WHAT IS ARTIFICIAL INTILLIGENCE (AI) AND ITS HISTORY, TYPES, IMPORTANCE?

 

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