keywords definition

Question 1

Creativity

Answer 1

Use of imagination or original ideas to create something. Creativity is characterized by the ability to perceive the world in new ways and to make connections between seemingly unrelated phenomena

Question 2

Emotion

Answer 2

Mental state associated with the nervous system, brought on by chemical changes and by the environment (e.g. relationships, circumstances). It is distinguished from reasoning or knowledge as it is primarily instinctive

Question 3

Deep Learning

Answer 3

Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly, to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning.

Question 4

PATTERN ASSOCIATOR

Answer 4

Consists of a set of input units, and output units, and connections from input to output. A training set of examples, consisting of inputs and their corresponding outputs

Question 5

AUTOMATION

Answer 5

Automation is the technology by which a process or procedure is performed with minimal human assistance.

Question 6

SOCIAL ROBOTICS

Answer 6

The field of social robotics concentrates on the development and design of robots which interact socially with humans, but sociality between robots (e.g., in multirobot systems) is not part of the field

Question 7

SWARM INTELLINGENCE

Answer 7

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence.
Or
Swarm intelligence (SI) is a branch of computational intelligence that discusses the collective behavior emerging within self-organizing societies of agents.

Question 8

DYNAMICAL SYSTEMS

Answer 8

Dynamical systems are those systems whose main features or properties are not time invariant. A dynamical system is simply a model describing the temporal evolution of a system. a dynamical system is a system in which a function describes the time dependence of a point in a geometrical space.

Question 9

SIMULATED ANNEALING

Answer 9

Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem.
Or
Simulated Annealing (SA) is an effective and general form of optimization.  It is useful in finding global optima in the presence of large numbers of local optima.

Question 10

ERROR

Answer 10

Human error means that something has been done that was “not intended by the actor; not desired by a set of rules or an external observer; or that led the task or system outside its acceptable limits”. In short, it is a deviation from intention, expectation or desirability.

Question 11

LOCAL REPRESENTATION

Answer 11

In a local representational scheme, each thing being represented is assigned a single representational element, that is, in a neural network, a single unit in the input or output layer of the networks. Conversely each unit is associated with only one represented thing. So each time a local input is presented to a network, one unit is turned on, and all the others are off.

Question 12

DISTRIBUTED REPRESENTATION

Answer 12

In a distributed representation, each thing being represented involves more than one representational element (unit in a neural network). Conversely each unit is associated with more than one represented thing. Instead of representing things, the units represent features of things. So each time a distributed input is presented to a neural network, multiple units are turned on.

Question 13

HILL CLIMBING

Answer 13

hill climbing is a mathematical optimization technique which belongs to the family of local search. It is an iterative algorithm that starts with an arbitrary solution to a problem, then attempts to find a better solution by making an incremental change to the solution.

Question 14

GRADIENT DESCENT

Answer 14

Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point.

Question 15

COLLECTIVE INTELLIGENCE

Answer 15

Collective intelligence (CI) is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making.

Question 16

COGNITIVE MODELLING

Answer 16

Cognitive modeling is an area of computer science that deals with simulating human problem-solving and mental processing in a computerized model. Such a model can be used to simulate or predict human behavior or performance on tasks similar to the ones modelled and improve human-computer interaction.

Question 17

FAULT TOLERANCE

Answer 17

Fault tolerance is the property that enables a system to continue operating properly in the event of the failure of (or one or more faults within) some of its components

Question 18

BACKPROPAGATION

Answer 18

Backpropagation is the central mechanism by which neural networks learn. It is the messenger telling the network whether or not the net made a mistake when it made a prediction.
Or
Backpropagation algorithms are a family of methods used to efficiently train artificial neural networks (ANNs) following a gradient descent approach that exploits the chain rule.

Question 19

CONNECTIONISM

Answer 19

an artificial intelligence approach to cognition in which multiple connections between nodes (equivalent to brain cells) form a massive interactive network in which many processes take place simultaneously and certain processes, operating in parallel, are grouped together in hierarchies that bring about results such as thought or action.

Question 20

RULE-BASED SYSTEM

Answer 20

the term rule-based system is applied to systems involving human-crafted or curated rule set

Question 21

GENERALIZATION

Answer 21

the term rule-based system is applied to systems involving human-crafted or curated rule set

Question 22

OPTIMIZATION

Answer 22

In machine learning, optimization algorithms are used to minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples

Question 23

DELTA RULE

Answer 23

The Delta rule in machine learning and neural network environments is a specific type of backpropagation that helps to refine connectionist ML/AI networks, making connections between inputs and outputs with layers of artificial neurons. The Delta rule is also known as the Delta learning rule.

Question 24

RECURRENT ANN

Answer 24

A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs.

Question 25

GLOBAL MAXIMUM

Answer 25

the maxima of a function is the largest value of the function on the entire domain of a function

Question 26

GLOBAL MINIMUM

Answer 26

the minimum of a function is the smallest value of the function on the entire domain of a function

Question 27

LOCAL MINIMUM

Answer 27

the minima of a function is the smallest value of the function within a given range

Question 28

LOCAL MAXIMUM

Answer 28

the local maximum of a function is the biggest value of the function within a given range

Question 29

GLOBAL OPTIMIZATION

Answer 29

Global optimization is a branch of applied mathematics and numerical analysis that attempts to find the global minima or maxima of a function or a set of functions on a given set.

Question 30

models of emotion

Answer 30

ITERA
Emotional cognition – connectionist approach
HOTCO
Model of emotional coherence that stimulates transfer of emotions – reacts to humorous analogies
GAGE
Model of emotions

Question 31

search

Answer 31

Artificial Intelligence is the study of building agents that act rationally. Most of the time, these agents perform some kind of search algorithm in the background in order to achieve their tasks. A search problem consists of: A State Space.

Question 32

EMA

Answer 32

Model of emotion and adaptation. Computational process model of appraisal dynamics. It is a model of emotion must explain both the rapid dynamics of some emotional reactions as well as the slower responses that follow deliberation

Question 33

BUTTERFLY EFFECT

Answer 33

In chaos theory, the butterfly effect is the sensitive dependence on initial conditions in which a small change in one state of a deterministic nonlinear system can result in large differences in a later state

Question 34

CHAOS THEORY

Answer 34

Chaos theory is a branch of mathematics focusing on the behavior of dynamical systems that are highly sensitive to initial conditions. “Chaos” is an interdisciplinary theory stating that within the apparent randomness of chaotic complex systems, there are underlying patterns, constant feedback loops, repetition, self-similarity, fractals, self-organization, and reliance on programming at the initial point known as sensitive dependence on initial conditions.

Question 35

CUSP CATASTROPHE

Answer 35

The cusp catastrophe model which has two control variables and one behavioral outcome, has been the most commonly applied in the social sciences. This model follows from catastrophe theory which is the study of the many ways in which continuous changes in a system’s parameters can result in discontinuous changes in one or several outcome variables of interest.

Question 36

TRI-LEVEL HYPOTHESIS

Answer 36

the idea that one must understand information processing systems at three distinct, complementary levels of analysis

or

It has been argued that for any information processing system to be understood completely, it must be described at three different levels of analysis. Marr has defined these levels as computational, algorithmic, and implementational. The computational level is a description of what information processing problem is being solved by the system. The algorithmic level is a description of what steps are being carried out to solve the problem. The implementational level is a description of the physical characteristics of the information processing system.

Question 37

GOAL STACK

Answer 37

A goal stack is part of the ACT-R (Adaptive Control of Thought—Rational) architecture. The stack contains goals that are not dealt with at the moment but that will still need to be dealt with later.

Question 38

CRUM

Answer 38

Computational-representational understanding of the mind based on the view that thinking can be best understood in terms of representational structures in the mind and computational procedures that operate on those structures. It applies computational procedures (algorithms) to mental representations (data strucutures)