Question 1
An important part of the techniques on which current artificial intelligence (AI) systems are based were developed:
In the middle Ages
During the Renaissance
In the 20th century
Last year
Question 2
When making a drawing in an image recognition application that uses machine learning (ML) techniques, the system is able to recognize what you are painting (for example, a turtle), this is due to:
That you have drawn a turtle very similar to the turtle that the creators of the system drew during their training.
That your drawing is very similar to the one made by the previous player who had to paint a turtle.
That your drawing is the same as the typical turtle that appears in Google Images searches.
That the system has recognized patterns in your drawing similar to those of the turtle drawings with which it was trained.
Question 3
When an artificial intelligence (AI) system provides results that discriminate in terms of, for example, gender, this is usually due to:
That the data that was used to train the system presented certain biases or was not balanced, that is, that much more data corresponding to men than to women was used, or vice versa.
That the developers of the system had sexist biases.
That the system is designed to be used by men to a greater extent than by women, or vice versa.
That the system reflects the sexist reality of human nature.
Question 4
When we implement machine learning (ML) techniques in a text classification system:
We present the computer with a set of example texts and it, after processing them, is able to recognize only the texts that exactly match these examples.
We present the computer with a set of example texts and it, after processing them, is able to recognize texts similar to said examples (that is, to recognize new texts that it has not seen before).
We present the computer with a set of example texts and it, after processing them, is able to recognize any text we present to it.
We present the computer with a set of example texts and it, after processing them, is able to recognize any text, image or sound that we present to it.
Question 5
Which of the following statements is true about machine learning (ML)?
Training data is essential for machine learning, without data it is not possible to do machine learning.
With machine learning, computers learn to think and can recognize any type of data (text, image, sound...), in the same way that a human being does.
The more data we use to train a system that incorporates machine learning, the worse (more inaccurate) are the results offered by said system.
Machine learning does not need data to work, precisely because it is automatic and does not depend on being fed data of any kind.
Question 6
Which of the following strategies would be most appropriate for teaching a computer to recognize photos of apples?
Train the computer with photos of dogs.
Train the computer with several photos of different apples, taken in different places and contexts.
Train the computer with several similar photos of the same apple, taken in the same place.
Train the computer with several identical copies of the same photo of an apple.
Question 7
Which of the following statements is correct regarding machine learning (ML)?
ML is a technique that allows computers to be trained with example data to perform certain tasks; rather than having to program them with an explicit set of steps and rules to follow.
ML implies that humans will no longer have to think about how to solve problems, since computers will think for us (and better than us).
ML implies that it is no longer necessary for humans to learn to program.
ML can only be implemented on supercomputers with enormous computing power.
Question 8
In which of the following tasks, to be performed by a computer, would it be most appropriate to apply machine learning (ML) techniques?
Add large numbers.
Recognize if an email is spam (junk mail).
Count the number of times a key is pressed.
Inform the hours of a specific business based on the day of the week.
Question 9
When training a computer using machine learning (ML) techniques, which of the following strategies is likely to be most successful and perform best?
Collect a small set of examples; train the system; check how good (reliable) the built model is; add more examples; retrain the system; check again how good it is... and repeat until you get a good enough model.
More starter examples are always better, so to get started you decide to collect millions of training examples. You start with such mass collection, and spend several months collecting more and more examples. Only then do you proceed to train the system.
Question 10
Both Alicia and Roberto want to train a machine learning (ML) system that can recognize whether a certain text is “happy/positive” or “sad/negative.” Alicia and Roberto follow two different training strategies. Which of the two will achieve the best system?
Alicia. She has collected 10 varied examples of happy/positive texts and another 10 varied examples of sad/negative texts.
Robert. He has collected 1000 examples of happy/positive texts and another 10 examples of sad/negative texts.
Question 11
The level of technical knowledge required to understand what artificial intelligence (AI) is is too high for most of the population.
Totally disagree.
Somewhat disagree.
Neither agree nor disagree.
Somewhat agree.
Totally agree.
Question 12
If an algorithm is able to predict the probability of victory in a tennis match, it is very likely that it will also be able to predict the beginning of World War 3.
Totally disagree.
Somewhat disagree.
Neither agree nor disagree.
Somewhat agree.
Totally agree.
Question 13
In less than 3 years, a Terminator-type artificial intelligence (AI) will be developed that will dangerously threaten the survival of all humanity.
Totally disagree.
Somewhat disagree.
Neither agree nor disagree.
Somewhat agree.
Totally agree.
Question 14
As a user, the legal regulation that is approved regarding artificial intelligence (AI) systems will affect my life:
Totally disagree.
Somewhat disagree.
Neither agree nor disagree.
Somewhat agree.
Totally agree.