Humans tend to not like change, particularly in institutions that are themselves resistant to change. Education today is very much like the education in the early 1900s - designed to educate a compliant workforce who would work in factories. Neat rows of desks lined up before an authority.
Wide availability of these tools will create a new "AI Native" generation. What this will do I don't know. Some effects will be good, some bad.
Narrowing to just me, I find the real value I get while learning is asking questions along the lines of "is this concept like this other concept that I know?" When I get an affirmative, then ChatGPT often goes on to tell me something else that I didn't know. These kinds of interactions are very valuable to me. Example from last night in the middle of a long chat:
"How is the query representation computed? Also, is the dot product between the query and key representations similar to computing the cosine similarity between two vectors?"
"The query representation is typically computed by applying a linear transformation to the hidden states of the input words, often represented as vectors. This linear transformation is typically implemented as a fully connected layer, or a matrix multiplication, followed by a non-linear activation function, such as a ReLU. The resulting query representation vector has the same dimension as the hidden state, and it's used to measure the similarity between the word and the other words in the input sequence.
The dot product between the query and key representations is similar to computing the cosine similarity between two vectors. The cosine similarity is a measure of the similarity between two vectors in a multi-dimensional space, and is defined as the dot product of the vectors normalized by their magnitudes.
The dot product of the query and key representations can be seen as an un-normalized version of the cosine similarity, in the sense that it computes the dot product of the two vectors. The result is a scalar value, which represents the similarity between the two vectors, the larger the scalar, the more similar the vectors are.
The dot product, however, is just one way of computing the similarity, other similarity measure, like the euclidean distance, the L1 norm, or even other more sophisticated similarity measures can also be used. The choice of similarity measure depends on the task and the model architecture, also the dot product has the advantage of being computationally efficient, which is important when dealing with large scale models like transformer based models."