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People getting into AI today follow courses like: LLM University - https://docs.cohere.com/docs/llmu https://docs.cohere.com/docs/llmu and never get to learn

People getting into AI today follow courses like: LLM University - https://docs.cohere.com/docs/llmu https://docs.cohere.com/docs/llmu and never get to learn about linear regression, bias and variance, cost function and gradient descent, regularisation and optimisation - all the good things taught by Andrew Ng in the amazing course he run 12 years ago just before creating Coursera. Is that a good thing?

simonw 3y ago
Learning about those things isn't particularly relevant to learning how to use LLMs for NLP tasks. Not saying they're not worth learning, but I think it's reasonable for them not to be included in the syllabus for that particular course. Kind of like how learning memory management in C doesn't need to be a pre-requisite for a course on Python. Note that learning about those things will likely make you a better LLM+NLP practitioner, in the same way that having a good grasp of memory management in C will help you be more effective at working with Python - but it's OK to leave them out of introductory courses.
photochemsyn 3y ago
I think people getting into AI today instead ask ChatGPT and similar models questions like: > "What field of modern science relies heavily on "linear regression, bias and variance, cost function and gradient descent, regularisation and optimisation"?" To dive into a particular topic: > "Provide a course outline for a four week course, meeting twice a week, that focuses on linear regression in the context of machine learning and the relationship between inputs and outputs." And to get to the actual material, zoom in some more: > "Please expand Session 2: Simple Linear regression into an hour-long talk focused on Python coding approaches to the problem" And again, to get some working code: > "For topic #2, please provide an explicit code example of using numpy, pandas and scikit-learn to load a dataset, preprocess the data, and split it into training and testing sets" Anyone can generate a course on any topic using this approach, with pretty good results.
- hammyhavoc 3y ago
  With the rate of hallucination, learning via ChatGPT is questionable at best, especially when someone doesn't know enough to know when it is hallucinating.

simonw 3y ago

Learning about those things isn't particularly relevant to learning how to use LLMs for NLP tasks. Not saying they're not worth learning, but I think it's reasonable for them not to be included in the syllabus for that particular course. Kind of like how learning memory management in C doesn't need to be a pre-requisite for a course on Python. Note that learning about those things will likely make you a better LLM+NLP practitioner, in the same way that having a good grasp of memory management in C will help you be more effective at working with Python - but it's OK to leave them out of introductory courses.

photochemsyn 3y ago

I think people getting into AI today instead ask ChatGPT and similar models questions like: > "What field of modern science relies heavily on "linear regression, bias and variance, cost function and gradient descent, regularisation and optimisation"?" To dive into a particular topic: > "Provide a course outline for a four week course, meeting twice a week, that focuses on linear regression in the context of machine learning and the relationship between inputs and outputs." And to get to the actual material, zoom in some more: > "Please expand Session 2: Simple Linear regression into an hour-long talk focused on Python coding approaches to the problem" And again, to get some working code: > "For topic #2, please provide an explicit code example of using numpy, pandas and scikit-learn to load a dataset, preprocess the data, and split it into training and testing sets" Anyone can generate a course on any topic using this approach, with pretty good results.

hammyhavoc 3y ago
With the rate of hallucination, learning via ChatGPT is questionable at best, especially when someone doesn't know enough to know when it is hallucinating.