AI Fundamentals: From Basics to Generative AI

Compare AI to the human brain, noting the brain's 86 billion neurons and quadrillions of connections that enable rapid processing and learning. Explore how neural networks mimic these connections.

Explore how AI and smart machines integrate into everyday life, from fraud detection and SEO optimization to healthcare predictions and autonomous driving.

Reflect on AI's evolving capabilities and limitations, explore benefits and risks, and consider ethical responsibilities as AI reshapes daily life, industries, jobs, and the skills valued in work.

Explore how we train ai systems to learn and decide with machine learning and neural networks, recognizing patterns and making predictions through three approaches—learning by example, rules, and understanding principles.

Explore the three main types of machine learning—supervised, unsupervised, and reinforcement—covering data inputs and tasks like classification, regression, clustering, dimensionality reduction, and anomaly detection.

Explore unsupervised learning through a block clustering analogy where the machine discovers patterns without labels. See a hands-on Claude demo that shows how shapes group by detected features.

Discover how unsupervised learning finds patterns in data without labels to power real-world marketing, NLP, finance, and healthcare. See customer segmentation, topic modeling, anomaly detection, and image segmentation across industries.

Explore supervised learning by using labeled shapes to train models that classify new data, like the yellow block, based on past labels.

Explore how machines learn versus humans, examining pattern recognition, trial and error, memorization versus understanding, and the roles of supervised, unsupervised, and deep learning with real world applications.

Discover how algorithms and mathematics power AI, examining foundational principles and their role in prediction and decision making, with approachable examples like estimating weight from height.

Explore how math underpins AI, using supervised learning and linear regression to predict weights from heights, with an interactive demo of adjusting heights to see changes.

Discover generative ai and its ability to create and generate new content, building on the foundations of ai and machine learning.

Trace the rise of generative AI and large language models, powered by self-supervised learning, and explore how ChatGPT and similar systems transform education, exams, and everyday life.

Explore how large language models work by breaking down their key components and mechanisms, revealing how they generate human-like text and power tools like ChatGPT.

Explore how the sequence to sequence model underpins language models and llms by predicting the next word from prior input, enabling tasks like translation and autocorrect.

Explore transformers, a model architecture that processes language efficiently by using attention across all words and multi-head attention, powering large language models like GPT.

Master self-attention in transformers to understand how LLMs focus on relevant words. It remembers and weighs earlier words to build accurate context and predictions.

Learn how large language models like ChatGPT develop through three stages: pre-training on an unlabeled corpus, instruction fine-tuning, and reinforcement learning from human feedback.

Identify the key players shaping AI, from commercial giants to open source communities like OpenAI, Google, Anthropic, Mistral, and Meta, and compare free basic versions.

Explore the current capabilities of large language models, including contextual language understanding, summarization, and intent recognition, while noting their basic reasoning and the risk of hallucinations.

Explore LLM capabilities like natural language understanding, a vast built-in knowledge base, and reasoning to solve problems, plus style emulation and prompt testing for diverse responses.

Explore hallucinations in llms through famous quotes, highlighting how ai generated content can sound profound yet be fabricated. Reflect on reliability, critical thinking, and responsible use of advanced ai capabilities.

Explore how to harness large language models and prompts in ai fundamentals to make tasks smarter and acquire new skills, while learning prompting fundamentals, best practices, and where llms excel.

Explore the OpenAI ChatGPT interface, log in with a user or Google ID, and use the default ChatGPT 4.0 model while navigating chats and the prompt area.

Learn how rephrasing prompts with llms improves clarity and accuracy, guiding AI to deeper explanations by asking the same question in a different way.

Learn when to start a new chat and how to use a prompt helper. Craft clear, concise prompts with iterative refinement and one-question clarifications.

Empower audiences with persona prompting to tailor the AI tone; craft messages for a six-year-old or a lawyer, then write a complex email advocating data-scientist consolidation into a company.

Use interactive prompting to tailor artificial intelligence responses by asking one question at a time, building a personalized 12-week workout plan after clarifying your fitness level, goals, and preferences.

Explore how generative ai excels in content generation (text, code, and stories), image and video creation, and in question answering and document summarization and investigation.

Upload your documents to large language models to tailor insights, identify top five points, and generate detailed summaries, while noting web-trained data and file-size limits.

Explore ai and generative ai through prompts, applying llms to daily tasks like writing emails and researching topics. Understand their capabilities, limits, and the importance of thoughtful use.