The coming era of
Artificial Intelligence will not be the era of war, but be the era of deep
compassion, non-violence, and love.”. Amit Ray, Pioneer of Compassionate AI
Movement.
In today’s fast-moving #
digital world, #Artificial #Intelligence
(AI) has already become a buzzword. AI continues impacting our work
#environment, business processes/ procedures/ practices, #productivity, #quality,
# networking and #lifestyle. AI is special software created and stored in
computers systems/computing/communication devices and #household #appliances so
that these can act like humans. These IT-enabled devices can efficiently
perform tasks such as recognition of #speech, #images, speedily analyze large
input data and assist in faster and accurate decision making. AI is based on well-designed #algorithms by a team of
experts from multi-disciplinary areas. The algorithms are coded and stored in
the equipment/devices as #embedded software. Thus,
the AI system has pre-stored #logic
(algorithm) to act like how we humans observe, feel, learn, reason and
appropriately respond to any emerging situation. AI can perform
many #repetitive tasks tirelessly, faster and more accurately than humans. Today, AI is transforming entire systems of
Production, Management, #Healthcare, #Entertainment/Gaming, #Animation, #Robotics,
#Navigation, #Transportation and #Governance.
Due to rapid growth in mobile #smart/intelligent
computing devices, data processing power and embedded #technologies,, AI
has become very efficient and affordable since late 2015. AI now forms an
essential and cost-efficient component in every product, equipment, machinery,
vehicle, #Intelligent /Cities/ Homes and various healthcare procedures. Particularly,
in the last 10 years, AI has picked up great momentum in innovation and
applications in many fields.
AI continues to move rapidly into the fields of
Medical #Diagnostics (MD), Machine Learning (ML), Deep Learning (DL), #Document Recording,
Storage, Retrieval, Processing, #Business Intelligence (BI), Industrial #Automation,
and #Research & Development (R&D). AI helps in speeding up
Digital Transformation and support the fast-developing Digital World.
Background of AI. AI has
not suddenly emerged in 21st century. Infect. AI as a
technology subject is being taught since 1935. At that time, Turing, a UK
scientist who thought of Machine Intelligence (MI) and published his first
report during World War II in 1943. However, a major part of AI implementation
was carried out from 1955 onward. During the 1960s, most researchers
considered AI use in a two-sided game like # Chess which is still in
vogue. In1995. IBM's Deep-Blue; chess
computer could beat Garry Kasparov (Russia) and become the
first computer to defeat a human world chess
champion.
Since the early 1970s, AI is
being taught as a subject of computer science at UG/PG level in most of the engineering colleges and universities
in India and abroad. However, despite this momentum, no real-life
application was made. During the 1970s LISP was used for AI
programming to solve simple problems related to health care, speech recognition
and image processing. During the 1990s, #ALGOL and #PROLOG became popular
languages for AI. However, AI still remained restricted to academic interest in
areas like neural networks, healthcare and speech recognition. Other areas
related to AI are computer science, mathematics, neuron science, psychology,
biology, philosophy, sociology. Currently, research in AI has been
focused mainly on learning, reasoning, problem-solving, perception, #pattern
matching, and #language understanding.
China had plans to catch up with the west in AI utilization by
2020 and lead the world in AI by 2030. China has very large data with 751
Million Internet users and therefore China wants to leverage AI for its speedy
economic growth. In October 2017, Chinese President Xi, Jin-Ping had set the
goal of promoting further integration of the Internet, Big Data and AI to
become a leading world economy. As per the 2021 economic survey, China has
already surpassed the USA and now China is the richest country in the world.
Likewise, many other nations like the USA, Russia, Japan, England, France,
Germany, Holland, Israel, Canada, Sweden, Australia, and India have given
priority to various AI initiatives in many fields.
Definitions and Terminologies. AI
relates to a software application where computer stored program (Algorithm) is
able to perform tasks normally performed by human intelligence (Natural
Intelligence). This included areas like visual perception, face reading, speech
recognition, translating between languages, decision-making, moving and
positioning of objects. It is computing /machine’s ability to emulate
some of the human behaviour. By embedding suitable algorithms, the
computing machine can learn like a child/technician/soldier and soon acquire
sufficient knowledge to function in autonomous mode. However, common use of AI
is often confused between Data Science, Data Analyst
(DA), ML and DL which are interrelated specializations. Simple definitions and
some commonly used terms are briefly given below:
·
Data Science. It is a multi-disciplinary field that
uses scientific methods, processes, algorithms and computing systems
to extract intelligence from structured and unstructured data.
Large data streaming from various sources is stored in data warehouses, network
nodes and various sensors/computing devices. This needs data mining to use the
outcome as input in an iterative way to generate required Business Intelligence
(BI).
·
Data Scientist. Data scientists are experts in mathematics, domain #knowledge,
and technology to work at the raw database level. They dig down and discover
new solutions. A data scientist has to be very inquisitive, asking new
questions, making new discoveries, and learning new things progressively.
·
Data Analyst. Data analysts look at data to try to gain insights. They may
interact with the database and decide how to meet Customer Requirement (CR).
·
Analytics. The term Analytics has become popular in the past few years.
They are specialists in critical thinking and #quantitative/ statistical
techniques for fast decision making.
·
Machine Learning (ML). ML is a term closely associated with data modelling. In an
iterative way, it uses a built-in algorithm to decipher patterns in data and
make predictions. The main concept of ML is to use tagged data to
train the machine to accurately predict future outcomes. It is similar to a
mother teaching her small child how to respond to various situations. More details are covered in Chapter 05.
·
Deep Learning (DL). DL is a special form of ML DL is finding its use in areas
like healthcare, autonomous driving, sign language reading, music generation,
image processing, document retrieval and summarizing.
AI Data Bases. AI database size and its processing are quite
different from traditional RDBMS, which is based on Relational Algebra and #SQL. AI database combines data warehousing, analytics, and
visualizations. AI database should be able to simultaneously digest, explore,
analyze, and visualize fast-moving and complex data within milliseconds. Many
computing companies have developed a special AI Chip to speedily handle large
volumes and complex data. The chip has to have an embedded algorithm which makes
the chip layout complex. AI chip will help in pattern recognition, image
classification or Natural Language Processing (NLP).
Business Intelligence (BI). BI is the most vital for strategic
planning and market competition in any business. BI is redefining many
businesses processes to achieve higher #productivity and #efficiency. In global
market, the speed and accuracy of information provides the cutting edge and for that AI is the best tool.
Attributes of a #Trustworthy AI System. Human intelligence and interpretation are
based on domain knowledge and social knowledge, built over many years. While
designing AI-based Decision Support Systems (DSS), we extrapolate human
judgment (ideas) and embed it in the AI system. From a user point of view, the
AI system must be very fast, secure and trustworthy, IBM team has proposed the following basic attributes of a reliable AI system.
• Understandability. AI systems should provide decisions or
suggestions that can be easily understood by their users and
developers. Easy to explain and easy to understand reduces uncertainty and
helps to further improvement of the model.
• Unbiased AI systems should use training data that are free of
bias. A bias in training data can result in unfair results by the ML
system. Establishing tests for identifying and minimizing bias in training
datasets should be a key factor to establishing fairness in AI systems.
• Robustness. AI systems should be safe and secure, not
vulnerable to tampering or hacking. The robustness of AI model is related to the following factors:
• Reliability. AI
reliability is the ability of an AI model to build knowledge that faithfully
incorporates government policies, regulations and societal norms. Ensuring
reliability with the consistency of AI models is an important criterion of a
trustworthy AI system.
• Security. AI
models are highly susceptible to Cyber-attacks. The accuracy of AI models is
directly correlated to their vulnerability to small fluctuation in the input
dataset. The hackers often try to alter specific datasets and change the
outcome of the AI models. Periodic testing and benchmarking of AI models
against expected cyber-attack is the key to establishing trust in AI systems.
• Traceability. AI
systems should include details of their development, deployment, and
maintenance so that these can be audited throughout their lifecycle.
Factors accelerating AI Growth. Some of the factors for the speedy advancement of AI are
briefly given below:
·
Participation
by Academic Institutes.
Active participation by the educational institutes/universities to quickly
evolve industry-oriented programs and offer courses related to AI skills. This
makes the students more employable and quickly billable by the industry.
·
Industry Participation. Active
participation by the industry in recent years for validating AI-related
products and processes have boosted AI development. Industry motivation is
to use AI systems and get higher productivity, better efficiency, and greater
Quality Assurance (QA) for their products and service.
·
Availability
of large data. The validation of
AI models needs very large and authentic data. This has now become possible due
to easy and affordable access to Big Data, generated from e-commerce,
businesses, government departments, science institutes, research labs and
social media.
·
Improved
Response Time. AI is
needed for fast accessing and interpreting of digitized legal proceedings,
medical diagnostics and insurance claims. By integrating AI system with Cloud
Computing and Big Data, response time has improved significantly. This is a good booster for AI applications and customer
participation.
·
Algorithms. With availability of large data for
training and validating algorithms, the quality of outcome and decision making
has improved considerably.
·
Computing
Power. Cloud Computing is
making available greater computing power and processing speed at an affordable
cost. This is another factor for the faster growth of AI.
·
Programming
Languages. The rise of OOP languages like Python,
Kotin, GO and Ruby has helped in fast and efficient coding of comprehensive
algorithms and embedding their code in the microchips.
·
Cloud
Computing Services. Easy
availability of affordable cloud-based services, for validating various algorithms.
Functional
Grouping of AI Applications. In
today’s economy, AI is a very versatile and useful software tool used in all
technology-driven digital applications. AI can help to solve complex
problems related to various organization/departments like the judiciary,
security, entertainment, education, healthcare, commerce, transport,
agriculture, and defence applications. AI applications can be categorized into
the following functional groups:
·
Knowledge: The ability to present knowledge about
the real world. e.g. financial market trading, purchase prediction, fraud
prevention, drug creation, medical diagnosis and making appropriate
recommendations.
·
Language
Translation: The ability
to understand spoken and written language for real-time translation to any
specified language. AI can also understand sign language, interpreting gestures
and facial expressions. This is very helpful during
international conferences
·
Perception: The ability to infer things about the
real world through sounds, images, and other sensory inputs which are needed
for medical diagnosis, autonomous vehicles, and surveillance.
·
Planning: The ability to set and achieve
goals. e.g. inventory management, demand forecasting, predictive
maintenance, physical and digital network optimization, navigation, scheduling,
and logistics.
·
Prediction
and Forecast: Ability
of AI tools to quickly sift through large data related to weather, industry
production, sales, project management, healthcare, education, agriculture, and
employment and predict /forecast for future activities.
·
Reasoning: The ability to solve problems through
logical reasoning and deduction in areas like financial application processing,
financial asset management, legal assessment, business alliance and
entertainment games.
Implementing AI. It is now well realized that AI is
not a threat to jobs but a lifeline of every organization and their business
process. For successful implementation of AI, the top management and all staff
should be fully acquainted with AI so that they become enthusiastic to get the best out
of AI,. Many CIOs, CKOs and top technology executives of leading
companies have identified AI as the most disruptive technology that is
transforming their business models. They have also now realized that
AI will not totally upset their business model. Instead, AI will enable them to
remodel their various processes and make their business cost-efficient and more
productive. The senior management must be fully involved and committed to
taking full advantage of AI wave, which is now sweeping the entire industrial
world at a great speed. They should review their existing processes
and their expectations from AI
Educating the top executives, about the capabilities, limitations,
and requirements of AI is the most vital and tedious job for any
CIO/CTO/CKO. CTO needs to acquaint top management with AI concepts,
guide them on how to identify areas where AI can be gainfully applied in their
own organization. To prepare the new young workforce in AI, most of
the engineering colleges/universities, IITs, NITs and other institutes of
higher learning have already introduced UG ( 4 Years) / PG ( 2 Years) level
programs in AI and related areas. AI, ML. DL, Data Science, Cloud Computing
are` becoming more popular programs than Computer Science and Engineering
(CSE).
Emerging trends in AI sectors. In 2015, India had
taken a very bold imitative “Digital India”. To give further impetus to its
digital economy, NITI Aayog (Planning Commission) of India had formulated
comprehensive guidelines in Jun 2018, for introducing AI, ML and Robotics in
various sectors. This
document is available on the web www.niti.gov.in
Future trends in AI use in major sectors are
briefly covered in the succeeding paragraphs.
·
Healthcare. AI and ML technologies have been very
useful in healthcare services because it generates large data to train and
fine-tune algorithms for locating disease patterns faster than human
analysts. Some popular utilization of AI and ML is in diagnostics of
cancer, eye ailments, diabetes, drug effect, DNA analysis, patient symptoms and
suggested treatment. AI has already proved its utility during 2020 to
accelerate research to find new vaccines against the Covid-19 pandemic.
·
Entertainment. The service providers of various Cinema
Screens, TV Channels and Websites use ML algorithms to analyze and compare
their viewership with that of other service providers. It also recommends specific shows to retain
their customers. This also helps to promote various products and services on
News channels and maximize their revenue.
·
Finance.
·
Brand
Promotion. Many leading
finance companies use AI-based NLP tools to analyze the brand sentiments of
potential customers from social media platforms and provide actionable inputs
to the business houses/promoters. Some finance companies use AI-powered B2C
robot advisors for portfolio management.
·
Fraud
Detection. Fraud detection in misuse of Master-Card /
Visa-Card is a very important application of AI. AI-enabled ATM machines can
quickly examine the transaction and give approval for the genuine transactions
and decline fraudulent transactions with intimation to the Credit Cardholder.
· Data Security. With faster and affordable internet facilities and the easy availability of smartphone-like devices, cyber-attacks are becoming a common feature. As most of the nations are moving toward the digital world there are many concerns about the security and privacy of data. This is a big risk for /financial institutions, stock market and even political campaigns like USA presidential election in 2016. Good news is that AI algorithms can automatically do bug fixing. Likewise, AI could also predict the likelihood of cyber-attacks. AI can also detect and remove malware code.
·
Manufacturing. ML and Robots/ Cobots enabled with AI have
revolutionized the entire manufacturing sector, be it automotive, household
appliances, textile, or processing industry. Some very common examples are
shown below:
·
ML
algorithm. It can assist to find microscopic defects in objects like circuit boards
·
Self-driving
robots. These can move
finished goods around without endangering anyone or anything around.
·
Collaborative
robots (Cobots). Cobots are
enabled by AI to accept fresh instructions from humans and work collaboratively
with them.
·
Supply
chain. AI can help in detecting the patterns of
demand for products across geographical regions, socioeconomic segments,
festival/seasonal rush and improve delivery time. This will be cost-effective
and will also delight the customers.
·
Inventory
Management. AI can help in efficient vendor management
for raw material sourcing, financing decisions, human staffing, energy
consumption, and maintenance of equipment.
·
Preventive
breakdown of vehicles/machines. AI
can assist in predicting malfunctions and breakdown of equipment/devices and
recommending timely pre-emptive actions.
·
Monitoring
work environment. AI can help in
tracking operating conditions and performance of factory environment,
·
Automotive Industry
·
Self-driven
cars. Google and Uber have already developed
and launched self-driven cars in the USA and Europe. This is helpful
for elderly or handicapped people to reach their destination safely.
·
Car
Safety. The AI-based system when installed in
the car will enable safety warnings, related to
speed, approaching vehicle, road conditions and lane changing, For this,
number of sensors will be installed along the roads.
·
Rail
safety. To avoid rail accidents, an AI-based system
can be installed in train driver’s compartment and installing sensors at level
crossings and track changing points. Such an AI-enabled system will control
train speed as per track conditions and also avoid any head-on collision due to
wrong track changing.
·
Car
theft alarm. Some
cars have microprocessors installed along with the ignition system. When a
person inserts a physical key AI system matches the car key with a pre-stored
software key and if there is mismatch, it immediately cuts off car ignition
system so that the unauthorized person cannot take away the car. In addition,
AI software can recognize biometric signs and set the alarm, if an unauthorized
person attempts to open the car.
Areas to build a career in AI. There are many areas where you can enter, build and excel in your
skill-set. AI is now used extensively in many applications: as shown
below:
·
Experts Systems. AI is already being gainfully used in agriculture, road
transportation, and weather prediction. You may have your choice.
·
Knowledge-Based Systems
(KBS). These are being used in many Defence and some Business
applications. It suits those who have a flair for R&D.It has been in
USA since the 1980s. With arrival of AI, KBS has got a big boost.
·
Fuzzy Logic. It is finding vast applications for household appliances like
washing machines, dishwashers and many business applications.
·
Speech recognition. This is quite a common application since the early 1970s.
It provides customer-friendly auto-response to queries for travel and
hotel/ resort reservations.
·
Robots. The heart of the Robot is AI and embedded
logic. Robots are already being used in many Military activities,
Healthcare, Business applications, Animated movies and Entertainment.
·
Machine Learning (ML). AI forms a major part of ML Systems which are being
used for diagnostics and interpretation of laboratory tests. ML also
playing a major role in industrial automation, record keeping and
information retrieval.
·
Navigation and control
system. This is particularly useful for aviation, aerospace missions,
air flights, and shipping. It suits aeronautical engineers.
·
Lie Detector. A lot of research in USA and UK has been carried out to
accurately detect micro-expressions on the face of the accused so that
police/law enforcement agency can accurately detect if the suspect is telling a
lie. This facility will also facilitate the judge to award appropriate
punishment or grant bail/parole to the convict.
·
Prediction in Casinos. In leading casinos like those in Loss Vegas, the owners are
using AI to recognize and record facial expressions to identify the risk
appetite of probable losers. There is risk of its misuse and cheating the
participants.
·
Guided weapon systems. AI has many applications in military warfare. It is
especially needed for surveillance, navigation/tracking, terrain
analysis,
·
Tracking transport fleet. AI gets integrated with satellite
imageries and Global Positioning / System (GPS) to monitor,
direct and control the movement of vehicles, boats and excursion teams..
·
Self-learning. There
are many tools for language translation and presentation of course content. It
helps learn any foreign language at your own place and pace and add value to
your CV.
·
Translation. A number of internet service
providers like Microsoft and Google are offering real-time
machine-based translation from one language to another language.
·
Video
gaming and entertainment. The
toy industry with embedded AI has a great market for children entertainment.
Video games needing higher skills and visualization need AI components suitably
embedded.
Sources for AI
Courses. To give impetus
to AI and meet the ever-increasing demand for AI qualified workforce, most of
the leading universities in the USA, UK, Canada, Australia and Indian
IITs, NITs have revised their curriculum of UG/PG level engineering courses in
Computer Science, Electronics and Communications, Instrumentation and
Control. You can choose a full time or part-time course at your convenient
time, pace and cost. In addition, you can choose online free
AI courses from:
· t USA. Learn AI with Google, ML from, Stanford
University, Harvard University – Data Science from Columbia
University, Fundamentals of DL and Computer Vision
from, Columbia University and Self
Driving Cars from. MIT and many
more
·
India. IIT Bombay -NLP, IIT, Kharagpur - AI,
IIT Rookie – ML, NIT. Warangal – ML and DL, Cisco
Bangalore-ML. There are many private training institutes offering AI/ML
courses that you find through Google search.
If you undertake any of the above online AI courses and do not pay
the fee for certification, the course will be free but considered an audit. It
is therefore recommended to pay a fee of $40 to $60 and get certified since
that will show your competency and value addition to your CV.
Required Skills for
Carrier in AI
·
Programming Languages for AI. Java, Python, Ruby, Scala, GO and C++ are popular
OOP programming languages for AI applications.. All these programming
languages are suitable for the development and design of different
AI-related applications. It is up to the developer to choose
which of the AI languages will satisfy the desired functionality and features
of the application. There are some advantages and some limitations of
these languages when we deal with Mobile phone devices, very high response time
and large heterogeneous data. However, if you have mastery of one of
these languages, switching to others programming languages is quite easy and it
takes just 2-3 weeks. Among most AI developers, Python is the
favourite programming language for AI development because of its syntax
simplicity and versatility. Python is a very portable language as it is used on
many platforms including Linux, Windows, Mac OS, and UNIX.
Python supports, multiprogramming, Neural networks and the development
of NLP solutions It has a
powerful library of many simple functions which speeds up coding.
·
Expertise in Mathematics. Building analytic models for various business applications
involves using applied/engineering mathematics and statistical
techniques. Therefore, Data scientists should have good knowledge of
applied mathematics covering linear algebra, matrix algebra, numerical methods,
and statistical techniques. These are needed for data mining of vast data. There
are correlations in data that need to be expressed mathematically while
developing algorithms.
·
Domain Knowledge. Having good business knowledge is as important as having good
knowledge of technology and algorithms. Remember the real value of a business
solutions doesn't come from data, mathematics, or technology alone. It comes
from judicious blend of Technology related skills and domain knowledge to build
an appropriate business model and develop a cost-efficient business
solution.
·
Statistical Tools. Besides SAS and Minitab, “R” has emerged as a more popular
statistical programming language for forecasting, predicting, interpreting
and decision making.
·
Embedded
Technologies. AI uses the
embedded software in microcontrollers and microprocessors which form the heart
of Robots and, Drones. The navigation system and driver-less vehicles. It uses
lots of inbuilt controllers, which are programmable with autonomous built-in
control and/or external remote control system. Embedded
software and optical devices are already facilitating many activities in areas
of healthcare, diagnostics, physiotherapy and surgery.
Summary. AI has been gaining a lot of momentum since 2005 though some less
informed people feel threatened about their jobs being taken away by AI. This
is just an apprehension by those who are a bit lazy to upgrade their knowledge
and add AI to supplement their skills. The idea that routine manual work can be
carried out by the machine is quite old and well known to all. AI-based
machines can perform tasks that were earlier done by the knowledge workforce.
We all know that computer/logic controlled machines can do many forms of
routine manual tasks, but now these machines are able to perform some routine
cognitive tasks too.
Although AI has been around since the 1950s,
yet it is only during last 15 years that AI has been actively applied to
real-world applications in diverse fields. The investment in AI by technology
leaders like Google, Microsoft, IBM, Cisco, Facebook as well as some start-ups
has increased 3 times and was having nearly $40 Billion
business as of 2017. The
report published in 2018 by Forbes suggests that AI will create 58 million
new jobs by 2022. Likewise a report
from the World Economic Forum (WEF), Sep 2018 indicates that Machines
and Algorithms are expected to create 133 million new jobs,
but also take away 75 million jobs by 2022. Another report published by Forrester
Research suggests that organizations that use AI and related
technologies will have an edge and take away $1.2 trillion per annum from their
less-informed peers by 2020. Post-Covid 19, the role of AI
is many times more than estimated.
As most of the countries are now becoming Digital Economies, AI
has a big role to play. In it's Budget 2019, India had given a special boost to
AI for agriculture, processing, transportation, education, and research. AI
also supports military warfare, political campaigning /psychological warfare. AI has also a role in controlling
global warming. In the coming years, there will be more integration
of technologies to reap the full benefits of AI and associated technologies.
An old view about AI was that it was a job
snatcher and makes people redundant earlier than expected. A similar
view was expressed even by Jack Ma, the founder of Ali Baba, at the
World Economic Forum 2018, at Davos- “AI and Big Data were a threat to the
human workforce, as it would restrict human’s natural
thinking. People will stop thinking, analyzing and making a
decision. They will depend heavily on machine-generated information”.
However, Jack Ma’s view is not fully supported by other economists
and technology experts. Instead, they all promote AI as a software
tool for achieving enhanced efficiency, more productivity, and higher
profitability. Given many real-world applications of AI, ML and continuing
advancements in AI, it rapidly changing the way we work and do our
business It is now well established that AI
will create many new jobs but require new skills. Definitely, AI
skilled workforce will draw a better salary. AI will further supplement human
effort in many fields leaving human beings to do more creative thinking and
innovation and better integration/interfacing systems and sub-systems.
Unexpected global spread of COVID 19, leading to shutting down of many and
extended lockdowns have forced large number of people to Work From Home (WFH).
From early 2020, the Covid 19 has seriously impacted manufacturing,
trading, education and travel. Indeed, future business environment will be quite
different and therefore all young professionals must quickly acquire AI skills
to stay in demand during global uncertainty.
Summary. AI has been gaining a lot of momentum since 2005
though some less informed people feel threatened about their jobs being taken
away by AI. This is just an apprehension by those who are a bit lazy to upgrade
their knowledge and add AI to supplement their skills. The idea that routine
manual work can be carried out by the machine is quite old and well known to
all. AI-based machines can perform tasks that were earlier done by the
knowledge workforce. We all know that computer/logic controlled machines can do
many forms of routine manual tasks, but now these machines are able to perform
some routine cognitive tasks too.
Although AI has been around
since the 1950s, yet it is only during last 15 years that AI has been actively applied to real-world applications in diverse fields.
The investment in AI by technology leaders like Google, Microsoft, IBM, Cisco,
Facebook as well as some start-ups has increased 3 times and was having nearly $40 Billion business
as of 2017. The
report published in 2018 by Forbes suggests that AI will create 58 million
new jobs by 2022. Likewise a report from the World Economic Forum (WEF),
Sep 2018 indicates that Machines and Algorithms are expected
to create 133 million new jobs, but also take away 75
million jobs by 2022. Another report published by Forrester
Research suggests that organizations that use AI and related
technologies will have an edge and take away $1.2 trillion per annum from their
less-informed peers by 2020. Post-Covid 19, the role of AI is many times more than estimated.
As most of the countries
are now becoming Digital Economies, AI has a big role to play. In it's Budget
2019, India had given a special boost to AI for agriculture, processing,
transportation, education, and research. AI also supports military warfare,
political campaigning /psychological warfare. AI has also a role
in controlling global warming. In the coming years, there will be
more integration of technologies to reap the full benefits of AI and associated
technologies.
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