Accidents, problem solving and systematic approaches to social issues

Accidents happen. E.g. a bus crashes into a metallic protection and people die.

When accidents happen 

it's important:

1. To be outraged, in order to mobilize resources.

But then it's also important:

2. To cool down, 

3. Identify root causes, 

4. Identify potential solutions,

5. Analyze the impact and cost of potential solutions, 

6. Prioritize and plan the implementation of these solutions,

7. Implement selected solutions.

Because there is no single cause, and no single solution

This is why:

3. for root-cause analysis we use tools like Ishikawa / fish-bone analysis.

Because each problem has multiple causes, and each cause has other causes.

Direct causes include driver fatigue, architectural and design problems, construction issues, signage issues.

Underlying causes include lack of professional standards and processes in construction and road design, lack of professional expertise and training of those responsible, societal cultural issues (disrespect of laws, misapplication of penalties, low standards and expectations), corruption, legislation, and political populism.

4. Identifying potential solutions can be done with solution solving techniques. Decomposition, brainstorming, Delphi, design thinking, design cycle.

There are lots of possible solutions for each of the causes.

5. You cannot solve all causes by implementing all possible solutions. But also, solving only one cause is probably not enough.

This is why causes and solutions must be prioritized. This is done with tools such as Paretto (80/20) and cost-benefit analysis.

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Can AI pass the Turing test? Let's ask ChatGbt

Beyond the fascination for technology, that I also share, it is important to recognize what technology does, and what are its limitations.

Can current AI tools such as ChatGbt pass the Turing test? Let's ask them.

ChatGpt's answer is irrelevant. It is a non-answer. 

ChatGpt cannot answer this question, and pretty much any meaningful question. It is not designed for answering abstract questions that go beyond what it already digested from somewhere else.

ChatGpt is the best boring web bullshit generator - just like your average politician

ChatGpt is great at generating boring truisms collected from the web, and at avoiding any real question. Pretty much like your average politician. 

From this point of view, I prefer simpler machines such as the New Age Bullshit generator. They are funnier. ChatGpt is just... the best boring web bullshit generator.

ChatGpt generates poetry, because of course. But no relevant information whatsoever. A simpler search engine such as Google generates better and simpler answers (and by the way, Google search also uses a lot of ML and NLP).

ChatGpt is also very confident in what it says - which can sometimes be dangerous. Again, pretty much like your average politician...

The Turing test

Of course, this type of machines still have a long way to go to pass the Turing test. As ChatGpt explained in the chat in the picture above, Turing measures if a machine can simulate human behaviour, to an undistinguishable degree. No machine has ever passed the Turing test yet, and neither does ChatGpt.

Turing is a simple test, somewhat fallacious, because it is just an immitation game (as the movie title says). 

Its underlying problem is actually the definition of "intelligence". We don't have a working definition of intelligence, yet. We tend to define intelligence using terms such as intentionality, free-will and self-awareness, but these are vague, abstract concepts, impossible to measure. And, somehow, they try to express a definition by example: "intelligence is what humans have". This is what Turing measures: if a machine behaves like a human. 

The miracle and limitations of technology

These smart ML and NLP tools, such as ChatGpt, Google or Lensa, are extraordinary. They do great things, and are wonderful exemples of the benefits of complexity in technology and engineering. 

They are great at crunching numbers, recognizing and combining images, patterns and keywords, recommending similar products, translating speech and text, at driving cars and trains under well defined conditions.

But this is what they are, and not more. 

They perform very well a very specific data-intensive operation.

They are smart, but not “intelligent”. They do not pass the Turing test. 

And they will not take over the world (yet).

Cleaning up Beatles' Revolver: is this better? Obviously, with AI

Abbey Road studio relaunched Beatles' Revolver with a cleaner sound, based on original studio recordings; and of course with AI. Because everything was once better with Bluetooth, but everything is now better with AI.

Is the new album better than the original?

I have the feeling that the sound is indeed cleaner.

The problem with these cleanups is that I don't know if cleaner is better. 

Was this the intention of the artists? Most likely not. If the Beatles wanted clean sound, they would have made it cleaner. But no. They were playing with strange instruments and sounds, and with strange studio effects. They would speed tracks up and down by ear, turn tracks upside down, change tempo and key in the middle of a song, mix voices, styles, instruments and tracks randomly. They were super high throughout the recordings, and it shows.

Somehow, cleaning up the original Beatles' sound made me think of clarifying the colors of Degas; or sharpening a bit the lips of Mademoiselle Pogany.

Ce este complexitatea? Omul major - ep. 2

Merci pentru invitația de a discuta live despre complexitate în tehnologie și societate, în această seară, Ciprian Mihali și Cristian Presura.

Https://www.youtube.com/watch?v=PD2CnEDSX2Y

Moderatori: Ciprian Mihali și Cristian Presură

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O abordare pozitivă asupra complexității în știința, tehnologia și societatea contemporană

Complexitatea ne înconjoară, este peste tot, și crește continuu.

Tehnologia pe care o utilizăm este din ce în ce mai complexă. Produsele, organizațiile, societatea. Trăim într-o lume din ce în ce mai complexă.

În primul rând, vom discuta despre ce este complexitatea, și cum poate fi ea definită și analizată. Vom vorbi despre sisteme complicate și complexe, despre haos și emergență.

Apoi vom discuta despre cum putem gestiona complexitatea, cum o putem simplifica sau elimina, folosind frameworkuri moderne precum Cynefin, dar și metode tradiționale precum divide-et-impera.

Și, mai ales, vom vorbi despre beneficiile complexității, și cum putem să o folosim pentru progres tehnic, științific și social. Aici vom atinge subiecte precum antifragilitatea, complexitatea necesară și complexitatea pozitivă. Adică cea responsabilă pentru tehnologiile disruptive din jurul nostru, inclusiv iPhone, Facebook și Viagra.

Despre invitat

Ștefan Morcov este inginer și arhitect software, are un MBA de la Universitatea Tiffin din US și a obținut diploma de doctor de la universitatea KU Leuven din Belgia, cu o teză inovatoare despre managementul complexității pozitive a proiectelor informatice.

A creat și condus mai multe firme în Belgia și Luxembourg, având experiență în managementul, marketingul și vânzarea de proiecte informatice complexe în educație și pentru Comisia Europeană.

În prezent lucrează la Hermix, un produs IT pentru analize inteligente pentru piața Business-to-Government, în special instituții europene.

Este pasionat de tehnologiile avansate, inteligența artificială, inovație, strategie și complexitate.

Models are (useful) simplifications of reality

The only true measure of a model is if it works.

Applies to Systems Thinking, as well as to Cynefin, or to my Positive Complexity model.

Because all models are simplifications of reality.

Yes, we need models, i.e. simplifications, so that we understand reality. But it is important to know that they are limited, and to know their limitations.

Some are too simple, some are too complicated or complex for a specific problem.

The simplest example is Newton's laws. They were super useful for hundreds of years, and still are. 

We know that they do not model exactly the reality - because no model does. You cannot fly to the moon using only Newtonian physics. Einstein's théories are a better model for space navigation. But still a model. Einstein's model is more advanced, more complex. It is nevertheless less practical for designing bicycles (you only need classic mechanics for bicycles).

So, there is no such thing as a correct or wrong model. Especially in social sciences, in management, or in engineering.

Thus models are better measured by appropriateness: useful or less useful.

The only true measure of a model or theory is if it works.