AI in Action — TechKnowSurge

About this interactive

Artificial intelligence means a computer doing tasks associated with human intelligence. Logic is a basic form: the computer follows rules, like a robot keeping to the left wall of a maze, and takes the same path every time. Machine learning learns from its mistakes, but in the lesson's maze it must start over on a new maze. Deep learning, a type of machine learning, recognizes patterns with artificial neural networks: inputs (one per pixel for a picture), layers of neurons in the middle, and outputs. More neurons allow more complex work but take longer to train, and training means rewarding right answers until the network recognizes, say, a car it has never seen. Generative AI uses what it learned to create something new. Large language models are trained on words; GPT means generative pre-trained transformer, already trained, so a chatbot such as ChatGPT can generate and restyle text for you. Voice assistants such as Siri interpret speech. AI also predicts from trends and suggests searches. AI can be wrong (hallucinations) or biased, and the hype cycle runs from inflated expectations down to the trough of disillusionment, then up the slope of enlightenment. Read each situation, predict, then read the reveal.

What you'll learn

Predict how logic, machine learning and deep learning behave, how a neural network is trained and sized, and what generative AI and a pre-trained model do
Tell what an AI tool is doing (a chatbot generating text, a voice assistant interpreting speech, prediction from a trend, suggestions from searches) and respond to a hallucination and the hype cycle

Aligned to

CompTIA Tech+

1.3 Illustrate the basics of computing and processing.

Key terms

Artificial Intelligence: The simulation of human intelligence by computers, enabling them to perform tasks typically associated with human thinking such as decision-making and pattern recognition.
Machine Learning: A subset of artificial intelligence in which systems learn from data and improve their performance over time without being explicitly programmed.
Deep Learning: A subset of machine learning that uses artificial neural networks with many layers to recognize patterns and make decisions in a manner similar to the human brain.
Neural Network: A computational model inspired by the human brain, consisting of interconnected nodes (neurons) organized in layers that process inputs to produce outputs.
Generative AI: A category of artificial intelligence that can create new and original content such as text, images, code, or audio based on patterns learned during training.
Chatbot: An AI-powered program designed to simulate conversation with users, generating text-based responses to queries.
Voice Assistant: An AI-powered application that interprets and responds to spoken language commands, such as Siri or Alexa.
AI Hallucination: A phenomenon where an AI model produces confidently stated but factually incorrect or fabricated information.
Gartner Hype Cycle: A graphical model representing the maturity and adoption of technologies through five phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity.
Large Language Model: A type of AI model trained on massive amounts of text data to understand and generate human language across a wide range of topics and tasks.

Topics

Artificial Intelligence Machine Learning Deep Learning Neural Network Generative Ai Chatbot Voice Assistant Interactive Predict

About TechKnowSurge

TechKnowSurge builds IT and cybersecurity professionals through hands-on, concept-first training built around real understanding — not memorization. Free interactive tools, structured programs, and 25+ years of real-world experience, all in one place.

Explore free tools and programs →