In the early 1990s, when computers became personal, popular, and mainstream, opponents said they would kill jobs and displace workers.
As of today, artificial intelligence (AI for short) has become mainstream. Now what?
Disclaimer
I’m no expert. Merely curious. A hobbyist. A dilettante. Nosey.
This article is based on the little I know, the research I did, and my conversations with people wiser than me. Much, much wiser. Compared to them, I’m insignificant.
But they inspire me to learn and to pass it on, for knowledge is to be shared. To teach is to learn.
And I’m an eternal learner.
In its current form, this article is more like a blank canvas with a few pencil markings. It’s begging to be painted. And for that, I need your help. As of this writing, the article is shallow and focused. Please reach out to me to say how deep and how wide I should go. I sincerely thank you.
Micro and personal computers
Microcomputers had existed for a long while, but other than a few specialized ones, they all had a text interface. For most of them, you would turn it on, insert a floppy disk, and see a familiar prompt on the screen: A> followed by a blinking cursor. Next you type a command and see it in action. For corporate use, that was good enough, where you would launch a word processor, electronic spreadsheet, or relational database manager.
A few companies, such as Apple, Atari, Commodore, and Tandy, among others, added graphical (or semi-graphical) capabilities, which made it easier to use certain programs (but not necessarily launch them). For the home user, they had become gaming systems.
Even the IBM PC, in its initial stage, only had a text interface, with a more sophisticated prompt: C:\>. Although its graphical capabilities were far superior than its predecessors, which allowed software vendors to publish more user-friendly products, such as image editors and desktop publishing. After Microsoft released Windows 3 in the early 1990s, that’s when microcomputers really became personal, especially because they could finally be considered easy to use.
Job killer
Opponents were saying personal computers would kill jobs and displace workers. Those opponents were only partially right: personal computers killed professions. For instance, secretaries were no longer needed. The boss would give them a letter to type on a typewriter, have them schedule and change meetings, or make phone calls. With a computer on the desk, the boss could then use a word processor to write letters and memos, use a calendar and scheduling software to manage their own schedule, and as for the phone calls… have the phone operator do it. With time, secretaries became executive assistants, serving more as gatekeepers to executives.
Other secretaries, throughout their career, had learned the job of their bosses. Some corporations converted secretaries into account managers, office managers, and team leaders, competing against their former bosses in an even field. Surely enough, the profession of a secretary disappeared.
As for displacements, in the beginning there were quite a few, but suddenly organizations learned they needed different kinds of people, and as a result new professions were created and the workforce grew. Only those who could not (or didn’t want to) adapt to the new reality were displaced.
Artificial intelligence
The concept of AI has existed for a very long time, but it came in and went away in waves, either due to scalability, pricing, or lack of interest altogether. I remember playing with ELIZA in the mid-1980s. It was a program that used pattern matching and substitution to give an illusion of understanding, but its responses, other than the curiosity of it, were nothing to write home about1.
Over the following years, we find LISP and Prolog, two programming languages that could be easily used for AI. We also find the animal guessing game, which asked you to think of an animal and then ask you yes/no questions about it; if it could guess it correctly, then it won, otherwise it would ask you the animal you were thinking of and what differentiates it from the one it guessed. It’s merely an ever-expanding decision tree, but it’s an early form of machine learning, where once a mistake is made and identified, it never repeats again.
There’s also SHRDLU. Imagine you have some being in a room full of colored objects. Using natural language, you ask the being to move the pieces around, for example, “Put the red pyramid on top of the green cube.” If you would then ask it to move the green cube elsewhere, it would tell you it’s removing the red pyramid first. If you would then ask it why it was doing it, it would say, “Because it’s on top of the green cube.” The little prank that Terry Winograd, the creator of the program, plays on you is that, if you ask it why the pyramid was on top of the cube, it would respond, “Because you asked me to.”
We can’t obviously forget the multiple computer-based board games where you’re playing against the computer. We’re talking Chess, Checkers, Tic-Tac-Toe, Reversi (or Othello), and others. The underlying logic is not an algorithm, but a heuristic2, such as Minimax, which also belongs to the AI universe.
The leap forward
Great ideas come to fruition when there is a breakthrough. A significant milestone was IBM Watson, which proved it was possible to talk to an AI model and expect intelligent responses, sometimes even funny. The problem was that it required an enormous amount of power-hungry servers3, something no home user should be willing to consider. For them, the real breakthrough happened just a few years ago4, when ChatGPT came into existence.
Simulating intelligence
Computers, as the name implies, compute. They perform calculations pretty fast. That’s what they do best. Everything else they do is a consequence or subproduct. Text processing is challenging for them; they do it, but slowly.
I want the computer to do something for me: create a report; find some information somewhere; draw a picture; give me the directions to go somewhere; you name it. But I want to ask the computer in my own language. No SQL statements. No spreadsheets. No online search. In plain English, I want to tell the computer (either typing or speaking) what I want and I let it do its thing. That’s natural language processing, a branch of AI.
Once the computer understands what I’m asking, it will perform a complex search. If you’re running your own AI model, the search is limited to the boundaries you specify, but if you’re using a public model (ChatGPT, Claude, or so many others), it will do a search online. And then, very quickly, will give you the result you’re looking for.
How is this possible? Computers are slow at processing text, but they still manage to search through millions of documents online and give you the answer in a matter of seconds. Hmm…
The science behind it
In computer science, a vector is a list of numbers arranged in a specific order. This can mean anything you want, but for the computer it’s merely a list of numbers. Let’s create a convention and make vectors meaningful. A vector with a single element, for example $[5]$, for us, is going to represent a point on a line; with two elements, for example $[3, -0.8]$, represents the $x$ and $y$ coordinates of a point on a plane; and with three elements, like $[-1.25, 3.14, 6.55]$, represents the $x$, $y$, and $z$ coordinates of a point in space.
Let’s now define that, for every point represented by a vector, there is a corresponding point at the origin ($[0]$, $[0, 0]$, or $[0, 0, 0]$). If we connect both points, we have a line that starts at the origin and ends at the point represented by the vector.
Vectors are not limited to three elements, though. We could have more than 3 dimensions. Us humans may have difficulty visualizing a 4th, 5th, or 6th dimension, but for the computer a vector is still merely a list of numbers.
If we have two vectors $a$ and $b$, representing lines connected at the origin, we can calculate the angle $\theta$ between them by using the dot product:
$a \cdot b = |a| \cdot |b| \cdot cos \theta \Rightarrow \theta = cos^{-1} \left( \frac{a \cdot b}{|a| \cdot |b|}\right)$
Steps:
- Compute the dot product: $a \cdot b = \sum_i a_i b_i$
- Compute magnitudes: $|a| = \sqrt{\sum_i a_i^2}$, $|b| = \sqrt{\sum_i b_i^2}$
- Compute angle: $\theta = cos^{-1} \left( \frac{a \cdot b}{|a| \cdot |b|}\right)$
- The result is in radians. If you want to convert to degrees: $deg = rad \cdot \frac{180}{\pi}$
Here’s a Python5 function to calculate the angle between two vectors:
from math import acos, sqrt
def vectors_angle(a: list[float], b: list[float]) -> float:
if len(a) != len(b):
raise RuntimeError("Vectors must have the same dimension")
def magnitude(v: list[float]) -> float:
return sqrt(sum([n * n for n in v]))
product = sum([x * y for x, y in zip(a, b)])
return acos(product / (magnitude(a) * magnitude(b)))
As an example, the angle between vectors $[1, 3, 5, 7, 9, 2]$ and $[0, 1, 3, 7, 9, 9]$ is approximately 0.5376 radians, or nearly 30.8°.
Large language models
Let’s build a practical vector, where each element represents, in ascending order, the position, inside a document, where a given word can be found. For example, in this article, the word computers can be found in position 6, 36, etc. That’s how we would build a vector for computers in this document. Our AI model will then check all its vectors for computers, which were created for all the documents within its reach. That’s an enormous amount of vectors.
Let’s now agree that, for two documents to be considered closely related, the angle between the vectors must be 20° (approximately 0.35 radians) or less. Because the computer is performing numerical computations on lists of numbers, the result comes up quickly. This is only for finding related documents; there are far more rules the computer needs to follow. More on that to come later.
It’s easy to infer that, in English itself, there are lots and lots of unique words, even if you ignore the declinations. Also, there could be millions, if not billions, of documents made available to AI models. If we are to track vectors for each word in each document, the number of vectors is gigantic, hence the name Large Language Model, or LLM for short.
References and notes
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An algorithm contains a number of steps that will take you to the solution of a problem, or to the firm conclusion that there is no solution. A heuristic, on the other hand, only takes you closer to a solution, but without any guarantees; it can be implemented as a decision tree, where the computer evaluates the impact of making a given move. ↩
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To make matters easier, Python has a function
math.degreesthat converts radians to degrees. You don’t have to code it yourself. ↩