# Practical Machine Learning with R and Python – Part 2

In this 2nd part of the series “Practical Machine Learning with R and Python – Part 2”, I continue where I left off in my first post Practical Machine Learning with R and Python – Part 2. In this post I cover the some classification algorithmns and cross validation. Specifically I touch
-Logistic Regression
-K Nearest Neighbors (KNN) classification
-Leave out one Cross Validation (LOOCV)
-K Fold Cross Validation
in both R and Python.

As in my initial post the algorithms are based on the following courses.

You can download this R Markdown file along with the data from Github. I hope these posts can be used as a quick reference in R and Python and Machine Learning.I have tried to include the coolest part of either course in this post.

1. Machine Learning in plain English-Part 1
2. Machine Learning in plain English-Part 2
3. Machine Learning in plain English-Part 3

Check out my compact and minimal book  “Practical Machine Learning with R and Python:Third edition- Machine Learning in stereo”  available in Amazon in paperback($12.99) and kindle($8.99) versions. My book includes implementations of key ML algorithms and associated measures and metrics. The book is ideal for anybody who is familiar with the concepts and would like a quick reference to the different ML algorithms that can be applied to problems and how to select the best model. Pick your copy today!!

The following classification problem is based on Logistic Regression. The data is an included data set in Scikit-Learn, which I have saved as csv and use it also for R. The fit of a classification Machine Learning Model depends on how correctly classifies the data. There are several measures of testing a model’s classification performance. They are

Accuracy = TP + TN / (TP + TN + FP + FN) – Fraction of all classes correctly classified
Precision = TP / (TP + FP) – Fraction of correctly classified positives among those classified as positive
Recall = TP / (TP + FN) Also known as sensitivity, or True Positive Rate (True positive) – Fraction of correctly classified as positive among all positives in the data
F1 = 2 * Precision * Recall / (Precision + Recall)

## 1a. Logistic Regression – R code

The caret and e1071 package is required for using the confusionMatrix call

source("RFunctions.R")
library(dplyr)
library(caret)
library(e1071)
# Read the data (from sklearn)
# Rename the target variable
names(cancer) <- c(seq(1,30),"output")
# Split as training and test sets
train_idx <- trainTestSplit(cancer,trainPercent=75,seed=5)
train <- cancer[train_idx, ]
test <- cancer[-train_idx, ]

# Fit a generalized linear logistic model,
fit=glm(output~.,family=binomial,data=train,control = list(maxit = 50))
# Predict the output from the model
a=predict(fit,newdata=train,type="response")
# Set response >0.5 as 1 and <=0.5 as 0
b=ifelse(a>0.5,1,0)
# Compute the confusion matrix for training data
confusionMatrix(b,train$output) ## Confusion Matrix and Statistics ## ## Reference ## Prediction 0 1 ## 0 154 0 ## 1 0 272 ## ## Accuracy : 1 ## 95% CI : (0.9914, 1) ## No Information Rate : 0.6385 ## P-Value [Acc > NIR] : < 2.2e-16 ## ## Kappa : 1 ## Mcnemar's Test P-Value : NA ## ## Sensitivity : 1.0000 ## Specificity : 1.0000 ## Pos Pred Value : 1.0000 ## Neg Pred Value : 1.0000 ## Prevalence : 0.3615 ## Detection Rate : 0.3615 ## Detection Prevalence : 0.3615 ## Balanced Accuracy : 1.0000 ## ## 'Positive' Class : 0 ## m=predict(fit,newdata=test,type="response") n=ifelse(m>0.5,1,0) # Compute the confusion matrix for test output confusionMatrix(n,test$output)
## Confusion Matrix and Statistics
##
##           Reference
## Prediction  0  1
##          0 52  4
##          1  5 81
##
##                Accuracy : 0.9366
##                  95% CI : (0.8831, 0.9706)
##     No Information Rate : 0.5986
##     P-Value [Acc > NIR] : <2e-16
##
##                   Kappa : 0.8677
##  Mcnemar's Test P-Value : 1
##
##             Sensitivity : 0.9123
##             Specificity : 0.9529
##          Pos Pred Value : 0.9286
##          Neg Pred Value : 0.9419
##              Prevalence : 0.4014
##          Detection Rate : 0.3662
##    Detection Prevalence : 0.3944
##       Balanced Accuracy : 0.9326
##
##        'Positive' Class : 0
##

## 1b. Logistic Regression – Python code

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
os.chdir("C:\\Users\\Ganesh\\RandPython")
from sklearn.datasets import make_classification, make_blobs

from sklearn.metrics import confusion_matrix
from matplotlib.colors import ListedColormap
(X_cancer, y_cancer) = load_breast_cancer(return_X_y = True)
X_train, X_test, y_train, y_test = train_test_split(X_cancer, y_cancer,
random_state = 0)
# Call the Logisitic Regression function
clf = LogisticRegression().fit(X_train, y_train)
fig, subaxes = plt.subplots(1, 1, figsize=(7, 5))
# Fit a model
clf = LogisticRegression().fit(X_train, y_train)

# Compute and print the Accuray scores
print('Accuracy of Logistic regression classifier on training set: {:.2f}'
.format(clf.score(X_train, y_train)))
print('Accuracy of Logistic regression classifier on test set: {:.2f}'
.format(clf.score(X_test, y_test)))
y_predicted=clf.predict(X_test)
# Compute and print confusion matrix
confusion = confusion_matrix(y_test, y_predicted)
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('Accuracy: {:.2f}'.format(accuracy_score(y_test, y_predicted)))
print('Precision: {:.2f}'.format(precision_score(y_test, y_predicted)))
print('Recall: {:.2f}'.format(recall_score(y_test, y_predicted)))
print('F1: {:.2f}'.format(f1_score(y_test, y_predicted)))
## Accuracy of Logistic regression classifier on training set: 0.96
## Accuracy of Logistic regression classifier on test set: 0.96
## Accuracy: 0.96
## Precision: 0.99
## Recall: 0.94
## F1: 0.97

## 2. Dummy variables

The following R and Python code show how dummy variables are handled in R and Python. Dummy variables are categorival variables which have to be converted into appropriate values before using them in Machine Learning Model For e.g. if we had currency as ‘dollar’, ‘rupee’ and ‘yen’ then the dummy variable will convert this as
dollar 0 0 0
rupee 0 0 1
yen 0 1 0

## 2a. Logistic Regression with dummy variables- R code

library(dummies)

# Remove rows which have NA
df1 <- df[complete.cases(df),]
dim(df1)
## [1] 30161    16
# Select specific columns
capital.loss,hours.per.week,native.country,salary)
# Set the dummy data with appropriate values

#Split as training and test

# Fit a binomial logistic regression
fit=glm(salary~.,family=binomial,data=train)
# Predict response
a=predict(fit,newdata=train,type="response")
# If response >0.5 then it is a 1 and 0 otherwise
b=ifelse(a>0.5,1,0)
confusionMatrix(b,train$salary) ## Confusion Matrix and Statistics ## ## Reference ## Prediction 0 1 ## 0 16065 3145 ## 1 968 2442 ## ## Accuracy : 0.8182 ## 95% CI : (0.8131, 0.8232) ## No Information Rate : 0.753 ## P-Value [Acc > NIR] : < 2.2e-16 ## ## Kappa : 0.4375 ## Mcnemar's Test P-Value : < 2.2e-16 ## ## Sensitivity : 0.9432 ## Specificity : 0.4371 ## Pos Pred Value : 0.8363 ## Neg Pred Value : 0.7161 ## Prevalence : 0.7530 ## Detection Rate : 0.7102 ## Detection Prevalence : 0.8492 ## Balanced Accuracy : 0.6901 ## ## 'Positive' Class : 0 ## # Compute and display confusion matrix m=predict(fit,newdata=test,type="response") ## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ## ifelse(type == : prediction from a rank-deficient fit may be misleading n=ifelse(m>0.5,1,0) confusionMatrix(n,test$salary)
## Confusion Matrix and Statistics
##
##           Reference
## Prediction    0    1
##          0 5263 1099
##          1  357  822
##
##                Accuracy : 0.8069
##                  95% CI : (0.7978, 0.8158)
##     No Information Rate : 0.7453
##     P-Value [Acc > NIR] : < 2.2e-16
##
##                   Kappa : 0.4174
##  Mcnemar's Test P-Value : < 2.2e-16
##
##             Sensitivity : 0.9365
##             Specificity : 0.4279
##          Pos Pred Value : 0.8273
##          Neg Pred Value : 0.6972
##              Prevalence : 0.7453
##          Detection Rate : 0.6979
##    Detection Prevalence : 0.8437
##       Balanced Accuracy : 0.6822
##
##        'Positive' Class : 0
##

## 2b. Logistic Regression with dummy variables- Python code

Pandas has a get_dummies function for handling dummies

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
# Drop rows with NA
df1=df.dropna()
print(df1.shape)
# Select specific columns
'hours-per-week','native-country','salary']]

'hours-per-week','native-country']]
# Set approporiate values for dummy variables

random_state = 0)

# Compute and display Accuracy and Confusion matrix
print('Accuracy of Logistic regression classifier on training set: {:.2f}'
print('Accuracy of Logistic regression classifier on test set: {:.2f}'
confusion = confusion_matrix(y_test, y_predicted)
print('Accuracy: {:.2f}'.format(accuracy_score(y_test, y_predicted)))
print('Precision: {:.2f}'.format(precision_score(y_test, y_predicted)))
print('Recall: {:.2f}'.format(recall_score(y_test, y_predicted)))
print('F1: {:.2f}'.format(f1_score(y_test, y_predicted)))
## (30161, 16)
## Accuracy of Logistic regression classifier on training set: 0.82
## Accuracy of Logistic regression classifier on test set: 0.81
## Accuracy: 0.81
## Precision: 0.68
## Recall: 0.41
## F1: 0.51

## 3a – K Nearest Neighbors Classification – R code

The Adult data set is taken from UCI Machine Learning Repository

source("RFunctions.R")
# Remove rows which have NA
df1 <- df[complete.cases(df),]
dim(df1)
## [1] 30161    16
# Select specific columns
capital.loss,hours.per.week,native.country,salary)
# Set dummy variables

#Split train and test as required by KNN classsification model
train.X <- train[,1:76]
train.y <- train[,77]
test.X <- test[,1:76]
test.y <- test[,77]

# Fit a model for 1,3,5,10 and 15 neighbors
cMat <- NULL
neighbors <-c(1,3,5,10,15)
for(i in seq_along(neighbors)){
fit =knn(train.X,test.X,train.y,k=i)
table(fit,test.y)
a<-confusionMatrix(fit,test.y)
cMat[i] <- a$overall[1] print(a$overall[1])
}
##  Accuracy
## 0.7835831
##  Accuracy
## 0.8162047
##  Accuracy
## 0.8089113
##  Accuracy
## 0.8209787
##  Accuracy
## 0.8184591
#Plot the Accuracy for each of the KNN models
df <- data.frame(neighbors,Accuracy=cMat)
ggplot(df,aes(x=neighbors,y=Accuracy)) + geom_point() +geom_line(color="blue") +
xlab("Number of neighbors") + ylab("Accuracy") +
ggtitle("KNN regression - Accuracy vs Number of Neighors (Unnormalized)")

## 3b – K Nearest Neighbors Classification – Python code

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler

df1=df.dropna()
print(df1.shape)
# Select specific columns
'hours-per-week','native-country','salary']]

'hours-per-week','native-country']]

#Set values for dummy variables

random_state = 0)

# KNN classification in Python requires the data to be scaled.
# Scale the data
scaler = MinMaxScaler()
# Apply scaling to test set also
# Compute the KNN model for 1,3,5,10 & 15 neighbors
accuracy=[]
neighbors=[1,3,5,10,15]
for i in neighbors:
knn = KNeighborsClassifier(n_neighbors = i)
knn.fit(X_train_scaled, y_train)
accuracy.append(knn.score(X_test_scaled, y_test))
print('Accuracy test score: {:.3f}'
.format(knn.score(X_test_scaled, y_test)))

# Plot the models with the Accuracy attained for each of these models
fig1=plt.plot(neighbors,accuracy)
fig1=plt.title("KNN regression - Accuracy vs Number of neighbors")
fig1=plt.xlabel("Neighbors")
fig1=plt.ylabel("Accuracy")
fig1.figure.savefig('foo1.png', bbox_inches='tight')
## (30161, 16)
## Accuracy test score: 0.749
## Accuracy test score: 0.779
## Accuracy test score: 0.793
## Accuracy test score: 0.804
## Accuracy test score: 0.803

Output image:

## 4 MPG vs Horsepower

The following scatter plot shows the non-linear relation between mpg and horsepower. This will be used as the data input for computing K Fold Cross Validation Error

## 4a MPG vs Horsepower scatter plot – R Code

df=read.csv("auto_mpg.csv",stringsAsFactors = FALSE) # Data from UCI
df1 <- as.data.frame(sapply(df,as.numeric))
df2 <- df1 %>% dplyr::select(cylinder,displacement, horsepower,weight, acceleration, year,mpg)
df3 <- df2[complete.cases(df2),]
ggplot(df3,aes(x=horsepower,y=mpg)) + geom_point() + xlab("Horsepower") +
ylab("Miles Per gallon") + ggtitle("Miles per Gallon vs Hosrsepower")

## 4b MPG vs Horsepower scatter plot – Python Code

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
autoDF.shape
autoDF.columns
autoDF1=autoDF[['mpg','cylinder','displacement','horsepower','weight','acceleration','year']]
autoDF2 = autoDF1.apply(pd.to_numeric, errors='coerce')
autoDF3=autoDF2.dropna()
autoDF3.shape
#X=autoDF3[['cylinder','displacement','horsepower','weight']]
X=autoDF3[['horsepower']]
y=autoDF3['mpg']

fig11=plt.scatter(X,y)
fig11=plt.title("KNN regression - Accuracy vs Number of neighbors")
fig11=plt.xlabel("Neighbors")
fig11=plt.ylabel("Accuracy")
fig11.figure.savefig('foo11.png', bbox_inches='tight')

## 5 K Fold Cross Validation

K Fold Cross Validation is a technique in which the data set is divided into K Folds or K partitions. The Machine Learning model is trained on K-1 folds and tested on the Kth fold i.e.
we will have K-1 folds for training data and 1 for testing the ML model. Since we can partition this as $C_{1}^{K}$ or K choose 1, there will be K such partitions. The K Fold Cross
Validation estimates the average validation error that we can expect on a new unseen test data.

The formula for K Fold Cross validation is as follows

$MSE_{K} = \frac{\sum (y-yhat)^{2}}{n_{K}}$
and
$n_{K} = \frac{N}{K}$
and
$CV_{K} = \sum_{K=1}^{K} (\frac{n_{K}}{N}) MSE_{K}$

where $n_{K}$ is the number of elements in partition ‘K’ and N is the total number of elements
$CV_{K} =\sum_{K=1}^{K} MSE_{K}$

$CV_{K} =\frac{\sum_{K=1}^{K} MSE_{K}}{K}$
Leave Out one Cross Validation (LOOCV) is a special case of K Fold Cross Validation where N-1 data points are used to train the model and 1 data point is used to test the model. There are N such paritions of N-1 & 1 that are possible. The mean error is measured The Cross Valifation Error for LOOCV is

$CV_{N} = \frac{1}{n} *\frac{\sum_{1}^{n}(y-yhat)^{2}}{1-h_{i}}$
where $h_{i}$ is the diagonal hat matrix

see [Statistical Learning]

The above formula is also included in this blog post

It took me a day and a half to implement the K Fold Cross Validation formula. I think it is correct. In any case do let me know if you think it is off

## 5a. Leave out one cross validation (LOOCV) – R Code

R uses the package ‘boot’ for performing Cross Validation error computation

library(boot)
library(reshape2)
df=read.csv("auto_mpg.csv",stringsAsFactors = FALSE) # Data from UCI
df1 <- as.data.frame(sapply(df,as.numeric))
# Select complete cases
df2 <- df1 %>% dplyr::select(cylinder,displacement, horsepower,weight, acceleration, year,mpg)
df3 <- df2[complete.cases(df2),]
set.seed(17)
cv.error=rep(0,10)
# For polynomials 1,2,3... 10 fit a LOOCV model
for (i in 1:10){
glm.fit=glm(mpg~poly(horsepower,i),data=df3)
cv.error[i]=cv.glm(df3,glm.fit)$delta[1] } cv.error ## [1] 24.23151 19.24821 19.33498 19.42443 19.03321 18.97864 18.83305 ## [8] 18.96115 19.06863 19.49093 # Create and display a plot folds <- seq(1,10) df <- data.frame(folds,cvError=cv.error) ggplot(df,aes(x=folds,y=cvError)) + geom_point() +geom_line(color="blue") + xlab("Degree of Polynomial") + ylab("Cross Validation Error") + ggtitle("Leave one out Cross Validation - Cross Validation Error vs Degree of Polynomial") ## 5b. Leave out one cross validation (LOOCV) – Python Code In Python there is no available function to compute Cross Validation error and we have to compute the above formula. I have done this after several hours. I think it is now in reasonable shape. Do let me know if you think otherwise. For LOOCV I use the K Fold Cross Validation with K=N import numpy as np import pandas as pd import os import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.cross_validation import train_test_split, KFold from sklearn.preprocessing import PolynomialFeatures from sklearn.metrics import mean_squared_error # Read data autoDF =pd.read_csv("auto_mpg.csv",encoding="ISO-8859-1") autoDF.shape autoDF.columns autoDF1=autoDF[['mpg','cylinder','displacement','horsepower','weight','acceleration','year']] autoDF2 = autoDF1.apply(pd.to_numeric, errors='coerce') # Remove rows with NAs autoDF3=autoDF2.dropna() autoDF3.shape X=autoDF3[['horsepower']] y=autoDF3['mpg'] # For polynomial degree 1,2,3... 10 def computeCVError(X,y,folds): deg=[] mse=[] degree1=[1,2,3,4,5,6,7,8,9,10] nK=len(X)/float(folds) xval_err=0 # For degree 'j' for j in degree1: # Split as 'folds' kf = KFold(len(X),n_folds=folds) for train_index, test_index in kf: # Create the appropriate train and test partitions from the fold index X_train, X_test = X.iloc[train_index], X.iloc[test_index] y_train, y_test = y.iloc[train_index], y.iloc[test_index] # For the polynomial degree 'j' poly = PolynomialFeatures(degree=j) # Transform the X_train and X_test X_train_poly = poly.fit_transform(X_train) X_test_poly = poly.fit_transform(X_test) # Fit a model on the transformed data linreg = LinearRegression().fit(X_train_poly, y_train) # Compute yhat or ypred y_pred = linreg.predict(X_test_poly) # Compute MSE * n_K/N test_mse = mean_squared_error(y_test, y_pred)*float(len(X_train))/float(len(X)) # Add the test_mse for this partition of the data mse.append(test_mse) # Compute the mean of all folds for degree 'j' deg.append(np.mean(mse)) return(deg) df=pd.DataFrame() print(len(X)) # Call the function once. For LOOCV K=N. hence len(X) is passed as number of folds cvError=computeCVError(X,y,len(X)) # Create and plot LOOCV df=pd.DataFrame(cvError) fig3=df.plot() fig3=plt.title("Leave one out Cross Validation - Cross Validation Error vs Degree of Polynomial") fig3=plt.xlabel("Degree of Polynomial") fig3=plt.ylabel("Cross validation Error") fig3.figure.savefig('foo3.png', bbox_inches='tight') ## 6a K Fold Cross Validation – R code Here K Fold Cross Validation is done for 4, 5 and 10 folds using the R package boot and the glm package library(boot) library(reshape2) set.seed(17) #Read data df=read.csv("auto_mpg.csv",stringsAsFactors = FALSE) # Data from UCI df1 <- as.data.frame(sapply(df,as.numeric)) df2 <- df1 %>% dplyr::select(cylinder,displacement, horsepower,weight, acceleration, year,mpg) df3 <- df2[complete.cases(df2),] a=matrix(rep(0,30),nrow=3,ncol=10) set.seed(17) # Set the folds as 4,5 and 10 folds<-c(4,5,10) for(i in seq_along(folds)){ cv.error.10=rep(0,10) for (j in 1:10){ # Fit a generalized linear model glm.fit=glm(mpg~poly(horsepower,j),data=df3) # Compute K Fold Validation error a[i,j]=cv.glm(df3,glm.fit,K=folds[i])$delta[1]

}

}

# Create and display the K Fold Cross Validation Error
b <- t(a)
df <- data.frame(b)
df1 <- cbind(seq(1,10),df)
names(df1) <- c("PolynomialDegree","4-fold","5-fold","10-fold")

df2 <- melt(df1,id="PolynomialDegree")
ggplot(df2) + geom_line(aes(x=PolynomialDegree, y=value, colour=variable),size=2) +
xlab("Degree of Polynomial") + ylab("Cross Validation Error") +
ggtitle("K Fold Cross Validation - Cross Validation Error vs Degree of Polynomial")

## 6b. K Fold Cross Validation – Python code

The implementation of K-Fold Cross Validation Error has to be implemented and I have done this below. There is a small discrepancy in the shapes of the curves with the R plot above. Not sure why!

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import train_test_split, KFold
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error
autoDF.shape
autoDF.columns
autoDF1=autoDF[['mpg','cylinder','displacement','horsepower','weight','acceleration','year']]
autoDF2 = autoDF1.apply(pd.to_numeric, errors='coerce')
# Drop NA rows
autoDF3=autoDF2.dropna()
autoDF3.shape
#X=autoDF3[['cylinder','displacement','horsepower','weight']]
X=autoDF3[['horsepower']]
y=autoDF3['mpg']

# Create Cross Validation function
def computeCVError(X,y,folds):
deg=[]
mse=[]
# For degree 1,2,3,..10
degree1=[1,2,3,4,5,6,7,8,9,10]

nK=len(X)/float(folds)
xval_err=0
for j in degree1:
# Split the data into 'folds'
kf = KFold(len(X),n_folds=folds)
for train_index, test_index in kf:
# Partition the data acccording the fold indices generated
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]

# Scale the X_train and X_test as per the polynomial degree 'j'
poly = PolynomialFeatures(degree=j)
X_train_poly = poly.fit_transform(X_train)
X_test_poly = poly.fit_transform(X_test)
# Fit a polynomial regression
linreg = LinearRegression().fit(X_train_poly, y_train)
# Compute yhat or ypred
y_pred = linreg.predict(X_test_poly)
# Compute MSE *(nK/N)
test_mse = mean_squared_error(y_test, y_pred)*float(len(X_train))/float(len(X))
# Append to list for different folds
mse.append(test_mse)
# Compute the mean for poylnomial 'j'
deg.append(np.mean(mse))

return(deg)

# Create and display a plot of K -Folds
df=pd.DataFrame()
for folds in [4,5,10]:
cvError=computeCVError(X,y,folds)
#print(cvError)
df1=pd.DataFrame(cvError)
df=pd.concat([df,df1],axis=1)
#print(cvError)

df.columns=['4-fold','5-fold','10-fold']
df=df.reindex([1,2,3,4,5,6,7,8,9,10])
df
fig2=df.plot()
fig2=plt.title("K Fold Cross Validation - Cross Validation Error vs Degree of Polynomial")
fig2=plt.xlabel("Degree of Polynomial")
fig2=plt.ylabel("Cross validation Error")
fig2.figure.savefig('foo2.png', bbox_inches='tight')

This concludes this 2nd part of this series. I will look into model tuning and model selection in R and Python in the coming parts. Comments, suggestions and corrections are welcome!
To be continued….
Watch this space!

Also see

To see all posts see Index of posts

# Close encounters with the future

Published in Telecom Asia, Oct 22,2013 – Close encounters with the future

Where a calculator on the ENIAC is equipped with 18,000 vacuum tubes and weighs 30 tons, computers in the future may have only 1,000 vacuum tubes and perhaps weigh 1.5 tons.—POPULAR MECHANICS, 1949

Introduction: Ray Kurzweil in his non-fiction book “The Singularity is near – When humans transcend biology” predicts that by the year 2045 the Singularity will allow humans to transcend our ‘frail biological bodies’ and our ‘petty, derivative and circumscribed brains’ . Specifically the book claims “that there will be a ‘technological singularity’ in the year 2045, a point where progress is so rapid it outstrips humans’ ability to comprehend it. Irreversibly transformed, people will augment their minds and bodies with genetic alterations, nanotechnology, and artificial intelligence”.

He believes that advances in robotics, AI, nanotechnology and genetics will grow exponentially and will lead us into a future realm of intelligence that will far exceed biological intelligence. This explosion will be the result of ‘accelerating returns from significant advances in technology”

Futurescape

Here is a look at some of the more fascinating key trends in technology. You can decide whether we are heading to Singularity or not.

Autonomous Vehicles (AVs): Self driving cars have moved from the realm of science fiction to reality in recent times. Google’s autonomous cars has already driven around half a million miles. All the major car manufacturers of the world from BMW, Mercedes, Toyota, Nissan, Ford or GM are all coming with their own versions of autonomous cars. These cars are equipped with Adaptive Cruise Control and Collision Avoidance technologies and are already taking away control drivers. Moreover AVs alert drivers, if their attention strays from the road ahead, for too long. Autonomous Vehicles work with the help of Vehicular Communication Technology.

Vehicular Communication along with the Intelligent Transport Systems (ITS) achieves safety by enabling communication between vehicles, people and roads. Vehicle-to-vehicle communications are the fundamental building block of autonomous, self-driving cars. It enables the exchange of data between vehicles and allows automobiles to “see” and adapt to driving obstacles more completely, preventing accidents besides resulting in more efficient driving.

Smart Assistants: From the defeat of Kasparov in chess by IBM’s Deep Blue in 1997, and then subsequently to  the resounding victory of IBM’s Watson in Jeopardy, capable of understanding natural human language, to the more prevalent Apple’s intelligent assistant Siri, Artificially Intelligent  (AI) systems have come a long way. The newest trend in this area is Smart Assistants.  Robots are currently analyzing documents, filling prescriptions, and handling other tasks that were once exclusively done by humans. Smart Assistants are already taking over the tasks of BPO operators, paralegals, store clerks, baby sitters. Robots, in many ways, are not only smarter than humans, but also do not get easily bored,

Intelligent homes and intelligent offices. Rapid advances in technology will be closer to the home both literally and figuratively. The future home will have the ability to detect the presence of people, pets, smoke and changes to humidity, moisture, lighting, temperature. Smart devices will monitor the environment and take appropriate steps to save energy, improve safety and enhance security of homes.  Devices will start learning your habits and enhance your comfort and convenience. Everything from thermostats, fire detectors, washing machines, refrigerators will be equipped electronics that will be capable of adapting to the environment. All gadgets at home will be accessible through laptops, tablets or smartphones from anywhere. We will be able to monitor all aspects of our intelligent home from anywhere.

Smart devices will also make major inroads into offices leading to the birth of intelligent offices where the lighting, heating, cooling will be based on the presence of people in the offices. This will result in an enormous savings in energy. The advances in intelligent homes and intelligent offices will be in the greater context of the Smart Grid.

Swarms of drones: Contrary to the use of weaponized drones for unmanned aerial survey of enemy territory we will soon have commercial drones. Drone will start being used for civilian purposes.  The most compelling aspect of drones these days is the fact that they can be easily manufactured in large quantities, are cheap and can perform complex tasks either singly or collectively. Remotely controlled drones can perform hundreds of civilian jobs, including traffic monitoring, aerial surveying, and oil pipeline inspections and monitoring of crop conditions. Drones are also being employed for conservation of wildlife. In the wilderness of Africa, drones are already helping in providing aerial footage of the landscape, tracking poachers and in also herding elephants. However, before drones become a common sight, it is necessary to ensure that appropriate laws are made for maintaining the safety and security of civilians. This is likely to happen in US in 2015, when the Federal Aviation Administration (FAA) will come up with rules to safely integrate drones into the American skies.

MOOC (Massive Online Open Course): The concept of MOOC, or the ‘Massive Open Online Course’ from top colleges, though just a few years old, is already taking the world by storm. Coursera, edX and Udacity are the top 3 MOOCs besides many others and offer a variety of courses on technology, philosophy, sociology, computer science etc.  As more courses are available online, the requirements of having a uniform start and end date will diminish gradually. The availability of course lectures at all times and through all devices, namely the laptop, tablet or smartphone, will result in large scale adoption by students of all ages.

Contrary to regimented classes MOOCs now allow students to take classes at their own pace. It is likely that some students will breeze through an entire semester worth of classes in a few weeks. It is also likely that a few students will graduate in 4 years with more than a couple of degrees. MOOCs are a natural development considering that the world is going to be more knowledge driven where there will be the need for experts with a diverse set of in-depth skills. Here is an interesting article in WSJ “What College will be like in 2023

3D Printing: This is another technology that is bound to become ubiquitous in our future. 3D printers will revolutionize manufacturing in ways we could never imagine. A 3-D printer is similar to a hot-glue gun attached to a robotic arm. A 3-D printer creates an object by stacking one layer of material, typically plastic or metal, on top of another.  3D printers have been used for making everything from prosthetic limbs, phone cases, lamps all the way to a NASA funded 3D pizza. Here is a great article in New York Times “Dinner is Printed” It is likely that a 3D printer would be indispensable to our future homes much like the refrigerator and microwave.

Artificial sense organs: A recent news items in Science 2.0 “The Future touch sensitive prosthetic limbs”   discusses the invention of a prosthetic limb that can actually provide the sense of touch by stimulating the regions of the brain that deal with the sense of touch. The researchers identified the neural activity that occurs when grasping or feeling an object and successfully induced these patterns in the brain. Two parallel efforts are underway to understand how the human brain works. They are “The Human Brain Project” which has 130 members of the European Union and Obama’s BRAIN project. Both these projects attempt to ‘to give us a deeper and more meaningful understanding of how the human brain operates”. Possibilities as in the movies ‘Avatar’ or ‘Terminator’ may not be far away.

The Others: Besides the above, technologies like Big Data, Cloud Computing, Semantic Web, Internet of Things and Smart Grid will also be swamp us in the future and much has already been said about it.

Conclusion: The above sets of technologies represent seismic shifts and are bound to explode in our future in a million ways.

Given the advances in bionic limbs, Machine Intelligent AI systems, MOOCs, Autonomous Vehicles are we on target for the Singularity?

I wouldn’t be surprised at all!