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• Online AI resources?

  - by bennybdbc

  What are the best freely available AI resources that I can find online? I'm looking for blogs, books or tutorials.

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• Natural Language Processing in Ruby

  - by Joey Robert

  I'm looking to do some sentence analysis (mostly for twitter apps) and infer some general characteristics. Are there any good natural language processing libraries for this sort of thing in Ruby? Similar to http://stackoverflow.com/questions/870460/java-is-there-a-good-natural-language-processing-library but for Ruby. I'd prefer something very general, but any leads are appreciated!

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• Another StackOverflow website?

  - by Betamoo

  It seems that StackOverflow is more concerned about programming techniques and coding skills (which is a good thing!).. But I am asking if anyone knows another "StackcOverflow"-like site, but which is mainly concerned about Machine Learning and AI? BTW: I have asked this question after nearly a week without an answer for Question

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• TicTacToe strategic reduction

  - by NickLarsen

  I decided to write a small program that solves TicTacToe in order to try out the effect of some pruning techniques on a trivial game. The full game tree using minimax to solve it only ends up with 549,946 possible games. With alpha-beta pruning, the number of states required to evaluate was reduced to 18,297. Then I applied a transposition table that brings the number down to 2,592. Now I want to see how low that number can go. The next enhancement I want to apply is a strategic reduction. The basic idea is to combine states that have equivalent strategic value. For instance, on the first move, if X plays first, there is nothing strategically different (assuming your opponent plays optimally) about choosing one corner instead of another. In the same situation, the same is true of the center of the walls of the board, and the center is also significant. By reducing to significant states only, you end up with only 3 states for evaluation on the first move instead of 9. This technique should be very useful since it prunes states near the top of the game tree. This idea came from the GameShrink method created by a group at CMU, only I am trying to avoid writing the general form, and just doing what is needed to apply the technique to TicTacToe. In order to achieve this, I modified my hash function (for the transposition table) to enumerate all strategically equivalent positions (using rotation and flipping functions), and to only return the lowest of the values for each board. Unfortunately now my program thinks X can force a win in 5 moves from an empty board when going first. After a long debugging session, it became apparent to me the program was always returning the move for the lowest strategically significant move (I store the last move in the transposition table as part of my state). Is there a better way I can go about adding this feature, or a simple method for determining the correct move applicable to the current situation with what I have already done?

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• Problems with real-valued input deep belief networks (of RBMs)

  - by Junier

  I am trying to recreate the results reported in Reducing the dimensionality of data with neural networks of autoencoding the olivetti face dataset with an adapted version of the MNIST digits matlab code, but am having some difficulty. It seems that no matter how much tweaking I do on the number of epochs, rates, or momentum the stacked RBMs are entering the fine-tuning stage with a large amount of error and consequently fail to improve much at the fine-tuning stage. I am also experiencing a similar problem on another real-valued dataset. For the first layer I am using a RBM with a smaller learning rate (as described in the paper) and with negdata = poshidstates*vishid' + repmat(visbiases,numcases,1); I'm fairly confident I am following the instructions found in the supporting material but I cannot achieve the correct errors. Is there something I am missing? See the code I'm using for real-valued visible unit RBMs below, and for the whole deep training. The rest of the code can be found here. rbmvislinear.m: epsilonw = 0.001; % Learning rate for weights epsilonvb = 0.001; % Learning rate for biases of visible units epsilonhb = 0.001; % Learning rate for biases of hidden units weightcost = 0.0002; initialmomentum = 0.5; finalmomentum = 0.9; [numcases numdims numbatches]=size(batchdata); if restart ==1, restart=0; epoch=1; % Initializing symmetric weights and biases. vishid = 0.1*randn(numdims, numhid); hidbiases = zeros(1,numhid); visbiases = zeros(1,numdims); poshidprobs = zeros(numcases,numhid); neghidprobs = zeros(numcases,numhid); posprods = zeros(numdims,numhid); negprods = zeros(numdims,numhid); vishidinc = zeros(numdims,numhid); hidbiasinc = zeros(1,numhid); visbiasinc = zeros(1,numdims); sigmainc = zeros(1,numhid); batchposhidprobs=zeros(numcases,numhid,numbatches); end for epoch = epoch:maxepoch, fprintf(1,'epoch %d\r',epoch); errsum=0; for batch = 1:numbatches, if (mod(batch,100)==0) fprintf(1,' %d ',batch); end %%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% data = batchdata(:,:,batch); poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1))); batchposhidprobs(:,:,batch)=poshidprobs; posprods = data' * poshidprobs; poshidact = sum(poshidprobs); posvisact = sum(data); %%%%%%%%% END OF POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% poshidstates = poshidprobs > rand(numcases,numhid); %%%%%%%%% START NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% negdata = poshidstates*vishid' + repmat(visbiases,numcases,1);% + randn(numcases,numdims) if not using mean neghidprobs = 1./(1 + exp(-negdata*vishid - repmat(hidbiases,numcases,1))); negprods = negdata'*neghidprobs; neghidact = sum(neghidprobs); negvisact = sum(negdata); %%%%%%%%% END OF NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% err= sum(sum( (data-negdata).^2 )); errsum = err + errsum; if epoch>5, momentum=finalmomentum; else momentum=initialmomentum; end; %%%%%%%%% UPDATE WEIGHTS AND BIASES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% vishidinc = momentum*vishidinc + ... epsilonw*( (posprods-negprods)/numcases - weightcost*vishid); visbiasinc = momentum*visbiasinc + (epsilonvb/numcases)*(posvisact-negvisact); hidbiasinc = momentum*hidbiasinc + (epsilonhb/numcases)*(poshidact-neghidact); vishid = vishid + vishidinc; visbiases = visbiases + visbiasinc; hidbiases = hidbiases + hidbiasinc; %%%%%%%%%%%%%%%% END OF UPDATES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end fprintf(1, '\nepoch %4i error %f \n', epoch, errsum); end dofacedeepauto.m: clear all close all maxepoch=200; %In the Science paper we use maxepoch=50, but it works just fine. numhid=2000; numpen=1000; numpen2=500; numopen=30; fprintf(1,'Pretraining a deep autoencoder. \n'); fprintf(1,'The Science paper used 50 epochs. This uses %3i \n', maxepoch); load fdata %makeFaceData; [numcases numdims numbatches]=size(batchdata); fprintf(1,'Pretraining Layer 1 with RBM: %d-%d \n',numdims,numhid); restart=1; rbmvislinear; hidrecbiases=hidbiases; save mnistvh vishid hidrecbiases visbiases; maxepoch=50; fprintf(1,'\nPretraining Layer 2 with RBM: %d-%d \n',numhid,numpen); batchdata=batchposhidprobs; numhid=numpen; restart=1; rbm; hidpen=vishid; penrecbiases=hidbiases; hidgenbiases=visbiases; save mnisthp hidpen penrecbiases hidgenbiases; fprintf(1,'\nPretraining Layer 3 with RBM: %d-%d \n',numpen,numpen2); batchdata=batchposhidprobs; numhid=numpen2; restart=1; rbm; hidpen2=vishid; penrecbiases2=hidbiases; hidgenbiases2=visbiases; save mnisthp2 hidpen2 penrecbiases2 hidgenbiases2; fprintf(1,'\nPretraining Layer 4 with RBM: %d-%d \n',numpen2,numopen); batchdata=batchposhidprobs; numhid=numopen; restart=1; rbmhidlinear; hidtop=vishid; toprecbiases=hidbiases; topgenbiases=visbiases; save mnistpo hidtop toprecbiases topgenbiases; backpropface; Thanks for your time

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• AI testing framework

  - by Jon

  I am looking at developing an AI player for a simple game I have created in C#. I will be creating a population of the bots and evolving them over generations. What I was wondering is there any frameworks out there that could be good for this sort of testing / development. Ideally I would like something that I could plug any / some type of games into and say, OK so have a population of X run it over Y generations and chart the results for me. I was having a think about how I would create something that would do this for me and allow me to reuse this later for different AI projects and all I could think of was to have some sort of core code and some interface contracts that the game and AI would use so that the server can script it. What are your thoughts, does anyone have any practical experience of this sort of thing?

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• Is there any open source AI engine?

  - by Andrei Savu

  I am searching for an open source AI engine implemented in C/C++, ActionScript or Java with no success. Do you know any open source implementation? Update: Thanks for answers! I had no idea how vast the AI field is. I am working on a sample application. I want to add intelligent behavior over a physics engine. I need some sort ai engine designed for games.

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• Activation Function, Initializer function, etc, effects on neural networks for face detection

  - by harry

  There's various activation functions: sigmoid, tanh, etc. And there's also a few initializer functions: Nguyen and Widrow, random, normalized, constant, zero, etc. So do these have much effect on the outcome of a neural network specialising in face detection? Right now I'm using the Tanh activation function and just randomising all the weights from -0.5 to 0.5. I have no idea if this is the best approach though, and with 4 hours to train the network each time, I'd rather ask on here than experiment!

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• Why is Lisp used for AI?

  - by Cristián Romo

  I've been learning Lisp to expand my horizons because I have heard that it is used in AI programming. After doing some exploring, I have yet to find AI examples or anything in the language that would make it more inclined towards it. Was Lisp used in the past because it was available, or is there something that I'm just missing?

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• Question about Convolutional neural network.

  - by Nhu Phuong

  I readed few book and acticles about Convolutional neural network, it seem I understand the concept but I don't know how to put it up like in image below: from 28x28 normalized pixel INPUT we get 4 feature map 24x24. but how to get them ? size the INPUT image ? or perform image transformation? but what kind of transformation? or cut up the input image to 4 piece 24x24 by 4 corner? I don't understand the process to me it seem they cut up or resize the image to more smaller at each step. please help thanks.

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• Prolog: Finding the Nth Element in a List

  - by Thomas

  I am attempting to locate the nth element of a List in Prolog. Here is the code I am attempting to use: Cells = [OK, _, _, _, _, _] . ... next_safe(_) :- facing(CurrentDirection), delta(CurrentDirection, Delta), in_cell(OldLoc), NewLoc is OldLoc + Delta, nth1(NewLoc, Cells, SafetyIdentifier), SafetyIdentifier = OK . Basically, I am trying to check to see if a given cell is "OK" to move into. Am I missing something?

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• Counting Sublist Elements in Prolog

  - by idea_

  How can I count nested list elements in prolog? I have the following predicates defined, which will count a nested list as one element: length([ ], 0). length([H|T],N) :- length(T,M), N is M+1. Usage: ?- length([a,b,c],Out). Out = 3 This works, but I would like to count nested elements as well i.e. length([a,b,[c,d,e],f],Output). ?- length([a,b,[c,d,e],f],Output). Output = 6

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• Searching in graphs trees with Depth/Breadth first/A* algorithms

  - by devoured elysium

  I have a couple of questions about searching in graphs/trees: Let's assume I have an empty chess board and I want to move a pawn around from point A to B. A. When using depth first search or breadth first search must we use open and closed lists ? This is, a list that has all the elements to check, and other with all other elements that were already checked? Is it even possible to do it without having those lists? What about A*, does it need it? B. When using lists, after having found a solution, how can you get the sequence of states from A to B? I assume when you have items in the open and closed list, instead of just having the (x, y) states, you have an "extended state" formed with (x, y, parent_of_this_node) ? C. State A has 4 possible moves (right, left, up, down). If I do as first move left, should I let it in the next state come back to the original state? This, is, do the "right" move? If not, must I transverse the search tree every time to check which states I've been to? D. When I see a state in the tree where I've already been, should I just ignore it, as I know it's a dead end? I guess to do this I'd have to always keep the list of visited states, right? E. Is there any difference between search trees and graphs? Are they just different ways to look at the same thing?

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• Improved Genetic algorithm for multiknapsack problem

  - by user347918

  Hello guys, Recently i've been improving traditional genetic algorithm for multiknapsack problem. So My Improved Genetic Algorithm is working better then Traditional Genetic Algorithm. I tested. (i used publically available from OR-Library (http://people.brunel.ac.uk/~mastjjb/jeb/orlib/mknapinfo.html) were used to test the GAs.) Does anybody know other improved GA. I wanted to compare with other improved genetic algorithm. Actually i searched in internet. But couldn't find good algorithm to compare.

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• Do you think the AI industry will ever come back?

  - by Isaiah

  I just spent some time reading about the collapse of the AI industry and realized a lot of the reason it failed was because technology was slow to catch up with their theories on when it would be available. I also read that it is believed computers that will be able to emulate human synapses may be made round 2015-2025. It's 2010 now and were getting pretty close to that time frame. I was wondering if anyone thinks that the AI industry will return as the technology lands? And if so, will it change the language market? Could Lisp like languages suddenly experience a burst of growth if it does? Idk I just thought it was interesting thinking about it.

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• Correct formulation of the A* algorithm

  - by Eli Bendersky

  Hello, I'm looking at definitions of the A* path-finding algorithm, and it seems to be defined somewhat differently in different places. The difference is in the action performed when going through the successors of a node, and finding that a successor is on the closed list. One approach (suggested by Wikipedia, and this article) says: if the successor is on the closed list, just ignore it Another approach (suggested here and here, for example) says: if the successor is on the closed list, examine its cost. If it's higher than the currently computed score, remove the item from the closed list for future examination. I'm confused - which method is correct ? Intuitively, the first makes more sense to me, but I wonder about the difference in definition. Is one of the definitions wrong, or are they somehow isomorphic ?

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• Hebbian learning

  - by Bane

  I have asked another question on Hebbian learning before, and I guess I got a good answer which I accepted, but, the problem is that I now realize that I've mistaken about Hebbian learning completely, and that I'm a bit confused. So, could you please explain how it can be useful, and what for? Because the way Wikipedia and some other pages describe it - it doesn't make sense! Why would we want to keep increasing the weight between the input and the output neuron if the fire together? What kind of problems can it be used to solve, because when I simulate it in my head, it certainly can't do the basic AND, OR, and other operations (say you initialize the weights at zero, the output neurons never fire, and the weights are never increased!)

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• Identifying voice as male or female

  - by duder

  I'm not much into audio engineering, so please be easy on me. I'm receiving an audio file as input, and need to detect whether the speaker is male or female. Any ideas how to go about doing this? I'm using php, but am open to using other languages, and don't mind learning a little bit of sound theory as long as the time is proportionate to the task.

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• Measuring the performance of classification algorithm

  - by Silver Dragon

  I've got a classification problem in my hand, which I'd like to address with a machine learning algorithm ( Bayes, or Markovian probably, the question is independent on the classifier to be used). Given a number of training instances, I'm looking for a way to measure the performance of an implemented classificator, with taking data overfitting problem into account. That is: given N[1..100] training samples, if I run the training algorithm on every one of the samples, and use this very same samples to measure fitness, it might stuck into a data overfitting problem -the classifier will know the exact answers for the training instances, without having much predictive power, rendering the fitness results useless. An obvious solution would be seperating the hand-tagged samples into training, and test samples; and I'd like to learn about methods selecting the statistically significant samples for training. White papers, book pointers, and PDFs much appreciated!

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• How to program simple chat bot AI?

  - by Larsenal

  I want to build a bot that asks someone a few simple questions and branches based on the answer. I realize parsing meaning from the human responses will be challenging, but how do you setup the program to deal with the "state" of the conversation? EDIT: It will be a one-to-one conversation between a human and the bot.

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• Searching Natural Language Sentence Structure

  - by Cerin

  What's the best way to store and search a database of natural language sentence structure trees? Using OpenNLP's English Treebank Parser, I can get fairly reliable sentence structure parsings for arbitrary sentences. What I'd like to do is create a tool that can extract all the doc strings from my source code, generate these trees for all sentences in the doc strings, store these trees and their associated function name in a database, and then allow a user to search the database using natural language queries. So, given the sentence "This uploads files to a remote machine." for the function upload_files(), I'd have the tree: (TOP (S (NP (DT This)) (VP (VBZ uploads) (NP (NNS files)) (PP (TO to) (NP (DT a) (JJ remote) (NN machine)))) (. .))) If someone entered the query "How can I upload files?", equating to the tree: (TOP (SBARQ (WHADVP (WRB How)) (SQ (MD can) (NP (PRP I)) (VP (VB upload) (NP (NNS files)))) (. ?))) how would I store and query these trees in a SQL database? I've written a simple proof-of-concept script that can perform this search using a mix of regular expressions and network graph parsing, but I'm not sure how I'd implement this in a scalable way. And yes, I realize my example would be trivial to retrieve using a simple keyword search. The idea I'm trying to test is how I might take advantage of grammatical structure, so I can weed-out entries with similar keywords, but a different sentence structure. For example, with the above query, I wouldn't want to retrieve the entry associated with the sentence "Checks a remote machine to find a user that uploads files." which has similar keywords, but is obviously describing a completely different behavior.

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• large test data for knapsack problem

  - by user347918

  i am researcher student. I am searching large data for knapsack problem. I wanted test my algorithm for knapsack problem. But i couldn't find large data. I need data has 1000 item and capacity is no matter. The point is item as much as huge it's good for my algorithm. Is there any huge data available in internet. Does anybody know please guys i need urgent.

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• Problems with real-valued deep belief networks (of RBMs)

  - by Junier

  I am trying to recreate the results reported in Reducing the dimensionality of data with neural networks of autoencoding the olivetti face dataset with an adapted version of the MNIST digits matlab code, but am having some difficulty. It seems that no matter how much tweaking I do on the number of epochs, rates, or momentum the stacked RBMs are entering the fine-tuning stage with a large amount of error and consequently fail to improve much at the fine-tuning stage. I am also experiencing a similar problem on another real-valued dataset. For the first layer I am using a RBM with a smaller learning rate (as described in the paper) and with negdata = poshidstates*vishid' + repmat(visbiases,numcases,1); I'm fairly confident I am following the instructions found in the supporting material but I cannot achieve the correct errors. Is there something I am missing? See the code I'm using for real-valued visible unit RBMs below, and for the whole deep training. The rest of the code can be found here. rbmvislinear.m: epsilonw = 0.001; % Learning rate for weights epsilonvb = 0.001; % Learning rate for biases of visible units epsilonhb = 0.001; % Learning rate for biases of hidden units weightcost = 0.0002; initialmomentum = 0.5; finalmomentum = 0.9; [numcases numdims numbatches]=size(batchdata); if restart ==1, restart=0; epoch=1; % Initializing symmetric weights and biases. vishid = 0.1*randn(numdims, numhid); hidbiases = zeros(1,numhid); visbiases = zeros(1,numdims); poshidprobs = zeros(numcases,numhid); neghidprobs = zeros(numcases,numhid); posprods = zeros(numdims,numhid); negprods = zeros(numdims,numhid); vishidinc = zeros(numdims,numhid); hidbiasinc = zeros(1,numhid); visbiasinc = zeros(1,numdims); sigmainc = zeros(1,numhid); batchposhidprobs=zeros(numcases,numhid,numbatches); end for epoch = epoch:maxepoch, fprintf(1,'epoch %d\r',epoch); errsum=0; for batch = 1:numbatches, if (mod(batch,100)==0) fprintf(1,' %d ',batch); end %%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% data = batchdata(:,:,batch); poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1))); batchposhidprobs(:,:,batch)=poshidprobs; posprods = data' * poshidprobs; poshidact = sum(poshidprobs); posvisact = sum(data); %%%%%%%%% END OF POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% poshidstates = poshidprobs > rand(numcases,numhid); %%%%%%%%% START NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% negdata = poshidstates*vishid' + repmat(visbiases,numcases,1);% + randn(numcases,numdims) if not using mean neghidprobs = 1./(1 + exp(-negdata*vishid - repmat(hidbiases,numcases,1))); negprods = negdata'*neghidprobs; neghidact = sum(neghidprobs); negvisact = sum(negdata); %%%%%%%%% END OF NEGATIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% err= sum(sum( (data-negdata).^2 )); errsum = err + errsum; if epoch>5, momentum=finalmomentum; else momentum=initialmomentum; end; %%%%%%%%% UPDATE WEIGHTS AND BIASES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% vishidinc = momentum*vishidinc + ... epsilonw*( (posprods-negprods)/numcases - weightcost*vishid); visbiasinc = momentum*visbiasinc + (epsilonvb/numcases)*(posvisact-negvisact); hidbiasinc = momentum*hidbiasinc + (epsilonhb/numcases)*(poshidact-neghidact); vishid = vishid + vishidinc; visbiases = visbiases + visbiasinc; hidbiases = hidbiases + hidbiasinc; %%%%%%%%%%%%%%%% END OF UPDATES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end fprintf(1, '\nepoch %4i error %f \n', epoch, errsum); end dofacedeepauto.m: clear all close all maxepoch=200; %In the Science paper we use maxepoch=50, but it works just fine. numhid=2000; numpen=1000; numpen2=500; numopen=30; fprintf(1,'Pretraining a deep autoencoder. \n'); fprintf(1,'The Science paper used 50 epochs. This uses %3i \n', maxepoch); load fdata %makeFaceData; [numcases numdims numbatches]=size(batchdata); fprintf(1,'Pretraining Layer 1 with RBM: %d-%d \n',numdims,numhid); restart=1; rbmvislinear; hidrecbiases=hidbiases; save mnistvh vishid hidrecbiases visbiases; maxepoch=50; fprintf(1,'\nPretraining Layer 2 with RBM: %d-%d \n',numhid,numpen); batchdata=batchposhidprobs; numhid=numpen; restart=1; rbm; hidpen=vishid; penrecbiases=hidbiases; hidgenbiases=visbiases; save mnisthp hidpen penrecbiases hidgenbiases; fprintf(1,'\nPretraining Layer 3 with RBM: %d-%d \n',numpen,numpen2); batchdata=batchposhidprobs; numhid=numpen2; restart=1; rbm; hidpen2=vishid; penrecbiases2=hidbiases; hidgenbiases2=visbiases; save mnisthp2 hidpen2 penrecbiases2 hidgenbiases2; fprintf(1,'\nPretraining Layer 4 with RBM: %d-%d \n',numpen2,numopen); batchdata=batchposhidprobs; numhid=numopen; restart=1; rbmhidlinear; hidtop=vishid; toprecbiases=hidbiases; topgenbiases=visbiases; save mnistpo hidtop toprecbiases topgenbiases; backpropface; Thanks for your time

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• How do you save a Neural Network to file using Ruby's ai4r gem?

  - by Jaime Bellmyer

  I'm using ruby's ai4r gem, building a neural network. Version 1.1 of the gem allowed me to simply do a Marshal.dump(network) to a file, and I could load the network back up whenever I wanted. With version 1.9 a couple years later, I'm no longer able to do this. It generates this error when I try: no marshal_dump is defined for class Proc I know the reason for the error - Marshal can't handle procs in an object. Fair enough. So is there something built in to ai4r? I've been searching with no luck. I can't imagine any practical use for a neural network you have to rebuild from scratch every time you want to use it.

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• what practical proofs are there about the Turing completeness of neural nets? what nns can execute c

  - by Albert

  I'm interested in the computational power of neural nets. It is generally accepted that recurrent neural nets are Turing complete. Now I was searching for some papers which proofs this. What I found so far: Turing computability with neural nets, Hava T. Siegelmann and Eduardo D. Sontag, 1991 I think this is only interesting from a theoretical point of view because it needs to have the neuron activity of infinite exactness (to encode the state somehow as a rational number). S. Franklin and M. Garzon, Neural computability This needs an unbounded number of neurons and also doesn't really seem to be that much practical. (Note that another question of mine tries to point out this kind of problem between such theoretical results and the practice.) I'm searching mostly for some neural net which really can execute some code which I can also simulate and test in practice. Of course, in practice, they would have some kind of limited memory. Does anyone know something like this?

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