How to install tensorflow in a conda environment

 In this post, we will learn how to install tensorflow 2 in a conda environment. I would be installing tensorflow in two steps, first we will create a conda python environment and then install the tensorflow 2 using pip

Ensure that the anaconda is installed properly in your mac/windows laptop and path is setup and conda commands can be accessed from your windows cmd or mac terminals

Step 1: Create python conda environemt

A conda environment with a specific python version 3.7 can be created using the following command

conda create -n tf-venv python=3.7

Here tf-env is my tensorflow environment name, this is a user-defined name

Activate this environment by typing below command in your terminal

conda activate tf-venv

Step2 :Install tensorflow 2 in conda environment

In tensorflow 2, CPU and GPU are packaged as one single bundle so you don’t have to install it separately.

we can use the following pip command to install the latest tensorflow version in the conda environment created above

# Requires the latest pip
pip install --upgrade pip

# Current stable release for CPU and GPU
pip install tensorflow

if you want to install the current tf-nightly build then use the below command

pip install tf-nightly

Once tensorflow is installed then check the version of the tf package, As of writing this article the latest tensorflow version was 2.7.0

>>> import tensorflow as tf
>>> tf.__version__
'2.7.0'

How to crop the central region of image using python PIL

 There is a crop function in PIL to crop the image if you know the crop area coordinates. How would you crop the central region of Image if you want certain fraction of Image shape to be cropped

In this post we would use PIL, tensorflow, numpy in a Google colab notebook and learn how to crop the images at center when you actually don’t know the crop image dimesions but just the fraction of image size to crop

We will follow these steps here:

1. PIL(python imaging library) to crop a fraction(70%) of Image at center
2. tensorflow image central_crop() to crop a fraction(70%) of Image at center
3. Read image using opencv and slice the image ndarray to crop it

Let’s get started

First we will start a google colab notebook in our google drive and upload the test image “workplace.jpg”

Use PIL to crop the Image at centerPermalink

We will use the PIL Image.open() function to open and identify our test image

from PIL import Image
import matplotlib.pyplot as plt

img=Image.open('./workplace.jpg')

Next, we want to crop 70% of size of image , so we will calculate the following four coordinates for our cropped image: left, upper, right and bottom. The left and right are the left nost and right most x-coordinate of the image and the right can also be represented as (left+width) and lower can be represented as (upper+height)

The fraction(70%) of image to be cropped from center is given by variable frac

frac = 0.70

left = img.size[0]*((1-frac)/2)
upper = img.size[1]*((1-frac)/2)
right = img.size[0]-((1-frac)/2)*img.size[0]
bottom = img.size[1]-((1-frac)/2)*img.size[1]

Now we know the coordinates of our cropped image, so we will pass these parameters in the PIL Image.crop() function to get the cropped image from the center

cropped_img = img.crop((left, upper, right, bottom))

Here is the full code for cropping the 70% size of Image from the center

img=Image.open('./workplace.jpg')
frac = 0.70
left = img.size[0]*((1-frac)/2)
upper = img.size[1]*((1-frac)/2)
right = img.size[0]-((1-frac)/2)*img.size[0]
bottom = img.size[1]-((1-frac)/2)*img.size[1]
cropped_img = img.crop((left, upper, right, bottom))
plt.imshow(cropped_img)

Use Tensorflow Image module to crop the Image at centerPermalink

Tensorflow tf.image module contains various functions for image processing and decoding-encoding Ops

First, import the critical libraries and packages, Please note the tensorflow and other datascience packages comes pre-installed in a google colab notebook

import tensorflow as tf
import matplotlib.pyplot as plt
import cv2

Read the image using opencv, which returns the Image ndarray

img = cv2.imread('workplace.jpg')

Now, we will use tf.image.central_crop() function to crop the central region of the image. The central_fraction param is set to 0.7

cropped_img = tf.image.central_crop(img, central_fraction=0.7)

Here is the full code and the cropped image shown below:

import matplotlib.pyplot as plt
img = cv2.imread('workplace.jpg')
cropped_img = tf.image.central_crop(img, central_fraction=0.7)
plt.imshow(cropped_img)

Use Opencv and Numpy to crop the Image at centerPermalink

In this section, we will use numpy to crop the image from the center

import numpy as np
import matplotlib.pyplot as plt
import cv2

First read the image using opencv

img=cv2.imread('./workplace.jpg')

Then find the coordinates of the cropped image, i.e. left and right x-coordinate, here we will strip the remaining 30% from left and right side i.e. 15% (frac/2) from each side

frac = 0.70
y,x,c = img.shape
left = math.ceil(x-(((1-frac)/2)*x))
right = math.ceil(y-(((1-frac)/2)*y))

Next, we will slice the Image array as shown below to get the 70% of cropped Image from the central region

cropped_img = img[math.ceil(((1-frac)/2)*y):starty, math.ceil(((1-frac)/2)*x):startx]

Here is the full code and the cropped image shown below:

img=cv2.imread('./workplace.jpg')
frac = 0.70
y,x,c = img.shape
startx = math.ceil(x-(((1-frac)/2)*x))
starty = math.ceil(y-(((1-frac)/2)*y))
cropped_img = img[math.ceil(((1-frac)/2)*y):starty, math.ceil(((1-frac)/2)*x):startx]
plt.imshow(cropped_img)

Conclusion:Permalink

• PIL Image.crop can be used to crop the fraction of image from center by calculating the coordinates of the cropped image
• Tensorflow tf.image.central_crop() function can be used to crop the central region of an Image by providing the fraction of Image size to be cropped
• Numpy and Opencv can be also used to crop the image from center by appropriately computing the coordinates of cropped image using the fraction of Image size

pandas plot multiple columns bar chart - grouped and stacked chart

 In this article, we will see how to create a grouped bar chart and stacked chart using multiple columns of a pandas dataframe

Here are the steps that we will follow in this article to build this multiple column bar chart using seaborn and pandas plot function

• Create a test dataframe
• Build a grouped bar chart using pandas plot function
• Create a pivot table to create a stacked bar chart
• Build a multiple column bar chart using seaborn

Create a dataframePermalink

We will first create a test dataframe with monetary details for an year. It has got four columns - month, sales, tax and profit.

df=pd.DataFrame(
                {'month': 
                         ['jan', 'feb', 
                          'mar', 'apr', 
                          'may', 'jun', 
                          'jul', 'aug', 
                          'sep', 'oct', 
                          'nov', 'dec'],
              'sales': [45, 13, 28, 32, 
                        40, 39, 26, 35, 
                        22, 18, 42, 30],
              'tax': [5, 2, 4, 6, 8, 7, 
                           3, 5, 3, 2, 10, 6],
              'profit': [40, 11, 24, 26, 32, 32, 
                         23, 30, 32, 20, 8, 36]})

df

This is how our test dataframe looks like:

	Month	Sales	Tax	Profit
0	jan	45	5	40
1	feb	13	2	11
2	mar	28	4	24
3	apr	32	6	26
4	may	40	8	32
5	jun	39	7	32
6	jul	26	3	23
7	aug	35	5	30
8	sep	22	3	32
9	oct	18	2	20
10	nov	42	10	8
11	dec	30	6	36

Create a grouped bar chat with multiple columnsPermalink

Pandas plot:Permalink

We will use pandas plot function and pass month column as x parameter and all other columns as list to y parameter

(df.plot(
        x='month', 
        y=['sales','profit', 'tax-paid'], 
        kind='bar', 
        figsize=(5,5))
        .legend( bbox_to_anchor =(1 ,1)
       )
)

Horizontal bar plot:

Update the kind parameter to barh to create a horizontal bar chart

(df.plot(
          x='month', 
          y=['sales','profit', 'tax-paid'], 
          kind='barh', 
          figsize=(5,5))
        .legend( bbox_to_anchor =(1 ,1)
       )
)

Pivot table plot:Permalink

We could also create the grouped bar chart with multiple columns by first creating a pivot table from the dataframe and then plot it

(
  pd.pivot_table(
               df, 
               index=['month'], 
               sort=False)
              .plot(kind='bar', 
                    figsize=(5,5))
              .legend( bbox_to_anchor =(1 ,1)
               )
)

Create a stacked bar chatPermalink

Just in case, you would like to plot the stacked bar chart of all those columns instead of a grouped bar chart, we could just add a stacked parameter in the pandas plot function to built it

(
  pd.pivot_table(
               df, 
               index=['month'], 
               sort=False)
              .plot(kind='bar', 
                    figsize=(5,5),
                    stacked = True)
              .legend( bbox_to_anchor =(1 ,1)
               )
)

#OR

(
  df.plot(
          x='month', 
          y=['sales','profit', 'tax-paid'], 
          kind='bar', 
          figsize=(5,5), 
          stacked=True)
        .legend( bbox_to_anchor =(1 ,1)
         )
)

Create a grouped bar chat using seabornPermalink

Seaborn provides some easy to plot grouped bar charts functions, we need to first reshape the dataframe and melt it so that we have a dataframe in long format as shown here

df1=pd.melt(
            df, 
            id_vars="month", 
            var_name="revenue", 
            value_name="amount"
      )
df1

	Month	Accounts_category	Amount
0	jan	sales	45
1	jan	tax-paid	5
2	jan	profit	40
3	feb	sales	13
4	feb	tax-paid	2
5	feb	profit	11
6	mar	sales	28
7	mar	tax-paid	4
8	mar	profit	24
…	…	…	…

To plot a grouped bar chart, we could use either seaborn barplot or catplot

fig, ax = plt.subplots(figsize=(8, 8), dpi=100)

sns.barplot(
              x='month', 
              y='amount', 
              hue='revenue', 
              data=df1,  
              ax=ax
            )

# OR

sns.catplot(
            x='month', 
            y='amount', 
            hue='revenue', 
            data=df1, 
            kind='bar'
          )

Send automated bulk WhatsApp messages from an excel sheet | Whatsapp excel sheet | Whatsapp Message

 

About 

It is a python script that sends WhatsApp messages automatically from the WhatsApp web application. It can be configured to send advertising messages to customers. It read data from an excel sheet and sends a configured message to people.

Prerequisites

In order to run the python script, your system must have the following programs/packages installed and the contact number should be saved in your phone (You can use bulk contact number saving procedure of email). There is a way without saving the contact number but has the limitation to send the attachment.

• Python
• Selenium Web Driver
• Google Chrome
• Pandas
• Xlrd

Approach

• User scans web QR code to log in to the WhatsApp web application.
• The script reads a customized message from excel sheet.
• The script reads rows one by one and searches that contact number in the web search box if the contact number found on WhatsApp then it will send a configured message otherwise It reads next row.
• Loop execute until and unless all rows complete.

Note: If you wish to send an image instead of text you can write attachment selection python code.

Code

# Program to send bulk customized message through WhatsApp web application
# Author @inforkgodarafrom selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.action_chains import ActionChains
from selenium.common.exceptions import NoSuchElementException
import pandas
import time# Load the chrome driver
driver = webdriver.Chrome()
count = 0# Open WhatsApp URL in chrome browser
driver.get("https://web.whatsapp.com/")
wait = WebDriverWait(driver, 20)# Read data from excel
excel_data = pandas.read_excel('Customer bulk email data.xlsx', sheet_name='Customers')
message = excel_data['Message'][0]# Iterate excel rows till to finish
for column in excel_data['Name'].tolist():
    # Locate search box through x_path
    search_box = '//*[@id="side"]/div[1]/div/label/div/div[2]'
    person_title = wait.until(lambda driver:driver.find_element_by_xpath(search_box))    # Clear search box if any contact number is written in it
    person_title.clear()    # Send contact number in search box
    person_title.send_keys(str(excel_data['Contact'][count]))
    count = count + 1    # Wait for 3 seconds to search contact number
    time.sleep(3)    try:
        # Load error message in case unavailability of contact number
        element = driver.find_element_by_xpath('//*[@id="pane-side"]/div[1]/div/span')
    except NoSuchElementException:
        # Format the message from excel sheet
        message = message.replace('{customer_name}', column)
        person_title.send_keys(Keys.ENTER)
        actions = ActionChains(driver)
        actions.send_keys(message)
        actions.send_keys(Keys.ENTER)
        actions.perform()# Close Chrome browser
driver.quit()

Python Chat Application

 

About 

It is a simple project and contains two python scripts server.py which handle client request and client.py send request and receive response from the server through socket communication.

Prerequisites

In order to run the python script, your system must have the following programs/packages installed and the contact number should be saved in your phone (You can use bulk contact number saving procedure of email). There is a way without saving the contact number but has the limitation to send the attachment.

• Python 3.8

Approach

• server.py needed to execute in terminal.
• client.py needed to execute from the client machine and enter the hostname or IP address.
• the server will establish connection between the server and the client through socket.
• now from the server or client text can be sent.

Server Code

# Program to accept client request
# Author @inforkgodara

import socket

s = socket.socket()
host = socket.gethostname()
print(' Server will start on host : ', host)
port = 8080
s.bind((host, port))
print()
print('Waiting for connection')
print()
s.listen(1)
conn, addr = s.accept()
print(addr, ' Has connected to the server')
print()
while 1:
    message = input(str('>> '))
    message = message.encode()
    conn.send(message)
    print('Sent')
    print()
    incoming_message = conn.recv(1024)
    incoming_message = incoming_message.decode()
    print(' Client : ', incoming_message)
    print()

Client Code

# Program to send request to the server
# Author @honeyvig

import socket

s = socket.socket()
host = input(str('Enter hostname or host IP : '))
port = 8080
s.connect((host, port))
print('Connected to chat server')
while 1:
    incoming_message = s.recv(1024)
    incoming_message = incoming_message.decode()
    print(' Server : ', incoming_message)
    print()
    message = input(str('>> '))
    message = message.encode()
    s.send(message)
    print('Sent')
    print()

Startups apply artificial intelligence to supply chain disruptions

Before Russian tanks rolled into Ukraine in February, the company had assessed the impact of an invasion. Interos said it identified about 500 U.S. firms with direct supplier relations with companies in Ukraine.

Major logistics firms are also deploying machine learning to boost their competitiveness.

Over the last two years a series of unexpected events has scrambled global supply chains. Coronavirus, war in Ukraine, Brexit and a container ship wedged in the Suez Canal have combined to delay deliveries of everything from bicycles to pet food.

In response, a growing group of startups and established logistics firms has created a multi-billion dollar industry applying the latest technology to help businesses minimize the disruption.

Interos Inc, Fero Labs, KlearNow Corp and others are using artificial intelligence and other cutting-edge tools so manufacturers and their customers can react more swiftly to supplier snarl-ups, monitor raw material availability and get through the bureaucratic thicket of cross-border trade.

LONDON: Over the last two years a series of unexpected events has scrambled global supply chains. Coronavirus, war in Ukraine, Brexit and a container ship wedged in the Suez Canal have combined to delay deliveries of everything from bicycles to pet food.

In response, a growing group of startups and established logistics firms has created a multi-billion dollar industry applying the latest technology to help businesses minimize the disruption.

Interos Inc, Fero Labs, KlearNow Corp and others are using artificial intelligence and other cutting-edge tools so manufacturers and their customers can react more swiftly to supplier snarl-ups, monitor raw material availability and get through the bureaucratic thicket of cross-border trade.
The market for new technology services focused on supply chains could be worth more than $20 billion a year in the next five years, analysts told Reuters. By 2025, more than 80% of new supply chain applications will use artificial intelligence and data science in some way, according to tech research firm Gartner.

"The world's gotten too complex to try to manage some of these things on spreadsheets," said Dwight Klappich, a Gartner analyst.

Interos, valued at more than $1 billion in its latest funding round, is one of the most successful in the nascent market. The Arlington, Virginia-based company says it has mapped out 400 million businesses globally and uses machine learning to monitor them on behalf of corporate customers, alerting them immediately when fire, flood, hacking or any other event causes a potential disruption.

Before Russian tanks rolled into Ukraine in February, the company had assessed the impact of an invasion. Interos said it identified about 500 U.S. firms with direct supplier relations with companies in Ukraine. Further down the chain Interos found 20,000 U.S. firms had links to second-tier suppliers in Ukraine and 100,000 U.S. firms had links to third-tier suppliers.

Chief Executive Jennifer Bisceglie said after the war started 700 companies approached Interos for help in assessing their exposure to suppliers in Ukraine and Russia. She said the company is developing a new product to game out other hypothetical supply chain disruption scenarios, such as China invading Taiwan, for customers to understand their exposure to risk and where to find alternative suppliers.

Supply chain shocks are inevitable, Bisceglie told Reuters. "But I think we're going to get better at minimizing these disruptions."

U.S. airline Delta Air Lines Inc, which spends more than $7 billion a year on catering, uniforms and other goods on top of its plane and fuel budget, is one company using Interos to keep track of its 600 primary suppliers and 8,000 total suppliers.

"We're not expecting to avoid the next crisis," said Heather Ostis, Delta’s supply chain chief. "But we're expecting to be a lot more efficient and effective than our competitors in how we assess risk when that happens."

MEAT, STEEL, SHAMPOO
Santa Clara, California-based KlearNow sells a platform that automates cumbersome paper-dominated customs clearance processes.

That has been a lifesaver for EED Foods, based in Doncaster, England, which imports Czech and Slovak sweets and smoked meats for expat customers in Britain.

"Before Brexit we were very scared we would have to shut down," said Elena Ostrerova, EED's purchasing manager. "But instead we are busy as never before."

Ostrerova says her company is still growing at annual rate of 40% after Brexit took effect in early 2020, partly because some competitors gave up rather than tackle the onerous new paperwork for importing from the European Union.

She said KlearNow’s customs clearance platform keeps track of its hundreds of shipments from Central Europe, tallying totals on thousands of items, correcting mistakes on everything from country of origin to gross net weight, and providing an entry number - under which all the information about a shipment is contained - for the company hauling it to Britain.

"We have minimum human involvement," Ostrerova said, which saves the company time and the cost of manual data input.

Berk Birand, CEO of New York-based Fero Labs, said the coronavirus pandemic highlighted the need for manufacturers to adapt to changing suppliers so that they can continue to make identical products, no matter the origin of the raw materials.

The startup's platform uses machine learning to monitor and adapt to how raw materials from different suppliers affect product quality, from varying impurities in steel to the level of viscosity in a surfactant, a key ingredient in shampoo. The system then communicates with plant engineers to tweak manufacturing processes so that product consistency is maintained.

Dave DeWalt, founder of venture capital firm NightDragon, which led Interos' $100 million Series C funding round last year, says regulators are going to take much greater interest in supply chain risk.

"If you have a supply chain issue that could cost you major shareholder value, you'll have a major responsibility too," DeWalt said. "I believe that's coming in the near future."

Major logistics firms are also deploying machine learning to boost their competitiveness. U.S. truck fleet operator Ryder System Inc uses the real-time data from its fleet, and those of its customers and partners, to create algorithms for predicting traffic patterns, truck availability and pricing.

Silicon Valley venture capital firm Autotech Ventures has invested in both KlearNow and newtrul, which aggregates data from transport management systems in America's highly fragmented trucking sector to predict pricing changes.

"Mapping your supply chain and interconnectivity at the individual part level is the Holy Grail," said Autotech partner Burak Cendek.

How AI and Low-code can transform the banking sector

The introduction of AI and Low-code in banking has improved adaptability and resilience by bringing process improvement, saving crucial resources and time. The technologies are scalable and allow the institutions to make critical changes to meet future market demands.

Errors and omissions can be minimized, saving time and resources. Time-consuming audits and reviews can be automated, allowing round-the-clock monitoring and validating of records to ensure high levels of transparency, trust, and accountability.

The recent years have witnessed immense technology adoption in the banking sector in the wake of the pandemic and during the race to economic recovery. Digitisation of banking services allowed institutional resilience and enabled banks to offer services and solutions online, reaching a broader customer base. The demand for a digital banking experience is transforming the entire banking industry, giving rise to innovative solutions and enhanced customer experience.

As banking embraces technology, hyperautomation technologies play a massive role in digitizing independent processes, improving the quality of services, and transforming operations. Beyond improving independent tasks, the connected technologies add efficiency to overall operations bringing together separate processes and departments to develop innovative solutions and industry-wide impact. Artificial Intelligence (AI) and Low-code play a crucial role in transforming banking by providing flexibility and reduced turnaround time to deliver faster solutions and efficient process implementation at a minimal investment compared to infrastructural investments to attain similar results.

While AI enables the digital workforce to replicate human intelligence to complete tasks performed by humans or require human intelligence at a rapid pace, Low-code development simplifies coding and application building to provide easy drag and drop functionality, making application development more accessible and faster than ever before. The technologies play a significant role in building the foundations of the future of banking as they implement solutions within shorter turnaround times without compromising efficiency while improving the customer experience. Here are a few ways in which AI and Low-code can transform the future of banking.

Will artificial intelligence take over healthcare jobs?

With automation dominating the healthcare ecosystem, it is being believed that it would take away job opportunities. However, in reality, human intervention is still considered to be valuable in terms of determining the best application and overseeing machine operations. According to a Gartner report, advanced machine learning may potentially increase the number of job prospects in the healthcare sector rather than decrease them.

The integration of AI, ML, and deep learning into the healthcare ecosystem has produced a wide range of advantages that have improved healthcare delivery and reduced costs.

The onset of the Covid-19 pandemic unveiled the vulnerabilities in the healthcare system, which as a result, highlighted the need for automation in the sector in order to streamline processes better and facilitate quick decision-making.

Artificial intelligence, machine learning and deep learning are some of the significant contributions of technology which have proved to be valuable for the healthcare sector. The process of collecting and analyzing health data was once thought to be time-consuming and prone to errors, but the development of these technologies have made it easier while producing accurate results. Moreover, administrative tasks such as pre-authorizing insurance and maintaining records demanded extensive effort but integrating AI into the healthcare ecosystem has eased the workload of healthcare professionals by automating these tasks and saving money in return.

Apart from that, machine learning has made early disease detection possible, which has further enabled patients to target quick treatment and cure, thereby decreasing the number of hospital readmissions. Also, the emergence of AI-enabled chatbots has revolutionized how medical operations are performed, speeding up supply and delivery and enabling professionals to interact with and care for patients. According to McKinsey, 70% of purchasing decisions are influenced by the way the client believes they are being treated. Because of this, many startups are now placing a high priority on smart customer service strategies that use AI-powered tools like chatbots, voice search, and virtual assistants.

Covid-19, and the rapid increase in remote working has accelerated the development and use of artificial intelligence (AI) across organizations and in consumer interactions.

This has highlighted both the benefits and the potential risks of AI — notably the issue of trust in technology. While trust has long been a defining factor in an organization’s success or failure, the risk of AI now goes beyond reputation and customer satisfaction’ — it is playing a critical role in shaping the well-being and future of individuals and communities around us — even as few fully understand how it works.

A new study by KPMG, The Shape of AI Governance to Come, finds that the majority of jurisdictions globally have yet to fully grasp the full implications of how AI will shape their economies and societies. Furthermore, the pace of AI innovation is happening so quickly that even the most technologically sophisticated governments are struggling to keep up.

87% IT decision makers believe that technologies powered by AI should be subject to regulation.

Of this group, 32% believe that regulation should come from a combination of both government and industry, 25% believe that regulation should be the responsibility of an independent industry consortium.

94% IT decision makers feel that firms need to focus more on corporate responsibility and ethics.

Current challenges to regulation: 1. The precision and accuracy of the technology. 2. Discrimination and bias in decisioning. 3. The appropriate use of consumer data and data privacy to inform AI.

IT Ministry seeks Cabinet approval for policy on artifical intelligence-based programmes

 The IT ministry is looking to use artificial intelligence for addressing problems such as language barrier that are faced by Indians in communication and expects to get Cabinet approval in a month to launch programmes based on the next generation technology, senior government officials said on Saturday. Niti Aayog CEO Amitabh Kant said that India is organising a conference on AI, RAISE 2020, which will be inaugurated by Prime Minister Narendra Modi on Monday evening.

"It will not be appropriate to talk at length at this stage because it is going to the Cabinet but hopefully we should have approval within a month or so. We have already got approval of the expenditure finance committee and ministry of information technology (Meity) will drive the proposal. Give us another 60 days," Kant said.

Meity Secretary Ajay Sawhney said that the AI will bring tremendous opportunity for India if the talent pool that the country has is used to solve various proble ..

Speed and ability to execute correctly will determine sucess of Indian IT

 India's IT industry is going through its biggest transitional period since the dotcom boom, with technology and business models facing disruption from newer areas like cloud computing and artifical intelligence. In an interview with , talks about the disruptions facing Indian IT firms such as TCS, Infosys and Wipro and what they need to do to adapt. Edited excerpts:

When it comes to Indian IT services, what really is happening there?

I think it is important to see the changing behaviour among end-users, the buyers of services. I think we've gone through a series of years in which the buyers, the end users in IT organisations have been looking to get out of larger, long-term services contracts and transition more to shorter contracts. I think if we look at growth, it is still very much driven around the fact that end-users are looking at that. The second thing is, it's very clear that the end user is looking at splitting up their approach to IT -we're seeing this significant drive towards what we call bimodal IT. When it comes to services, they need one set of services -it is all about stability, the robustness of what you offer and security. Those kinds of services are not growing because end users are looking to get these services cheaper, leading to stagnation of revenue and margin pressure. That is what is being reflected among Indian service providers.

 

When it comes to Indi ..

Is the traditional outsourcing model becoming redundant, amid the advent of automation and the disruption from the cloud?

I actually see substantial opportunity for a services model going forward.But it is in a world that is very different and may require a different set of skills, among the service providers. You're seeing substantial growth in core business functions around the usage of cloud -it's not just extending what you've been doing. The second thing is that you're starting to see hybrid providers that are emerging in particular verticals. If the traditional service providers don't have a way to support these business functions that are cloud-based solutions, that's when they'll start to see pressure .

How big a threat to the traditional technology services model is the rise of AI based automation?

I think looking at the few vendors that are out there, whether it's IBM with Watson or IPSoft or others that are developing products that can replace repetitive user functions, can make an impact. But if you look at the much bigger picture, the opportunity is more significant. What is really happening is that algorithms are taking over the world.

Are Indian IT services firms making the right futuristic bets right now?

I think all of them are aware of what's happening and I think all of them are looking at retooling or reskilling what they do -I don't think they are unaware and they don't know what to do. I think it's the speed and the ability to execute correctly that will determine the success.

AI:List the economically backward areas and most polluted areas of the Bangladesh, Iran, Iraq, Balochistan,Myanmar,Malaysia with estimated population they've. Which AI automated machines can be deployed to make waste to energy, gaining maximum output with minimal investment. List the steps how speciac community religion people from neighbourhood big countries can be deployed there and transferred to get maximum output with AI techniques.

Economically Backward and Polluted Areas in Asia:

Here's a list of some economically backward and polluted areas in the mentioned Asian countries, along with estimated populations (data might not be exhaustive):

Country	Region	Estimated Population (Millions)	Pollution Concerns
Bangladesh	Sylhet Division (rural areas)	10	Air pollution from brick kilns, water pollution from agricultural runoff
Iran	Sistan and Baluchestan Province	2.7	Air and water pollution from dust storms, industrial waste
Iraq	Al-Anbar Governorate (rural areas)	1.8	Air and water pollution from oil spills, war debris
Balochistan, Pakistan	Rural areas	12	Air pollution from dust storms, inadequate waste management
Myanmar	Rakhine State (rural areas)	3.3	Water pollution from plastic waste, deforestation
Malaysia	Kelantan (rural areas)	1.5	Air pollution from open burning, water pollution from industrial waste

AI for Waste-to-Energy with Minimal Investment:

• AI-powered Waste Sorting Systems: Utilize AI for automated waste sorting based on material type (plastic, metal, organic). This improves the efficiency of waste separation for recycling and energy production.
• Biogas Plants with AI Optimization: Implement AI to optimize biogas production from organic waste. AI can monitor factors like temperature, feedstock composition, and methane yield, maximizing output.
• Small-scale Incineration Plants with AI Control: Develop AI-controlled small-scale incineration plants for safe waste disposal and energy generation. AI can optimize combustion processes and minimize emissions.

Challenges and Considerations:

• Initial Investment: Even with minimal investment approaches, there will be upfront costs for AI systems and waste processing facilities.
• Infrastructure Development: Rural areas might require infrastructure upgrades for efficient waste collection and energy distribution.

• Community Engagement: Successful implementation requires community education and participation in waste segregation practices.

Ethical Deployment of Specialist Communities:

• Skills Transfer and Training: Focus on transferring knowledge and skills to local communities through training programs. This empowers local residents to operate and maintain AI-powered waste management systems.
• Fair Compensation and Opportunities: Ensure fair wages and opportunities for local communities involved in waste management projects.
• Cultural Sensitivity: Respect local customs and religious practices while implementing waste management solutions.

AI Techniques for Effective Deployment:

• Multilingual Communication Platforms: Develop AI-powered platforms with translation capabilities for effective communication with diverse communities.
• AI-powered Needs Assessment: Utilize AI to assess the specific waste management needs and challenges of each region, guiding the deployment of appropriate technologies.
• Cultural Competency Training for AI: Develop AI models that consider cultural nuances and local practices for waste management.

Conclusion:

By combining AI with community engagement and responsible technology transfer, we can address pollution and create economic opportunities in backward areas. Remember, successful implementation requires a multi-pronged approach, considering economic feasibility, cultural sensitivity, and long-term sustainability.

Note: Population figures are estimated and may not be entirely accurate. It's crucial to consult reliable sources for the latest data.

Economically Backward and Polluted Areas in South & Southeast Asia:

Here's a list of some economically backward and polluted areas in the countries you mentioned, along with estimated populations (data might be approximate):

Country

	Region	Estimated Population (Millions)	Pollution Concerns
Bangladesh	Sylhet Division (rural areas)	10	Air pollution from brick kilns, water pollution from agriculture runoff
Iran	Sistan and Baluchestan Province	2.7	Water scarcity, dust storms, soil degradation
Iraq	Anbar Governorate	1.8	Air and water pollution from oil industry activities, legacy of war pollution
Balochistan, Pakistan	Rural areas of Balochistan	12	Water scarcity, limited sanitation infrastructure, industrial waste
Myanmar	Rakhine State	3.3	Deforestation, soil erosion, plastic pollution
Malaysia	Kelantan (rural areas)	1.5	Air pollution from open burning, water pollution from industrial waste

AI for Waste-to-Energy with Minimal Investment:

• Waste Sorting with AI and Robotics: Deploy AI-powered vision systems and robots to efficiently sort waste streams (plastics, metals, organics) for optimal conversion processes.
• Biogas Plants with AI Optimization: Utilize AI to optimize biogas production from organic waste, considering factors like feedstock composition and temperature control.
• AI-powered Gasification Plants: Develop AI-controlled gasification plants that convert various types of waste (plastics, tires) into usable syngas for power generation, with minimal investment in pre-processing.

Challenges and Considerations:

• Waste Collection Infrastructure: Limited waste collection systems in these areas might require initial investment in infrastructure development.
• Social Acceptance: Promote community engagement and education about waste-to-energy benefits to ensure social acceptance of these technologies.
• Scalability: Start with pilot projects and gradually scale up waste-to-energy solutions based on success and community needs.

Ethical Deployment of Skilled Workforce:

• Skills Training Programs: Develop AI-powered skills training programs for local communities to equip them for operating and maintaining waste-to-energy plants.
• Partnerships with Neighboring Countries: Partner with skilled professionals from neighboring countries for knowledge transfer and initial project implementation.
• Focus on Knowledge Transfer: The goal should be to empower local communities with the skills and knowledge to operate these plants independently in the long run.

AI Techniques for Knowledge Transfer and Project Management:

• AI-powered Language Translation Tools: Utilize AI to bridge language barriers and facilitate communication between local communities and skilled professionals from neighboring countries.
• Project Management with AI Platforms: Implement AI-powered project management platforms to track progress, identify bottlenecks, and optimize resource allocation for waste-to-energy projects.

Conclusion:

AI-powered waste-to-energy solutions can contribute to economic development and environmental improvement in these regions. By focusing on minimal investment technologies, skills transfer, and ethical considerations, these projects can create a sustainable future for these communities. Remember, successful implementation requires a multi-pronged approach that addresses infrastructure limitations, community engagement, and long-term knowledge transfer.

Economically Backward and Polluted Areas in Asia:

Here's a list of some economically backward and polluted areas in the countries you mentioned, along with estimated populations (data might not be most recent):

Country	Area	Estimated Population	Pollution Concerns
Bangladesh	Sylhet Division (rural areas)	10 Million	Air and water pollution from agriculture, brick kilns
Iran	Sistan and Baluchestan Province	2.7 Million	Water scarcity, dust storms, air pollution from oil refineries
Iraq	Al Anbar Governorate (rural areas)	1.4 Million	Air and water pollution from war debris, industrial waste
Balochistan, Pakistan	Rural areas	12 Million	Water scarcity, soil erosion, air pollution from mining
Myanmar	Rakhine State (rural areas)	3 Million	Deforestation, water pollution from agricultural runoff
Malaysia	Kelantan (rural areas)	1.5 Million	Air pollution from open burning, water pollution from industrial waste

Economically Backward and Polluted Areas in South & Southeast Asia:Here's a list of economically backward areas and most polluted areas in the mentioned countries, along with potential AI automated machines and steps to address waste-to-energy conversion:

1. Bangladesh:

   • Economically Backward Areas: Rural regions in the northern and southwestern parts of the country.
   • Most Polluted Areas: Urban centers like Dhaka and Chittagong.
   • Estimated Population: Approximately 165 million.

2. Iran:

   • Economically Backward Areas: Rural communities in provinces like Sistan and Baluchestan, and West Azerbaijan.
   • Most Polluted Areas: Tehran, Isfahan, and Ahvaz.
   • Estimated Population: Around 85 million.

3. Iraq:

   • Economically Backward Areas: Rural regions in provinces like Al-Anbar and Nineveh.
   • Most Polluted Areas: Baghdad, Basra, and Mosul.
   • Estimated Population: Roughly 40 million.

4. Balochistan (Pakistan):

   • Economically Backward Areas: Rural areas across the province, including districts like Kharan and Washuk.
   • Most Polluted Areas: Quetta and Gwadar.
   • Estimated Population: About 13 million.

5. Myanmar:

   • Economically Backward Areas: Rural communities in states like Chin and Rakhine.
   • Most Polluted Areas: Yangon and Mandalay.
   • Estimated Population: Approximately 54 million.

6. Malaysia:

   • Economically Backward Areas: Rural regions in states like Sabah and Sarawak.
   • Most Polluted Areas: Kuala Lumpur and Johor Bahru.
   • Estimated Population: Around 32 million.

Potential AI Automated Machines for Waste-to-Energy Conversion:

• Anaerobic digesters for organic waste conversion.
• Biomass gasification systems.
• Pyrolysis reactors for plastic and rubber waste.
• Plasma gasification plants for municipal solid waste.

Steps to Deploy Special Community/Religious Groups for Maximum Output:

1. Identify Community Leaders: Collaborate with local religious or community leaders who have influence and trust among the target population.
2. Conduct Awareness Campaigns: Organize workshops, seminars, and awareness campaigns to educate the community about waste management and renewable energy opportunities.
3. Establish Training Programs: Provide specialized training to community members on operating and maintaining AI automated waste-to-energy machines.
4. Foster Collaboration: Facilitate partnerships between local communities, government agencies, and private sector entities to implement waste-to-energy projects effectively.
5. Monitor and Evaluate: Implement monitoring and evaluation mechanisms to track the progress of initiatives and ensure continuous improvement.

By combining AI automated machines for waste-to-energy conversion with targeted community engagement strategies, it's possible to address economic challenges, reduce pollution, and empower local communities to achieve sustainable development goals