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