Module 2: AI Concepts, Terminology, and Application Domains
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In this post, I provide accurate answers and detailed explanations for Module 2: AI Concepts, Terminology, and Application Domains of Course 1: Introduction to Artificial Intelligence (AI) – IBM Generative AI Engineering Professional Certificate
Whether you’re preparing for quizzes or brushing up on your knowledge, these insights will help you master the concepts effectively. Let’s dive into the correct answers and detailed explanations for each question.
Graded: AI Concepts, Terminology, and Application Areas
1. Which of these statements is true?
- Cognitive systems can only process neatly organized structured data
- Cognitive systems can only translate small volumes of audio data into their literal text translations at massive speeds
- Cognitive systems can derive mathematically precise answers following a rigid decision tree approach
- Cognitive systems can learn from their successes and failures ✅
Explanation:
Cognitive systems mimic human thought processes. They learn and adapt over time by analyzing data, recognizing patterns, and improving from interactions. Unlike rigid systems, cognitive systems don’t just follow pre-programmed instructions—they evolve based on feedback, making them more versatile and intelligent.
2. Which of these statements is true?
- Al is the subset of Data Science that uses Deep Learning algorithms on structured big data
- Deep Learning is a specialized subset of Machine Learning that uses layered neural networks to simulate human decision-making ✅
- Data Science is a subset of Al that uses machine learning algorithms to extract meaning and draw inferences from data
- Artificial Intelligence and Machine Learning refer to the same thing since both the terms are often used interchangeably
Explanation:
Deep Learning is a branch of AI where algorithms are structured in layers, mimicking the human brain’s neural networks. It excels at identifying patterns and improving decision-making over time. While AI encompasses a broad range of techniques, Deep Learning focuses specifically on these layered architectures.
3. Which of the following is NOT an attribute of Machine Learning?
- Takes data and answers as input and uses these inputs to create a set of rules that determine what the Machine Learning model will be
- Machine Learning defines behavioral rules by comparing large data sets to find common patterns
- Takes data and rules as input and uses these inputs to develop an algorithm that will give us an answer ✅
- Machine Learning models can be continuously trained
Explanation:
Machine Learning models require training data with inputs and their corresponding outputs to learn the relationships between them. This training allows the algorithm to create a model (rules) that can make predictions for new, unseen data. Machine Learning does not rely on predefined rules but discovers patterns and creates rules automatically.
4. Which of the following is NOT an attribute of Unsupervised Learning?
- Takes data and rules as input and uses these inputs to develop an algorithm that will give us an answer ✅
- The algorithm ingests unlabeled data, draws inferences, and finds patterns from unstructured data
- It is useful for finding hidden patterns and or groupings in data and can be used to differentiate normal behavior with outliers such as fraudulent activity
- It is useful for clustering data, where data is grouped according to how similar it is to its neighbors and dissimilar to everything else
Explanation:
Unsupervised learning involves analyzing unlabeled data to find patterns or groupings without predefined rules. Algorithms autonomously identify relationships, such as clusters or anomalies, making this approach valuable for tasks like market segmentation or fraud detection.
5. Which of the following is an attribute of Supervised Learning?
- Relies on providing the machine learning algorithm unlabeled data and letting the machine infer qualities
- Relies on providing the machine learning algorithm human-labeled data – the more samples you provide, the more precise the algorithm becomes in classifying new data ✅
- Relies on providing the machine learning algorithm with a set of rules and constraints and letting it learn how to achieve its goals
- Tries its best to maximize its rewards by trying different combinations of allowed actions within the provided constraints
Explanation:
In Supervised Learning, labeled datasets (where inputs are paired with correct outputs) train the algorithm. The more diverse and comprehensive the dataset, the better the model becomes at recognizing patterns and predicting outcomes for new data.
6. Which of the following statements about datasets used in Machine Learning is NOT true?
- Validation data subset is used to validate results and fine-tune the algorithm’s parameters
- Training subset is the data used to train the algorithm
- Training data is used to fine-tune algorithm’s parameters and evaluate how good the model is ✅
- Testing data is data the model has never seen before and is used to evaluate how good the model is
Explanation:
Training data is used solely to train the model. Validation data is used to adjust and fine-tune the model’s parameters, while testing data evaluates the model’s performance on new, unseen examples.
7. When creating deep learning algorithms, developers configure the number of layers and the type of functions that connect the outputs of each layer to the inputs of the next.
- True ✅
- False
Explanation:
Deep Learning involves stacking multiple layers of neurons. Developers decide the number of layers and the mathematical functions connecting them. These layers process input data step-by-step, extracting complex features and patterns.
8. Which of the following fields of application for Al can be used at the airport to flag weapons within luggage passing through the X-ray scanner?
- Chatbots
- Computer Vision ✅
- Natural Language
- Speech
Explanation:
Computer Vision focuses on enabling machines to interpret and analyze visual data from the real world. At airports, it’s used to analyze X-ray images and detect objects like weapons.
9. Which of these activities is NOT required in order for a neural network to synthesize human voice?
- Ingest numerous samples of a person’s voice until it can tell whether a new voice sample belongs to the same person
- Deconstruct sentences to decipher the context of use ✅
- Generate audio data and run it through the network to see if it validates it as belonging to the subject
- Continue to correct the sample and run it through the classifier, repetitively, till an accurate voice sample is created
Explanation:
Speech synthesis focuses on creating human-like speech by analyzing voice data, generating audio, and refining it. Deconstructing sentence context is a task of Natural Language Processing, not speech synthesis.
10. Which one of these ways is NOT how Al learns?
- Supervised Learning
- Unsupervised Learning
- Proactive Learning ✅
- Reinforcement Learning
Explanation:
AI learns using three primary methods:
- Supervised Learning: Training on labeled datasets.
- Unsupervised Learning: Discovering patterns in unlabeled data.
- Reinforcement Learning: Learning by trial and error to maximize rewards.
“Proactive Learning” is not a recognized AI learning method.