Author Archives: azurefinops

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About azurefinops

I'm a cloud architect and writer, focused on Azure, Azure cost control, Azure Security and Azure Cognitive Services. Maximizing your cloud investment is the shared responsibility of business and IT but often, there's a disconnect; the teams speak past each other. I act as the bridge, speaking the language of both groups to help organizations achieve their goals and maximize value.

Techno-Skepticism: A Tactical Skill

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Techno-skepticism is a vital and necessary response to a world awash in self-promoting boosterism and the capitalist utilized ideologies of techno-optimism and techno-determinism.

To define terms, techno-optimism is the belief any proposed technology is possible and good. Optimists look to past examples of things that were once impossible which became possible – such as machine flight – and infer this tendency is universal.

Techno-determinism (which can be considered a species of determinism) builds on tech-optimism’s ideological framework by asserting not just possibility, but inevitability.

For example, a techno-optimist views a development such as ‘robot’ kitchens as being both positive and possible as presented – determinists assert there’s nothing to stop such a development: it’s inevitable and beyond resistance, like gravity.

Robotic Chef Marketing Video

Skepticism, correctly practiced, isn’t the denial of technological change or the reality of, or potential for, benefits from such change. Skepticism is remembering to ask three questions:

  • How does this work? A technical inspection
  • Is it possible as described? A feasibility interrogation

Consider, for example, Amazon’s failed drone delivery service, which Cory Doctorow analyzed here – As Doctorow describes, this idea was inexplicably taken seriously:

“When Amazon announced “Prime Air,” a forthcoming drone delivery service, in 2016, there was a curious willingness on the part of the press – even the tech press – to take the promise of a sky full of delivery drones at face value.

This despite the obvious problems with such a scheme: the consequences of midair collisions, short battery life, overhead congestion, regulatory hurdles and more. Also despite the fact that delivery drones, like jetpacks, are really only practical as sfx in an sf movie.”

At the time this proposed service was announced, I read detailed analyses and excited Tweet threads about the supposed meaning of a bold new age of drone delivery. I noticed however, that simple questions regarding feasibility were rarely asked – optimism and determinism (with a good amount of self-interested boosterism in the mix) prevented a skeptical response

When you read about a technological system, such as delivery via drone, remembering to ask questions about function (the how), benefit (who’s promoting this and why) and feasibility (can this be done at all or as the promoters describe?) is a reliable way to avoid being fooled and knocked from delusion to delusion.

Pointillistic But Useful: A Machine Learning Object Lesson

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I devote a lot of time to understanding, critiquing and criticizing the AI Industrial Complex. Although much – perhaps most- of this sector’s output is absurd, or dangerous (AI reading emotions and automated benefits fraud determination being two such examples) there are examples of uses that are neither which we can learn from.

This post briefly reviews one such case.

During dinner with friends a few weeks ago, the topic of AI came up. No, it wasn’t shoehorned into an otherwise tech-free situation; one of the guests works with large-scale engineering systems and had some intriguing things to say about solid, real world, non-harmful uses for algorithmic ‘learning’ methods.

Specifically, he mentioned Siemens’ use of machine vision to automate the inspection of wind turbine blades via a platform called Hermes. This was a project he was significantly involved in and justifiably proud of. It provides an object lesson for the types of applications which can benefit people, rather than making life more difficult through algorithm.

You can view a (fluffy, but still informative) video about the system below:

Hermes System Promotional Video

A Productive Use of Machine Learning

The solution Siemens employed has several features which make it an ideal object lesson:

1.) It applies a ‘learning’ algorithm to a bounded problem

Siemens engineers know what a safely operating blade looks like; this provides a baseline against which variances can be found.

2.) It applies algorithms to a bounded problem area that generates a stream of dynamic, inbound data

The type of problem is within the narrow limits of what an algorithmic system can reasonably and safely handle and benefits from a robust stream of training data that can improve performance

3.) It’s modest in its goal but nonetheless important

Blade inspection is a critical task and very time consuming and tedious. Utilizing automation to increase accuracy and offload repeatable tasks is a perfect scenario.

How Is This Different from AI Hype?

AI hype is used to convince customers – and society as a whole – that algorithmic systems match, or exceed the capabilities of humans and other animals. Attempts to proctor students via machine vision to flag cheating, predict emotions or fully automate driving are examples of overreach (and the use of ‘AI’ as a behavioral control tool). I use ‘overreach‘ because current systems are, to quote Gary Marcus in his paper ‘The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence‘, “pointillistic” – often quite good in narrow or ‘bounded’ situations (such as playing chess) but brittle and untrustworthy when applied to completely unbounded, real world circumstances such as driving, which is a series of ‘edge cases’.

Visualization of Marcus’ Critique of Current AI Systems

The Siemens example provides us with some of the building blocks of a solid doctrine to use when evaluating ‘AI’ systems (and claims about those systems) and a lesson that can be transferred to non-corporate uses.