AI For Everyone Coursera Quiz Answer

AI For Everyone course. In this post you will get AI For Everyone Quiz Answer | 100% Correct Answer Of Week (1-4)

AI For Everyone

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Week- 1

Week 1 Quiz

1. Which of these terms best describes the type of AI used in today’s email spam filters, speech recognition, and other specific applications?

Artificial Narrow Intelligence (ANI)
Artificial General Intelligence (AGI)

2. What do you call the commonly used AI technology for learning input (A) to output (B) mappings?

Artificial General Intelligence
Unsupervised learning

Supervised learning
Reinforcement learning

3. undefined
You want to use supervised learning to build a speech recognition system. The figure above suggests that in order for a neural network (deep learning) to achieve the best performance, you would ideally use: (Select all that apply)

A large dataset (of audio files and the corresponding text transcript)
A small dataset (of audio files and the corresponding text transcript)

A large neural network
A small neural network

4. The only way to acquire data for a supervised learning algorithm is to manually label it. I.e., given the input A, to ask a human to provide B.

True
False

5. Which of these statements regarding data acquisition do you agree with?

It doesn’t matter how data is acquired. The more data, the better.

Only structured data is valuable; AI cannot process unstructured data.
It doesn’t help to give data to an AI team, because they can always produce whatever they need by themselves.
Some types of data are more valuable than others; working with an AI team can help you figure out what data to acquire.

6. You run a company that manufactures scooters. Which of the following are examples of unstructured data? (Select all that apply.)

The maximum speed of each of your scooters

The number of scooters sold per week over the past year
Audio files of the engine sound of your scooters
Pictures of your scooters

7. Suppose you run a website that sells cat food. Which of these might be a good result from a Data Science project? (Select all that apply.)

Insights into how to market cat food more effectively, depending on the breed of cat.

A slide deck presenting a plan on how to modify pricing in order to improve sales.
A large dataset of images labeled as “Cat” and “Not Cat”
A neural network that closely mimics how cats’ brains work.

8. Based on the terminology defined in Video 4, which of the following statements do you agree with? (Select all that apply.)

Deep learning is a type of machine learning. (I.e., all deep learning algorithms are machine learning algorithms.)

The terms “Machine learning” and “data science” are used almost interchangeably.
AI is a type of deep learning. (I.e., all AI algorithms are deep learning algorithms.)
The terms “Deep learning” and “neural network” are used almost interchangeably.

9. Which of these do AI companies do well?

Strategic data acquisition

Invest in unified data warehouses
Spot automation opportunities
All of the above

10. Say you want to input a picture of a person’s face (A), and output whether or not they are smiling (B). Because this is a task that most humans can do in less than 1 second, supervised learning can probably learn this A-to-B mapping.

True

False

Week- 2

Week 2 Quiz

1. Machine learning is an “iterative” process, meaning that an AI team often has to try many ideas before coming up with something that’s good enough, rather than have the first thing they try work.

True
False

2. Say you want to use Machine Learning to help your sales team with automatic lead sorting. I.e., Input A (a sales prospect) and output B (whether your sales team should prioritize them). The 3 steps of the workflow, in scrambled order, are:

(i) Deploy a trained model and get data back from users

(ii) Collect data with both A and B

(iii) Train a machine learning system to input A and output B

What is the correct ordering of these steps?

(i) (ii) (iii)

(ii) (iii) (i)
(ii) (i) (iii)
(i) (iii) (ii)

3. What are the key steps of a Data Science project?

Collect data

Analyze the data
Suggest hypothesis or actions
All of the above

4. Machine Learning programs can help: (select all that apply)

Automate resume screening

Automate visual inspection in a manufacturing line
Customize product recommendations
Automate lead sorting in sales

5. Unless you have a huge dataset (“Big Data”), it is generally not worth attempting machine learning or data science projects on your problem.

True

False

6. Say you want to build an AI system to help recruiters with automated resume screening. Which of these steps might be involved in “technical diligence” process? (Select all that apply.)

Ensuring that this is valuable for your business (e.g., estimating the project ROI)
Making sure that an AI system can meet the desired performance
Making sure you can get enough data for this project

Defining an engineering timeline

7. Which of these statements about “business diligence” do you agree with?

Business diligence applies only if you are launching new product lines or businesses.
Business diligence is the process of ensuring that the envisioned AI technology is feasible.
Business diligence is the process of ensuring that the AI technology, if it is built, is valuable for your business.

Business diligence can typically be completed in less than a day.

8. You want to use supervised learning for automated resume screening, as in the example above. Which of the following statements about the Training Set are true? (Select all that apply.)

The Training set and Test set can be the same dataset.
It should give examples of the input A (resume) but not necessarily the desired output B (whether to move forward with a candidate).
It will be used by the AI team to train the supervised learning algorithm.

It should give examples of both the input A (resume) and the desired output B (whether to move forward with a candidate).

9. For your automated resume screening application, you are now providing a Test Set to the AI team. Which of the following statements about the Test Set are true? (Select all that apply.)

It should give examples of the input A (resume) but not necessarily the desired output B (whether to move forward with a candidate).
The Test Set should ideally be identical to the Training Set.
It should give examples of both the input A (resume) and the desired output B (whether to move forward with a candidate)

It will be used by the AI team to evaluate the performance of the algorithm.

10. Which of these are reasons that it’s often unrealistic to expect an ML system to be 100% accurate?

You might not have enough data
Data can be mislabeled
Data can be ambiguous

All of the above.

Week- 3

Week 3 Quiz

1. Because smart speakers can carry out multiple functions (such as tell a joke, play music, etc.) it is an example of Artificial General Intelligence (AGI).

True
False

2. What are the key steps to a smart speaker function?

Trigger detection -> intent recognition -> speech recognition -> command execution.
Speech recognition → Trigger word detection -> intent recognition -> command execution.

Trigger word detection -> speech recognition -> intent recognition -> command execution.
Trigger word detection -> intent recognition -> speech recognition -> command execution.

3. Consider this system for building a self-driving car:

undefined
The component for pedestrian detection is usually built using:

Reinforcement learning

A motion planning algorithm
Supervised learning
GANs

4. Suppose you are building a trigger word detection system, and want to hire someone to build a system to map from Inputs A (audio clip) to Outputs B (whether the trigger word was said), using existing AI technology. Out of the list below, which of the following hires would be most suitable for writing this software?

AI Product Manager

Machine learning engineer
Data engineer
Machine learning researcher

5. What is the first step in the AI Transformation Playbook for helping your company become good at AI?

Build an in-house AI team

Execute pilot projects to gain momentum
Provide broad AI training
Develop an AI strategy

6. Of the following options, which is the most important trait of your first pilot project?

Succeed and show traction within 6-10 months

Drive extremely high value for the business
Be executed by an in-house team
None of the above

7. Say you are building a smart speaker, and want to accumulate data for your product through having many users. Which of these represents the “Virtuous circle of AI” for this product?

8. Why is developing an AI strategy NOT the first step in the AI Transformation Playbook?

The strategy should be to use the Virtuous Circle of AI, which comes after building a product.
There is no reason. Developing an AI strategy IS the first step in the AI Transformation Playbook.
When transforming a company into an AI company, one does not need a strategy, therefore it can’t be the first step.
Without having some practical AI experience and knowing what it feels like to build an AI project, a company usually does not know enough to formulate a sound strategy.

9. According to the AI Transformation Playbook, broad AI training needs to be provided not only to engineers, but also to executives/senior business leaders and to leaders of divisions working on AI projects.

True
False

10. Which of the following are AI pitfalls to avoid? (Select all that apply)

Expecting AI to solve everything
Expecting traditional planning processes to apply without changes
Pairing engineering talent with business talent to identify feasible and valuable projects.
Expecting AI based projects to work the first time

Week- 4

Week 4 Quiz

1. What are the current limitations of AI technology? (Select all that apply)

AI technology is susceptible to adversarial attacks
AI technology can discriminate
AI technology can be biased
There are no limitations to AI technology
Explainability is hard

2. What is the Goldilocks Rule of AI?

AI’s technology will continue to grow and can only benefit society
One should allocate many resources to defend the world from giant killer robots
An AI winter is coming
One shouldn’t be too optimistic or too pessimistic about AI technology

3. Say you are building an AI system to help make diagnoses from X-ray scans. Which of the following statements about explainability of AI do you agree with?

AI systems are intrinsically “black box” and cannot give any explanation for their outputs.
Lack of explainability can hamper users’ willingness to trust and adopt an AI system.
Most AI systems are highly explainable, meaning that it’s easy for a doctor to figure out why an AI system gave a particular diagnosis.
Explainability is usually achieved through building a chatbot to talk to the user to explain its outputs.

4. Using current AI technology, if a machine learning system learns from text that reflects unhealthy biases/stereotypes, then the resulting AI software may also exhibit similarly unhealthy biases/stereotypes.

True
False

5. Using current AI technology, if a machine learning system learns only from text that is completely neutral and does not reflect any gender biases, then we would expect it to exhibit no, or at most minimal, gender bias.

True
False

6. Which of these are good practices for addressing bias in AI? (Select all that apply)

Using an adversarial attack on the AI system to change its outputs to be less biased
Systematic auditing processes to check for bias
Technical solution such as “zeroing out” bias
Using more inclusive/less biased data

7. Which of these are examples of adversarial attacks on an AI system? (Select all that apply)

Adding a sticker to a stop sign to make an AI system fail to detect it.
Subtly modifying an audio clip to make a speech recognition system think someone said “Yes, authorized” when they actually said “No, reject.”
Subtly changing an image to make an AI system mistakenly recognize a dog as a cat.
Using AI to synthesize a fake video of a politician saying something they never actually said.

8. If a developing economy has a strong and thriving coffee bean manufacturing industry (or some other vertical industry), then it has an advantage in applying AI to coffee bean manufacturing (or other vertical industry).