How is AI’s process of categorizing words similar to human learning?

C) It recognizes patterns and groups words with similar meanings

A) It categorizes words based on emotions

B) It uses memory to remember words

D) It categorizes words randomly

What role does the internet play in providing data for AI models?

B) It provides vast amounts of unstructured data for AI to learn from

A) It provides structured data exclusively

C) It is not relevant to AI learning

D) It only provides images for AI models

How does AI learn from the data it is trained on to build a vocabulary?

B) By finding relationships between words in the data

A) By memorizing every word it encounters

C) By creating its own definitions for words

D) By ignoring words it doesn’t understand

What is bias in AI, and how does it occur in generative AI models?

B) When AI makes decisions based on imperfect or skewed data

A) When AI intentionally favors certain outcomes

C) When AI avoids using any data

D) When AI creates perfect results every time

How can bias in AI data impact the outcomes it produces?

B) It can lead to unfair or unrepresentative results

A) It can ensure fair and balanced outcomes

C) It has no effect on outcomes

D) It only affects the input process

What are some examples of biases that can emerge in generative AI?

A) Gender, racial, or geographical bias

B) Bias towards futuristic technologies

C) Bias towards more complicated data

D) No bias, as AI models are neutral

How can bias in AI data be identified and addressed?

B) By diversifying the data sources to include a broader range of perspectives

C) By focusing on the most popular data points

D) By eliminating all historical data

What is a potential consequence of unaddressed bias in AI models?

C) Models may produce skewed or unfair results

B) Models produce more fair and equitable outcomes

D) Models work more efficiently

What are embeddings in AI, and why are they important?

B) A way to represent words as numbers so AI can process them

A) A method to measure physical distances

C) A programming technique for creating visuals

D) A way to compress large datasets

How do embeddings help language models understand the meanings of words?

B) By representing words with numbers that capture relationships between them

A) By assigning random values to words

C) By categorizing words alphabetically

D) By making word choices based solely on frequency

What does it mean for two words to be "close" in meaning in the context of embeddings?

B) They are numerically close in the model’s representation

D) They are located near each other in a dictionary

How can numerical representations of words help AI models process language?

C) By allowing AI to compare words based on their meanings

A) By making words easier to spell

B) By simplifying grammar rules

D) By eliminating all irrelevant words

Why do similar words have similar numerical representations in an AI system?

B) Because they appear in similar contexts or sentences

A) Because AI assigns random numbers

C) Because they are alphabetically adjacent

D) Because they are synonyms only

How are word embeddings created based on context?

B) By using the word’s meaning in multiple contexts to form a numerical representation

A) By ignoring surrounding words

C) By categorizing words by their letter count

D) By randomly selecting numbers for each word

What is the importance of context in determining the meaning of words?

B) Words can have different meanings in different contexts

A) Context doesn’t matter when understanding word meaning

C) Context only matters for nouns

D) Context limits the meaning of a word to one definition

How do different contexts lead to different word embeddings?

B) Because the model adjusts its representation based on the context it appears in

A) Because the word's meaning is stable regardless of context

C) Because context doesn’t affect word meaning

D) Because the model ignores context when creating embeddings

What happens when a word’s meaning changes depending on its context?

B) The AI model creates different embeddings for the word in different contexts

A) The word is removed from the model’s vocabulary

C) The AI model cannot process the word

D) The word becomes unimportant in all contexts

Why do different contexts lead to different word embeddings?

B) Because the model adjusts its representation based on the context it appears in

A) Because the word's meaning is stable regardless of context

C) Because context doesn’t affect word meaning

D) Because the model ignores context when creating embeddings

What is a neural network, and how does it function in AI systems?

A) A system that mimics the way the human brain processes information

B) A series of connected rules for making decisions

C) A program that performs simple arithmetic

D) A database for storing information

How does a neural network help AI models make decisions?

B) By adjusting its parameters based on input data

A) By following a fixed set of rules

C) By randomly selecting answers

What is the purpose of hidden layers in a neural network?

B) To process data before generating an output

How do weights influence the decision-making process in a neural network?

B) Weights determine the strength of connections between nodes and influence decisions

A) Weights are irrelevant in decision-making

C) Weights adjust the model’s training speed

D) Weights only affect input data

What does a neural network need to learn from training data?

B) Feedback from errors and corrections

D) Only labels for each data point

What is supervised learning, and how is it related to training neural networks?

A) Learning with labeled data and feedback from errors

B) Learning through random data selection

C) Learning without any input data

D) Learning through observation without any feedback

How does trial and error help a neural network improve its performance?

A) By learning from its mistakes and adjusting accordingly

B) By ignoring errors and focusing on correct answers

C) By memorizing previous outcomes

D) By randomly selecting actions

How do neural networks adjust based on feedback from previous outcomes?

A) They update their weights to improve accuracy

B) They ignore all past results

C) They randomly change their structure

D) They rely solely on new data inputs

Is training a neural network the same as teaching it true intelligence? Why or why not?

B) No, because neural networks rely on patterns and feedback rather than true understanding

A) Yes, because neural networks can learn like humans

C) Yes, because neural networks can think independently

D) No, because neural networks don’t require any feedback

Why is context important for understanding language in AI models?

A) It helps the model determine the correct meaning of words

B) It has no impact on understanding language

C) It limits the model’s capacity to process information

D) It ensures the model only processes specific words

What is the role of attention in generative AI models?

A) It helps the model focus on the most relevant parts of a sentence

B) It allows the model to randomly choose which words to focus on

D) It reduces the model’s accuracy

How do AI models use attention to update word meanings?

A) By considering the surrounding context when interpreting words

B) By ignoring context and processing words in isolation

D) By storing all word meanings in a fixed list

How can ambiguous words be clarified through context?

A) By analyzing the surrounding words to determine the intended meaning

B) By removing them from the sentence

C) By focusing on only one meaning

D) By ignoring all ambiguous words

Why is attention essential for AI models when processing language?

A) Because it helps the model understand and interpret ambiguous words based on context

B) Because it simplifies grammar rules

C) Because it makes AI models faster

D) Because it reduces computational requirements

How do AI models generate output based on probabilities?

A) By choosing the word with the highest likelihood based on training data

B) By ignoring probability and focusing on frequency

D) By following predefined rules

What role does probability play in selecting words for a language model’s output?

A) It ensures the model selects the most likely word based on context

C) It limits word options to only a few possibilities

D) It has no role in word selection

Why do AI models sometimes produce results that are nonsensical even if they are highly probable?

B) Because the training data might not cover all possible scenarios

A) Because the model cannot process probability correctly

C) Because the model always focuses on coherence

D) Because the model avoids randomness at all costs

How do AI models ensure that their outputs are coherent?

A) By selecting words that fit the overall structure and context

C) By following a fixed pattern

D) By excluding less likely words

What factors influence the probability of a word being selected by an AI model?

B) The alphabetical order of words

What are hallucinations in the context of generative AI?

A) Mistakes where the AI creates false or inaccurate information

B) Accurate outputs generated by the model

C) Explanations that always match human expectations

D) Outputs based solely on probabilities

How can bias in training data lead to hallucinations in AI-generated content?

A) By providing the AI with skewed or incomplete data that influences its output

B) By helping the AI focus on more accurate results

C) By improving the model’s understanding of language

D) By eliminating irrelevant information

Why is it important for AI systems to avoid fabricating or hallucinating information?

A) Because fabricated information can be harmful or misleading

B) Because hallucinations help improve the system’s learning

C) Because fabricated content is less predictable

D) Because it makes the AI system less complex

How does understanding the probabilistic nature of generative AI help explain why hallucinations occur?

B) It explains how the model uses probability to create unexpected or false outputs

A) It shows that AI is always accurate in its predictions

C) It demonstrates that hallucinations never happen

D) It indicates that AI doesn't rely on probabilities at all

What measures can AI auditors take to reduce the risk of hallucinations and biased outputs in deployed AI systems?

A) Regularly review and refine training data and models

C) Ignore any inaccuracies in outputs

D) Rely solely on large data sets without context review

What is the primary goal of demystifying generative AI through education?

B) To teach the ethical implications and real capabilities of AI

A) To demonstrate how AI is always accurate

C) To show how AI can replace all human jobs

D) To highlight the dangers of AI without understanding its workings

Computer Literacy Midterm

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Nicole Matthews

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C) By using pre-programmed rules

D) By focusing only on the most common words

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