Introduction to Artificial Intelligence

Bradford Communications, LLC

Silicon Slopes, Utah

Section 1

Student Handout

20241120

The first computers during their testing made mistakes. The calculators too and they were seen. However, the <Nazi produced machine for encryption> trumped the United States’ techniques. The United States was able to reverse engineer it and win World War II because they were able to because of < famous gay engineer, check Enigma movie>. Computer research development labs were put together in the United States and progressed quickly afterward.

I think generative AI (exemplified by openAI) is a financial catastrophe already being held up by companies trapped in a sunken cost fallacy and an extremely predatory idea on fair use. The repeated attempts to deploy AI into the general user space has exacerbated and exposed the tech industry as a whole in the separation of human beings and the act of simple commerce. Everyone already hates phone trees asking a million questions before getting a person, and AI says "do that but for the McDonald's drive through!"

As a consumer, AI generated media is the equivalent of a meme or reaction gif. Low-skill, low-value, disposable uses sourced from content I likely have never engaged with, and certainly don't put any value in the creators of in considering their work transformative.

I do think it has uses, and there are large datasets or predictive applications that could be served by the technological goals of the technology, if the industry leaders don't burn down the reputation of the entire technology by crushing public goodwill and presenting itself as an invasive technology designed to create more problems than it solves.

I also think the Pandora's box metaphor is extremely dangerous for AI advocates to continue running with. It's a threatening escalation that has already caused their opponents to reach for more extreme demands than payment or opt in processes for trolling data across the internet, and created a growing segment of detractors to want to see a disgorgement of their datasets, which risks associated models and algorithms with becoming unusable en masse.

Rick Bradford this is one of those areas that I think it has value and could be served well in! Same with large data lakes like healthcare data, linguistic study, actuarial tables, and the realms of hard science like chemistry, or physics.

From what I've read, those applications generally take a model, and then isolate it into more specific data sets to limit the chances of hallucinations. There's some conflicting reports on if the generative AI models work "better" for those applications, and I worry the "move fast break things" mentality in silicon valley is going to end up getting the potential answers to that conflict thrown out rushing to create AGI or push AI into realms its ill served to fit.

</Charles Xavier Lacerte>

AI is following the same flow…

Add to AI Foundation Curriculum - NVIDIA Introduces Hymba 1.5B: A Hybrid Small Language Model Outperforming Llama 3.2 and SmolLM v2 - MarkTechPost 20241122

https://www.marktechpost.com/2024/11/22/nvidia-introduces-hymba-1-5b-a-hybrid-small-language-model-outperforming-llama-3-2-and-smollm-v2/

Course Description: This course provides an overview of the field of Artificial Intelligence, covering its history, core concepts, and applications. Students will learn about the different branches of AI, including machine learning, natural language processing, and robotics, and explore ethical considerations and societal impacts.

Introduction: AI is transforming industries and everyday life. This course introduces students to the fundamentals of AI, setting the stage for deeper exploration in subsequent courses.

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Terminal Learning Objectives

Enabling Learning Objectives

References

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