โ† All Artificial Intelligence Flashcard Decks

Artificial Intelligence Flashcards

23 cards from real Artificial Intelligence practice questions. Tap to flip, then mark Knew It or Still Learning โ€” missed cards come back until you master them.

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  1. What is the term for human and animal intelligence?

    Answer: Natural intelligence

    Natural intelligence is the term used to describe the intelligence exhibited by humans and animals. It encompasses cognitive abilities such as learning, reasoning, problem-solving, perception, and understanding language. This term is often used in contrast to Artificial Intelligence, which refers to intelligence demonstrated by machines.

  2. When was Artificial Intelligence first recognized as a field of study?

    Answer: 1956

    Artificial Intelligence was formally recognized as a distinct field of study at the Dartmouth Workshop in the summer of 1956. This seminal event brought together leading researchers who coined the term "Artificial Intelligence" and laid out the foundational goals and challenges for the field. This workshop is widely considered the birth of AI as an academic discipline.

  3. What basically is artificial intelligence (AI)?

    Answer: Making a Machine intelligent

    Artificial Intelligence (AI) fundamentally involves developing machines that can perform tasks typically requiring human intelligence. This includes capabilities like learning, problem-solving, decision-making, understanding language, and perception. The core goal of AI is to enable machines to exhibit intelligent behavior, rather than simply executing predefined instructions.

  4. Which of the following is an Artificial Intelligence tool?

    Answer: Neural networks

    Neural networks are a fundamental and powerful tool within the field of Artificial Intelligence, particularly in machine learning. Inspired by the structure and function of the human brain, they are algorithms designed to recognize patterns and learn from data. They are widely used for tasks such as image recognition, natural language processing, and predictive modeling.

  5. DARPA is a government entity that has supported a lot of artificial intelligence research in the United States. The Department of Research is a division of the Department of

    Answer: Defense

    DARPA stands for the Defense Advanced Research Projects Agency, and it is an agency of the United States Department of Defense. DARPA has played a crucial role in funding and advancing groundbreaking research in various technological fields, including a significant amount of early and ongoing work in Artificial Intelligence. Its investments have led to many foundational AI breakthroughs.

  6. The problem known as the Artificial Intelligence Paradox arose from an evolving notion of Artificial Intelligence.

    Answer: AI Effect

    The AI Effect describes the phenomenon where, as AI capabilities advance and tasks previously considered 'intelligent' are accomplished by machines, those tasks are no longer considered true AI. This paradox arises because once an AI solves a problem, the problem is often reclassified as 'just computation,' leading to an ever-shifting definition of what constitutes 'true' artificial intelligence.

  7. Who is artificial intelligence's founding father?

    Answer: John McCarthy

    John McCarthy is widely recognized as the 'father of Artificial Intelligence.' He coined the term 'Artificial Intelligence' in 1955 and organized the Dartmouth Conference in 1956, which is considered the birth of AI as a field. His contributions also include the development of the Lisp programming language, a foundational tool for AI research.

  8. Which of these is a closely related field to AI?

    Answer: Mathematics

    Mathematics is a foundational and closely related field to AI because AI relies heavily on mathematical concepts and tools. Areas like linear algebra, calculus, probability, statistics, and discrete mathematics are essential for developing algorithms, understanding data, and building intelligent systems. These mathematical principles underpin machine learning, neural networks, and various AI reasoning techniques.

  9. KEE is a creation of:

    Answer: lntelliCorpn

    KEE (Knowledge Engineering Environment) was an early and influential expert system shell developed by IntelliCorp. It was designed to help build knowledge-based systems and expert systems, providing tools for knowledge representation, inference, and user interfaces. IntelliCorp was a prominent company in the early AI commercialization efforts.

  10. Which of the following is a primary goal of Artificial Intelligence research?

    Answer: Reasoning

    Reasoning is a primary goal of Artificial Intelligence research because it involves enabling machines to draw inferences, make decisions, and solve problems logically, similar to human cognitive processes. AI systems strive to mimic or surpass human reasoning abilities to interpret information, understand contexts, and generate appropriate responses or actions. This capability is fundamental to developing intelligent agents that can operate autonomously and effectively.

  11. Another sort of reasoning is default reasoning.

    Answer: Non-monotonic reasoning

    Default reasoning is a form of non-monotonic reasoning, which is a type of logical reasoning where conclusions can be retracted or changed when new information becomes available. In default reasoning, conclusions are drawn based on typical or assumed facts, but these conclusions are not absolute and can be overridden if exceptions or contradictory evidence emerge. This contrasts with monotonic reasoning, where adding new information never invalidates previous conclusions.

  12. What is the study of computer algorithms that improve themselves over time?

    Answer: Machine learning

    Machine learning is a subfield of artificial intelligence focused on developing algorithms that allow computers to learn from data without being explicitly programmed. These algorithms identify patterns, make predictions, and improve their performance over time as they are exposed to more data. This iterative improvement is a core characteristic that distinguishes machine learning from traditional programming.

  13. What is an important field in Artificial Intelligence research?

    Answer: Knowledge engineering

    Knowledge engineering is a crucial field in AI research that focuses on acquiring, representing, and maintaining knowledge for use in AI systems, particularly expert systems. It involves transforming human expertise into a structured, machine-readable format that AI programs can utilize for reasoning and problem-solving. This process is vital for building intelligent systems that can make informed decisions based on a rich understanding of a domain.

  14. It is said that a robot is capable of changing its own course in reaction to external variables. to be considered:

    Answer: intelligent

    A robot capable of changing its course in reaction to external variables demonstrates adaptability and responsiveness, which are key characteristics of intelligent behavior. This ability implies sensing its environment, processing information, and making decisions to adjust its actions, rather than simply following a predefined, rigid path. Such dynamic interaction with its surroundings is a hallmark of an intelligent agent.

  15. The ability to recognize patterns in a stream of data is known as?

    Answer: Unsupervised learning

    Unsupervised learning is a type of machine learning where algorithms learn patterns from data that has not been labeled or categorized. Its primary goal is to discover hidden structures, relationships, or groupings within the data without any prior knowledge of the output. This contrasts with supervised learning, which requires labeled examples to train a model.

  16. The Robotics Institute, which is based at ____ is one of the major American robotics centers.

    Answer: CMU

    The Robotics Institute at Carnegie Mellon University (CMU) is indeed one of the world's leading research and education centers in robotics. Established in 1979, it has been at the forefront of numerous advancements in robotics, artificial intelligence, and computer vision. Its significant contributions have solidified its reputation as a major hub for robotics innovation in the United States.

  17. Any gadget that senses its surroundings and takes measures to improve its chances of achieving a goal is referred to as?

    Answer: Intelligent agent

    An intelligent agent is a fundamental concept in AI, referring to an autonomous entity that perceives its environment through sensors and acts upon that environment through effectors. Its actions are designed to maximize its chances of achieving specific goals. This definition encompasses a wide range of AI systems, from simple software agents to complex robots.

  18. What is the name of the computer software that includes the knowledge that has been distilled of an expert?

    Answer: Expert system

    An expert system is a computer program designed to emulate the decision-making ability of a human expert. It achieves this by incorporating a knowledge base filled with facts and heuristics (rules of thumb) distilled from human experts in a specific domain. These systems use an inference engine to apply this knowledge to solve complex problems or provide advice.

  19. Which of the following is not an AI programming language?

    Answer: Perl

    Perl is a general-purpose programming language known for its strong text processing capabilities and use in web development and system administration. While it can be used for various tasks, it is not specifically designed for or historically associated with AI research in the way that LISP and PROLOG are. LISP and PROLOG were developed specifically for AI applications and became foundational languages in the field.

  20. A hybrid Bayesian network is made of with

    Answer: Both discrete and continuous variables

    A hybrid Bayesian network is designed to model relationships between variables that can be both discrete (e.g., true/false, categories) and continuous (e.g., temperature, height). This allows for a more comprehensive and realistic representation of complex systems where different types of data interact. Standard Bayesian networks typically handle only discrete variables, making hybrid networks essential for broader applications.