AI Is a Branch of Computer Science
- Terminology and concepts to look out for: program · model · training
- Relevance to me: You have used AI tools all semester. For the next eight days, you will learn what these tools are. Today you learn which part of computer science they come from.
- Board Question: Is the autocorrect on your phone AI? Yes or no, and one reason. You answer this three times today: now, at your table, and on the exit ticket.
First, the diagnostic. Open it on your Chromebook. It locks your Chromebook until you submit.
DO NOT CLICK OPEN FORM FROM A LAB COMPUTER
Open the diagnosticAI Is a Branch of Computer Science
This section explains where artificial intelligence fits inside computer science and how a model is different from an ordinary program.
Where AI Fits in Computer Science
Computer science is the study of computers and the step-by-step processes they carry out. Networking, data management and security are three branches of computer science, and artificial intelligence, usually called AI, is another. The name "artificial intelligence" comes from the 1950s, when four researchers proposed a summer study of the topic at Dartmouth College. Machine learning is one area inside AI, and the language models that write text belong to machine learning. The models behind the class tools MuggsOfPrompts and MuggsOfVibes are language models of this kind. Figure 1 shows these areas as boxes drawn inside one another.
Programs: Rules Written by People
A program is a set of instructions that a person wrote for a computer to follow. For example, a calculator app is a program. When a user enters 2 + 2, the app follows an addition rule that a programmer wrote, so the same input gives the same output every time. A model is also a program in the broad sense that it is software that runs on a computer, but nobody typed its rule. The calculator fits this description of a program, while the face-recognition feature described below does not.
How Models Are Made by Training
A model is a piece of software whose behavior came from examples instead of from rules a person wrote. Inside, a model is a large collection of numbers that connect an input to an output. Training is the process of showing a model a very large number of examples and adjusting its numbers until its output matches them. For each example, the model makes a prediction, the prediction is compared to the correct answer, which is called a label, and the model's numbers are adjusted to bring the next prediction closer. A person typing in a rule is not training, because the rule came from the person and not from examples. A language model is trained on a very large amount of text, and it then generates text one word or part of a word at a time. A chatbot is one use of a language model, which generates each reply as a continuation of the conversation so far.
One Phone, Two Kinds of Software
The calculator app runs on the phone's chips and is not a model, because every one of its rules was typed by a person. The feature that unlocks the phone by recognizing its owner's face works differently, because it relies on a model that was made by training. No person wrote a rule that lists what the owner's face looks like. Some features have been built both ways. For example, an earlier design of one phone keyboard described typing mistakes with rules, and a newer design replaced those rules with a trained model, so the answer to the test depends on how the feature was built.
Summary
AI is one branch of computer science, and machine learning and language models are areas inside it, as Figure 1 shows. A program follows rules that a person wrote, while a model gets its behavior from training on examples. The test for any computer feature is whether a person wrote the rule or whether the rule was learned from examples.
Sources: McCarthy et al., A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence (1955); Tucker et al. via K–12 CS Framework, Defining Computer Science; ACM, IEEE-CS and AAAI, Computer Science Curricula 2023 (2024); Russell and Norvig, Artificial Intelligence: A Modern Approach, contents; CompTIA, Tech+ (FC0-U71); Samuel, Some Studies in Machine Learning Using the Game of Checkers (1959); Google, What is Machine Learning? and Supervised Learning; Google, LLMs: What's a large language model?; Microsoft, Windows Calculator; Beaufays and Riley, The Machine Intelligence Behind Gboard (2017); Apple, About Face ID advanced technology and Biometric security; Mistral AI, Models Overview; course repository, MuggsOfPrompts and MuggsOfVibes model ids.
I Do questions
-
notebook cue: program
Which one is following rules a person wrote: a calculator adding 2 + 2, or a phone recognizing your face?
Check
The calculator is following rules a person wrote, because a programmer typed its addition rule. The face feature uses a trained model, so no person wrote its rule. See Programs: Rules Written by People and One Phone, Two Kinds of Software.
-
notebook cue: model
A model runs on a computer the same way a calculator does. What is the one thing about it that a calculator does not have?
Check
A model's behavior came from training on examples. Every rule in a calculator was typed by a person. See How Models Are Made by Training.
-
notebook cue: training
What goes into training, and what comes out?
Check
Many examples, each with its correct answer, go into training. Out comes a model whose numbers were adjusted until its output matched the examples. See How Models Are Made by Training.
I Do questions
-
notebook cue: program
Which one is following rules a person wrote: a calculator adding 2 + 2, or a phone recognizing your face?
Check
The calculator is following rules a person wrote, because a programmer typed its addition rule. The face feature uses a trained model, so no person wrote its rule. See Programs: Rules Written by People and One Phone, Two Kinds of Software.
-
notebook cue: model
A model runs on a computer the same way a calculator does. What is the one thing about it that a calculator does not have?
Check
A model's behavior came from training on examples. Every rule in a calculator was typed by a person. See How Models Are Made by Training.
-
notebook cue: training
What goes into training, and what comes out?
Check
Many examples, each with its correct answer, go into training. Out comes a model whose numbers were adjusted until its output matched the examples. See How Models Are Made by Training.
AI Is a Branch of Computer Science
This section explains where artificial intelligence fits inside computer science and how a model is different from an ordinary program.
Where AI Fits in Computer Science
Computer science is the study of computers and the step-by-step processes they carry out. Networking, data management and security are three branches of computer science, and artificial intelligence, usually called AI, is another. The name "artificial intelligence" comes from the 1950s, when four researchers proposed a summer study of the topic at Dartmouth College. Machine learning is one area inside AI, and the language models that write text belong to machine learning. The models behind the class tools MuggsOfPrompts and MuggsOfVibes are language models of this kind. Figure 1 shows these areas as boxes drawn inside one another.
Programs: Rules Written by People
A program is a set of instructions that a person wrote for a computer to follow. For example, a calculator app is a program. When a user enters 2 + 2, the app follows an addition rule that a programmer wrote, so the same input gives the same output every time. A model is also a program in the broad sense that it is software that runs on a computer, but nobody typed its rule. The calculator fits this description of a program, while the face-recognition feature described below does not.
How Models Are Made by Training
A model is a piece of software whose behavior came from examples instead of from rules a person wrote. Inside, a model is a large collection of numbers that connect an input to an output. Training is the process of showing a model a very large number of examples and adjusting its numbers until its output matches them. For each example, the model makes a prediction, the prediction is compared to the correct answer, which is called a label, and the model's numbers are adjusted to bring the next prediction closer. A person typing in a rule is not training, because the rule came from the person and not from examples. A language model is trained on a very large amount of text, and it then generates text one word or part of a word at a time. A chatbot is one use of a language model, which generates each reply as a continuation of the conversation so far.
One Phone, Two Kinds of Software
The calculator app runs on the phone's chips and is not a model, because every one of its rules was typed by a person. The feature that unlocks the phone by recognizing its owner's face works differently, because it relies on a model that was made by training. No person wrote a rule that lists what the owner's face looks like. Some features have been built both ways. For example, an earlier design of one phone keyboard described typing mistakes with rules, and a newer design replaced those rules with a trained model, so the answer to the test depends on how the feature was built.
Summary
AI is one branch of computer science, and machine learning and language models are areas inside it, as Figure 1 shows. A program follows rules that a person wrote, while a model gets its behavior from training on examples. The test for any computer feature is whether a person wrote the rule or whether the rule was learned from examples.
Sources: McCarthy et al., A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence (1955); Tucker et al. via K–12 CS Framework, Defining Computer Science; ACM, IEEE-CS and AAAI, Computer Science Curricula 2023 (2024); Russell and Norvig, Artificial Intelligence: A Modern Approach, contents; CompTIA, Tech+ (FC0-U71); Samuel, Some Studies in Machine Learning Using the Game of Checkers (1959); Google, What is Machine Learning? and Supervised Learning; Google, LLMs: What's a large language model?; Microsoft, Windows Calculator; Beaufays and Riley, The Machine Intelligence Behind Gboard (2017); Apple, About Face ID advanced technology and Biometric security; Mistral AI, Models Overview; course repository, MuggsOfPrompts and MuggsOfVibes model ids.
Each table decides: autocorrect, AI or not AI. The table agrees on one answer and on the test it used to decide.
Chromebooks stay closed while your table talks. When the discussion form pops up, answer it right here under My View. Switch slides whenever you need the passage, and submit before the exit ticket.
Use this form at your table. At the start of class, you answered today's Board Question: Is the autocorrect on your phone AI? Yes or no, and one reason. Talk with your table about it, the three positions and the three statements. Then answer on your own.
MC 1. (Where you stand) At the start of class, you answered this question: Is the autocorrect on your phone AI? Yes or no, and one reason. After talking with your table, which position is closest to yours now?
- Yes. Autocorrect learned from examples of how people type.
- No. A person wrote a list of words and rules for it to follow.
- It depends on how that autocorrect was built.
CR 1. (Board Question · answer 2) Think about the Board Question again. Did you reach the same conclusion you had at the start of class, or a different one? Say so in one sentence. Then write your answer as a claim: (1) State your answer as if it is a fact. (2) Support it with evidence from the passage. (3) Explain how that evidence supports your claim.
Write your answer in your lab notebook, labeled CR 1.
MC 2. (Statement 1) A calculator is a kind of AI because it solves problems.
- Agree
- Disagree
MC 3. (Statement 2) Every model is a program, but not every program is a model.
- Agree
- Disagree
MC 4. (Statement 3) If a program gives a smart answer, it must have been trained.
- Agree
- Disagree
CR 2. (My view) Write one thing you said or thought at your table.
Write your answer in your lab notebook, labeled CR 2.
CR 3. (A different view) Write the view at your table that was most different from yours. If everyone agreed, write the strongest argument against your table's answer.
Write your answer in your lab notebook, labeled CR 3.