R&A IT Strategy & Architecture
Articles about IT, IT Strategy, and Enterprise Architecture
R&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.
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.
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.
What is the information revolution doing to us humans? A very condensed journey from essences of digital technology and human intelligence to the role of talk, trust and the impact of IT on society.
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.
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.
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.
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.
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.
If we ask GPT to get us “that poem that compares the loved one to a summer’s day” we want it to produce the actual Shakespeare Sonnet 18, not some confabulation. And it does. It has memorised this part of the training data. This is both sought-after and problematic and provides a fundamental limit for the reliability of these models.
I came across a 2 minute video where Ilya Sutskever — OpenAI’s chief scientist — explains why he thinks current ‘token-prediction’ large language models will be able to become superhuman intelligences. How? Just ask them to act like one.
Google’s Gemini has arrived. Google has produced videos, a blog, a technical background paper, and more. According to Google: “Gemini surpasses state-of-the-art performance on a range of benchmarks including text and coding.”
But hidden in the grand words lies another generally overlooked aspect of Large Language Models which is important to understand.
And when we use that aspect to try to trip up GPT, we see something peculiar. Shenanigans, shenanigans.
Should we be hopeful or scared about imminent machines that are as intelligent or more than humans? Surprisingly, this debate is even older than computers, and from the mathematician Ada Lovelace comes an interesting observation that is as valid now as it was when she made it in 1842.
After watching my talk that explains GPT in a non-technical way, someone asked GPT to write critically about its own lack of understanding. The result is illustrative, and useful. “Seeing is believing”, true, but “believing is seeing” as well.
We should stop labelling the wrong results of ChatGPT and friends (the ‘hallucinations’) as ‘errors’. Even Sam Altman — CEO of OpenAI — agrees, they are more ‘features’ than ‘bugs’ he has said. But why is that? And why should we not call them errors?
On 10 October I gave an (enthusiastically received) explainer talk at the EABPM Conference Europe 2023, making clear what ChatGPT and friends actually do — addressing the technology in a non-technical but correct way — and what that means. That presentation fills the gap between the tech and the results. At the end you will understand what these models really do in a practical sense (so not the technical how) when they handle language, see not only how impressive they are, but also how the errors come to be (with a practical example), and what that means what we may expect from this technology in the future.
R&A IT Strategy & Architecture 