Row 93455

Row ID: 93455 | Dataset Entry | Axioma AXP Content Repository

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The ability to use reasoning to correctly perform tasks it has never seen before during its training phase is called 0 shot generalisation. Current 0 shot generalisation performance from even the most cutting edge models is extremely poor, and recent research has shown that even by infinitely increasing model size, this cannot increase 0 shot generalisation performance. Realistically, the only way to solve for this core feature of any ‘AGIentic’ system is to produce a transformer architecture that so radically new it has nothing in common with any current approach.

For OpenAI to have reached AGI, they would have to have created this architecture, which, has 0 known or theoretical approaches across scientific literature to achieve. This is highly unlikely to have happened as this area of study is so new. The ‘true’ solving of this problem (ie a pseudo solution not powered by agentic workflows) is likely decades away, and I think will coincide with research on how we understand the human brain.

FieldValue
text The ability to use reasoning to correctly perform tasks it has never seen before during its training phase is called 0 shot generalisation. Current 0 shot generalisation performance from even the most cutting edge models is extremely poor, and recent research has shown that even by infinitely increasing model size, this cannot increase 0 shot generalisation performance. Realistically, the only way to solve for this core feature of any ‘AGIentic’ system is to produce a transformer architecture th…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-25
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{
  "text": "The ability to use reasoning to correctly perform tasks it has never seen before during its training phase is called 0 shot generalisation. Current 0 shot generalisation performance from even the most cutting edge models is extremely poor, and recent research has shown that even by infinitely increasing model size, this cannot increase 0 shot generalisation performance. Realistically, the only way to solve for this core feature of any ‘AGIentic’ system is to produce a transformer architecture that so radically new it has nothing in common with any current approach.\n\nFor OpenAI to have reached AGI, they would have to have created this architecture, which, has 0 known or theoretical approaches across scientific literature to achieve. This is highly unlikely to have happened as this area of study is so new. The ‘true’ solving of this problem (ie a pseudo solution not powered by agentic workflows) is likely decades away, and I think will coincide with research on how we understand the human brain.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-25",
  "username_encoded": "Z0FBQUFBQm5Lak10blFmUklmWkVVdm9BTVlzX21XYWlYQnZ1T1FfbjBWVVB5OEZtZTR5b01NbmF5ekQtU3dmS09tNExyeENCUm5UbWg3b1VtNlFnN0RoMzQ0T0dlalpQZmc9PQ==",
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}
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