Introduction:

For the last 5 years I have been independently developing a unique product.

This product is an Artificial Intelligence that specializes in ingenuity, creativity, and curiosity. The objective of the AI was originally to be able to write and create its own programs, essentially granting the AI the capability to not only reproduce itself, but to evolve. It has, however, broadened it's application to generically cover all-things-related to ingenuity, creativity, and curiosity.

Background to this post:

I have decided to share how it works with the community, as I have just recently enlisted into the United States Navy as a Nuclear Physics Engineer--I am concerned that with current career choice, I won't have that much time to continue my research or that my research may be confiscated by the US armed forces for their own agendas.

So, I am hoping by sharing this I can inspire someone to continue my work.

The project details:

So let me break the A.I. down simply for you.

The primary objective was to make an A.I. creative, but not merely that, but give it the capacity to invent or create something completely new.

Let's discuss "completely new" and "creative." I have discovered through reading several published works (primarily the works cited in this read - (APA citation) Wu, Q., & Miao, C. (n.d). Curiosity: From Psychology to Computation. Acm Computing Surveys, 46(2) ) that human creativity is primarily dependent on human curiosity, and can be simply described using the analogy "creativity is to curiosity as solutions are to problems or answers to questions."

-(Recap checkpoint): Let's take a brief moment to summarize simply the breakdown and essentially the possible dependencies for the research:

Programmer/Engineer -(needs to be)-> creative -(which requires)-> curiosity

Let's leave this simple idea alone for a moment and expand on the topic of "completely new." In order to discuss this, we need to keep the conversation simple by limiting it to talking about "what is 'new?'"

According to Dr. Daniel E. Berlyne in his work on human curiosity novelty in the eyes of the human mind is a characteristic defined as something fundamentally similar to what we have experienced before, yet fundamentally different. His argument was quite simple. Imagine coming across a two-headed lady. You may ask yourself tons of questions about this two-headed lady, but if you were posed with an amorphous not-seen before shape on a piece of paper, you may be less interested. He coined this phenomenon the "novelty collative variable."

The idea here is that something is considered "new," not necessarily if it has been experienced before, but if it defies your expectations of what you should be experiencing--fundamentally similar, yet fundamentally deviating.

Now, of course this is not what you would imagine "pure novelty" is like, because something that is completely new is something that cannot be possibly compared to other objects, but unfortunately these novelties don't exist are technically impossible. Just take a minute to think about it. You see something new, your first response is to describe it. In order to describe it, you have to compare it to something else you have experienced, if it is not comparable to anything, then does it really exist? because if it does exist, then it is comparable to other things that exist.

So, let's make sure the point is communicated. Novelty is a phenomenon that is fundamentally similar to other things, yet is fundamentally different, and the more fundamentally similar while simultaneously being fundamentally different an object is, the more "new" it is.

-Recap checkpoint:

Programmer/engineer -(needs to be)-> creative -(which requires)-> curiosity -(which depends on)-> novelty -(which is defined as)-> being fundamentally similar while being fundamentally different

So, let's expand on similarity and difference.

Can a computer understand similarity and difference? Yes, and no. Detecting similarity and difference depends on the capability to understand the basic properties and definitions of the things it is comparing or contrasting. So does a computer have the capacity to define objects in the real world? Again yes, and no. So why "yes?" NLP. Natural Language Processing helps computers have an extremely good understanding of the real world based on multiple corpi of text. The most notable way that it chooses to understand the real world is using word2vec which is a software/technique that allows computers to understand context. However, it has a limitation, which is why I had said "no." That limitation is that it struggles with strong antonyms. The reason being is that pure antonyms often share the same verbal context. For example, NLP software struggles with understanding the words "like" and "hate," because they are often used in similar sentences. Furthering that example, take a look at the following sentences:

"I like ice cream, because it is a cold food."

"I hate ice cream, because it is a cold food."

Both sentences drastically mean 2 different things, but the context of the words "like" and "hate" are exactly the same, therefore word2vec is incompetent at fully understanding the real world.

But can this be solved? I believe so. The problem with the NLP approach to allowing computers to understand the real world is that it is limited to the verbal world, and does nothing to incorporate outside contexts such as basic visual, contact, and auditory to cues or complex emotional and neurological cues.

What my plans were for future research:

Sentiment analysis - aids NLP by adding emotional context

Robotics - aids overall AI by adding basic visual, contact, and auditory context.

-Recap checkpoint:

Programmer/engineer -(needs to be)-> creative -(which requires)-> curiosity -(which depends on)-> novelty -(which is defined as)-> being fundamentally similar while being fundamentally different -(which calls forth the ability to)-> compare/contrast -(which requires the ability to)-> define objects -(which depends on)-> the context the object is associated with <-This is where my research left off at, and needs improvement.

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