COURSE 2 – INTRODUCTION TO ARTIFICIAL INTELLIGENCE (AI)

Module 2: AI Concepts, Terminology, and Application Areas

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TABLE OF CONTENT

INTRODUCTION – AI Concepts, Terminology, and Application Areas

This week’s agenda delves into fundamental AI concepts, offering insights into how AI acquires knowledge and the diverse array of applications it encompasses. By exploring the mechanisms through which AI learns and uncovering its practical implementations, you’ll gain a solid understanding of the underlying principles driving this transformative technology.

Learning Objectives

  • Define basic AI concepts.
  • Explain Machine Learning, Deep Learning, and Neural Networks.
  • Explain the application areas of AI.

GRADED: AI CONCEPTS, TERMINOLOGY, AND APPLICATION AREAS

1. Which of these statements is true?

  • Cognitive systems can derive mathematically precise answers following a rigid decision tree approach
  • Cognitive systems can learn from their successes and failures (CORRECT)
  • 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 learn, adapt, and keep getting smarter by learning from their interactions with us and from their own successes and failures, just like humans do. 

2. Which of these statements is true?

  • Deep Learning is a specialized subset of Machine Learning that uses layered neural networks to simulate human decision-making (CORRECT)
  • Artificial Intelligence and Machine Learning refer to the same thing since both the terms are often  used interchangeably
  • Data Science is a subset of AI that uses machine learning algorithms to extract meaning and draw inferences from data
  • AI is the subset of Data Science that uses Deep Learning algorithms on structured big data

Data Science is not a subset of AI. It is a separate interdisciplinary field encompassing the entire data processing methodology while sometimes using AI techniques to derive insight from data.

3. Which of the following is NOT an attribute of Machine Learning? 

  • Takes data and rules as input and uses these inputs to develop an algorithm that will give us an answer (CORRECT)
  • Machine Learning defines behavioral rules by comparing large data sets to find common patterns
  • Machine Learning models can be continuously trained
  • 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 uses computer algorithms to analyze data and make intelligent decisions by defining behavioral rules based on what it has learned, without being explicitly programmed. These algorithms continue to learn on the job.

4. Which of the following is NOT an attribute of Unsupervised Learning?

  • It is useful for clustering data, where data is grouped according to how similar it is to its neighbors and dissimilar to everything else
  • 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
  • Takes data and rules as input and uses these inputs to develop an algorithm that will give us an answer (CORRECT)
  • The algorithm ingests unlabeled data, draws inferences, and finds patterns from unstructured data

This statement is not an attribute of either Machine Learning or Unsupervised Learning. Machine Learning techniques such as unsupervised learning are not fed rules. Rather they determine the rules from data.

5. Which of the following is an attribute of Supervised Learning?

  • Relies on providing the machine learning algorithm with a set of rules and constraints and letting it learn how to achieve its goals
  • 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 (CORRECT)
  • Tries its best to maximize its rewards by trying different combinations of allowed actions within the provided constraints
  • Relies on providing the machine learning algorithm unlabeled data and letting the machine infer qualities

Supervised learning relies on giving the algorithm human-labeled data for training. The greater the number of samples that the algorithm is trained on, the greater is its precision in classifying 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
  • Testing data is data the model has never seen before and is used to evaluate how good the model is
  • Training data is used to fine-tune algorithm’s parameters and evaluate how good the model is (CORRECT)

Training data is used to train the algorithm. It is the Validation data that is used to  fine-tune algorithm’s parameters and evaluate how good the model is.

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 (CORRECT)
  • False

Deep Learning algorithms rely on several layers of processing units, or neurons, where each layer passes on its output to the next layer, which processes it and passes it onto the next. The number of layers and the types of functions that connect the outputs of each layer to the inputs of the next are configured by developers.

8. Which of the following fields of application for AI can be used at the airport to flag weapons within luggage passing through the X-ray scanner?

  • Computer Vision (CORRECT)
  • Speech
  • Chatbots
  • Natural Language

Computer Vision enables machines to interpret digital images and video sequences and perform tasks like object identification.

9. Which of these activities is NOT required in order for a neural network to synthesize human voice?

  • Continue to correct the sample and run it through the classifier, repetitively, till an accurate voice sample is created
  • Deconstruct sentences to decipher the context of use (CORRECT)
  • Generate audio data and run it through the network to see if it validates it as belonging to the subject
  • Ingest numerous samples of a person’s voice until it can tell whether a new voice sample belongs to the same person

The process of generating natural voice starts with a neural network ingesting samples of a person’s voice until it can tell whether a new voice sample belongs to the same person. Then, a second neural network generates audio data and runs it through the first network to see if it validates it as belonging to the subject, which it does till such time that it generates an accurate voice sample.

10. Which one of these ways is NOT how AI learns?

  • Unsupervised Learning
  • Reinforcement Learning
  • Supervised Learning
  • Proactive Learning (CORRECT)

AI learns in three different ways – Supervised, Unsupervised, and  Reinforcement Learning.

11. Cognitive Systems can interpret data to generate hypotheses about what it means

  • True (CORRECT)
  • False

Cognitive systems use processes similar to the decision-making process of humans to interpret and generate hypotheses about the information they read.

12. Is the following an application of Machine Learning and AI: 

A machine that beats human in a game in which all rules and moves have been pre-programmed into the machine – Yes or No?

  • Yes
  • No (CORRECT)

Programming all rules and moves of a game is not a true application of AI, rather training the machine to learn from data and enabling it figure out the moves and strategies for winning is how Machine Learning works. 

13. Data Science is a subset of AI that uses machine learning algorithms to extract meaning and draw inferences from data.

  • True
  • False (CORRECT)

Data Science is an interdisciplinary field encompassing the entire data processing methodology. While it uses AI techniques to derive insight from data, it is NOT a subset of AI.

14. Which of the following are attributes of Machine Learning?

  • Defines behavioral rules by comparing large data sets to find common patterns (CORRECT)
  • Machine learning algorithms can be continuously trained and used in the future to predict values (CORRECT)
  • Takes data and answers as input and use these inputs to create a set of rules that determine what the Machine Learning model will be (CORRECT)
  • In Machine Learning models, when we submit inputs, we get answers based on predefined rules

Machine learning uses computer algorithms to analyze large data sets to find common patterns and and define behavioral rules based on what it has learned.

Unlike traditional algorithms which give answers based on predetermined set of rules, machine learning algorithms analyze input data and desired results to define behavioral rules based on which desired results can be achieved. The model can be continuously trained with new data sets. 

Machine learning analyzes input data and answers to determine the set of rules and create the learning algorithm. 

15. Which of the following are attributes of Classification?

  • Forms of classification include decision trees, support vector machines, logistic regression and random forests (CORRECT)
  • Classification is the process of predicting the class of given data points (CORRECT)
  • Using classification models we extract features from data and classify results into multiple categories (CORRECT)
  • Classification models are built by looking at the relationships between features and results, where results are a continuous variable

Classification is the process of extracting features from data and classifying the results into one or more categories. 

16. Neural networks are the reason deep learning algorithms become more efficient as the datasets increase in volume.

  • True (CORRECT)
  • False

Neural networks are the reason deep learning algorithms can continuously learn on the job and improve the quality and accuracy of results as datasets increase in volume over time.

17. Which of the following are attributes of Perceptrons?

  • Input layers forward the input values to the next layer by means of multiplying by a weight and summing the results (CORRECT)
  • Perceptrons are single-layered neural networks consisting of input nodes connected directly to an output node (CORRECT)
  • Each layer of neurons conducts a mathematical operation on the output of the previous layer
  • An activation function determines how a node responds to its inputs (CORRECT)

Perceptrons are single-layered neural networks consisting of input nodes connected directly to an output node. Input layers forward the input values to the next layer, by means of multiplying by a weight and summing the results. An activation function determines how a node responds to its inputs and is a critical component to the success of a neural network.

18. Which of these is the most complex data for machine learning to work with?

  • Structured data
  • Big data 
  • Natural Language (CORRECT)
  • Training data 

Natural language is one of the most complex data for machine learning to work with. Natural language is highly contextualized, moreover humans view and use language conceptually rather than literally. Other types of data, be it auditory or visual data, have some form of discernible patterns, making it easier to work with than natural language.

19. Which of the following is an attribute of Natural Language Processing (NLP)?

  • NLP systems can identify the emotion in which a word is spoken, for example, frustration, confusion, irritation, or fun etc. (CORRECT)
  • NLP systems are provided recorded voice samples with corresponding text to help them discern common patterns
  • NLP systems can understand intent  (CORRECT)
  • NLP systems use a broad array of linguistic models and algorithms to draw inferences from language (CORRECT)

Natural Language Processing uses machine learning and deep learning algorithms to deconstruct sentences grammatically, relationally, and structurally, to understand a word’s semantic meaning and it’s context of use. NLP systems are also able to understand intent and emotion, by drawing inferences through a broad array of linguistic models and algorithms.

20. In order for a self-driving vehicle to navigate accurately, it needs to piece together a complete view of its driving environment, which it does with the help of:

  • Laser data (CORRECT)
  • Vision data (CORRECT)
  • Radar data (CORRECT)
  • Data Science

Self-driving vehicles fuse laser data, vision data, and radar data to create a three-dimensional view of their driving environment helping them make accurate decisions on the road.

21. Which is the biggest limitation of human vision that computer vision can help make up for?

  • Gauging distance between objects
  • Visual Attention
  • Being able to predict in which direction objects are headed (CORRECT)

Visual attention is a limitation of human vision that computer vision can help makeup for. Human vision cannot attend to everything in its visual field, all at the same time, like computer vision can.

CONCLUSION – AI Concepts, Terminology, and Application Areas

In conclusion, this week’s exploration of basic AI concepts has provided a foundational understanding of this rapidly advancing field. By delving into how AI learns and examining its real-world applications, you’ve gained valuable insights into its potential and significance in various domains.

As you continue your journey into the realm of artificial intelligence, remember that this is just the beginning—a springboard for further learning and discovery in this ever-evolving field.

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