What is AI?

What is AI?

AI is such a trendy and overhyped term that I hardly need to tell you what it stands for. Every single company on the planet is now calling themselves an “AI Company” and they're not incorrect. I mean, my toaster is technically an AI engine because there's silicon in there somewhere and it's “intelligent” enough not to burn bread.

And when you get your dream job as an overpaid Software Engineer doing LLMs at a major tech company, the phrase “AI Engineer” is a great term to impress your kids. Your official title, “ML Engineer”, not so much.

This cuts both ways, though. If you're haggling the price of your new car at the dealership, maybe stick to ML Engineer. Similarly, if you send your resume to a major tech company with “AI Engineer” as your career aspiration, they'll throw it in the trash and say, “Noob!” with a bemused look on their face.

AI means anything you want it to, but ML means “Machine Learning” to anyone important enough to have that title. The category of ML is specific to a piece of software that actually “learns” to be smarter (e.g. by “training”). The main ones this book is about are:

• Transformers (e.g. ChatGPT's “engine”)
• Large Language Models (LLMs) (e.g. GPT-3 or GPT-4)
• Neural Networks (NNs) (i.e., an “artificial brain” simulated in C++.)

The general category of Deep Learning (DL) is the subset of ML involving neural networks. Hence, Transformers are a subset of DL. Some of the other more specific types of ML include:

• Computer Vision (CV)
• Autonomous Vehicles (AVs) — self-driving cars
• Product Recommendation Systems — e-commerce
• Machine Translation (MT) — foreign language translation
• Content relevancy algorithms — social media feeds

Looking forward, some of the aspirations of the AI industry are capabilities such as:

• Artificial General Intelligence (AGI) — human-like reasoning
• Artificial Super-Intelligence (ASI) — who knows what?

• Next: • Up: Table of Contents • Buy: Generative AI in C++: Coding Transformers and LLMs

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