Module 2 TECH FOUNDATIONS: Fourth Industrial Revolution (4IR) & AI

Artificial Narrow Intelligence (ANI)

• focused on one single narrow task

• it possesses a narrow-range of abilities

• this is the only AI in existence today, for now

• Narrow AI is something most of us interact with on a daily basis

• think of Google Assistant, Google Translate, Siri, Cortana, or Alexa; They are all machine intelligence that use Natural Language Processing (NLP)

Artificial General Intelligence

• when we talk about Artificial General Intelligence (AGI) we refer to a type of AI that is about as capable as a human

• however, AGI is still an emerging field

• since the human brain is the model to creating General Intelligence, it seems unlikely that will happen relatively soon because there is lack of a comprehensive knowledge of the functionality of the human brain

Artificial SuperIntelligence (ASI)

• way into the future, or, that is what we believe

• to reach this point and to be called an ASI, an AI will need to surpass humans at absolutely everything

• the ASI type is achieved when AI is more capable than a human

Artificial Neural Networks (ANN)

• a core algorithm that enables enhanced decision making and interaction

• network of neurons, modeled on how the brain functions

• neurons can be on or off, and pass signals from one layer to the next.

Machine Capabilities that Emulate Human Cognition and Communication

1. Enhanced decision-making

2. Human-centered interaction

3. Generativity

4. Extensible resource sharing

Backpropogation

the process of the neural network working backwards to adjust weights proportionately

Regularlization

• techniques used to fine-tune machine learning models to minimize the adjusted loss function and prevent overfitting or underfitting

• using regularization, we can fit machine learning model appropriately on a given test set and reduce the errors

Activation Function

how the weighted sum of the input is transformed into an output from a node or nodes in a layer of the network

Features

• Each feature or a column in a data set represents a measurable piece of data that can be used for analysis: Name, Age, Sex, Fare, and so on

• Features are also sometimes referred to as “variables” or “attributes”

• Depending on what you’re trying to analyze, the features you include in your dataset can vary widely

Classification

• the algorithm assigns labels to data based on the predefined features

• inputs are classified or categorized

• this is an example of supervised learning; it generalizes existing patterns (i.e., labels) to new data

Clustering

• an algorithm splits data into several clusters based on the similarity of features

• this is an example of unsupervised learning; it discovers new patterns in existing data

Regression

• regression algorithms try to find a relationship between variables and predict unknown dependent variables based on known data

• it is based on supervised learning.

Logistics Regression

• a classification algorithm

• used to predict a binary outcome based on a set of independent variables