31
Mar

On the brighter prospects of a world with more tasks handled by machines

2-Color-Design-Hi-Res-100px-widthSince the advent of the world wide web in the early 1990s it has been possible to craft viable business models from highly specific — and limited — market niches. Now, in 2015, with the promise of an expansion in the capabilities of computing machines to handle more tasks of, perhaps, a mundane nature, this opportunity horizon has widened even further. (If you would like more information about why I have specifically connected the enormous popularity of web pages exposed over Ethernet networks for the general public as an important milestone leading to an enormous expansion in the range of viable tech business notions, please contact me as I offer consulting services in this area).

I think it makes sense for readers to keep this factor in mind as they witness public debate about the notion of just whether or not the proliferation of robots, hardware computing machines powered by algorithms, and even what are colloquially referred to as “smart” applications (and apps) will, in sum, result in a net positive, or negative, result for the sheer number of people employed.

An OPED piece published on the CNN web site on March 18, 2015 communicates the seriousness of this debate and adds a raw edge to it: Silicon Valley to millennials: Drop dead. The piece is written by David R. Wheeler. I could not find any information about him, beyond his picture on the CNN web site. So I can provide no background on why CNN decided to post his article.

The raw and right-to-the-point flavor of Wheeler’s chosen title for his piece certainly captures one’s attention. When this factor is combined with CNN’s decision to go to press, and prominently, with this piece, I would hope my readers will agree the topic has a lot of interest behind it, as it should given what I take to be Wheeler’s core point: “The commonly held belief is that with hard work and a good education, a young person in America can get a good job”.

Given the statistics Wheeler provides in his piece, he is probably correct in his conclusion the employment horizon has darkened. But if I replace “can get a good job” in the above quote with “can achieve financial security and even wealth”, then the horizon opens up for another phenomenon we are all witnessing today: an explosion in the number of small businesses and, particular, technology startups.

As recently as Sunday, March 29, 2015, an article appeared on the Financial Times web site about an entrepreneur by the name of Bart Van der Roost. Mr. Van der Roost has started a business by the name of neoScores. I hope readers can share my appreciation for Van der Roost to craft what may become a very promising business from an especially narrow niche market — musicians requiring scores on digital devices. Perhaps we can extrapolate from his notion an opportunity for literally millions of these niches just waiting for entrepreneurs to expose.

Sure code is required. But isn’t code one of the skills people can go to college to learn? I hope we can all take a more sunny view of a new world of computing with hardware devices (powered by algorithms) capable of executing a widened vista of tasks.

Ira Michael Blonder

© IMB Enterprises, Inc. & Ira Michael Blonder, 2015 All Rights Reserved

15
Jan

Just how accurate are advertising predictions produced by machine learning systems?

2-Color-Design-Hi-Res-100px-widthThanks to Mikio Braun, who on Thursday, January 2015 published an article on the InfoQ website. Braun’s article includes mention of a Google acknowledgement about the role played by machine learning (also known, at least in part, as data processing by algorithms) as a predictive tool in its ad placement technology for its click ad business. Readers interested in this topic should read Braun’s article, which is titled Google on the Technical Debt of Machine Learning.

I have written about the inaccuracy of click advertising in earlier posts to this blog. To quickly summarize my opinion on this topic, I found the systemic tendency towards poor ad placement to be especially difficult to overcome when the items to be promoted provide subjective, intangible benefit. So gaining a perspective on just how much of the ad placement technology behind Google Adwords and, in all likelihood, its direct competitors (principally Microsoft’s Bing advertising system), as Braun points out in this short article is very helpful.

What is also very helpful in Braun’s article is the manner in which the Google researchers (Braun’s article is really a news report on a presentation at a recent conference event held in Montreal, the Software Engineering for Machine Learning workshop, part of the annual Neural Information Processing Systems, NIPS, conference held in Montreal) shed light on the precariousness of proper performance for machine learning systems, in this (online advertising) context, given the effect they have on other related computer processes. These researchers make clear how the basic assumptions powering Neural Networks can actually adversely affect these siblings, and, thereby, produce erroneous results along with very little value to people depending on them. Readers should note this conclusion is my own, and not a conclusion expressed anywhere in Braun’s article.

From Braun’s article, and the technical précis of a research paper on the algorithmic process behind machine learning, which was also published by Google researchers, online advertisers should be careful to set realistic expectations about paid placements. Perhaps it will make sense to horizontally structure these campaigns, with a panoramic reach wherever possible, if they are to produce any meaningful results.

Ira Michael Blonder

© IMB Enterprises, Inc. & Ira Michael Blonder, 2015 All Rights Reserved