The Next Frontier: Generative AI for Absolute Beginners

Explore the big picture of AI by defining AI, tracing its evolution through machine learning and deep learning, and introducing generative AI as the next evolution wave.

Understand why AI is a general purpose technology and how it mirrors human intelligence. See how automation and everyday applications harness AI and the impact of innovations like ChatGPT.

Explore how machine learning enables computers to learn patterns from data and improve artificial intelligence, with algorithms, training models, and deep learning driving the evolution.

Explore how deep learning, a subfield of machine learning, uses layered artificial neural networks to learn complex patterns from large data, shaping the AI puzzle toward generative AI.

Discover how generative AI analyzes text as a language and creates new content, boosting productivity across domains and signaling a broad, early-stage general-purpose technology.

Explore the AI landscape from artificial intelligence to machine learning, deep learning, and generative AI, and see how these layers form a digital brain that analyzes text and generates content.

Discover the soft introduction to machine learning, uncover its building blocks, technologies, and market terms, and understand how machine learning forms the foundation of generative AI for absolute beginners.

Apply machine learning box concept to classify defective products and analyze sentiment, then generate a video from text. See how input becomes output and boxes connect with apis in pipelines.

Explore the four core machine learning tasks: prediction, classification, clustering, and content generation, and learn how each category is trained to derive valuable outputs from input data.

Train the machine learning box through data preparation and algorithm selection to produce a trained model, deploy it via inference in production, and retrain periodically.

See how a machine learning box maps input x to output y with a simple function f. Explore linear regression and a four layer neural network as examples.

Identify the three main data types: structured, semi-structured, and unstructured, and recognize that structured data is highly organized and searchable, while unstructured data is not predefined.

Explore how structured and unstructured data types shape feature design for training and predictions, and how feature engineering—like age binning—enhances disease risk prediction.

Explore supervised, unsupervised, semi-supervised, and reinforcement learning, and how labeled data, training data sets, and rewards guide model optimization and pattern discovery.

Explore the machine learning box concept, core tasks: prediction, classification, clustering, and content generation; training processes, data types, features, and learning methods, with a peek at generative AI.

Explore the magic behind generative AI as we reveal how AI systems understand language and generate content from text, uncovering the key principles and secrets you can use wisely.

Explore artificial neural networks as deep learning models with input, hidden, and output layers, where features and weights drive training toward classification, prediction, or data generation.

Explore deep learning architectures, from recurrent and convolutional networks to transformer architectures, and learn how parallel processing and attention power generative ai with large language models.

Discover foundation models: large-scale, generic models trained on massive data that can be adapted to many tasks, exemplified by GPT and ChatGPT.

Discover how foundation models power large language models, and why parameters, model size, and training data influence performance and cost in AI applications.

Compare general purpose and domain specific LMS, then contrast open source versus closed source LLMs with examples like ChatGPT and Gemini.

Discover how prompts drive large language models, how prompts are analyzed and broken into tokens, and how tokenization, vocabulary, and tokenizers convert text into numerical representations for completions.

Explore how tokens translate text into numbers and how context windows shape prompts, costs, and long-document chunking for APIs.

Explore how LLMs operate in a sequential mode, predicting the next token using probability distributions learned from massive data, and why slight randomness creates diverse, creative outputs.

Explore self-supervised learning in large language models trained on unstructured data with masked language modeling to predict missing words from context, enabling scalable pre-training and tuning of foundation models.

Explore three methods to tailor large language models: contextual prompting with prompt engineering, retrieval augmented generation using private data and external sources, and cost-effective fine tuning via transfer learning.

Generative AI creates content by analyzing text with transformer foundation models and LLMs, covering prompts, tokens, context windows, domain-specific versus general models, and retrieval or fine tuning.

Explore the momentum and opportunities of generative ai for beginners while acknowledging its limitations and risks, and learn responsible usage and preparation for practical applications.

Explore prompt sensitivity and prompt engineering in generative ai, showing how input quality and context shape outputs. Learn to craft prompts to maximize accuracy, avoiding garbage in, garbage out.

Explore knowledge cutoff in generative ai, learn strategies to stay current: retrain at intervals or connect to external tools like search engines and databases to close the knowledge gap.

Explore how Gen II models introduce non-deterministic, creative outputs by adjusting temperature, influencing randomness and word probabilities for varied responses in brainstorming and consistent answers in sensitive domains.

Understand structured data as tabular formats, like product reviews in a spreadsheet, and learn why generative AI struggles with context windows and tabular preprocessing.

Explore how generative AI models can hallucinate by producing incorrect, confidently presented information, and understand causes like insufficient training data, data quality, and knowledge cutoff that affect safe production use.

Explore how generative artificial intelligence relies on pattern recognition and statistical predictions, yet lacks human-like common sense, while safety protocols and a washing machine example illustrate the gap.

Examine how biases in online content like wikipedia shape ai outputs and fairness challenges. Explore how providers reduce bias through diverse data, monitoring, and ongoing updates.

Explore data privacy and security risks in AI, including data leakage from third party tools and safer data handling and user education, plus rising misuse by bad actors.

Explore the challenges and limitations of generative AI, including prompt sensitivity, knowledge cutoff, and model randomness, and learn how to mitigate risks such as hallucinations and data privacy.

Discover practical applications of generative AI and market use cases shaping many industries to boost efficiency, creativity, and innovation while navigating an evolving landscape of tools and setting realistic expectations.

Compare web-based AI tools and software-based JNI modules embedded in larger apps, connected via APIs to exchange data and drive tasks like review classification and routing.

Discover how a brainstorming assistant uses chatbots to generate blog titles and refine prompts, offering practical, iterative ideation for knowledge-base optimization and marketing ideas.

Discover how generative AI summarizes long texts by extracting key points into concise, structured outputs, while noting limits like token size and potential omissions.

Learn how to use free chatbot JNI tools to enhance text by copy-pasting content and guiding prompts to fix grammar and improve flow, while preserving your human touch.

Discover how JNI-powered code generation makes programming accessible for non developers, helping analysts scaffold SQL queries and starter code, while acknowledging knowledge cutoff and the need to refine outputs.

Explore AI use cases beyond basics, drafting blogs, articles, scripts, and product descriptions; use AI as a starting framework for drafts, and add a human touch to ensure ethical content.

Generate images on demand from text prompts, using color scales and visuals to speed up presentations, blogs, and reports; it is not a replacement for real images.

Explore how generative ai models integrate into end-to-end business workflows across domains such as retail, driving product recommendations and the growth of ai-based applications.

Explore ai content types—text, image, video, and audio—and consumption modes: web based and application based. Practice prompts for brainstorming, summarizing, enhancing text, and generating code or images on demand.

Recap AI basics, machine learning, deep learning, and generative AI, highlighting transformer architectures, foundation models, LLMs, prompts, tokens, and key use cases like content generation and brainstorming.

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