Foundations of AI: Bridging Mind, Brain and Machine

Explore the foundations of intelligence by launching the philosophical journey into module 1, questioning what intelligence means and why it remains a debated, elusive concept.

Examine how intelligence is defined across psychology, philosophy, and AI—from g-factor tests to functional and adaptive views—and assess whether machines can truly pass the Turing test and exhibit creativity.

Explain the Turing test, its imitation game setup, and major criticisms like Searle's Chinese room argument; highlight limitations including language bias, deception, and neglect of embodiment, creativity, and understanding.

Differentiate weak AI from strong AI and AGI, weighing Chinese room, semantics, grounding, and emergence to decide whether machines truly understand or merely simulate.

Examine substrate independence and functionalism, asking whether intelligent processes can be replicated on different hardware through computation. Assess embodiment, neuromorphic chemistry, and the limits of replicating conscious experience.

Explore embodied cognition, arguing that intelligence emerges from mind, body, and environment interaction, not abstract computation, grounded by sensorimotor experience and the extended mind concept.

Explore the definition of consciousness, including phenomenal consciousness and qualia, and identify neural correlates of consciousness that link brain activity to subjective experience.

Explore the mind-body problem through dualism, physicalism, and idealism, examine functionalism's role in consciousness and assess whether AI and silicon can realize it amid the hard problem.

Explore the hard problem of consciousness, contrasting physicalism with subjective experience, and examine epistemic and ontological gaps, Mary’s thought experiment on color vision, and AI implications.

Explore substrate independence for conscious experience, comparing functionalism, global workspace theory (GWT) and integrated information theory (IIT) views, biological naturalism, emergentism, and Orch-OR implications for silicon consciousness.

Explore how determinism, quantum randomness, and compatibilism shape ai agency and what free will could mean for complex algorithms.

Explore whether autonomous AI can be morally accountable and legally responsible by examining free will, responsibility gap, and the role of explainability in assigning accountability.

Examine how near-term ai decisions reflect and amplify societal biases from data and design choices. Discuss fairness definitions, accountability, transparency, nudging, and the quest for meaningful human control.

Explore long-term questions about artificial general intelligence and artificial superintelligence, focusing on value alignment, control, existential risk, and approaches like rlhf, interpretability, robustness, and scalable oversight.

Shift from asking if machines can think to how we might build one, using the human mind and cognitive science and neuroscience as a blueprint for AI via cognitive architecture.

Define cognitive architecture as a blueprint for intelligence, detailing key components—perception, memory, reasoning, learning—and the dynamic information flow and feedback loops that adapt human and artificial systems.

Explore why AI researchers look to the human mind for inspiration, viewing the brain as a general-purpose, energy-efficient model and benchmark for progress, and contrast it with narrow AI.

Explore how perception, attention, memory, learning, reasoning, decision-making, language, and motor control interact as the building blocks of thought, shaping human cognition and cognitive architectures.

Compare symbolic AI and connectionist AI to reveal how explicit rules enable reasoning and memory, while neural networks learn from data and handle perception.

Explore symbolic and connectionist cognitive architectures, with examples like SOAR and ACT-R, and learn how deep learning complements these frameworks in modeling cognition.

Explore the hybrid approach that blends symbolic and connectionist systems to combine reasoning, learning, and perception, moving toward more flexible AI and AGI.

Explore how the brain's neurons and synapses process signals—from dendrites to axon terminals. Understand synaptic plasticity, neurotransmitters, and the Hebbian principle that strengthens connections when cells fire together.

Explore how sensation detects environmental stimuli and converts them into neural signals. Then see how perception interprets and stabilizes signals to form meaningful representations, accounting for context and prior knowledge.

Trace the human visual pathway from the retina to V1, then through V2–IT, highlighting bottom-up feature detection and the ventral what and dorsal where streams.

Examine how top-down processing, guided by prior knowledge, expectations, context, and attention, collaborates with bottom-up input to shape perception and perceptual constancy through neural feedback.

Discover how computer vision turns raw pixel data into meaningful perception, enabling image classification, object detection, segmentation, scene understanding, and motion analysis through deep learning and top-down reasoning.

Explore the basic structure of artificial neural networks—layers of nodes with weights, biases, and activation functions—driving hierarchical representations and CNNs with convolution and pooling for vision tasks.

Discover how an artificial neural network uses forward propagation to process input data through hidden and output layers with weighted sums, biases, and activation functions to produce predictions.

Neural networks learn by predicting, measuring error with a loss function, and adjusting weights via backpropagation after comparing outputs to true labels across many training examples.

Compare convolutional neural networks with the human visual system, highlighting parallels in parallel processing, hierarchical learning, and experience-driven adaptation, while noting fundamental gaps and emerging transformer approaches.

Analyze intelligence, consciousness, and AI ethics through cognitive architectures and vision, highlighting convolutional neural networks, bottom-up perception, data efficiency, and the gaps toward true semantic understanding.