I completely agree. I have limited experience with ML which is a fascinating topic but I believe all experts agree AGI is still far off.

linear regression=statistical analysis on the fly. Nothing special just number crunching to make predictions on future inputs.

Supervised learning=spoon-fed human curated information to make basic inferences, but over fitting is an issue.

Unsupervised learning=better than a human for spotting unseen relations in inputs, but not always useful or correct in correlation, overfitting is a huge problem just like in humans.

Reinforcement learning=reward learning but also requires tons of training data, may not provide anything useful.

There are some great uses for ML in it's current state, and ML does amazing amounts of number crunching and statistical analysis, but humans still need to mostly supervise all the data and inferences, and under the hood the ML hasn't really learned anything quite how a human brain does over many years, but chatbots are able to fake it very well. AGI seems way off still honestly, but again I am not deep in the industry so idk.