Unit 3: Neuroscience and Cognitive Science

The ability to see possibilities beyond what is immediately being perceived.

The ability to recall coded and stored information in the brain.

The process of searching for possible reasons or causes.

The study of the nervous system, including the brain and its functions.

The study of mental processes and how they are implemented in the brain.

Mathematical models inspired by the biological nervous system. They are used in artificial intelligence to simulate learning.

A biological cell that transmits information through electrochemical signals.

The process of strengthening the connection between two neurons when they are activated together.

The outer layer of the brain, responsible for higher functions like sensory interpretation, emotions, reasoning, speech, and language understanding.

The connection between the two hemispheres of the cerebrum, allowing communication and exchange of signals.

A structure located beneath the cerebrum, primarily responsible for motor control, balance, and coordination.

The base of the brain, serving as a relay station between the brain and the spinal cord.

The nervous system controlling the body's involuntary actions, such as breathing, heart rate, and temperature regulation.

The nervous system responsible for voluntary movements, sending signals from the brain to the muscles.

The right hemisphere of the brain controls the movements of the left side of the body.

The part of the nervous system outside the brain and spinal cord, connecting the central nervous system to the rest of the body.

The idea that artificial intelligence could surpass human intelligence.

The hypothetical moment when artificial intelligence surpasses human intelligence, leading to an exponential increase in its capabilities.

A machine that, like humans, can learn, adapt, and improve its abilities. Importantly, it can apply its intelligence to solve problems and achieve new goals.

The idea that super intelligence could be created by machines themselves, rather than by humans.

The process where AI can use its intelligence to gain knowledge, analyze vast information, and improve its own capabilities.

The concept that AI could improve itself in a cycle of continuous development, each generation exceeding the previous one, leading to a rapid increase in intelligence.

A futurist who predicted the possibility of a singularity within 30 years, where machines surpass human intelligence.

A renowned inventor and futurist, known for advocating the concept of singularity and predicting its arrival in the near future.

A neural network where information flows in one direction, from input to output, without loops. It consists of input, hidden, and output layers.

A neural network where information can flow in loops, allowing for the processing of temporal sequences. It has connections that feedback to previous layers.

Layers in a feed-forward neural network that process information between the input and output layers. They contribute to the network's ability to learn complex patterns and relationships.

The layer in a neural network that receives the input data, which is then processed by the network.

The layer in a neural network that produces the final output based on the processed information.

The process of modifying the connection strengths (weights) between neurons in a network to improve its performance based on presented data. This is how networks learn.

The concept that artificial neural networks are inspired by biological neural systems, but are highly simplified models. They don't accurately capture all the complexity of biological brains.

The use of neural networks to process information that has a temporal component, like speech or handwriting.

Deep learning refers to neural network models that are characterized by their many layers, often described as deep, rather than their resemblance to biological networks.

Modern CPUs contain a large number of transistors, approaching the number of neurons in the human brain. However, this does not demonstrate that CPUs are as intelligent as the human brain.

While current artificial neural networks have a significant number of units, they still fall short of the complexity of the human brain.

The number of neurons in the human brain is approximately 10^11, while bees have about 10^6 and frogs have 10^8.

Artificial intelligence research often focuses on tackling specific tasks, such as playing games, object recognition, robot control, or language translation.

The term 'deep learning' emphasizes the depth of layers in neural network models, rather than drawing a direct biological analogy.

The history and current state of AI research show a focus on solving specific tasks, not necessarily replicating human-level intelligence.

Although the number of transistors in CPUs and the number of units in artificial neural networks are increasing, it's not a guarantee of achieving human-level intelligence.

The principle that as you increase something (like AI capabilities), the amount of benefit gained from further increases will eventually decrease.

A curve that shows growth initially, then plateaus as it approaches a limit, often depicting the rate of improvement of a technology like AI.

The idea that the combined intelligence of many individuals, working together and sharing knowledge, can be more powerful than a single genius.

The idea that the digital content of a human brain could be transferred to a computer, essentially preserving the brain.

The argument that the most significant impact of AI will be felt in the social and collaborative aspects of human life, rather than just individual intelligence.