AI can automate complex shipping and factory workflows

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 40:01

   about the practical applications of this technology that you're building. You know, it's interesting. You were talking a little bit about AI being used for physics, AI being used in other research applications, AI being used for defense. None of this sounds like a chatbot. So talk a little bit about the applications that you are working on and whether we're going to see AI move beyond the chatbot. I mean, the chatbot is oftentimes the interface because artificial intelligence is a... Generative AI allows you to interact with machines in a human way. So say chatbot is a human machine interface, but it's not the the rest it's it's only that um now if you look at the at the actual applications that are strongly exciting for us you have two things you have the things that are really on the end-to-end workflow automation that effectively changes

2. 40:53

   the way your business is fully run. So examples are like cargo dispatching. When we work with CMA CGM, which is a shipping company, and we help them dispatch all of their containers when the cargo, the ship comes into the ports and they need to dispatch everything. They need to contact like hundreds of people. They need to contact the harbor. They need to contact the regulators. They need to actionate 20 software differently.

3. 41:16

   And so that takes like, I mean, I think a few hundred people to do it. And by working together around how to automate those things, suddenly you can save 80%. So the LM is making those communications. The open creator, yeah. And also deciding, not just making the call, but deciding who gets what. It decides and it wires the things and you measure whether it's doing the right thing. And if it doesn't, then you improve the system.

4. 41:38

   How's it doing? So it's working, it's live actually in certain agencies. So that's very, like it has, to me, it's very exciting because it has a physical footprint. It takes decisions in a safe way, and it's effectively bringing a very large efficiency gain to a company. Now, another example, which is more on the growth side, are things that we do with ASML. We are working with them on vision systems.

5. 42:03

   And talk a little bit about what ASML is for those that don't know. So ASML is a company that is doing computational lithography and scanning. and their role is to build those big machines that are effectively engraving the wafers that are then used as the chips in NVIDIA, for instance. Right, so they're like key industrial component of the semiconductor manufacturing process. They provide the machines for semi-files.

6. 42:26

   Right, and something so specialized, you would think, how's generative AI going to help them? Well, generative AI models are predictive AI models. And one good thing they have is that they can see and reason about what they see. And so one of the things that SML needs to reason about are the images coming out of their scanners that are verifying whether there are errors in the engraving of the chips.

7. 42:53

   And it's actually fairly complex because there's some logical thinking to be done. And the combination of images and logical thinking is what enables us to actually automate do things much faster, which means that the throughput down the line of FABs is going to increase. And so in that setting, customization is key because the kind of input that is coming in is nowhere to be found elsewhere. SML is the only one who has access to these images.

8. 43:18

   And so we find like a physical problem that is effectively a bottleneck in like a manufacturing process. And we go and we train models that are effectively solving it. So that's, and this is going to occur in like many, many different places. And general TVI is needed there because you need a model that can reason about images. And so the reasoning capabilities are critical. But customizing those reasoning models for a specific problem with a specific kind of input is the one thing that is the unlock there.

9. 43:50

   Yeah, the industrial applications of Genre AI to me have been super surprising and interesting. Like there has been technology, for instance, computer vision technology that can take a look at a piece of machinery or an output and be like, that's not good or actually that's what we need. But there hasn't been this nerve center that that information can be channeled to and then sort of have a decision made about it and then communicate it to somebody in the field.

10. 44:21

    And that's what this stuff is enabling, is that full line of technical work is starting to be able to be done by this technology. Basically, what you need are models that can perceive multiple kinds of information. and oftentimes in manufacturing, information is visual. So having very strong visual models is super useful. And then based on those vision models, you can, on these inputs, you can make choices and you can rely on the LLMs themselves to orchestrate calling an agent or going into the next step.

11. 44:53

    the workflow or actually calling a tool or writing something in a database and and that's uh having dynamic agents that are able to see what's happening in a factory that are able to see what's happening in a process and that can take the next step whether it's actually an automatic step or a call an agent step so that they validate a decision is where a lot of the value can be created and that's going to reorganize manufacturing you know manufacturing had to reorganize itself multiple times.

12. 45:21

    When we invented the steam engine, we had to rebuild the entire factories around like a central steam machine because that was the energy provider. And so what's going to happen, I think in the next 10 years, is that all of the manufacturing processes will be rebuilt around LLM orchestrators. And it's super interesting because you have physical problems to solve. The system has a physical footprint, so there's some safety issue that you need to solve.

13. 45:48

    Just the complexity of the system itself is huge. And so that's a fascinating problem for engineers like us.