On AI Snobs

The ongoing hype around generative AI has led to an influx of tech influencers and enthusiasts. This, in turn, has led to an influx of snobs and cynics who will shake their fists at those who dare claim AI expertise without advanced degrees and experience with statistical methods and "standard" machine learning.

These AI snobs will give you a long study plan of all the things you have to master before putting anything AI-related into your LinkedIn headline:

• Linear algebra

• Vector calculus

• Statistics

• Classical ML methods (support vector machines, logistic regression, k-means clustering)

• Stochastic Gradient Descent and other optimization methods

and on and on.

I call nonsense. First, if it's all about foundations, why stop at the math parts? I'd like to demand that anyone using a computer first learn about the quantum properties of semiconductors. 😬

Second, the best way to achieve valuable outcomes is to take a top-down approach. Use whatever tools are available, and only if you encounter sharp edges will you spend the time and effort to go deeper. (Note: The courses on fast.ai are a masterclass in this principle.)

Concrete examples

No-code platforms to rig LLM workflows and agents together don't require any deep ML expertise. If those fit your bill, you won't need that deep expertise, AI snobs we damned.

On the other hand, if you are contemplating an AI project with an uncharted course and an unknown approach, it's helpful if you can rely on someone who has extensive experience with different techniques.