Pt.1

June 4, 2023

Evolution

Throughout human history, there have been few developments as transformative as the advent of artificial intelligence (AI). It has been hailed as the dawn of a new era, a revolution that promises to reshape our world in ways we are only beginning to understand. But as we stand on the precipice of this brave new world, it is incumbent upon us to consider the philosophical implications of this seismic shift. For in the rush to embrace AI, we risk outsourcing not just our labor, but our very thought processes, to machines.

“By outsourcing our thought processes to AI, we risk diminishing our capacity for critical thinking, for independent judgment, for creativity.”

The philosopher René Descartes famously declared, "Cogito, ergo sum" - "I think, therefore I am." This statement encapsulates the essence of human existence: our capacity for thought, for self-reflection, for creativity. But as we delegate more and more of our cognitive tasks to AI, we must ask ourselves: what does it mean to be human in a world where machines can think for us?

The outsourcing of human thought to AI is not a future hypothetical; it is a present reality. A 2022 study by the McKinsey Global Institute found that up to 20% of business processes could be automated by existing technology, with AI playing a significant role in this transformation. But the impact of AI extends far beyond the workplace. From personalized recommendations on Netflix to the predictive text on our smartphones, AI is increasingly shaping our thoughts, our decisions, our very perceptions of the world.

This trend is not without precedent. Throughout history, humans have used tools to augment their abilities, from the plow to the printing press. But AI represents a fundamental departure from these earlier technologies. As the historian Yuval Noah Harari notes in his book "Homo Deus," "In the past, machines competed with humans mainly in manual skills. Now they are beginning to compete with us in cognitive skills."

Lets take a moment to glance over how we got here:

1820s - The Birth of Mechanical Computing

The journey begins with Charles Babbage, an English mathematician and inventor. In the 1820s, he conceived of a mechanical device called the Difference Engine, designed to compute mathematical tables. Later, he proposed an even more ambitious machine, the Analytical Engine, which was intended to use punched cards to carry out complex calculations. Although Babbage never built his machines, his ideas laid the groundwork for modern computing.

1930s - Theoretical Foundations

In the 1930s, the British mathematician and logician Alan Turing developed the concept of a universal machine, capable of simulating any other machine given the right inputs and instructions. This theoretical construct, now known as the Turing Machine, forms the basis of modern computer science.

1940s - Birth of the Digital Age

The 1940s saw the creation of the first electronic digital computers, such as the ENIAC in the United States. These machines, although primitive by today's standards, marked the beginning of the digital age.

1950s - The Dawn of AI

The term "artificial intelligence" was coined in 1956 at a conference at Dartmouth College. Early AI research focused on problem-solving and symbolic methods. This period also saw the development of the first programming languages, such as LISP, which became closely associated with AI research.

1960s-1980s - AI Winter and the Rise of Personal Computing

The initial optimism about AI led to high expectations, but by the late 1960s and early 1970s, progress had slowed, leading to reduced funding and interest, a period known as the "AI winter." However, during this time, the rise of personal computing and the development of the internet set the stage for the next wave of AI innovation.

1990s - Machine Learning Emerges

In the 1990s, a shift occurred from knowledge-driven approaches to data-driven approaches. The field of machine learning, where computers learn from data, began to take shape. The development of the World Wide Web provided a vast new array of digital data for these machine learning algorithms to learn from.

2000s - Big Data and the Internet

The explosion of the internet led to the generation of 'big data'. Innovations in data storage and processing made it possible to handle this data, providing the raw material for advanced machine learning algorithms. This period also saw the development of more sophisticated forms of AI, such as natural language processing and computer vision.

2010s - Deep Learning Revolution

The 2010s saw the rise of deep learning, a type of machine learning that uses neural networks with many layers (hence "deep"). These techniques, powered by increased computational power and large datasets, led to dramatic advances in AI capabilities, from self-driving cars to speech recognition.

2020s - AI Today

Today, AI is an integral part of our daily lives, powering everything from search engines to recommendation systems. It has also raised new ethical and philosophical questions about autonomy, identity, and what it means to be human in an increasingly automated world.

Outsourcing Thought

The outsourcing of thought to technology has undergone significant evolution over the past two decades. This process has been driven by rapid advancements in technology, particularly in the fields of artificial intelligence (AI) and machine learning. Here's a brief overview of this evolution:

Early 2000s - The Dawn of the Digital Age

At the turn of the century, the internet was becoming increasingly integrated into daily life. Search engines like Google began to serve as external repositories of information, changing the way we seek and store knowledge. Instead of remembering information, we began to remember where to find it. This was the beginning of a shift towards the outsourcing of memory and knowledge retrieval to technology.

Mid 2000s - The Rise of Social Media

The advent of social media platforms like Facebook and Twitter further accelerated the outsourcing of thought. These platforms not only changed the way we communicate but also influenced our opinions and behaviors. The 'like' button, for instance, introduced a new form of social validation, subtly shaping our preferences and self-perception.

Late 2000s - The Smartphone Revolution

The introduction of smartphones brought about a new era of constant connectivity. With access to the internet in our pockets, we began to rely on technology for a wider range of cognitive tasks. Navigation apps like Google Maps began to replace our need to remember routes, while calendar apps took over the task of remembering appointments.

Early 2010s - The Big Data Era

As technology companies began to collect and analyze vast amounts of data, they started to develop algorithms that could predict our behavior. Recommendation algorithms on platforms like Amazon and Netflix began to shape our consumption habits, while personalized news feeds on social media started to influence our perceptions of the world.

Mid 2010s - The Advent of AI Assistants

The introduction of AI assistants like Apple's Siri and Amazon's Alexa marked a new stage in the outsourcing of thought. These tools took over routine cognitive tasks like setting reminders, finding information, and even controlling our home appliances.

Late 2010s - The Rise of Machine Learning

Advancements in machine learning led to more sophisticated AI that could learn from our behavior and make predictions or decisions on our behalf. For instance, predictive text and autocorrect features began to influence our writing, while algorithmic trading systems started to make financial decisions with minimal human intervention.

2020s - The Era of Deep Learning

Deep learning, a subset of machine learning, has enabled even more advanced AI capabilities. From generating realistic deepfake videos to powering self-driving cars, these technologies are increasingly taking over tasks that require complex decision-making and perceptual abilities.

The implications of this shift are profound. By outsourcing our thought processes to AI, we risk diminishing our capacity for critical thinking, for independent judgment, for creativity. We risk becoming, in the words of the philosopher Hannah Arendt, "thoughtless creatures who, unable to rely on their own thinking, are forced to fall back on routines and clichés."

But the outsourcing of thought to AI also raises deeper, more existential questions. If our thoughts are shaped by algorithms, to what extent can we claim to be autonomous beings? If our decisions are guided by AI, to what extent can we claim to be responsible for our actions? As the philosopher John Locke observed, "The power to act according to the determination of one's own will is what constitutes a man."

Insourcing Bias

The rise of AI also threatens to exacerbate existing social inequalities. As the economist Thomas Piketty has argued, the increasing automation of labor could lead to a further concentration of wealth in the hands of those who own the machines. But the outsourcing of thought to AI could lead to a new form of inequality: a cognitive divide between those who can afford to think for themselves and those who must rely on machines.

“But the outsourcing of thought to AI could lead to a new form of inequality: a cognitive divide between those who can afford to think for themselves and those who must rely on machines.”

In the face of these challenges, it is tempting to retreat into nostalgia, to yearn for a simpler time before the rise of AI. But such a retreat would be a mistake. The genie cannot be put back in the bottle. Instead, we must confront these challenges head-on, with clear eyes and open minds.

In the post-modern age, the outsourcing of human thought to AI presents us with a paradox. On the one hand, it offers unprecedented opportunities for progress, for innovation, for the betterment of the human condition. On the other hand, it poses profound challenges to our sense of self, our social fabric, our very humanity.

The philosopher Friedrich Nietzsche once wrote, "Man is something that shall be overcome." In the age of AI, we must ensure that we do not overcome ourselves. We must ensure that in our quest for progress, we do not lose sight of what makes us human: our capacity for thought, for self-reflection, for creativity.

We must remember that AI is a tool, a means to an end, not an end in itself. We must remember that AI is a creation of human thought, not a replacement for it. We must remember that AI, for all its power, for all its potential, is not a panacea.

Indeed, the existential questions raised by the outsourcing of thought to AI are among the most profound and challenging of our time. They strike at the very heart of our understanding of what it means to be human, to be autonomous, to be responsible.

The philosopher John Locke's assertion that the power to act according to one's own will is what constitutes a person is a cornerstone of liberal philosophy. It underpins our notions of individual rights, of freedom. But in a world where our thoughts and decisions are increasingly shaped by algorithms, this foundational principle is thrown into question.

“Yet it is important to remember that AI is not an autonomous entity, but a tool created and controlled by humans. As such, it is not AI that shapes our thoughts and decisions, but the humans who design and deploy AI. The algorithms that guide us are not neutral, objective arbiters, but reflect the values, biases, and interests of their creators.”

The philosopher Jean-Paul Sartre famously declared that "man is condemned to be free." By this, he meant that we are always free to choose, even when it seems that we have no choice. But in a world where our choices are increasingly influenced by AI, this freedom may seem illusory. We may feel that we are not so much condemned to be free, as condemned to be guided, shaped, even controlled by algorithms.

Yet it is important to remember that AI is not an autonomous entity, but a tool created and controlled by humans. As such, it is not AI that shapes our thoughts and decisions, but the humans who design and deploy AI. The algorithms that guide us are not neutral, objective arbiters, but reflect the values, biases, and interests of their creators.

This realization brings us back to Locke's assertion about the power to act according to one's own will. For it is not AI that threatens our autonomy and responsibility, but the humans who use AI to shape our thoughts and decisions. It is not AI that we must fear, but the misuse of AI by those who seek to control rather than empower us.

Indeed, the realization that AI is a tool, not an autonomous entity, is a crucial one. It reframes the debate about AI and autonomy, shifting the focus from the technology itself to the humans who design and deploy it. This shift is not just a matter of semantics, but a fundamental reorientation of our approach to AI.

AI, in and of itself, does not have values, biases, or interests. It does not have a will or a consciousness. It is a tool, a product of human ingenuity, a reflection of our values, biases, and interests. .

In the final analysis, the outsourcing of thought to AI is not just a technological issue, but a moral one. It forces us to confront the most fundamental questions of our existence: Who are we? What do we value? What kind of world do we want to create?