Row 57021

Row ID: 57021 | Dataset Entry | Axioma AXP Content Repository

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It can. for example, AlphaFold predicts protein structures. It was trained on a dataset of DNA amino acid sequences and their structures. Once it was trained on the dataset, they could then feed it (prompt it) new amino acid sequences it was never trained on and have it accurately predict the structures of more sequences. That’s because training doesn’t just memorize a dataset, it learns capabilities like reasoning, logic, pattern recognition, etc that allow it to accurately predict the data in the dataset. Once it’s trained, you can prompt it with new data and it uses the capabilities it’s learned to provide novel conclusions/responses. It’s limited to what it’s learned from its dataset but what it learns can be applied in very useful ways across a variety of tasks.

They used it to predict every protein in the human body which has never been done before. deepmind is expected to receive the nobel prize.

Consider this, humans can never be smarted than humans…but does that mean that humans are stuck with our current laws of physics? If human brains never become more intelligent, can they not continue to gather data about the universe, analyze that data, and keep discovering new laws of physics?

Models don’t need to be smarter than humans to process data, and make novel discoveries and contributions to science.

FieldValue
text It can. for example, AlphaFold predicts protein structures. It was trained on a dataset of DNA amino acid sequences and their structures. Once it was trained on the dataset, they could then feed it (prompt it) new amino acid sequences it was never trained on and have it accurately predict the structures of more sequences. That’s because training doesn’t just memorize a dataset, it learns capabilities like reasoning, logic, pattern recognition, etc that allow it to accurately predict the data in …
label r/technology
dataType comment
communityName r/technology
datetime 2024-05-23
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  "text": "It can. for example, AlphaFold predicts protein structures. It was trained on a dataset of DNA amino acid sequences and their structures. Once it was trained on the dataset, they could then feed it (prompt it) new amino acid sequences it was never trained on and have it accurately predict the structures of more sequences. That’s because training doesn’t just memorize a dataset, it learns capabilities like reasoning, logic, pattern recognition, etc that allow it to accurately predict the data in the dataset. Once it’s trained, you can prompt it with new data and it uses the capabilities it’s learned to provide novel conclusions/responses. It’s limited to what it’s learned from its dataset but what it learns can be applied in very useful ways across a variety of tasks. \n\nThey used it to predict every protein in the human body which has never been done before. deepmind is expected to receive the nobel prize. \n\nConsider this, humans can never be smarted than humans…but does that mean that humans are stuck with our current laws of physics? If human brains never become more intelligent, can they not continue to gather data about the universe, analyze that data, and keep discovering new laws of physics?\n\nModels don’t need to be smarter than humans to process data, and make novel discoveries and contributions to science.",
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  "datetime": "2024-05-23",
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