Human Information Processing

The human mind processes everything we encounter in daily life. Sensory inputs can take various forms, such as seeing a landscape, hearing a song, or feeling a touch. These inputs are the information received through our brain's sensory organs. Information processing allows us to make sense of these sensory inputs. At this stage, cognitive processes such as attention, memory, and language come into play. We transform physical information into symbolic, meaningful knowledge. Processed information is then transferred to long-term memory, where past experiences, knowledge, and skills are stored. This memory strengthens over time through repeated learning processes, and when needed, this information is recalled and used.

AI and Human Learning: A Comparison

AI and human learning involve processing information through different methods but share fundamental principles. Humans and AI systems process inputs from their environments to carry out learning processes. As I mentioned above, humans receive sensory inputs (visual, auditory, tactile), which involves interpreting, comprehending, and storing information received through the brain's sensory organs. AI, on the other hand, analyzes vast datasets using mathematical models and algorithms to find patterns. For instance, image recognition systems are trained on millions of images to identify objects in new images. This process relies on data processing and pattern recognition abilities. AI learns from datasets and is often trained using supervised or unsupervised learning methods. Techniques like deep learning involve performing learning processes on multi-layered artificial neural networks to analyze data.

Deep Learning and Pattern Recognition

Deep learning algorithms, much like how children learn, are trained by exposing them to numerous accurate examples. This iterative learning process helps build intricate neural networks capable of autonomously categorizing new inputs. This capability is vividly demonstrated in applications such as facial recognition systems, where the algorithms efficiently identify and classify faces across diverse settings. For example, when you upload a photo to social media, the system can automatically tag friends based on their faces, showcasing the algorithms' ability to interpret complex visual data. This parallels human learning in its ability to generalize from specific instances to broader categories.

Imitation and Reinforcement Learning

Imitation and reinforcement learning methods are critical areas in artificial intelligence where machines learn through experience as well as by trial and error. Imitation learning involves a learner following an expert's policy by observing and imitating their decisions, essentially learning by copying someone else. In reinforcement learning, AI explores its environment and optimizes its actions based on received feedback. This ability highlights AI's capacity for independent learning.

AI's Limitations and Human Cognitive Abilities

Despite AI's ability to solve challenging problems, sometimes even surpassing human capability, advancements in AI have shown that AI does not achieve minds equivalent to the human mind. Computer scientists have conducted studies indicating that AI algorithms exhibit thinking patterns similar to humans. However, AI researcher Rodney Brooks, aiming to create a robot that learns through imitation, encountered the following problem:

"The robot observes a person opening a glass jar. The person approaches the robot and places the jar on a table near the robot. The person prepares to open the jar with their hands. They hold the glass jar with one hand and the jar lid with the other hand, then start turning the lid counterclockwise to open it. After opening the jar, the person replaces the lid and continues with other tasks. The robot then attempts to replicate this action. However, it is crucial to determine which part of the action should be imitated (such as turning the lid counterclockwise) and which parts are irrelevant (like wiping the hand). How will the robot abstract the knowledge gained from this experience and apply it in a similar situation?"

The answer lies in equipping the robot with the ability to read the mind of the person being imitated, thus understanding their intentions and extracting the necessary aspects of the behavior to achieve the goal. Cognitive scientists refer to this ability as intuitive psychology, or theory of mind.

The Role of Theory of Mind

Individuals with autism spectrum disorder struggle with such impairment. They can grasp physical symbols but struggle with mental symbols and cannot read others' intentions. They mimic through pathways where they have partial imitation ability. Some are adept at mimicking sounds and repeating grammatical structures that allow them to form their own sentences. Autistic individuals who achieve speech often use "you" as if it were their own name because others address them as "you," and they never grasp that the meaning of this word depends on who is addressing whom.

Human vs. AI Relational and Cognitive Growth

This ability is crucial for humans, distinguishing our ability to learn and communicate from simpler forms of imitation and reinforcement seen in animals. Nim Chimpsky, a chimpanzee involved in linguistic studies in the 1970s, exemplified this distinction. While Nim could mimic human actions to a degree, such as dishwashing movements, he did not grasp the underlying intentions or meanings behind those actions. For Nim, mimicking was limited to physical replication without understanding the deeper concepts involved, like the purpose of warm water in dishwashing.

Therefore, Nim's studies underscored that human language and communication involve more than mere imitation and reinforcement—they require abstract thinking, symbolic meaning-making, and the ability to grasp complex relationships between actions and intentions. These cognitive abilities set human language acquisition apart from the capabilities of chimpanzees and other primates, highlighting the unique ways in which humans learn, communicate, and understand the world around them.

The Role of the Innate Nervous System

It also refers to the role of an innate nervous system in learning, language acquisition, and information processing. Without this, no matter the variety of learning methods, cultural learning and being a social and emotional being become impossible.

Contemporary philosopher of mind John Searle argues that creating a conscious artificial system is not possible merely by writing sophisticated programs; it is also necessary to replicate the causal power that the human brain possesses to create consciousness.

According to Searle, no matter how complex a computer program is, it cannot create a conscious experience. This is because consciousness is a product of the causal powers arising from the physical and biological structure of the human brain. Consciousness is not only related to information processing but also to the biological processes that support this processing.

At this point, it would be appropriate to refer to Searle's famous thought experiment known as the "Chinese Room Argument." This experiment emphasizes that a person can mimic a linguistic conversation by responding to texts written in a language they do not understand according to certain rules, but this does not demonstrate that the person understands the language. This analogy is used by Searle to support his argument that machines cannot generate true "meaning" or "consciousness."

The thought experiment aims to show that the mind possesses qualities beyond what a computer program can replicate. A computer processes symbols syntactically, whereas the human mind attributes meaning to them, engaging in semantic understanding. According to Searle, no computer program can achieve this.

Flaws and Challenges in AI Systems

AI systems are not without their flaws. They can make errors when interpreting complex information or evaluating critical evidence in legal or healthcare settings. AI algorithms trained on biased datasets may produce inaccurate results based on factors like ethnicity, gender, or socioeconomic status. The complexity and opacity of AI decision-making processes raise significant ethical, legal, and technical concerns, particularly regarding accountability and transparency in decision-making.

Human Error and Understanding

While humans also make errors, harbor biases, and misinterpret information, we can understand the reasons and processes behind these mistakes. This understanding allows us to identify the sources of our biases and mistakes, which in turn enables us to make corrections and learn from our experiences. By recognizing the distortions in our perception and cognition, as well as understanding their connections to various factors, we can improve and transform ourselves personally and relationally. In contrast, the black box problem of AI makes it impossible to provide comprehensible explanations when AI systems make similar errors.

Relational Learning and AI Limitations

Besides, our understanding of ourselves often hinges on our interactions with others. Through these interactions, we gain insights into our blind spots, mistakes, biases, and distorted beliefs. This relational perspective arises from viewing both ourselves and others as independent minds, each with a unique identity and internal subjective world. In essence, it's about acknowledging that we perceive our inner worlds through our own individual thoughts, emotions, and experiences.

Unlike humans, AI cannot relate to our minds in the same way. Similar to individuals with autism who struggle with understanding others' minds, AI cannot perceive both itself and others as having minds.

Conclusion: Ethical and Responsible AI

In conclusion, AI technologies hold immense potential but require careful management, particularly in domains like law and healthcare that directly impact human life and rights. Upholding principles of transparency, accuracy, and fairness in the development and application of AI systems can enhance their positive contributions while mitigating risks. Ensuring ethical and responsible AI implementation is crucial for maximizing benefits and minimizing potential harms in our increasingly AI-driven world.

References

Steven Pinker. The Blank Slate: The Modern Denial of Human Nature

John Searle. Minds, Brains, and Programs