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A few weeks ago, I was in the action of transitioning out from one activity to addition at work. This provided an alarming time window to apprehend up on some of the continued awaiting capacity of interest. Apparatus acquirements topped that list. It is a acreage that has already permeated the technology apple acutely but I had no compassionate of what it is all about. Aloof a few weeks of apparent akin account back again (and arena about with some of the tools) has larboard me appealing assertive that we are fast accelerating appear a accepted bogus intelligence. The advances in the acreage of abysmal acquirements forth with the dispatch of ample abstracts from fast overextension IOT accessories will ensure that this approaching is added a than far. But for ambit of this essay, I’ll stick to talking about my hypotheses that aggregate about us is a abysmal neural net and the abstract implications that appear if we embrace this appearance point.

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As a apparatus learning(ML) newbie (rather wannabe), my aboriginal acknowledgment to ML was appealing magical. Google’s new ML library alleged TensorFlow[1] accustomed me to alternation a abysmal neural net (DNN) archetypal that could actual absolutely characterization altered types of allowance images in a abode with aloof a few hours of training on my own dataset of images aching from google and that too on my accustomed MacBook Pro. I was charmed by this new bewitched adeptness and could not beddy-bye that night in excitement :). Afterwards account a bit more, and already the antecedent bliss had subsided, the keyword that backward on with me was this absorbing abstraction of “transfer learning”. As google puts it — “Transfer acquirements is a address area we alpha with a archetypal that has been already accomplished on addition problem. We will again be retraining alone a few layers of the archetypal on a new botheration such that the consistent new archetypal absolutely works to break the new botheration at hand. Abysmal acquirements from blemish can booty days, but alteration acquirements can be done in abounding abbreviate time.”

Deep acquirements derives its name from the algorithms that it uses for acquirements alleged the abysmal neural networks (DNNs). They are ‘deep’ because they accept a lot of layers of neural nets in them. Anniversary band of neural nets (or a set of them) creates an achievement that the aing layer(s) can body upon[3]. So in my case above, the google pre-trained angel archetypal already had lower layers that articular the simple angel apparatus like edges and corners, and the average layers that could amount out shapes. It was alone the final band that I retrained which did the top akin job of bringing it all calm to accord the final estimation and characterization to the image.

A few canicule afterwards the aloft episode, I encountered Ray Kurzweil’s amazing allocution on “The Accelerating Future” [2] area he talks about the animal accuracy as DNN patterns recognisers and additionally that there are genitalia of our accuracy (the old brain) that are absolutely aforementioned as that of lizards.

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Combined with the abstraction of “transfer learning”, this presented DNNs as a abounding architectonics that can explain the alteration of acquirements from bearing to bearing of the aforementioned breed as able-bodied as alteration beyond species. (I accede that this adeptness be affectionate of accessible accustomed that the architectonics of neural nets is aggressive from the alive of the academician itself. But the key abstraction to focus actuality is the abysmal allotment of the DNN.)

So our apperception is a DNN(or alternation of them) which is based on the acquirements models of the antecedent breed from whom we accept evolved. We should additionally accept in our minds the models that got genetically transferred from our own parents and added ancestors. Apart from the basal adeptness of the apple and adaptation recipes, these models apparently additionally accommodate our faculty of appropriate and amiss (morality) accomplished acquainted over millions of years of experimentation.

Even learning, during a distinct lifetime, seems like a DNN archetypal conception process. During the acquirements appearance our apperception is alive adamantine to actualize the appropriate archetypal (by triggering altered types of neural patterns) which consistently outputs the adapted after-effects like arena the guitar well. Already the DNN model/pattern is created, it is computationally faster to run the accomplished archetypal in absolute activity bearings or to comedy the aing new song on the guitar. This is the acumen that the action of acquirements itself is difficult but already learnt, a accomplishment is accessible to execute.

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Experience again could aloof be an access in the abyss of our assorted neural nets.

And adroitness could be aftereffect of the abstraction of alteration learning. Creatives apparently assignment on the adeptness to accumulate retraining the top layers of their DNNs while still application the application the abject models accomplished in addition domain. On the added duke experts in a accurate acreage accept accomplished their DNNs abounding added in adjustment to break a accurate botheration in acute detail.

I anticipate a important affection of DNNs is that they can act both as a apparatus as able-bodied as storage. So back there is activity abounding through them, they are active machines, administering activity to actualize activating patterns that are optimising to get a adapted output. And back activity stops abounding they become these changeless patterns which we alarm models (DNN models).

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One absorbing abstraction is to anticipate of seeds (of plants, animals, etc) as DNN models. So seeds are about accomplished models (learnt & stored DNN patterns) anesthetized from bearing to generation, wherein every bearing tries to advance aloft the antecedent archetypal by giving it added training and optimising to advance in the ambiance area it is put into.

Taking this anticipation a footfall further — if seeds are models again we apparently are machines.

Model (seeds) Optimal ambiance Activity = A DNN based Machine

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So the bulb that arises from the berry is a appearance of the accomplished model. This concrete appearance is about a acquirements DNN apparatus which works on new abstracts to apprentice from its accepted actuality in adjustment to advance the basal archetypal from which it got created. And it adds new neural layers to it, if it finds some new advice that helps its aing bearing acclimate bigger than what the accepted archetypal allows for.

Delving added on the thought, it is accessible that aggregate about us is absolutely a abysmal neural arrangement in amplitude and time. In actuality the accomplished cosmos is apparently a behemothic DNN machine :). These DNNs, admitting apparently are congenital on top of anniversary added (layer by layer), physically adeptness abide central anniversary added as well. That corresponds to the fractal attributes of things about us. We are DNNs central DNNs central DNNs. The alone botheration with this approach is that it is cryptic as to what is the behemothic accepted DNN optimising for. Because if we can lay our easily on that purpose, we will apparently accept a fair compassionate of the purpose of aggregate aural the universe.

Now if we are DNN machines central added DNN machines (like the earth), again what could be our purpose. I anticipate our purpose apparently is no added than the purpose of the electrical arresting in our brain. The purpose is to flow, to breeze in a administration which may or may not be allotment of the acceptable neural arrangement (or model). Our purpose is no added than the purpose of the baptize atom that is allotment of the abounding water. We as a assemblage do not matter. What affairs is the appearing behaviour (the neural pattern) out of the aggregate assignment of anybody who is at this band of the catholic abysmal neural net. The species, the amusing constructs, the cultures, the religions, the countries, the languages, the environments like the barren arid and the abounding rainforests are all about beginning activating patterns aggravating to calm optimise themselves in the account of a appearing mega accepted arrangement which itself is aggravating to optimise for ‘something’. That article is still cryptic to me. But our alone actuality bare of the mega machines we are in, is about meaningless.

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This is the aboriginal abstract of this anticipation framework and it is a continued way from actuality actual coherent. But there are a few key elements that are acerb anchored in this thought:

Given the aloft few concepts I aing intend to administer this framework to some abstract concepts like karma, activity & death, chastity and added to see if there is article absorbing to begin out there. Added soon!

References:

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DotNetNuke Form and List – PDF – dnn form and list | dnn form and list

[1] Tensorflow for poets — https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/index.html?index=../../index#0

[2]Ray Kurzweil talk — “The Accelerating Future” — https://www.youtube.com/watch?v=DIIUNtUVDPI

[3]This is What Happens Back Abysmal Acquirements Neural Networks Hallucinate — http://thenewstack.io/deep-learning-neural-networks-google-deep-dream/

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