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All right, everyone, so let me introduce our next speaker, my fellow Devroom, co-organized

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and actually, a company's co-founder Tatiana Dada Shiva, who will be talking about some of

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the interesting projects between hardware, software, and AI, so take it away, thank you.

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So this is actually an AI application talk, which will not technically allow them this

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Devroom, so since it's applied to AI Plum, and I guess it works, so if anybody wants

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to do, then use the same LiveHard next time, you welcome.

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So I haven't wanted to talk about load in the better fountains in different kind of hardware

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development, and if you ask, how well, actually, I'd say as low as it gets, because I'm

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not even an engineer, so I'm even worse, I'm CEO of hardware company, and if anybody

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asks me, can I have a design hardware, now that it doesn't know, so how did I do that?

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Well, in this particular case, and for the purpose of this discussion, I'll talk about how

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to use AI to be helpful to your company when you know nothing, and what I do know, I do

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know how to manage people, I do know how to kind of know when somebody's bullshit in

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the company, and I wanted to use it all in the AI, and I absolutely am not here to produce

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the best code ever, or be the smartest person in the room, I'm usually just for the result.

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So what does it mean?

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It means that I use code code, and absolutely shoots which double-nercarnities, but again,

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for me, the model doesn't matter, usually don't even pay attention, which one clue chooses,

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I'm not writing code myself, so it's not about being a copilot, and again, safety

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matters, so especially if you're not in professional engineer, don't allow cloth mess up

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your stuff, and it did start it to kind of motivate people in the company to use more

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AI, but then, again, I figure out that there are so many ways that how people use AI,

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so here are some questions that I suggest people to ask themselves, so I do use it in just

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one instance of AI, and since we are, like as we discussed today, in the edge of open

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core, I guess you should think twice if you use it on the one.

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Do give the AI tasks like a bigger than junior developer can handle?

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I know people who ask AI to just create a core and validate it, and do the whole design,

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I don't believe in that, you should absolutely not do that.

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Again, personal opinion, but pretty strongly hold, and I suggest, like, you think about

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these questions, not going to focus on each one of them, but it's always good for lots of things,

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but not for everything, so what do they do in my company?

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So I think one of the good first examples is we got this novel, I accelerated a card, and then I said

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in all, I mean, there was no documentation, and I didn't want to have the source code.

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I mentioned years in the company, we are trying to figure out what is there, how to use it,

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that is a win-work, and they have been trying to understand it all for a few weeks, and then

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another insure of it works, and we've got code in about three hours and made it work, and again,

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some ideas of how to use it, is asked to run just about every model you can, and you

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can make conclusions as, like, what work, what dawn, what is the difference?

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You can bring us that of human being, like, they would think about the structures, and this,

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and that, and again, you can slow a lot of things and ask it to compare, to experiment.

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It's basically we call it software at a college now, so when you have something you don't understand,

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but you want to make it work. So that's one example, some important D files.

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Another one, so we're doing a lot of open source efforts, and since again,

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all the engineers in the company actually doing useful stuff, and I want an open source code to be nice,

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and people be able to use it. So one of the ideas was, let's make it applicable.

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So how do we do that? I got the cheapest of PGA as I could, so this, for example, was

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a sprinkler, it cost, like, 80 bucks. I got a few others. So my knowledge of PGA actually

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is very limited. It's limited to one hour talk. I've heard that the RISC file sent it,

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and I've been able to port a couple of cores that are open already, and well documented,

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and again, by comparing and contrasting, asking the I to basically figure out how to do it

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for the next one that I happen to have. So that's another application. By the way,

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I'm not the only one doing all of this. So this is our friend who basically

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tweeted recently about exactly the same. So while some people are afraid to use the I,

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others are asking them to figure out the path and put easy complicated tasks. So why and how does it work?

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Actually, I'll quickly jump to this one. It's basically asking to find the solution,

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experimenting a lot, comparing things, and treating everything as a system. So there are lots of

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tools that appear actually, I didn't find any open source ones. But there are lots of tools

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that allow you to kind of throw documentation with the white papers, whatever you have the

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data sheets at the AI system like to and program the system. So if your system is open and

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it's a system on the chip, you can do it a similar thing. So again, if you know any open ones,

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that's another really interesting way to work with it all. But again, I'm not touching platform

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blocks that would be other people talking about it. But if you have enough platform blocks,

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yeah, it's a really good way to kind of free combine them, experiment with them, and basically

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allow us people who have never done hardware developments that are doing that. Thank you.

