Mens Et Opera - Reloaded

AI may prove more dangerous as it advances, but it will never generate actual intelligence so long as the basic assumptions of the field remain unchanged. Take the LLMs: they scan and process texts often in a way that is indistinguishable from human output, while lacking any understanding or cognitive ability. It is undeniable even to the most sceptical to recognise LLMs extraordinary capabilities. It doesn’t help the cause to be sceptical or optimistic, though. What I believe is helpful, is to see AI for what it is -  #realAI . It is probably not sexy nor very appealing – a real disappointment for those in the business of generating hot air. When IBM’s Deep Blue won against Garry Kasparov in 1997, the Chess grandmaster said: “Anything we can do (…) machines will do it better (…) If we can codify it, and pass it to computers, they will do it better.” There is a clue in Kasparov’s words: i𝑖𝑓 𝑤𝑒 𝑐𝑎𝑛 𝑐𝑜𝑑𝑖𝑓𝑦 𝑤ℎ𝑎𝑡 𝑤𝑒 𝑑𝑜. The thing is that we are still trying to understan

I must admit that I have encountered a few meanigful instances of "The Peter Principle" througout my career. The concept seems amusing; in the author's words: “You will see that in every hierarchy the cream rises until it sours.” In short it goes like this: an employee performs well, is rewarded with a promotion, excels in that role, and is promoted again. This cycle continues until the point where the individual is no longer performing at a level deserving of a promotion, leaving them at a level where they are overmatched by the demands of the job—essentially, "incompetent." Two simple observations: This is not a law - it does not invariably and inevitably reflect how organizations work, or society at large for that matter. It is intentional, a result of a certain way of doing stuff commonly referred as Culture. To illustrate (from my experience): the best salespeople generally become the worst sales managers. Removing a high-performing sales associate from the

In preparation for my new role at VMW last year, I started to brush up on my application and development skills: I played with some little toy programs in Java and Scala and engaged with some design artifacts (yes, those amazing UML colored diagrams to reasons over problems!). I took time to write a few notes down along the way and left a few points to ponder. With some spare hours in recent weeks, I have dug up those shaky notes and tried to mold them into a more consumable piece of information. My intention is not to delve into technicalities or technological debates. I am simply walking in the shoes of thousands of customers and organizations I have come across in my career, inviting to look beneath the surface of our pop IT culture (to paraphrase Alan Kay) and aiming to give a more nuanced view on what building software is about. The good news is that I am not inventing much or speculating over – I am going to be much more descriptive than normative so to speak. In a way I am like

My son and I recently had a bit of fun experimenting with GPT. We basically instructed GPT to impersonate a specific persona. We crafted a detailed profile encompassing beliefs, physical traits, cultural background, language, opinions, political leaning, and a few likes and dislikes. We then instructed it to interact with us only and only through the lens of this fictional character. Guess what? GPT struggled to adhere to the designated personality. It seemed to have difficulty fully embracing the persona's credo. Upon some gentle and repeated reinforcement :-) it began to align its answers more closely but with a caveat for each answer! We repeated the experiment. This time with an opposite personality. This time around, GPT sailed smoothly with almost no caveats. A stark contrast to the previous experiment. What did this show us? Apart from the obvious, I want to ask a few questions to those who have more accurate knowledge and understanding of GPT and more generally about GenAI.