Extending Neural Networks to higher dimensions (article)

In summary, the conversation discusses the advancements of neural networks in the field of computer science, with the use of big data and increased computing power. The potential for artificial general intelligence (AGI) using predictive analytics is also mentioned, with the idea of using neural networks to discover patterns and create algorithms for various purposes. The conversation also touches on the importance of anticipating situations, which can be achieved through methods of predictive analytics.
  • #1
scottdave
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I came across this interesting article: An Idea from Physics helps AI see in Higher Dimensions. The idea is something called gauge equivariance.
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  • #2
There’s been a real explosion in this area of comp sci with the advent of big data and greater computing power. It’s hard to see how it will all fit together in the end. Basically a collision of big data, comprehensive surveillance, privacy, quantum computing into the beginnings of AGI Ie artificial general intelligence using predictive analytics.

one comment I’ve heard from AI researchers is that the worst solution to any problem is machine learning. And yet that is what is happening right now. I imagine the next steps will be to use the nn to discover a pattern, convert it to a concrete algorithm for legal purposes ie non-discrimination issues or quantum computer algorithm for speed. Already NNs are being used to train NNs by adjusting their hyper parameters, so what will come next.
 
  • #3
jedishrfu said:
AGI Ie artificial general intelligence using predictive analytics
Well, that expands my vocabulary. Did you mean, "AGI i.e. artificial general intelligence using predictive analytics"?
 
  • #4
Yes, somehow AGI will need to anticipate situations. We do that now from our experience and what we’ve learned. The methods of predictive analytics can provide that anticipation feature.
 

1. What are the benefits of extending neural networks to higher dimensions?

Extending neural networks to higher dimensions allows for more complex and accurate data representations, which can lead to better performance in tasks such as image and speech recognition. It also allows for a larger number of input features to be considered, which can improve the network's ability to handle complex and diverse data.

2. How is dimensionality defined in the context of neural networks?

In the context of neural networks, dimensionality refers to the number of input features or variables that the network is trained on. Higher dimensionality means that the network is able to consider a larger number of input features, which can lead to better performance in certain tasks.

3. Can neural networks be extended to an unlimited number of dimensions?

No, there are practical limitations to how many dimensions a neural network can be extended to. As the number of dimensions increases, the network's complexity and training time also increase. Additionally, there may not be enough data available to effectively train a network with an unlimited number of dimensions.

4. How does extending neural networks to higher dimensions impact the interpretability of the model?

Extending neural networks to higher dimensions can make the model more complex and harder to interpret. This is because there are more parameters and connections between layers, making it difficult to understand how the network is making its predictions. However, there are techniques and tools that can help visualize and interpret the learned representations in higher dimensions.

5. Are there any drawbacks to extending neural networks to higher dimensions?

One potential drawback is the risk of overfitting the model to the training data. With a larger number of dimensions, there is a higher chance of the model memorizing the training data instead of learning general patterns. This can result in poor performance on new, unseen data. Additionally, training a network in higher dimensions may require more computational resources and time.

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