# Tag Info

## Hot answers tagged prediction

15

If we are talking about a perfect RNG, the answer is a clear no. It is impossible to predict a truly random number, otherwise it wouldn't be truly random. When we talk about pseudo RNG, things change a little. Depending on the quality of the PRNG, the problem ranges from easy to almost impossible. A very weak PRNG like the one XKCD published could of course ...

15

Early success on prime number testing via artificial networks is presented in A Compositional Neural-network Solution to Prime-number Testing, László Egri, Thomas R. Shultz, 2006. The knowledge-based cascade-correlation (KBCC) network approach showed the most promise, although the practicality of this approach is eclipsed by other prime detection algorithms ...

11

Some back of the envelope calculations : number of neurons in AI systems The number of neurons in AI systems is a little tricky to calculate, Neural Networks and Deep Learning are 2 current AI systems as you call them, specifics are hard to come by (If someone has them please share), but data on parameters do exist, parameters are more analogous to ...

9

I think you're coming at your problem slightly wrong... what you're essentially talking about is a belief network. You may want to look into existing Bayesian Learning techniques to get your head around this, but belief networks commonly use the exact scenario you're talking about; using a set of known (or uncertain facts) statements to produce some ...

7

Soon enough but that doesn't mean anything at all. In machine learning the word neuron represents a calculation whereas in brain the word neuron represent a specific type of cell which is a biochemical system.

6

Yes. For instance, the popular softmax regression gives you probability distribution for each class. Yes. Softmax is a regression over a set of discrete classes. We can use regression for classification, the most common strategy is to grab the most likely class for the prediction.

6

Neural networks are good at classifying. In some situations that comes down to prediction, but not necessarily. The mathematical reason for the neural networks prowess at classifying is the universal approximation theorem. Which states that a neural network can approximate any continuous real-valued function on a compact subset. The quality of the ...

5

Yes, many people have worked on this sort of thing, due to its obvious industrial applications (most of the ones I'm familiar with are in the pharmaceutical industry). Here's a paper from 2013 that claims good results; following the trail of papers that cited it will likely give you more recent work.

4

By definition, artificial intelligence includes all forms of computer systems capable of completing tasks that would ordinarily warrant human intelligence. A superintelligent AI would have intelligence far superior to that of any human and therefore would be capable of creating systems beyond our capabilities. As a consequence, if a technology superior to ...

4

Old question, but I thought it's worth one practical answer. I happened to stumble upon it right after looking at a guide of how to build such neural network, demonstrating echo of python's randint as an example. Here is the final code without detailed explanation, still quite simple and useful in case the link goes offline: from random import randint from ...

3

There is no defined rules for choosing a machine learning algorithm to learn some type of pattern. However, there are some guidelines to help you better select an algorithm which will yield a higher probability of success. Some important considerations are: Number of features: This is the number of questions that each patient had to answer. Number of ...

3

Being a complete newbie in machine learning, I did this experiment (using Scikit-learn ): Generated a large number (N) of pseudo-random extractions, using python random.choices function to select N numbers out of 90. Trained a MLP classifier with training data composed as follow: ith sample : X <- lotteryResults[i:i+100], Y <- lotteryResults[i] In ...

3

If a pseudorandom number generator is throwing out numbers, then, in the analysis of these numbers, you will be able to determine the algorithm that produced them, because the numbers aren't random; they are determined by that algorithm and not by chance. If the world is made up of physical laws that are able to be understood and replicated, then the ...

3

The answers so far haven't answered the question numerically, so here is my attempt to steer them in the direction I was seeking: The freely available Deep Learning Book has the following figure on page 27: I question the blue fit line, as it seems that data points may be better described by a parabolic or exponential function. In any case, based upon ...

3

While interesting, this is all rendered somewhat moot if you think about what will happen once we understand how the brain works. After all, once we understood flight, we didn't start making birds. The same goes for AI. Here are just a few ways in which human brains and digital brains can't be compared. The digital brain won't have to worry about food and ...

3

First of all, sigmoid does not output 0 or 1, it outputs any real number in the range between 0 and 1. Furthermore, normal neural networks doesn't output binary values, unless the output layer uses the step function as activation function (which is rare). I'm not really sure if you want the NN to be a classifier or regressor, but it sounds like you want a ...

3

ANNs & RNNs can be used to create some great models in many different domains, including time-series forecasting. However, across all of these domains, they suffer from the problem of hyper-parameter optimization. Because neural networks are so flexible, it is not clear, at the outset, which arrangement of neurons will be most effective to solve a given ...

3

It might be more informative to: Label each combination of location, type, and time of crime with a crime rate. For example, theft, in Crystal City, at 11pm at night, occurs 20 times per year, or 0.4 times per resident per year. Predict the crime rate, rather than individual events. This avoids the need to have explicit examples of "non-crime", and lets ...

3

The post you linked to clearly states that pseudo random number cannot be predicted. Their randomness is made to be nearly perfect, and if you ever found a way to even predict a pseudo random number with 20% chance of correct, the security of the entire world would be vulnerable to attacks, as things ranges from cryptocurrency and secure data transfer is all ...

3

You should look into "missing values". This is an entire research field in itself. First, you need to identify the type of missing values: They can be missing purely at random. Whether they are missing or not is itself a useful feature, and should be treated as a class of its own. (Those two are the best case scenarios.) Whether they are missing ...

2

Yes, there were successful attempts at predicting the interaction between molecules and biological proteins which have been used to identify potential treatments by using convolutional neural networks. For example in 2015, the first deep learning neural network has been created for structure-based drug design which trains 3-dimensional representation of ...

2

"Regression", is to estimate a continuous value as a function of some other parameters. There are different forms of regression, such as linear regression and logistic regression, that differ in the assumptions they make on the data. You could try looking up those for a start.

2

Adding to what Demento said, the extent of randomness in the Random Number Generation Algorithm is the key issue. Following are some designs that can make the RNG weak: Concealed Sequences Suppose this is the previous few sequences of characters generated: (Just an example, for the practical use larger range, is used) lwjVJA Ls3Ajg xpKr+A XleXYg 9hyCzA ...

2

Impossible to solve until you define an error measurement ( by example $|R-R'|$ or $(R-R')^2$ ) and how this error changes when A, B and C change. Extreme example: assume $R()$ is random (unrelated to A, B, C values) but static (always same $R(A,B,C)$ for same values of A,B,C). Given some values of A, B, C, you can only answer the value of $R(A,B,C)$ when A,...

2

People have used machine learning models on aspects of weather forecasting, as here: Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting or here Predicting Solar Generation from Weather Forecasts using Machine Learning. I've been loosely associated with an effort to use ML techniques to predict utility demand from weather ...

2

Can one use an Artificial Neural Network to determine the size of an object in a photograph? Yes: Learning Depth from Single Monocular Images In the end, depth is just one special form of size. Of course, you need something partially known, e.g. another car. You don't need to know the exact size of the car, but you know which size cars in general have. If ...

2

In my thesis I actually solve the problem of depth estimation with a CNN based on a single monocular image so I can share my experiences for understanding that problem. As you already stated in general you have the problem that you cannot recover the scale of the scene in an image by geometrical approaches directly. And that is still not the case even if ...

2

It is correct that climate and economic models are distinct from waste models. A memory based model is the correct approach because the time domain is key in prediction based on existing trend data. However, the RNN is not a practical production model with others that have proven more productive. Although this article is trendy and not accurate in every ...

2

This sounds like a great project, although this exact setup limits your options somewhat. Supervised machine learning approaches are effectively ruled out because you don't have the necessary training data to develop a model (i.e. the dependent variable: whether it is raining or not). You could look at accessing similar data (from what source depends on ...

2

Given the (usual) higher architectural complexity of ML models compared to more classical forecasting models, ML models might also require more data, otherwise they might just overfit the training dataset. Furthermore, online learning (or training) of a neural network using stochastic gradient descent (that is, one example at a time) might also be ...

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