The turbulent history of Artificial Intelligence

Introduction

As we speak of artificial intelligence in the present day, it sometimes feels as if it were an overnight phenomenon that in a burst of technological magic, it suddenly materialized in such systems as ChatGPT. But if we peel away and examine this narrative, a far different tale emerges one both ancient and endlessly human. AI history spans not only from abacus in ancient Mesopotamia to neural nets in this 21st century but represents a continuum of imagination, discovery, and sometimes disillusion, all exemplified in our eternal need to build a thinking machine.

Here’s why it’s important for us to grasp the origin of AI in order to realize what AI really is: The desire to create a thinking machine is, in essence, a human story and one that predates computers by a very long time, in fact dating back to the first moment when a human considered the possibility of recreating thought with logic.

The origins: from abacus to logic machines

Well before the time of computers, people invented devices to augment their brains. The abacus, which dates to Around 2400 BCE in ancient Mesopotamia, allowed merchants and record keepers to do math calculations much faster. While not very smart, it marked a beginning of mechanical thinking the premise that a machine could imitate a part of our thinking.

Centuries later, this interest in reasoning engendered a new child, formal logic. Aristotle’s syllogisms in the 4th century BCE laid the groundwork for the belief in a predictable pattern of thought that can be imitated. In the 17th century, these thinkers such as René Descartes and Gottfried Wilhelm Leibniz were working towards a universal calculus of reason, a language in which thought itself could be calculated. Further, Leibniz talked of a machine which would calculate arguments, Let us calculate!

The Enlightenment’s preoccupation with mechanism led to designs such as Charles Babbage’s Analytical Engine in the 1830s and Ada Lovelace’s algorithms, which were a first attempt at making a machine do abstract calculations. Lovelace conceived a vision of machines not only carrying out calculations but being able to compose music of any degree of complexity. What a vision of artificial intelligence before computers were able to make a sound.

The birth of computer intelligence

The 20th century saw these philosophical visions become reality in physical form. In the 1940s, when electronic computers were invented, not only was it not a question of whether machines were capable of calculation the answer to which, clearly, was yes but rather whether they were capable of thinking.

One man epitomized this vision above all others: Alan Turing. In 1950, in his paper Computing Machinery and Intelligence, he posed this simple but profoundly radical question: Can machines think? To answer it, he proposed a thought experiment called the Turing Test: a machine would be proven intelligent if it were impossible for a man to distinguish its responses from those given by another human.

Just a short time later, in 1956, The Dartmouth Conference in New Hampshire breathed the first life into the term artificial intelligence. Attendees such as John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon had a vision: intelligence could be defined so clearly a machine could imitate it. They thought human thought could be translated into logic. The hope ran high with money pouring in and projects underway to do math problems, play checkers, and prove mathematical theorems.

The first AI winter

But reality proved far tougher than expectations. The early AI systems were working well in constrained domains but were unable to cope with the complexities of reality. But in the 1970s, research started incurring a massive slowdown. Computers were not powerful enough, and a lack of algorithms with flexibility exacerbated this problem. Research started getting reduced government funding in a disappointed manner. The first AI winter era began with a phase of skepticism and disillusionment.

However, in spite of all this, and in the cold environment, seed Development was happening. The emergence of expert systems in the 1980s breathed life into AI. Expert systems, such as XCON in Digital Equipment Corporation, were able to model human expertise in a domain such as medical Diagnosis or engineering design. For a brief time, AI reentered mainstream focus. However, new shortcomings were discovered, and these systems were inflexible, costly, and not capable of learning.

The quiet years and the rebirth of learning

The 1990s were a bit more laid back but very important years. Scientists finally understood the limits of codified intelligence. They saw that a part of it had to be learned. As a consequence, neural networks were reborn, based on a model of the human brain. Backpropagation networks, in particular, were instrumental in teaching computers how to adjust parameters in order to gain more experience.

Then in 1997, a turning point emerged. IBM’s Deep Blue beat world chess champion Garry Kasparov, which demonstrated a computer’s capability to surpass a human in a cognitive function. Although it was not cognitive thinking, this event marked a milestone. Artificial intelligence reentered public imagination this time, and it never left.

The age of data and deep learning

In the 2000s, with the explosion of the internet, the volume of available data the fuel for AI also grew in leaps and bounds. Moreover, computational power increased exponentially, allowing for larger and larger models to be built. Minds such as Geoffrey Hinton, Yoshua Bengio, and Yann LeCun also resurrected deep learning, developing architectures for speech, image, and pattern recognition with incredible accuracy.

The 2010s saw AI leave research institutions behind. AI is present in our smartphones, cameras, autos, social media sites. Google Translate, Siri, Alexa, and self driving cars are examples of AI entering our lives. AI concepts have shifted from theoretical to our lives online.

After this, transformers emerged, which was a new model architecture developed by Google in 2017. Transforming all previous models, transformers were capable of processing language in a way that incorporated meaning, resulting in the development of large language models, which form the basis of most programs such as GPT, Claude, Gemini, and LLaMA.

The ChatGPT revolution

Then, when OpenAI developed ChatGPT in late 2022, everything changed. Suddenly, a machine would converse with millions of people across the world, complete essays, poetry, code debugging, and two way conversations. ChatGPT took less than a year to have over 100 million people using it, which is the fastest adoption rate of technology in history.

ChatGPT, from our perspective, represents a cultural rather than a technological breakthrough. Suddenly, AI seemed not just a possibility but a reality, so personal and accessible that it stood out for its creative potential. For the first time in history, people in general have had a chance to relate to what it means to encounter a synthetic intelligence. The world suddenly began to wonder not just what AI can do but what it ought to do.

The ethical crossroads

Progress, however, is never without friction. The emergence of generative AI has ignited an international discourse on ethics, ownership, and control. Who can claim rights over AI generated content? How do we address bias or misinformation? Can AI benefit humanity or displace it? Some of these early responses to innovation include the European Union’s AI Act effective in Europe, Latin America, and Asia.

We believe these are not technology questions; they are deeply human ones, they continue. AI is a reflection of the data, intentions, and biases of those creating it. The future of AI will be shaped by the values we put into it.

Conclusion

Artificial intelligence, from abacus to ChatGPT, is not a simple record of machine history but a reflection of ourselves. With each machine built in this sequence, our drive and interest in comprehending and pushing the boundaries of our brains keep shining through. Of course, with each machine we have produced the calculating machine, the reasoning machine, and most recently, a machine that can talk the core urge remains deeply human: creating meaning.

AI’s history has been stormy full of hope, failure, and rebirth. But beneath all this, one thing remains unchanged: curiosity. Whether it is ancient merchants pushing beads on an abacus or modern day scientists working on trillion parameter models, the objective all along has remained the same: thinking deeply about thinking.

As we move forward, let us not forget that intelligence, whether human or artificial, is never an end in itself but a manifestation of purpose. The intentions our machines will have inherited will not just come from their designs but from ours. Ultimately, the future of AI will not be simply smarter algorithms but wiser human beings.

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Lucas Chiesa

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