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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

The Technology of Automation and Artificial Intelligence

Mobarak Inuwa
Mobarak Inuwa Last Updated : 27 Jun, 2022
4 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Automation and intelligence are two technologies that are widely used in modern systems, making them cutting-edge. Without the two technologies of automation and intelligence, a computer system or program will revert to outdated design and development concepts, which are currently obsolete and less productive.

The two concepts are frequently confused as one and can be interchanged. However, the two technologies are distinct and should be used in different ways depending on the situation. In some cases, the two may be used interchangeably, while in others, they may be quite distinct.

Both intelligence and automation can be combined in a single system, as the case may be. In this case, intelligence could lead to automation. The system is designed to run and do things on its own, without the need for assistance, direct action, or external control.

Automation

What is Automation?

Automation refers to a system’s ability to move, function, or perform on its own without being controlled externally, by utilizing its internal capabilities and a set of predetermined variables. This definition defines automation as the ability to be self-sufficient and independent. When a single system can perform all of its expected tasks using all of its sub-systems without external assistance, it is considered automated in this capacity.

What is Intelligence?

The ability of a system to function or perform on its own without being controlled externally in the presence of changing or random variables is known as intelligence. Unlike automation, intelligence is distinguished by its ability to function and perform in unexpected situations. Unexpected circumstances are similar to random variables that the system takes in while performing its expected tasks.

The ability to use random variables or unexpected circumstances distinguishes intelligence from mere automation. Take, for example, a self-driving car. The car should be able to move on its own without the need for a human driver to turn the steering wheel or apply the brakes. It should be capable of doing all of this on its own; this is referred to as automation. But, in order to make the self-driving car a so-called cutting-edge technology, we also need to be able to teach it when to perfectly hold breaks or make turns, regarding the constant dynamics or changing situations on the highway. Intelligence will be needed to accomplish this.

Automation

This same self-driving car could be designed to perform its function simply by moving a fixed distance. We’ll be able to accomplish this by simply automating the process by using a fixed or set of fixed paths between the two distances.

Without intelligence, the car may lose control and fall off the track if an unexpected item enters the picture. Intelligence is the feature that allows the car to, for example, hold the break even if an unexpected object is in the way. The car can make random dodges and miss obstacles as they come into its path, depending on the sophistication of the system. In addition to automation and fixed variables, this will necessitate the ability to work with random variables and repetitive circumstances.

When to use Intelligence?

Automation does not require intelligence and does not yet constitute artificial intelligence. Automation simply goes about its business based on preprogrammed, expected fixed conditions. We don’t always need intelligence in our systems. In any system, implementing intelligence can be very costly and complicated. As a result, we may be able to achieve our goal solely through automation in some problem domain. Only when dealing with an open system that is dynamic and constantly unpredictable should we use intelligence.

Let’s look at a car manufacturing system as an example. The assembling machine is housed in a room dedicated to this purpose by the company. It is common for businesses to declare the environment restricted and only allow employees access. As a result, the factory becomes a functioning closed system in which everything is expected and fixed. In an assembly line, for example, attaching doors is a repetitive task. The machines can simply be set to the correct lengths and angles and all will be well.

If the task is repetitive, an automated set of machines set to move and turn at fixed lengths and angles may just do the trick. This same machine may fail if it is expected to operate outside of the factory or if an unexpected event occurs that was not measured or factored into the system’s design.

This same machine can be improved to be more capable of handling even the most unusual scenarios. This is when intelligence emerges.

Intelligence, on the other hand, necessitates automation. In most cases, we can even say that intelligence’s goal is automation. Except that in this case, the automation will be intelligent, because automation in general refers to the concept of independence and self-control.

Conclusion

To summarize, intelligence is the combination of independence and automation in a single system. A robot, for example, could be intelligent or not, but it must be automated. A smart robot is one that uses intelligence, and the extent to which this is implemented determines the robot’s level of sophistication. We can say that a robot is more often associated with automation than intelligence.

A robot is a mechanical system that can move independently while performing a task, and an advancement to this system will be the addition of intelligence to its functionality.

A smart system is a term used in the market to describe a system that has a high level of intelligence. One who is able to adapt and perform admirably in a variety of situations. A hybrid system with cutting-edge concepts involving intelligence and automation will be considered advanced.

Key take away:

1. The terms “automation” and “intelligence” are not interchangeable.

2. A single system can contain both automation and intelligence.

3. Intelligence is not required or necessary for automation.

4. Intelligence requires automation.

5. Independence is achieved through automation.

6. Intelligence entails autonomy and automation.

7. Robots use automation, which can be intelligent or not.

8. An intelligent and automated system is referred to as a smart system.

9. A sophisticated system combines automation and intelligence.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Mobarak Inuwa
Mobarak Inuwa

I am an AI Engineer with a deep passion for research, and solving complex problems. I provide AI solutions leveraging Large Language Models (LLMs), GenAI, Transformer Models, and Stable Diffusion.

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Responses From Readers

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Kalvin
Kalvin

Rich content. Was helpful in seeing the contrast between AI and automation. Thanks for updating

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