Joke aside, you can train a LLM to give the result of 1+1 and it can sometimes be wright. That's an expensive way of solving that problem.
You can also develop a simple calculator, that will always get an accurate awnser.
My point being that simply because the algorithms you mentioned 'can' solve a problem, doesn't mean they are the best solution for that. That are a bunch of NLP problems that LLMs are supbar for exemple.
The future of ML in startups will be the same as it currently is: find the best solution to the problem given the particularities of the problem and the business constraints (i.e. money).
The same as always, using lightgbm.
Joke aside, you can train a LLM to give the result of 1+1 and it can sometimes be wright. That's an expensive way of solving that problem.
You can also develop a simple calculator, that will always get an accurate awnser.
My point being that simply because the algorithms you mentioned 'can' solve a problem, doesn't mean they are the best solution for that. That are a bunch of NLP problems that LLMs are supbar for exemple.
The future of ML in startups will be the same as it currently is: find the best solution to the problem given the particularities of the problem and the business constraints (i.e. money).