Autocompleting Reality: The Rise of Large Event Models

This episode unpacks large event models—AI that can understand, represent, and forecast real-world event sequences over time, not just generate text. We explore how LEMs extract underlying rules with schema induction, marry neural nets with symbolic planners for safety, and use sparse attention to manage massive timelines. We discuss real-world uses in public safety and healthcare, the safety nets that keep predictions grounded in reality, and imagine how a personal LEM could optimize your day.

Note:  This podcast was AI-generated, and sometimes AI can make mistakes.  Please double-check any critical information.

Sponsored by Embersilk LLC

Transcript

SPEAKER_01 0:00

You know how uh like one tiny decision can just completely unravel your entire morning?

SPEAKER_00 0:05

Oh, absolutely. The classic butterfly effect.

SPEAKER_01 0:08

Right, exactly. Like today I hit snooze just once, and um that meant rushing breakfast, which led to a smoking toaster, which meant opening the windows, and then, you know, inevitably missing my train.

SPEAKER_00 0:20

Well, from a data perspective, that is just a perfect sequence of discrete events. I mean, cause, effect, and time.

SPEAKER_01 0:27

Yeah. And if you've ever felt like your life is just one giant chain reaction like that, you are going, absolutely love today's deep dive.

SPEAKER_00 0:34

Aaron Powell We are looking at a really massive breakthrough in AI called uh large event models or lems.

SPEAKER_01 0:41

Right. Because we are all super familiar with large language models, you know, predicting the next word in a sentence. But lems are this entirely new class of AI.

SPEAKER_00 0:49

Aaron Powell Yeah, they are designed to understand and represent and actually forecast real-world events over time. It is a huge shift.

SPEAKER_01 0:56

So instead of generating text, they're built to optimize these complex sequences in reality, right? Like from healthcare protocols to um global logistics.

SPEAKER_00 1:04

Exactly. To a lem, your burn toast isn't a word. It's a structured object. It has a specific timestamp, participants, a location.

SPEAKER_01 1:12

Aaron Powell Okay. So it's almost like uh autocomplete for reality.

SPEAKER_00 1:16

I love that phrase, yeah. Autocomplete for life.

SPEAKER_01 1:19

Aaron Powell But wait, if it's just looking at patterns and massive data sets, how does it learn the actual rules of the physical world? Like why doesn't it just guess wildly and you know hallucinate impossible sequences?

SPEAKER_00 1:30

Well, that brings us to a really fascinating mechanism called schema induction.

SPEAKER_01 1:34

Schema induction. Okay, unpack that for me.

SPEAKER_00 1:37

Aaron Powell Sure. So instead of just finding statistical correlations like saying um A usually follows B, lems actively extract the underlying logical rules of a system.

SPEAKER_01 1:47

Aaron Powell Wait, how does it extract a rule from raw data though?

SPEAKER_00 1:50

Aaron Powell Think of it like watching thousands of hours of a sport you had never seen before.

SPEAKER_01 1:54

Okay, I'm with you.

SPEAKER_00 1:54

Eventually you deduce the rules of the game just by watching the events play out, right?

SPEAKER_01 1:59

Oh, right, because you see the same patterns repeat.

SPEAKER_00 2:01

Exactly. The model does this and translates those rules into explicit symbolic logic using frameworks like PDDL. Yeah, it's essentially a programming language built specifically for complex planning.

SPEAKER_01 2:13

Yeah.

SPEAKER_00 2:14

So it explicitly learns, for instance, that a pan must possess the physical attribute of being hot before an event where a finger gets burned can logically occur.

SPEAKER_01 2:23

Oh wow. So it is mapping out the actual physics of the situation, not just parroting vocabulary.

SPEAKER_00 2:28

You got it. But you know, knowing the rules of the game is very different from knowing exactly when the next play is going to happen.

SPEAKER_01 2:34

Right. Time is the hardest variable here. And actually, before we get into how LEM's master time, I want to quickly mention that just as LEM's Optimize Event Sequences, today's sponsor, EmberSilk, optimizes workflows.

SPEAKER_00 2:46

They are fantastic for that.

SPEAKER_01 2:48

Truly. If you need help with AI training, automation, integration, or you know, custom software development, they are the ones to call.

SPEAKER_00 2:55

Yeah, if you are uncovering where agents could make the most impact for your business or even your personal life, definitely check out EmberSilk.com for all your AI needs.

SPEAKER_01 3:04

Absolutely. So back to mastering time. Our sources mention something called temporal point processes.

SPEAKER_00 3:10

Yes, and specifically the Transformer Hawks process.

SPEAKER_01 3:13

Hawks process, I saw that term. But usually it was related to predicting things like earthquakes, right?

SPEAKER_00 3:19

Precisely. So classical Hawks models predict self-exciting events. Mathematically, an earthquake increases the probability of an aftershock.

SPEAKER_01 3:28

Okay, that makes total sense.

SPEAKER_00 3:29

Well, LEMS take that exact math and apply it to human behavior. Like a missed car payment mathematically increases the probability of a future loan default. One event excites the likelihood of another.

SPEAKER_01 3:43

But wait, mapping millions of these cascading events over years of data, wouldn't that require an impossible amount of computing power?

SPEAKER_00 3:51

It absolutely would if the AI looked at every single second of the day.

SPEAKER_01 3:55

Right.

SPEAKER_00 3:55

That is why researchers integrated something called sparse self-attention.

SPEAKER_01 3:59

Sparse self-attention, meaning it ignores something.

SPEAKER_00 4:02

Exactly. Instead of analyzing every single moment of a five-year timeline, the model learns to selectively focus only on the few past events that actually matter to the current moment.

SPEAKER_01 4:12

Oh, so it just ignores the dead space.

SPEAKER_00 4:14

Right, which makes processing years of complex timelines incredibly efficient.

SPEAKER_01 4:18

That is brilliant. Let's talk about where this is actually going because the applications in our sources are incredibly optimistic.

SPEAKER_00 4:26

They really are. I mean, in public safety, limbs are being used to predict human mobility. So they can help manage crowds at massive public events to keep people safe before bottlenecks even form.

SPEAKER_01 4:38

That is amazing.

SPEAKER_00 4:39

And in healthcare, they can simulate patient trajectories through hospital systems to foresee health risks days in advance.

SPEAKER_01 4:47

Okay, let me stop you there because the healthcare application makes me a little nervous. If a LEM is forecasting medical treatments, couldn't it um suggest a physically impossible or dangerous sequence of events?

SPEAKER_00 4:59

It is a valid concern, but that is exactly where the schema induction we talked about earlier acts as a safety net. It uses a process called neurosymbolic integration.

SPEAKER_01 5:08

Meaning it combines the neural network guessing with the hard logic.

SPEAKER_00 5:11

Precisely. The neural side makes a prediction about what happens next, but before that prediction is finalized, it's run through an external planner that checks it against the strict symbolic rules it learned.

SPEAKER_01 5:22

Oh, wow. So if the neural net predicts a medical event that violates physical or medical logic, the system just rejects it.

SPEAKER_00 5:29

Exactly. It is fundamentally constrained by reality.

SPEAKER_01 5:32

It is incredible to see how we are building these safety nets directly into the architecture. It really makes you optimistic about our ability to engineer solutions to massive complex problems.

SPEAKER_00 5:45

The capacity to foresee and optimize the future is honestly just beginning. It is going to help us so much.

SPEAKER_01 5:52

It really is. And it leaves you with a really fun thought experiment, too. Like imagine applying a personal lem to your own habits.

SPEAKER_00 5:58

Well, that'd be amazing.

SPEAKER_01 5:59

Right. What hidden rules and positive chain reactions could you optimize to design your absolute perfect day? Definitely something to think about.

SPEAKER_00 6:07

For sure.

SPEAKER_01 6:08

Well, if you enjoyed this deep dive, please subscribe to the show. Hey, leave us a five star review if you can. It really does help get the word out.

SPEAKER_00 6:14

Thanks for tuning in, everyone.

SPEAKER_01 6:15

Keep dreaming big because the future of humanity is looking incredibly bright.