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Slightly off topic, but I hate how publications are written. It seems like authors are purposely using big words and sentences that are often 5-6 lines long in order to make it seem more clever. I find myself often having to reread a sentence in order to understand it. These algorithms are often very simple and can be easily explained. Don't over complicate them.
Slightly off topic, but I hate how publications are written. It seems like authors are purposely using big words and sentences that are often 5-6 lines long in order to make it seem more clever. I find myself often having to reread a sentence in order to understand it. These algorithms are often very simple and can be easily explained. Don't over complicate them.
Earlier quoted context omitted.
I would have said 30 years, and I thought I was the optimist :) Anyway I hope you're right and I'm wrong!
That's not a thing to hope for. We haven't solved the value alignment problem, so the longer until we have human or better-than-human AI, the better.
Earlier quoted context omitted.
Out of curiosity: how far away do you think we are?
I'll say... 5 - 7 years. This is all based on pure speculation, and being a little more than a ML hobbiest. One thing is for sure though - when we do reach that point, everything changes forever.
Then again.. I also don't understand how researchers come up with a yearly budget for making discoveries.
Slightly off topic, but I hate how publications are written. It seems like authors are purposely using big words and sentences that are often 5-6 lines long in order to make it seem more clever. I find myself often having to reread a sentence in order to understand it. These algorithms are often very simple and can be easily explained. Don't over complicate them.
It never ceases to amaze me that the best steps towards achieving AI is to look at how we perceive that a Neuron works and simulate it. And the thing is, we aren't exactly sure why exactly that is.. it's amazing. Sometimes the best thing we can do is imitate nature
- "We specifically consider the case of...a stacked deep autoencoder (AE), which is a type of neural network designed to encode a set of data samples such that they can be decoded to produce data sample reconstructions with minimal error
- "The first step of the NDL algorithm occurs when a set of new data points fail to be appropriately reconstructed by the trained network...When a data sample’s RE is too high, the assumption is that the AE level under examination does not contain a rich enough set of features to accurately reconstruct the sample.
- "The second step of the NDL algorithm is adding and training a new node, which occurs when a critical number of input data samples (outliers) fail to achieve adequate representation at some level of the network.
- "The final step of the NDL algorithm is intended to stabilize the network’s previous representations in the presence of newly added nodes. It involves training all the nodes in a level with both new data and replayed samples from previously seen classes on which the network has been trained.
Neurogensis? How about neural death as a way to prune large neural networks into more compact ones--now that is a research idea!
I believe several papers have examined efficiently pruning neural networks, but neural death would be better branding ;) ( https://arxiv.org/pdf/1506.02626v3.pdf https://arxiv.org/pdf/1510.00149v5.pdf )
Basically trying to achieve a certain level of plasticity in deep neural nets by getting inspiration from https://en.wikipedia.org/wiki/Adult_neurogenesis
Earlier quoted context omitted.
I would have said 30 years, and I thought I was the optimist :) Anyway I hope you're right and I'm wrong!
That's not a thing to hope for. We haven't solved the value alignment problem, so the longer until we have human or better-than-human AI, the better.