Empower yourself with a clear understanding of AI and Generative AI fundamentals and analogies to the human brain so you can better understand how modern AI systems work.
This course aims to build confidence in using Generative AI technologies like CoPilot to boost your productivity and enhance your daily workflows.
Throughout this series, we'll cover key topics from fundamentals to practical applications of Generative AI in professional settings.
Each session includes live demonstrations and Q&A portions to ensure you gain hands-on experience with these powerful tools.
GenAI Basics & Neural Networks
Real-world Use Cases
Integration & Workflows
Artificial Intelligence has evolved dramatically since its conceptualization in the 1950s, progressing from theoretical frameworks to the sophisticated systems we use today.
The journey from early AI concepts to modern generative models spans decades of research breakthroughs, with each milestone building upon previous innovations.
Today's AI landscape is dominated by Large Language Models and generative systems that can understand and create human-like content across multiple modalities.
Alan Turing proposed the Turing Test to determine whether a machine could exhibit human-like intelligence, laying the foundation for defining machine intelligence.
The Dartmouth Conference officially coined the term "Artificial Intelligence," marking the beginning of focused AI research and development.
The rise of Expert Systems introduced rule-based AI programs designed to mimic human decision-making in specialized fields like medical diagnosis.
AlexNet demonstrated the power of deep neural networks for image recognition, proving that multi-layered neural networks could outperform traditional machine learning.
The Transformer architecture enabled AI to process entire sequences at once, leading to breakthroughs in natural language processing and generation.
Large Language Models like GPT-3 brought powerful text generation capabilities, making AI-powered conversation accessible to businesses and the public.
An improved version with better accuracy, faster responses, and more nuanced conversations, marking a shift toward more interactive and useful AI.
Anticipated next generation of AI models with enhanced multimodal capabilities, advanced reasoning, personalization, and real-time interactions.
As compute costs decline exponentially, we're witnessing the democratization of artificial intelligence. What once required million-dollar supercomputers can now be achieved with affordable hardware, making AI accessible to organizations of all sizes.
The plummeting cost of compute is driving a paradigm shift in how AI integrates into every aspect of society. As intelligence becomes ubiquitous and nearly free, we're entering an era where AI augmentation will become the norm rather than the exception.
The economics of AI are following an exponential curve that will continue to accelerate innovation. This trajectory suggests we're only at the beginning of understanding how accessible AI will transform industries, scientific discovery, and human potential.
Global computing operations per day likely exceed 10²⁶ - 10²⁸ FLOPS (2025)
AI will be embedded in everyday devices and systems, from personal assistants to smart cities, making intelligence a utility as common as electricity.
Lower costs enable more experimentation, leading to faster iterations and breakthroughs in medicine, engineering, and scientific research.
AI is no longer just for big tech; smaller companies, educational institutions, and developing nations can now leverage AI for growth.
As compute becomes cheaper, AI will develop real-time inference and reasoning capabilities, shifting from reactive to proactive systems.
AI will transition from being a tool to a true collaborator, enhancing human abilities in creativity, decision-making, and problem-solving.
AI will accelerate discoveries in materials science, drug development, and climate modeling, solving problems once thought to take decades.
The field of artificial intelligence is filled with specialized terminology that often overlaps. This glossary provides clear definitions from leading experts to help you navigate conversations about AI technologies.
These definitions show the nested relationship between AI concepts, from the broader field of artificial intelligence to more specialized applications like machine learning, generative AI, and large language models.
As the field rapidly advances, terminology evolves—what was once considered cutting-edge deep learning may now be referred to as "traditional AI" in some contexts. Understanding these terms helps track the evolution of the technology.
Computer systems designed to perform tasks that typically require human intelligence. These systems can perceive their environment, learn from data, and take actions to achieve specific goals.
Example: Smart assistants like Siri, Alexa, and Google Assistant that can understand voice commands and perform various tasks.
A subset of AI that allows systems to learn and improve from experience automatically without being explicitly programmed. ML algorithms build models based on sample data to make predictions or decisions.
Example: Netflix and Spotify recommendation systems that learn from your viewing or listening habits to suggest new content.
A specialized type of machine learning that uses artificial neural networks with multiple layers (hence "deep") to progressively extract higher-level features from raw input data, mimicking how the human brain processes information.
Example: Face recognition in smartphone cameras and social media that can identify individuals in photos with remarkable accuracy.
Advanced AI systems trained on vast amounts of text data that can understand, generate, and manipulate language. They work by predicting the most likely next word in a sequence, enabling them to write essays, answer questions, and more.
Example: ChatGPT and Claude that can answer questions, write essays, and engage in natural-sounding conversations.
Early AI programs that use explicit "if-then" rules created by human experts to make decisions. Unlike learning systems, rule-based systems follow predetermined logic paths and cannot adapt to new situations without being reprogrammed.
Example: Spam filters that use predefined rules to determine whether an email is spam based on specific keywords or sender information.
AI systems that can create new content such as text, images, music, and videos that didn't exist before. Unlike traditional AI that classifies or predicts data, generative AI produces original outputs based on patterns learned from training data.
Example: DALL-E and Midjourney that create realistic images and artwork based on text descriptions provided by users.
Computing systems inspired by biological brain structures, consisting of interconnected nodes ("neurons") that process and transmit information. They excel at recognizing patterns and can learn to perform tasks by analyzing example data.
Example: Google Translate uses neural networks to provide increasingly accurate translations between languages, capturing nuances and context.
AI systems that can operate autonomously to achieve specific goals on behalf of users. They can perceive their environment, make decisions based on that information, and execute tasks with minimal human intervention.
Advanced autonomous AI systems that can chain together multiple steps, interact with software or services, make decisions, and adapt their approach based on outcomes. Agentic AI can complete complex tasks much like a personal assistant.
Just as the human brain processes information through interconnected neurons, artificial neural networks use mathematical representations of neurons to process data and identify patterns.
The brain's synapses strengthen with repeated use, similar to how neural network connections adjust their "weights" during training to improve outcomes over time.
Humans refine skills through practice and experience; similarly, AI models improve through iterative training on diverse examples, gradually enhancing their ability to generate relevant outputs.
Generative AI refers to technologies that create new content (text, images, code, etc.) based on patterns learned from existing data. These systems automate creative and repetitive tasks, enabling unprecedented productivity.
GenAI transforms traditional workflows by saving time, enhancing accuracy, and unlocking new business insights that were previously inaccessible or time-consuming to obtain.
Modern generative AI systems integrate directly into familiar applications, helping you draft content, summarize documents, and analyze data with simple natural language prompts.
Crafting precise, detailed prompts is key to getting high-quality outputs from generative AI. Learning to communicate effectively with AI systems is a skill that improves with practice.
AI models like those powering CoPilot are trained on vast datasets, including business content, to understand context, language, and domain-specific knowledge relevant to your work.
When you provide a prompt, the AI processes it through its neural network, drawing on patterns from its training to generate contextually relevant content that meets your needs.
"Draft a quarterly report summary highlighting key financial metrics"
Pattern recognition, context analysis, content generation
Structured summary with key metrics and insights highlighted
From professional emails and marketing copy to creative stories and technical documentation, GenAI can produce various text formats tailored to specific audiences, tones, and purposes.
Create images, illustrations, design mockups, and visual concepts by describing what you envision. AI can generate entirely new visuals or variations on existing design themes.
Generate functioning code in various programming languages, debug existing code, and create technical solutions to specific problems with simple natural language instructions.
Produce audio content, music compositions, voiceovers, video concepts, and even interactive experiences by defining parameters and creative direction.
These organizations create the large language models and multimodal AI systems that power most generative AI applications. They invest billions in research, training infrastructure, and dataset curation.
The computing hardware that enables AI model training and inference is crucial to the GenAI ecosystem. These companies design specialized processors optimized for AI workloads.
Building on foundation models, these companies develop user-friendly interfaces and specialized applications that make generative AI capabilities accessible to businesses and consumers.
These collaborative ecosystems democratize access to cutting-edge AI by developing freely available models, tools, and frameworks that reduce barriers to entry in the field.
The AI landscape evolves rapidly with new models released regularly. Each company's flagship model represents its latest research and capabilities in the field.
While some companies prioritize tightly controlled access to their models (closed source), others make their models available to the public (open source), often with specific licensing terms.
Access to advanced AI models varies significantly by region due to multiple factors:
Open source models provide a partial solution to these geographic limitations, as they can be deployed locally without API restrictions—though they often require significant technical expertise and computing resources.
As AI models grow more sophisticated, researchers use standardized tests to measure their capabilities against human performance. The "Humanity's Last Exam" benchmark evaluates models on complex reasoning, knowledge application, and problem-solving.
The latest generation of AI models has shown significant gains in accuracy and capabilities. Most impressive is the o3-mini series, which despite its smaller size has outperformed earlier, larger models on these challenging benchmarks.
As models continue to improve at this pace, we can expect to see even more capable AI systems emerging throughout 2025. These advancements will likely transform knowledge work, education, and scientific research in fundamental ways.
Higher percentages indicate better performance, with launch dates noted for each model
Featured models: GPT-4o (3.1%), Grok-3 (3.9%), Claude 3.5 Sonnet (4.8%), GPT-4.5 Preview (6.4%), Gemini Thinking (7.2%), DeepSeek-R1 (8.6%), o1 (8.8%), Claude 3.7 Sonnet (16K) (8.9%), o3-mini variants (11.1-14.0%)
The newest models demonstrate unprecedented performance across specialized fields, with the o3-mini (high) variant showing a remarkable 14% accuracy on the most challenging benchmark tests ever created.
Recent models excel at breaking down complex problems into logical sequences, with each generation showing improved capacity to maintain consistency across extended reasoning chains.
The trend toward smaller yet more capable models (as seen with o3-mini) suggests that architectural improvements are now outpacing raw scale, potentially democratizing access to powerful AI capabilities.
Create stunning AI-generated images with our new tool
Compare responses from different AI models with adjustable parameters