I made this at https://deepai.org/machine-learning-model/fast-style-transfer
Hehe cool.
There’s a lot of them. Heh Parsey McParseface API https://deepai.org/machine-learning-model/parseymcparseface
[
{
"tree": {
"ROOT": [
{
"index": 1,
"token": "What",
"tree": {
"cop": [
{
"index": 2,
"token": "is",
"pos": "VBZ",
"label": "VERB"
}
],
"nsubj": [
{
"index": 4,
"token": "meaning",
"tree": {
"det": [
{
"index": 3,
"token": "the",
"pos": "DT",
"label": "DET"
}
],
"prep": [
{
"index": 5,
"token": "of",
"tree": {
"pobj": [
{
"index": 6,
"token": "this",
"pos": "DT",
"label": "DET"
}
]
},
"pos": "IN",
"label": "ADP"
}
]
},
"pos": "NN",
"label": "NOUN"
}
],
"punct": [
{
"index": 7,
"token": "?",
"pos": ".",
"label": "."
}
]
},
"pos": "WP",
"label": "PRON"
}
]
},
"sentence": "What is the meaning of this?"
}
]
Some curated research too, https://deepai.org/research – one article https://arxiv.org/pdf/2007.05558v1.pdf showing that deep learning is too resource intensive.
Conclusion
The explosion in computing power used for deep learning models has ended the “AI winter” and set new benchmarks for computer performance on a wide range of tasks. However, deep learning’s prodigious appetite for computing power imposes a limit on how far it can improve performance in its current form, particularly in an era when improvements in hardware performance are slowing. This article shows that the computational limits of deep learning will soon be constraining for a range of applications, making the achievement of important benchmark milestones impossible if current trajectories hold. Finally, we have discussed the likely impact of these computational limits: forcing Deep Learning towards less computationally-intensive
methods of improvement, and pushing machine learning towards techniques that are more computationally-efficient than deep learning.
Yeah, well, the neocortex has like 7 “hidden” layers, with sparse distributions, with voting / normalising layers. Just a 3d graph of neurons, doing some wiggly things.