What's the Difference Between a Model and an Application?
AI, ML, DL, CNN, RNN, LLM, APIs, GANs, AGI, NLP, MLops...No, not a recipe for alphabet soup. Just a handful of the plentiful acronyms of AI-related jargon. (And not to be confused with the broader lexicon of current AI buzzwords like Generative AI, models, applications, supervised, unsupervised, semi-supervised learning, big data, cognitive computing, hallucinations…we could go on.)
AI is for sure an exciting field to be in with innovative technology in the daily.? But with the staggering amount of publicity, information can become convoluted and confusing. In an attempt to help people understand, explanations are sometimes oversimplified. Or conversely – explanations are bogged down by overly specific details that don’t apply to a general understanding.?
AI can feel both simple and straightforward and then suddenly complex and complicated. And while acronyms can be useful in shortening language, too many can only make the problem of clarity worse. (LOL if you DM a teen IRL. BTW NVM if U don’t.)
As practical AI experts, our goal is to provide useful content – with just enough detail and specificity to help you understand, but not so much as to bury you in unnecessary, overly technical details.?
How Computer Vision Works
To understand the differences between models and applications, it’s important to understand the basics of what computer vision is and how it works...
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