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Our world’s a giant computer built with machine learning.

There are many ways to attempt answering “how does the world work?”. We’re all trying to understand how we function, as humans (cue social sciences), and beyond, extending into psychics, astronomy and all the sciences including chemistry, biology, etc. We even have religion to explain all the things we have yet to understand, and the reliable fate we can always turn to.

Source: The Social Sciences in Modern Research, in: The Impact of the Social Sciences: How Academics and Their Research Make a Difference

How does a computer fit in?

When I think of a computer, I think of hardware, software, and all the parts you need in the build. You also need algorithms and instructions. With all the talk about augmented reality (or alternate reality?), virtual reality and ha, the metaverse, I can’t help but wonder, isn’t it all based on what’s your programming? What do I mean by programming? It’s a complex one. One we’ve been trying to explain as a race and an equation we’ve been trying to solve — what makes people tick? Why do certain things happen? What’s right and what’s wrong?

The machine learning process by TechTarget. Seeing the parallel yet?

The code and data sets that feed the algorithm

Let’s start small and with something quantifiable and thus tangible — economics. We measure many indicators. We try to assess nations and what policies are required to keep it afloat. Then we have laws — the set of rules we live by to differentiate what’s right from wrong. We also have the many facets of sociology, psychology and philosophy — the soft sciences that tell us about social learning and how to interpret different things.

Source: “Methods in Microeconomic and Macroeconomic Issues” by Jarmila Zimmermannová, published in Springer Link

So here’s throwing out an oversimplified formula: Complex social fabric of the human world + infrastructure +psychology + philosophy = (an inference that) the world we live in is just a super computer, with evolving algorithms, and rarely any NPCs (depending on who’s story you’re in).

Everything’s connected, subjective, and data points can be interdependent

My first job was in a credit bureau and business information house, where I learnt of the importance of credit scores, criminal records, personal and public data like addresses and next of kin. These are key data points in discerning whether you’re a decent human being for another human to trust in. Couple all that with what I was exposed to in university as a social scientist — the impact of social engineering, how demographics affect your social mobility, how groupthink and extreme behaviors result, the role that culture plays. It’s only natural that humans are trying to seek answers to “who are we” and form an understanding of our identities, derived from all these variables in our environment.

Source: “How is your credit score determined” by Louis DeNicola of Experian

We keep learning as a race. We let our findings edge us closer to the “truth”. We scour each other’s brains and ethics at our very cores to understand the world. It’s all about a balance between subjectivity and tolerance of other “truths”. Can we ever truly get the algorithm or programme written correctly? We are going to have bugs to debug. We are always going to have loop holes in algorithms that hackers are going to exploit. We are going to have to accept that everything is connected and we’re still in the quest of understanding how the world functions.

I was reading Scott Bicheno’s book, Identity Crisis, when all these thoughts came darting through my brain. (I highly recommend it if you like a book that makes you think.) His novel reminded me of Douglas Adams’ humour, as well as got me comparing some of the concepts in Douglas’ books in parallel to that of Scott’s. What Scott does in his novel is to explore what forms one’s identity and the impact that society has on individuals, laced with witty humor (that I don’t often get given how dense I am with these).

Concepts explored include that of civil disobedience, social contract, social capital and self awareness (and the looking-glass self). Because we’re talking about social credit scores — already a reality in China, it also explores racism, gender bias and all the things that we stereotype just because we can see them. Scott eludes to stereotypes and biases being perpetuated by its certainty of a cue. We find it harder to discern if someone is safe or can be trusted, when the information you have at hand can’t be seen at a glance. (Not the same but reminds me of this study: “Stereotype Threat and Women’s Math Performance”.)

What stood out to me too was the idea of “teams”. I wrote in my reading notes: “when you force people into teams, of course they’re going to compete”. So should we reinforce teams? Do we stand with <insert cause here>? Or do we see ourselves as 1 team, a human race. Causes are the result of teams too. However, on the other hand, with competition, we push ourselves and go beyond the status quo. So is competition good? Can humans handle competition?

So many questions, so many words, so many synapses firing, and yet, we’ve a giant computer that is the world we exist in, that somehow works and keeps reiterating its programming. We’re no where near perfect but perhaps we’ll never be, and we’ll never get the answer to the ultimate question of life, the universe and everything (spoiler alert: it’s 42), or find out what the question is.

Source: “Douglas Adams — The Hitchhiker’s Guide and his Legacy”, by Bindu Bala for Bookish Santa.

p.s.: I wrote another piece when I was only 30 per cent through Scott’s book. Read it here: Tech and the commoditization of talk & silence, and let me know what you think!

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