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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
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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

Who Will Be The Useless Species of 2050? Human Intelligence v/s Artificial Intelligence

Hrishabh
Hrishabh Last Updated : 17 Mar, 2021
7 min read
This article was published as a part of the Data Science Blogathon.

Overview

In this article,

  • You will travel with me through various chapters from our history of humankind and collect shreds of evidence on the impact of Artificial Intelligence in human lives.
  • I will try to describe our current situation and our moderately possible future, based on these shreds of evidence and my understanding of the humanist creed.

So let’s come to our first question.

 

Is This Article For Me?

If you are intrigued by reading about a moderately possible future— that involves heavy co-dependency of carbon-based human beings on highly intelligent algorithms housed in silicon bodies — based on our habits from the past 2 centuries, then you will enjoy reading this article with a cup of your favorite drink.

Artificial Intelligence Human should I read

 

What Is This Article About?

This article is about a future that is based on our ideas and hopes of the past 200 years. Obviously, the real future could be something absolutely different, but even if there is a teeny-tiny bit of possibility, I am delighted to write about it.

Now, for some people, it may sound a bit pessimistic but it’s best not to jump to conclusions. Our planet has witnessed the rise and fall of many cultures and civilizations. Egypt was continually governed, by native pharaohs for approximately 2500 years. If someone could break it out to an Egyptian, in the time of Cleopatra VII, that soon the Egyptian empire will be gone, he would probably have been horrified. [1]

So here is a thought to start with-

 

What Will Happen When Artificial Intelligence Will Do Practically Everything that Human does?

Artificial Intelligence Human What if..?

To answer this question, let us rewind, to a time when we could greet each other by shaking hands. Actually, a bit more. I want you to meet me at the inception of the 19th CE. Most of the economies in the world were divided into 3 sectors:

  1. Agriculture
  2. Industry
  3. Services

The majority of the population were engaged in agricultural activities, that provided them with direct employment to sustain themselves and their families. There was a minority of the population who were dependent on industry and services for their livelihood.

A few years later, we witnessed the Industrial Revolution in many countries. People migrated from their field flocks to the new mechanized factories. The revolution augmented job opportunities in the industrial sector as well as the service sector.

Now, let us fast-forward time, in 2x, to the beginning of the 21st Century, developed nations underwent another revolution wherein industrial jobs evaporated and jobs opportunities in the service sector hail-stormed.

As of the year 2010, 2% of the population living in the United States of America was working in the agriculture sector, 20% of the inhabitants were working in the industry and a whopping 78% of the people were employed as teachers, doctors, full-stack developers, graphic designers and in other service-based jobs.

Based on this trend from the last 2 centuries, have you wondered:

 

What Will Happen When Artificial Intelligence Starts Educating, Diagnosing, and Designing Better Than a Human?

Now, this is not a brand new question. You and I have seen such questions before. For instance, following the era of the industrial revolution, people protested that mechanization might cause mass unemployment and chaos. But we witnessed old jobs becoming obsolete and new jobs emerging and evolving!

And people? People comfortably urbanized society to what it is today.

But I have heard many people — and I believed in it too — say,

“There Is Always Something That Humans Can Do Better Than Machines”

But this is not a law of nature. This might not be permanent.

Let us look at ourselves from scratch. Broadly categorized, we have 2 major types of abilities:

  • Cognitive Abilities: Brain-based skills that we need to carry out any task from the simplest to the most complex problems.
  • Physical Abilities: Our ability to perform some physical activity without much application of brain-based skills.

Machines made our physical tasks easier and made us concentrate more on our cognitive abilities. But, imagine a day when machines overtake us in retaining, recognizing, and understanding patterns.

I know what you might be thinking right now,

“Hey Hrishabh, there are numerous things humans are better at than machines thanks to our unique cognitive abilities. After all, we are incrementally superior to any other animal or hominid on the surface of this planet!”

But before we come to any conclusion, let us go out for a spin around our recent history.

Chess: The Biometric for Human Intelligence

In the 1980s, people boasted the unique nature of humanity by habitually showcasing chess as an endorsement of human superiority. They concluded that machines could never conquer humans in this game.

Skipping to February of 1997, IBM’s supercomputer called Deep Blue defeated Garry Kasparov at the heart of New York City. [2]

A similar account in the world of Go (game), Google’s AlphaGo taught itself how to play Go (game) and defeated the South Korean Go champion Lee Sedol in March 2016. [3]

Baseball: The Curious Case of Moneyball

Ever since the 19th CE, professional baseball managers and scouts have used their instincts, wisdom, experience, and expertise in the game to recruit and manage their players. They believed the selection of baseball players is an art that only intimate and highly experienced individuals can master. A mere machine or a computer program could never unlock the secrets and spirit of baseball.

In 2002, Billy Beane of the Oakland Athletics decided to break the system and introduced an algorithm developed by statisticians and computer geeks to create a team of underdogs who were overlooked by the managers. The result? Oakland Athletics became the first team in the American League history to ever win 20 consecutive games. [4]

Facial Recognition: The Birthright of Human Intelligence

According to research by Stanford, face recognition is one of the earliest features to develop in an infant’s vision system. [5]

For a long time, facial recognition had been considered as an iron fence between most powerful computers and humans.

Today, facial recognition programs are widely used by intelligence services and police forces for scanning countless hours of surveillance content as they are far more efficient and quicker than humans ever were.

Transportation: Enter Elon Musk

In 2004, Professors from MIT and Harvard published research on the job market wherein they categorized truck driving as an example of a job that did not have any opportunities for substitution by automation in the foreseeable future. [6]

Today, companies like Tesla are not only promoting this but making it a reality, by implementing it as well. [7]

Corporation: Ai On Board

Being a board member for a company or organization is a sophisticated portfolio. One needs to demonstrate the breadth and depth of management and leadership qualities along with business acumen and relevant industry experience.

But, in May 2014, Hong Kong-based Deep Knowledge ventures appointed an algorithm called VITAL to its board who gets to vote on whether or not the firm should invest in a specific company, based on a meticulous analysis of huge amounts of data. [8]

Doctor iPhone

In 2015, researchers at Yale University successfully demonstrated the use of artificial pancreas. A tiny sensor along with a tiny pump, connected to a small tube of insulin was installed in the patients’ abdomen. The sensor was responsible for measuring and reporting the sugar levels of the patient to an iPhone. The iPhone hosted an application that analyzed information and gave orders to the sensor which in return injected controlled amounts of insulin in the abdomen of patients without any help from humans. [9]

Therapy: Dr (IBM) Watson

When was the last time you visited a therapist? I hope you did not have to. But you might have been to some therapist at some point in your life. (Even if you haven’t trust me, you haven’t missed out on anything.)

So what does a therapist do? He understands your emotional state by examining external signals such as your facial expressions and the tone of your voice.

IBM’s Watson is an Artificial Intelligence that can not only analyze your external signals more precisely than a doctor but it can also concurrently analyze various internal indicators that are normally hidden from the human ears and eyes. [10]

Music is Art

Yes! Music is Art. And according to Merriam-Webster: “Art is the conscious use of skill and creative imagination especially in the production of aesthetic objects”

It reflects cultural values, it chronicles people’s experiences in life over time. It needs emotions. Art is uniquely human.

Really?

Take a second. Assume that Artificial Intelligence takes over as our doctors, as our drivers, as our teachers, they even handle our accounts and even our legal cases. So what exactly are we left with? Art seems like a virgin profession. What if everyone tries to become an artist? I do not believe so.

In 1989, a professor from the University of California developed an algorithm called EMI. During a blind test, EMI’s compositions were praised precisely for their soulfulness and emotional resonance, even more than the original artist’s composition. [11]

How Will Artificial Intelligence Affect Us?

There is no question that the upcoming technological advancements constructed over the foundation of Artificial Intelligence will make our lives easier. It might even be possible for Artificial Intelligence to feed and support humans without much effort from their side. But the crucial question is:

What will keep us occupied and content?

Drugs and Virtual Reality Worlds might keep us engaged and provide far more intense and exciting engagements than the dull reality outside but without work and purpose in our lives, we might feel worthless and yes, might as well end up becoming useless.

 

How Can We Avoid Being The Useless Class of 2050?

As of the time I am writing this article, Artificial Intelligence is nowhere close to existence as a human, but more than 95% of human traits and skills are inefficient in finishing most modern jobs. For Artificial Intelligence to squeeze us out of our jobs, is by only outperforming our abilities at a particular profession.

So our objective should not revolve about creating new jobs for Human Intelligence. Our primary objective should be to create new jobs where Human Intelligence can perform better than Artificial Intelligence.

Since you and I have no idea about the circumstances of the job market in 2050, we have no clue so as to what to teach our future generation. Most of the knowledge that they acquire in their schooling days, might be obsolete by the time they turn 30.

Broadly categorizing, our life consists of 2 stages:

  • The Theoretical Stage: where we learn and attain knowledge at school and colleges
  • The Practical Stage: where we work and apply our knowledge in real projects.

Soon a time might come when these 2 stages of life become obsolete and the only way to succeed in life will be by constantly learning and developing our skills, throughout our lives.

 

References

  • Linked at respective reference points.

Thanks For Reading, Have a Great Coffee! ☕

About Me

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

Hrishabh
Hrishabh

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SkillsStreet
SkillsStreet

The human race's dwindling capabilities is not something that has been thought of in the modern world. The most intelligent and technologically advanced society in the history of mankind, it is easy to assume that their dominance will never cease to exist. However, as technology continues to improve at a rapid rate it may be possible for robots and other artificial intelligence programs such as AlphaGo to take over from them.

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