[whataretensors]

I don't understand why this sentiment is gaining so much traction lately. I'm not claiming to understand the complex world of economics, but I do recognize great inventions - and our world is full of them.

ML is the one I'm most excited about at the moment.

[daxorid]

A few reasons:

1. We are currently inside the second longest bull market in US history. Markets are cyclical. We are far more likely to be toward the end of the bull market than the beginning.

2. Interest rates have begun to normalize, albeit very slowly.

3. Source rock fracturing and enhanced oil recovery has bought us another 20 years of bumpy plateau. We are squandering this by employing civilization's best minds building Tinder, Uber, Snapchat, etc. The last "great inventions" were the microprocessor and the lithium polymer battery. Every disruption in the last forty years has been variations of ever more hedonistic consumerist enablement, while the clock continues to tick on future energy supply.

[jazzyk]

The reason investors are not interested in funding enterprise software (which usually increases productivity, and eventually standards of living) is that big companies have not been interested in optimizing their operations for many years now, because:

- business is good due to lowered competition (market consolidation)

- cheap/free debt can be used for various forms of financial engineering (stock buybacks, etc), making EPS look good

[mempko]

recessions and depressions usually happen after federal gov decides to reduce deficit spending. I'm curious if there is any indication of this happening. Possibly after elections...

EDIT: Judging from this site it seems they plan to reduce deficit spending to about 500bln a year. So yes, I expect a recession if that turns out true. http://www.usgovernmentspending.com/federal_deficit_chart.ht...

You can see here that every time there was a slowdown in deficit spending or a surplus there was a recession or depression. Notice that before the great depression, there was a long period of surplus and following surplus during Clinton years there was huge issues.

Let's hope Hillary does not repeat Bill's mistake.

http://www.usgovernmentdebt.us/spending_chart_1900_2021USp_1...

[gajjanag]

> ML is the one I'm most excited about at the moment.

I don't deny that ML is useful (I work in the field of statistical inference), but I am bothered by the characterization of "ML" as a great invention.

First off, "ML" is not really new per se; it has gone through iterations over a long time frame. Pattern recognition (via discriminant analysis, a method that is still popular in a variety of circles) was introduced in 1936. One could argue that there were statistical works addressing this at even earlier time frames - see e.g "The History of Statistics" by Stigler that focuses on linear regression, a method with at least 250 years of history. Popular methods like nearest neighbor date to at least the 1970's. Even the "revolutionary" deep learning paradigm had most of its key architectural ingredients mapped out by the 1980's or early 1990's - the main changes have been the scale at which one can operate these algorithms, tweaks of algorithms, etc due to improvements in hardware. This in turn has fed a bunch of further developments, the stage we are at right now.

Essentially, what has happened is that "ML" has appropriated a variety of disciplines and techniques under its umbrella, many of which have existed for a long time.

As for why concretely (i.e from an economic perspective) I lack the same level of enthusiasm as many others here, I have the following test: Amazon (or for that matter Google or your favorite search engine) has rarely been able to recommend me a nontrivial product (nontrivial in the sense of transaction magnitude) that I liked within the first few links. The best nontrivial products that I have acquired have always needed meticulous searching through a variety of diverse sources on the web (reddit, youtube, random blog posts/discussion forums including hn, etc), conversations with people, etc. Same goes for movie recommendations, and miscellaneous other things. This was true 7 years back, it is still true today for me.

This suggests either that these problems are genuinely hard (e.g at an NP-complete fundamental level), or that we still don't know how to come up with good recommendation systems, or that companies have failed to incorporate/package techniques into a good product, or that I am just an outlier with respect to recommendation engines.

[whataretensors]

I don't disagree with you, but this is the first time in history that ML(deep learning in particular) has become useful due to hardware and software availability. I can now buy commodity hardware and run ML experiments.

Additionally on a theoretical level there are new things being discovered every day it seems. I mean, batch normalization wasn't even discovered until 2015.

There's a long road ahead, but I believe it will be filled with better and better inventions.