WHAT IS ARTIFICIAL INTILLIGENCE (AI) AND ITS
HISTORY, TYPES, IMPORTANCE?
written by M Arshad Sohail dated 16 nov-2022
TABLE OF CONTENT:
1- Introduction
2- Definition
3- History of artificial intelligence
4- Types of artificial intelligence
5- Importance
6- Benefits
7- Ethics and transparency
1-INTRODUCTION TO ARTIFICIAL
INTELLIGENCE
Artificial intelligence allows machines to model and even
improve the capabilities of the human mind. From the development of
self-driving cars to the proliferation of smart assistants like Siri and Alexa,
artificial intelligence is becoming a bigger part of everyday life. As a
result, many technology companies across various industries are investing in
artificially intelligent technologies.AI
Artificial intelligence enables machines to understand and
achieve specific goals. AI involves machine learning through deep learning. The
first refers to machines that automatically learn from existing data without
the help of human beings. Deep learning allows a machine to absorb vast amounts
of unstructured data such as text, images, and audio.
2-DEFINITION OF AI
Artificial intelligence is the ability of machines to
perform certain tasks that require the intelligence exhibited by humans and
animals. This definition is often attributed to Marvin Minsky and John McCarthy
from the 1950s, who were also known as the fathers of the field.
Artificial intelligence is a constellation of many different
technologies that work together to enable machines to perceive, understand, act
and learn with a human-like level of intelligence. Perhaps this is why everyone
seems to have a different definition of artificial intelligence: AI is not just
one thing.
3-History of
artificial intelligence
Intelligent robots and artificial beings first appeared in
ancient Greek myths. And Aristotle's development of the syllogism and his use
of deductive reasoning was a pivotal moment in humanity's efforts to understand
its own intelligence. While the roots are long and deep, the history of
artificial intelligence as we think of it today spans less than a century. The
following is a quick look at some of the highlights in AI.
AI was a term first coined at Dartmouth College in 1956.
Cognitive scientist Marvin Minsky was optimistic about the future of this
technology. From 1974-1980, there was a decline in government funding in the
field, a period known as the "artificial intelligence winter", when
several criticized progress in the field.
However, enthusiasm was renewed later in the 1980s, when the
British government began funding the technology again, especially as it feared
competition from the Japanese. In 1997, IBM's Deep Blue became the first
computer to beat a Russian grandmaster and made history.
4 types of artificial
intelligence
Artificial intelligence can be divided into four categories
based on the type and complexity of tasks the system is capable of performing.
For example, automated spam filtering falls into the most basic class of AI,
while the remote potential of machines that can sense people's thoughts and
emotions is part of an entirely different subset of AI.
WHAT ARE THE FOUR TYPES OF ARTIFICIAL INTELLIGENCE?
I. Reactive machines: able to perceive and respond to the
world in front of them while performing limited tasks.
II. Limited memory: the ability to store past data and
predictions to inform predictions of what may come.
III. Theory of mind: is able to make decisions based on how
others feel and make decisions.
IV. Self-awareness: the ability to work with human-level
consciousness and understand one's own existence.
I-Reactive machines
A reactive machine follows the most basic principles of
artificial intelligence and, as its name suggests, is only able to use its
intelligence to perceive and react to the world in front of it. A reactive
machine cannot store memory, and as a result cannot rely on past experience to
make real-time decisions.
HOW ARTIFICIAL INTELLIGENCE WORK?
Direct perception of the world means that reactive machines
are designed to perform only a limited number of specialized tasks. However,
deliberately narrowing the world view of a reactive machine is not some kind of
cost-cutting measure, and instead means that this type of AI will be more
trustworthy and reliable – it will respond in the same way to the same stimuli
every time.
An example of a reactive gaming machine is Google's Alpha
Go. Alpha Go is also unable to evaluate future moves, but relies on its own
neural network to evaluate the evolution of the current game, giving it an edge
over Deep Blue in a more complex game. Alpha Go also defeated the game's global
competitors when it beat Go champion Lee Sedol in 2016.
II-Limited memory
AI with limited memory has the ability to store previous
data and predictions as it gathers information and considers potential
decisions – essentially looking into the past to see what might come next.
Artificial intelligence with limited memory is more complex and offers more
capabilities than reactive machines.
There are six steps to follow when using AI's limited memory
in ML: Training data must be created, an ML model must be created, the model
must be able to make predictions, the model must be able to receive feedback
from a human or environment that must have feedback. be stored as data and
these steps must be repeated as a cycle.
III-Theory of Mind
Theory of mind is just that – theoretical. We have not yet
achieved the technological and scientific capabilities necessary to achieve
this next level of artificial intelligence. The concept is based on the
psychological premise of understanding that other living beings have thoughts
and emotions that influence human behavior.
In terms of AI machines, this would mean that AI can
understand how humans, animals and other machines feel and make decisions
through self-reflection and determination, and then use that information to
make its own decisions. Essentially, machines would need to be able to grasp
and process the concept of “mind,” the fluctuations of emotion in
decision-making, and a litany of other psychological concepts in real time,
creating a two-way relationship between humans and AI.
IV-Self-awareness
Once a theory of mind can be created, sometime in AI's
future, the final step will be for AI to become self-aware. This kind of
artificial intelligence has human-level consciousness and understands its own
existence in the world, as well as the presence and emotional state of others.
It would be able to understand what others may need based not only on what they
communicate to them, but also on how they communicate it.
Self-awareness in AI relies on both human researchers
understanding the premise of consciousness and then learning how to replicate
it so it can be built into machines.
TYPES OF ARTIFICIAL INTELLIGENCE | ARTIFICIAL INTELLIGENCE
EXPLAINED | WHAT IS AI?
Another classification
There are three ways to classify AIs based on their
abilities. Rather than types of AI, these are stages through which AI can
evolve – and only one of these is possible right now.
A. Narrow (or
"weak") AI
Some go further and define artificial intelligence as
"narrow" and "general" AI. Most of what we experience in
our daily lives is narrow AI that performs a single task or a set of closely
related tasks. Examples:
These systems are powerful, but the playing field is narrow:
they tend to focus on driving efficiency. But with the right application,
narrow AI has enormous transformative power—and continues to influence how we
work and live globally
Scale.
EXAMPLES OF ARTIFICIAL INTELLIGENCE: NARROW AI
Siri, Alexa and other smart assistants
Self-driving cars
Google Search
Email spam filters
Netflix recommendations
Weather application
b. General (or
"strong") AI
General AI is more like what you see in science fiction
movies, where sentient machines mimic human intelligence, thinking
strategically, abstractly and creatively, with the ability to handle a range of
complex tasks. While machines can perform some tasks better than humans (such
as data processing), this fully realized vision of general artificial
intelligence does not yet exist outside of the silver screen. That's why
human-machine collaboration is key – in today's world, artificial intelligence
remains an extension of human capabilities, not a replacement.
What is machine
learning?
Machine learning is a type of artificial intelligence that
allows systems to learn patterns from data and subsequently improve future
experiences.
#ARTIFICIAL INTELLIGENCE SERVICE HUMANITY
C. superintelligence
In addition to narrow AI and AGI, some believe there is a
third category known as superintelligence. For now, this is a completely
hypothetical situation where machines are fully self-aware, even surpassing
human intelligence in virtually every field, from science to social skills. In
theory, this could be achieved through a single computer, a network of
computers, or something else entirely if it is conscious and has subjective
experiences.
Nick Bostrom, founding professor and head of Oxford's Future
of Humanity Institute, apparently coined the term back in 1998, predicting that
we would reach superhuman artificial intelligence in the first third of the
21st century. he added, it will require not only sufficiently powerful
hardware, but also "adequate initial architecture" and "a rich
flow of sensory input."
8- Meaning of
artificial intelligence
Artificial intelligence has many uses, from supporting
vaccine development to automating the detection of potential fraud. Private
activity in the AI market saw a record year in 2021, according to CB
Insights, with global funding up 108 percent compared to 2020. AI is making
waves across industries thanks to rapid adoption.
Business Insider Intelligence's 2022 AI in Banking report
found that more than half of financial services companies are already using AI
solutions to manage risk and generate revenue. The application of AI in banking
could lead to savings of up to 400 billion dollars.
In terms of medicine, a 2021 World Health Organization
report says that while the integration of artificial intelligence into
healthcare poses challenges, the technology "holds great promise" as
it could lead to benefits such as more informed health policy and improved
diagnostic accuracy patients. .
Artificial intelligence has also made inroads into
entertainment. The global market for artificial intelligence in media and
entertainment is estimated to reach $99.48 billion by 2030, growing from $10.87
billion in 2021.
THE AI TIMELINE: A HISTORY OF ARTIFICIAL INTELLIGENCE
A critical source of
business value – when done right
AI has long been seen as a potential source of business
innovation. With the enablers now in place, organizations are beginning to see
how AI can multiply value for them. Automation lowers costs and brings new
levels of consistency, speed and scalability to business processes; in fact,
some Accenture clients see time savings of up to 70 percent. But even more
compelling is AI's ability to drive growth. Companies that scale successfully
see three times the return on their AI investment compared to those stuck in
the pilot phase. No wonder 84 percent of C-suite executives believe they must
use AI to achieve their growth goals.
B- Agility and
competitive advantage
Artificial intelligence is not just about efficiency and
streamlining laborious tasks. With machine learning and deep learning, AI
applications can learn from data and results in near real-time, analyze new
information from multiple sources, and adapt accordingly with a level of
accuracy that is invaluable to business. (Product recommendations are a prime
example.) This ability to self-learn and optimize means AI is constantly
increasing the business benefits it creates.
In this way, AI helps businesses adapt quickly with a
regular flow of insights that drive innovation and competitive advantage in a
world of constant disruption. Once scaled, AI can become a key driver of your
strategic priorities—and even a pillar of survival: Three out of four C-suite
executives believe that if they don't scale AI in the next five years, they
risk going out of business altogether. Clearly, the stakes are high when it
comes to the scope of AI.
3 out of 4 C-suite executives believe that if they don't
scale AI in the next five years, they risk going out of business altogether.
6-Advantages of AI
There are many ways to define AI, but the more important
conversation revolves around what AI allows you to do. According to Accenture's
report, AI: Built to Scale, 84 percent of business executives believe they need
to use AI to achieve their growth goals. However, 76 percent admit they
struggle with how to scale AI within their business. Until now, there hasn't
been a plan to get an accurate proof of concept into production and scale, a
transition that many are struggling to make. At this inflection point, it is
imperative that businesses take the necessary steps to scale successfully.
i- End-to-end efficiency: Artificial intelligence eliminates
friction and improves analysis and resource utilization across the
organization, leading to significant cost reductions. It can also automate
complex processes and minimize downtime by anticipating maintenance needs.
ii- Improved accuracy and decision-making: Artificial
intelligence augments human intelligence with rich capabilities of pattern
analysis and prediction to improve the quality, efficiency and creativity of
employee decision-making.
iii- Intelligent Offers: Because machines think differently
than humans, they can more quickly identify gaps and opportunities in the
market, helping you to introduce new products, services, channels and business
models with a speed and quality not previously possible.
iv- Empowering employees: AI can handle mundane activities
while employees spend time on high-value tasks. By fundamentally changing the
way work is done and empowering the role of humans in driving growth, AI is
expected to increase labor productivity. The use of artificial intelligence can
also unlock the incredible potential of talent with disabilities while helping
all workers thrive.
v- Superior customer service: Continuous machine learning
provides a constant stream of 360-degree customer insights for hyper
personalization. From 24/7 toaster rerouting, businesses can use AI to manage
real-time information and deliver high-touch experiences that drive growth,
retention and overall satisfaction.
AI is used in many ways, but the prevailing truth is that
your AI strategy is your business strategy. To maximize the return on
investment in AI, identify your business priorities and then determine how AI
can help you.
Identify your business priorities and then determine how AI
can help you.
vii- Define the value of your business
There are countless ways to use AI. How do organizations
decide what to focus on? To scale successfully, start by defining what value
means to your business. Then evaluate and prioritize different AI applications
against those strategic goals.
VIII- Rework your workforce
The growing momentum of AI requires a diverse, reconfigured
workforce to support and scale it. Despite initial fears that AI and automation
will lead to job losses, the future of AI depends on human-machine
collaboration and the need to reshape talent and ways of working.
IX- Establish governance and ethical frameworks
Organizations must design their AI strategy with trust in
mind. This means building the right governance structures and ensuring that
ethical principles are translated into algorithm and software development.
7-Ethics and
transparency
Successful application of these factors can help organizations
unlock exponential value and remain competitive. Artificial intelligence is no
longer just a "nice-to-have", it is essential to the future of
business.
a- Ethics
No introduction to artificial intelligence would be complete
without addressing the ethics of artificial intelligence. Artificial
intelligence is evolving at a breakneck pace, and as with any powerful
technology, organizations need to build trust with the public and be
accountable to their customers and employees.
At Accenture, we define "responsible AI" as the
practice of designing, building and deploying AI in a way that empowers
employees and businesses and equitably impacts customers and society – enabling
companies to build trust and scale AI with confidence.
b- Trust
every company using AI is subject to scrutiny. Ethical
theater is a regular theme, where companies promote the responsible use of AI
through PR while also engaging in unpublished gray area activities. Unconscious
bias is another. Responsible AI is an emerging capability that aims to build
trust between organizations and their employees and customers.
c- Data security
Data privacy and unauthorized use of artificial intelligence
can damage reputation and systemically. Companies must design confidentiality,
transparency and security into their AI programs from the start, and ensure
that data is collected, used, managed and stored securely and responsibly.
d- Transparency and ability to explain
Whether it's building an ethics committee or revising their
code of ethics, companies must create a governance framework to guide their
investments and avoid ethical, legal and regulatory risks. As AI technologies
become increasingly responsible for decision-making, businesses need to be able
to see how AI systems achieve a given outcome and take those decisions out of
the "black box". A clear governance framework and ethics committee
can help develop procedures and protocols to ensure that their code of ethics
is properly translated into the development of AI solutions.
e-control
Machines don't have minds of their own, but they do make
mistakes. Organizations should have risk frameworks and contingency plans in
place in the event of a problem. Clarify who is responsible for decisions made
by AI systems and define a governance approach to help escalate issues when
necessary.
8-Conclusion:
When you consider the computational costs and the technical
data infrastructure running behind AI, actually executing on AI is a complex
and expensive undertaking. Fortunately, there have been massive advances in
computing technology, as indicated by Moore's Law, which states that the number
of transistors on a microchip doubles approximately every two years, while the
cost of computers is halved.
Although many experts believe that Moore's Law is likely to
end sometime in 2020, it has had a major impact on modern artificial
intelligence techniques—without it, deep learning would be financially
impossible. Recent research has found that AI innovation has actually outpaced
Moore's Law, doubling every six months or so versus two years. The organization
must build trust with the public and be accountable to its customers. He
concludes that the future of the world is bright as ARTIFICIAL INTELLIGENCE
