Empower a CEO-led GenAI transformation by championing experimentation, building a cross-functional AI team, establishing ethical guidelines, and sustaining an ongoing learning playbook across the organization.

Utilize gen ai as a strategic thought partner for CEOs, leveraging large language models for idea generation, summarization, and impact–effort evaluation to prioritize features and upskill teams.

Explore hands-on prompt engineering with Claude and ChatGPT, mastering summarization, idea refinement, analysis, and competitive analysis through practical prompts and chain-of-thought demonstrations.

Master prompting strategies with ChatGPT to build implementation and evaluation frameworks for day-to-day work, using context, budgets, timelines, and a Portland expansion plan as a practical example.

Generative ai will transform work and unlock opportunities. Leaders should bridge gaps to align vice presidents and middle management with a clear ai vision, boosting efficiency and value.

Generative AI, led by GPT-4, automates cognitive tasks, risking over half of jobs while boosting productivity and reshaping roles across developed and developing economies.

Generative AI reshapes work in a hybrid culture, prompting CXOs to champion AI adoption, bridge middle management, and unlock productivity and new opportunities for freelancers.

Discover how generative AI adds insane value to customers by turning feedback from product touchpoints and product interactions into prioritized, high-impact features using an impact-effort framework.

Identify and communicate ai risks to employees, including model hallucination, regulatory, data security, and ip concerns. Build a safer, smarter risk-management plan with a dedicated team.

This lecture reframes AI ethics as a risk management process, outlining a practical, collaborative framework to manage bias, privacy, and societal risks through stakeholder engagement, policy, training, and continuous improvement.

Lead with a bold first step to change your company's trajectory, mastering skills known to only 2–3% and implemented by less than 1%, a bonus set simplified for mainstream adoption.

Experience the biggest GPT-4o image generation update, turning photos into anime, replacing faces, and generating ads, wedding invitations, headshots, room designs, and infographics with diffusion rendering.

Explore the basics of AGI and how leaders can understand and prepare for it, recognizing the significant change it may bring.

Artificial intelligence enables computers to learn, adapt, and decide by analyzing data and forecasting outcomes, from narrow AI to artificial general intelligence, with machine learning powering generative AI and collaboration.

Explore how machine learning, a subset of AI, learns from data through supervised, unsupervised, and reinforcement methods. Apply it to weather forecasting, market forecasting, fraud detection, and recommender systems.

Explore unsupervised learning in machine learning, where algorithms find patterns and cluster data without labels, grouping items by similarities such as shape or color.

Reinforcement learning trains an agent to act in an environment by actions and rewards or penalties, using feedback like upvotes and downvotes to reinforce positive and negative reinforcement signals.

Master supervised learning, the simplest training method using labeled data to teach models to predict outputs; recognize false positives, false negatives, and spam classification.

Explore deep learning as a subset of machine learning that uses neural networks with many layers, including input, hidden, and output layers, with pre-processing and feature extraction for predictions.

Natural language processing, a subfield of artificial intelligence, enables computers to understand and respond to human language, powering models like ChatGPT and Llama 2.

Explore computer vision, a subset of artificial intelligence that enables machines to interpret visual data, using supervised and unsupervised learning to convert images to binary and generate images or video.

Explore how expert systems encode human expertise to enhance decision making, using supervised data and knowledge databases alongside neural networks and robotics in the era of generative ai.

Explore neural networks, computing systems inspired by the brain, foundational to machine learning for pattern recognition, with input, hidden, and output layers and training.

Discover how generative AI blends machine learning and unsupervised learning to create unique music, art, and text, using models like GANs, VAEs, RNNs, and Transformers.

Explore rag, or retrieval augmented generation, through a high-level conceptual overview and an optional video on building a rag-based system, ideal for coding ai and understanding how ai is evolving.

Build a local app-based chatbot powered by retrieval augmented generation using llama 2, with LangChain and Streamlit, storing data in a vector DB to avoid OpenAI API keys.

Explore AI agents and AGI as future tech, with a conceptual understanding and optional hands-on pursuit that builds confidence and new skills.

Explore how artificial intelligence agents are autonomous software that interact with their environment, coordinating tasks like newsletters and evolving from simple reflex to learning agents.

Discover how AI agents automate end-to-end tasks, from content creation to debugging, using Autogen and no-code tools, and why these agents are going mainstream.

Explore the true AI framework CrewAI to build low-code agents. Learn prerequisites, tool setup, and running your first agents with OpenAI and other LLMs.

Build an artificial intelligence-powered sales outreach agent that automatically researches target companies, generates personalized emails, and runs outreach campaigns using templates and email tools.

Build an operator agent with the open source deep seek-r1 model to automate newsletters, using google search results and a three-agent crew (researcher, fact checker, writer).

Define AGI as a system that can reason, learn, and adapt across varied scenarios, enabling automation and rapid research while balancing safety and regulation.

Trace the evolution of AGI from Turing, Minsky, and McCarthy to breakthroughs like GPT-3, GPT-4, AlphaGo, Watson, and AlphaFold, and discuss the AI winter and progress toward AGI.

Explore level three AGI, expert systems that learn from experience, adapt to new situations, and handle uncertainty with human-like intuition and language, surpassing the 90th percentile of skilled human adults.

Explore the five levels of AGI, from level zero no general AI to level five superhuman AI. Focus on level one emergent, task-specific AGI and human-in-the-loop feedback.

OpenAI leads AGI progress with GPT-3, GPT-4, GPT-4.5, and GPT-5, while Anthropic, DeepMind, IBM, Microsoft, and xAI pursue safety and open and closed models like Claude and Group one.

Track agi project milestones from GPT-4 omni and turbo with text and image inputs, real-time video, and multilingual translation. AlphaFold three enables faster drug discovery and improved protein mapping.

Apply AI ethics by protecting individual rights, privacy, and non-discrimination, with clear policies and reviews under regulatory oversight. Address existential risk, alignment, autonomy, economic disruption, accountability, and misuse in AGI.