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Which learning tasks do brains use to train themselves to see?
2019 Community Moderator ElectionCan I use unsupervised learning followed by supervised learning?Which type of machine learning to useWhich supervised learning algorithms are available for matching?Is what I did supervised or unsupervised Machine learning?How to use deep learning to add local (e.g. repairing) transformations to images?Training an AI to play Starcraft 2 with superhuman level of performance?Supervised Learning could be biased if we use obsolete dataWhat machine learning algorithms to use for unsupervised POS tagging?Reinforcement learning - How to deal with varying number of actions which do number approximationWhich reinforcement learning methods can be trained off-policy?
$begingroup$
In computer vision is very common to use supervised tasks, where datasets have to be manually annotated by humans. Some examples are object classification (class labels), detection (bounding boxes) and segmentation (pixel-level masks). But animals don't need anybody to show them bounding boxes or masks on top of things in order for them to learn to detect objects and make sense of the visual world around them. This leads me to think that brains must be performing some sort of self-supervision to train themselves to see. What does current research say about the learning paradigm used by brains to achieve such an outstanding level of visual competence? Which tasks do brains use to train themselves to be so good at processing visual information? Finally, can we apply these insights in computer vision?
machine-learning computer-vision
asked 3 hours ago
Pablo MessinaPablo Messina
435
$endgroup$
add a comment |
$begingroup$
In computer vision is very common to use supervised tasks, where datasets have to be manually annotated by humans. Some examples are object classification (class labels), detection (bounding boxes) and segmentation (pixel-level masks). But animals don't need anybody to show them bounding boxes or masks on top of things in order for them to learn to detect objects and make sense of the visual world around them. This leads me to think that brains must be performing some sort of self-supervision to train themselves to see. What does current research say about the learning paradigm used by brains to achieve such an outstanding level of visual competence? Which tasks do brains use to train themselves to be so good at processing visual information? Finally, can we apply these insights in computer vision?
machine-learning computer-vision
asked 3 hours ago
Pablo MessinaPablo Messina
435
$endgroup$
add a comment |
$begingroup$
In computer vision is very common to use supervised tasks, where datasets have to be manually annotated by humans. Some examples are object classification (class labels), detection (bounding boxes) and segmentation (pixel-level masks). But animals don't need anybody to show them bounding boxes or masks on top of things in order for them to learn to detect objects and make sense of the visual world around them. This leads me to think that brains must be performing some sort of self-supervision to train themselves to see. What does current research say about the learning paradigm used by brains to achieve such an outstanding level of visual competence? Which tasks do brains use to train themselves to be so good at processing visual information? Finally, can we apply these insights in computer vision?
machine-learning computer-vision
asked 3 hours ago
Pablo MessinaPablo Messina
435
$endgroup$
In computer vision is very common to use supervised tasks, where datasets have to be manually annotated by humans. Some examples are object classification (class labels), detection (bounding boxes) and segmentation (pixel-level masks). But animals don't need anybody to show them bounding boxes or masks on top of things in order for them to learn to detect objects and make sense of the visual world around them. This leads me to think that brains must be performing some sort of self-supervision to train themselves to see. What does current research say about the learning paradigm used by brains to achieve such an outstanding level of visual competence? Which tasks do brains use to train themselves to be so good at processing visual information? Finally, can we apply these insights in computer vision?
machine-learning computer-vision
machine-learning computer-vision
asked 3 hours ago
Pablo MessinaPablo Messina
435
asked 3 hours ago
Pablo MessinaPablo Messina
435
edited 8 mins ago
Pablo Messina
asked 3 hours ago
Pablo MessinaPablo Messina
435
asked 3 hours ago
Pablo MessinaPablo Messina
435
asked 3 hours ago
Pablo MessinaPablo Messina
435
435
add a comment |
add a comment |
1 Answer
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$begingroup$
I think this kind of question is better fit for the Artitifical Inteligence SE, but it works here as well (I guess).
So Natural Neural Networks had a lot of time to develop using Genetic Algorithms (evolution). Even the complex human eye might have started with bacteria search for light (energy) sources using simple light intensity sensing.
Having enough time, our brains developed and we have about 5 know regions in the Visual Cortex, each responsible for a kind of feature (check on Mind Field)
Also, little is know about the learning process/otimization of a natural neuron but your question is on the data used...
Well, we cluster things in utility for survival: We detect human faces and perform person identification really well, this is one of the most advanced features of our visual cortex and this can be traced to our social needs which are intrinsically related to our survival ability. It is really important for us to identify the people that are friendly to us and those that may cause us harm.
When the object is brain diseases diagnosis using imaging, CNNs are already beating our brains.
So summarizing my answer: Fitness to environment allow us to define what to learn, correct predictions allow us to survive and evolve, while premature deaths avoid bad genes from propagating
Our environment provide us the label by Reinforced Learning + Genetic Algorithms.
Adding: We also developed the capability of propagating our knowledge (sometimes by genetic code and sometimes by teaching others).
answered 1 hour ago
Pedro Henrique MonfortePedro Henrique Monforte
1257
$endgroup$
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
$begingroup$
I think this kind of question is better fit for the Artitifical Inteligence SE, but it works here as well (I guess).
So Natural Neural Networks had a lot of time to develop using Genetic Algorithms (evolution). Even the complex human eye might have started with bacteria search for light (energy) sources using simple light intensity sensing.
Having enough time, our brains developed and we have about 5 know regions in the Visual Cortex, each responsible for a kind of feature (check on Mind Field)
Also, little is know about the learning process/otimization of a natural neuron but your question is on the data used...
Well, we cluster things in utility for survival: We detect human faces and perform person identification really well, this is one of the most advanced features of our visual cortex and this can be traced to our social needs which are intrinsically related to our survival ability. It is really important for us to identify the people that are friendly to us and those that may cause us harm.
When the object is brain diseases diagnosis using imaging, CNNs are already beating our brains.
So summarizing my answer: Fitness to environment allow us to define what to learn, correct predictions allow us to survive and evolve, while premature deaths avoid bad genes from propagating
Our environment provide us the label by Reinforced Learning + Genetic Algorithms.
Adding: We also developed the capability of propagating our knowledge (sometimes by genetic code and sometimes by teaching others).
answered 1 hour ago
Pedro Henrique MonfortePedro Henrique Monforte
1257
$endgroup$
add a comment |
$begingroup$
I think this kind of question is better fit for the Artitifical Inteligence SE, but it works here as well (I guess).
So Natural Neural Networks had a lot of time to develop using Genetic Algorithms (evolution). Even the complex human eye might have started with bacteria search for light (energy) sources using simple light intensity sensing.
Having enough time, our brains developed and we have about 5 know regions in the Visual Cortex, each responsible for a kind of feature (check on Mind Field)
Also, little is know about the learning process/otimization of a natural neuron but your question is on the data used...
Well, we cluster things in utility for survival: We detect human faces and perform person identification really well, this is one of the most advanced features of our visual cortex and this can be traced to our social needs which are intrinsically related to our survival ability. It is really important for us to identify the people that are friendly to us and those that may cause us harm.
When the object is brain diseases diagnosis using imaging, CNNs are already beating our brains.
So summarizing my answer: Fitness to environment allow us to define what to learn, correct predictions allow us to survive and evolve, while premature deaths avoid bad genes from propagating
Our environment provide us the label by Reinforced Learning + Genetic Algorithms.
Adding: We also developed the capability of propagating our knowledge (sometimes by genetic code and sometimes by teaching others).
answered 1 hour ago
Pedro Henrique MonfortePedro Henrique Monforte
1257
$endgroup$
add a comment |
$begingroup$
I think this kind of question is better fit for the Artitifical Inteligence SE, but it works here as well (I guess).
So Natural Neural Networks had a lot of time to develop using Genetic Algorithms (evolution). Even the complex human eye might have started with bacteria search for light (energy) sources using simple light intensity sensing.
Having enough time, our brains developed and we have about 5 know regions in the Visual Cortex, each responsible for a kind of feature (check on Mind Field)
Also, little is know about the learning process/otimization of a natural neuron but your question is on the data used...
Well, we cluster things in utility for survival: We detect human faces and perform person identification really well, this is one of the most advanced features of our visual cortex and this can be traced to our social needs which are intrinsically related to our survival ability. It is really important for us to identify the people that are friendly to us and those that may cause us harm.
When the object is brain diseases diagnosis using imaging, CNNs are already beating our brains.
So summarizing my answer: Fitness to environment allow us to define what to learn, correct predictions allow us to survive and evolve, while premature deaths avoid bad genes from propagating
Our environment provide us the label by Reinforced Learning + Genetic Algorithms.
Adding: We also developed the capability of propagating our knowledge (sometimes by genetic code and sometimes by teaching others).
answered 1 hour ago
Pedro Henrique MonfortePedro Henrique Monforte
1257
$endgroup$
I think this kind of question is better fit for the Artitifical Inteligence SE, but it works here as well (I guess).
So Natural Neural Networks had a lot of time to develop using Genetic Algorithms (evolution). Even the complex human eye might have started with bacteria search for light (energy) sources using simple light intensity sensing.
Having enough time, our brains developed and we have about 5 know regions in the Visual Cortex, each responsible for a kind of feature (check on Mind Field)
Also, little is know about the learning process/otimization of a natural neuron but your question is on the data used...
Well, we cluster things in utility for survival: We detect human faces and perform person identification really well, this is one of the most advanced features of our visual cortex and this can be traced to our social needs which are intrinsically related to our survival ability. It is really important for us to identify the people that are friendly to us and those that may cause us harm.
When the object is brain diseases diagnosis using imaging, CNNs are already beating our brains.
So summarizing my answer: Fitness to environment allow us to define what to learn, correct predictions allow us to survive and evolve, while premature deaths avoid bad genes from propagating
Our environment provide us the label by Reinforced Learning + Genetic Algorithms.
Adding: We also developed the capability of propagating our knowledge (sometimes by genetic code and sometimes by teaching others).
answered 1 hour ago
Pedro Henrique MonfortePedro Henrique Monforte
1257
answered 1 hour ago
Pedro Henrique MonfortePedro Henrique Monforte
1257
answered 1 hour ago
Pedro Henrique MonfortePedro Henrique Monforte
1257
answered 1 hour ago
Pedro Henrique MonfortePedro Henrique Monforte
1257
1257
add a comment |
add a comment |
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