So you think you can AI

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So You Think You Can AI?

Test your knowledge on Artificial Intelligence with our engaging quiz! Dive into various topics and see how well you understand the rapidly growing field of AI.

Multiple choice questions
Learn about AI technologies and concepts
Challenge yourself and track your progress

54 Questions14 MinutesCreated by ThinkingTree42

Which one of the following is the largest and fastest growing sector for AI-related global investment (2018-2019)?

Facial Recognition

Robotic automation

Drug, cancer study

Autonomous driving

Which country got the most private investments (for startups) in Artifical Intelligence (in 2018) in terms of per capita (dollar per person) ?

Singapore

China

Israel

United States

Many people consider Artificial Intelligence as the

Sixth industrial revolution

Third industrial revolution

Fourth industrial revolution

Fifth industrial revolution

Which of the following is true for General Artificial Intelligence ?

Takes knowledge from one domain and transfers it to other domain

Machines which are an order of magnitude as intelligent or more intelligent than humans

Dedicated to assist with or take over specific tasks

Chatbots and Voice assistants (Siri, Alexa, Google assistant) are examples of

Super AI

Narrow AI

General AI

What is a Turing test in Artificial Intelligence?

A method for determining whether or not a computer is capable of thinking like a human being

A method for determining whether or not a computer is capable of thinking like Super AI

A method for determining whether or not a computer is capable of thinking like General AI

While working with creating Artificial Intelligence applications, In which area do AI programmers spend most of their time

Model development

Data processing (cleaning, labeling etc)

A.I programming

Model deployment

A data point which differs significantly from other observed data points is called

Outlier

Synthetic data

Labeled data

What is the figure below an example of?

Data labeling

Data anonymization

Synthetic data generation

Feature engineering

The process of using domain knowledge of a data set to create new attributes from existing data points/attributes is called

Feature engineering

Synthetic data generation

Data labeling

Which type of machine learning is shown in this image ?

Reinforcement learning

Recommender systems

Supervised learning

Unsupervised learning

In a specific kind of machine learning, an agent can learn in an interactive environment by trial and error using feedback from its own actions and experiences. This is

Recommender systems

Reinforcement learning

Supervised learning

Unsupervised learning

What kind of algorithm is Logistic regression ?

Clustering algorithm

Classification algorithm

Association algorithm

Regression algorithm

The output of a sigmoid function (for classification algorithms) has a range from

0 to 100

0 to 1

0 to 1000

0 to 10

Suppose that you are given the previous tax information of all individuals and you now have to develop an algorithm which predicts how much tax will they submit next year. Which type of algorithm would you use ?

Clustering

Regression

Classification

Association

What kind of algorithm assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature

Polynomial regression

Linear regression

Naive Bayes algorithm

Logistic regression

What is the maximum number of hyperplanes one can use

3 dimensional

N dimensional

2 dimensional

10 dimensional

Suppose you are given a data set of student complaints from OsloMets customer service center. The data set is labelled. You are now given a task to understand how angry or happy the students are in those complaints. What kind of algorithms would you use ?

Regression

Classification

Clustering

Suppose you are given a data set of X ray images of Covid patients. The data set is not labelled and you do not have the opportunity to label it. You are now given the task to identify if the patient has covid or not. What kind of algorithm would you use ?

Clustering

Regression

Classification

Suppose you operate a successful eCommerce store. You want to boost your sales and think you can encourage people to buy more based on their previous purchases. What kind of algorithm would you use to show customers what should they buy ?

Classification

Clustering

Association

An equation that describes a relationship between two quantities that show a constant rate of change is called

Linear regression

Logistic regression

Naive Bayes

Support vector machine

The following image which shows a tight formation of data points which is usually produced by

K-means algorithm

Gaussian algorithm

Linear regression

A recommendation system (e.g. Used by social media companies) usually belongs to the following category of AI:

Super A.I.

Narrow A.I.

General A.I.

Suppose you are given the task to predict your income for the next year. You need data for the last 15 years and you only have data for the last 5 years. How will you get that missing data ?

Feature engineering

Data anonymization

Data warehousing

Synthetic data

In what kind of algorithms do we need to use data labeling ?

Reinforcement learning

Supervised learning

Unsupervised learning

In Machine learning, Linear Regression falls within the category of:

Recommender systems

Unsupervised learning

Reinforcement learning

Supervised learning

Regression models are used with

Random data

None of the above

Continuous data

All of the above

What is NOT valid for a hyperplane ?

We can only use maximum 2 hyperplanes for any number of features

Hyperplanes work with support vector machines

They are boundaries that help classify data points

Which statement is true about outliers ?

Outliers should be part of the training data set but should not be present in the test data

The nature of the problem determines how outliers are used

Outliers should be part of the test data set but should not be present in the training data

Outliers should be identified and removed from the data set

The correlation between the number of years an employee has worked for a company and the salary of the employee is 0.75. What can be said about employee salary and years worked ?

There is no relationship between salary and years worked

The majority of employees have been with the company a long time

Individuals that have worked for the company the longest have lower salaries

Individuals that have worked for the company the longest have higher salaries

What is TRUE for a machine learning algorithm ?

It is harder to train the first 90% than the remaining 10%

It is harder to train the remaining last 10% than the first 90%

None of the above.

"You may also like" or "recommended for you" kind of applications (used primarily in Amazon, Facebook etc) can be implemented by using algorithms such as

K-Means algorithm

Apriori algorithm

Neural network algorithms

What kind of problem does this statement highlight in your data : Most facial recognition systems today use a higher proportion of white faces as training data (study by IBM in 2019)

Data Bias

Unlabeled data

Clustered data

None of the above

In the following image the self driving car came to an abrupt stop. What do you think went wrong ?

A handful of stickers and graffiti have confused the car to misinterpret the sign

Self driving cars have problems with blue color

The human took the floppy drive out of the car and the car cannot move

The smiley face picture on the board is interpreted as a human face and the car cannot move

If the software follows a logical series of steps to reach a conclusion, is easy to explain and the programmer has complete control over the code, then what kind of programming is it ?

Conventional programming

Artificial Intelligence programming

The major reason behind the increased use of Artificial Intelligence today is due to

What is the preferred way to work with an A.I. algorithm ?

Identify the problem -> prepare data -> choose algorithms -> train the algorithm -> run the algorithm

Identify the problem -> choose algorithms -> run the algorithm -> prepare data -> train the algorithm -> export data to algorithms

Identify the problem -> choose algorithms -> train the algorithm -> run the algorithm -> prepare data -> export data to algorithms

All of the above

In approximate terms, how much time of an A.I programmer is spent on pure A.I programming ?

85%

30%

15%

75%

Which of the following is FALSE regarding regression ?

It may be used for interpretation

It is used for prediction

It discovers causal relationships (if the occurrence of the first causes the other)

It relates inputs to outputs

Which of the following is a common use of unsupervised learning ?

Detect outliers

Determine if meaningful relationships can be found in a dataset

Determine a base set of input attributes for supervised learning

Evaluate the likely performance of a supervised learning model

Suppose you have to build a machine learning model to predict the price of housing market in Norway. What kind of models would you choose ?

Classification models

Reinforcement models

Regression models

What does a classification model do ?

Predicts real number responses such as changes in temperature, date, or time

Predicts the class of the data

Clusters responses in groups based on similarity, to find patterns

None of the above

Suppose your model is not giving a good prediction. What is NOT a valid way to increase prediction score ?

Improve the optimization algorithm being used for error minimzation

Reduce the noise in the data

Decrease the model complexity

Get as much data as you can

Logistic regression is a ____ regression technique that is used to model data having a ____ outcome

Nonlinear, numeric

Nonlinear, binary

Linear, numeric

Linear, binary

Which one of these classification algorithms is easiest to start with for prediction?

Naive Bayes

Neural Networks

Logistic regression

Bagged decision tree

Two ways to more reliable improve an algorithms performance?

Train a bigger network

Change dataset

Remove data

Get more data

How large should the dataset be?

Small enough to detect differences in dataset

Not to large to minimize power use

As large as possible with all the data you can ad

Large enough to detect differences in dataset

What are the processes of mining data?

Collecting, manage, cleane and delete

Extract, cleane, sort and present data

Collecting, transport and clean data

Collecting, manage, prepareing, sort and present data

What is gradient descent?

A cost function

A optimizing algorithm

A performance function

A demand function

What does not fit under Logistic regression?

Binary logistic regression

Multiclass logistic regression

Ordinal regression

Polynomial regression

What is wrong about support vector machines?

Classifies unseen data

Can be used both in classification and regression

Can be used only for classification

Finds a clear seperation between data points

What is support vector NOT normally used for?

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