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Artificial and Human Intelligence in Business
Being a Webinar Paper Presented at the Institute of Chartered Accountants of Nigeria (ICAN)
Ikorodu & District Society on Saturday, 13th May 2023.
Prof. Godwin Emmanuel
Oyedokun
Professor of Accounting and Financial
Development
Department of Management & Accounting
Faculty of Management and Social Sciences
Lead City University, Ibadan, Nigeria
Principal Partner; Oyedokun Godwin Emmanuel &
Co
(Chartered Accountants, Tax Practitioners &
Forensic Auditors)
ND (Fin), HND (Acct.), BSc. (Acct. Ed), BSc (Fin.), BSc. (Bus. Admin), MBA (Acct. & Fin.), MSc. (Acct.), MSc. (Bus & Econs), MSc (Tax),
MTP (SA), PhD (Acct), PhD (Fin), PhD (FA), FCA, FCTI, FCIB, ACS, ACIS, MNIM, FCNA, FCFIP, FCE, FERP, CICA, CFA, CFE,
CIPFA, CPFA, ACAMS, ABR, CertIFR, IPA, IFA, FFAR, FPD-CR, FSEAN, FNIOAM, ACIrb
Artificial and Human
Intelligence in Business
Introduction
Artificial intelligence (AI) is an interdisciplinary field that has roots in computer science,
mathematics, psychology, and philosophy
AI is a form of technology that enables machines to think and act like humans. This
technology has advanced rapidly in recent years and has revolutionized the way we live and
work
AI has a wide range of applications in various fields, including healthcare, finance, education,
transportation, and entertainment
Artificial Intelligence (AI) is a rapidly emerging technology that is transforming businesses,
human intelligence as the controlling factor manages and is used for a wide range of
applications from customer service and marketing to automation and operational efficiency
Narrow AI and General AI
Narrow AI involves
designing systems that
can perform specific
tasks, such as image or
speech recognition,
automated reasoning,
and game playing
General AI, also known as
strong AI or Artificial
General Intelligence
(AGI), involves designing
systems that can perform
any intellectual task that
a human can do
General AI is still a
theoretical concept, and
no system has yet been
able to surpass human-
level intelligence
History of Artificial Intelligence
In 1950, Alan Turing published the milestone paper "Computing machinery and intelligence" (Turing 1950), considering
the fundamental question "Can machines think?”
Turing proposed an imitation game, known as the Turing test afterwards, where if a machine could carry on a
conversation indistinguishable from a conversation with a human being, then it is reasonable to say that the machine is
intelligent
The Turing test was the first experiment proposed to measure machine intelligence
The first “AI period” began with the Dartmouth conference in 1956, where AI got its name and mission McCarthy
coined the term “Artificial Intelligence," which became the name of the scientific field
The primary conference assertion was, "Every aspect of any other feature of learning or intelligence should be
accurately described so that the machine can simulate it” (Russell and Norvig 2016)
People were excited because for the first-time computers were solving problems like humans and seemed intelligent
The wider AI research community shared an initial optimism making bold claims and boosting popularity in solving
problems like algebraic application problems, language translation, geometric theorem proving, etc
List of Several Important AI breakthroughs
1952 Checkers was the first program to demonstrate that computers can learn and not just
perform what they are programmed to do
Checkers attracted media attention and learned to play at a level high enough to challenge a
decent amateur human player (Samuel 1960)
1955 The Logic Theorist had proven 38 theorems from Principia Mathematical and introduced
critical concepts in artificial intelligence, like heuristics, list processing, ‘reasoning as search,' etc.
(Newell et al. 1962).
1957 Inspired by the human brain, Rosenblatt discovered the perceptron
The perceptron was predicted to be "the embryo of an electronic computer that will be able to
walk, talk, see, write, reproduce itself and be conscious of its existence
"The perceptron was the birth of connectionism, the foundation of Neural Networks (NN) and
Deep Learning (Rosenblatt 1961).
List of Several Important AI breakthroughs
1961 Machine Educable Nougats and Crosses Engine (MENACE) was one of the first programs capable of
learning to play a perfect game of TicTacToe (Michie 1963)
1965 ELZA was a natural language processing system that imitated a doctor. ELIZA responded to questions like a
psychotherapist
Some users believed they are interacting with another human being until it reached its limitations, and the
conversation became nonsense (Weizenbaum 1966)
1969 Shakey the Robot was the first general-purpose mobile robot capable of reasoning its actions. This project
integrated research in robotics with computer vision and natural language processing, thus being the first
project that combined logical reasoning and physical action (Bertram 1972)
1969 The book “Perceptrons” highlighted unrecognized limits of the feed forward, two layered perceptron
structure. The authors' pessimistic predictions made a fundamental shift in the AI research direction to symbolic
and disregarding connectionism
"Perceptrons" marks the beginning of the AI winter of the 1970s (Minsky and Seymour 1969)
Concept of Artificial Intelligence
Rule-based systems
• They are pre-programmed with a set of
rules that guide their decision-making
process
Machine learning
• It involves teaching machines through
training data
Deep learning
• It involves the use of neural networks to
learn and improve on its own
Evolution of Artificial Intelligence (AI)
Artificial Intelligence has been around for centuries, with early examples including the Mechanical Turk and
Ada Lovelace until the 1950s when AI began to take off with the advent of digital computers and new
algorithms
In 1956, a team of researchers at Dartmouth College organized a conference on AI, which is often
considered to be the birth of the field
Interest in AI continued to grow throughout the 1960s and 1970s, with major advances in natural language
processing and expert systems
AI experienced a renaissance in the 1990s due to improved computer hardware and software and
increased interest from the business community
AI has made rapid progress since 1990s and is now used in many fields such as healthcare, finance,
transportation, and manufacturing
Artificial Intelligence-related terms
• A set of step-by-step instructions
• Computer algorithms can be simple (if it’s 3 p.m., send a reminder) or complex (identify
pedestrians)
Algorithm
• The way many neural nets learn
• They find the difference between their output and the desired output, then adjust the
calculations in reverse order of execution
Back-Propagation
• A description of some deep learning systems
• They take an input and provide an output, but the calculations that occur in between are not
easy for humans to interpret
Black Box
• How a neural network with multiple layers becomes sensitive to progressively more abstract
patterns
• In parsing a photo, layers might respond first to edges, then paws, then dogs
Deep Learning
• A form of AI that attempts to replicate a human’s expertise in an area, such as medical
diagnosis
• It combines a knowledge base with a set of hand-coded rules for applying that knowledge
• Machine-learning techniques are increasingly replacing hand coding
Expert System
Artificial Intelligence-related terms
• A pair of jointly trained neural networks that generates realistic new data and improves
through competition
• One net creates new examples (fake Picassos, say) as the other tries to detect the fakes
Generative Adversarial
Networks
• The use of algorithms that find patterns in data without explicit instruction
• A system might learn how to associate features of inputs such as images with outputs such as
labels
Machine Learning
• A computer’s attempt to “understand” spoken or written language
• It must parse vocabulary, grammar, and intent, and allow for variation in language use
• The process often involves machine learning
Natural Language
Processing
• A highly abstracted and simplified model of the human brain used in machine learning
• A set of units receives pieces of an input (pixels in a photo, say), performs simple
computations on them, and passes them on to the next layer of units
• The final layer represents the answer
Neural Network
• A computer chip designed to act as a neural network. It can be analog, digital, or a
combination
Neuromorphic Chip
Artificial Intelligence-related terms
Perceptron
• An early type of neural network, developed in the 1950s
• It received great hype but was then shown to have limitations, suppressing interest in neural nets for years
Reinforcement Learning
• A type of machine learning in which the algorithm learns by acting toward an abstract goal, such as “earn a high video
game score” or “manage a factory efficiently.”
• During training, each effort is evaluated based on its contribution toward the goal
Strong AI
• AI that is as smart and well-rounded as a human
• Some say it’s impossible, Current AI is weak, or narrow
• It can play chess or drive but not both and lacks common sense
Supervised Learning
• A type of machine learning in which the algorithm compares its outputs with the correct outputs during training
• In unsupervised learning, the algorithm merely looks for patterns in a set of data
Artificial Intelligence-related terms
TensorFlow
• A collection of software tools developed by Google for use in deep learning
• It is open source, meaning anyone can use or improve it
• Similar projects include Torch and Theano
Transfer Learning
• A technique in machine learning in which an algorithm learns to perform
one task, such as recognizing cars, and builds on that knowledge when
learning a different but related task, such as recognizing cats
Turing Test
• A test of AI’s ability to pass as a human
• In Alan Turing’s original conception, an AI would be judged by its ability to
converse through written text
Build-Ups of Artificial Intelligence
Fostering
collaboration
across
functions
Structuring
and
formalizing
AI
management
through a
framework
Focusing on
AI as a
strategic
asset
Defining
how and
who makes
decisions.
Developing
supporting
artifacts
(policy,
standards, and
procedures)
Monitoring
compliance
Principles on Artificial Intelligence
Transparency
Justice and
fairness
Non-
maleficence
Responsibility Privacy Reliability
Safety Security Inclusiveness
Guidelines on Artificial Intelligence
Safety
• It is a primary concern when it comes to AI governance, as AI systems can be used in ways that create safety risks
• To ensure safety, AI must be designed and implemented in such a way that it minimizes potential risks and hazards
• Ensuring that AI systems are built with robust security measures, such as access control and intrusion detection, and
that they are tested and monitored for potential safety issues
Security
• Given the sensitive and confidential nature of data, it is important to ensure that AI systems are secure
• This requires implementing rigorous security measures, such as authentication, encryption, and auditing, to protect
data from unauthorized access and to ensure that data remains secure
Ethics
• It is important to consider the ethical implications of using AI in business in addition to safety and security
• AI is often used for decision-making and predictive analytics, which can create ethical concerns that must be
addressed
• When using AI to make decisions, it is important to make sure that the decisions are fair and unbiased
• Ensuring that the decision-making process is transparent and that it is based on valid data and ethical principles
Pros and Cons of Artificial Intelligence
Cost savings
Improved decision-making
Increased efficiency
Job loss
Ethical
concerns
Security
risks
Pros Cons
Examples of Artificial Intelligence Models
Siri
• Apple’s assistant, Siri, is a friendly voice-activated computer that many people interact with daily
• She helps find information, gives directions, helps send messages, and much more
Alexa
• The smart home’s hub can interpret speech from anywhere in the room, help search the web for
information, shop, schedule appointments, set alarms, and perform hundreds of other daily tasks
Amazon.com
• Amazon uses AI with algorithms that the company uses to predict what you’re interested in purchasing
based on your online behaviour
Netflix
• Netflix provides highly accurate predictive technology based on customer’s reactions to films
• This tech is getting brighter as each year passes
Pandora
• Pandora, available only in the United States, is a leading music and podcast discovery platform, providing a
highly personalised listening experience
Technical Initiatives in Artificial Intelligence
The most prominent initiative from the technical community can be found in the Institute of Electrical and Electronic
Engineers (IEEE)’s work on AI, in the form of its Global Initiative on Ethics of Autonomous and Intelligent Systems
The IEEE has involved its membership of technical experts but also reached beyond to non-IEEE members to participate
in this project
The initiative has produced two versions to date of Ethically Aligned Design, involving ‘hundreds of participants over six
continents’ (IEEE 2018)
Version 2 includes five General Principles to guide the ethical design, development, and implementation of autonomous
and intelligent systems
In line with IEEE’s general activities, the development of technical standards based on these discussions on ethics is
envisaged by the Global Initiative, and a series of working groups have been set up under the Global Initiative to work
towards this goal
Criticism of Artificial Intelligence (AI)
There is still a high level of
uncertainty about how AI
technology can be used
effectively to generate value
in organizations and how to
specifically leverage the
technology to make profits for
organizations
It is accompanied by disquiet
over problematic and
dangerous implementations,
or indeed, even AI itself
deciding to do dangerous and
problematic actions, especially
in fields such as the military,
medicine, and criminal justice
It also attracted criticism
during this time, with some
believing that it would never
be possible to create
intelligent machines
Types of Artificial Intelligence
• These machines do not have any memory or data to work with, specializing in just one field of work
• For example, in a chess game, the machine observes the moves and makes the best possible decision to win
Purely Reactive
• These machines collect previous data and continue adding it to their memory
• They have enough memory or experience to make proper decisions, but memory is minimal
• This machine can suggest a restaurant based on the location data that has been gathered
Limited Memory
• This kind of AI can understand thoughts and emotions, as well as interact socially
• However, a machine based on this type is yet to be built
Theory of Mind
• Self-aware machines are the future generation of these new technologies
• They will be intelligent, sentient, and conscious
Self-Aware
• It is machine learning that gives AI the ability to learn
• This is done by using algorithms to discover patterns and generate insights from the data they are exposed to
Machine
Learning
• Deep learning, which is a subcategory of machine learning, provides AI with the ability to mimic a human brain’s
neural network
• It can make sense of patterns, noise, and sources of confusion in the data
Deep Learning
Advantages of AI
Reduction in
Human Error
Zero Risks
Unbiased
Decisions
Perform
Repetitive Jobs
Medical
application
Disadvantages of AI
High Costs No creativity
Unemployme
nt
Make
Humans Lazy
No Ethics Emotionless
Human Intelligence
Human intelligence is a mental attribute that consists of the capacities to learn through
experience, adjust to changes and new conditions, grasp and manage complex ideas, and
influence one's environment using knowledge
human intelligence, mental quality that consists of the abilities to learn from experience,
adapt to new situations, understand and handle abstract concepts, and use knowledge to
manipulate one’s environment
A general mental ability for reasoning, problem solving, and learning, People can reason
about virtually any issue, and many problems may be solved
Intelligence integrates cognitive functions such as perception, attention, memory, language,
or planning
Simple and highly complex behavioral repertoires can be learned throughout the lifespan
Development of Human "Cognitive Intelligence"
Being able to understand a
language
Being able to understand
numbers
Being able to understand abstract
thinking
Types of Human Intelligence
Intelligence Quotient
Spiritual Quotient
Emotional Quotient
Intelligence Quotient
A measure of a person's thinking capacity
It is meant to assess how well someone can utilize facts and reasoning to
answer questions or make predictions
Short- and long-term memory is measured in IQ tests to begin to examine this
They also assess how quickly people can solve puzzles and recollect information
they've heard
Spiritual Quotient
Spirituality
improves a
person's ability
to be creative,
alert, and
analytical
Spirituality can
help to
strengthen the
power of
intuition and
awareness
It greatly aids
in coping with
and
eradicating
current
problems like
terrorism, self-
centeredness,
and a lack of
humanity
SQ makes us
more
accountable to
ourselves, our
families,
society, our
surroundings,
our country,
and finally our
cosmos
It assists us in
connecting our
own vision
with the
greater welfare
of humanity
It makes us
more modest
in our outlook,
It significantly
diminishes our
selfish
intentions
Emotional Quotient
Capacity to understand, use, and control one's own emotions in constructive
ways to reduce stress, communicate effectively, sympathize with others,
overcome problems, and diffuse conflict
It assists you in developing stronger connections, succeeding in school and at
work, and achieving your professional and personal objectives
The most prevalent method of measuring things like personality
characteristics is self-report
Warmth, empathy, anxiousness, and other characteristics are examples of
personality qualities
Artificial Intelligence and Automation
(AI) is the science of creating machines that are tasked to solve problems and accomplish some work that is too complicated
for the human brain to process by itself
AI is an effort to make computers think like humans, creating machines with minds, in the full and literal sense
AI is not science fiction, but real science, based on the concept of intelligent machines
Examples of AI includes ride-sharing apps like Uber and Lyft, speech recognition, problem-solving, machine learning, smart
email categorization, spam filters, Amazon Alexa devices, Chatbots, Apple’s Siri, Google Now, Internet of Things, Google
Predictive searching, Product recommendations, and more
It is the development of computers system that can perform tasks that typically require human intelligence such as visual
perception, speech recognition, decision making and problem solving
AI systems are typically designed to be able to learn from experience, adapt to new inputs, and improve their performance
Artificial Intelligence
Artificial Intelligence and Automation
Refers to the use of
technology to
automate tasks that
were previously
performed by
humans
Automation can be
powered by a variety
of technologies,
including AI,
robotics, and
machine learning
that uses technology
to automate
processes with
minimal or no
human intervention
Automation is all
about making
machines capable
enough of doing
monotonous tasks
automatically and
effectively
Automation is not a
new concept, It is the
ability of machines
to perform tasks that
would otherwise be
impossible or
difficult when
performed by
humans
Automation is
everywhere, any
device or machine
that operates
autonomously is an
automated system
Automation
Artificial Intelligence and Automation
There are numerous real-life examples of automation you come across every day such as
payment apps, thermostats, coffee makers, an ATM, automated parking systems, automated
machine assembly, shopping apps, and so much more
Automated systems can be generally classified as fixed automation and programmable
automation
Examples of automation include form auto-filling, customer support, digital signatures,
automatic assembling, employee analytics, online banking, payment processing, numerical
control machines, material handling robots, highway systems, automated waste
management, automated retail, video surveillance, etc
Automation
Relationship Between AI and Automation
Both the terms Artificial Intelligence (AI) and Automation are often used interchangeably
but were two different terms
Artificial Intelligence
• AI is the science of creating machines that are tasked to solve problems and accomplish some tasks that are too complicated
for the human brain to process by itself
• AI focuses on creating highly intelligent machines to accomplish tasks that in normal sense would be called intelligent thinking
or behavior
• AI is the science and engineering of making machines smart enough to mimic human intelligence and behavior
• The idea behind AI is to create technology that allows computers and machines to think like humans, behave like humans and
learn from them
Automation
• Automation is the use of technology to automate processes with minimal or no human intervention
• Automation is the ability of machines to perform tasks that would otherwise be impossible or difficult when performed by
humans whereas AI is the simulation of human intelligence
• The main objective of automation is to simplify and speed up common, repetitive tasks to increase productivity and efficiency
of output, with less or no human involvement
Reflects on Application of AI to own Context
Healthcare
• AI-powered tools can help doctors diagnose
and treat diseases more accurately and
efficiently, leading to better outcomes for
patients
• AI algorithms can also analyze vast amounts
of medical data and help researchers identify
new drugs and treatments for various illnesses
Finance
• AI-powered chatbots can help customers with
banking and investment queries, while
machine learning algorithms can analyze
financial data and identify trends and patterns
that can help businesses make informed
decisions
Education
• AI is being used to create personalized
learning experience, adapt to students
’individual learning styles and automate
administrative tasks
Transportation
• AI algorithms can optimize routes, reduce
traffic congestion, and enhance driver safety
• Self-driving cars that use AI technology are
also being tested and developed, which could
change the way we commute and travel
significantly
Entertainment
• AI is being used to create personalized
recommendations for movies, TV shows, and
music
• AI is also being used to create virtual
assistants and gaming characters that can
interact with the users in more realistic ways
Key Shift Identification in Workplace Influence by AI
Automation and Efficiency
Improved Decision Making
Upskilling the Workforce
Collaborations between Human and Machines
Enhanced Customer Experience
New Business Models
Assessing Impact Shifts in Workplace
AI Lacks Emotional
Intelligence
AI’s Creative process
is limited to data it
receives
AI does not have soft
skills
Humans make AI work
AI is meant to
complement human
ability and
intelligence, not
compete with it
AI need to be fact
checked
Artificial and Human Intelligence
• A branch of data science that specializes in creating smart
machines that can execute a broad range of tasks that
typically need human intelligence, reasoning and
capabilities
Artificial Intelligence
• Ability that enables us to learn from various experiences,
think, understand intricate concepts, apply reasoning and
logic, solve mathematical problems, identify patterns, make
decisions, hold on to information, communicate with fellow
human beings, and so on
Human Intelligence
Relationship Change Between AI and Humans
• Human Intelligence looks to adjust to new environments by using a combination of various
cognitive processes, while AI aims to create machines that can imitate human behavior and
perform human-like actions
• Machines are digital, but the human brain is analogous
Nature
• Humans use the brain’s memory, computing power and ability to think, while AI-powered
machines depend on data and instructions fed into the system
Functioning
• Human Intelligence is all about learning and understanding from different incidents and
past experiences
• However, AI falls behind in this area, as AI cannot think.
Learning Power
Identifying Required Skills to Work and Interact With AI
Technological adaptation
Active learning with a growth mindset
Basic computer skills
Programming and automation
Analytical thinking
Creativity
Producing Action Plan For The Future
Stay up to date
with the latest
developments
Develop a
solid
understanding
of the
fundamentals
Build your
technical skills
Cultivate soft
skills
Embrace
lifelong
learning
Consider
ethical and
societal
implications
Conclusion
AI is a rapidly advancing field that
has the potential to revolutionize the
way we live and work
It involves developing intelligent
agents that can perform tasks that
typically require human cognitive
abilities, such as perception, critical
thinking, reasoning, learning, and
problem-solving
AI has a wide range of applications
in various fields with its numerous
benefits and applications, it also
poses challenges and ethical
concerns
These concerns include job
displacement, privacy and security,
bias and discrimination, and
accountability and transparency
As AI continues to evolve, it is
important to ensure that it is
developed and deployed with
ethical considerations in mind, to
maximize its benefits while
minimizing its risks
Recommendations
AI systems should be designed to be transparent, explainable, and accountable
Developing algorithms that are fair and unbiased
Promoting diversity and inclusion in the development of AI
Active learning with a growth mindset
Developing humans’ intellectual ability
Prof. Godwin Emmanuel
Oyedokun
Professor of Accounting and Financial
Development
Lead City University, Ibadan, Nigeria
Principal Partner; Oyedokun Godwin
Emmanuel & Co
(Chartered Accountants, Tax Practitioners &
Forensic Auditors)

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  • 1. Artificial and Human Intelligence in Business Being a Webinar Paper Presented at the Institute of Chartered Accountants of Nigeria (ICAN) Ikorodu & District Society on Saturday, 13th May 2023. Prof. Godwin Emmanuel Oyedokun Professor of Accounting and Financial Development Department of Management & Accounting Faculty of Management and Social Sciences Lead City University, Ibadan, Nigeria Principal Partner; Oyedokun Godwin Emmanuel & Co (Chartered Accountants, Tax Practitioners & Forensic Auditors)
  • 2. ND (Fin), HND (Acct.), BSc. (Acct. Ed), BSc (Fin.), BSc. (Bus. Admin), MBA (Acct. & Fin.), MSc. (Acct.), MSc. (Bus & Econs), MSc (Tax), MTP (SA), PhD (Acct), PhD (Fin), PhD (FA), FCA, FCTI, FCIB, ACS, ACIS, MNIM, FCNA, FCFIP, FCE, FERP, CICA, CFA, CFE, CIPFA, CPFA, ACAMS, ABR, CertIFR, IPA, IFA, FFAR, FPD-CR, FSEAN, FNIOAM, ACIrb
  • 4. Introduction Artificial intelligence (AI) is an interdisciplinary field that has roots in computer science, mathematics, psychology, and philosophy AI is a form of technology that enables machines to think and act like humans. This technology has advanced rapidly in recent years and has revolutionized the way we live and work AI has a wide range of applications in various fields, including healthcare, finance, education, transportation, and entertainment Artificial Intelligence (AI) is a rapidly emerging technology that is transforming businesses, human intelligence as the controlling factor manages and is used for a wide range of applications from customer service and marketing to automation and operational efficiency
  • 5. Narrow AI and General AI Narrow AI involves designing systems that can perform specific tasks, such as image or speech recognition, automated reasoning, and game playing General AI, also known as strong AI or Artificial General Intelligence (AGI), involves designing systems that can perform any intellectual task that a human can do General AI is still a theoretical concept, and no system has yet been able to surpass human- level intelligence
  • 6. History of Artificial Intelligence In 1950, Alan Turing published the milestone paper "Computing machinery and intelligence" (Turing 1950), considering the fundamental question "Can machines think?” Turing proposed an imitation game, known as the Turing test afterwards, where if a machine could carry on a conversation indistinguishable from a conversation with a human being, then it is reasonable to say that the machine is intelligent The Turing test was the first experiment proposed to measure machine intelligence The first “AI period” began with the Dartmouth conference in 1956, where AI got its name and mission McCarthy coined the term “Artificial Intelligence," which became the name of the scientific field The primary conference assertion was, "Every aspect of any other feature of learning or intelligence should be accurately described so that the machine can simulate it” (Russell and Norvig 2016) People were excited because for the first-time computers were solving problems like humans and seemed intelligent The wider AI research community shared an initial optimism making bold claims and boosting popularity in solving problems like algebraic application problems, language translation, geometric theorem proving, etc
  • 7. List of Several Important AI breakthroughs 1952 Checkers was the first program to demonstrate that computers can learn and not just perform what they are programmed to do Checkers attracted media attention and learned to play at a level high enough to challenge a decent amateur human player (Samuel 1960) 1955 The Logic Theorist had proven 38 theorems from Principia Mathematical and introduced critical concepts in artificial intelligence, like heuristics, list processing, ‘reasoning as search,' etc. (Newell et al. 1962). 1957 Inspired by the human brain, Rosenblatt discovered the perceptron The perceptron was predicted to be "the embryo of an electronic computer that will be able to walk, talk, see, write, reproduce itself and be conscious of its existence "The perceptron was the birth of connectionism, the foundation of Neural Networks (NN) and Deep Learning (Rosenblatt 1961).
  • 8. List of Several Important AI breakthroughs 1961 Machine Educable Nougats and Crosses Engine (MENACE) was one of the first programs capable of learning to play a perfect game of TicTacToe (Michie 1963) 1965 ELZA was a natural language processing system that imitated a doctor. ELIZA responded to questions like a psychotherapist Some users believed they are interacting with another human being until it reached its limitations, and the conversation became nonsense (Weizenbaum 1966) 1969 Shakey the Robot was the first general-purpose mobile robot capable of reasoning its actions. This project integrated research in robotics with computer vision and natural language processing, thus being the first project that combined logical reasoning and physical action (Bertram 1972) 1969 The book “Perceptrons” highlighted unrecognized limits of the feed forward, two layered perceptron structure. The authors' pessimistic predictions made a fundamental shift in the AI research direction to symbolic and disregarding connectionism "Perceptrons" marks the beginning of the AI winter of the 1970s (Minsky and Seymour 1969)
  • 9. Concept of Artificial Intelligence Rule-based systems • They are pre-programmed with a set of rules that guide their decision-making process Machine learning • It involves teaching machines through training data Deep learning • It involves the use of neural networks to learn and improve on its own
  • 10. Evolution of Artificial Intelligence (AI) Artificial Intelligence has been around for centuries, with early examples including the Mechanical Turk and Ada Lovelace until the 1950s when AI began to take off with the advent of digital computers and new algorithms In 1956, a team of researchers at Dartmouth College organized a conference on AI, which is often considered to be the birth of the field Interest in AI continued to grow throughout the 1960s and 1970s, with major advances in natural language processing and expert systems AI experienced a renaissance in the 1990s due to improved computer hardware and software and increased interest from the business community AI has made rapid progress since 1990s and is now used in many fields such as healthcare, finance, transportation, and manufacturing
  • 11. Artificial Intelligence-related terms • A set of step-by-step instructions • Computer algorithms can be simple (if it’s 3 p.m., send a reminder) or complex (identify pedestrians) Algorithm • The way many neural nets learn • They find the difference between their output and the desired output, then adjust the calculations in reverse order of execution Back-Propagation • A description of some deep learning systems • They take an input and provide an output, but the calculations that occur in between are not easy for humans to interpret Black Box • How a neural network with multiple layers becomes sensitive to progressively more abstract patterns • In parsing a photo, layers might respond first to edges, then paws, then dogs Deep Learning • A form of AI that attempts to replicate a human’s expertise in an area, such as medical diagnosis • It combines a knowledge base with a set of hand-coded rules for applying that knowledge • Machine-learning techniques are increasingly replacing hand coding Expert System
  • 12. Artificial Intelligence-related terms • A pair of jointly trained neural networks that generates realistic new data and improves through competition • One net creates new examples (fake Picassos, say) as the other tries to detect the fakes Generative Adversarial Networks • The use of algorithms that find patterns in data without explicit instruction • A system might learn how to associate features of inputs such as images with outputs such as labels Machine Learning • A computer’s attempt to “understand” spoken or written language • It must parse vocabulary, grammar, and intent, and allow for variation in language use • The process often involves machine learning Natural Language Processing • A highly abstracted and simplified model of the human brain used in machine learning • A set of units receives pieces of an input (pixels in a photo, say), performs simple computations on them, and passes them on to the next layer of units • The final layer represents the answer Neural Network • A computer chip designed to act as a neural network. It can be analog, digital, or a combination Neuromorphic Chip
  • 13. Artificial Intelligence-related terms Perceptron • An early type of neural network, developed in the 1950s • It received great hype but was then shown to have limitations, suppressing interest in neural nets for years Reinforcement Learning • A type of machine learning in which the algorithm learns by acting toward an abstract goal, such as “earn a high video game score” or “manage a factory efficiently.” • During training, each effort is evaluated based on its contribution toward the goal Strong AI • AI that is as smart and well-rounded as a human • Some say it’s impossible, Current AI is weak, or narrow • It can play chess or drive but not both and lacks common sense Supervised Learning • A type of machine learning in which the algorithm compares its outputs with the correct outputs during training • In unsupervised learning, the algorithm merely looks for patterns in a set of data
  • 14. Artificial Intelligence-related terms TensorFlow • A collection of software tools developed by Google for use in deep learning • It is open source, meaning anyone can use or improve it • Similar projects include Torch and Theano Transfer Learning • A technique in machine learning in which an algorithm learns to perform one task, such as recognizing cars, and builds on that knowledge when learning a different but related task, such as recognizing cats Turing Test • A test of AI’s ability to pass as a human • In Alan Turing’s original conception, an AI would be judged by its ability to converse through written text
  • 15. Build-Ups of Artificial Intelligence Fostering collaboration across functions Structuring and formalizing AI management through a framework Focusing on AI as a strategic asset Defining how and who makes decisions. Developing supporting artifacts (policy, standards, and procedures) Monitoring compliance
  • 16. Principles on Artificial Intelligence Transparency Justice and fairness Non- maleficence Responsibility Privacy Reliability Safety Security Inclusiveness
  • 17. Guidelines on Artificial Intelligence Safety • It is a primary concern when it comes to AI governance, as AI systems can be used in ways that create safety risks • To ensure safety, AI must be designed and implemented in such a way that it minimizes potential risks and hazards • Ensuring that AI systems are built with robust security measures, such as access control and intrusion detection, and that they are tested and monitored for potential safety issues Security • Given the sensitive and confidential nature of data, it is important to ensure that AI systems are secure • This requires implementing rigorous security measures, such as authentication, encryption, and auditing, to protect data from unauthorized access and to ensure that data remains secure Ethics • It is important to consider the ethical implications of using AI in business in addition to safety and security • AI is often used for decision-making and predictive analytics, which can create ethical concerns that must be addressed • When using AI to make decisions, it is important to make sure that the decisions are fair and unbiased • Ensuring that the decision-making process is transparent and that it is based on valid data and ethical principles
  • 18. Pros and Cons of Artificial Intelligence Cost savings Improved decision-making Increased efficiency Job loss Ethical concerns Security risks Pros Cons
  • 19. Examples of Artificial Intelligence Models Siri • Apple’s assistant, Siri, is a friendly voice-activated computer that many people interact with daily • She helps find information, gives directions, helps send messages, and much more Alexa • The smart home’s hub can interpret speech from anywhere in the room, help search the web for information, shop, schedule appointments, set alarms, and perform hundreds of other daily tasks Amazon.com • Amazon uses AI with algorithms that the company uses to predict what you’re interested in purchasing based on your online behaviour Netflix • Netflix provides highly accurate predictive technology based on customer’s reactions to films • This tech is getting brighter as each year passes Pandora • Pandora, available only in the United States, is a leading music and podcast discovery platform, providing a highly personalised listening experience
  • 20. Technical Initiatives in Artificial Intelligence The most prominent initiative from the technical community can be found in the Institute of Electrical and Electronic Engineers (IEEE)’s work on AI, in the form of its Global Initiative on Ethics of Autonomous and Intelligent Systems The IEEE has involved its membership of technical experts but also reached beyond to non-IEEE members to participate in this project The initiative has produced two versions to date of Ethically Aligned Design, involving ‘hundreds of participants over six continents’ (IEEE 2018) Version 2 includes five General Principles to guide the ethical design, development, and implementation of autonomous and intelligent systems In line with IEEE’s general activities, the development of technical standards based on these discussions on ethics is envisaged by the Global Initiative, and a series of working groups have been set up under the Global Initiative to work towards this goal
  • 21. Criticism of Artificial Intelligence (AI) There is still a high level of uncertainty about how AI technology can be used effectively to generate value in organizations and how to specifically leverage the technology to make profits for organizations It is accompanied by disquiet over problematic and dangerous implementations, or indeed, even AI itself deciding to do dangerous and problematic actions, especially in fields such as the military, medicine, and criminal justice It also attracted criticism during this time, with some believing that it would never be possible to create intelligent machines
  • 22. Types of Artificial Intelligence • These machines do not have any memory or data to work with, specializing in just one field of work • For example, in a chess game, the machine observes the moves and makes the best possible decision to win Purely Reactive • These machines collect previous data and continue adding it to their memory • They have enough memory or experience to make proper decisions, but memory is minimal • This machine can suggest a restaurant based on the location data that has been gathered Limited Memory • This kind of AI can understand thoughts and emotions, as well as interact socially • However, a machine based on this type is yet to be built Theory of Mind • Self-aware machines are the future generation of these new technologies • They will be intelligent, sentient, and conscious Self-Aware • It is machine learning that gives AI the ability to learn • This is done by using algorithms to discover patterns and generate insights from the data they are exposed to Machine Learning • Deep learning, which is a subcategory of machine learning, provides AI with the ability to mimic a human brain’s neural network • It can make sense of patterns, noise, and sources of confusion in the data Deep Learning
  • 23. Advantages of AI Reduction in Human Error Zero Risks Unbiased Decisions Perform Repetitive Jobs Medical application
  • 24. Disadvantages of AI High Costs No creativity Unemployme nt Make Humans Lazy No Ethics Emotionless
  • 25. Human Intelligence Human intelligence is a mental attribute that consists of the capacities to learn through experience, adjust to changes and new conditions, grasp and manage complex ideas, and influence one's environment using knowledge human intelligence, mental quality that consists of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment A general mental ability for reasoning, problem solving, and learning, People can reason about virtually any issue, and many problems may be solved Intelligence integrates cognitive functions such as perception, attention, memory, language, or planning Simple and highly complex behavioral repertoires can be learned throughout the lifespan
  • 26. Development of Human "Cognitive Intelligence" Being able to understand a language Being able to understand numbers Being able to understand abstract thinking
  • 27. Types of Human Intelligence Intelligence Quotient Spiritual Quotient Emotional Quotient
  • 28. Intelligence Quotient A measure of a person's thinking capacity It is meant to assess how well someone can utilize facts and reasoning to answer questions or make predictions Short- and long-term memory is measured in IQ tests to begin to examine this They also assess how quickly people can solve puzzles and recollect information they've heard
  • 29. Spiritual Quotient Spirituality improves a person's ability to be creative, alert, and analytical Spirituality can help to strengthen the power of intuition and awareness It greatly aids in coping with and eradicating current problems like terrorism, self- centeredness, and a lack of humanity SQ makes us more accountable to ourselves, our families, society, our surroundings, our country, and finally our cosmos It assists us in connecting our own vision with the greater welfare of humanity It makes us more modest in our outlook, It significantly diminishes our selfish intentions
  • 30. Emotional Quotient Capacity to understand, use, and control one's own emotions in constructive ways to reduce stress, communicate effectively, sympathize with others, overcome problems, and diffuse conflict It assists you in developing stronger connections, succeeding in school and at work, and achieving your professional and personal objectives The most prevalent method of measuring things like personality characteristics is self-report Warmth, empathy, anxiousness, and other characteristics are examples of personality qualities
  • 31. Artificial Intelligence and Automation (AI) is the science of creating machines that are tasked to solve problems and accomplish some work that is too complicated for the human brain to process by itself AI is an effort to make computers think like humans, creating machines with minds, in the full and literal sense AI is not science fiction, but real science, based on the concept of intelligent machines Examples of AI includes ride-sharing apps like Uber and Lyft, speech recognition, problem-solving, machine learning, smart email categorization, spam filters, Amazon Alexa devices, Chatbots, Apple’s Siri, Google Now, Internet of Things, Google Predictive searching, Product recommendations, and more It is the development of computers system that can perform tasks that typically require human intelligence such as visual perception, speech recognition, decision making and problem solving AI systems are typically designed to be able to learn from experience, adapt to new inputs, and improve their performance Artificial Intelligence
  • 32. Artificial Intelligence and Automation Refers to the use of technology to automate tasks that were previously performed by humans Automation can be powered by a variety of technologies, including AI, robotics, and machine learning that uses technology to automate processes with minimal or no human intervention Automation is all about making machines capable enough of doing monotonous tasks automatically and effectively Automation is not a new concept, It is the ability of machines to perform tasks that would otherwise be impossible or difficult when performed by humans Automation is everywhere, any device or machine that operates autonomously is an automated system Automation
  • 33. Artificial Intelligence and Automation There are numerous real-life examples of automation you come across every day such as payment apps, thermostats, coffee makers, an ATM, automated parking systems, automated machine assembly, shopping apps, and so much more Automated systems can be generally classified as fixed automation and programmable automation Examples of automation include form auto-filling, customer support, digital signatures, automatic assembling, employee analytics, online banking, payment processing, numerical control machines, material handling robots, highway systems, automated waste management, automated retail, video surveillance, etc Automation
  • 34. Relationship Between AI and Automation Both the terms Artificial Intelligence (AI) and Automation are often used interchangeably but were two different terms Artificial Intelligence • AI is the science of creating machines that are tasked to solve problems and accomplish some tasks that are too complicated for the human brain to process by itself • AI focuses on creating highly intelligent machines to accomplish tasks that in normal sense would be called intelligent thinking or behavior • AI is the science and engineering of making machines smart enough to mimic human intelligence and behavior • The idea behind AI is to create technology that allows computers and machines to think like humans, behave like humans and learn from them Automation • Automation is the use of technology to automate processes with minimal or no human intervention • Automation is the ability of machines to perform tasks that would otherwise be impossible or difficult when performed by humans whereas AI is the simulation of human intelligence • The main objective of automation is to simplify and speed up common, repetitive tasks to increase productivity and efficiency of output, with less or no human involvement
  • 35. Reflects on Application of AI to own Context Healthcare • AI-powered tools can help doctors diagnose and treat diseases more accurately and efficiently, leading to better outcomes for patients • AI algorithms can also analyze vast amounts of medical data and help researchers identify new drugs and treatments for various illnesses Finance • AI-powered chatbots can help customers with banking and investment queries, while machine learning algorithms can analyze financial data and identify trends and patterns that can help businesses make informed decisions Education • AI is being used to create personalized learning experience, adapt to students ’individual learning styles and automate administrative tasks Transportation • AI algorithms can optimize routes, reduce traffic congestion, and enhance driver safety • Self-driving cars that use AI technology are also being tested and developed, which could change the way we commute and travel significantly Entertainment • AI is being used to create personalized recommendations for movies, TV shows, and music • AI is also being used to create virtual assistants and gaming characters that can interact with the users in more realistic ways
  • 36. Key Shift Identification in Workplace Influence by AI Automation and Efficiency Improved Decision Making Upskilling the Workforce Collaborations between Human and Machines Enhanced Customer Experience New Business Models
  • 37. Assessing Impact Shifts in Workplace AI Lacks Emotional Intelligence AI’s Creative process is limited to data it receives AI does not have soft skills Humans make AI work AI is meant to complement human ability and intelligence, not compete with it AI need to be fact checked
  • 38. Artificial and Human Intelligence • A branch of data science that specializes in creating smart machines that can execute a broad range of tasks that typically need human intelligence, reasoning and capabilities Artificial Intelligence • Ability that enables us to learn from various experiences, think, understand intricate concepts, apply reasoning and logic, solve mathematical problems, identify patterns, make decisions, hold on to information, communicate with fellow human beings, and so on Human Intelligence
  • 39. Relationship Change Between AI and Humans • Human Intelligence looks to adjust to new environments by using a combination of various cognitive processes, while AI aims to create machines that can imitate human behavior and perform human-like actions • Machines are digital, but the human brain is analogous Nature • Humans use the brain’s memory, computing power and ability to think, while AI-powered machines depend on data and instructions fed into the system Functioning • Human Intelligence is all about learning and understanding from different incidents and past experiences • However, AI falls behind in this area, as AI cannot think. Learning Power
  • 40. Identifying Required Skills to Work and Interact With AI Technological adaptation Active learning with a growth mindset Basic computer skills Programming and automation Analytical thinking Creativity
  • 41. Producing Action Plan For The Future Stay up to date with the latest developments Develop a solid understanding of the fundamentals Build your technical skills Cultivate soft skills Embrace lifelong learning Consider ethical and societal implications
  • 42. Conclusion AI is a rapidly advancing field that has the potential to revolutionize the way we live and work It involves developing intelligent agents that can perform tasks that typically require human cognitive abilities, such as perception, critical thinking, reasoning, learning, and problem-solving AI has a wide range of applications in various fields with its numerous benefits and applications, it also poses challenges and ethical concerns These concerns include job displacement, privacy and security, bias and discrimination, and accountability and transparency As AI continues to evolve, it is important to ensure that it is developed and deployed with ethical considerations in mind, to maximize its benefits while minimizing its risks
  • 43. Recommendations AI systems should be designed to be transparent, explainable, and accountable Developing algorithms that are fair and unbiased Promoting diversity and inclusion in the development of AI Active learning with a growth mindset Developing humans’ intellectual ability
  • 44. Prof. Godwin Emmanuel Oyedokun Professor of Accounting and Financial Development Lead City University, Ibadan, Nigeria Principal Partner; Oyedokun Godwin Emmanuel & Co (Chartered Accountants, Tax Practitioners & Forensic Auditors)