Marketing
Nov 7, 2023
What is AI (Artificial Intelligence)?
Maria Ruocco
Table of Contents
Introduction
A look at the history of AI evolution
What is Artificial Intelligence?
Different types of AI based on ability
Different types of AI based on functionality
Generative AI
Artificial intelligence impact on business
Benefits of using AI in business
Risk of using AI in business
How to implement AI into business
1. Introduction
Artificial intelligence is revolutionizing the lives of people and businesses that are benefiting from this technology in terms of innovation and efficiency.
In this guide we want to focus on why companies can benefit from this revolution and how they can implement it within their organization.
According to Techtarget, companies can benefit from AI in terms of:
better decision assumed
efficiency and productivity gains
improved speed of business
new capabilities and business model expansion
personalized customer service ans experiences
improved services
improving monitoring
better quality and reduction of human herror
betetr talen management
more innovation
increase profitability
industry-specific improvements
Image credits: TechTarget
To give you some data that can give you an understanding of the context in which we move:
Today, computers have become faster and faster. Some computers have now crossed the threshold, that is, they can perform in a single second as many calculations as an individual could do in 31,688,765,000 years.
Despite its many advantages, like anything new, the introduction of this technology within business contexts requires special attention.
It is not enough to implement advanced algorithms or invest in cutting-edge tools, but you need an end-to-end approach that integrates strategy, process redesign, data management, ethical considerations, and people transformation.
This comprehensive guide aims to provide guidance on how to incorporate AI into your business effectively and responsibly for better results.
2. A look at the history of AI evolution
The idea of a "thinking machine" is nothing new. Even in ancient Greece, people were trying to figure out whether it was feasible to create "thinking" systems to aid human activities.
However, when you look at the data, it is clear that significant developments have only been made in the field in the last century.
In 1950, Alan Turing published Computing Machinery and Intelligence which raised the question, "Can machines think?"
All of Turing's research would culminate in the development of the famous Turing Test, which is used to determine whether a computer can demonstrate the same level of intelligence (or equivalent intelligence) as a human.
A few years later, in 1956, John McCarthy coined the term artificial intelligence at the first AI conference held at DartmouthCollege and defined it as:
"the science and engineering of making intelligent machines, in particular intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to restrict itself to methods that are biologically observable."
In 1957, Allen Newell, J.C. Shaw, and Herbert Simon created the Logic Theorist, which was the world's first AI software program. In 1961, Joseph Weizenbaum created ELIZA, a natural language processing program that simulated a Rogerian psychotherapist.
In 1967, Frank Rosenblatt developed the Mark 1 Perceptron, which was an early neural network architecture.
In 1968, Marvin Minsky and Seymour Papert published their book Perceptrons, which provided an in-depth analysis of neural networks.
In the 1980s, neural networks that used a backpropagation algorithm to train became widely used in artificial intelligence applications.
In 1995, Stuart Russell and Peter Norvig published their book Artificial intelligence: a modern approach, which delved into four potential objectives or definitions of artificial intelligence, which differentiate computer systems on the basis of rationality and thought versus action.
In 1997, IBM's DeepBlue defeated world chess champion Garry Kasparov in a six-game match.
Image credits: Harvard University
In 2015, Baidu's Minwa supercomputer used a special type of deep neural network called a convolutional neural network to identify and classify images with a higher accuracy rate than the average human.
In 2016, DeepMind's AlphaGo program, powered by a deep neural network, defeated Lee Sedol, the Go world champion, in a five-game match. The victory was significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves!). Subsequently, Google purchased DeepMind for an estimated $400 million.
In 2023, several large language models like ChatGPT are emerging.
3. What is Artificial Intelligence?
To get the most out of AI in your business, it's crucial to have a good understanding of what AI is and how it operates.
Artificial intelligence (AI) refers to the ability of machines to imitate human intelligence.
AI is not just about performing calculations, but showing intelligence in a broader sense: it is the ability of a machine to perform cognitive functions that we relate to the human mind, such as perceiving, reasoning, learning, interacting with an environment, resolving problems and even carry out inventiveness.
A critical component in the development of artificial intelligence is machine learning, where computer systems are “trained” to find the best answers by learning from their own errors. Neural networks are a prevalent technique that exploits the concept of neurons in the human brain so by attempting to complete a task multiple times, algorithms "learn".
For example, the neural network of a self-driving car is trained by exposing it to various traffic conditions. With each attempt, he gets better at recognizing and responding to variables on the road.
Take a look at the video below to better understand how it works.
4. Different types of AI on ability
Artificial intelligence is a complex concept that encompasses within itself several classifications. The first classification to pay attention to is the one that distinguishes artificial intelligence by ability.
Artificial Narrow Intelligence (ANI)
ANI, or Artificial Narrow Intelligence, is the most commonly experienced type of AI, encompassing nearly everything in the field. Also known as weak AI, it operates under specific constraints and performs a single task using human-like capabilities. These machines are limited to their programmed functions and lack the ability to perform beyond their designated tasks.
As the simplest form of AI, ANI focuses on one or, at most, two tasks.
For instance, object detection systems can recognize specific objects within a given frame.Artificial General Intelligence (AGI)
AGI, or artificial general intelligence, refers to an AI system's ability to learn, perceive, understand, and function in a manner similar to humans.
Although AGI remains largely theoretical, advancements in the field are expected to enable these systems to develop multiple competencies independently and form connections. This development will significantly reduce training time and render AI systems as capable as humans. AGI heavily involves the Theory of Mind, as it entails understanding and expressing emotions. However, the extent of self-awareness in AGI systems remains a topic of ongoing debate.Artificial Super Intelligence (ASI)
ASI systems represent the pinnacle of AI achievement, potentially marking the final invention of humanity. Their superiority to human intelligence is assured, as they not only replicate the multifaceted intelligence of humans but surpass it in every way. These systems will possess exceptional memory, lightning-fast data processing and analysis capabilities, and superior decision-making skills.
5. Different types of AI based on functionality
The second classification criterion we need to pay attention to distinguishes artificial intelligence on the basis of functionality.
Reactive Machines
Reactive Machines represent the most elementary forms of AI systems that respond to cues based strictly on pre-set rules.
They lack both memory and learning capacity from past experiences.
Prime examples of reactive machines are Deep Blue, the computer program that outsmarted Garry Kasparov in a chess match in 1997, and IBM Watson which triumphed over human competitors on Jeopardy in 2011.Limited Memory
Limited Memory AI systems are capable of learning from previous experiences and using that knowledge to make decisions.
These systems can retain past experiences and use this data to make predictions and decisions.
A great example of a limited memory AI system is autonomous vehicles, which use sensors and data to navigate roadways and avoid obstacles.Theory of Mind
AI systems with the Theory of Mind can understand the feelings and thoughts of others (like people or pets).
Based on these sentiments and ideas, these AI systems can predict behavior.
Theory of Mind AI is currently in its early stages, with ongoing research to improve it.Self-Aware
Self-aware AI systems have a consciousness similar to that of humans, and they can think and learn independently.
They understand their existence and their place in the world.
Most research into self-aware AI is still in its theoretical phases, as it has not yet been fully realized.
6. Generative AI
In the commercial sector, special attention should be paid to generative AI
Generative AI is a subset of artificial intelligence that focuses on generating new content, data, or information based on existing data.
It uses sophisticated algorithms and models to create outputs that closely mimic real-world examples. This is achieved by the models learning and understanding patterns, structures, or relationships embedded within the input data.
The goal of generative models is to create original yet familiar results that are based on the learned patterns and structures.
Image Credit: TechTarget
One of the writing tools that uses generative AI is Editby. Editby in fact collects all the input that the untente enters neal (video, text, etc.) and reworks it to create unique, new and unprecedented content. These allows users to use the tool in order to create their own content for use in their own distribution network.
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Maria Ruocco
