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1
General Project Discussion / Re: Using Genetic Algorithms To Create Neural Simulations
« Last post by Art on January 16, 2018, 04:44:04 pm »
Hello Danlovy,

Welcome!
I didn't actually see that you had joined our Forum and certainly did want to ignore your presence.

Hopefully, everyone will benefit from the informational exchange, no matter great or small. Please enjoy your time here! O0

- Art -

2
General Project Discussion / All-in language draft
« Last post by Zero on January 16, 2018, 02:26:03 pm »


primitive data type
    number                  | a Javascript number
    text                    | a Javascript string
    outcome                 | success/failure of an evaluation
    function                | an anonymous function in Tcl-like code
composite data type
    object                  | an Io-like object
    list                    | a plain old list
    knowledge               | a coherent set of interrelated meanings
        meaning                 | a knowledge item
            relation                | a relation between items
            meta-data               | a semi-structured data chunk
            behavior                | a behavior tree
    schema                  | a data pattern


number syntax example
    23
    -56.9
    2e5


text syntax example
    bluesky
    "blue sky"
    'blue sky'
    (blue sky)
    {blue sky}


outcome syntax example
    success
    failure


function syntax example
    set incr [
        function {
            set [arg] [= [[arg]] + 1]
        }
    ]


object syntax example
    object {
        <tag1>key1: expr1,
        <tag2>key2: expr2
    }


list syntax example
    list {
        <tag1>expr1,
        <tag2>expr2
    }


knowledge syntax example
    meaning {

        item1 -> ownership {
            owner: Janis;
            owned: Mercedes;
        }

        item2 -> [email|
            [from|john@doe.com]
            [to|jane@tarzan.net]
            [title|meta/data test]
            [body|
                Hi, this is John sending
                semi-structured data.
            ]
        ]

        item3 ->
        (select,
            open the door,
            (sequence,
                get the key,
                unlock the door,
                open the door))
    }



The "goal directed execution" concept is borrowed from the Icon programming language. There's also the pub/sub thing from Birdy, and lessons learned from Semantic JSON, which don't appear here, but are meant to be included.

This could be my idea of an "AiDream" language...  :)
3
Robotics News / Re: Robot Ice Cream Cone Server
« Last post by ranch vermin on January 16, 2018, 10:21:37 am »
love it.  u could say im into this kind of thing.
 Some places are more computerized than others around the place.

that would be awesome at an arcade.

To do this kind of thing really well,  you have to think all the little things the kids (and us) are going to think when they are around the machine.   like, no cones left,  some guy running past too quickly... more and more states,  makes for a more filled complete experience of automation.

Just like making a video game,  just in real life like a pinball machine.
4
Robotics News / Robot Ice Cream Cone Server
« Last post by Art on January 16, 2018, 04:05:55 am »
Yep, it's the only thing it does...make soft-serve ice cream cones. And it does them very well.

Slowly these robots are taking over these types of dangerous human jobs.
After all, one could get frostbite, cone allergies or some such malady.  ;)

Seriously, one does have to appreciate the ease with which it performs its task.

https://www.chonday.com/28436/icrsofse6/
5
General AI Discussion / Re: I want to crack Neural Networks
« Last post by LOCKSUIT on January 15, 2018, 11:34:23 pm »
I actually got my amount of open work-tabs down recently. However my knowledgebase is a little scarier to look at. Good thing I know where everything is.

So IF the filtered feature images DID get layered back as one, it would sorta ruin the whole point of the filters right?, however it would mean there is actual faces higher up in the network, lol. While if the filtered feature images DON'T get layered back as one, then the purpose of the filters is kept alive right?, however there is no actual faces in higher layers, rather they are there but as encryption-like and are detected by the fully-connected layer of weights with the lines voting in by however they were trained right? Also, after the first layer of filters, the lines/curves/etc features filtered are the only things that um, have an appearance, I mean, when a nose etc is detected and then a face is detected, these things detected will never have an appearance right, they are only an encryption and weight votings right? Cus I was thinking that the detected lines/curves would become shapes, and then higher, hehe...

If I stare at a human's face, I will detect "face" at the end of the network. But, why and how am I able to concentrate on just the nose and see the nose? If the encryption for "nose" "I see a nose I see a nose" is in a layer behind the "face" layer, then, that means it would need to stop there, and output a layer early, right? Also my concentration gives more score to that area I guess.

Is korrelan working hard on Deep Sensory Cortices because that will pave the way for the rest? Like The Deep Motor Cortex?

Btw it's bugging me so I wanted to make it clear, I know korrelan, you are the father of wisdom with the ANN / machine learning.

In the brain there is sensory cortices and the motor cortex. Why does Machine Learning have no Motor Cortex ANN algorithms????? For example, take CNNs, or Logistic Regression, or HHMMs, they are not motor cortexes in the sense of the human brain's motor cortex. Why does it seem Machine Learning is focusing on "senses" but not "motor actions" ?!?!? I know I've seen Machine Learning projects have spiders learn to crawl BUT, never mention the motor cortices, only the ex. CNN. Half the story is missing. Omg guys.
6
General AI Discussion / Re: I want to crack Neural Networks
« Last post by korrelan on January 15, 2018, 10:15:57 pm »
Your first picture shows a convolutional network.  The detected line features are fed through a max pooling network/ layer.  The pooling layer is just a way to keep the values/ vectors within reasonable bounds before being fed into the next layer.  The pooling layer also usually shrinks the detected image features, with no loss of detail.

There are many variations on the CNN schema, some pool features into larger features (eyes, nose, etc). Usually only at the end of the CNN chain of networks/ processes are the detected features fed into a fully connected network (FCN).  The full set of detected line features are never actually recombined into the original image.  The FCN (last stage) is where the machine learns to name an object from the features present in the image; all the stages prior to the FCN are just used to extract recognisable features from the image.

Your second picture is of a standard feed forward classifier, very similar to the FCN mentioned earlier.  This is a totally different method of using a NN to detect features/ numbers, the image matrix of pixels is fed in and the NN learns to classify a output.  This method is not usually as versatile/ accurate as the CNN approach but requires less processing.

Keep in mind that there is no silver bullet solution, there are thousands of different variations on the NN, CNN, RNN, architectures.

ED: OMG close some of those tabs on the browser... my OCD is twitching lol

 :)
7
General AI Discussion / Re: I want to crack Neural Networks
« Last post by LOCKSUIT on January 15, 2018, 08:20:46 pm »
I have 2 new questions. See the image attachment in this post. So the stick man is made into convolved feature maps, one for vertical lines, one for diagonally left lines, and one for diagonally right lines. It's extracting features and looking for matches at a small level. BUT, now for my questions, as you can see at the bottom left in question 1, do those feature maps ever get layered on top of each other to you know, use lines n curves to make noses and eyes, then use eyes and noses to make faces, creating higher-level features as you go up the 6 layers in the mammalian brain? Or does that give us the same picture that we started with? I.e. in my attachment I show a OR in the bottom left asking if or does it use a fully connected layer with the curve/line features to sum up probability weights to what it is, like out of the 3 options, it is a eye or nose or mouth, or say a man or box or fox, ? If it does that then, then, then, that means it never actually builds higher-level features, i.e. lines/curves, eyes/nose/moth, face/body/limbs, human/chair/pillow, dining table with chairs with humans with pillow, that scene with music playing, and even higher. What that means is that it just uses the lines and curves and well, any higher-level features like eyes in the next layer and then faces in the layer after that are really just fully connected layers that where the image does-not look like a face but instead is just a number / representation, then uses that nose/eye representation in the next layer for ex. faces.
Question 2 now, is, at bottom right of attachment 1, how does it detect just a nose (or ear/s as I drew) by lines n curves if those lines n curve features will light up some of the stickman at the same time?

Another issue I'm having is, see attachment 2 explaining CNNs? In this image you can draw a number and that thing detects if it's a 1 or 8 or 0. 784 pixels get fed into the first layer, and it clearly stated it uses multiple features, yet if that were so then on the far left at the first layer of 784 pixels there would be a divergence of 3 separate layers each having 784 pixels input. It seems as if it is just 1 feature map summing its own self rofl! Helppp!
8
General AI Discussion / Re: I want to crack Neural Networks
« Last post by LOCKSUIT on January 15, 2018, 05:14:23 pm »
No implementation yet. Just anylising the situation right now. Prep stage.
9
General Project Discussion / Re: Using Genetic Algorithms To Create Neural Simulations
« Last post by keghn on January 15, 2018, 03:00:49 pm »
 
  What is next 3 d FPV space?



10
XKCD Comic / XKCD Comic : Memorable Quotes
« Last post by Tyler on January 15, 2018, 12:00:14 pm »
Memorable Quotes
15 January 2018, 5:00 am



Source: xkcd.com

Pages: [1] 2 3 ... 10

Robot Ice Cream Cone Server
by ranch vermin (Robotics News)
January 16, 2018, 10:21:37 am
Three EECS professors join leadership team
by Tyler (Robotics News)
January 13, 2018, 12:00:04 pm
Give your robot some muscles!
by Freddy (Robotics News)
January 12, 2018, 08:41:21 pm
Comparison of Digital Assistants
by Art (AI News )
January 07, 2018, 09:04:51 pm
Let's shed some light on the subject
by Art (AI News )
January 03, 2018, 03:44:32 pm

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