| No offense taken. I feel humbled. Indeed, your argument supports my view. I have deep and very high regard for the people who are able to apply ML to fields like DNA analysis or NLP which can take the dreaded "Turing test". I stand nowhere in the ML arena, but I had tried once and got a good shock of my life: how hellishly difficult the ML can get and how quickly. I really feel humbled. If anything, I learnt to appreciate the width and depth of human brain capabilities. It seems entirely magical now to me how on earth does my brain process/understand such complex things like this very paragraph. Prior to some exposure to ML, I couldn't have appreciated this thing. >>Just because you find something extremely challenging, doesn't mean it is inherently challenging. Agreed. I never claimed it anyway. But the kind of problems, for which ML is being applied, the state of the art existing "analyzable algorithms" (like, finding approximate near-optimal solutions for TSP) are far from trivial. In addition to this, we must realize that the ML solution must "beat" these algorithms hands-down in "non-trivial" cases. All this makes ML extremely difficult. I agree that for real world (and not necessarily state-of-the-art) ML applications, you have to handle many more fields in addition to these 3 fields. All I say is even these three things, when taken together, are very complex things to handle. edit: typo |
On a morning where I woke up feeling anxious and worried after the events of Monday, this was a small but appreciated little reminder that there's still hope.