Demystifying Genius: How AI Neural Networks and Emotional Intelligence Interact

In our brain, we have something called neurons, a type of cells that can send and receive messages.

Our neurons (billions of them) come together to form a neural network that transmits our thoughts, emotions, feelings, and actions.

When we become thirsty while sitting at our desks, our neural network starts sending and receiving impulses that trigger a series of actions into motion.?

We will move our feet closer together, lean our torso forward, and start pushing on our legs and feet to unbend our knees so we can stand up and start walking left foot and right foot towards the kitchen.?

Once in the kitchen, we lift our arms, open our hands, and use our fingers to open the cupboard to fetch a glass that we will fill with a liquid to quench our thirst.

Between the moment we were thirsty (input) and drank our liquid (output), our neural network involves many steps.?

Artificial intelligence also relies on a neural network to send and receive messages.?

In AI, the steps involved between input and output are called layers.

The more layers AI has, the more complex operations it can perform.

Deep Learning (DL) is a term that refers to the depth (complexity) of a neural network in AI.?

This article explains the nine layers involved in an AI neural network. We will define each layer and how they work before transposing them into a framework to show how AI neural networks and emotional intelligence interact.?        

Depiction Accuracy: the Elephant, the Tutu, and the Umbrella

The Human Brain

While in elementary school, I learned my numbers and their order by playing a game called Connect The Dots. The dots were numbers (1, 2, 3, etc.) placed in random order on a white sheet of paper.

The first thing I did was find the number 1. Then I looked for the number 2. Once I knew where it was on the sheet, I drew a line connecting 1 and 2. After that, I looked for the number 3 and drew a line between the numbers 2 and 3, and so on.

Once I had connected all the numbers correctly, I got a drawing!?

One of those drawings was an elephant standing on one foot while wearing a tutu and holding an umbrella! It was magnificent, and the kid in me liked that image very much.?

The AI Brain

The AI brain passes a picture through a nine-layer process to determine what is on the image. Let us see how AI would assess the elements in our Connect The Dots (CTD) picture with the elephant, the tutu, and the umbrella.

Convolutional Layer

This layer has learnable filters. Learnable filters look for simple patterns (shapes, lines, etc.) and create a map to remember where those patterns are on the image.?

(CTD) AI scans a white sheet of paper and notes three categories: straight lines, shapes, and numbers. It creates a map showing where those categories are on the sheet.

Activation Layer

This layer makes decisions based on the data received from the layer above. It says yes or no to allow or stop certain things from happening.?

(CTD) While AI connects the dots to get the elephant in a tutu picture, all the numbers (1,2,3, etc.) are a yes, and the shapes/lines are a no. You will not see AI connect the number 2 to an eyeball.?

Pooling Layer

This layer goes one level deeper than the layer above. It divides the picture into a grid and examines/summarizes one grid section at a time.?

(CTD) AI looks at the tutu section, goes into its data bank, and retrieves similar pictures until it can say, This is a tutu. But what would happen if the grid section was a mesh of colourful paint strokes? How would AI be able to tell what is there? AI would look at what is different. For example, it might say, This section has more blue strokes than red strokes.

Repeat Convolutional and Pooling Layers

These two layers can be stacked multiple times to form a deeper neural network. Each layer further examines each grid section to get a more accurate picture.?

(CTD) AI now knows the elephant to have two eyes, a smile, and two ears flapping open like Dumbo, the elephant!

Flattening Layer

This process groups things in a way that makes more sense. All the elements in the grid get flattened into a long, single list with a one-liner to describe it.

(CTD) AI pools all the numbers (1,2,3,4,5, etc.) into one ‘flat’ line and says, This picture contains the numbers 1 to 100. That sentence becomes a single vector with a lot of information.

Fully Connected Layers

Each vector gets connected to one or more layers to allow for higher-level reasoning and decision-making.?

(CTD) AI looks at the picture in new ways. Here is an answer ChatGPT gave me: Elephant ballet: the act of putting elephants in a tutu.?

Output Layer

This layer gives us a final answer. AI looks at all the information it has received from all the layers and tells us its conclusion.?

(CTD) AI looks at the elephant, the tutu, the umbrella, the elephant standing on one foot, the elephant wearing a tutu, the elephant holding an umbrella, etc., and replies, 'This is an elephant standing on one foot while wearing a tutu and holding an umbrella.'

Training Layer

This level adjusts internal parameters (weights and biases). AI considers how many accurate answers it has. The backpropagation algorithm goes back and updates all the parameters layer by layer for AI to get more and more accurate answers in the future.

(CTD) AI relies on feedback to get more accurate answers. What if you looked at the picture and decided the elephant looked more like a giraffe? In that case, you could tell AI it got it wrong.

Inference Layer

Once its training is over, we can use AI to make predictions.?

(CTD) We could ask the AI, What colour is the tutu? And AI might infer that tutus are usually pink.?Or it might ask, Who is Dumbo?

Humans and AI Deep Learning: Eliciting Strokes of Genius

Awareness (Convolutional Layer)

Awareness is about noticing our learnable filters (thoughts, words, feelings, decisions, etc.) It is about looking for the patterns in our life picture. Those patterns may or may not be strokes of genius. At this point, we are collecting data.

Intent (Activation Layer)

Our intent depends on the level of awareness that we have. We say yes or no to allow or stop certain things from happening. Our intention shows whether we are being open-minded or acting closed-minded.

Self-reflection (Pooling Layer)

Self-reflection goes deeper than intent. It questions the quality of our intention and the quality of our awareness. At this level, we become more mindful of the meanings we assign and more open to receiving outside perspectives.

Repeat Awareness and Self-reflection (Repeat Convolutional and Pooling Layers)

Awareness and self-reflection can be stacked multiple times, forming a neural network. Each layer dives deeper into its analysis of each part of our life to get a more accurate picture of our life.

We need more self-reflection to develop more self-awareness. And we need more self-awareness to invite more self-reflection, and so on. It is an open loop.

Discernment (Flattening Layer)

This process is about organizing things more simply in our lives.

Discernment is about establishing a vector of truth: it is what works for us at an emotional level based on what we have learned and experienced.

We take all the collected data and derive our truth from that process. A single vector of truth expresses much information about our level of self-knowledge (more on that in a moment).

External Accountability (Fully Connected Layers)

We connect our discernment to more layers, which act as a traditional neural network.

At this level, we rely on external accountability (having a mentor) to help us look at the “old” picture of our life (what we think we know) in new ways.

Simply put, we go beyond what we think our life is. We start generating new meanings.?

Self-knowledge (Output Layer)

Self-knowledge is the part of our process that gives us the “final” answer or prediction about who we are and what we do.

Self-knowledge looks at all the information it has received from all the other layers and gives its full answer. It tells us what it has learned from the data it has examined about us. At this level, we declare, “This is who I am and what I do.”

Integration (Training Layer)

During integration, we take the elements in our life picture and adjust our internal parameters (weights and biases) to experience more of what we want.

With the help of our mentor (external accountability), we go back (revisit) past experiences and update our parameters (weights and biases) layer by layer to get more and more of what we want to experience in the future.

Eliciting strokes of genius (Inference Layer)

At this level, we use our neural network to infer (make predictions) about new, unseen images. We elicit strokes of genius in our life and the life of others. We awaken, uplift, and inspire. We seek to feel fulfilled and build a bold legacy that is a testament to our unwavering spirit. We become genuinely soulful.

This article showed how AI neural networks and emotional intelligence interact. Reach out when ready to take this discussion deeper for yourself and your organization.

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Emotional Tech? Engineer

Human-Centric AI Advocacy | Generative AI | Responsible AI?

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