human brain is [entertaining thoughts](https://www.science.org/content/article/ai-re-creates-what-people-see-reading-their-brain-scans) of a plane, a teddy bear, or a clock tower. Similarly, Anthropic has plunged into the digital tangle of the neural net of its LLM Claude, and pinpointed which combinations of its crude artificial neurons evoke specific concepts, or “features.” The company’s re…
label r/artificial
dataType comment
communityName r/artificial
datetime 2024-05-21
username_encoded Z0FBQUFBQm5Lak1HMHpKVksyYkpDOWUwUEVfc0ZFOFpBSklxVXU2eGtWNFp3UWdoWUs3MWExc1hVdlVxYUMxTTUwdjVQM24yNVpLZkpYLUNkQi1tRElTRWhUREliTENEWkE9PQ==
url_encoded Z0FBQUFBQm5Lak9WdmJQbE4wSEQ1Skp4WVdyQnVvc3BqcmV1RXM2bkttSm5IVXhSVHlDNFkyRDFYR0FUTjVYVVBidkJkNmhDVnNJVF8wM2MtTUxiVUstQndTQzV1cHFKbVI5clNkX2lENHdlYVlOcWxKalBYYkNDeGlJN1pqbzhHQWFBeHJrM29oN1FSMEswM1NfbEVrc0tpcDRwS0JCZExhRDJJUm9hY0FCMEY5MlBZUkRVZ3FqaE95SWlTclFuR1RYRlZiaWNXaWdkdjg1X3pzbW01VVN3SVdUV0RFejFqQT09

Raw Record

{
  "text": "By Steven Levy\n\nMaybe you’ve seen neuroscience studies that interpret MRI scans to identify whether a human brain is [entertaining thoughts](https://www.science.org/content/article/ai-re-creates-what-people-see-reading-their-brain-scans) of a plane, a teddy bear, or a clock tower. Similarly, Anthropic has plunged into the digital tangle of the neural net of its LLM Claude, and pinpointed which combinations of its crude artificial neurons evoke specific concepts, or “features.” \n\nThe company’s researchers have identified the combination of artificial neurons that signify features as disparate as burritos, semicolons in programming code, and—very much to the larger goal of the research—deadly biological weapons. Work like this has potentially huge implications for AI safety: If you can figure out where danger lurks inside an LLM, you are presumably better equipped to stop it.\n\nRead the full story: [https://www.wired.com/story/anthropic-black-box-ai-research-neurons-features/](https://www.wired.com/story/anthropic-black-box-ai-research-neurons-features/)",
  "label": "r/artificial",
  "dataType": "comment",
  "communityName": "r/artificial",
  "datetime": "2024-05-21",
  "username_encoded": "Z0FBQUFBQm5Lak1HMHpKVksyYkpDOWUwUEVfc0ZFOFpBSklxVXU2eGtWNFp3UWdoWUs3MWExc1hVdlVxYUMxTTUwdjVQM24yNVpLZkpYLUNkQi1tRElTRWhUREliTENEWkE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9WdmJQbE4wSEQ1Skp4WVdyQnVvc3BqcmV1RXM2bkttSm5IVXhSVHlDNFkyRDFYR0FUTjVYVVBidkJkNmhDVnNJVF8wM2MtTUxiVUstQndTQzV1cHFKbVI5clNkX2lENHdlYVlOcWxKalBYYkNDeGlJN1pqbzhHQWFBeHJrM29oN1FSMEswM1NfbEVrc0tpcDRwS0JCZExhRDJJUm9hY0FCMEY5MlBZUkRVZ3FqaE95SWlTclFuR1RYRlZiaWNXaWdkdjg1X3pzbW01VVN3SVdUV0RFejFqQT09"
}
Explore Dataset Explore Row

Entry Information