Description : Feature of ANN in which ANN creates its own organization or representation of information it receives during learning time is A. Adaptive Learning B. Self Organization C. What-If Analysis D. Supervised Learniing
Last Answer : B. Self Organization
Description : Artificial intelligence is A . It uses machine-learning techniques. Here program can learn From past experience and adapt themselves to new situations B. Computational procedure that takes some ... performs tasks that would require intelligence when performed by humans D . None of these
Last Answer : C. Science of making machines performs tasks that would require intelligence when performed by humans
Description : What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ... true b) (i) and (iii) are true c) Only (i) d) All of the mentioned
Last Answer : d) All of the mentioned
Description : What is needed to make probabilistic systems feasible in the world? a) Reliability b) Crucial robustness c) Feasibility d) None of the mentioned
Last Answer : b) Crucial robustness
Description : Generality is the measure of _____________ a) Ease with which the method can be adapted to different domains of application b) The average time required to construct the target knowledge structures ... unreliable feedback and with a variety of training examples d) The overall power of the system
Last Answer : a) Ease with which the method can be adapted to different domains of application
Description : The best strategies to increase stress tolerance are: a. Planning, experience and self-control (fewer unexpected situation) b. Learning, experience and anticipation c. Learning, experience and CRM d. Planning, experience and CRM
Last Answer : c. Learning, experience and CRM
Description : ______________is a branch of science that deals with programing the systems in such a way that they automatically learn and improve with experience A. Machine Learning B. Deep Learning C. Neural Networks D. None of these
Last Answer : A. Machine Learning
Description : Genetic Algorithm are a part of A . Evolutionary Computing B. inspired by Darwin's theory about evolution - "survival of the fittest" C. are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics D . All of the above
Last Answer : D . All of the above
Description : Experiential readiness to learn refers to the patient's a) Past history with education and life experience. Experiential readiness refers to past experiences that influence a person's ability to learn ... to the patient's ability to cope with physical problems and focus attention upon learning.
Last Answer : a) Past history with education and life experience. Experiential readiness refers to past experiences that influence a person’s ability to learn.
Description : Ability to think, puzzle, make judgments, plan, learn, communication by its own is known as ___ AI. A. Narrow AI B. General AI C. Super AI D. None of above
Last Answer : C. Super AI
Description : K-means, self-organizing maps, hierarchical clustering are the example of _____. A. Supervised learning B. Unsupervised learning C. Machine learning D. Deep learning
Last Answer : B. Unsupervised learning
Description : Which is the only way to learn about the different kinds of human faces? a) Perception b) Speech c) Learning d) Hearing
Last Answer : c) Learning
Description : Membership function can be thought of as a technique to solve empirical problems on the basis of A. knowledge B. examples C. learning D. experience
Last Answer : D. experience
Description : Wider span of control is effective in organization where– (A) authority delegation is inadequate (B) tasks are complex (C) thorough subordinate training scheme exists (D) the leadership style is authoritarian
Last Answer : Answer: thorough subordinate training scheme exists
Description : Modern NLP algorithms are based on machine learning, especially statistical machine learning. a) True b) False
Last Answer : a) True
Description : Computational learning theory analyzes the sample complexity and computational complexity of __________ a) Unsupervised Learning b) Inductive learning c) Forced based learning d) Weak learning
Last Answer : b) Inductive learning
Description : Which agent deals with happy and unhappy states? a) Simple reflex agent b) Model based agent c) Learning agent d) Utility based agent
Last Answer : d) Utility based agent
Description : Unsupervised learning is A. learning without computers B. problem based learning C. learning from environment D. learning from teachers
Last Answer : C. learning from environment
Description : Computational learning theory analyzes the sample complexity and computational complexity of a) Unsupervised Learning b) Inductive learning c) Forced based learning d) Weak learning e) Knowledge based learning
Description : Define knowledge based Inductive learning.
Last Answer : KBIL algorithm finds inductive hypotheses that explain sets of observations with the help of background knowledge.
Description : Classifying email as a spam, labeling webpages based on their content, voice recognition are the example of _____. A. Supervised learning B. Unsupervised learning C. Machine learning D. Deep learning
Last Answer : A. Supervised learning
Description : Decision Trees can be used for Classification Tasks. a) True b) False
Description : Which of the following task/tasks Artificial Intelligence could not do yet? a) Understand natural language robustly b) Web mining c) Construction of plans in real time dynamic systems d) All of the mentioned
Description : What are the tasks in probabilistic language model?
Last Answer : i. Information retrieval ii. Information Extraction iii. Machine Translation
Description : Which set of learner characteristics may be considered helpful in designing effective teaching-learning systems? Select the correct alternative from the codes given below: (i) Prior experience of learners in respect of the subject. (ii) ... iii), (iv) and (v) (D) (iii), (iv), (v) and (vi)
Last Answer : Answer: B
Description : In a backward chaining system you start with the initial facts, and keep using the rules to draw new conclusions (or take certain actions) given those facts. a) True b) False
Last Answer : b) False
Description : Which of the following is not an application of learning? a) Data mining b) WWW c) Speech recognition d) None of the mentioned
Last Answer : d) None of the mentioned
Description : A completely automated chess engine (Learn from previous games) is based on? a) Strong Artificial Intelligence approach b) Weak Artificial Intelligence approach c) Cognitive Artificial Intelligence approach d) Applied Artificial Intelligence approach
Last Answer : a) Strong Artificial Intelligence approach
Description : n(log n) is referred to A. A measure of the desired maximal complexity of data mining algorithms B. A database containing volatile data used for the daily operation of an organization C. Relational database management system D. None of these
Last Answer : A. A measure of the desired maximal complexity of data mining algorithms
Description : _________ is one of the more expensive and complex areas of network computing. a) Multi-Factor Authentication b) Fault tolerance c) Identity protection d) All of the mentioned
Last Answer : Identity protection
Description : In your study of the fossil record of early mammals, you notice a changing environment is followed by the initial appearance of a tree-climbing species, which is then followed by many later tree-climbing ... is an example of A) anagenesis. B) gradualism. C) species selection. D) adaptive radiation.
Last Answer : D) adaptive radiation.
Description : Treatment chosen by doctor for a patient for a disease is based on _____________ a) Only current symptoms b) Current symptoms plus some knowledge from the textbooks c) Current symptoms plus some knowledge from the textbooks plus experience d) All of the mentioned
Last Answer : c) Current symptoms plus some knowledge from the textbooks plus experience
Description : . In an Unsupervised learning ____________ a) Specific output values are given b) Specific output values are not given c) No specific Inputs are given d) Both inputs and outputs are given
Last Answer : b) Specific output values are not given
Description : In an Unsupervised learning a) Specific output values are given b) Specific output values are not given c) No specific Inputs are given d) Both inputs and outputs are given e) Neither inputs nor outputs are given
Description : The extent to which a software tolerates the unexpected problems, is termed as: (A) Accuracy (B) Reliability (C) Correctness (D) Robustness
Last Answer : (D) Robustness
Description : The extent to which a software performs its intended functions without failures, is termed as (A) Robustness (B) Correctness (C) Reliability (D) Accuracy
Last Answer : (C) Reliability
Description : ………….. gives defining the flow of the data through and organization or a company or series of tasks that may or may not represent computerized processing. A) System process B) System flowchart C) System design D) Structured System
Last Answer : B) System flowchart
Description : Factors which affect the performance of learner system does not include? a) Representation scheme used b) Training scenario c) Type of feedback d) Good data structures
Last Answer : d) Good data structures
Description : Which of the factors affect the performance of learner system does not include? a) Representation scheme used b) Training scenario c) Type of feedback d) Good data structures
Description : Factors which affect the performance of learner system does not include a) Representation scheme used b) Training scenario c) Type of feedback d) Good data structures
Description : For an efficient and durable learning, learner should have (A) ability to learn only (B) requisite level of motivation only (C) opportunities to learn only (D) desired level of ability and motivation
Last Answer : (D) desired level of ability and motivation
Description : What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the ... the network to allow weights to be adjusted so that the network can learn d) None of the mentioned
Last Answer : c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn
Description : What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the ... network to allow weights to be adjusted so that the network can learn. d) None of the mentioned
Last Answer : c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.
Description : What is the problem space of means-end analysis? a) An initial state and one or more goal states b) One or more initial states and one goal state c) One or more initial states and one or more goal state d) One initial state and one goal state
Last Answer : a) An initial state and one or more goal states
Description : What will happen if a predecessor description is generated that is satisfied by the initial state of the planning problem? a) Success b) Error c) Compilation d) Termination
Last Answer : d) Termination
Description : The initial state and the legal moves for each side define the __________ for the game. a) Search Tree b) Game Tree c) State Space Search d) Forest
Last Answer : b) Game Tree
Description : A game can be formally defined as a kind of search problem with the following components. a) Initial State b) Successor Function c) Terminal Test d) All of the mentioned
Description : Flexible CSPs relax on _______ a) Constraints b) Current State c) Initial State d) Goal State
Last Answer : a) Constraints