Abandon ACL and Others?

For the past few years, I’ve been outspoken about auditors that 1) don’t do much data analysis, OR 2) rely only on tools like ACL, IDEA, Arbutus, and the like to do their data analysis.

In this post, I’m going to provide some reasons auditors should not rely on only on these tools. I’ve dealt with this before, but I want to look at it from some different angles.

In this post, I’m speaking only to auditors, as they alone are called to audit the technology and processes their companies use.

In this post, when I mention ‘ACL+’, I am referring to ACL, IDEA, Arbutus, and any other tool that typically only auditors use. I’m also including ACL ‘Robotics’ in this list.

In this post, I’m going to step on people’s toes, but my readers should be used to that by now.

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My Python Journey, Part 4

In this fourth post of the Python Journey, I want to discuss WHY I keep going on these journeys despite poor management support. And how I stay sane doing it.

While this post goes beyond my Python journey, previous journeys have been very similar, so in a sense, it has been one looong journey.

My first journey started with ACL, then came SQL, databases, virtual machines and virtual servers, and a host of other technologies, and finally Python and machine learning, all of which I pretty much learned/am learning on my own.

Not only because my audit management didn’t have much foresight or vision, but also because company management approved and launched tools without much guidance or training. Yeah, really.

So what keeps me going and why do I stay here?

See my previous Python journey posts 1, 2, and 3.

My Python Journey, Part 3

In my first Python post, I described the first steps of my python journey.

In my second Python post, I shared my thoughts about whether auditors could learn programming and Python (yes).

In this third post of the series, I want to describe how my audit management has supported my Python journey (spoiler: poorly).

My Python Journey, Part 2

In my previous Python post, I described the first steps of my python journey.

In this post, I want to respond to Grant’s comments that he left here re: auditors getting into programming, and specifically python.

[For my readers who don’t know, Grant is the founder, President and Chief Architect of Arbutus Software Inc, which specializes in audit analytics (he usually doesn’t mention that he was involved in writing the first versions of ACL too).

So his words have experience to back them up, and while I’m flattered he pops in here and there to comment on my little blog, I don’t hesitate to disagree with him occasionally .]

My Python Journey, Part 1

As I mentioned in my previous post, I’ve been learning Python.

As a result, I haven’t posted for a long while, so I thought I’d crawl out of my Python den and discuss my journey so far. It has been an interesting slither with some sunshine, as well as a few dark days.

My Background

I’ve been an auditor for many years, and before that I managed a data/computer security department (see my ABOUT post for other details). I don’t consider myself a programmer by any means, but have automated several processes with a variety of tools, including ACL, command line, Visual Studio, SQL Server Integration Services (SSIS), Power BI, and other tools.

4 Common AI Fallacies

An article posted on MachineLearningTimes.com discusses 4 common fallacies or myths regarding artificial intelligence (AI). These misconceptions lead to many misunderstandings and fear* regarding AI.

Wikipedia defines AI as “intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality.”

I like Investopedia’s definition better*: “the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.”

In the post, Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute and author of Artificial Intelligence: A Guide For Thinking Humans, lists the 4 most common fallacies that I would summarize as follows:

1. Narrow intelligence (being really good at one task) leads to general intelligence (being good at many things, the way humans are). In other words, computers will become super-smart and take over the world.
2. Easy tasks are hard to automate/hard tasks are easy to automate.
3. AI works like the human mind. This comes from using ‘human-y” terms like learn, understand, read, and think, which leads some to believe AI can achieve humanness.
4. Intelligence is all in the AI brain. In other words, “the right algorithms and data…can create AI that lives in servers and matches human intelligence.”