We are further down the road to A.I. As we grow in understanding, so, too, do we grow in understanding its differences.

In 2020, we can classify artificial intelligence into 4 different categories. The categories are loosely similar to Maslow's hierarchy of needs, where the simplest level requires only basic functioning and the highest level is Muhammad, Buddha, Christian saint, omniscient, all-seeing, Self-awareness is consciousness.

Four A.I. There are types

1 Reactive machines

2 Limited memory

3 Theory of Mind

4 Self-aware

Reactive machines

Reactive machines perform basic functions. This level of A.I is the easiest. These types react to certain inputs with certain outputs. There is no learning that happens. This is the first step in any A.I. A machine learning system that takes a human face as input and outputs a box around the face to identify it as a face is a simple, reactive machine. A model does not store any input, it does not do any learning.

Static machine learning models are reactive machines. Their architecture is the simplest and can be found in GitHub repos all over the web. These models can be easily downloaded, traded, passed around and loaded into a developer's toolkit.

Limited memory

Limited types of memory refer to the AI's ability to store previous data and/or predictions, using that data to make better predictions. With limited memory, the machine learning architecture becomes a bit more complex. Each machine learning model requires limited memory to build, but the model can be deployed as a type of reactive machine.

There are three major categories of machine learning models that capture this type of limited memory:

Reinforcement learning

These models learn to make better predictions through several cycles of trial and error. This type of model is used to teach computers how to play games like chess, Go, and DOTA2.

Long Short Term Memory (LSTMs)

The researchers reasoned that past data would help predict the sequence of next items, especially in language, so they developed a model called long-short-term memory. To predict the next elements in a sequence, LSTM tags recent information as more important and past items as less important.

Evolutionary Generative Adversarial Networks (E-GAN)

The memory of E-GAN is such that it evolves on every evolution. The model produces a type of growing object. Growing things don't follow the same path every time, the paths are slightly modified because statistics is the math of chance, not the math of accuracy. In modification, the model can find a better path, the path of least resistance. The next generation of models changes and evolves along the path that its ancestors found in error.

In a way, E-GAN creates a simulation of the evolution of humans on this planet. Each child, in perfect, successful reproduction, is better equipped to lead an extraordinary life than its parents.

Practically limited types of memory

Although every machine learning model is created using limited memory, they are not always built that way when deployed.

Limited memory A.I. Works in two ways:

A team continuously trains a model on new data.

A.I. The environment is designed in such a way that models are automatically trained and updated based on model usage and behavior.

For a machine learning infrastructure to maintain a limited memory type, the infrastructure needs to incorporate machine learning into its architecture.

Theory of Mind

We have yet to get to Theory of Mind types of artificial intelligence. These are only in their early stages and can be seen in things like self-driving cars. In this type of A.I., A.I. Interactions begin with human thoughts and emotions.

Currently, machine learning models do much more than guide a person to achieve a task. Current models have a one-way relationship with A.I. Alexa and Siri bow to every command. If you angrily yell at Google Maps to take you in another direction, it doesn't provide emotional support and say, "This is the fastest direction. Who do I call to tell you that you're going to be late?" ?" Google Maps, instead, continues to return the same traffic reports and ETAs

Self-aware

Eventually, in some distant future, perhaps A.I. attains Nirvana. It becomes self-aware. This type of A.I. exists only in the story, and as stories often do, creates immense hope and fear in the audience. A superhuman self-aware intelligence has an independent intelligence, and it is likely that humans will have to negotiate terms with the entity it created. What happens, good or bad, is anyone's guess.