Standards in this Framework
| Standard | Description |
|---|---|
| 1-A-ii | Describe the limitations and advantages of various types of computer sensors. |
| 1-A-iii | Explain how radar, lidar, GPS, and accelerometer data are represented. |
| 1-B-i | Explain perception algorithms and how they are used in real-world applications. |
| 1-B-ii | Explain how features are extracted from waveforms and images. |
| 1-B-iii | Illustrate the abstraction hierarchy for speech understanding, from waveforms to sentences, showing how knowledge at each level is used to resolve ambiguities in the levels below. |
| 1-B-iv | Demonstrate how perceptual reasoning at a higher level of abstraction draws upon earlier, lower levels of abstraction. |
| 1-C-i | Analyze one or more online image datasets and describe the information the datasets provide and how this can be used to extract domain knowledge for a computer vision system. |
| 1-C-ii | Describe some of the technical difficulties in making computer perception systems function well for diverse groups. |
| 2-A-i | Describe how to represent a concept as a schema. |
| 2-A-ii | Translate the premises of a syllogism expressed in English into logical notation and complete the syllogism correctly. |
| 2-A-iii | Describe how schemas are used to structure information about people, places, or things in knowledge graphs. |
| 2-A-iv | Describe how a transformer network operates. |
| 2-B-i | Identify types of real-world problems that are search problems and describe their states and operators. |
| 2-B-ii | Illustrate breadth-first, depth-first, and best- first search algorithms to grow a search tree for a graph search problem. |
| 2-C-i | Categorize real-world problems as classification, prediction, sequential decision problems, combinatorial search, heuristic search, adversarial search, logical deduction, or statistical inference. |
| 2-C-ii | For each of these types of reasoning problems (classification, prediction, sequential decision making, combinatorial search, heuristic search, adversarial search, logical deduction, and statistical inference), list an algorithm that could be used to solve that problem. |
| 3-A-i | Define supervised, unsupervised, and reinforcement learning algorithms, and give examples of human learning that are similar to each algorithm. |
| 3-A-ii | Model how machine learning constructs a reasoner for classifcation or prediction by adjusting the reasoner's parameters (its internal representations). |
| 3-A-iii | Use either a supervised or unsupervised learning algorithm to train a model on real world data, then evaluate the results. |
| 3-A-iv | Illustrate what happens during each of the steps required when using machine learning to construct a classifier or predictor. |
| 3-A-v | Describe how various types of machine learning algorithms learn by adjusting their internal representations. |
| 3-A-vi | Select the appropriate type of machine learning algorithm (supervised, unsupervised, or reinforcement learning) to solve a reasoning problem. |
| 3-B-i | Describe the following neural network architectures and their uses: feed-forward network, 2D convolutional network, recurrent network, generative adversarial network. |
| 3-B-ii | Train a multilayer neural network using the backpropagation learning algorithm and describe how the weights of the neurons and the outputs of the hidden units change as a result of learning. |
| 3-C-i | Compare two real world datasets in terms of the features they comprise and how those features are encoded. |
| 3-C-ii | Evaluate a dataset used to train a real AI system by considering the size of the dataset, the way that the data were acquired and labeled, the storage required, and the estimated time to produce the dataset. |
| 3-C-iii | Investigate imbalances in training data in terms of gender, age, ethnicity, or other demographic variables that could result in a biased model, by using a data visiualization tool. |
| 4-A-i | Identify portions of a text that would be difficult for a computer to understand, and explain why. |
| 4-A-ii | Illustrate how understanding a sentence could be challenging for a computer by describing multiple senses of a given word. |
| 4-A-iii | Demonstrate how a small context-free grammar can be used to parse or generate simple sentences. |
| 4-A-iv | Describe several approaches to Natural Language Processing, ranging from simple to more sophisticated. |
| 4-B-i | Explain the cultural and naive physics knowledge required for a computer to correctly interpret a fable or fairytale. |
| 4-C-i | Identify ways AI applications can modify their behavior to respond to people's emotional states. |
| 4-D-i | Debate alternative perspectives on human vs. artificial intelligence |
| 5-A-i | Explain how use of AI systems has led to disparate impacts on different groups. |
| 5-A-ii | Analyze an AI system to determine whether it satisfies ethical design criteria. |
| 5-A-iii | Design an AI System using an ethical design process. |
| 5-B-i | Explain the kinds of debates that might arise as AI technology continues to evolve and is further woven into our culture. |
| 5-B-ii | Identify areas where it is appropriate to regulate use of AI technologies and evaluate regulations that have been proposed. |
| 5-C-i | Predict how a sector of society is likely to change in the short and intermediate term as a result of AI technology. |
| 5-C-ii | Investigate the skills needed for AI-enabled careers. |
| 5-D-i | Create a novel application using some of the AI tools available in the programming framework of your choice. |
| 5-D-ii | Evaluate an AI for Social Good project in terms of the problem it is addressing and the project's actual or potential impact. |
