Here's why the pipes metaphor is a bad one: we already are doing everything we can and ever will do with pipes. Pipes have been around for a really long time, we know what they are capable of, we've explored all of their uses.
OTOH, the current progress in AI has enabled us to do things we couldn't do before and is pointing towards totally new applications. It's not about making existing functionality cheaper, or incrementally improving results in existing areas, it's about doing things that have been heretofore impossible.
I agree that deep nets are overkill for lots of data analysis problems, but the AI boom is not about existing data analysis problems.
> we already are doing everything we can and ever will do with pipes.
I'm pretty sure industrial engineers would disagree here. The romans certainly didn't clean their lead pipes with plasma and neither did they coat them with some fancy nano materials to reduce stickyness.
If there is a curse of our industry, it is almost willful ignorance of just how hard the physical engineering fields are.
The simple things with pipes are simple. Yes. However, to think we haven't made advances, or have no more to make, is borderline insulting to mechanical engineers and plumbers.
Ironically, deep learning will likely help lead to some of those advances.
> However, to think we haven't made advances, or have no more to make
Not what I was saying at all. My point was that pipes are used to transport something from point A to point B, and that regardless of what advances we make, they are still going to be used for that purpose, and that this is unlike the situation with AI.
My apologies for twisting your point, then. I confess I do not think I see it, still. :(
Pipes do much more than just transport from a to b. Though, often it is all a part of that. Consider how the pipes of your toilette work. Sure, ultimately it is to get waste out of your house. Not as simple as just a pipe from a to b, though. You likely have a c, which is a water tank to provide help. And there are traps to keep air from sewage getting back in.
Basically, the details add up quick. And the inner plumbing for such a simple task are quite complicated and beyond simple pipes.
So, bringing it back to this. Linear algorithms are actually quite complicated. So are concerns with moving all of the related data. And that is before you get to things that are frankly not interpretable. Like most deep networks.
> I agree that deep nets are overkill for lots of data analysis problems, but the AI boom is not about existing data analysis problems.
This hits the nail on the head for me. The author's observations in the first 9/10 of the article could all be perfectly valid, but the conclusions he draws I. The last two don't follow for exactly this reason.
The interaction of a system of many small pipes to accomplish computation is an active area of research, and new improvements are used in devices pretty routinely. (Really, the behavior of networks of pipes in general is still pretty open, if you want instantaneous details rather than statistical averages.)
Along similar lines, HFLP systems and systems that require laminar flow to be effective are both more recent techniques that come out of a better understanding and engineering of pipes. HFLP upgrades are a current engineering change over very recent and modern high-pressure systems.
Another way the analogy breaks down: Let's think about cars/trucks. Let's say in the 1930's , they're only availble to big business. What do we get ? A few big companies(Walmart/Sears/etc) doing all retail, at significantly lower prices. A big change.
OTOH, the current progress in AI has enabled us to do things we couldn't do before and is pointing towards totally new applications. It's not about making existing functionality cheaper, or incrementally improving results in existing areas, it's about doing things that have been heretofore impossible.
I agree that deep nets are overkill for lots of data analysis problems, but the AI boom is not about existing data analysis problems.