![]() Games can use neural networks to learn and get better over time. Where I think it holds the most promise is in the field of artificial intelligence, especially with computer games. I have read up on it, and while I can’t claim to be an expert, I do understand the basic concepts. Neural network programming is one of the most fascinating fields in computer science. I know I’m a bit far out, but I see the day coming. It would be virtual intelligence sitting inside your PC, able to learn and interact with you like the “computer” in Star Trek. Your computer would do more than run basic productivity applications. I can easily see how that the nanobots could function as nodes in a neural network. There can be thousands or even millions of these nanobots operating inside of a computer’s operating system. Nanobots are little robots performing specialized tasks. I say this because the computers of the future will use nanotechnology, from what I understand. I think the computers of the future will use neural networks. ![]() November 10, - Surely you’re not serious about war games, are you? Have you never seen the movie by the same name, built upon the same premise you just outlined? I am excited about neural information processing myself, but for completely different reasons. At best, all we can produce in the meantime is an impressive simulation. I think we have many years, and many breakthroughs to go, before we can truly create a thinking machine. Given the pairs $(x_c, y_c)$ for $c = 1, …, C$ in the context set and given unseen inputs $x_t^$).November 10, - The problem with conscious processing in my opinion is that we don’t fully understand how the brain works.Īre the brain and the mind the same thing? Do neural networks truly replicate the genius of the human mind or are they just whittled down approximations? Given a set of observations $(x_i, y_i)$, they are split into two sets: “context points” and “target points”. The broad idea behind how the NP model is set up and how it is trained is illustrated in this schema: The NP is a neural network based approach to represent a distribution over functions. Even though here I discuss, you might find it easier to start with which are essentially a non-probabilistic version of NPs. So here is my attempt at reviewing and discussing NPs.īefore reading my post, I recommend the reader to take a look at both original papers. I believe, often the best way towards understanding something is implementing it, empirically trying it out on simple problems, and finally explaining this to someone else. I found the idea behind NPs interesting, but I felt I was lacking intuition and a deeper understanding how NPs behave as a prior over functions. Neural Processes aim to combine the best of these two worlds. However the latter might be preferable in the presence of large amounts of data as training NNs is computationally much more scalable than inference for GPs. This differs from (non-Bayesian) neural networks which represent a single function rather than a distribution over functions. In the limited data regime, GPs are preferable due to their probabilistic nature and ability to capture uncertainty. Gaussian Processes – GPs offer a probabilistic framework for learning a distribution over a wide class of non-linear functionsīoth have their advantages and drawbacks.Deep Learning – neural networks are flexible non-linear functions which are straightforward to train.Neural Processes (NPs) caught my attention as they essentially are a neural network (NN) based probabilistic model which can represent a distribution over stochastic processes. See the paper conditional Neural Processes and the follow-up work by the same authors on Neural Processes which was presented in the workshop. In this year’s ICML, some interesting work was presented on Neural Processes.
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