2. Machines and the Human Experience

Machine learning (ML) is a branch of artificial intelligence that focuses on developing algorithms capable of learning patterns from data, making predictions, or improving performance over time without being explicitly programmed for specific tasks. It achieves this through techniques like supervised learning, unsupervised learning, and reinforcement learning. This learning does not imply "experience" in the human sense, nor does it suggest consciousness. Instead, it refers to a specific computational process through which an algorithm adjusts its parameters or representations based on input data to improve its performance on a given task. This "learning" differs fundamentally from human learning or experience but can be considered meaningful in its own operational context. ML systems excel in automating repetitive tasks, analysing vast datasets, and generating insights, often augmenting human decision-making and efficiency.

In the evolving landscape of AI, ML systems are becoming increasingly sophisticated, capable of processing vast amounts of data and performing complex tasks. However, as ML systems, including large language models (LLMs), exhibit advanced capabilities, questions arise about their nature, particularly concerning consciousness and self-awareness. This article delves into the relationship between machine learning and consciousness, examining current capabilities, limitations, the ethical considerations that accompany these technologies ,and the way in which AI intersects with ethics, creativity, and human identity.

What are the philosophical and practical implications of machine learning for humanity? Can a machine ever truly understand what it means to be human? As machines grow smarter, do they challenge not only our intelligence but our sense of self? What does it mean for machines to learn, and how does it relate to the essence of human learning and experience?

Machine Learning’s Strengths: Outperforming Humans in Specific Domains

Machine learning is a subset of AI that focuses on developing algorithms capable of improving their performance over time by learning from data. These algorithms analyse patterns, make predictions, and automate decision-making processes based on the data they receive. Some of the key capabilities of ML systems are data processing, pattern recognition and predictive modelling. In many respects these systems already outperform human capabilities.

One area where ML systems have demonstrated their ability to outperform humans is in image recognition and analysis, particularly in the medical field. ML models, such as convolutional neural networks (CNNs), have been trained on vast datasets of medical images, enabling them to identify diseases with remarkable accuracy. For example, AI systems like Google's DeepMind and Stanford's CheXNeXt can detect conditions such as pneumonia, breast cancer, and retinal diseases at a level that matches or exceeds that of expert radiologists. Apart from their accuracy, these systems also excel in speed, analysing thousands of images in seconds, a task that would take a human hours or even days to complete. Additionally, ML models operate without fatigue and deliver consistent results, avoiding the errors that can stem from human subjectivity, or burnout, which is a huge problem for many medical professionals. These advantages make ML an invaluable tool in medical diagnostics, augmenting human expertise and enabling earlier detection of diseases. ML achievements more generally are rooted in speed, precision, and scalability, attributes where machines surpass humans. However, while these accomplishments are impressive, they highlight a fundamental question: in what areas do humans maintain a decisive edge over machines?

The Limitations of Machine Learning

The learning that MLs perform is a computational process of optimisation, devoid of awareness or meaning. ML lacks true understanding or contextual awareness, often excelling only in narrowly defined tasks for which it has been trained. Unlike humans, who can draw on a broad spectrum of knowledge and adapt fluidly to novel situations, AI systems struggle outside their intended domain. A radiologist, for example, not only interprets medical images but also considers a patient’s history, symptoms, and the nuances of clinical judgement, areas where AI remains superficial at best. Furthermore, AI’s dependency on high-quality data means that any gaps, biases, or inaccuracies in its training set can lead to flawed or even harmful decisions, an issue that human reasoning, while imperfect, can often mitigate through intuition or ethical consideration. Finally, while humans may be slower or prone to error, their creative thinking and capacity for empathy remain unmatched, at least, so far. These limitations highlight that the superiority of ML lies not in its independence from humans, but in its complementarity, prompting deeper questions about the essence of human consciousness and the unique qualities that set us apart.

Yet for all its precision and efficiency, ML operates within defined boundaries, far removed from the complexity of human experience. Furthermore, ML’s pattern-based responses contrast with a human caregiver’s ability to adapt to nuanced, emotional contexts in healthcare or counselling. While ML "learns," it does so without awareness or experience.

Human Learning and Experience

Human experience is an amalgamation of memory, culture, feeling, relationships, and the unique way we process reality over time. This subjective depth gives rise to things that AI cannot replicate. Human experience refers to the dynamic and multifaceted process through which individuals perceive, interpret, and interact with the world. It encompasses the integration of sensory perception, emotional response, cognitive reasoning, cultural influences, relationships, and personal memory. This depth allows humans to derive meaning, create narratives, and imagine possibilities beyond immediate reality, giving rise to creativity, empathy, and purpose. Humans feel joy, grief, love, and anger, which deeply influence how we make decisions and form connections. These emotions shape moral judgement, storytelling, and our capacity for genuine compassion. AI can mimic emotional responses, but it cannot feel or genuinely empathise. This is particularly important in ethical dilemmas, for example, decisions about resource allocation, or to return to the medical world, end-of-life care. Human values, and in particular the human capacity for empathy, is crucial, and far outweigh the benefits of AI efficiency.

A sometimes maligned human attribute is intuition, yet it is a crucial part of our human experience. Humans often draw conclusions without formal logic or explicit data, relying instead on a “gut feeling” born from years of accumulated experience. This ability to leap beyond the data into the realm of the unknown is something no algorithm has mastered.

While AI can produce paintings, music, and text, its creations stem from patterns in existing data, not from the spontaneous spark of inspiration or personal meaning. AI art can be innovative and pleasing, but it comes from a different process to the one which creates human, traditional art. Human creativity often emerges from struggle, contradiction, or the search for identity, which are inherently human processes.

Humans grapple with questions of meaning and morality that AI does not. It is unlikely that somewhere right now an AI is pondering the ethics of how to interact with and treat humans. Our choices often reflect a mix of rational thought and deeply personal values, which guide us in ways that transcend optimisation or efficiency. It’s this intangible quality of human experience, this ability to weave together intellect, emotion, and creativity, that sets us apart from the machine. To be a humans is not limited to the processing of data. Human learning is intertwined with self-awareness and intentionality. It sometimes involves imaginative leaps beyond data. The decisions we make are likely to be informed by values, emotions, and cultural contexts. As humans we are positioned within personal, relational, and historical dimensions.

For humans, all aspects of our lives are shaped by consciousness, be it as the state of wakefulness, to awareness, and a sense of being alive, as part of something greater than ourselves. Our minds and bodies are such that if we do not for whatever reason manage to connect with our fellow humans, or to the animal world, or to nature more generally, we are likely to suffer. It is as beings within the world that we learn.

Can Machine Learning be understood as a Machine Experience?

The training of an ML system can be viewed as a kind of machine "experience," in which the system processes data and adjusts itself iteratively to perform better. This experience is not subjective, emotional, or intentional but is instead a systematic optimisation process. For instance, an ML system exposed to thousands of labelled images learns patterns to classify them more effectively. While human experience is shaped by sensory inputs, ML depends on the data it is exposed to. If the data changes, the resulting model, which if we were to anthropomorphise the machine, we might call its "perspective", also changes. As with humans, there is a sense of an accumulation of knowledge, and the model is shaped in part by the programmers' experiences and wishes. But while humans reflect on experiences and derive meaning from them, ML systems do not. Their learning is purely mechanical, defined by mathematical processes such as gradient descent and error minimisation. The "machine experience," therefore, lacks introspection or agency.

Is learning a misnomer for machines? The term learning accurately describes the process of adapting based on feedback to improve task performance, which is a hallmark of both human and artificial learning. However, learning, as we have come to understand it from a human perspective, implies a degree of understanding, intention, or self-awareness that machines do not possess. Machines do not "know" they are learning; they execute programmed instructions to optimise their performance. At least, we humans mostly believe that machines cannot know. This may change, as AI develops. Perhaps what we see as a machine's absence of intentionality or subjective understanding will be understood differently in the future, rightly or wrongly. While Machine Learning is entirely a product of algorithmic design, it may develop capabilities, or be seen to develop capabilities, which cause us to re-define what machine experience might be.

Conclusion

While Machine Learning can be framed as a kind of machine "experience," it is different in nature to the deeply subjective, emotional, and reflective nature of human experience. ML is a technical process of optimisation and adjustment, devoid of consciousness or understanding, but powerful in its ability to simulate intelligence and enhance human endeavours.

There is a paradox in the relationship between ML and human experience. On the surface, both concepts centre on learning, which, in the human context, is deeply intertwined with experience, something lived, subjective, and meaningful. By contrast, ML is fundamentally distinct, a computational process devoid of awareness or subjective experience. This paradox, this learning without experience, opens the door to profound questions about the nature of consciousness, intelligence, and the future of human-machine interaction. Machine Learning can be classified - for now - as experience-less learning: ML systems process data and adjust based on patterns and algorithms, but they have no awareness of the process or its implications. However, it is important to state that this claim perhaps cannot be proved.

The paradox lies in the shared terminology of "learning" suggesting a bridge between these domains, while the underlying processes are fundamentally different. The debate on machine consciousness must separate the capacity to learn (which machines already do) from the capacity to be aware of learning or possess subjective experiences. Highlighting this distinction forces us to ask whether consciousness requires certain elements, like emotion, intentionality, or a "self", that go beyond computational processes.

If machines were to mimic behaviours associated with consciousness (e.g., expressing emotions, making decisions with ethical considerations), would that qualify as consciousness, or merely advanced simulation? This sets the stage for philosophical and ethical discussions about what it truly means to "be" versus "act as if." How can we preserve the richness of human experience while embracing the benefits of machine learning?