This is a sneak peek into the brain(s) of AI.
Brains are like parties. Connections are made and information is exchanged.
Think of it as a social party where each of the colorful spheres is a guest. We’ve got a someone in blue, pink, yellow, green, and purple, all mingling through these orange lines that are the conversations happening between them.
Brains be they real or virtual also connect in a simular fasion. Instead of people brains have neurons. Imagine each sphere as a neuron inside an AI's neural network.
Those lines? They're the digital chit-chat—data zipping back and forth. Now, if you're diving into AI development, this is your bread and butter. You're the host of this shindig, and it's your job to make sure these guests connect, share info, and learn from each other.
Keep this picture in mind when you're designing learning experiences. Each connection represents potential growth, a pathway to learning something new. And remember, just like in a real network, the strength of the connection matters. Weak connections? They're like those forgettable small talks. Strong connections are your deep, memorable conversations.
Next time you're piecing together an AI, think about these vibrant little spheres. Your goal? To create those strong, meaningful links that turn a casual meet-up into a transformative gathering. Keep it colorful, keep it connected, and watch the magic happen!data:image/png;base64,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 is a sneak peek into the brain(s) of AI.
Brains are like parties. Connections are made and information is exchanged.
Think of it as a social party where each of the colorful spheres is a guest. We’ve got a someone in blue, pink, yellow, green, and purple, all mingling through these orange lines that are the conversations happening between them.
Brains be they real or virtual also connect in a simular fasion. Instead of people brains have neurons. Imagine each sphere as a neuron inside an AI's neural network.
Those lines? They're the digital chit-chat—data zipping back and forth. Now, if you're diving into AI development, this is your bread and butter. You're the host of this shindig, and it's your job to make sure these guests connect, share info, and learn from each other.
Keep this picture in mind when you're designing learning experiences. Each connection represents potential growth, a pathway to learning something new. And remember, just like in a real network, the strength of the connection matters. Weak connections? They're like those forgettable small talks. Strong connections are your deep, memorable conversations.
Next time you're piecing together an AI, think about these vibrant little spheres. Your goal? To create those strong, meaningful links that turn a casual meet-up into a transformative gathering. Keep it colorful, keep it connected, and watch the magic happen!what's your name?Alert!choose!title§_objectsMenuoptionleftleft
righthey!open my definition to look at examples how to use the menu blocksThis block creates new variables on the selected scope: global (for all sprites), sprite (for this sprite only) or script (only for that blocks stack) with the names given (in 'names' list).
If there is already a variable with that name in that scope, it does nothing: no errors and no overwrites.pt:cria as variáveis _ _
ca:crea les _ variables _
es:crear las _ variables _
de:erstellen _ var _
globalglobal
sprite
scriptThis block deletes all the variables with the names given (inside input list).
Each name will make only one variable deletion, and this will be the variable found following the scope order: 'script' -> 'sprite' -> 'global'.
If we have a "testing" sprite variable and also a "testing" global one, deleting "testing" will delete only the sprite one (Yes! we can also do "delete var (testing, testing)" to delete both.
If one variable does not exists (in any scope) an error happens, stopping block action in that point. You can check it before with the "does var (name) exists?"block. pt:remove as variáveis _
ca:esborra les variables _
es:borrar variables _
de:löschen var _
This block sets the given value (last input) to the variable named with the name givent (var input).
It looks for that variable following the scope order 'script' -> 'sprite' -> 'global' (the first match it finds).
If that variable does not exist (in any scope) an error happens, stopping their script. You can check it before using the "does var (name) exists?" block. ca:assigna a _ el valor _
es:asignar a _ el valor _
pt:altera _ para _
de:setze var _ auf _
§_getVarNamesDictThis block reports the value of the variable with the name given.
It looks for that variable following the scope order 'script' -> 'sprite' -> 'global' (the first match it finds).
If that variable does not exist (in any scope) an error happens, stopping their script. You can check it before using the "does var (name) exists?" block. pt:o valor de _
§_getVarNamesDictThis block reports "true" if there is a variable with this given name (input slot) in that context.
It can be a global, sprite or script variable.
Otherwise it reports "false".pt:a variável _ existe
ca:existeix la variable _ ?
es:existe la variable _ ?
de:existiert var _ ?
err_resetThis block turns on (show) the watcher view on stage (if it was not already activated) of the variable with the given name (slot input).
It can only access to the closest variable scope (if there different variables with the same name in different scopes) following the order 'script' -> 'sprite' -> 'global'.
No errors if that variable does not exist.ca:mostra la variable _
es:mostrar variable _
pt:mostra a variável _
de:zeige var _
§_getVarNamesDictThis block turns off (hide) the watcher view on stage (if it was not already activated) of the variable with the given name (slot input).
It can only access to the closest variable scope (if there different variables with the same name in different scopes) following the order 'script' -> 'sprite' -> 'global'.
No errors if that variable does not exist.ca:amaga la variable _
es:esconder variable _
pt:esconde a variável _
de:verstecke var _
§_getVarNamesDictredrawde:animiere Setter _ Getter _ Delta _ über _ Sek. beschleunigt _
ca:modifica setter _ i getter _ amb delta _ en _ segons de forma _
pt:anima com modificador _ e inspector _ de _ em _ s de forma _
501de:Grad _
ca:graus de _ radians
pt:_ radianos em graus
0.5de:Fehler _
pt:assinala o erro _
de:gleite _ in _ Sek. beschleunigt: _
ca:llisca _ en _ segons de forma _
fr:glisser _ en _ sec. accélérer _
pt:desliza _ em _ s de forma _
501linearlinear
quadratic={
quadratic-in
quadratic-out
quadratic-in-out
}
cubic={
cubic-in
cubic-out
cubic-in-out
}
quart={
quart-in
quart-out
quart-in-out
}
sinusoidal={
sine-in
sine-out
sine-in-out
}
elasticde:animiere _ um _ in _ Sek. beschleunigt: _
ca:modifica _ amb _ en _ segons de forma _
pt:anima _ de _ em _ s de forma _
x positionsteps
x position
y position
direction
size
ghost effect
color effect
saturation effect
brightness effect
fisheye effect
whirl effect
pixelate effect
mosaic effect
negative effect
tempo
volume
balance501linearlinear
quadratic={
quadratic-in
quadratic-out
quadratic-in-out
}
cubic={
cubic-in
cubic-out
cubic-in-out
}
quart={
quart-in
quart-out
quart-in-out
}
sinusoidal={
sine-in
sine-out
sine-in-out
}
elasticdeltay positiondirectionsizetempovolumebalance112de:Beschleunigung _
ca:de forma _
pt:a forma _
linearlinear
quadratic={
quadratic-in
quadratic-out
quadratic-in-out
}
cubic={
cubic-in
cubic-out
cubic-in-out
}
quart={
quart-in
quart-out
quart-in-out
}
sinusoidal={
sine-in
sine-out
sine-in-out
}
elasticquadratic-outquadratic-inquadratic-in-outcubic-outcubic-incubic-in-outquart-outquart-inquart-in-outsine-outsine-insine-in-outelasticunknown easing function ""de:für _ = _ bis _ _ in _ Sek. beschleunigt: _ _
ca:per _ = _ fins _ _ en _ segons de forma _ _
pt:para _ de _ a _ _ em _ s de forma _ _
01001linearlinear
quadratic={
quadratic-in
quadratic-out
quadratic-in-out
}
cubic={
cubic-in
cubic-out
cubic-in-out
}
quart={
quart-in
quart-out
quart-in-out
}
sinusoidal={
sine-in
sine-out
sine-in-out
}
elasticdata:image/png;base64,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0