CSF 7.6 Assessment - Automation

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How are you feeling today? Baby edition.

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Which of the following is a benefit of automation?
Options:
Greater potential for human error,
Increased labor costs,
Improved production speed and efficiency,
All of these

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Improved production speed and efficiency

Answer explanation

Automation is the use of technology to perform tasks with little or no human intervention. One of the main benefits of automation is that it can improve production speed and efficiency. By automating certain tasks, machines and systems can work faster and with greater precision than humans, which can lead to increased productivity and output.

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Which term best describes a summary of the rules a computer will follow to make predictions about similar data in the future? Options: Natural language processing, Automation, Bias

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Model

Answer explanation

The term that best describes a summary of the rules a computer will follow to make predictions about similar data in the future is "Model". A model is a set of mathematical algorithms and statistical methods that are used to analyze and predict outcomes based on data. In machine learning and artificial intelligence, models are trained on large datasets to identify patterns and relationships, and then used to make predictions about new data.

Option A) Natural language processing refers to the ability of computers to understand, interpret, and generate human language. It is a subfield of artificial intelligence and computational linguistics.

Option C) Automation refers to the use of technology to perform tasks with little or no human intervention. While models can be a component of automation, the term "automation" is more general and can refer to a wide range of technologies and applications.

Option D) Bias refers to systematic errors or inaccuracies in a model or dataset that result in incorrect or unfair predictions or decisions. While models can be affected by bias, the term "bias" specifically refers to the presence of unfair or discriminatory factors.

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Which of the following best describes machine learning? Machine learning is when computers think for themselves, Machine learning is only used for tasks humans cannot complete, Machine learning is when computers predict results based on input data, Machine learning is only used for tasks that humans are naturally worse at than computers

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Machine learning is when computers predict results based on input data

Answer explanation

The option that best describes machine learning is: "Machine learning is when computers predict results based on input data."

Machine learning is a subset of artificial intelligence that involves training algorithms to learn patterns and relationships in data, and then using those patterns to make predictions or decisions about new data. In machine learning, a computer is not thinking for itself in the way that humans do, but rather is using statistical models and algorithms to identify patterns in data and make predictions based on that analysis.

Option A) "Machine learning is when computers think for themselves" is not an accurate description, as machine learning algorithms are programmed to learn from data and make predictions based on that learning, but they do not have the ability to "think" or make decisions in the way that humans do.

Option B) "Machine learning is only used for tasks humans cannot complete" is also not an accurate description, as machine learning can be used for a wide range of tasks, both those that humans can do and those that they cannot. For example, machine learning can be used to identify patterns in data that would be too complex for humans to discern on their own.

Option D) "Machine learning is only used for tasks that humans are naturally worse at than computers" is also not an accurate description, as machine learning can be used for tasks where humans are equally or even more skilled than computers, such as image recognition or natural language processing.

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Which of the following statements best describes the differences between AI and machine learning?

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AI is the process of creating intelligent machines, while machine learning is a subset of AI that focuses on learning from data.

Answer explanation

AI is short for artificial intelligence. The scientific study of AI aims to develop computer hardware and software which emulates processes of the human brain, including the processing of photographic images and human languages.

Machine learning, or ML, is a branch of AI focused on creating computer systems that accomplish tasks without explicit instructions. Instead of being told step-by-step and case-by-case how to do something, ML systems "learn" by repeatedly processing "training data" (representative sets of example information). The results of the processing are graded by how close they are to a desired result. This process is repeated with the best-scoring algorithms and parameters used as the basis for new ones.

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Which of the following best estimates how many images a machine learning model might need to be able to identify an apple in a variety of settings? Thousands, Dozens, 100, Two: one image containing an apple and one image not containing an apple

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Thousands

Answer explanation

The number of images that a machine learning model might need to be able to identify an apple in a variety of settings is "Thousands".

Training a machine learning model to accurately identify objects such as apples requires a large dataset of images that includes a variety of settings, angles, lighting conditions, and other variables. While the exact number of images needed can vary depending on the complexity of the model and the specific application, a common rule of thumb is that thousands of images are needed to train a model that can accurately identify objects in a variety of settings.

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Which of the following best describes an algorithm?

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A set of instructions or rules used to solve a problem or perform a task

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Which of the following demonstrates machine learning algorithmic bias? A computer program is given input data of résumés from mostly 20-year-old men, and then the program recommends men over women for job openings, A computer program is coded with if statements to deny credit cards to people in a specific neighborhood, A computer program is coded with if statements to consider race during college admissions as part of an affirmative action policy, A computer program is given input data of pictures of oranges and incorrectly identifies a frog as an orange

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A computer program is given input data of résumés from mostly 20-year-old men, and then the program recommends men over women for job openings

Answer explanation

The option that demonstrates machine learning algorithmic bias is: "A computer program is given input data of résumés from mostly 20-year-old men, and then the program recommends men over women for job openings."

Algorithmic bias occurs when a machine learning model produces results that are systematically unfair or discriminatory towards certain groups of people. In this case, the bias arises from the fact that the training data for the machine learning model is not representative of the broader population, and as a result, the model has learned to make recommendations based on gender and age, rather than on the qualifications and abilities of the job applicants.

Option B) and Option C) describe situations where the bias is the result of the programming of the algorithm, rather than a reflection of the data it was trained on. In these cases, the algorithm has been programmed to consider certain factors, such as location or race, in making decisions. While these decisions may be controversial or unfair, they do not necessarily involve algorithmic bias in the way that the first option does.

Option D) describes a situation where the machine learning model has made an error in classification, which is different from algorithmic bias. While such errors can be problematic, they are not the same as bias, which involves systematic patterns of unfairness or discrimination.

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How confident do you feel about this topic?

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Very confident, Mostly confident, Somewhat confident, Not confident at all