Why AIs dream of gluing cheese to your pizza

Feed AIs Reddit Garbage, get Reddit Garbage answers

Jun 09, 2024

Perfecting Equilibrium Volume Three, Issue 9

At the far end of your tether
And your thoughts won't fit together
So you sleep light or whatever
And the night goes on forever
Then your mind change like the weather
You're in need of Doctor Tarr and Professor Fether
Just what you need to make you feel better
Just what you need to make you feel
Just what you need to make you feel better

The Sunday Reader, June 9, 2024

The judge was too puzzled to be angry. “You’re objecting to your own agreement for your client pleading guilty to starting the fire?

“He didn’t start the fire,” the defense attorney said. “He’s pleading guilty to causing the fire.”

This was so odd that the judge decided to entertain an explanation.

Here’s the thing to understand: every movie and TV bad guy is a twisted genius leading the detectives in a deadly game of cat and mouse.

The accused in criminal court...not so much.

The defense attorney proceeded to finally explain the bizarre scene I had covered a week before the previous Thanksgiving. I’d covered fires and explosions, but I’ve never seen anything before or since like that exploded home in Plymouth, Connecticut.

The roof was the only thing intact. It was also in the front yard of the neighbor across the street.

The rest of the house was…just gone. There were a few studs sticking up here and there, but mostly just ground covered with shattered wood.

It was immediately obvious that it was arson. While the ground was strewn with shattered wood, there was nothing else. No clothes. No furniture. No cars. No boats. No pots. No pans. The house had been completely emptied out.

While we examined the wreckage the arson dogs, who had been trained to sit when they smelled an accelerant like gasoline, were dragging their butts around the yard like itchy puppies.

The defense attorney unraveled the mystery. Connecticut real estate had been booming, and people were making fortunes flipping houses. A group of guys made a lot of money, and spent it on boats, cars and coke, plus more houses to flip.

Then the market crashed, and they were stuck with a house with a huge mortgage and no way to unload it. So they decided to burn it down for the insurance. They talked an old down-on-his-luck high school friend who had been hanging around the parties into setting the fire.

This was the defense attorney’s client. He’d never burned down a house before, and he wanted to make sure to do a good job, so he brought five-gallon jerry cans filled with gas. Five of them.

What could go wrong?

A gallon of gas has about the same explosive force as 14 sticks of dynamite. So our arsonist was dumping into the house as much explosive force as 350 sticks of dynamite.

He had dumped four of the cans around the house and was pouring the fifth, the attorney said. In New England. In November. What could go wrong? As our arsonist was pouring the fifth can, the temperature dropped enough for the thermostat to kick in and set off the heat.

The heater’s pilot light fired. So did the house.

The explosion blew the roof across the street. The arsonist was standing in front of sliding glass doors and blew through them.

The judge grimaced at the defendant. “Let me put you in prison before you hurt yourself any worse.”

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One of those funny but true things about reporters back in the day; every one of us had a half-finished novel in the desk bottom drawer, right under the bottle of work Scotch.

Another funny but true thing; none of us ever finished that novel, because when you spend your nights and days nosing around streets and neighborhoods and courts and police stations, you deeply understand that fiction is futile when the world is so very, very weird.

Now everyone knows this, of course. Because everyone has the internet, and gets to see humaniti’s weirdness archived in places like Reddit.

So what do you think you get when you train Artificial Intelligence software on Reddit?

You get this:

And this:

“LLMs aren’t necessarily the best approach to get at factuality. Hallucination is still an unsolved problem. In some ways it’s an inherent feature. It’s what makes these models creative,” Google CEO Sundar Pichai said in response to the criticism of these and a host of other preposterous answers by Google’s AI.

Except-that’s not true. At all. Google’s AI is not hallucinating. It is not being the slightest bit creative. Instead, it is accurately regurgitating its training. Google and others are paying Reddit $20 million a year to use their posts for AI training.

That’s where the AI found this:

Note that this is not only an actual Reddit answer to the same question, it’s a highly rated one with 342 upvotes! That means the AI was trained that this is an excellent answer.

Google is paying millions annually to use Reddit for AI training. Meta is training LLaMA on Facebook and Instagram data. They and the rest of the AI companies are doing this because the current fastest way to train Large Language Models requires trillions of tokes – effectively, words or even syllables – that the LLMs can strain for patterns.

So why is anyone surprised when an LLM trains by straining sarcasm, meme lord postings and conspiracy theories for patterns, it then “hallucinates” answers comprised of sarcasm, memes and conspiracy theories? Worse, many of these models are being released as open source for use by those who don’t have the enormous resources needed for LLM training, so they are getting used in all sorts of enterprise applications.

But at least there is some discernible logic to memes and sarcasm and conspiracy theories. Sure, maybe it isn’t good logic, but it’s there and can be used to fit these things into the human experience.

Refer a friend

But none of that begins to plumb the true weirdness of humanity, much of which is on full Reddit display. Consider, if you will, the subreddit r/BreadStapledToTrees.

To start, it’s important not to confuse r/BreadStapledToTrees with r/BreadStabbedToTrees or r/BreadScrewedToTrees, never mind the completely different r/BreadStapledToWalls.

If you happen to staple some pumpernickel to a pine and want to share, be sure to follow the BreadStapledToTrees Rules; there are 25. Here are a few:

4. Many things are BREAD. Please read the Acceptable Bread List below for what constitutes BREAD.

5. Most things are NOT STAPLES. Figure this one out by yourself; if you cannot then this sub is not for you.

6. Apply Rule #5 again, but substitute the word TREES in place of the word STAPLES.

7. NO cacti. NO young trees. NO bonsai trees.

The way Large Language Models are trained today is very much like the old joke about the guy who loses his keys at night and looks for them under a street light despite dropping them elsewhere.

Because it’s easier to look for them under the street light. But you aren’t going to find anything useful there. And LLMs trained on Reddit and Facebook and Instagram and the like will also fail to be of any use. Sure, if you’re developing an application you can have LLaMA up and running in a few hours.

And then you can spend years enjoying the “hallucinations.”