Question 1

In natural language processing (NLP), which of the following can introduce ambiguity in interpretation and thereby complicate parsing or understanding?

Accepted Answer

Introduction / Context: Ambiguity is a central challenge in natural language processing (NLP). Systems must disambiguate syntax, semantics, and reference to correctly interpret sentences. The question asks which sources can cause ambiguity and thereby increase the difficulty of accurate parsing, tagging, and semantic analysis. Given Data / Assumptions: Syntactic structures can have multiple parses. Words often have multiple senses depending on context. Pronouns and definite descriptions may lack clear antecedents. Concept / Approach: NLP pipelines typically include part-of-speech tagging, parsing, word sense disambiguation, and coreference resolution to address these issues. Each listed factor—syntax, lexical semantics, and reference—can independently or jointly create ambiguity that must be resolved for correct understanding by downstream tasks such as question answering or information extraction. Step-by-Step Solution: Consider syntactic ambiguity: the same tokens may form different parse trees.Consider multiple word meanings: polysemy and homonymy require sense selection.Consider unclear antecedents: pronouns like ‘‘it’’ or ‘‘they’’ may not map uniquely to entities.Conclude that all listed factors contribute to ambiguity. Verification / Alternative check: Standard NLP textbooks detail modules dedicated to resolving each kind of ambiguity, confirming their roles in complexity and error rates. Why Other Options Are Wrong: Picking only one factor ignores the multi-layered nature of language ambiguity. None of the above: Incorrect because each factor is a well-known source of ambiguity. Common Pitfalls: Assuming syntax alone determines meaning; overlooking context required for sense and reference resolution. Final Answer: All of the above

Question 2

In the LISP programming language, how should the addition of 3 and 2 be written using prefix notation and proper list structure?

Accepted Answer

Introduction / Context: LISP (LISt Processing) uses prefix (Polish) notation and parenthesized lists to represent expressions. Operators precede their operands, and evaluation proceeds by applying the first element (a function or operator) to the remaining elements (arguments). The question tests recognition of this canonical syntax for a simple arithmetic operation. Given Data / Assumptions: The task is to express 3 + 2 in valid LISP syntax. LISP requires parentheses to form an s-expression (symbolic expression). Operators are functions written before their operands. Concept / Approach: In LISP, an addition is written with the plus symbol as a function followed by its arguments inside a list. Thus the correct expression is an s-expression containing the operator and operands: (+ 3 2). This is evaluated by the interpreter to produce the number 5. The same pattern generalizes to any number of operands, e.g., (+ 1 2 3). Step-by-Step Solution: Recall that LISP uses prefix notation: operator first, then operands.Construct a list with parentheses: (operator operand1 operand2).Insert + as the operator and 3, 2 as operands: (+ 3 2).Confirm that this evaluates to 5 in a LISP REPL. Verification / Alternative check: A quick test in any Common Lisp or Scheme environment returns 5 for (+ 3 2), validating the syntax and semantics. Why Other Options Are Wrong: ‘‘3 + 2’’ and ‘‘3 + 2 =’’: Infix notation; not LISP list form. ‘‘3 add 2’’: Not valid LISP syntax for addition. None of the above: Incorrect because (+ 3 2) is valid. Common Pitfalls: Forgetting parentheses around the list; writing infix out of habit from other languages. Final Answer: (+ 3 2)

Question 3

Which academic field most directly studies the mechanisms and computational principles underlying human intelligence, bridging psychology, neuroscience, linguistics, and computer science?

Accepted Answer

Introduction / Context: Understanding intelligence requires multiple perspectives: how the brain represents information, how the mind reasons and learns, how language is processed, and how these processes can be modeled computationally. The field that integrates these perspectives into a coherent science of the mind and intelligent behavior is the focus of this question. Given Data / Assumptions: The goal is to investigate mechanisms of human intelligence. Methods span experiments, theory, and computational modeling. Interdisciplinarity is essential, drawing from psychology, neuroscience, linguistics, philosophy, and AI. Concept / Approach: Cognitive science is the interdisciplinary study of mind and intelligence. It merges empirical work (e.g., psychophysics, neuroimaging) with formal models (e.g., Bayesian cognition, symbolic reasoning, neural networks). Psychology is a major contributor, but cognitive science explicitly seeks cross-disciplinary unification and computational explanations of cognition, differentiating it from single-discipline approaches. Step-by-Step Solution: Identify the field defined by mechanisms and computational principles of intelligence.Distinguish it from adjacent disciplines that do not necessarily emphasize computation or integration.Select ‘‘cognitive science’’ as the integrative discipline.Verify by noting its canonical subfields (cognition, language, perception, learning, AI modeling). Verification / Alternative check: University programs and textbooks explicitly define cognitive science as the interdisciplinary science of mind and intelligence, confirming the selection. Why Other Options Are Wrong: Psychology: Central contributor but not inherently computational or integrative across all listed fields. Sociology: Focuses on social systems, not mechanisms of individual cognition. History: Studies past events; not about mechanisms of intelligence. None: Incorrect because cognitive science precisely fits. Common Pitfalls: Equating psychology with cognitive science; the latter explicitly includes computational modeling as a core pillar. Final Answer: cognitive science

Question 4

Before the advent of interactive time-sharing systems, what was the predominant method by which users accessed and ran programs on large computers?

Accepted Answer

Introduction / Context: Early mainframe computing emphasized throughput over interactivity. Users prepared jobs offline and submitted them to be executed later by operators. Only later did time-sharing allow multiple interactive users. The question asks for the dominant access method in the pre–time-sharing era. Given Data / Assumptions: Time-sharing was not yet available. Users could not interact with programs in real time. Operational practices focused on queues of jobs processed in sequence or batches. Concept / Approach: Batch processing executes groups of jobs without user interaction. Users delivered decks of punched cards or magnetic media to operators, who scheduled them. Output was collected later (printouts, listings). This model dominated until interactive terminals and operating systems enabled time-sharing and conversational computing. Step-by-Step Solution: Identify the historical period before interactive terminals were common.Recall that users submitted jobs to a queue handled by operators.Recognize this as batch processing, not remote interactive access.Select ‘‘batch processing’’ as the correct answer. Verification / Alternative check: Computing histories detail the shift from batch queues to time-sharing (e.g., CTSS, MULTICS), confirming batch as the earlier predominant mode. Why Other Options Are Wrong: Remote access/telecommunication: Became practical with terminals and networks; not predominant before time-sharing. All of the above: Overbroad and historically inaccurate for the pre-time-sharing norm. None: Incorrect because batch processing is the recognized answer. Common Pitfalls: Projecting modern remote access paradigms backward in time; underestimating the operational role of operators and card decks. Final Answer: batch processing

Question 5

In LISP, which predicate returns t (true) if the supplied <object> is a CONS cell and returns nil (false) otherwise?

Accepted Answer

Introduction / Context: LISP lists are built from CONS cells—pairs consisting of a head (car) and a tail (cdr). Recognizing list structure at runtime is a common need in symbolic processing. LISP provides type predicates to test the nature of objects. This question asks for the predicate that checks whether an object is specifically a CONS cell. Given Data / Assumptions: We need a boolean test that distinguishes CONS cells from atoms or other objects. The result should be t for CONS and nil otherwise. Standard Common Lisp predicate names are assumed. Concept / Approach: The predicate consp returns t when its argument is a CONS cell. By contrast, cons constructs a new CONS cell and is not a predicate, and eq tests identity between two objects, not type. The misspelled form ‘‘(cous = )’’ is invalid syntax and not a standard function. Step-by-Step Solution: Recall standard predicates: consp, listp, atom, null, etc.Match the requirement ‘‘true if argument is a CONS’’ to consp.Eliminate constructor and equality functions (cons, eq) and invalid tokens.Select ‘‘(consp )’’ as correct. Verification / Alternative check: In a Common Lisp REPL: (consp '(a b)) → t; (consp 'a) → nil. This confirms the predicate's behavior. Why Other Options Are Wrong: (cons ): Creates a cell; does not test type. (eq ): Compares two arguments for identity; wrong arity and purpose here. (cous = ): Not valid LISP syntax or function. None: Incorrect because consp is the precise predicate. Common Pitfalls: Confusing constructors with predicates; overlooking arity requirements in predicate forms. Final Answer: (consp )

Question 6

PROLOG is an AI programming language based on predicate calculus. It was developed in 1972 at the University of Marseille by a team of specialists. Who headed this development team?

Accepted Answer

Introduction / Context: PROLOG (from ‘‘PROgrammation en LOGique’’) is a declarative AI language that expresses knowledge as relations and solves problems via logical inference (resolution and unification). Understanding its origins provides context for logic programming’s influence on AI, natural language processing, and symbolic reasoning systems. Given Data / Assumptions: Development occurred around 1972 at the University of Marseille (often spelled Marseilles in older texts). The language is rooted in predicate calculus and Horn clauses. We need the name of the project lead. Concept / Approach: Alain Colmerauer led the team that created PROLOG, collaborating with Philippe Roussel and influenced by work in automated deduction. The language's operational semantics, based on unification and depth-first search with backtracking, enabled concise problem statements and rapid prototyping for AI tasks. Step-by-Step Solution: Identify the university and timeframe: Marseille, 1972.Recall prominent figures associated with PROLOG’s inception.Select ‘‘Alain Colmerauer’’ as the head of the team.Eliminate distractors known for other contributions (McCarthy for Lisp, Papert for LOGO, Wirth for Pascal). Verification / Alternative check: Historical accounts of logic programming attribute the initial PROLOG implementation and design leadership to Alain Colmerauer's group, confirming this selection. Why Other Options Are Wrong: John McCarthy: Led Lisp and AI research at Stanford, not PROLOG. Seymour Papert: Known for LOGO and constructionism. Nicklaus Wirth: Designed Pascal, Modula, Oberon. None: Incorrect because Colmerauer is the recognized leader. Common Pitfalls: Confusing logic programming (PROLOG) with functional or list-processing traditions (LISP) due to their shared AI heritage. Final Answer: Alain Colmerauer

Question 7

Human cognitive strengths versus computers: Historically, which tasks have people performed better than computers—recognizing relative importance, finding similarities, and resolving ambiguity?

Accepted Answer

Introduction / Context: Before modern advances in machine learning, humans consistently outperformed computers on tasks requiring nuanced judgment, contextual interpretation, and flexible pattern perception. This question examines three classic human strengths: recognizing relative importance, spotting similarities, and resolving ambiguity. Given Data / Assumptions: ‘‘Recognizing relative importance’’ means prioritizing factors by context rather than by rigid rules. ‘‘Finding similarities’’ includes analogy, metaphor, and recognizing partially matching patterns. ‘‘Resolving ambiguity’’ requires interpreting incomplete, noisy, or conflicting information. Concept / Approach: Symbolic programs and early expert systems relied on explicit rules and struggled with uncertainty, vague categories, and shifting priorities. Humans, conversely, use background knowledge, common sense, and pragmatic cues to weigh signals, generalize from few examples, and disambiguate meanings in real time. Step-by-Step Solution: Identify the three capabilities: prioritization, similarity detection, ambiguity resolution.Compare traditional computers (rule-bound, brittle) with humans (context-aware, adaptive).Conclude that humans have historically excelled at all three tasks. Verification / Alternative check: Human performance in language understanding, visual analogy, and decision-making under uncertainty has long exceeded classical algorithms. Only with recent data-driven AI have machines begun to narrow these gaps in specific domains. Why Other Options Are Wrong: Recognizing relative importance only: ignores similarity and ambiguity strengths. Finding similarities only: omits the other two cognitive advantages. Resolving ambiguity only: too narrow; humans excel at all listed skills. (a) and (c) only: still excludes similarity perception, which is a key human faculty. Common Pitfalls: Assuming current AI capabilities generalize across all contexts; many successes are narrow and data- or task-specific. Final Answer: All of the above

Question 8

Leading U.S. robotics research centers: At which institution is the Robotics Institute (a prominent American robotics center) located?

Accepted Answer

Introduction / Context: Robotics research in the United States has flourished at several universities and labs. The question targets knowledge of a flagship academic center: the Robotics Institute, widely recognized for pioneering work spanning perception, manipulation, and autonomous systems. Given Data / Assumptions: The Robotics Institute is a distinct, named entity. It is affiliated with a major U.S. university known for engineering and computer science. Other organizations listed engage in research but are not the home of this specific institute. Concept / Approach: Map the official institute name to the correct university. Both MIT and SRI are historic AI/robotics sites, and RAND contributed to AI theory, but the ‘‘Robotics Institute’’ specifically resides at Carnegie Mellon University (CMU) in Pittsburgh. Step-by-Step Solution: Identify the distinctive term ‘‘Robotics Institute.’’Recall its location and founding within CMU’s School of Computer Science.Select ‘‘CMU (Carnegie Mellon University)’’. Verification / Alternative check: The institute’s long-standing contributions include self-driving vehicles, humanoid robots, and field robotics, peer-reviewed publications, and renowned faculty—hallmarks of CMU’s leadership. Why Other Options Are Wrong: MIT: A robotics powerhouse, but ‘‘Robotics Institute’’ is not its formal unit name. SRI: Influential research lab, different organization. RAND: Policy and research, not the institute named here. None of the above: incorrect because CMU is correct. Common Pitfalls: Confusing leading robotics programs with the specific branded institute name. Final Answer: CMU (Carnegie Mellon University)

Question 9

AI technique recognition: Which method enables a computer system to understand associations and relationships among objects and events by comparing patterns?

Accepted Answer

Introduction / Context: Many AI systems must determine whether new inputs resemble known templates or previously seen examples. The question probes the term for recognizing associations by evaluating similarity structures in data. Given Data / Assumptions: Inputs can be symbols, strings, images, or event sequences. The goal is to detect correspondence or similarity to known patterns. Associations arise from matching features, tokens, or structures. Concept / Approach: Pattern matching refers to techniques that check whether and how an input conforms to a defined pattern. In AI, it underpins rule firing (matching conditions), natural language parsing, vision feature detection, and event correlation. While heuristics guide search, and cognitive science studies mind mechanisms, pattern matching is the direct method for associating inputs with stored representations. Step-by-Step Solution: Identify the task: determine relationships via similarity or structure.Map the task to the term used in rule systems, regex/string processing, and symbolic AI.Select ‘‘Pattern matching’’ as the precise descriptor. Verification / Alternative check: Expert systems match facts to rules; NLP engines match tokens to grammars; vision pipelines match features to models—all are pattern matching instances. Why Other Options Are Wrong: Heuristic processing: search/control strategies, not association per se. Cognitive science: academic discipline, not a matching method. Relative symbolism: not a standard technical term. Common Pitfalls: Confusing high-level disciplines or strategies with the concrete operation of matching inputs to patterns. Final Answer: Pattern matching

Question 10

Origins of commercial Lisp machines: The first systems from LMI and Symbolics were primarily based on research conducted at which institution?

Accepted Answer

Introduction / Context: Lisp machines—specialized workstations optimized for symbolic computation—transitioned from academic prototypes to commercial products in the late 1970s and early 1980s. This question checks historical grounding in their academic origin. Given Data / Assumptions: LMI (Lisp Machines, Inc.) and Symbolics commercialized Lisp-machine technology. Foundational work occurred at a specific university AI lab. The research focused on hardware support for Lisp, interactive development, and garbage collection. Concept / Approach: MIT’s Artificial Intelligence Laboratory incubated the designs (e.g., the MIT Lisp Machine/CONS architecture), which later informed the systems produced by LMI and Symbolics. The commercial efforts extended MIT’s prototypes into robust products. Step-by-Step Solution: Identify the companies (LMI, Symbolics) and their academic lineage.Recall that MIT AI Lab hosted the formative Lisp machine projects.Select ‘‘MIT (Massachusetts Institute of Technology)’’. Verification / Alternative check: Historical accounts consistently trace Symbolics and LMI to MIT-originated designs and personnel. Why Other Options Are Wrong: Stanford and CMU had significant AI programs, but not the seed of these specific machines. RAMD: not a relevant origin for Lisp machines. None of the above: incorrect because MIT is correct. Common Pitfalls: Conflating general AI history with the specific Lisp machine lineage. Final Answer: MIT (Massachusetts Institute of Technology)

Question 11

Special programs that assist programmers are called:

Accepted Answer

intelligent programming tools

Question 12

The expert system developed at MIT to solve mathematical problems is known as:

Accepted Answer

MACSYMA

Question 13

LISP machines also are known as:

Accepted Answer

AI workstations

Question 14

In LISP, which of the following function assigns the value 10 to the symbol a?

Accepted Answer

(setq a 10)

Question 15

In LISP, the function (endp <list>)

Accepted Answer

returns t if is empty.

Question 16

Which of the following is an advantage of using an expert system development tool?

Accepted Answer

All of the above

Question 17

Which of the following function returns t if the object is a number in LISP?

Accepted Answer

(numberp )

Question 18

LISP was created by:

Accepted Answer

John McCarthy

Question 19

The area of AI that investigates methods of facilitating communication between people and computers is:

Accepted Answer

natural language processing

Question 20

The characteristics of the computer system capable of thinking, reasoning and learning is known is

Accepted Answer

artificial intelligence

Artificial Intelligence Questions