⚠ A mathematical model from 103 years ago predicted something that was seen for the first time today: a #black_hole.
#MachineLearning could never do that: it needs observations to model anything. This is a major weak-point of ML. Let's fix it.
A stark contrast between Machine Learning vs other forms of mathematical modeling is that ML models often don't model extreme corner cases very well, because #data in those areas is rare. Gathering data in important areas is as important a skill as building fancy neural networks.
Sadly, too often, using extreme inputs to a model is more useful: e.g. by modeling physics of levers on light objects with short levers, we then built very long levers to lift extremely heavy things. Instead, ML is better suited at modeling everyday phenomena with complex models.
https://twitter.com/Reza_Zadeh/status/1053771110410375168?s=19
#MachineLearning could never do that: it needs observations to model anything. This is a major weak-point of ML. Let's fix it.
A stark contrast between Machine Learning vs other forms of mathematical modeling is that ML models often don't model extreme corner cases very well, because #data in those areas is rare. Gathering data in important areas is as important a skill as building fancy neural networks.
Sadly, too often, using extreme inputs to a model is more useful: e.g. by modeling physics of levers on light objects with short levers, we then built very long levers to lift extremely heavy things. Instead, ML is better suited at modeling everyday phenomena with complex models.
https://twitter.com/Reza_Zadeh/status/1053771110410375168?s=19
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Reza Zadeh
A stark contrast between Machine Learning vs other forms of mathematical modeling is that ML models often don't model extreme corner cases very well, because data in those areas is rare. Gathering data in important areas is as important a skill as building…