Artificial Intelligence: The Three Waves of AI

Artificial intelligence (AI) has become a transformative force in the modern world, revolutionizing various industries and reshaping the way we live, work, and interact.  

Over the years, AI has undergone significant advancements, paving the way for groundbreaking technologies and applications. To better understand the evolution of AI, it is essential to delve into its history and identify the key waves that have shaped its development.  

In this blog, we will explore the concept of the "Three Waves of AI," a framework that categorizes the major milestones and breakthroughs in AI's evolution. 

The Evolution of AI

Before the advent of artificial intelligence (AI), various tasks that required human intelligence were slow, cumbersome and prone to errors. For instance, data analysis, language translation and voice recognition were challenging and time-consuming. This often led to bottlenecks in workflows, delayed decision-making and reduced productivity. 

However, in 1950, Alan Turing posed a question in his paper "Computing Machinery and Intelligence," which marked the beginning of the development of artificial technology.

Today, AI has opened new opportunities for progress in every field. Despite the enormous strides made in AI, experts have failed to agree on a single universally accepted definition of what artificial intelligence is. However, for the purpose of this discussion, we define AI as the ability of a system to perform tasks that would typically require human intelligence, such as data analysis, learning, planning and generalization. 

AI is not only limited to corporations, scientists or tech companies, but is also present in many everyday devices such as smartphones, watches, television sets and vehicles, enabling features that simplify tasks, automate processes and provide intelligent solutions. 

According to John Launchbury, former Innovation Information’s director of the United States’ Defense Advanced Research Projects Agency (DARPA), the development of AI comes in three waves:

First Wave AI (Handcrafted Knowledge) 

The first wave of AI, also known as Handcrafted Knowledge AI, is a technology that simplifies complex problems by mapping out the best possible outcomes or steps that can be taken. It relies on human expertise to provide the system with the necessary facts and rules of the process that it aims to improve. 

First wave AI is effective at handling narrowly defined problems, and it is proven to be useful in various applications such as scheduling logistics, automating tax software and virtual board games. This type of AI technology works through all possible outputs to generate the best recommendations for a given problem. 

However, one of the limitations of first wave AI is its lack of learning capability, which means it cannot adapt to uncertainties or changes in facts or rules without human input. Nevertheless, it still has a significant impact on improving processes, product testing and identifying potential threats and opportunities. 

Overall, while the first wave AI is a powerful tool, it has its limitations and it is crucial to understand its capabilities before applying it in different fields. As AI technology advances, it is expected that newer and more sophisticated AI models will emerge, capable of addressing the current limitations of the first wave AI. 

RELATED: How Tech Leaders See the Future of Artificial Intelligence 

Second Wave AI (Statistical Learning) 

The second wave of AI called Statistical Learning is the AI technology that makes visual recognition, speech recognition and other perceptive technologies possible. This wave in AI development works through complex algorithms to sort through big data and identify data from one another. Second wave data is powerful in going through vast amounts of data in a matter of seconds and classifying them according to the output intended. 

Additionally, second wave AI, owing to its nature as a perceptive technology, can also be programmed to learn and adapt from the new data that it receives. This enables the second wave AI to present reliable predictions by identifying patterns in large data sets. 

While second wave data is excellent in perception and its predictive capacity, this AI development is not without caveat. Second wave AI can make calculations through perception, but it cannot perceive the context of the object being presented to it. This vulnerability can easily distort the system’s learning and perceptive capability. 

These two waves of AI development fall under on what is classified as narrow AI. Narrow AI helps with the improvement of basic and complex human activities. This technology enables the conduct of fast, accurate and highly specialized analysis, calculations, predictions and recommendations. All current deployment and implementation of AI falls under this classification. 

Third Wave AI is (Artificial General Intelligence) 

On the other hand, the third and future wave of artificial intelligence is classified as Artificial General Intelligence (AGI). 

AGI refers to the concept of AI that can match the full cognitive capacity of human intelligence. This technology would allow AI to perceive real-world phenomenon with the proper context. With this development, AI will be able to address the limitations of the first and second waves of AI development and perform tasks similar, or even superior, to any human being. 

The increased interest in artificial intelligence research and development by leading countries, tech corporations and academic institutions brings the third wave of AI closer to reality. Once AGI becomes a reality, it is expected to revolutionize various industries, including the finance industry. 

Read: Should My Firm be Worried About AI and Audit? 

Where the Third Wave Might Take Us 

The third wave of AI in the finance industry is expected to bring about significant changes and advancements that could revolutionize the way we conduct financial transactions, manage risks and make investment decisions. This wave is characterized by the development and application of more sophisticated algorithms, increased computational power and vast amounts of data. 

Real-life applications of the third wave AI 

• Risk Management: One of the major areas where the third wave of AI is expected to have a significant impact is in financial risk management. By leveraging these algorithms, it can analyze vast amounts of data and help in detecting anomalies and identifying patterns that can indicate potential risks.  

• Customer Service: Another area where the third wave of AI is expected to bring about significant changes is customer service. By leveraging natural language processing (NLP) and machine learning algorithms, financial institutions can create more personalized and engaging experiences for their customers. Chatbots and virtual assistants, for example, can provide customers with instant access to information and assistance, helping to improve customer satisfaction. 

• Fraud Detection: The third wave of AI is also expected to bring about significant changes in fraud detection and prevention. By leveraging advanced machine learning algorithms and data analytics, financial institutions can better detect and prevent fraudulent activities. These algorithms can analyze vast amounts of data, including transactional data and user behavior data, to identify potentially fraudulent activities in real-time.  

• Trading: Another area where the third wave of AI is expected to have a significant impact is in  investment management. AGI can use its predictive abilities to analyze market trends, identify potential trading opportunities and execute trades with greater efficiency and accuracy. 

The Bottom Line 

Overall, AI is expected to bring about significant changes in the finance industry, enabling financial institutions to better manage risks, provide more personalized customer experiences, prevent fraud and make more informed investment decisions. As AI technology continues to evolve, we can expect to see even more exciting advancements in the years to come.  

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This post was first published 11 December 2018 and edited 23 May 2023. Edited by: Angelica Garcia