Introduction to Artificial Intelligence (AI) – Coursera Quiz Answers

OIntroduction to Artificial Intelligence (AI) – Coursera Quiz Answers

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In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project.
This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

Introduction to Artificial Intelligence (AI) – Coursera Quiz Answers

Graded: What is AI? Applications and Examples of AI


1. Which of the following is NOT a good way to define AI?

  • AI is Augmented Intelligence and is not intended to replace human intelligence rather extend human capabilities
  • AI is the application of computing to solve problems in an intelligent way using algorithms.
  • AI is the use of algorithms that enable computers to find patterns without humans having to hard code them manually
  • AI is all about machines replacing human intelligence.
2. Which of the following is an attribute of Strong or Generalized AI?
  • Perform independent tasks
  • Cannot teach itself new strategies
  • Operate with human-level consciousness
  • Can perform specific tasks, but cannot learn new ones
3.AI is the fusion of many fields of study. Which of these fields, along with Computer Science, plays a role in the application of AI?
  • Statistics
  • Philosophy
  • Mathematics
  • All responses are correct
4. Which of these is NOT a current application of AI?
  • Self-Driving vehicles utilizing Computer Vision to navigate around objects
  • Collaborative Robots helping humans lift heavy containers
  • Classifying rock samples to identify best places to drill for oil
  • Making precise patient diagnosis and prescribing independent treatment
5. Natural Language AI algorithms that learn by example are the reason we can talk to machines and they can talk back to us.
  • True
  • False
6. Advances in the field of Computer Vision make which of the following possible?
  • Detecting fraudulent transactions
  • Detecting cancerous moles in skin images
  • On-demand online tutors
  • Real-time transcription
7. Which of these is currently NOT an application of Collaborative Robots or Cobots?
  • Robots helping humans lift heavy containers
  • Robots assisting or replacing humans in jobs that may be dull, dangerous, ineffective or inefficient when done by humans
  • Robots helping move items on shelves for stocking purposes
  • Personal use in the home such as doing the laundry and cooking for example
8. Which of the following aspects involved in converting the stethoscope into a digital device to support patient diagnoses involves the use of AI?
  • An app on the mobile device that applies learnings from previous diagnosis data to assist the physicians in their current diagnoses
  • Sending digital signals to a mobile device with a machine learning app via bluetooth
  • Inserting a digitizer into the stethoscope tube to convert the analog sound of the heart beat into a digital signal
  • Graphing heart beat data on the mobile device allowing a physician to spot trends
9. Which of the following are applications of Artificial Intelligence in action?
A. IBM Watson utilizing its information retrieval capabilities to provide technical information to oil and gas company workers.
B. Watson analyzing Grammy nominated song lyrics over a 60-year period and categorizing them based on their emotions.
C. Assisting patients with neurological damage by detecting patterns in massive movement related datasets and using robots to trigger specific movements in the human body to create new neural pathways in the brain.
D. Law enforcement authorities using facial recognition algorithms to identify suspects in multiple streams of video footage
  • Only options A, B, and C are correct

    • All of the options are correct
  • All of the options are correct
  • Only option A is correct
  • None of the
  • options are correct

Graded: AI Concepts, Terminology, and Application Areas

1. Which of these statements is true?

  • Cognitive systems can only translate small volumes of audio data into their literal text translations at massive speeds
  • Cognitive systems can learn from their successes and failures
  • Cognitive systems can derive mathematically precise answers following a rigid decision tree approach
  • Cognitive systems can only process neatly organized structured data

2. Which of these statements is true?

  • AI is the subset of Data Science that uses Deep Learning algorithms on structured big data
  • Data Science is a subset of AI 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
  • Deep Learning is a specialized subset of Machine Learning that uses layered neural networks to simulate human decision-making

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

  • 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
  • Takes data and rules as input and uses these inputs to develop an algorithm that will give us an answer

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

  • The algorithm ingests unlabeled data, draws inferences, and finds patterns from unstructured data
  • 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

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
  • 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
  • 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

6.Which of the following statements about datasets used in Machine Learning is NOT true?

  • 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
  • Validation data subset is used to validate results and fine-tune the algorithm’s parameters
  • Testing data is data the model has never seen before and is used to 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
  • False

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?

  • Speech
  • Computer Vision
  • Natural Language
  • Chatbots

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

  • 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
  • Deconstruct sentences to decipher the context of use
  • Continue to correct the sample and run it through the classifier, repetitively, till an accurate voice sample is created

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

  • Proactive Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Graded: AI Issues, Ethics and Bias

1. Ethics in artificial intelligence is:
  • Something that we need to apply today.
  • Something that is not an issue.
  • Something that somebody else is going to do in the future.
  • Something that is entirely solved in current AI systems.
2. One approach that helps developers avoid unintentionally creating bias in AI systems is:
  • Using a wide variety of appropriately diverse data for training.
  • Using highly specific training data from a narrow range.
  • Not using any training data.
  • None of the above.
3. Which of the following statements about IBM’s views on AI are correct?
  • Data and insights belong to the people and businesses who created them. Organizations that collect, store, manage, or process data have an obligation to handle it responsibly.
  • Knowing how an AI system arrives at an outcome is key to trust. To improve transparency, we should define how we build, deploy, and manage AI systems through scientific research.
  • Unbiased models and a spirit of diversity and inclusion are necessary to create fair AI systems, which can mitigate, rather than magnify, our existing prejudices.
  • AI can be applied to solve some of humanity’s most pervasive problems and create opportunity for all.
  • All of the above.
4. Which of the following are examples of bias in an AI system?
  • Customers not being aware that they are interacting with a chatbot on a company website.
  • AI systems in call centers providing context sensitive assistance to staff.
  • Image recognition systems associating images of kitchens, shops, and laundry with women rather than men.
  • Facial recognition systems performing well for individuals of all skin tones.
5. There is concern that some jobs will be replaced by AI systems. Which of the following characteristics make a job a good candidate for replacement?
  • Features highly creative tasks.
  • Requires innovative problem solving.
  • Has very varied, unpredictable tasks.
  • Rules-based decision-making.
6. Ethical concerns with AI systems are:
  • Not genuinely troubling, and the concern of very few AI experts.
  • Short term and easily addressed when developing new AI systems.
  • Something that should be the concern of every AI developer, so they can be mitigated for as AI systems are developed.
  • Something that can’t be mitigated for.
7.What are some of the ethical concerns around artificial intelligence?
A. Racial, gender or other types of bias.
B. Loss of jobs due to AI replacing workers performing repetitive tasks.
C. Concern about the trustworthiness of decision-making supported by AI systems.
D. Privacy, for example, as human faces are photographed and recognized in public spaces.
  • Only options A, B, and D are correct
  • None of the options are correct
  • Only options A and B are correct
  • All of the options are correct
8. Which of the following NOT a way AI is being used to benefit humanity?
  • In healthcare, AI is being used to interpret scans for early detection of cancer, eye disease, and other problems.
  • Crime: to identify criminals before they commit a crime.
  • In healthcare, AI is being used to predict where the next outbreak of a disease will occur.
  • In agriculture, AI is being used to identify and recommend treatment for plant diseases.
9. How many new opportunities and job roles does the World Economic Forum expect that AI will create in the next few years?
  • 165 million
  • 7 million
  • 133 million
  • 48 million
10. What is a significant way in which developers of AI systems can guard against introducing bias?
  • Using only examples from their own environment as training data.
  • Using government approved algorithms.
  • Providing effective training data and performing regular tests and audits.
  • Using less varied AI systems and datasets.

Final Assignment Part One

1. How would YOU define AI?

Your definition of AI can be similar or different from the ones given in the course.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

2. Explain an application or use-case of AI that fascinates YOU.

It may or may not be something that is mentioned in the course.
  1. Artificial Intelligence in Healthcare :
  2. Artificial Intelligence in Business
  3. Artificial Intelligence in Education
  4. Artificial Intelligence in Autonomous Vehicles
  5. Artificial Intelligence in Social Media
  6. Artificial Intelligence for a Better World
  7. Artificial Intelligence in Tourism

3. Pick a specific industry or an aspect of our lives or society and describe how YOU think it will be impacted by Artificial Intelligence in future.

What you discuss may or may not be something that is mentioned in the course.
Artificial Intelligence in Education:
It must be very tedious for a teacher to grade homework and tests for large lecture courses. A significant amount of time is consumed to interact with students, to prepare for class, or work on professional development. But, this will not be the case anymore.
Though it can never replace human work, it is pretty close to it. So, with the automated grading system checking multiple-choice questions, fill-in-the-blank testing and automated grading of students can be done in a jiffy.
It can tell the areas, where there is a need for improvement –
A lot of times, it happens that the teachers may not be aware of the gaps that a student might face in the lectures and educational materials. This can leave students confused about certain concepts. With AI, the system alerts the teacher and tell what is wrong. It gives students a customized message which offers hints to the correct answer.

Peer-graded Assignment: Final Assignment Part Two

Upload the screenshot that you saved in Exercise 4, Task 3 of the previous hands-on exercise titled “Classify your images with AI”. Ensure the screenshot includes the picture you uploaded along the labels and confidence scores below it.


Refer to the screenshot you uploaded for the previous question. Based on the confidence scores for labels provided by the AI model, versus what you can identify as a human, indicate whether the AI model did a good job of classifying your image? Explain.

I uploaded Upload the screenshot that you saved in Exercise 4, Task 3 of the previous hands-on exercise titled “Classify your images with AI“. Ensure the screenshot includes the picture you uploaded along the labels and confidence scores below it.


The above questions are from “Introduction to Artificial Intelligence (AI)” You can discover all the refreshed questions and answers related to this on the “Introduction to Artificial Intelligence (AI) – Coursera Quiz Answers” page. If you find the updated questions or answers, do comment on this page and let us know. We will update the answers as soon as possible.

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