Welcome to R&A

Logo-RnA-100dpi.jpgR&A is the vehicle for Gerben Wierda’s ‘extracurricular activities’ (what he does next to his day job and family life — or what’s left of that). Mostly writing and sometimes a bit of training or consultancy. R&A is also the publisher of Mastering ArchiMate and Chess and the Art of Enterprise Architecture.

This site combines the previous separate content of masteringarchimate.com and enterprisechess.com.

Recent Posts

On the Psychology of Architecture and the Architecture of Psychology

Advisors need (a) to know what they are talking about and (b) be able to convince others. For architects, the first part is called ‘architecture’ and the second part could be called ‘the psychology of architecture’.

We tend to do that already, but most attention is paid to the role of the advisor. But it takes two to tango. The ‘receiving end’ (the one being advised) plays a key role and it is here that psychological and neurological research of the last few decades on ‘the architecture of psychology’ can be put to good use.

For the Board: Essential Reading on IT Strategy

IT is notoriously hard to manage and it has been so for decades. As a result, the execution of new strategies is often exceedingly difficult. These 4 articles (2 serious, 2 a bit tongue-in-cheek) are meant to enlighten non-IT-savvy board members.

Hello Human Intelligence, meet Complexity Crunch

Just an announcement that the video of the ‘fundamentals’ part of my recent talks on essential insights on technology and psychology in relation to the digital revolution is now online on my YouTube channel.

Mastering ArchiMate 3.2 has been released (PDF version)

Mastering ArchiMate 3.2 has been released. Finally. This post contains release information, and a link to the book’s page where you can order the free excerpt (with the entire language description as well as a short BPMN primer) or the entire book (both PDF).

Can we break through the inertia that plagues IT-change?

You’re on the Titanic. The engineers are shouting: “The bulkheads are too low! The rudder is too small! There aren’t enough life boats!”. The sailors mumble: “It has been cold, there will be many more icebergs than usual and further south”. The owners are pressing the captain: “You should be in New York in six days, we desperately need a record!”. And the captain thinks: “I have execution power. I can break through. I will be successful.” and orders: “Northerly course and full steam ahead!”. 

Move fast and break things…

Like we don’t see air, we don’t see the Digital Revolution

Fundamental properties of digital IT have set ons on a road not to a Singularity Point, but towards Complexity Crunch. That has consequences for our strategic (IT) choices and landscapes.

A ‘long read’ (sorry) about lessons we can learn by now after half a century of Digital Revolution so far. Written as I have been giving talks about the subject this pas half year.

Generative AI doesn’t copy art, it ‘clones’ the artisans — cheaply

The early machines at the beginning of the Industrial Revolution produced ‘cheap’ (in both meanings) products and it was the introduction of that ‘cheap’ category that was actually disruptive. In the same way, where ‘cheap’ is acceptable (and no: that isn’t coding), GenAI may disrupt today.

But there is a difference. Early machines were separate inventions creating a comparable product. GenAI is trained on the output of humans, their skill is ‘cloned’ and it is this ‘cloned skill’ that produces the ‘comparable product’. GenAI is not ‘copying art’, it is ‘cloning the artisan’. And our intellectual rights haven’t yet caught up.

No-IT.   Really.    No.    I.    T.

What happens when your organisation suddenly loses all of its IT? There are enough realistic ways for that to happen. Think: a really successful ransomware attack.

As it turns out, first turning ourselves into ‘digital organisations’, and then requiring a speedy recovery from ‘digital armageddon’ creates a weapons grade challenge.

A story about ‘Out-of-Systems’, ‘Out-of-Sync’, and your ‘Minimal Viable Organisation’ (MVO), and a ‘fix’ that may only make matters worse.

When ChatGPT summarises, it actually does nothing of the kind.

One of the use cases I thought was reasonable to expect from ChatGPT and Friends (LLMs) was summarising. It turns out I was wrong. What ChatGPT isn’t summarising at all, it only looks like it. What it does is something else and that something else only becomes summarising in very specific circumstances.

Microsoft lays a limitation of ChatGPT and friends bare

Microsoft researchers published a very informative paper on their pretty smart way to let GenAI do ‘bad’ things (i.e. ‘jailbreaking’). They actually set two aspects of the fundamental operation of these models against each other.

Don’t forget all the things that a core team performs to a tee, but that you never see

The third ‘fragmentation wave’ of the IT-revolution is upon us, it seems.
Fragmentation/encapsulation is a repeated pattern in the IT-revolution for managing complexity. First as object oriented programming (for code) and later as agile (for IT landscape change).
Now, it is the organisation’s turn to fragment. How strong is your mission, your ‘why’? You might soon find out, thanks to IT.

Ain’t No Lie — The unsolvable(?) prejudice problem in ChatGPT and friends

Thanks to Gary Marcus, I found out about this research paper. And boy, is this is both a clear illustration of a fundamental flaw at the heart of Generative AI, as well as uncovering a doubly problematic and potentially unsolvable problem: fine-tuning of LLMs may often only hide harmful behaviour, not remove it.

Will Sam Altman’s $7 Trillion Plan Rescue AI?

Sam Altman wants $7 trillion for AI chip manufacturing. Some call it an audacious ‘moonshot’. Grady Booch has remarked that such scaling requirements show that your architecture is wrong. Can we already say something about how large we have to scale current approaches to get to computers as intelligent as humans — as Sam intends? Yes we can.

The Department of “Engineering The Hell Out Of AI”

ChatGPT has acquired the functionality of recognising an arithmetic question and reacting to it with on-the-fly creating python code, executing it, and using it to generate the response. Gemini’s contains an interesting trick Google plays to improve benchmark results.

These (inspired) engineering tricks lead to an interesting conclusion about the state of LLMs.