[JohnnyWaffles]

So I'll get straight to the point, I have built a simple machine learning platform at my place of work. It still needs work, but its mostly done. At some point soon I'd like to share it with my co-workers and my supervisor with the intent on how it can make our day to day job easier. The intent is not too expose the ANN or its data handling in anyway to other users, other than it's predictive data output. I will handle the maintenance and training of the network myself.

Here's the thing; how do you convey the concepts of how Neural Network works without diving into the math behind it. If I do that, I will lose everybody. In some cases, the proof is in the pudding as they say. If I demonstrate it's accuracy without going behind the scenes that may be enough. Otherwise, how would you guys approach this?

It's a simple feed forward Neural Network I designed in Excel by the way. Nothing fancy. Thanks in advance.

[LOCKSUIT]

What does it do? How good? Does any one's else top yours?

If it works, why not show the results to them? Again, tell us what it does.


As for conveying concepts, Lock is ace on this!!! I HATE math. AGI ain't math! Nor code. And I know neither besides their names n very very basic stuff. It's an idea, a mechanism, a concept that can be explained easy! Tell them ANNs are a memory device, and they compute. Tell them the memory is a hierarchy where features get re-used ex. triangle feature is used to make cubes and sandtimer features, and triangle is made of 3 line features. Then tell them when input comes in, it travels through the net forward a certain way depending on the times (x) against the weights. Tell them ex. it learns about the world knowledge or imagery, and when new input enters it can analogate it to stuff it knows (generalize). If I know the goal I can better help you. Also these tags: Recognition, generation, prediction.

False flag to think AGI is math. Reallly? The mechanism for discovery is simple. It's literally A=B>C!   :D

[JohnnyWaffles]

Hey LOCKSUIT,

So, thank you for your help. The neural network is feed forward binary classifier. It consist of  3 layers. The input layer has 3 input nodes. The hidden layer has two nodes, and the output layer has 3 nodes, with each output node putting out a 1 or 0. The activation function used is the Sigmoid activation function.

My input data consist of vectors of 3, like below:
[1,0,0]
[0,1,0]
[0,0,1]
[-1,0,0]
etc...

I am using it for predicting document locations that could exist on a series of databases. Trouble is, I have to find certain documents in my job, and there is no rhyme or reason as to where they might exist, and it can take tedious hours to search ALL the databases. So I'm using existing data to train a neural network and predict where a document exists. Thereby decreasing the time it takes to find them. I've had some success so far. Still needs work though.

The output classification should correspond to the input where each node outputs a single binary number, and all 3 of them make a prediction as to where I will find it:

Databases_One = 100
Database_Two = 010
etc...

So, I don't know much about AGI, and I'd certainly prefer to have one of those on hand. Maybe I'm just ignorant (probably), but how could you make an AGI using simple logic? I've never attempted to make an AGI, so I've never though about it before.

[ivan.moony]

Otherwise, how would you guys approach this?

Show final results to co-workers. If the results can convince you, the results can convince them too.

[LOCKSUIT]

AGI not so simple.

Well, you'd have to find documents by them having meaning X, the same meaning you'd look for yourself.

If it says cats, lick, paws, poop, sleep, maybe that's enough, and maybe they all relate, and maybe some phrase like "it keeps slurping into itself resources" is just "licking into its mouth kibble", and you'd never find it by searching for those exact words.