So you think you can AI
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
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 is an example of
Polynomial regression
Linear regression
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.
Has there been any software which claims to have passed the Turing test ?
YES
NO
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
Availability of increased data
Cloud computing
Powerful processors
Increased connectivity between devices
All of the above
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?
Face detection
Predict driving routes
Sorting sensor measurements
Detect cancerous cells
Image classification
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