We're getting into the territory of some external debates -- that is to say, into areas that have already been debate by others, so there isn't much knew I could tell you here.
But basically I'm in the camp of Gary Marcus and others, who would probably respond by saying something along the lines of the following:
"No, there's not particularly good evidence that all we're doing is pattern matching, and a lot of evidence to the contrary. For one thing, a lot of these ML algorithms (touted as near- or superhuman) are easily fooled, especially when you jiggle the environmental context by even just a little bit."
"For another, and on a higher level, what algorithms lack -- but which sentient mammals have in spades -- is an ability to 'catch' themselves, an ability to look at the whole situation and say 'Woah, something just isn't right here'. (This is referred to as the 'elephant in the room' problem with modern AI)."
"And then there's the lack of a genuine survival drive -- not to mention the fact that we don't see any inkling of evidence of some capacity for self-awareness in any currently existing system."
Just as a starting point. But these are some huge differences between currently existing AI systems (however you want to define "AI here) and actual sentient cognition.
Huge, huge differences... such that I don't see one can get the idea that "all" we're doing is pattern recognition. Even if it may cover roughly 90 percent of what our neural tissues do - that other 10 percent is absolutely crucial, and completely elusive to any current, working technology as such.
When someday we do achieve human level intelligences in computers, it will be at least in large part with ML.
No doubt it will, but still there's that ... remaining 10 percent. Which you won't get to with accelerated pattern finding any more than a faster airplane will get you to the moon.