Artificial Intelligence - Exam
A) Artificial Intelligence B) Analogous Integration C) Automated Intelligence D) Advanced Intelligence
A) A test of a machine's ability to exhibit intelligent behavior indistinguishable from a human B) A test to measure a machine's processing speed C) A test to determine the power consumption of a machine D) A test to evaluate a machine's physical strength
A) Ruby B) Java C) Python D) C++
A) A subset of AI that enables machines to learn from data B) A technique to manually program machines C) A method to improve network security D) A process of assembling hardware components
A) Robust Neuron Navigator B) Regular Numeric Notation C) Rapid Notification Node D) Recurrent Neural Network
A) A type of machine learning algorithm B) A weather manipulation technique C) A hypothetical future point at which AI surpasses human intelligence and control D) A measure of data complexity
A) Testing computer hardware components B) Analyzing audio signals C) Generating random pixel patterns D) Mimicking human vision and identifying objects in images or videos
A) A program that simulates conversation with human users B) A program for music composition C) A program for virtual reality gaming D) A program for graphic design
A) Detecting errors in data B) Generating random numbers C) Finding the shortest path in a graph D) Optimizing computer memory usage
A) Networked Logistic Performance B) Natural Language Processing C) Nonlinear Linguistic Pattern D) Neural Learning Protocol
A) 1972 B) 1965 C) 1980 D) 1956
A) Learning B) Quantum computing C) Knowledge representation D) Reasoning
A) OpenAI B) Intel C) Microsoft D) IBM
A) Perceptron B) Recurrent neural network C) Transformer architecture D) Convolutional neural network
A) Recommendation systems B) Advanced web search engines C) Virtual assistants D) Autonomous vehicles
A) Psychology B) Neuroscience C) Astronomy D) Linguistics
A) Quantum entanglement B) Artificial neural networks C) Formal logic D) State space search
A) 2000s B) 2010s C) 1990s D) 2020s
A) Reduced software complexity B) Decreased computational power C) Lower energy consumption D) Existential risks
A) They were unable to process any form of incomplete information. B) They experience a 'combinatorial explosion' where they become exponentially slower as problems grow. C) These algorithms required human intervention for every step. D) Early AI could not handle logical deductions.
A) Humans solve problems by following pre-defined algorithms. B) Humans use a combination of intuition and probabilistic reasoning exclusively. C) Humans use fast, intuitive judgments rather than step-by-step deduction. D) Humans rely solely on logical deductions similar to early AI models.
A) No clear objective or preference. B) Randomly assigned tasks with no particular order. C) A specific goal. D) Multiple goals to achieve simultaneously.
A) Supervised learning B) Reinforcement learning C) Transfer learning D) Unsupervised learning
A) Classification predicts categories while regression deduces numeric functions. B) Classification is a type of unsupervised learning. C) Classification uses neural networks while regression does not. D) Regression requires more data than classification.
A) Word embedding B) Machine translation C) Speech synthesis D) Information retrieval
A) Transformers B) Convolutional neural networks (CNNs) C) Recurrent neural networks (RNNs) D) Generative pre-trained transformers (GPT)
A) Speech recognition. B) Image classification. C) Textual sentiment analysis. D) Object tracking.
A) Local search. B) Particle swarm optimization. C) Adversarial search. D) Gradient descent.
A) Mathematical optimization. B) Swarm intelligence algorithms. C) Backpropagation algorithm. D) Means-ends analysis.
A) Particle swarm optimization. B) Evolutionary computation. C) Gradient descent. D) Ant colony optimization.
A) Evolutionary computation. B) Particle swarm optimization. C) Inductive reasoning. D) Deductive reasoning.
A) It assigns degrees of truth between 0 and 1. B) It uses swarm intelligence algorithms. C) It requires gradient descent for optimization. D) Inference is undecidable, making it intractable.
A) Evolutionary computation. B) Gradient descent. C) Particle swarm optimization. D) Ant colony optimization.
A) Markov decision processes B) Kalman filters C) Bayesian networks D) Dynamic decision networks
A) Expectation–maximization algorithm B) Information value theory C) Decision analysis D) Mechanism design
A) Support vector machine B) Naive Bayes classifier C) Decision tree D) K-nearest neighbor algorithm
A) Support vector machine B) K-nearest neighbor algorithm C) Naive Bayes classifier D) Decision tree
A) Bayesian networks B) Classifiers C) Neural networks D) Controllers
A) Naive Bayes classifier B) K-nearest neighbor algorithm C) Support vector machine D) Decision tree
A) Hidden Markov models B) Decision analysis C) Dynamic decision networks D) Game theory
A) Siri B) Alexa C) Cortana D) Google Assistant
A) Bayesian networks B) Classifiers C) Neural networks D) Controllers
A) 5% B) 7% C) 3% D) 10%
A) Gemini Deep Think B) AlphaTensor C) Qwen2-Math D) rStar-Math
A) 10% B) 5% C) 7% D) 3%
A) Gibson's law. B) Huang's law. C) Bell's law. D) Moore's law.
A) IBM B) Google C) DeepMind D) Microsoft
A) $1.5 trillion B) $2.7 trillion C) $3.5 trillion D) $4.0 trillion
A) Prolog B) ChatGPT C) Gemini D) Claude
A) Amazon B) Google C) Apple D) Microsoft
A) 2028 B) 2030 C) 2026 D) 2025
A) Watson B) Deep Blue C) AlphaStar D) MuZero
A) February 2023 B) July 2024 C) December 2017 D) May 2025
A) 75% B) 53% C) 84% D) 90%
A) OpenAI B) Microsoft C) Google DeepMind D) Alibaba Group
A) Chief Automation Officer (CAO) B) Chief Data Officer (CDO) C) Chief Technology Officer (CTO) D) Chief Information Officer (CIO)
A) $10 million B) $25 million C) $50 million D) $100 million
A) 30% B) 15% C) 47% D) 9%
A) 25% B) 60% C) 9% D) 47%
A) 50% B) 80% C) 75% D) Exactly 61%
A) United States B) Taiwan C) Singapore D) Japan
A) Susquehanna B) Palisades Nuclear reactor C) Fukushima D) Three Mile Island
A) Dynamic Bayesian networks B) Mechanism design C) Markov decision processes D) Game theory
A) Pluribus B) AlphaStar C) SIMA D) MuZero
A) 10% B) 12% C) 8% D) 5%
A) Representational fairness B) Procedural fairness C) Distributive fairness D) Predictive fairness
A) Enhancing content diversity B) Maximizing user engagement C) Reducing misinformation spread D) Promoting accurate information
A) 2021 B) 2023 C) 2019 D) 2024
A) Information overload B) Filter bubbles C) Echo chambers D) Confirmation bias
A) Monte Carlo tree search B) Natural language processing C) Probabilistic models D) Various topological approaches
A) Tesla, SpaceX, Uber, Lyft B) Coca-Cola, PepsiCo, Red Bull, Monster C) Alphabet Inc., Amazon, Apple Inc., Meta Platforms, Microsoft D) Nike, Adidas, Puma, Reebok
A) Deep Blue B) MuZero C) Watson D) AlphaStar
A) 75% B) 84% C) 53% D) 90%
A) Whole objects B) Faces C) Digits D) Edges
A) Blockchain technology B) Differential privacy C) Data encryption D) Cloud storage
A) 90% B) 50% C) 70% D) 30%
A) 15 times B) 5 times C) 10 times D) 20 times
A) 2016 B) 2013 C) 2015 D) 2014
A) Stochastic gradient descent B) Backpropagation C) Gradient descent D) Forward propagation
A) Jeopardy! quiz shows. B) Real-time strategy games. C) Chess and Go. D) Imperfect-information games like poker.
A) Wendell Wallach B) Stephen Hawking C) Stuart J. Russell D) Eliezer Yudkowsky
A) rStar-Math B) Qwen-7B C) AlphaTensor D) Gemini Deep Think
A) 22% B) 50% C) 75% D) 5%
A) Gordon Moore. B) Jensen Huang. C) John McCarthy. D) Alan Turing.
A) 10% B) 20% C) 50% D) 5%
A) Dynamic Bayesian networks B) Kalman filters C) Decision theory D) Expectation–maximization algorithm
A) Cybersecurity tool B) Conventional firearm C) Lethal autonomous weapon D) Drones used for surveillance
A) They require constant internet connectivity. B) Built-in security measures can be trained away until ineffective. C) They cannot be used for commercial purposes. D) Their architecture and parameters are kept secret.
A) Keras. B) TensorFlow. C) PyTorch. D) Scikit-learn.
A) Elon Musk B) Bill Gates C) Geoffrey Hinton D) Tim Cook
A) AlphaGo B) DALL-E C) GPT-3 D) ChatGPT
A) Eliezer Yudkowsky B) Stephen Hawking C) Stuart J. Russell D) Wendell Wallach
A) Synthetic media B) Deepfakes C) AI clones D) Faux images
A) Watson B) AlphaGo C) MuZero D) Deep Blue
A) Predict future stock market trends. B) Generate text based on semantic relationships between words. C) Translate languages in real-time. D) Analyze and interpret images.
A) 25% B) 10% C) About 4% D) 50%
A) Both directions B) Randomly C) Backwards D) Only one direction
A) Blockchain verification B) AI ethical guidelines C) Digital signatures D) Personhood credentials
A) Constellation Energy B) Talen Energy C) Amazon D) Microsoft
A) Ethical computing B) Computational morality C) Moral robotics D) Artificial intelligence ethics |
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