Gerard Beenen, who, among other things, is a professor at Cal State Fullerton, recently published a video explaining AI and LLMs that we think does an excellent job of breaking down what is an extremely complex subject. Viewers of this video (above) can learn five key lessons:

1. The High Stakes of AI: The video begins with a cautionary tale about Google’s Bard, a counterpart to ChatGPT, which made a factual error in its response to a query. This incident, known as a “hallucination” in LLMs, led to a significant drop in Google’s market value, highlighting the high stakes involved in AI development and deployment. AI will play a crucial role in shaping the future economy, creating and destroying jobs, and raising philosophical and ethical questions.
2. Understanding AI Taxonomy: The video provides a clear taxonomy of AI, distinguishing between machine learning, deep learning, large language models (LLMs), and generative AI. Understanding these concepts is crucial for grasping the potential and limitations of AI technologies.
3. The Mechanics of LLMs: LLMs like ChatGPT use deep learning algorithms to generate original content. The video explains the four key concepts behind LLMs: word vectors, layers and parameters, attention scores, and feed-forward steps. This technical breakdown helps demystify how LLMs process and generate language.
4. The Power of Generative AI: Generative AI, which includes LLMs, goes beyond text generation to create content such as images, sounds, and even music. The video touches on examples like OpenAI’s DALL-E and Microsoft’s Copilot, showcasing the versatility and creative potential of generative AI.
5. Ethical and Philosophical Considerations: The video concludes by urging viewers to think about the ethical and philosophical implications of AI. As AI technologies continue to evolve and impact various aspects of society, it’s crucial to consider how they align with human values and ethical principles.

Overall, Gerard Beenen’s video offers a comprehensive and accessible overview of generative AI and LLMs, highlighting their potential, complexity, and the important questions they raise for the future of work and society.