The Ultimate Artificial Intelligence Quiz

Artificial intelligence (AI) is the ability of machines to perceive, synthesize, and infer information.
AI applications include speech recognition, computer vision, translation, self-driving cars, and automated decision-making.
AI research has experienced waves of optimism, disappointment, and renewed funding.
The sub-fields of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and object manipulation.
The two visions for achieving machine intelligence are Symbolic AI and the connectionist approach.
The field of AI research was born at a workshop at Dartmouth College in 1956.
Progress slowed in the 1970s, leading to an "AI winter" when funding for AI projects was difficult to obtain.
AI research was revived in the 1980s by the commercial success of expert systems.
Researchers began to doubt the symbolic approach and turned to sub-symbolic approaches and soft computing tools.
Advances in machine learning and perception enabled by faster computers, algorithmic improvements, and access to large amounts of data have led to the dominance of data-hungry deep learning methods.
AI usage in Google increased from sporadic usage to over 2,700 projects in 2015
1 in 5 companies reported incorporating AI in some offerings or processes in 2017
AI research has developed subfields including artificial general intelligence (AGI)
AI capabilities include reasoning, problem-solving, knowledge representation, learning, natural language processing, perception, social intelligence, and general intelligence
Machine learning is a fundamental concept in AI research
Natural language processing enables machines to read and understand human language
Machine perception involves using input from sensors to deduce aspects of the world
Affective computing comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood
AI can solve problems by intelligently searching through many possible solutions
Simple exhaustive searches are rarely sufficient for most real-world problems
AI uses various methods including search, logic, probabilistic methods, classifiers, and neural networks.
Heuristics prioritize choices likely to reach a goal in a shorter number of steps.
Optimization algorithms refine guesses incrementally until no more refinements can be made.
Logic is used for knowledge representation and problem-solving.
Probabilistic methods are used for uncertain reasoning and decision-making.
Classifiers are functions that use pattern matching to determine the closest match.
Neural networks model complex relationships between inputs and outputs and find patterns in data.
Deep learning uses several layers of neurons to extract higher-level features from raw input.
Convolutional neural networks are often used in deep learning for image processing.
Recurrent neural networks allow feedback and short-term memory of previous input events.
AI is an umbrella term for any intellectual task that can be performed by machines.
Deep learning techniques, such as N and LSTM, are used to train RNNs.
Specialized languages and hardware have been developed for AI, such as Lisp, Prolog, TensorFlow, and AI accelerators.
AI has become a ubiquitous feature of daily life, used in search engines, recommendation systems, virtual assistants, autonomous vehicles, automatic language translation, facial recognition, image labeling, spam filtering, chatbots, and more.
AI content detector tools are commonly used to identify inappropriate content in digital media.
Smart traffic lights have been developed and installed in 22 cities, reducing drive time by 25% and traffic jam waiting time by 40%.
AI is the most prolific emerging technology in terms of patent applications and granted patents.
The Turing test measures the ability of a machine to simulate human conversation.
No established unifying theory or paradigm has guided AI research for most of its history.
Symbolic AI simulated high-level conscious reasoning and was highly successful at "intelligent" tasks such as algebra or IQ tests, but had limits.