Introduction:
Artificial intelligence (AI) has long been hailed as a transformative force, promising to revolutionize industries, augment human capabilities, and even replicate aspects of human cognition. Yet, beneath the surface of AI’s technological advancements lies a profound convergence with the realms of psychology and philosophy. In this article, we explore how the goals of AI intersect with the fundamental inquiries of psychology and philosophy, uncovering shared objectives and illuminating the implications for our understanding of intelligence, consciousness, and ethical decision-making.
AI distinguishes itself from other endeavors aimed at comprehending the mechanisms of human and animal cognition by focusing on the construction of functioning models. These synthetic models enable AI to investigate and refine theories regarding intelligent behavior through practical experimentation.
“We cannot hold back AI any more than primitive man could have suppressed the spread of speaking”
– Doug Lenat and Edward Feigenbaum
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The Evolution of Cognitive Psychology:
In the late 1950s, psychology began to diverge from Behaviorism, exploring alternative avenues to comprehend human behavior. Concurrently, the notion of computers as models of thought gained traction. The synergy of these concepts laid the groundwork for a computational theory of mind within psychology. By the end of the 1960s, cognitive psychology emerged, focusing on explaining cognitive functions through the lens of information processing, with the computer serving as a metaphor for cognition.
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Common Interests of AI and Psychology:
AI and cognitive psychology share a profound interest in understanding the mental processes underlying behavior.
Both fields seek to unravel the intricacies of human and animal cognition, albeit through different methodologies.
While psychology employs empirical research and experimentation, AI constructs synthetic models to test and develop theories of intelligent action.
Despite the variance in approaches, the overarching goal remains the same: to decipher the mechanisms driving behavior and cognition.
Consider the example of understanding memory processes in humans
In cognitive psychology, researchers may conduct empirical studies using methods such as observation, surveys, and experiments to investigate how memory works. They might design experiments to test different hypotheses about memory encoding, storage, and retrieval. For instance, psychologists might use a recognition memory task where participants are shown a list of words and later asked to identify which words they remember seeing before. Through such experiments, cognitive psychologists aim to uncover the underlying mechanisms of memory formation and recall.
On the other hand, AI researchers might approach the study of memory using computational modeling. They could develop artificial neural networks that simulate aspects of human memory, such as pattern recognition and associative learning. These models would be trained on large datasets and then tested to see how well they perform memory-related tasks, such as image recognition or language translation. By constructing these synthetic models, AI researchers can explore different theories of memory and refine their understanding of how memory functions.
Despite the differences in methodology—psychology relying on empirical research and AI employing synthetic modeling—the overarching goal remains the same: to decipher the mechanisms underlying memory processes in humans. Both fields aim to uncover how information is encoded, stored, and retrieved in the human brain, albeit through different approaches. This example illustrates how AI and cognitive psychology share a common interest in understanding mental processes, each contributing unique insights to our understanding of cognition.
AI’s Relationship with Philosophy:
The parallels between AI and philosophy run deep, particularly concerning fundamental questions about the nature of the mind.
The mind-body problem, which traces back to philosophers like René Descartes, questions the relationship between mental phenomena and physical processes.
The mind-body problem is a philosophical inquiry that delves into the relationship between the mind (mental phenomena) and the body (physical processes).
It grapples with fundamental questions about the nature of consciousness, identity, and the relationship between mental experiences and physical reality.
At its core, the mind-body problem seeks to answer how mental states (such as thoughts, emotions, and perceptions) relate to physical states (such as brain activity and physiological processes).
There are various perspectives and theories that attempt to address this problem, leading to different philosophical positions.
René Descartes & the Mind Body Problem
One of the key historical figures associated with the mind-body problem is René Descartes, a 17th-century philosopher.
Descartes famously proposed a dualistic view, suggesting that the mind and body are separate substances with distinct attributes. According to his theory of substance dualism, the mind (or soul) is immaterial and non-extended, while the body is material and extended.
This dualistic perspective posits that mental and physical phenomena exist in different realms, with the mind exerting influence over the body through the pineal gland.
However, Descartes’ dualism has faced criticism and alternative perspectives have emerged over time. For instance, materialism asserts that only physical entities exist, including mental phenomena, which are reducible to physical processes in the brain.
On the other hand, idealism contends that reality is fundamentally mental or experiential, with physical phenomena being manifestations of mental states.
Contemporary discussions on the mind-body problem often revolve around neuroscience, cognitive science, and philosophy of mind.
These interdisciplinary inquiries aim to reconcile the subjective nature of consciousness with the objective findings of empirical science, posing significant challenges and inviting diverse perspectives.
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Bridging Disciplinary Boundaries:
The convergence of AI, psychology, and philosophy underscores the interdisciplinary nature of cognitive science.
While each discipline approaches the study of cognition from distinct perspectives, their goals and inquiries intersect in profound ways.
AI’s synthetic modeling provides psychologists with tools to explore theoretical constructs, while philosophical inquiries enrich AI research by probing the ethical and metaphysical implications of artificial intelligence.
This symbiotic relationship fosters a holistic understanding of cognition, transcending disciplinary boundaries and fostering collaboration across diverse fields.
Conclusion:
In the pursuit of understanding cognition, AI psychology, and philosophy converge on common goals and inquiries.
Through synthetic modeling, AI elucidates the mechanisms of intelligent action, aligning with the objectives of cognitive psychology. Simultaneously, AI engages in philosophical discourse, offering insights into age-old inquiries about the nature of the mind and consciousness.
This interdisciplinary synergy not only enriches each discipline but also propels our collective understanding of the complex tapestry of human and animal cognition.
As we navigate the frontier of cognitive science, collaboration across disciplines remains paramount, forging new pathways towards unraveling the mysteries of the mind.
Absolutely, AI is as enticing as forbidden fruit from Garden Of Eden. Technology whose time has come to grow,no one can hold it back
Absolutely, AI is as enticing as forbidden fruit from Garden Of Eden. Technology whose time has come to grow,no one can hold it back
It’s becoming clear that with all the brain and consciousness theories out there, the proof will be in the pudding. By this I mean, can any particular theory be used to create a human adult level conscious machine. My bet is on the late Gerald Edelman’s Extended Theory of Neuronal Group Selection. The lead group in robotics based on this theory is the Neurorobotics Lab at UC at Irvine. Dr. Edelman distinguished between primary consciousness, which came first in evolution, and that humans share with other conscious animals, and higher order consciousness, which came to only humans with the acquisition of language. A machine with only primary consciousness will probably have to come first.
What I find special about the TNGS is the Darwin series of automata created at the Neurosciences Institute by Dr. Edelman and his colleagues in the 1990’s and 2000’s. These machines perform in the real world, not in a restricted simulated world, and display convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. They are based on realistic models of the parts of the biological brain that the theory claims subserve these functions. The extended TNGS allows for the emergence of consciousness based only on further evolutionary development of the brain areas responsible for these functions, in a parsimonious way. No other research I’ve encountered is anywhere near as convincing.
I post because on almost every video and article about the brain and consciousness that I encounter, the attitude seems to be that we still know next to nothing about how the brain and consciousness work; that there’s lots of data but no unifying theory. I believe the extended TNGS is that theory. My motivation is to keep that theory in front of the public. And obviously, I consider it the route to a truly conscious machine, primary and higher-order.
My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there, by applying to Jeff Krichmar’s lab at UC Irvine, possibly. Dr. Edelman’s roadmap to a conscious machine is at https://arxiv.org/abs/2105.10461