Imagine AI as the ultimate apprentice-- one that never sleeps, never complains and keeps learning from every job it sees and does. But unlike a regular apprentice, this one lives inside your tools, machines and even your phone!
When you use AI, you might just sign in and ask it a question. But for the question you are asking are you using the right tool. Knowing the differences in all the versions of AI will help you as an employee provide the company the best results. Because if you were working in green industries why use scissors when a lawn mower would work?
Things to consider:
Understand what Machine Learning is and Generative AI
Types of AI
Difference between software and AI
When you see the ICON on the left it means you need to record something in your notebook. It could be a question or a place to jot down notes for the quiz at the end of this certificate. Please record your answer in your Student WORKBOOK.
Select which colume you would like to read from to gather an understanding of what AI is about. One is more technical and the other gives you information.ย
TECHNICALย
Select which colume you would like to read from to gather an understanding of what AI is about. One is more technical and the other gives you information.ย
INFORMATIVEย
Predictive AI leverages statistical modeling, machine learning, and data mining techniques to identify patterns in historical data and forecast future outcomes. Itโs often used in scenarios where understanding trends, behaviors, or failures in advance can save time or resources. For instance, in finance, predictive AI models are used to assess credit risk or anticipate market movements. In manufacturing, predictive maintenance systems analyze sensor data to determine when equipment is likely to fail, reducing downtime and repair costs. These systems often rely on supervised learning algorithms such as regression models, decision trees, or ensemble methods like random forests and gradient boosting.
Generative AI refers to systems that can generate new content such as text, images, music, or code. These models are trained on vast datasets and use deep learning techniquesโespecially transformer-based architectures like GPT (Generative Pre-trained Transformer)โto learn the patterns, structure, and style of the input data. Once trained, these models can produce outputs that resemble human-generated content. For example, a generative language model like ChatGPT can write essays, solve math problems, or simulate conversation. Image generators like DALLยทE or Midjourney use diffusion models or GANs (Generative Adversarial Networks) to synthesize high-quality images from text prompts. These systems involve large-scale training using billions of parameters and require significant compute power to develop.
AI-powered robotics blends mechanical engineering with computer vision, machine learning, and control systems to create machines capable of performing physical tasks autonomously or semi-autonomously. In industrial automation, robots equipped with AI can identify objects, navigate spaces, and optimize workflows without explicit programming for each scenario. For example, collaborative robots (cobots) in manufacturing environments can adapt their movements based on sensor data and real-time feedback. Self-driving vehicles combine multiple AI modelsโperception, localization, decision-making, and controlโto interpret surroundings and make navigation decisions. These systems rely heavily on reinforcement learning, real-time sensor fusion, and edge AI processing for rapid response.
Natural Language Processing (NLP) is a branch of AI focused on enabling machines to understand, interpret, generate, and respond to human language. NLP combines computational linguistics with machine learning and deep learning to handle tasks like sentiment analysis, language translation, speech recognition, and conversational agents. Techniques like tokenization, part-of-speech tagging, syntactic parsing, and semantic analysis help models understand linguistic structure. Transformer models, like BERT and GPT, have significantly advanced NLP by enabling contextualized language understanding across a variety of domains. NLP is essential in industries such as customer service (chatbots), legal tech (document summarization), and healthcare (transcribing medical notes).
Computer vision enables machines to extract, analyze, and understand useful information from visual data such as images and videos. It combines convolutional neural networks (CNNs), image processing techniques, and deep learning to detect objects, recognize faces, read text, and interpret scenes. In industry, computer vision is used for automated quality inspection in manufacturing, detecting anomalies in X-rays or MRIs in healthcare, and enabling autonomous navigation in drones and vehicles. The systems often work in real time and require edge computing capabilities when deployed in environments with limited connectivity or strict latency requirements.
Decision support systems use AI to analyze complex datasets and provide actionable recommendations. These systems are often embedded in enterprise software to help professionals make data-informed decisions in areas like finance, logistics, healthcare, and policy-making. They may use a combination of AI techniques including predictive analytics, simulation models, optimization algorithms, and knowledge graphs. Unlike fully autonomous systems, decision support AI is designed to augment human decision-making, not replace it. For example, a hospital might use AI to recommend treatment options based on patient data and historical outcomes, but a doctor still makes the final call.
Definition: AI is the ability of a machine or computer program to perform tasks that typically require human intelligence.
Examples of AI abilities:
Understanding language (like Siri or ChatGPT)
Recognizing images or faces
Making decisions or recommendations (like Netflix or Spotify suggestions)
Driving a car (self-driving tech)
Definition: A subset of AI where computers learn from data instead of being explicitly programmed.
Key idea: The machine gets better at a task by learning from patterns in data. Example: A spam filter that learns what messages are junk based on what you've marked before.
Definition: A type of AI that creates new content like text, images, music, or code.
It learns from patterns in massive datasets and generates original content based on that training.
Examples:
ChatGPT (writes essays, scripts, etc.)
DALLยทE (creates images from text prompts)
MusicLM (generates music from text descriptions)
Traditional Software vs Artificial Intelligence
Follows exact rules set by programmers
Learns patterns from data and adapts
Output is predictable and fixed
Output can vary depending on inputs and learned behavior
No improvement unless re-coded
Can improve performance through training
Example: Calculator app
Example: Language translator that improves over time
Purpose: Predict future outcomes using historical data.
Examples:
Predicting stock trends
Maintenance forecasting in manufacturing
Customer churn in businesses
Purpose: Create new content or ideas.
Examples:
AI-generated ads or videos
Product mockups
Virtual writing assistants
Purpose: Perform physical tasks or automate manual processes.
Examples:
Assembly line robots
Self-driving vehicles
Warehouse automation (like Amazon robots)
Purpose: Understand and process human language.
Examples:
Chatbots
Virtual assistants
AI content moderation
Purpose: Interpret and understand visual information.
Examples:
Facial recognition
Quality inspection in factories
Medical image analysis (e.g., spotting cancer)
Purpose: Help people make better decisions based on data.
Examples:
AI in finance to assist investment choices
Healthcare diagnostics support
Risk management in insurance
Both of the above were generated by AI, which do you think is was better for you to gain the information needed to respond to the questions in your WORKBOOK.ย ย
Next...
While you watch the video on "What is Artificial Intellegence", start to think about how you might use AI, in your regular daily life, in your SHSM and then in your job after high school.ย