In essence, Artificial Intelligence (AI) is the use of computer systems to simulate human cognitive functions. Computers are programmed to think and learn like humans so that they can perform tasks that typically require human intelligence. Such tasks include recognizing speech, making decisions, and identifying patterns.

The notion of intelligence is hard to define, and experts in AI distinguish between strong AI and weak AI. Strong AI is Artificial General Intelligence (AGI) that can perform tasks that it has not been specifically trained for. It’s capable of understanding, learning, and applying knowledge, just like human intelligence. This is the kind of AI we generally think of in movies such as “2001: A Space Odyssey” and “Terminator.”

The fear and apprehension that people have for AI is due to the concept of Strong AI. However, this is still a theoretical concept and not a realized technology.

The recent advancements in the field of AI with Large Language Models (LLM), where humans interact with machines using natural language (OpenAI’s ChatGPT, Google’s Gemini, Microsoft’s Copilot), have renewed people’s fear of Strong AI, as these interactions truly feel like you are conversing with an intelligent machine. Yet these are still a very long way off from being truly sentient machines.

Weak AI, or Narrow AI, is more limited in scope and is a simulation of human intelligence applied to a very specific problem. This is not general intelligence or consciousness. This is the AI that we’ve encountered in our daily lives. Some examples of Weak AI are:

• Facial recognition systems
• Autonomous vehicles
• Virtual assistants like Siri, Alexa, and Google Assistant
• Recommendation systems like Amazon, Netflix, and Spotify
• Predictive Text and Autocorrect
• Generative AI (creation of text, images, and music) such as ChatGPT and Gemini

The essence of AI lies in Machine Learning (ML) and Deep Learning (DL). Machine Learning stands as a technique that empowers computers to assimilate knowledge from data and, subsequently, make predictions or take decisions. This process does not rely on explicit programming for each distinct task but rather on the ability of algorithms to learn from data and improve over time.

Deep Learning, a sophisticated subset of Machine Learning, is characterized by its use of artificial neural networks. These networks are structured in layers and designed to process data in a hierarchical manner. Each layer computes outcomes from the data it receives, which then becomes the input for subsequent layers, somewhat akin to the cognitive processes of the human brain. This architecture enables Deep Learning models to recognize intricate patterns and nuances within large datasets.

The distinction between the two lies in their depth and complexity. While Machine Learning can handle tasks that involve data prediction and classification, Deep Learning is particularly adept at handling tasks that involve unstructured data like images and speech.

The significance of these technologies cannot be overstated, as they underpin many of the rapid advancements in various fields, such as computer vision, natural language processing, and autonomous vehicles. By harnessing the power of vast datasets and computational strength, AI is able to perform tasks that once required the intricate understanding and decision-making capabilities of human intelligence.

Machine Learning and Deep Learning represent the core of AI’s ability to mimic and potentially surpass human cognitive functions in complexity, efficiency, and scalability. As we continue to refine these technologies, the potential applications and advancements they can bring to various sectors of society are enormous.

Where did AI come from?

Ancient History and Philosophy

The roots of Artificial Intelligence are deeply embedded in ancient history and philosophy, far preceding the advent of computers and modern technology. This fascination with creating life-like intelligence through artificial means can be traced back to ancient myths and tales. Cultures around the world dreamed of animated statues, golems, and automata, which were believed to possess intelligence or consciousness bestowed by their creators. These stories reflect an early desire to understand and replicate the essence of human intelligence and consciousness.

Philosophers played a crucial role in this journey, laying the philosophical and logical foundations necessary for the development of AI. Aristotle, one of the most influential figures in this narrative, introduced formal logic, which would later evolve into the computational logic that underpins AI. His work on categorization, syllogisms, and deductive reasoning offered the first steps toward creating systems that could simulate aspects of human thought and decision-making processes.

The interplay between ancient myths and philosophical thought provided the foundation for the growth of AI. It was the combination of humanity’s imagination, as seen in the stories of artificial beings, and the rigorous logical structures provided by philosophers like Aristotle that set the stage for the eventual emergence of AI as a scientific discipline. These early concepts and speculations about intelligence and consciousness outside of the human mind laid the groundwork for centuries of progress, leading to the computational and algorithmic approaches that characterize AI today.

Mid-20th Century

The mid-20th century ushered in a transformative era in the realm of science and technology, marking the inception of AI as a new discipline. This period was characterized by the convergence of visionary scientists and mathematicians whose collective curiosity and innovative thinking laid the groundwork for AI. Among these pioneers, Alan Turing stands out as a seminal figure. His work not only pushed the boundaries of mathematics and computing but also introduced a philosophical dimension to the discussion on machine intelligence.

The creation of the first computers during this time, though primitive by today’s standards, was nothing short of revolutionary. These machines, capable of performing calculations at unprecedented speeds, opened up new possibilities for research and application in various fields, including AI. Turing’s contribution was particularly significant; his Turing Test provided a simple yet profound criterion for evaluating a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human. This concept not only challenged existing notions of intelligence but also set a benchmark that continues to influence AI research and development.

Turing’s ideas on machine intelligence, coupled with the advent of digital computing, created a fertile ground for AI as a scientific discipline. The mid-20th century thus represents a crucial turning point not just in the technological advancements it brought about but in the philosophical and ethical questions it raised regarding the nature of intelligence and the potential of machines to emulate human thought and behavior. This era laid the foundation for the explosive growth and diversification of AI research in the subsequent decades, shaping the trajectory of modern computing and significantly impacting society at large.

The Dartmouth Conference of 1956 stands as a pivotal moment in the history of AI, marking its inception as a distinct field of scientific inquiry. Held at Dartmouth College, this seminal event was convened by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, who were pioneers in the nascent domain of AI. Their collective vision was to explore the possibility of machines not just calculating at unprecedented speeds but actually mimicking the nuanced processes of human thought.

The conference proposal, authored primarily by McCarthy, held that every feature of learning or any other manifestation of intelligence could, in theory, be so precisely described that a machine could be made to simulate it. This bold assertion laid the groundwork for the field’s ambitious objectives, focusing on the creation of machines capable of learning, problem-solving, and self-improvement.

Although the conference itself did not produce immediate breakthroughs, it succeeded in galvanizing interest and investment in AI research. It also established a community of researchers dedicated to advancing the field, fostering collaboration and exchange of ideas that would fuel decades of innovation.

The Dartmouth Conference’s legacy is profound, setting the stage for the development of AI technologies that have become integral to modern life. It symbolizes the moment when AI transitioned from the realm of science fiction and philosophical speculation into a legitimate scientific endeavor focused on understanding and replicating the cognitive capabilities of the human mind.

AI Summers and Winters

The journey of AI has been characterized by alternating periods of rapid advancement and stagnation, known as “AI summers” and “AI winters,” respectively. After the initial excitement in the mid-20th century, the AI field saw significant advancements, including the development of machine learning algorithms and natural language processing. These innovations promised to revolutionize the way machines interacted with the world and processed information, mirroring human capabilities to an extent previously deemed impossible.

However, the ambitious expectations set for AI led to periods known as AI winters, the first of which occurred in the mid-1970s and again in the late 1980s to early 1990s. These were times of reduced funding and skepticism from both the public and the research community, driven by the realization that AI had not yet lived up to its lofty promises. The complexity of replicating human intelligence was far greater than anticipated, and the limitations of existing technology became apparent.

Despite these setbacks, AI winters were not devoid of progress. They were times of reflection, theoretical development, and laying the groundwork for future advancements. Researchers focused on refining algorithms, improving computational efficiency, and understanding the theoretical underpinnings of AI, which would later enable the resurgence of the field. The quiet progress made during these winters set the stage for the AI renaissance in the late 1990s and early 21st century, fueled by increased computational power, the advent of the Internet, and large datasets.

Late 20th and early 21st Centuries

The resurgence of AI in the late 20th and early 21st centuries speaks to the transformative power of technological advancements. This period marked a significant shift in the capabilities and applications of AI, driven by two key developments: the exponential increase in computing power and the widespread adoption of the Internet.

The advent of more powerful and affordable computing resources meant that researchers could process and analyze data at scales previously unimaginable. Simultaneously, the Internet facilitated unprecedented access to vast amounts of data and enabled global collaboration among AI researchers and practitioners. Deep learning and neural networks, in particular, emerged as pivotal technologies during this resurgence. These factors combined to create a fertile environment for innovation in AI.

This era of AI has been characterized by rapid advancements and the practical application of AI technologies in everyday life. From virtual assistants and recommendation systems to autonomous vehicles and advanced healthcare diagnostics, the impact of AI is evident across various sectors, heralding a new era of innovation and technological integration.

The resurgence of AI has not only propelled the field into a new era of research and development but has also raised important questions about ethics, bias, privacy, and the future of work. As AI continues to evolve, these considerations will play a crucial role in shaping its trajectory and ensuring that the technology benefits society as a whole.

(George Wilson is a member of our National Advisory Committee and was integral in the launch of The Patriot Post in 1996. He has been on the leading edge of technology for his entire career, including leading the IT departments for one of the world’s largest companies. His primary interest is the dual-edged impact of technology on our lives — both its potential to enhance our world and the challenges it presents.)