If you have been around these parts long enough, you know how the classic chatbots like Eliza, Parry, and Megahal work. Then there were other important approaches to AI, like Perceptrons, SHRDLU, Mathematica, Watson, and so on. Some of those things have open source versions, and some do not. One thing that they all had, and perhaps still have in common, was, and perhaps there still is the fact that there always seems to be a "great breakthrough" in AI, which almost always seems as if it were as great a leap forward, as it would be, that is to say, as if sustainable controlled fusion had also been solved. And then of course, no, not really. Better luck next time.
Then perhaps one of the biggest breakthroughs ever came with the landmark observation, "What if attention is all you really need?" Purely attentional-based systems, however they actually work, do have their own sets of problems, and among those, of course, is a lack of efficiency. And yet there are some very interesting things that can be said about "attention" in and of itself! So maybe a digression into the history of all of this will prove to be worthwhile.

Most people reading this were not even born when Don Lancaster wrote the article for the very first build-it-yourself "TV Typewriter", as if all that mattered at the time was putting "your message on the screen."
Ah, the TV Typewriter!
Somewhere, somehow, back in the day, beyond the valley of the shadows, in the once upon a long time ago, long before I ever wanted to write a novel where every chapter began with "It was a dark and stormy night, and as the swamp thing staggered from the crypt, suddenly there was a need for words", there was, of course, this:

Now that I have your attention. Yeah, that thing. Back when Bill Gates was still in high school. Then one thing happened, and another, another, and we all know what happened. Even if Eliza came out in the 60's, and then SHRDLU amazed so many more, while requiring a PDP-10 so as to enable a user to interact with a wire-frame virtual world that allowed the input of commands, like "Pick up the red block and place it under the blue pyramid."
Now, all of a sudden, here it is, 2026, and vibe coding seems to be the new meme. Or perhaps the new mess, since obviously, we are at a crossroads. Some people are saying that up to 50% of white-collar jobs will be gone within the next couple of years, and then what? Will AI also somehow eliminate the need for half the world's population? Will the presence of data centers in rural areas lead to vanishing water supplies, dead fish, and no more birds?
What if open-source training can be done "at home", and if models could be shared, Wiki-style? Would that work? Would that solve the data center problem?
Well, that is all good and fine, if it should turn out to be possible, but how do we know what is or is not possible, that is, until we try some things for ourselves? So let's take a look at something different, I think, from anything that anyone else is doing, at least insofar as "open source" is concerned. What if we wanted to try to use AI, let's say, to try to "predict the Lottery!?!"

Now some people will be wondering, of course, what does trying to predict the Lottery have to do with LLMs? Perhaps the answer will be obvious to some, while for others, we will need to take a very deep dive into the whole theory of just how "training an LLM works", that is, if it actually does. Well, as it turns out, there are certain Lottery systems that have a great deal in common with how LLMs work. Well, maybe. Like what if we had a program that looks at the draw history for a particular game that we are interested in, to find potential "hot pairs", that might be useful in trying to create not just lists of so-called hot numbers. Rather, we might want to find...
Read more »
glgorman






