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AI in Business: From Past to Present, A Brief History

The History of Innovation Cycles

Artificial Intelligence (AI) is transforming how we do business, and industries worldwide are experiencing a renaissance thanks to AI-powered tools. In the current fast-paced and competitive business world, AI is becoming a staple in various industries, enabling significant time and cost savings while enhancing customer experiences. AI is a technology that has been under development for decades, and its evolution has brought new and exciting possibilities to the business world.

In this blog post, we will dive deeper into the history of AI in business, from its beginning to the present, to understand its impact on small businesses.

The Origins of AI

Artificial Intelligence, as an idea, has been around since ancient times. Ancient myths and folklore often contain narratives and legends that touch upon themes and concepts related to artificial intelligence, although not in the same technological sense as we understand it today.

In Greek mythology, Talos was a giant bronze automaton created by Hephaestus, the god of craftsmanship. Talos guarded the island of Crete and was tasked with circling it three times a day to keep intruders at bay. It showcased the concept of an intelligent and powerful humanoid automaton created for protection and defence.

In Hindu mythology, there are references to Brahma’s Yantra, a mystical device that possesses profound powers and intelligence. It is often depicted as a complex geometric symbol representing the universe and embodying knowledge and cosmic principles.

Some of the earliest modelling of AI was competing in better understanding the human brain through the creation of the neural network. A simple electrical circuit simulating brain function termed “connectionism” was created in 1943 by the neurophysiologist Warren McCulloch and mathematician Walter Pitts. This was one of the first attempts to understand how a machine could learn by processing information like a human brain does.

A McCulloch-Pitts neuron

In 1954, MIT created the first implementation of a neural network, and in 1958 the Mark I Perceptron was created by Frank Rosenblatt, a psychologist at Cornell, to study the flee response of a fly by integrating an early digital camera.

The AI Winter

In 1959 at Stanford, Bernard Widrow and Marcian Hoff developed systems to eliminate noise in phone lines as an applied business problem that still remains in use today. It was around this time that the idea of “Thinking Machines” became popular, and the idea of AI gained attention.

This ended in 1969 with the publication of the book “Perceptrons” by Marvin Minsky, founder of the MIT AI Lab, and Seymour Papert, director of the lab. The book argued that models around neural networks created would require enormous computational power and time that was beyond the capability of computing. Convincing the research and, more importantly, the funding institution that further development was futile.

Hence, advancements in this area declined significantly for the next decade, which is often referred to as the “AI Winter”. It was not until the 1980s that research began around the development of AI.

A rediscovery of backpropagation (identified in the 1960s), is the method for calculating the weight gradients, while gradient descent (later developed) is the algorithm that uses those gradients to adjust the weights and optimize the neural network’s performance.

To visualize this, imagine training a model to descend a mountain. Backpropagation continuously calculates the slope or steepness of the terrain at each step, ensuring the descent is moving in the right direction. Gradient descent is then taking small steps in that direction to gradually descend to the lowest point, where the loss is minimized.

Essentially, backpropagation is checking for error and gradient descent is optimizing the results from the backpropagation to improve performance. In a sense, as data is fed through the model, it learns and becomes more efficient.

The renaissance of AI began.

AI Learns Language

Natural Language Processing (NLP) is a field of study focusing dating back to the 1950s on the interaction between computers and human language. It involves various techniques for processing, analyzing, and understanding human language data. Early systems used rule-based and statistical models to extract meaning.

The first significant use of NLP in business was the development of automated customer support systems known as chatbots. These chatbots gain traction in the mid-2000s to understand natural language inputs from users, interpret their intent, and hopefully provide a relevant response.

Chatbot implementation allowed businesses to automate customer support to handle large volumes of inquiries and provide faster responses and round-the-clock support. Common tasks included answering frequently asked questions, guiding users through troubleshooting steps, and even performing basic transactions or bookings. This freed customer support representatives to handle more complex tasks and improve productivity.

AI Gets Smarter

With the availability of large datasets and advances in computing power, researchers began leveraging deep learning architectures for NLP. Deep learning involves the training of neural networks with many layers. It aims to learn hierarchical representations of data and extract patterns and features.

Prior traditional approaches to programming required the creation of explicit rules for the computer to follow. NLP took a specific neural network and optimized its predictability as more data was processed. By contrast, deep learning is the creation of a neural network that learns by itself.

Deep learning can be applied to various types of data, including text, images, audio, and more. While it can be used for NLP tasks, its application is not limited to language data. Deep learning excels at learning representations from raw data and has shown significant success in tasks like image recognition, speech recognition, and natural language generation.

In essence, deep learning allows computers to learn and make predictions by building and adjusting complex neural networks. It mimics how the human brain processes information, allowing computers to recognize patterns and make decisions based on data.

Deep learning has enabled advancements in self-driving cars, virtual assistants, voice assistants, recommendation systems, and much more.

AI Learns Conversation

ChatGPT, like other variants of GPT (Generative Pre-trained Transformers), is built upon the concepts of deep learning. GPT specifically employs a deep learning architecture known as Transformers. Transformers are neural network models that excel at capturing long-range dependencies and contextual relationships in sequential data, such as text. They are composed of multiple self-attention layers and feed-forward neural networks, allowing for efficient and effective language modelling.

The training process for GPT involves utilizing large amounts of text data (leveraging Internet data) to pre-train the model in an unsupervised learning manner. During pre-training, the model learns to predict the next word in a sentence by considering the context of previous words. This enables the model to develop an understanding of grammar, syntax, and semantic relationships.

After pre-training, GPT is fine-tuned using supervised learning, where it is trained on specific datasets with labelled examples to adapt the model to perform tasks such as text completion, translation, or conversational responses. Fine-tuning further refines the model’s ability to generate relevant and coherent responses.

By leveraging deep and machine learning techniques and the power of Transformers, GPT can generate human-like text and engage in conversations with users in a more natural and context-aware manner.

Defining the Different Forms of AI

There are three categories of AI:

  • Artificial Narrow Intelligence (ANI)

  • Artificial General Intelligence (AGI)

  • Artificial Superintelligence (ASI)

Artificial Narrow Intelligence (ANI)

ANI or sometimes called narrow, is a type of artificial intelligence technology that is designed to perform a specific task. This could be anything from recognizing speech, recommending songs, or playing chess. ANI is also referred to as “weak AI” because it does not possess true understanding or consciousness.

ANI systems can excel at the tasks they’re designed for, often surpassing human abilities. However, they can’t perform tasks outside their specific domain or learn new tasks without being specifically trained or programmed to do so.

Examples of ANI are abundant in our everyday life. They include systems like Google’s search algorithm, Amazon’s recommendation system, Apple’s Siri, and Tesla’s self-driving car software. Despite being highly advanced in their respective tasks, these systems don’t understand the broader context of their actions — they simply do what they’re programmed to do.

Artificial General Intelligence (AGI)

AGI, sometimes called just general, is a form of artificial intelligence that has the capacity to understand, learn, and apply knowledge across a wide array of tasks at a level equal to or beyond a human being. It’s also sometimes referred to as “strong AI” or “full AI.”

Unlike Artificial Narrow Intelligence (ANI), which is designed to excel in a specific task, AGI can theoretically perform any intellectual task that a human can do. It has the ability to understand context, make judgments, learn from experience, and apply its knowledge to different domains.

For example, an AGI could read a book, understand the content, make logical inferences based on that understanding, write a review of the book, and then use the knowledge gained from the book in a completely different context, such as solving a complex problem or creating a piece of art.

The debate of whether tools like ChatGPT, etc., are AGI is still being weighed. These are very Large Language Models (LLM) that have been trained on the vast amount of data that is available on the Internet. Hence, are these models just better at summarizing the massive conversations on the Internet, or do they truly understand and comprehend the ideas behind these conversations and create new ideas?

Artificial Superintelligence (ASI)

ASI, or Artificial Superintelligence, refers to a type of artificial intelligence that possesses the capacity to surpass human intelligence and capability in practically every field, including scientific creativity, general wisdom, and social skills.

The concept of ASI is largely theoretical at present since we have not yet achieved Artificial General Intelligence (AGI), which is considered a necessary precursor to ASI. If and when ASI is realized, it would theoretically be able to outperform humans at most economically valuable work, come up with better ideas, make more accurate predictions, and even exhibit superior emotional intelligence.

The concept of ASI raises important questions and concerns around ethics and control. The late physicist Stephen Hawking, Tesla and SpaceX founder Elon Musk, and many other prominent figures have expressed concerns about the potential risks associated with ASI, including the idea that it could potentially pose existential risks to humanity if not properly controlled.

In Business Today

Artificial Intelligence (AI) is an important technology that has recently seen increased popularity with the release of ChatGPT, a popular chatbot from OpenAI. Monthly active users are estimated to have reached 100 million active users in January 2023, just two months after its release to the public. This represents one of the fastest adoptions of any technology to date.

Beyond the impressive interest in ChatGPT, businesses are looking to embrace AI. According to the 2021 AI Adoption Report by McKinsey, 50% of organizations have adopted AI in at least one business function. In another survey by Salesforce, 70% of customer service agents believe that AI can help improve their jobs, pointing to an increased acceptance of AI tools like ChatGPT, with the goal of enhancing customer interactions, driving operational efficiency, and generating insightful data for businesses.

However, the integration of AI doesn’t stop at customer service. Businesses are leveraging AI for marketing purposes as well. Salesforce reports that 60% of marketers’ jobs will be transformed by AI.

According to a study conducted by Adobe, AI is changing the face of digital marketing, with over 75% of marketers now using some form of AI technology in their strategies. The impact of AI on digital marketing strategies is unparalleled, with AI tools providing data-driven insights, improving customer experiences, and streamlining operations.

This highlights the growing significance of AI in driving personalized customer experiences and more targeted marketing strategies. The use of AI in marketing is demonstrating increased customer engagement, improved customer loyalty, and enhanced revenues, further underlining its importance in the contemporary business landscape.

AI is a significant innovation that will likely affect the global economy, as we’ll discuss in the next section.

Innovation and the Economy

Schumpeter’s concept of creative destruction and Kondratieff waves (or K-waves) are both key concepts in the field of economics, particularly those related to business cycles and economic development. Here is how the two concepts are related.

Creative Destruction

Creative destruction is a term coined by Austrian economist Joseph Schumpeter to describe the process of transformation that accompanies radical innovations like AGI and ASI. In Schumpeter’s vision of capitalism, innovative solutions by entrepreneurs were the force that sustained long-term economic growth, even as it destroyed the value of established companies and labourers that enjoyed some degree of monopoly power derived from previous technological, organizational, regulatory, and economic paradigms.


Kondratieff waves (K-waves) are a theory named after Nikolai Kondratieff, a Russian economist, who proposed that economies move through long-term (45-60 years) cycles of high and low growth. These cycles are driven by technological innovations that drive periods of economic growth, followed by periods of stagnation or decline as the innovation reaches maturity.

The Relationship

The concept of creative destruction is often used to explain the dynamics that Kondratieff described in his theory of long waves. When a new, innovative technology (like the steam engine, electricity, or the internet) is introduced, it can lead to a period of rapid economic growth (the upswing of a K-wave) as industries based on the old technology are destroyed (Schumpeter’s creative destruction) and new industries are created.

However, as the new technology matures and becomes widely adopted, the rate of growth can slow, leading to a period of stagnation or decline (the downswing of a K-wave). Eventually, a new wave of innovative technologies will emerge, leading to another round of creative destruction and the start of a new K-wave.

The History of Innovation Cycles

Therefore, Schumpeter’s creative destruction can be seen as the driving force behind the Kondratieff waves, with each wave being driven by a new round of technological innovations and the creative destruction that they cause.

Is AI the Sixth Wave?

The earliest forms of AI, as discussed previously, were Artificial Narrow Intelligence (ANI) which has been around since the 1960s but did not gain popularity till the 1990s and 2000s as a business solution. This early form of AI contributed to the Information Technology Revolution (Fifth Wave) but was not a major contributor.

However, the next iteration of AI research into developing Artificial General Intelligence (AGI) will certainly have a significant impact on economic growth.

Impact of Innovation Waves Charged by AI

History of past Innovation Waves gains insight into the likely impact of AI as a primary driver of the next wave.

Increased Productivity and Efficiency Through AI

One of the key impacts of innovation waves on economic growth is increased productivity and efficiency. The adoption of AI and innovative business uses will allow firms to produce more output with fewer inputs, leading to higher levels of productivity. In the past, the introduction of assembly line production during the Industrial Revolution (Third Wave) enabled manufacturers to produce goods at a much faster rate, reducing costs and increasing output. With AI, greater productivity will occur from the use of AI by knowledge workers.

Similarly, advancements in information technology have improved productivity by automating tasks, streamlining processes, and enabling faster and more accurate decision-making. The use of artificial intelligence and other machine learning algorithms will further transform industries such as finance, healthcare, and transportation, leading to greater efficiency and productivity gains.

AI Will Create New Industries and Job Opportunities

Innovation waves often lead to the creation of new industries and job opportunities, as well as the transformation of existing industries. New technologies and business models open up new avenues for economic activity, generating demand for goods and services that did not exist before. For example, the rise of e-commerce during the Information Technology Revolution created new opportunities for online retailers, logistics providers, and digital marketers.

Innovation waves also drive job creation by requiring workers with new skills and expertise, such as prompt engineers and robotics. Industries will adapt to technological changes created by AI, and new occupations and professions will emerge, leading to the creation of new jobs.

Improved Living Standards and Quality of Life

Innovation waves have a significant impact on living standards and the overall quality of life. As new technologies become more widespread, they often become more affordable and accessible to the general population, leading to improvements in the standard of living. While some of the early AI research was limited to academia and businesses, the latest release of Open Source models and future releases of software with AI integration will make this innovation accessible to many.

Innovation waves also have positive effects on various aspects of daily life. For instance, the availability of smartphones and the internet has revolutionized communication and access to information, enabling people to connect with others around the world and access a wealth of knowledge. Now, AI as an assistant to the vast network of information, will help users find solutions to their daily life.

Positive Effects of Innovation Waves

Advancements in Technology

Innovation waves drive advancements in technology, leading to the development of cutting-edge tools, devices, and systems. These advancements have far-reaching implications for various industries, enabling them to improve their products and services, optimize processes, and explore new possibilities. For example, advancements in robotics and automation have revolutionized manufacturing, allowing for precise and efficient production processes. Now with AI takes this advancement a step further.

Additionally, innovation waves often spur breakthroughs in scientific research, leading to a greater understanding of the natural world and paving the way for new inventions and discoveries. AI will aid in the research and development of AI systems, thus contributing to scientific knowledge and fostering the evolution of technology.

Increased Competitiveness of Industries

Innovation waves enhance the competitiveness of industries by enabling firms to differentiate their products and services from competitors. Through the adoption of new technologies and innovative business models, companies can gain a competitive edge by offering unique features, lower costs, or improved customer experiences. Current technology is becoming more of a commodity rather than a competitive advantage, but AI will likely renew the impact of technology.

Moreover, the emergence of new industries during innovation waves creates new markets and opportunities for growth. Companies that are able to identify and capitalize on these opportunities can expand their operations, attract new customers, and increase their market share.

Development of New Products and Services

Innovation waves often lead to the development of new products and services that address unmet needs or improve existing solutions. As new technologies emerge, entrepreneurs and businesses seize the opportunity to innovate and create value for customers. This leads to a proliferation of new products and services that offer enhanced functionality, convenience, or cost-effectiveness.

AI will transform the way people work, communicate, and entertain themselves. These innovations will not only improve efficiency and productivity but will enrich the lives of individuals and provide new avenues for self-expression and creativity.

Negative Effects of Innovation Waves

Job Displacement and Unemployment

While innovation waves drive economic growth and create new job opportunities, they can also result in job displacement and unemployment in certain sectors. As new technologies and business models emerge, they often replace traditional methods of production and employment. This can lead to the automation of tasks, which reduces the need for human labour.

For example, the advent of machines and automation during the Industrial Revolution led to the displacement of many workers in the agricultural and manufacturing sectors. Similarly, advancements in artificial intelligence and robotics are expected to reshape the job market and cause significant disruptions in industries such as transportation, retail, and customer service.

Economic Inequality

Innovation waves can also exacerbate economic inequality, as the benefits of technological advancements are not evenly distributed. In some cases, certain individuals or groups may benefit disproportionately from the opportunities created by innovation, while others may be left behind.

For example, the Information Technology Revolution has generated tremendous wealth for a small number of individuals and companies in the technology sector, while many traditional industries and workers have struggled to adapt. This growing wealth gap can lead to social tensions and contribute to the concentration of economic and political power in the hands of a few. AI will likely reshuffle this concentration of economic and political power.

Disruption of Traditional Industries

Innovation waves often disrupt traditional industries, leading to the decline or even extinction of certain sectors. As new technologies and business models emerge, they can render existing products and services obsolete or less competitive. This can have significant economic and social consequences, particularly for communities that rely heavily on these industries for employment and economic stability.

For example, the rise of digital media and online streaming platforms has disrupted the traditional print and broadcast media industries, leading to the decline of newspapers, magazines, and television networks. This disruption has resulted in job losses and the need for industry-wide restructuring and adaptation.

What future industries will be disrupted by AI?

Societal Response to Innovation Waves

Fear and Resistance to Change

Innovation waves often evoke fear and resistance to change as they disrupt established practices and ways of life. People may fear the loss of jobs, the erosion of traditional values or the unknown consequences of new technologies. This resistance can be driven by a lack of understanding or a belief that the benefits of innovation will not outweigh the costs.

However, embracing innovation is crucial for societies to adapt and thrive in the face of rapid technological advancements. Overcoming fear and resistance requires education and awareness about the potential benefits of innovation, as well as support programs for workers who may be displaced by technological change.

Adaptation and Retraining of Workforce

To mitigate the negative impacts of innovation waves, societies must invest in the adaptation and retraining of the workforce. As industries evolve, workers need opportunities to acquire new skills and expertise that are in demand. This can be achieved through vocational training programs, lifelong learning initiatives, and partnerships between educational institutions and businesses.

Supporting workers in their transition to new employment opportunities and providing them with the skills needed to succeed in the changing economy is essential for inclusive economic growth. Governments, businesses, and educational institutions must work together to ensure that individuals have access to the resources and support they need to adapt to technological changes.

Government Policies and Regulations

Government policies and regulations play a critical role in shaping the impact of innovation waves on economic growth and societal well-being. Policies that promote innovation, investment in research and development, and access to capital can encourage entrepreneurship and the development of new industries.

At the same time, regulations are necessary to ensure that innovation is harnessed in a responsible manner and that the benefits of technological advancements are shared broadly. Regulation can address concerns related to data privacy, worker protection, and the ethical implications of emerging technologies.

What are the ethical impacts of AI spreading throughout our lives?

In conclusion

The history of Artificial Intelligence in business spans decades, and it has undergone AI development and significant transformations over time. AI has grown from being a rudimentary concept to becoming an essential part of the business landscape, and its future is more promising than ever. Small businesses have an excellent opportunity to leverage AI to transform how they do business as the technology becomes more accessible. Ultimately, AI’s ability to streamline operations, automate mundane tasks, and provide invaluable insights has cemented its place in businesses today and in the future.

Article Summary

What is AI in simple terms

AI, or “Artificial Intelligence,” is when machines do things that usually only humans can do, like solve problems, learn from experience, understand language, and spot patterns. AI lets machines make decisions, learn from new knowledge, and get better over time without having to be programmed for each task individually.

What is a basic example of AI

A simple chatbot is one of the simplest kinds of AI. It can talk to users through text and answer their questions based on rules or simple patterns that have already been set up. These robots can’t learn on their own, but they can give pre-programmed answers to specific questions. This makes them a good way to start learning about how AI works.

What are the types of AI?

There are three types Narrow, General, and Super AI. Narrow is preprogrammed with limited abilities. General has the ability to learn and apply that learning to new tasks. Finally, super AI is theoretical with reason and makes its own judgement with an understanding far beyond any human intelligence.

When did AI Start

In 1950, Theseus a robot mouse that could find its way out of a maze. Theseus was one of the first machines that could learn, and it could “remember” its way by using telephone relay switches.

Who first invented AI

In 1950, Alan Turing developed the Turing Test to assess a machine’s ability to exhibit intelligent behaviour (AI). There were many talks about the idea of AI, but Alan Turing is attributed to defining AI by creating the Turing Test.

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