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