AI Literacy

How does AI Work?

• Competency 7 (Representation)
• Understand what a knowledge representation is (they model the world in a way that is understandable to a computer) and describe some examples of knowledge representations
• Competency 8 (Decision-Making)
• Recognize and describe examples of how computers reason and make decisions.
• Competency 9 (Machine Learning Steps)
• Understand the steps involved in machine learning and the practices and challenges that each steps entails.
• Competency 10 (Human Role in AI)
• Recognize that humans play an important role in programming, choosing models, and fine-tuning AI systems.
• Competency 11 (Data Literacy)
• Understand basic data literacy concepts.
• Competency 12 (Learning from Data)
• Recognize that computers often learn from data (including one’s own data).
• Competency 13 (Critically Interpreting Data)
• Understand that data cannot be taken at face-value and requires interpretation. Describe how the training examples provided in an initial dataset can affect the results of an algorithm.
• Competency 14 (Action & Reaction)
• Understand that some AI systems have the ability to physically act on the world (robotics) This action can be directed by higher-level reasoning (e.g. walking along a planned path) or it can be reactive (e.g. jumping backwards to avoid a sensed obstacle).
• Competency 15 (Sensors)
• Understand what sensors are, recognize that computers perceive the world using sensors, and identify sensors on a variety of devices. Recognize that different sensors support different types of representation and reasoning about the world.

While it’s one thing to know what AI is, it’s another to understand the underlying functions. Artificial intelligence operates by processing data through advanced algorithms. It combs large data sets with its algorithms, learning from the patterns or features in the data. There are many theories and subfields in AI systems including:

Machine learning. Machine learning uses neural networks to find hidden insights from data, without being programmed for what to look for or what to conclude. Machine learning is a common way for programs to find patterns and increase their intelligence over time.

Deep learning. Deep learning utilizes huge neural networks with many layers, taking advantage of its size to process huge amounts of data with complex patterns. Deep learning is an element of machine learning, just with larger data sets and more layers.

Cognitive computing. Cognitive computing has a goal for a human-like interaction with machines. Think robots that can see and hear, and then respond as a human would.

Computer vision. In AI, computer vision utilizes pattern recognition and deep learning to understand a picture or video. This means the machine can look around and take pictures or videos in real time, and interpret the surroundings. 

The overall goal of AI is to make software that can learn about an input, and explain a result with its output. Artificial intelligence gives human-like interactions, but won’t be replacing humans anytime soon.

Code.org Video Series on AI