AI- finally, pattern recognition

I have made some serious advances with my AI. Now I have definite pattern learning + pattern discrimination = pattern recognition. No doubts anymore, that this is working, but I also know that this is only the very first 0.1% of the development needed to truly claim a different type of AI – the Hebbian AI..

Here is the most relevant data:

First we have the learning step, before the black line. each pattern will activate a different neuron from the output layer, I only use 4 output neurons here. Then after the black line I “read” the patterns again, expecting that, the same pattern to light up the same output neuron.

I’ve also created a some sort of graphical interface and made a video with the whole process:

I have tested the code with 19 different patterns and 1 of the 19 patterns was misunderstood (letter M is sometimes confused with letter H), you can see it on the video. That was to be expected, at some point, some patterns, are going to be misunderstood. How many patterns can it learn ? All possible patterns within that matrix (25 by 25 pixels), but as more patterns are added more misunderstandings should occur. Why ? because I don’t really have what in current AI is called “feature extraction”… I only have a single feature extracted, the whole pattern. This works with basically one (1) hidden layer and the extraction is made by the output layer…

This is also observed in the fact that as you go with more and more pattern learning, the learning process, becomes more difficult, it take more cycles to be learned. This should not happen with more “feature extraction” along the way… Learning should become faster, as more an more small pattern become common among the bigger patterns.

Any way this is a big achievement and a proof of concept, even if in infancy.

What’s next ? Of course feature extraction… My first target is to have a big line learned and then a smaller line be recognized as the same “line”. Right now, the smaller line pattern is considered an incomplete pattern and blocked from reaching the output layer. How am I planning to go ahead ? Well, I have wondered for a long time, why our brain, has neurons that bind among themselves within the same “layer”. I cannot find information showing differences between neuron that bind to neurons from the same layer and neurons that bind to neurons from a higher layer… They appear to be the same… I don’t know the ratio.. how many bind intra-layer, how many bind to the next layer. Do they have the same learning rate ? Do they form as many/as few dendrites/synapses ? Do they have a shorter axon from the beginning ? are they attracted the same by the Nitric Oxide (retrograde signalling) ? Is mirroring happening only for inter-layer binding neurons or it also happens to inter-layer binding neurons ?

( Mirror neurons are neurons that fire both when an individual performs an action and when the individual sees[14] or hears[15] another perform a similar action from wiki).

So there are many many unknowns, not sure about the time frame I’ll be able to put them all together… I’m hoping to have something working by the end of the year… but who knows, I may decide to take a more brute force approach and use what I already have, multiply the single feature extraction, but that should prove slow, inefficient and inflexible… Anyway, small line/big line – here I come !!!!

What is a Hebbian AI ?

“Cells that fire together wire together.” – see it on wiki

A Hebbian AI is the term I used to describe my AI project, yet, what a Hebbian AI might be is not clearly defined. The mathematical description is extremely limited and is totally different from what I’m using for this AI.

SO what do I mean by Hebbian AI ?

  • it has a learning algorithm based on: ” Cells that fire together wire together “, so it is not using regression for learning, so no back-propagation function. It does not do ANY kind of regressions with all the downsides it brings.
  • The network is made WITHOUT connections. Connections between neurons are made dynamically. There is both creation and destruction of connections.
  • The neurons are using only 0 and 1 as responses, so every type of signal can be correlated and understood, for example an IR sensor can be integrated with an audio signal … They are both treated the same.

Possible advantages over current AIs:

  • It could be way faster for some tasks. That remains to be determined with some sort of benchmarks. So far the code is in Python with zero optimization… no multi thread no GPU use.. nothing. Even so, for what it does at this moment, should still prove faster than whatever sophisticated AI out there.
  • It can immediately integrate all kind of data, visual, audio, pressure, IR, 2-3 video inputs .. everything.
  • It has causality, as far as causality can be determined from observable data, built in.
  • It has temporal awareness, so it matters what is learned first
  • Understanding is probabilistic, sort of a Bayesian learning

Possible dis-advantages over current AIs:

  • It does not do regressions, so it should not be able to do some task, that are possible today with current AI.
  • It is somehow more imprecise (no values between 0 and 1).
  • It can’t be made totally parallel when computing neuronal responses… many processes need to remain serial (so postsynaptic neuron cannot fire before the pre-synaptic neuron ).

I’ll update this article once I understand more :).

AI – Houston we have touchdown

The bad news first. Seem my previous post was some sort of a random luck error. The code did not behave as expected, but I was too exited anyway, and rushed to post it. (not that anyone read this So I went back to the drawing board working only with enough data to have some meaning but otherwise the simplest data possible: [1,0,0,1]. Then after I was convinced everything worked well ( I moved onto a bit more complex data.. a 5×5 array with two patterns:

  • a right line /
  • a left line \

Then after even more work, mostly figuring out how my important variables (number of connections, filter size, learning factor and so on) affect the results. I am no where near understanding all the subtle influences are having, but I have some important rules to guide me. Last layer has to have all neurons firing to decide when something was learned, and read data from the discrimination layer (second to last). Then the magic happened.. I was able to repeatedly see the same difference between the two patterns: \, /.

Also, as predicted, I needed to see a learning curve (which was not happening in my previous post, there was no apparent learning, the signal would just drop)

Here’s the data:

Learning two patterns: / and \. The patterns are 2 png files of 5 by 5 pixels each. Left graph showing the discrimination layer, second one (number of cycles vs active neurons in the learning layer (last layer in this configuration)

How confident am I that this is not yet another fluke ? Well, is not 100%, that’s for sure, I need to run more (simple) patterns and then more complex patterns to get to 95% confidence :). I’ve changed my mind too many times and now I’m more skeptical than ever.. even when things do go as predicted.

My set up so far: Input Layer (5 by 5 matrix), 2 Hidden Layers (5 by 5) and an Output Layer (4 by 4), a total of 91 neurons. Output Layer = Learning Layer, Last Hidden layer acts as a discrimination layer.

AI-Update – learning hand written numbers

Progress has been slow for a while, but after a certain development threshold was reached, things have started moving faster. I now have a good release candidate for my neuron code. I also spent a lot of time reading and doing theoretical calculations. While the Neuron can be well described in mathematical terms, the network is too complex to describe in mathematical terms. The code can handle now 2D images, with at least thousands of cycles and at least thousands of neuron into the net. Perhaps more, I have not tried. Speed is not limiting me at the moment, 1000 cycles with 535 neurons took 20 seconds to finish, so I’m delaying any speed improvements for now, I was planing moving parts of code to C but other things are more important for now.

So what is new ? LEARNING hand written numbers from images.

learning hand written numer zero and 3
Learning hand written number zero ( 0 ) and 3

The output layer is a 4 by 4 matrix, but neurons are presented on a single row. All output neurons have a 0 / 1 response. After 150 cycles learning number 0, all neurons have “learned” the number and I was expecting all neurons to light up. They do, on certain configurations, but here one is off. After the initial process, I fed the network more images (different images) of number 0 (zero). I expect common features to be kept and at some point to obtain only a single output neuron in an active state, rest of them to be off. In the image above I’m showing some preliminary results where I get results only for different images of number zero, and I get no result for images of number 3. For training I’m using a very small MNIST data base with hand written numbers. What about CNN ( Convolutional Neural Networks ), pooling and such ? I’m using none of those. The image if fed directly into the hidden layer neurons. I worked for a bit to implement a receptive field algorithm. While I believe that to be a very useful and smart way of reducing complexity, I don’t believe is needed for simple images like this.

What’s Next?

Learning all numbers and letters, at the same time. I have hoped that the current set up will allow for at least 2 numbers learning at the same time, but I had no success so far. Keep in mind that the network has many parameters that need to be optimized, such as number of synapses (connections per neuron), number of hidden layers, neurons per layer, learning factor and so on. Right now I’m using this configuration: Input layer a 25 by 25 matrix with 3 hidden layers (25 by 25) and a 4 by 4 output layer.

Artificial Intelligence -Update- One Neuron in action !

After many trials and many grandiose ideas, I simplified everything to bare minimum. Only a single neuron. Fix that part first. I gave up on sophisticated connections, but those functions are still available, gave up on complex distributions. And here I am… a single neuron + 3 input Neurons. The input neurons do almost nothing, they are there to establish input connections with “real” neurons. And without further due, this is it:

I have a pattern of 3 digits but the pattern can be infinite. The learning part comes from repetition. See the same pattern twice and that is it… the third time the pattern is recognized. I taught the AI 2 patterns [1,1,0] and [0,0,1]. Then I asked it for other 3 patterns: [1,0,0], [1,1,1] and [0,1,0]. I was hoping for a “Pattern Unknown”, all over, but instead I got an unexpected: “Pattern recognized !” for pattern [1,1,1]. So if it’s composed of two know patterns, is still considered known.


I went for an actual neural net, meaning many neurons connected somehow. The full connection (each neuron with each neuron), proved useless, all neurons became symmetric with same output. So next step is to go for an asymmetrical connection between neurons. I’ll first try the asymmetrical_1D and then asymmetrical_2D, and eventually get back to random spherical distribution.

Artificial Intelligence – some ideas

While taking a brake from game development, I though to myself, why not ?? Everybody is doing it ! So I started working on an AI project. Did some literature research for a month or so and then decided what I wanna do. Some sort of a Hebbian type of AI without a correction function (backpropagation).. Well sort of, there is a self correction based on frequency of data and what data is associated with what. Working in Pyhton with the Matplotlib , using, for now, a random spherical distribution for network topology.

Blue -is the input layer, Green dots is the output layer. Trying to learn 3 letters.

Advertising with Google

We’ve started a small advertising campaign with Google Ads for our new game BackSO. For some reasons I always believed this should be something very costly and maybe complex. Well, it was not. In fact was very simple and the prices were actually very good. You can limit the showings to a certain budget/day, and the budget can actually be very small. Overall I’m very happy with how Google is doing its job. Of course the game is doing rather poorly but that is not because of Google :). For for those of you running small gigs don’t be afraid of Google. You can get very fast some users and if you have the Firebase statistics implemented you can get a very large amount of data for the price of a Starbucks coffee. You can the go on on improving your project of maybe decide to start anew.

I also tried a Fiverr small gig, my conclusion is that it is not worth it. The audience is not targeted and get your money wasted for no real benefit. Better to get with the giants, at least you get some real data that can be used to improve your game, before trying out other campaigns.

For Google Ads, start with a global campaign, all over the world, at the smallest price Google can offer per install. Adjust your game and perhaps do a targeted campaign in richer countries, that perhaps require higher prices for installs but may give you better return on your investment.

A new project – BackSO on google play

We’ve recently published another game on the Google Store: BackSO

BackSO is a casual game to play on short breaks to get your mind off things. Most levels are strategy based and only few require a bit of eye hand coordination, but nothing to be scared about. All in good fun.

Your goal is to place all the balls in the correct placement, considering that two balls cannot occupy the same place.

Here are some screen shots:

We Published, now what ???

We rushed through and the game is out. I thought I will get some peace and quiet now.. but nooo. It turns out that nobody reads the HELP section. They start the game, fail to even move the character in some cases, get angry and uninstall right away 🙂  The ones that can get the character moving get frustrated because they don’t know what to do next. So I have about 130 player that failed level one several times, and God knows how many uninstalled right away. So I’ve started working hard on a HELP level, this should start once you hit PLAY and then can be replayed from the HELP menu. After two day, I was done and went to a tester. Was I disappointed or what ???

The guy went through the HELP level without reading anything, did at random all the scripted actions then start wondering “How to finish the LEVEL ?” Totally ignored the “BACK” option and got stuck, doing all kinds of actions trying to Finish the level. There are of course many things to learn from this debacle:

  • People Don’t read.
  • People want everything to be as it is in other games they’ve played, anything different and they get frustrated and leave.
  • They want to Win at all cost, no way they’d go through the first level, fail a couple of times and learn how to play..
  • We don’t really understand our target audience thinking.
  • No way you drop a game in the Google Store and from the billion of users some will randomly download and try out your game. You would think the probability will work your way and maybe 10-100 people will just download your game at random. The 100 people that downloaded the game, were only users that we kind of brought ourselves directly.

The Shifter has Arrived !!!!

After couple of days of intense testing the Space Shifter is in the store. Get it from here.

That was intense. Working without knowing if your work has any value is… challenging. We hope for the best, of course, but there are many times when the doubt sneaks up on you.

Anyway, enough with the self loathing. Great job team !!

And you brave warriors go help the Shifter.