Vīta Brevis, Wit Artefāctōrum Ætērna Podcast

What if one of the most important breakthroughs in the story of human progress happened not in a flash of genius, but through an act of genuine courage? Most people believe innovation works like lightning strikes. Sudden. Brilliant. Instantaneous. But that's what we might call the eureka fallacy. The real story of transformative breakthroughs is far more fascinating, far more human, and far more r...

In the previous episode, we looked at the birth of industry - large-scale mechanized production, as part of the story of the Industrial Revolution that began in Britain in the late 1700s. Towards the latter part of the Industrial Revolution, some erudite and expansive mind surveying and chronicling the breadth and scale of change and progress this movement had wrought could be forgiven for proclai...

In previous episodes I have laid out the broad theory of the technocentric paradigm including the ‘duonomy’ therein - artefactual and institutional, which I touched upon in light of the historical development of technology as a whole. Let’s next jump into the first great acceleration, where, for the first time in all human history, these forces came together to enable the most consequential revolu...

I have previously ranted that the term ‘technology’ is often misused, solely to mean ‘computer technology’ or worse whatever digital device one is holding and perhaps fumbling with at a given moment. To take a wholesome view instead, I’ve proposed a much broader definition of this term. Let me now get into the details of this along with why this seeming quibble matters and how it’ll enhance our ob...

In the previous episode, we looked at the very first stepping stone of the very origin of technology - very broadly defined, within our technocentric paradigm in this series, as the sum total of all human action that modifies something in the environment around us. Quite literally, this stepping stone was about the very first tool - the handaxe! Let’s now explore, as promised, how we got from ther...

Last time I proposed to you one of several ways of looking at reality - the technocentric view, one based on what I’m calling ‘technology’, which is in fact not what you would normally think I’m referring to. It’s not about phones and fancy gadgets, it’s not about code and complicated configurations on that dreaded device low on battery! And it’s certainly not about getting that damn printer to wo...

You've probably interacted with AI that generates human-like text - perhaps ChatGPT, Claude, or similar systems. The experience can feel almost magical: you type a question, and back comes a response that sounds remarkably human, sometimes insightful, occasionally surprising. But what's actually happening in that moment between your question and its answer? If you've ever wondered whether these sy...

In Econ 05 and Econ 06 I covered what is unique about software as opposed to the physical world. I fleetingly mentioned the concept of the ‘creative-commons free market of open source AI’. Why is it creative commons? How is it a free market? What exactly is open source and how does it matter to AI? Let’s explore these questions. Let’s start with the very idea of open source. Software is written in...

This is a series about the economics of AI. The economic question is somewhat related to the ecological question - the environmental impact. Let’s dig deeper into these related questions - energy, efficiency, economy and ecology / environment, for these are, I’m sure you’ll agree, very important. In conventional media, you’ve no doubt come across articles as to how energy-intensive a data center t...

In the last two episodes, I chronicled the rapid development of the fields of software and hardware. Let’s now examine in further detail how a great many aspects of activity and production are moving from the physical (hardware) world to the digital (software). Originally, a ‘computer’ referred to a woman, in a team, who carried out computation - calculations on a large sheet of paper spread acros...

In the previous episode, we discussed the rapid advancement of software and the advantages that made it possible. However, the advancements in hardware right from the earliest days to date are an equally important part of the AI story. Let’s get into the details of this, as it has both technological and economic implications. British inventor and engineer Charles Babbage can be seen as the first e...

In the previous episode we saw how progress in services automation got ahead of goods thanks to the rapid development of software engineering and the special characteristics and advantages of the digital realm compared to the physical. To get a fuller picture of why this matters in the current AI and Economics context, let’s dig deeper into, well, how software ate the world! We touched upon these...

In the last episode I touched upon the fact that the historical expectation was for manufacturing to be automated first and cognitive areas such as professions like doctors and lawyers coming only much later; but it has turned out to be the reverse so far. Let’s explore this further to identify why this might be so. It’s perhaps easy to, at least in hindsight, speculate why this made sense. Manufa...

In the previous episode, while discussing how AI/Robotics could be applied in industrial production, I touched upon goods and services. Let’s explore this dichotomy further. While we all know the distinction, for completeness’s sake, and in the first-principles spirit of these articles, let’s lay out definitions of these to start with. Goods are physical outputs as part of a standard manufacturing...

In the last two episodes I discussed how a ‘job’ is part of the wider economic question and how AI/Robotics will inevitably be applied across industry, simply because it’s going to be cheaper and more efficient. Let’s see if we can explore some concrete examples of how this might pan out. Like we’ve touched upon before, any economically meaningful activity can be considered a system , with inputs...

In my previous article, the first of this new series, I asked the question: Will AI take my job? I did not give a conclusive answer, however. This is largely because, well, nobody knows the future. However, we can look at how things might change based on how things have changed in the past. Let’s set the stage to explore this further and take the discussion beyond where we left off last time. One...

In the 7 articles that make up this series so far, we have explored the notion of how machines do human language, digging deeper into some of the conceptual foundations underpinning this phenomenon. Let’s take a step back. First, let’s acknowledge that this is a truly revolutionary development: The creation of machines that can talk convincingly like humans is one of the greatest technological lea...

We started this series by raising the question: are language models just parrots? In that context, we discussed the notion of non-deterministic, probabilistic, stochastic (save this one for the next dinner party!). Let’s explore some of the implications of this. As we touched upon in the first episode, it is not easy to know how language flows: you cannot necessarily guess what your interlocutor i...

Previously we explored the concept of inference, and the notion that the large language models underlying popular AI chatbots are in fact next-word-predictors. Let’s explore this idea further: how they turn from simply predicting the next word to having a useful conversation. But let’s start with a joke. Man finds bottle, rubs it, out comes a genie, and grants him one wish (this one’s a bit stingy...

So far we have explored how AI, language models, in particular, mimic the activities of the human brain. They can do meaning - by way of semantic space, they can generate phrases and sentences through a statistical process. Let’s see how it all fits together for practical use in the real world. A large language model (LLM), as hinted, is like the human brain. In particular, its ability to generate...

In previous episodes in this series, we discussed the notion of meaning, specifically how meaning is implemented in large language models (LLMs). We also discussed the distinction between conventional software and AI, specifically that conventional software is deterministic whereas AI is just the opposite - going by terms such as stochastic, probabilistic or non-deterministic. Let’s explore a clos...

The word ‘job’ can have strong emotional connotations. Apart from the idea of putting bread on the table, it is tied up with our sense of oneself, our place in society and indeed our purpose in life. With the rapid advancement of Artificial Intelligence in recent times, the question whether you’ll be replaced by AI is getting hotter and hotter. Let’s look at this question from first principles. Th...

In my previous article, we discussed the concept of meaning, in particular how meaning is represented in large language models. At one point I hinted about the notion that words are not the same as their meaning. Let’s explore this concept further. As humans, we have an instinct for spoken language. We are genetically programmed to be able to speak and hear language, barring a few unfortunate case...

No, not life! We haven’t gone that far enough to be able to answer that question! Right now, the more pertinent question here, and one that’s easier to answer, is: how does AI, specifically how do large language models (LLMs), represent meaning? In my previous article, we touched upon the concept of a bit : a binary unit of information, that simply encodes a true/false, yes/no, I do/I don’t kind o...

Computers, we’ve been told, operate using just zeroes and ones. On the face of it, for most of us, that might sound ridiculous. Hard to fathom. How can all information be reduced to just zeroes and ones? But yes, it is possible. Because the smallest unit of data is a bit - a binary unit - a simple true/false, yes/no, 1/0 dichotomy. It is that signal that was delivered from atop a castle in medieva...