3 ms·

Wrong on so many levels. If AI has been well-researched in 90s, why it is DL that really pushes the boundary of several essential tasks, namely image/speech rec

by eva1984 10y ago

Wrong on so many levels. If AI has been well-researched in 90s, why it is DL that really pushes the boundary of several essential tasks, namely image/speech recognition, machine translation to near human performance? I am curious about your definition of 'AI' that seems only obvious to the term 'Artificial'.

lngnmn 10y ago
> namely image/speech recognition, machine translation to near human performance? The key word here is recognition. Better, faster, more efficient classifiers. The algorithms for training a better representation of the features, not just features itself is a big deal, but it is, again, supervised learning. The adversary approach, which gives additional feedback for better training, is mere sophisticated supervised learning. Take one of the best papers survey (which is an emergent new genre in hipster's blogs) and one will see that there is nothing fundamentally new, apart from sophistication and additional feedback.
- eva1984 10y ago
  While if you consider that human achieves intelligence through a lot of supervision(homework and exams) too, I won't go directly to disclaim only unsupervised learning can be hailed as the king. In fact, there is a discussion about the definition of unsupervised learning, because of the emergence of techniques like word2vec, where it essentially supervised learning but construct all its tasks from the data itself without any labelling.
p1esk 10y ago
You do realize that main DL ideas have been developed in the 90s, or even earlier? CNNs, RNNs, LSTMs, reinforcement learning, gradient descent, backpropagation - all that was used in the 90s, but given the limited computing power they had, people simply couldn't run large enough models on large enough data to get results.
- eva1984 10y ago
  Without development of technique like Dropout Batch normalization, residual connection, better optimizer like Adam, even with enough data and computational power, DL won't be nearly as powerful as it is today.

by eva1984 10y ago

lngnmn 10y ago

> namely image/speech recognition, machine translation to near human performance? The key word here is recognition. Better, faster, more efficient classifiers. The algorithms for training a better representation of the features, not just features itself is a big deal, but it is, again, supervised learning. The adversary approach, which gives additional feedback for better training, is mere sophisticated supervised learning. Take one of the best papers survey (which is an emergent new genre in hipster's blogs) and one will see that there is nothing fundamentally new, apart from sophistication and additional feedback.

eva1984 10y ago
While if you consider that human achieves intelligence through a lot of supervision(homework and exams) too, I won't go directly to disclaim only unsupervised learning can be hailed as the king. In fact, there is a discussion about the definition of unsupervised learning, because of the emergence of techniques like word2vec, where it essentially supervised learning but construct all its tasks from the data itself without any labelling.

p1esk 10y ago

You do realize that main DL ideas have been developed in the 90s, or even earlier? CNNs, RNNs, LSTMs, reinforcement learning, gradient descent, backpropagation - all that was used in the 90s, but given the limited computing power they had, people simply couldn't run large enough models on large enough data to get results.

eva1984 10y ago
Without development of technique like Dropout Batch normalization, residual connection, better optimizer like Adam, even with enough data and computational power, DL won't be nearly as powerful as it is today.