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<v Speaker 1>How'd you like to listen to dot NetRocks with no ads? Easy?

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<v Speaker 1>Become a patron For just five dollars a month you

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<v Speaker 1>you that and a special dot NetRocks patron mug. Sign

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<v Speaker 1>up now at Patreon dot dot NetRocks dot com. Hey,

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<v Speaker 1>Carl and Richard here with your twenty twenty four NDC schedule.

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<v Speaker 2>Will be at as many NDC conferences as possible this year,

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<v Speaker 2>and you should consider attending no matter what. The Copenhagen

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<v Speaker 2>Developers Festival happens August twenty sixth through the thirtieth. Tickets

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<v Speaker 2>at Cphdevfest dot com.

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<v Speaker 1>NDC Porto is happening October fourteenth through the eighteenth. The

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<v Speaker 1>early bird discount ends June fourteenth. Tickets at Ndcporto dot com.

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<v Speaker 2>And we'll see you there, we hope.

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<v Speaker 1>Hey, guess what it's dot net rocks again. I'm Carl

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<v Speaker 1>Franklin and I'm Richard Campbell, and this is episode nineteen nineteen.

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<v Speaker 1>And Buddy, I went and I looked up you know

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<v Speaker 1>what happened in this year in history? So some significant

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<v Speaker 1>events included the collapse of the Habsburg and German empires.

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<v Speaker 2>Yes, the end of World War One, end of World War.

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<v Speaker 1>One, the redrawing of the map of Europe at the

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<v Speaker 1>Paris Peace Conference, the influence of Woodrow Wilson's fourteen points.

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<v Speaker 2>Interesting they call it the Paris Peace Conference. It was

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<v Speaker 2>the Versiah Cords, and they were not They were not very.

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<v Speaker 1>Peaceful, not very peaceful.

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<v Speaker 2>Yeah, they were a very good setup for World War two, yep.

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<v Speaker 1>And sowing the seeds of World War two, growing demands

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<v Speaker 1>for self determination by smaller nations, the growing involvement of

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<v Speaker 1>the United States and European politics and trade all that. Yeah.

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<v Speaker 2>Yeah, one would argue this is the ess of the

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<v Speaker 2>nation stage. Yeah, basically the model we've been functioned ever since.

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<v Speaker 1>I bet you didn't know Richard Campbell knew so much

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<v Speaker 1>about history. O. Who am I kidding? You knew you knew?

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<v Speaker 1>Go ahead and roll the music, because I got a

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<v Speaker 1>pretty good better no framework, awesome? All right? What do

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<v Speaker 1>you got? So you know, I'm cutting down on travel

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<v Speaker 1>to conferences. At the end of the year, I'm not

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<v Speaker 1>going outside the country. I'm not even sure that there

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<v Speaker 1>are any more domestic conferences that I'm going to at

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<v Speaker 1>the end of this year, but I still want to

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<v Speaker 1>do some Blazer hands on training.

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<v Speaker 2>Right.

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<v Speaker 1>So what I've done is if you go to APPVNX

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<v Speaker 1>dot com, slash training or nineteen nineteen dot po dot me,

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<v Speaker 1>you'll see the training page which links to a form

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<v Speaker 1>that you can fill out. I've decided I want to

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<v Speaker 1>do these online classes on Mondays. Oh yeah, mostly Mondays

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<v Speaker 1>from now until the end of the year, most of

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<v Speaker 1>them except for you know, some week off here and there.

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<v Speaker 1>And this is a form where you can select the

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<v Speaker 1>dates that you want to attend. Each Monday, we'll have

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<v Speaker 1>one starting at nine am Eastern and nine am Pacific,

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<v Speaker 1>so that you know, if more people want to do

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<v Speaker 1>in a Pacific time blah blah blah. But the deal

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<v Speaker 1>is that once we hit that magic number of ten,

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<v Speaker 1>then the class is going to happen, Right, So you

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<v Speaker 1>should select all of the dates that you can possibly make,

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<v Speaker 1>and then there's a greater chance that one of them.

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<v Speaker 2>Will will take But online classes.

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<v Speaker 1>Will online classes, but you know it's interactive. You'll be

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<v Speaker 1>able to do the hands on work and even though

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<v Speaker 1>we won't get to all the materials in one day,

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<v Speaker 1>you still get all the materials right so you can

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<v Speaker 1>go back on your own time.

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<v Speaker 2>Well in the class size sounds like assuming nice as

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<v Speaker 2>small too, so people go be able to interact a lot.

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<v Speaker 1>Well, ten is the minimum. I haven't really set a

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<v Speaker 1>maximum yet, but of course if worry about that problem

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<v Speaker 1>when it happened, I'll worry about that problem. So that's

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<v Speaker 1>what it is at Phoenix dot com slash training. Who's

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<v Speaker 1>talking to us Richard crawbd to.

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<v Speaker 2>Comment on top of show nineteen sixteen, So just last

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<v Speaker 2>one published. To be honest, this is the show we

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<v Speaker 2>did with Mark Rendalls. We called how Simple is as

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<v Speaker 2>Simple as Possible? And in finding conversation with Mark, as

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<v Speaker 2>always he's a thoughtful guy as well as very funny.

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<v Speaker 2>I think we spent most of the show laughing. And

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<v Speaker 2>Rob King has this great comment. He said, once again

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<v Speaker 2>another great episode which reaffirms my position that everything old

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<v Speaker 2>is new again. I feel like there's a whole generation

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<v Speaker 2>of developers coming up with only known JavaScript frontends and

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<v Speaker 2>think the server is only for APIs or serving the JavaScript.

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<v Speaker 2>I've started going back to a server side first approach

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<v Speaker 2>of building apps, keeping get all in the server and

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<v Speaker 2>then build it out towards the client only required. Yes,

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<v Speaker 2>Lazer made this is really simple, and it's approached to

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<v Speaker 2>adding interactivity as required. I've even converted a Blazer WASM

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<v Speaker 2>app to pure service side because it's actually faster than

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<v Speaker 2>the first page and the app doesn't need any interactivity

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<v Speaker 2>beyond the forms. To that end, I've found that you

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<v Speaker 2>can integrate htmx something that Mark talked about right seamless

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<v Speaker 2>see into Blazer server and a built out of a

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<v Speaker 2>little demo repo which I'll include in the show notes

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<v Speaker 2>to Rob's GitHub repository. It's just a sort of hey,

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<v Speaker 2>these two things will play together, Blazer and HGMX.

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<v Speaker 1>Yeah, there you go.

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<v Speaker 2>You know, it's just sort of a sort of recognition

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<v Speaker 2>of yeah, we're kind of calling in a cycle. Like

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<v Speaker 2>there's clearly cases for putting more load on the client,

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<v Speaker 2>but one would argue we swung too far that way. Yes,

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<v Speaker 2>although I know as we get into these conversations about

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<v Speaker 2>large language models and machine learning so forth, there's now

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<v Speaker 2>a big push about how do you get more of

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<v Speaker 2>that workload on the client because folks are concerned now

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<v Speaker 2>that too much of that workload is on the server.

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<v Speaker 2>So I think it'll be some good talking points for

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<v Speaker 2>today's show.

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<v Speaker 1>I'm really happy that a guy like Mark Render, who

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<v Speaker 1>you know, go back and listen to his stuff before

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<v Speaker 1>a Blazer came out. He was not really a service

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<v Speaker 1>side guy at all, like very much in the in

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<v Speaker 1>the composable c I CD pipeline kind of thing and

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<v Speaker 1>and all you know JavaScript, and a guy like Mark

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<v Speaker 1>comes out and says, you know what, I took Blazer

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<v Speaker 1>server for a ride and I really like it.

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<v Speaker 2>Yeah, it's really great. Well, you know, and again his whole,

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<v Speaker 2>that whole how simple is simple is getting back to

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<v Speaker 2>the intention of what we're trying to do for the

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<v Speaker 2>customer exactly right, Like I do think we get enamored

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<v Speaker 2>of the technology. And again I brought this show and

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<v Speaker 2>these comments out because we're about to go down a

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<v Speaker 2>path about a whole other class of development, and I

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<v Speaker 2>think it's good to push back on those fundamentals and say,

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<v Speaker 2>what are we trying to deliver to customers?

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<v Speaker 1>Yep, no pressure pre shant, Yeah for sure.

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<v Speaker 2>So Rob, thank you so much for your comment and

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<v Speaker 2>a copy of Music co Buy. It's on its way

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<v Speaker 2>to you. And if you'd like a copy of Music

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<v Speaker 2>co By, I write a comment on the website at

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<v Speaker 2>dot at rocks dot com or on the facebooks. We

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<v Speaker 2>publish every show there, and if you comment there and

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<v Speaker 2>never read on the show, we'll send you a copy

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<v Speaker 2>of Music to.

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<v Speaker 1>Go By, Music to Code by, Still Going Strong. I

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<v Speaker 1>just sold another Flak collection, which is good, and I

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<v Speaker 1>got inspired last night to do a new one.

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<v Speaker 2>How many how many have you made?

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<v Speaker 1>Is it like twenty one?

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<v Speaker 2>Twenty one? Okay, so you know you're digging around for

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<v Speaker 2>another track? Huh, that's awesome.

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<v Speaker 1>Well, I was listening to it last night. I just

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<v Speaker 1>decided to.

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<v Speaker 2>I don't know if you've heard it. Stuff.

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<v Speaker 1>It's pretty good. It is pretty good. Yeah, I've been

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<v Speaker 1>listening for a long time. Yeah, I mean I started.

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<v Speaker 1>I was listening to the more recent ones okay last

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<v Speaker 1>night and I was like, yeah, you know, this is

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<v Speaker 1>pretty good stuff. I think I got inspired to do

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<v Speaker 1>another one.

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<v Speaker 2>Hey, you should meet that guy. He's pretty clever.

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<v Speaker 1>Yeah, so we'll see. I'm not announcing it now, I'm

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<v Speaker 1>just announcing my intent.

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<v Speaker 2>You got it?

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<v Speaker 1>Some itch?

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<v Speaker 2>I like when you have an itch? Yeah, stuff happens

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<v Speaker 2>when you have an itch. Yeah yeah.

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<v Speaker 1>Well, anyway, you can also follow us on ex Twitter.

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<v Speaker 1>We've been there for years, but the cool kids are

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<v Speaker 1>hanging out. I'm Astdon, I'm Carl Franklin at tech Hub dot.

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<v Speaker 2>Social, and I'm Rich Campbell at master do Social.

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<v Speaker 1>Send us a two. That's another way you can get

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<v Speaker 1>a copy of music to code buy. Okay, let us

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<v Speaker 1>introduce Prashant Boyar. He is six times AI MVP, three

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<v Speaker 1>times Business Applications MVP, Microsoft Certified Trainer, speaker, author, and

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<v Speaker 1>leading AI architect from the Washington, DC area of the

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<v Speaker 1>United States. Currently, he works as an AI architect in

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<v Speaker 1>the Office of the Chief Technology Officer at Applied Information

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<v Speaker 1>Sciences That's AI at dot com. Braschant is on the

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<v Speaker 1>leadership committee for AI Fest, AI and mL User Group

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<v Speaker 1>and the Northern Virginia Data Platform User Group. As a

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<v Speaker 1>renowned international speaker, he frequently presents a tech conferences. Additionally,

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<v Speaker 1>Prashant was awarded the Antarctic Service Medal of the United

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<v Speaker 1>States of America for his outstanding service in Antarctica. You know,

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<v Speaker 1>I was going to tease you for not having enough

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<v Speaker 1>stuff on your CV, but let's go right to Antarctica.

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<v Speaker 2>What was that?

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<v Speaker 3>So, first of all, thank you very much for having me.

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<v Speaker 3>That was once in a lifetime experience for me. Like

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<v Speaker 3>I grew up in India and two thousand and seven

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<v Speaker 3>is the year I came to the United States for

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<v Speaker 3>my master's degree and I got I was really fortunate

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<v Speaker 3>that I got a scholarship in the university and as

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<v Speaker 3>part of that scholarship, I got a chance to work

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<v Speaker 3>with a lot of brilliant scientists. And those scientists were

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<v Speaker 3>studying the costmes raise and everyone in that room except

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<v Speaker 3>me was like really brilliant. So I had to, you know,

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<v Speaker 3>fit in and I know, and you.

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<v Speaker 1>Might be a little unbiased towards yourself.

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<v Speaker 3>Now, Like if you if you talk to any experimental

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<v Speaker 3>physics physicists or in theoretical physicists, not only they know

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<v Speaker 3>software development really well, but they also know all this

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<v Speaker 3>machine learning match and some advanced mathematics really well. So

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<v Speaker 3>I have like after that, I have really high respect

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<v Speaker 3>for all these physicists because for the work they do.

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<v Speaker 3>And if you compare the salaries against that, you will

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<v Speaker 3>be surprised.

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<v Speaker 2>Yeah, they don't get paid much for yes, as hard

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<v Speaker 2>as they as they work. Antarctica is a great location

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<v Speaker 2>for doing stuff like measuring comic cause of rays. You know,

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<v Speaker 2>the atmosphere is a little thinner, and it's very very dark,

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<v Speaker 2>and there's a minimum amount of noise, but it's and

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<v Speaker 2>it's sad. That's like the best time to do that

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<v Speaker 2>work is midwinter of Antarctica where there's no sunlight for months,

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<v Speaker 2>so for the tough place to be for shann you

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<v Speaker 2>hung out there.

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<v Speaker 3>Yes, So as part of that experiment, like we built,

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<v Speaker 3>it was a joint collaboration WITHWEN multiple universities, and ASA

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<v Speaker 3>was the main sponsor because typically NASA sponsors this, you know,

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<v Speaker 3>give this huge grant to the unisity so that they

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<v Speaker 3>can do research on their behalf. And as part of

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<v Speaker 3>the research, you know, the best results to get is

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<v Speaker 3>via satellite, but you know, sending any payload to satellite

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<v Speaker 3>is very expensive. So they came up with an approach

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<v Speaker 3>about how we send this really large duration balloons in

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<v Speaker 3>Antarctic continent which fly around you know the south you know,

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<v Speaker 3>South Pole for around twenty to eighty days, so not

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<v Speaker 3>really twenty to thirty days, collect as much data as possible,

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<v Speaker 3>and then we take years and years to analyze the data.

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<v Speaker 3>And not many people know, but US government has a

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<v Speaker 3>really big station there, like Unartic continent is huge and

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<v Speaker 3>a lot of countries have stations, but US in particular

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<v Speaker 3>has like huge stations over there. And the station I

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<v Speaker 3>went to, it's called Macmurdo Station. So when I was

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<v Speaker 3>there into like in the end of twenty eighteen, sorry,

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<v Speaker 3>two thousand and eight, I was there for almost a

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<v Speaker 3>couple of months. And that is like a typical summer

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<v Speaker 3>time in Antarctica, and that's the time where they will

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<v Speaker 3>have like around thousand people on the base and everybody

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<v Speaker 3>will be there to conduct some kind of scientific experiment

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<v Speaker 3>and to support those experiment. They also need a lot

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<v Speaker 3>of people, like they need cooks, they need chefs, you know,

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<v Speaker 3>they knew it, people who can like blow the roads

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<v Speaker 3>and all this kind of stuff. And I met a

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<v Speaker 3>lot of interesting people there who like, let's say a

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<v Speaker 3>software engineering by trade. They work here nine months in

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<v Speaker 3>US continent, and for three months they just go to

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<v Speaker 3>Antarctica and work as a chef or as a cook

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<v Speaker 3>because they just like it there and the pay is good.

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<v Speaker 1>And the base has good internet access. I hear yes,

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<v Speaker 1>and I know that because I have a friend I

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<v Speaker 1>went to grammar school with who is messaging me from Antarctica.

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<v Speaker 1>I'm like, what you're aware, but he was at that

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<v Speaker 1>base and yeah, so pretty cool. How in the summertime

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<v Speaker 1>in Antaro would you think of it as bomby? Would

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<v Speaker 1>you go out in shorts? What's the temperature?

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<v Speaker 3>So temperature is not that bad. You can go out

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<v Speaker 3>and go and go out in shots only for ten

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<v Speaker 3>to fifteen minutes, that's it.

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<v Speaker 1>Wow.

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<v Speaker 3>So like the during the summer time, you know there

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<v Speaker 3>the temperature was in the range of you know, like

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<v Speaker 3>minus let's say ten degrees celsius to plus five degrees celsius. Okay,

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<v Speaker 3>but there used to be some drays where you know,

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<v Speaker 3>it was like very very bad, Like we had like

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<v Speaker 3>one day where it was minus twenty degrees celsius and

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<v Speaker 3>things were pretty rough over there. But you have heated

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<v Speaker 3>rooms and you know, like food is awesome, there are

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<v Speaker 3>some recreational activities and you know, and that when you

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<v Speaker 3>go to those kinds of places, you kind of realize, okay,

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<v Speaker 3>how fast paced your life is back in like you know,

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<v Speaker 3>you know, the civilized world, and how much of a

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<v Speaker 3>time you got when you go to these kind of places.

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<v Speaker 1>Did you ever have to slice open a banta with

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<v Speaker 1>your lightsaber to keep warm? No? No, that's the question.

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<v Speaker 2>I was.

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<v Speaker 3>I was fortunate enough. No, I went there on a

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<v Speaker 3>good good time over.

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<v Speaker 2>There, Yeah, very good. You only had to smell them

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<v Speaker 2>on the outside.

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<v Speaker 1>Oh my, sorry for that little diversion, but I just

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<v Speaker 1>had to know.

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<v Speaker 2>It's great.

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<v Speaker 1>So let's talk about Microsoft co Pilot Studio. I know

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<v Speaker 1>because I went to build with Richard, and we know

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<v Speaker 1>that it's for building your own custom agents and you

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<v Speaker 1>can connect them to a bunch of sources of data,

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<v Speaker 1>and we know that you can hook them up into

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<v Speaker 1>M three sixty five and all that. But what don't

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<v Speaker 1>we know?

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<v Speaker 3>Okay, so let me go back to the history and

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<v Speaker 3>start with Microsoft first, foray into the cornosential AI. Like

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<v Speaker 3>if you remember in March twenty sixteen, I think it's

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<v Speaker 3>the first time when Satya Nadela went onto the stage

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<v Speaker 3>and he announced about Microsoft bought framework. Because still that time,

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<v Speaker 3>there was no good story when it comes to building

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<v Speaker 3>let's say, any kind of child bought or conversational experiences.

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<v Speaker 3>And after maybe two or three, after a month after that,

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<v Speaker 3>I know Microsoft is getting a lot of good price

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<v Speaker 3>right now because of their partnership with open AI. But

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<v Speaker 3>they also had their fair share of failures as well.

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<v Speaker 3>I think In March twenty sixteen, they launched a Twitter

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<v Speaker 3>bought called day oh yeah yes, yes yeah, and its

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<v Speaker 3>long form was thinking about you And they launched on

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<v Speaker 3>Twitter and that pot was from Microsoft Research and it

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<v Speaker 3>was their first public foray into the world of AI.

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<v Speaker 3>And people soon figure out on Twitter, you know, to

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<v Speaker 3>teach that bot to say some nasty stuff and Microsoft yes, yes, yeah, Microsoft.

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<v Speaker 3>Microsoft try to like you know again reteaching like good stuff,

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<v Speaker 3>but within sixteen hours they had to take it down

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<v Speaker 3>and they still now a lot of key executives at

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<v Speaker 3>Microsoft talks about that particular failure saying like, hey, we

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<v Speaker 3>learn a lot from that failure, like how what other

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<v Speaker 3>things not to do with AI stuff? And so they

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<v Speaker 3>continue to work on board framework for next three years.

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<v Speaker 3>But board framework, like is very complex.

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<v Speaker 2>Yeah, we did a show, We did shows on bot framework,

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<v Speaker 2>like you said, like twenty seventeen. It's hard to think

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<v Speaker 2>about that now because the opening eye things overwhelmed everything. Yes,

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<v Speaker 2>and they made a lot of sense, like the fact

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<v Speaker 2>bot to me was totally logical that you could point

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<v Speaker 2>a piece of software at a fact and it would

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<v Speaker 2>give you sort of a natural language interface to it.

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<v Speaker 3>But still, like Microsoft struggle to get really good traction

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<v Speaker 3>with a lot of customers and developers because it was

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<v Speaker 3>extremely hard to get you know, your conversational experiences right.

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<v Speaker 3>And then in November twenty nineteen they launch a low

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<v Speaker 3>code version of it called Power Virtual Asian and the

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<v Speaker 3>name Power itself can give you indication which product family

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<v Speaker 3>belongs to. It was from a power platform. So they

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<v Speaker 3>continued on that journey and then basically there was a

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<v Speaker 3>time where the entire investment on board framework stopped and

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<v Speaker 3>all the things like Microsoft was doing on Power virtualation side.

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<v Speaker 3>They had decent success. But then the real success or

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<v Speaker 3>real i'll call life changing moment for that particular product

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<v Speaker 3>or group was Ignite last year. Fen went on stage

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<v Speaker 3>and say, hey, Microsoft now is the co pilot company,

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<v Speaker 3>and if you would like to create a low code,

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<v Speaker 3>no code co pilots, then Copilot Studio is the product.

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<v Speaker 3>And I know Microsoft get a lot of flak, especially

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<v Speaker 3>their marketing department, for naming like weird products, but I

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<v Speaker 3>think this time really nailed it.

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<v Speaker 2>To be clear, GitHub came up with the name, right,

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<v Speaker 2>That's why it's such a good name, because Microsoft didn't

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<v Speaker 2>think of it.

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<v Speaker 1>We have a history of making really long names that

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<v Speaker 1>are very kind of academic sounding.

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<v Speaker 2>Well they're named by Kimmittee.

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<v Speaker 3>Yes, yeah, and someone I heard. I don't know how

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<v Speaker 3>true it is, but this product was supposed to have

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<v Speaker 3>another name. They already have a lot of printed name

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<v Speaker 3>and banners on it, but just three one week or

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<v Speaker 3>two weeks before Ignite, someone came up with this name

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<v Speaker 3>and they have to, you know, do a lot of

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<v Speaker 3>you know, last minute stuff.

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<v Speaker 2>There's been a lot of name twitching, right copilot for

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<v Speaker 2>Microsoft three sixty five, Microsoft three sixty five copilot like it's.

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<v Speaker 1>It's you know, the AI thing has been sort of

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<v Speaker 1>like a gold rush, hasn't it. Yes, like every company

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<v Speaker 1>wants to get their put their stake in the ground

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<v Speaker 1>and say we're doing AI or we're doing you know,

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<v Speaker 1>generative AI. And I mean I don't I don't want

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<v Speaker 1>our listeners who are dot developers to be thinking that, oh,

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<v Speaker 1>development is gone. Microsoft is just focusing completely from now

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<v Speaker 1>on on AI. Because if you went to build, there

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<v Speaker 1>wasn't a whole lot about dot net, you know what

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<v Speaker 1>I mean. There wasn't a whole lot of code, plumbing code.

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<v Speaker 1>It was all focused on AI. But you know, we're,

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<v Speaker 1>as Richard said in the beginning when he read that comment,

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<v Speaker 1>we need to stay focused on delivering value to our customers.

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<v Speaker 1>And the sort of AI thing just smacked this in

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<v Speaker 1>the face and came out of nowhere and says, hey,

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<v Speaker 1>guess what this is going to allow you developers to

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<v Speaker 1>provide better value to your customers. So really interested to

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<v Speaker 1>hear some scenarios about that, and you.

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<v Speaker 3>Write a call like, you know, just don't think that

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<v Speaker 3>copilot Studio is the only product that I will need

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<v Speaker 3>to use, you know, when I had to create custom copilots.

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<v Speaker 3>There is another story in Azure world called AI Studio.

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<v Speaker 3>So if you are a professional developer and you don't

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<v Speaker 3>want to use let these drag and drop kind of

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<v Speaker 3>tools or low code no code tools and you would

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<v Speaker 3>have full control. Studio is your choice where since it's

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<v Speaker 3>part of Azure, you know it has a really good

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<v Speaker 3>pro developer story. You can be a dot net developer,

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<v Speaker 3>you can be a Python developer, and you can use

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<v Speaker 3>all the good services in Azure. And one other thing

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<v Speaker 3>I like about AI service a Azure EI studio is

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<v Speaker 3>the model selection. Like. One thing Microsoft has done which

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<v Speaker 3>not many people know is Microsoft launched something called Model

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<v Speaker 3>as a service where you can bring in some open

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<v Speaker 3>source model as well and host those in Azure and

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<v Speaker 3>use those in your Genia applications, so you don't have

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<v Speaker 3>to go with let's say models from open Ai or

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<v Speaker 3>models from let's say Microsoft for that matter. You can

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<v Speaker 3>use Lama hugging pace. You know, those models are also available.

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<v Speaker 3>So if you want to create a co pilot or

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<v Speaker 3>agent using that, you can do that now in Asure.

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<v Speaker 1>But they still have GPT four GPT four oh dally

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<v Speaker 1>all that stuff from AI Open ai. But all right,

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<v Speaker 1>so wow, So that is really the fundamental difference between

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<v Speaker 1>these two is that co pilot studio is maybe for

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<v Speaker 1>draggy dropping business people and Azure AI studios more for us.

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<v Speaker 3>There is one fundamental difference which not many people tay

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<v Speaker 3>pay attention. So when it comes to building a co

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<v Speaker 3>pilot or any kind of a application, a lot of

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<v Speaker 3>time people don't think about the UX part of it.

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<v Speaker 3>I know. That's where a copilot studio shines really well.

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<v Speaker 3>Like out of the box, it has really good capacity

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<v Speaker 3>of you know, having a really good UX. Let's say

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<v Speaker 3>I launch a co pilot for my existing enterprise app.

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<v Speaker 3>But then that copilot should able to understand or behave

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<v Speaker 3>like actual human right. The reason chad jipt got so

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<v Speaker 3>popular so fast is because we can interact with it

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<v Speaker 3>and you know, it can understand like at least what

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<v Speaker 3>I'm trying to say maybe ninety to ninety percent of time,

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<v Speaker 3>and it can give me good information back. And if

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<v Speaker 3>you compare that with old version of the chatboards that

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<v Speaker 3>more of these public facing website used to have. Those

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<v Speaker 3>used to be like, oh the major retailers or bank

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<v Speaker 3>used to have those, like used to be simple time

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<v Speaker 3>st where the first thing I used to google that

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<v Speaker 3>time is ourbing is what's the best way to get

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<v Speaker 3>to an actual human agent rather than you know, going

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<v Speaker 3>through their automated.

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<v Speaker 2>Response because that's what everybody does.

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<v Speaker 3>Yes, But now with this, after the generative AI and

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<v Speaker 3>some of the advancement, you know, you can at least

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<v Speaker 3>have some decent conversation. And that's where this co Pilot

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<v Speaker 3>Studios shine where it has a lot of widgets, it

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<v Speaker 3>has a lot of you know, building capabilities that if

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<v Speaker 3>I would like to build a good conversational experience, Copilot

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<v Speaker 3>Studio really shines as compared to Azure Eye Studio.

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00:22:43.279 --> 00:22:45.400
<v Speaker 1>Can I take the agents that I make in copiled

424
00:22:45.440 --> 00:22:48.319
<v Speaker 1>studio and move them over into Azure Ai studio and

425
00:22:48.480 --> 00:22:49.119
<v Speaker 1>enhance them.

426
00:22:49.519 --> 00:22:53.160
<v Speaker 3>Right now, the create direct conversation story is not there.

427
00:22:53.799 --> 00:22:55.839
<v Speaker 3>But what you can do is, let's say, if you

428
00:22:55.880 --> 00:22:59.279
<v Speaker 3>have already an agent in Azure AI side, you can

429
00:22:59.279 --> 00:23:02.440
<v Speaker 3>directly call that from a Copilot studio, so you can

430
00:23:02.480 --> 00:23:04.759
<v Speaker 3>do integration. But there is no like Magic vand to

431
00:23:04.839 --> 00:23:05.680
<v Speaker 3>convert those.

432
00:23:05.680 --> 00:23:06.599
<v Speaker 1>That's actually better.

433
00:23:06.960 --> 00:23:09.640
<v Speaker 2>Yes, well, it also reminds you that there's more than

434
00:23:09.640 --> 00:23:12.039
<v Speaker 2>one team instead of Microsoft working on this stuff, right,

435
00:23:13.039 --> 00:23:15.319
<v Speaker 2>you know, that's that's sort of the real reality is

436
00:23:15.319 --> 00:23:17.680
<v Speaker 2>that there's lots of different places moving at once.

437
00:23:17.920 --> 00:23:21.119
<v Speaker 3>And I'm a big believer of Fusion Team where hey, developers,

438
00:23:21.160 --> 00:23:23.440
<v Speaker 3>you focus on you know, doing really crazy stuff behind

439
00:23:23.440 --> 00:23:26.160
<v Speaker 3>the scene, you know, make the magic, but also gave

440
00:23:26.200 --> 00:23:29.079
<v Speaker 3>a lot of information worker or business users and ability

441
00:23:29.119 --> 00:23:31.960
<v Speaker 3>to you know, take the advantage of what they know

442
00:23:32.039 --> 00:23:34.759
<v Speaker 3>from the business and then you know, have an easy

443
00:23:34.799 --> 00:23:39.680
<v Speaker 3>way to interact with the back end system. So so

444
00:23:39.759 --> 00:23:42.200
<v Speaker 3>Copilo Studio also have that functionality way. Let's say I

445
00:23:42.240 --> 00:23:44.480
<v Speaker 3>have my logic app I would like to call or

446
00:23:44.599 --> 00:23:46.880
<v Speaker 3>I would like to call my EPI. I can do

447
00:23:46.960 --> 00:23:49.839
<v Speaker 3>that directly. There is an STTP connector available or I

448
00:23:49.880 --> 00:23:54.400
<v Speaker 3>can also invoke a power Automate cloud flow, and power

449
00:23:54.400 --> 00:23:56.720
<v Speaker 3>Automate has more than twelve hundred out of the box

450
00:23:56.720 --> 00:23:58.759
<v Speaker 3>connectors where you know, or I can build my custom

451
00:23:58.799 --> 00:24:01.599
<v Speaker 3>connector as well, which then can you know, call my

452
00:24:01.640 --> 00:24:03.559
<v Speaker 3>back end APIs and then can get the job done.

453
00:24:03.640 --> 00:24:06.759
<v Speaker 1>I have a quick story here. I got an email

454
00:24:07.000 --> 00:24:10.279
<v Speaker 1>last week from somebody who said, you know, I see

455
00:24:10.319 --> 00:24:12.559
<v Speaker 1>that you have a YouTube channel and you've got some

456
00:24:12.680 --> 00:24:16.480
<v Speaker 1>great content there, and uh, you know, for a very

457
00:24:16.559 --> 00:24:20.240
<v Speaker 1>low fee. Actually it was pretty good. I will, I will,

458
00:24:20.839 --> 00:24:28.000
<v Speaker 1>I will generate SEO happy titles, descriptions, tags and stuff

459
00:24:28.000 --> 00:24:30.440
<v Speaker 1>for your YouTube content. So I said, okay, sure, I'll

460
00:24:30.480 --> 00:24:33.559
<v Speaker 1>try it. And what I got back was a RAR

461
00:24:33.799 --> 00:24:36.839
<v Speaker 1>file full of text files for and a lot of

462
00:24:36.839 --> 00:24:41.480
<v Speaker 1>them were for the dot net rocks videos. And they

463
00:24:41.599 --> 00:24:45.240
<v Speaker 1>kept mentioning the dot net Rocks videos are simply a

464
00:24:45.279 --> 00:24:49.480
<v Speaker 1>still shot with our podcast that plays on it. And

465
00:24:49.519 --> 00:24:52.079
<v Speaker 1>this thing went on and on about how you will

466
00:24:52.200 --> 00:24:57.160
<v Speaker 1>learn from these training videos, hands on experience and stuff.

467
00:24:59.039 --> 00:25:03.279
<v Speaker 1>They got to the they got to the songs that

468
00:25:03.759 --> 00:25:07.519
<v Speaker 1>my band has put up there, and they called, you know, Nachelle,

469
00:25:07.559 --> 00:25:10.319
<v Speaker 1>who's our background singer, but she does sing on some

470
00:25:10.359 --> 00:25:12.960
<v Speaker 1>of them, but on this one that I was singing

471
00:25:13.160 --> 00:25:17.119
<v Speaker 1>like all of it lead singer Nachelle Rollins blah blah blah,

472
00:25:17.200 --> 00:25:20.480
<v Speaker 1>this big, grandiose stuff, and I wrote it back. I'm

473
00:25:20.480 --> 00:25:23.039
<v Speaker 1>not gonna say it is by her own back and say, dude,

474
00:25:23.079 --> 00:25:27.640
<v Speaker 1>this is bullsh this is some AI generated crap that

475
00:25:27.720 --> 00:25:31.440
<v Speaker 1>doesn't know a still video from you know, from a

476
00:25:31.640 --> 00:25:35.119
<v Speaker 1>training video for crying out loud. So yeah, I won't

477
00:25:35.119 --> 00:25:40.960
<v Speaker 1>be using their actually incorrect, factually incorrect and being sold

478
00:25:41.160 --> 00:25:44.599
<v Speaker 1>as you know, the I don't know whatever.

479
00:25:45.359 --> 00:25:48.680
<v Speaker 2>So I mean, you say, quietly said Fusion development there,

480
00:25:48.680 --> 00:25:51.319
<v Speaker 2>which is that power platform working with dot net dev

481
00:25:51.400 --> 00:25:53.519
<v Speaker 2>and so forth. So you see a sort of same

482
00:25:53.640 --> 00:25:57.400
<v Speaker 2>dynamic where we have domain experts, which I think is

483
00:25:57.440 --> 00:25:59.440
<v Speaker 2>the big biggest challenge when you're building a lot of

484
00:25:59.480 --> 00:26:04.039
<v Speaker 2>these language models, is you need a domain expert checking

485
00:26:04.079 --> 00:26:07.279
<v Speaker 2>for factual accuracy, knows enough to know that is right

486
00:26:07.400 --> 00:26:10.519
<v Speaker 2>or that is wrong. Yes, who actually sang on this track,

487
00:26:11.279 --> 00:26:13.880
<v Speaker 2>as well as some coding knowledge to be able to

488
00:26:13.920 --> 00:26:14.920
<v Speaker 2>pull the pieces together.

489
00:26:15.079 --> 00:26:17.480
<v Speaker 3>Yes, that is, that is correct. And one of the

490
00:26:17.480 --> 00:26:21.400
<v Speaker 3>things is, you know a lot of people like really

491
00:26:21.400 --> 00:26:25.680
<v Speaker 3>got excited after the rise of generative EI, but there

492
00:26:25.720 --> 00:26:29.680
<v Speaker 3>are like legitimate you know application, A lot of organizations

493
00:26:29.680 --> 00:26:33.279
<v Speaker 3>built which doesn't use generative AI at all. Like one

494
00:26:33.319 --> 00:26:36.799
<v Speaker 3>of my customers, they did use Copilot Studio, and they

495
00:26:36.839 --> 00:26:40.160
<v Speaker 3>did launch a custom Copilot for their public facing website,

496
00:26:40.759 --> 00:26:43.319
<v Speaker 3>but they didn't use any of the generative AI features

497
00:26:43.359 --> 00:26:46.880
<v Speaker 3>at all, Like, because there will be some instances where

498
00:26:46.960 --> 00:26:50.200
<v Speaker 3>you don't want to give a sort of correct or

499
00:26:50.200 --> 00:26:53.400
<v Speaker 3>incorrect answer, right, You want to get give a factually

500
00:26:53.440 --> 00:26:56.119
<v Speaker 3>correct answer. And that are some of the things that

501
00:26:56.160 --> 00:26:59.119
<v Speaker 3>are there in Copilot Studio where for some of the

502
00:26:59.160 --> 00:27:02.720
<v Speaker 3>information you absolutely want to give the appropriate answers maybe

503
00:27:02.720 --> 00:27:06.039
<v Speaker 3>from your own knowledge pace or maybe from your own

504
00:27:06.200 --> 00:27:09.519
<v Speaker 3>database or you and that you can do. And there

505
00:27:09.519 --> 00:27:12.599
<v Speaker 3>are certain things where you think, okay, I can use

506
00:27:12.640 --> 00:27:14.960
<v Speaker 3>generative AI for some of the answers. You can do

507
00:27:15.000 --> 00:27:17.240
<v Speaker 3>that segregation wasy very easily as well.

508
00:27:17.880 --> 00:27:22.240
<v Speaker 1>I'm just another story. I love telling these stories my

509
00:27:22.359 --> 00:27:24.359
<v Speaker 1>Facebook feed. I don't know about you guys, if you're

510
00:27:24.400 --> 00:27:26.799
<v Speaker 1>on Facebook at all, but every once in a while

511
00:27:26.839 --> 00:27:31.960
<v Speaker 1>I scroll and I'm seeing these accounts that are something

512
00:27:32.119 --> 00:27:37.799
<v Speaker 1>like tiny houses, right, and then this clearly generated beautiful

513
00:27:37.960 --> 00:27:42.240
<v Speaker 1>scene of this wonderful house with multiple staircases and flowing

514
00:27:42.279 --> 00:27:45.000
<v Speaker 1>waterfalls by a brook. And it's just like a place

515
00:27:45.000 --> 00:27:47.480
<v Speaker 1>that you'd want to be, right And the caption is

516
00:27:47.599 --> 00:27:51.480
<v Speaker 1>like peaceful place. It's not. Hey, this is where I

517
00:27:51.519 --> 00:27:56.319
<v Speaker 1>am right now, it's not. And to get millions of

518
00:27:56.400 --> 00:28:02.599
<v Speaker 1>views and hundreds of thousands of comments, it just makes

519
00:28:02.599 --> 00:28:07.319
<v Speaker 1>me mad. Welcome to the new world.

520
00:28:07.680 --> 00:28:10.240
<v Speaker 2>Well, and again it's you used a bought to generate

521
00:28:10.279 --> 00:28:12.160
<v Speaker 2>the content. Who's to say you didn't use a bought

522
00:28:12.240 --> 00:28:16.799
<v Speaker 2>to generate the hits? Well, there you go, all right,

523
00:28:16.880 --> 00:28:20.240
<v Speaker 2>So let's take a break, and when we come back,

524
00:28:20.279 --> 00:28:21.400
<v Speaker 2>I've got a couple of questions.

525
00:28:21.480 --> 00:28:25.599
<v Speaker 1>Great, did you know there's a dot net on aws community?

526
00:28:26.279 --> 00:28:30.279
<v Speaker 1>Follow the social media blogs, YouTube influencers, and open source

527
00:28:30.359 --> 00:28:34.039
<v Speaker 1>projects and add your own voice. Get plugged into the

528
00:28:34.079 --> 00:28:38.759
<v Speaker 1>dot net on aws community at aws dot Amazon dot com,

529
00:28:38.839 --> 00:28:43.400
<v Speaker 1>slash dot net. Hey Carl, here are you maximizing your

530
00:28:43.440 --> 00:28:48.000
<v Speaker 1>dot net applications potential? Rayguns tools not only catch errors,

531
00:28:48.200 --> 00:28:52.839
<v Speaker 1>they provide actionable insights to optimize your code experience the

532
00:28:52.920 --> 00:28:56.559
<v Speaker 1>power of detailed reports and real time monitoring to enhance

533
00:28:56.599 --> 00:29:00.839
<v Speaker 1>your applications performance. Join thousands of dot next developers who

534
00:29:00.839 --> 00:29:04.559
<v Speaker 1>trust Raygun to help them keep their applications running smoothly.

535
00:29:05.240 --> 00:29:08.960
<v Speaker 1>Visit raygun dot com, slash dot net rocks. That's raygun,

536
00:29:09.240 --> 00:29:11.920
<v Speaker 1>r A y g U N dot com, slash do

537
00:29:12.200 --> 00:29:15.319
<v Speaker 1>tn E t r O c ks for your free

538
00:29:15.400 --> 00:29:16.480
<v Speaker 1>fourteen day trial.

539
00:29:17.680 --> 00:29:20.920
<v Speaker 2>We're back. It's dot at rocks. I'mrogard Campbell. Let's call Franklin. Hey, Hey,

540
00:29:21.319 --> 00:29:23.960
<v Speaker 2>I'm talking to our friend Forshett about the role of

541
00:29:24.680 --> 00:29:27.880
<v Speaker 2>as your AI studio as well as the co pilot studio,

542
00:29:27.960 --> 00:29:29.400
<v Speaker 2>because what's better than two?

543
00:29:31.759 --> 00:29:33.960
<v Speaker 1>And I like how you can use them together.

544
00:29:34.119 --> 00:29:36.680
<v Speaker 2>What's the call out for Patreon there, Carl.

545
00:29:36.960 --> 00:29:39.480
<v Speaker 1>Yeah, And if you don't want to hear those ads

546
00:29:39.559 --> 00:29:43.480
<v Speaker 1>during the show, you can subscribe to our Patreon account

547
00:29:43.519 --> 00:29:45.279
<v Speaker 1>for five bucks a month. You can get a feed

548
00:29:45.319 --> 00:29:47.920
<v Speaker 1>that has no ads. So there you go.

549
00:29:48.359 --> 00:29:53.720
<v Speaker 2>Awesome, Forsham, you mentioned like the ux of a co

550
00:29:53.960 --> 00:29:56.039
<v Speaker 2>pilot app. Isn't it just a textbox?

551
00:29:56.880 --> 00:30:00.039
<v Speaker 3>Not necessarily? Sometimes it can be text paused, but some

552
00:30:00.119 --> 00:30:03.279
<v Speaker 3>times you may want to show a really nice adapt card.

553
00:30:03.480 --> 00:30:06.039
<v Speaker 3>Sometimes you may have to show an image or maybe

554
00:30:06.079 --> 00:30:08.680
<v Speaker 3>the user is looking for a video. So those kind

555
00:30:08.720 --> 00:30:11.759
<v Speaker 3>of things like if I go with traditional board framework way,

556
00:30:12.480 --> 00:30:14.200
<v Speaker 3>those kind of things are really hard to you know,

557
00:30:14.319 --> 00:30:19.200
<v Speaker 3>get it right. And another thing way where Copilot Studio

558
00:30:19.279 --> 00:30:23.079
<v Speaker 3>really shines is you know, like how I can support

559
00:30:23.079 --> 00:30:26.759
<v Speaker 3>my copilot across multiple devices or multiple experience. That is

560
00:30:26.920 --> 00:30:29.000
<v Speaker 3>like one of the biggest challenges I have seen with

561
00:30:29.079 --> 00:30:33.359
<v Speaker 3>multiple conversational experiences. Like, yeah, like I build it, it's

562
00:30:33.359 --> 00:30:35.920
<v Speaker 3>worse on my machine, or it works in the environment

563
00:30:35.920 --> 00:30:38.720
<v Speaker 3>where I have deployed, But what about now the customer

564
00:30:38.799 --> 00:30:42.000
<v Speaker 3>wants to use the same copilot on Microsoft Teams, or

565
00:30:42.039 --> 00:30:44.279
<v Speaker 3>they want to use it on a public facing website,

566
00:30:44.400 --> 00:30:46.960
<v Speaker 3>or the customer loves Slack they want to use it there.

567
00:30:47.000 --> 00:30:50.240
<v Speaker 3>Like how I can you know, now redo everything so

568
00:30:50.279 --> 00:30:52.759
<v Speaker 3>that you know, my custom copilot will work on that

569
00:30:52.839 --> 00:30:55.279
<v Speaker 3>experience as well or on that service as well. So

570
00:30:55.359 --> 00:30:59.240
<v Speaker 3>copilot Studio has a lot of these channels or services

571
00:30:59.279 --> 00:31:05.200
<v Speaker 3>already prebuilt where I don't have to redesign my experiences.

572
00:31:05.279 --> 00:31:07.480
<v Speaker 3>All I do is do some back end like changes

573
00:31:07.599 --> 00:31:11.000
<v Speaker 3>in the studio itself, and the same copilot then can

574
00:31:11.039 --> 00:31:13.839
<v Speaker 3>be accessible on let's say SLAG or Microsoft Teams.

575
00:31:14.240 --> 00:31:17.039
<v Speaker 1>In both of these things, I noticed that you know,

576
00:31:17.119 --> 00:31:20.960
<v Speaker 1>you can connect it to your database. Yes, And apparently

577
00:31:21.359 --> 00:31:25.920
<v Speaker 1>the coolest thing is you know how many reverse threaded

578
00:31:26.000 --> 00:31:32.359
<v Speaker 1>left handed hemptas are left in inventory in Atlanta, Georgia warehouse, right,

579
00:31:32.759 --> 00:31:35.200
<v Speaker 1>and it'll just tell you that kind of stuff. But

580
00:31:36.400 --> 00:31:41.559
<v Speaker 1>the cooler things about generative AI for me are building

581
00:31:41.720 --> 00:31:47.440
<v Speaker 1>models from you know, training and unreal data. And is

582
00:31:47.480 --> 00:31:52.960
<v Speaker 1>that something that is not necessarily as valuable to regular,

583
00:31:53.039 --> 00:31:55.920
<v Speaker 1>run of the mill businesses as it would be to say,

584
00:31:56.119 --> 00:31:58.839
<v Speaker 1>you know, a chat gpt, which is the genie in

585
00:31:58.920 --> 00:32:00.359
<v Speaker 1>the bottle that knows everything.

586
00:32:00.519 --> 00:32:03.799
<v Speaker 3>So, if I understand your question correctly, you're alluding more

587
00:32:03.839 --> 00:32:06.119
<v Speaker 3>to you know. How about I have my own data

588
00:32:06.559 --> 00:32:09.119
<v Speaker 3>and I would like to use that data into generative AI.

589
00:32:10.160 --> 00:32:12.039
<v Speaker 3>That's one way of doing it. Or maybe I have

590
00:32:12.119 --> 00:32:14.279
<v Speaker 3>my own data and I would like to generate my

591
00:32:14.400 --> 00:32:18.200
<v Speaker 3>own model using that data because I want the answers

592
00:32:18.240 --> 00:32:20.640
<v Speaker 3>in a specific format or in a specific way rather

593
00:32:20.680 --> 00:32:24.000
<v Speaker 3>than what you know, Chat Jupiter gives me. So there

594
00:32:24.000 --> 00:32:25.519
<v Speaker 3>are a couple of things you can do. And you

595
00:32:25.559 --> 00:32:28.359
<v Speaker 3>may have heard this a lot. Very few companies these

596
00:32:28.440 --> 00:32:31.960
<v Speaker 3>days are investing building their own model because it's very

597
00:32:31.960 --> 00:32:35.799
<v Speaker 3>hard to compete with companies like Amazon, Google, or open

598
00:32:35.839 --> 00:32:39.920
<v Speaker 3>AI or even Microsoft. So most common techniques which most

599
00:32:39.960 --> 00:32:43.000
<v Speaker 3>of the enterprises are using these days is either you

600
00:32:43.079 --> 00:32:47.400
<v Speaker 3>go with a rack pattern like retrieval augumented generation, where

601
00:32:47.480 --> 00:32:50.480
<v Speaker 3>you bring your own data and then feed that context

602
00:32:50.559 --> 00:32:54.319
<v Speaker 3>or data to your large language model and then get

603
00:32:54.359 --> 00:32:57.720
<v Speaker 3>the inside. Second very common is not very common, Less

604
00:32:57.720 --> 00:33:01.799
<v Speaker 3>common and more expensive out is fine tuning this models

605
00:33:02.599 --> 00:33:04.839
<v Speaker 3>like where as part of fine tuning, then I provide

606
00:33:04.880 --> 00:33:08.119
<v Speaker 3>series of prompts some training data. So then after a

607
00:33:08.200 --> 00:33:10.759
<v Speaker 3>fine tuned model is there, then it will be responding

608
00:33:10.759 --> 00:33:13.480
<v Speaker 3>in a specific way, and it will be also having

609
00:33:13.519 --> 00:33:17.400
<v Speaker 3>a specific information that let's say the generic large language

610
00:33:17.400 --> 00:33:19.680
<v Speaker 3>model or foundation on a large language model will not have.

611
00:33:19.960 --> 00:33:22.839
<v Speaker 1>Now, when you say that, do you mean cleaning the data,

612
00:33:23.000 --> 00:33:25.680
<v Speaker 1>like taking out the things that aren't relevant, or do

613
00:33:25.720 --> 00:33:27.720
<v Speaker 1>you mean when you say fine tuning, do you mean

614
00:33:28.240 --> 00:33:30.279
<v Speaker 1>telling it what to ignore in the data.

615
00:33:30.400 --> 00:33:33.759
<v Speaker 3>Yes, so you have to basically provide custom instructions. You

616
00:33:33.759 --> 00:33:36.000
<v Speaker 3>have to provide custom prompts, and you can also provide

617
00:33:36.000 --> 00:33:38.319
<v Speaker 3>your own data as well. Where okay, these are the

618
00:33:38.440 --> 00:33:40.920
<v Speaker 3>sete of things that you should be doing in this

619
00:33:40.960 --> 00:33:43.440
<v Speaker 3>particular way. Don't include that or don't include these, so

620
00:33:43.480 --> 00:33:46.079
<v Speaker 3>those kinds of additional things you can do. One thing

621
00:33:46.119 --> 00:33:48.680
<v Speaker 3>I do want to highlight is fine tuning is not

622
00:33:48.880 --> 00:33:53.119
<v Speaker 3>for everyone it's only you know, for companies where you

623
00:33:53.200 --> 00:33:56.599
<v Speaker 3>know you're going to have large workload or your consumption

624
00:33:56.720 --> 00:33:59.559
<v Speaker 3>is going to be very high. And let's say with

625
00:33:59.640 --> 00:34:03.160
<v Speaker 3>this large language model and or this large language model

626
00:34:03.200 --> 00:34:07.000
<v Speaker 3>will not work in its current capacity because under the hood,

627
00:34:07.000 --> 00:34:10.880
<v Speaker 3>if you read the licensing documentation from both open ai

628
00:34:11.039 --> 00:34:14.760
<v Speaker 3>or even from Microsoft on Azure side, not only you

629
00:34:14.760 --> 00:34:17.159
<v Speaker 3>had to pay money to find tune the model, but

630
00:34:17.400 --> 00:34:19.760
<v Speaker 3>once the model is fine tuned, you had to also

631
00:34:19.760 --> 00:34:22.159
<v Speaker 3>pay Microsoft money or open am money to host that

632
00:34:22.199 --> 00:34:24.679
<v Speaker 3>fine tune model. And then on the top of that

633
00:34:24.840 --> 00:34:27.360
<v Speaker 3>the charges for your tokens that you will be exchanging.

634
00:34:28.159 --> 00:34:30.000
<v Speaker 2>It sounds like rags the way to go there.

635
00:34:30.119 --> 00:34:32.400
<v Speaker 1>Yes, oh you know what you say that, Richard, But

636
00:34:33.039 --> 00:34:36.440
<v Speaker 1>we have some mutual friends in Europe, and one in

637
00:34:36.480 --> 00:34:43.280
<v Speaker 1>particular whose company decided to do away with RAG in

638
00:34:43.320 --> 00:34:47.960
<v Speaker 1>their company because the percentage of errors that came back

639
00:34:48.280 --> 00:34:54.480
<v Speaker 1>was too high. And I just asked this person literally

640
00:34:54.800 --> 00:34:57.719
<v Speaker 1>two weeks ago, has your opinion changed? And no, no

641
00:34:57.840 --> 00:35:01.360
<v Speaker 1>it hasn't. It's still still or prone. So what do

642
00:35:01.360 --> 00:35:02.079
<v Speaker 1>you think about that?

643
00:35:02.320 --> 00:35:06.280
<v Speaker 3>So RAG, you know, on theory, it looks simple, but

644
00:35:06.400 --> 00:35:08.480
<v Speaker 3>one of the hardest challenge I have seen with the

645
00:35:08.559 --> 00:35:12.519
<v Speaker 3>RAG is let's say I'm looking for a specific information

646
00:35:12.639 --> 00:35:15.800
<v Speaker 3>from my company data. There is a limit on how

647
00:35:15.880 --> 00:35:18.000
<v Speaker 3>much of the day, like how much of a token

648
00:35:18.079 --> 00:35:20.280
<v Speaker 3>size or context size you can send to a large

649
00:35:20.320 --> 00:35:24.079
<v Speaker 3>language model. So most challenging part with the RAG implementation

650
00:35:24.239 --> 00:35:27.639
<v Speaker 3>is how I will make sure for that particular specific

651
00:35:27.679 --> 00:35:31.039
<v Speaker 3>question or conversation I'm having, how I can get the

652
00:35:31.159 --> 00:35:35.000
<v Speaker 3>least amount of data that is more relevant, like how

653
00:35:35.000 --> 00:35:37.599
<v Speaker 3>I can get that, And that's where you know, then

654
00:35:37.679 --> 00:35:40.039
<v Speaker 3>you have to have vector databases. That's where you need

655
00:35:40.079 --> 00:35:43.079
<v Speaker 3>to have your ash eye search and those are like

656
00:35:43.159 --> 00:35:46.480
<v Speaker 3>configuring those and getting those right are easier said than done.

657
00:35:46.960 --> 00:35:49.760
<v Speaker 3>And let's say if you're let's say, if you the

658
00:35:49.800 --> 00:35:54.039
<v Speaker 3>example you gave Carl is in case they're saying less errorrates, sorry,

659
00:35:54.039 --> 00:35:56.119
<v Speaker 3>more errorrates on RAG, maybe you know they will go

660
00:35:56.159 --> 00:36:00.800
<v Speaker 3>with fine tuning because there they have the build capacity

661
00:36:01.159 --> 00:36:03.039
<v Speaker 3>in terms of you know, fine tune the model. Because

662
00:36:03.079 --> 00:36:05.239
<v Speaker 3>fine tuning is not everyone's cup of tea, you know,

663
00:36:05.320 --> 00:36:08.400
<v Speaker 3>to have certain skills. And also they may be have

664
00:36:08.480 --> 00:36:11.880
<v Speaker 3>a use case where the cost will justify, you know,

665
00:36:12.119 --> 00:36:16.360
<v Speaker 3>aware or the cost the amount they had to put

666
00:36:16.360 --> 00:36:18.559
<v Speaker 3>in the fine tuning will justify in terms of the

667
00:36:19.239 --> 00:36:20.760
<v Speaker 3>errate they're getting on the right side.

668
00:36:20.960 --> 00:36:24.599
<v Speaker 1>Yeah, how much would it cost for the rag of

669
00:36:24.639 --> 00:36:27.000
<v Speaker 1>the Franklin Brothers band to get the lead singer? Right?

670
00:36:28.079 --> 00:36:28.599
<v Speaker 3>I don't know.

671
00:36:33.039 --> 00:36:35.039
<v Speaker 1>Those are the questions you're gonna be asking, though, you know,

672
00:36:35.079 --> 00:36:39.079
<v Speaker 1>we want we want accuracy because sometimes you can ask

673
00:36:39.119 --> 00:36:42.920
<v Speaker 1>a question if you're if you're looking for information, right,

674
00:36:43.039 --> 00:36:47.119
<v Speaker 1>real information. Typically you're asking a very specific question. Yes,

675
00:36:47.159 --> 00:36:49.880
<v Speaker 1>the answer could be yes, or it could be no, yes,

676
00:36:50.400 --> 00:36:52.280
<v Speaker 1>or the answer could be, you know, one of a

677
00:36:52.440 --> 00:36:57.639
<v Speaker 1>hundred possibilities, and that is usually a fundamental question that

678
00:36:57.840 --> 00:37:01.119
<v Speaker 1>business decisions will be made on because if it wasn't,

679
00:37:02.000 --> 00:37:03.760
<v Speaker 1>what's the point of rag.

680
00:37:04.000 --> 00:37:04.280
<v Speaker 2>Right.

681
00:37:05.039 --> 00:37:07.519
<v Speaker 1>We need to ask these fundamental questions, and then you

682
00:37:07.559 --> 00:37:09.719
<v Speaker 1>need to base decisions on See, we have to be really,

683
00:37:09.760 --> 00:37:12.920
<v Speaker 1>really sure that that answer is correct.

684
00:37:12.960 --> 00:37:15.599
<v Speaker 2>And one of these challenges I remember from you know,

685
00:37:16.039 --> 00:37:19.199
<v Speaker 2>decades ago, where you only have to generate a report

686
00:37:19.239 --> 00:37:22.840
<v Speaker 2>once that has inaccurate numbers, and everybody questions the numbers

687
00:37:22.840 --> 00:37:25.719
<v Speaker 2>forever after that. Yeah, that's right, So you know, you

688
00:37:25.840 --> 00:37:27.840
<v Speaker 2>can't afford to lose that confidence.

689
00:37:28.760 --> 00:37:32.559
<v Speaker 3>And that's where you know, a lot of companies, especially

690
00:37:32.599 --> 00:37:35.280
<v Speaker 3>after the rise of Jenny I especially the big companies

691
00:37:35.320 --> 00:37:38.719
<v Speaker 3>where if they get out any wrong information or incorrect information,

692
00:37:38.800 --> 00:37:41.400
<v Speaker 3>if there is like a significant financial penalty against it,

693
00:37:42.400 --> 00:37:44.599
<v Speaker 3>the first thing they are doing is cleaning up their house,

694
00:37:44.679 --> 00:37:47.360
<v Speaker 3>like cleaning the data. Like what if your data you

695
00:37:47.360 --> 00:37:49.760
<v Speaker 3>have multiple versions of the data. One is saying good thing,

696
00:37:49.800 --> 00:37:53.239
<v Speaker 3>but another saying is bad thing, and then somehow your

697
00:37:53.280 --> 00:37:56.039
<v Speaker 3>AI search or whatever other two you're using picked up

698
00:37:56.039 --> 00:37:59.159
<v Speaker 3>the bad information and then use that to the for

699
00:37:59.280 --> 00:37:59.960
<v Speaker 3>you rag pattern.

700
00:38:00.039 --> 00:38:03.760
<v Speaker 1>I remember Gary Short and Seth Warez both saying that

701
00:38:04.280 --> 00:38:06.719
<v Speaker 1>the majority of their job, and this was maybe ten

702
00:38:06.800 --> 00:38:11.559
<v Speaker 1>years ago, was cleaning data, you know, just to get

703
00:38:11.679 --> 00:38:16.000
<v Speaker 1>more accurate. And so the ability the fine tuning, as

704
00:38:16.039 --> 00:38:20.760
<v Speaker 1>you say, to omit things that are outliers or standard

705
00:38:20.760 --> 00:38:22.880
<v Speaker 1>deviations that are way high or something.

706
00:38:23.880 --> 00:38:26.159
<v Speaker 2>And we now up here in Canada we have a

707
00:38:26.199 --> 00:38:30.039
<v Speaker 2>core precedent around this with Air Canada. So Air Canada

708
00:38:30.119 --> 00:38:35.800
<v Speaker 2>deployed a chat bot using an LM for providing advice

709
00:38:35.920 --> 00:38:41.039
<v Speaker 2>for travel, and a fellow used that tool to figure

710
00:38:41.039 --> 00:38:43.199
<v Speaker 2>out if he could change a flight for it was

711
00:38:43.239 --> 00:38:46.599
<v Speaker 2>a bereavement. Fares are called sover Yeah, a funeral or

712
00:38:46.639 --> 00:38:48.440
<v Speaker 2>something like that. This is a great story. Yeah, And

713
00:38:48.480 --> 00:38:51.159
<v Speaker 2>the software said, yeah, no problem, you can change that.

714
00:38:51.320 --> 00:38:53.599
<v Speaker 2>And so then he went to actually change it and

715
00:38:53.639 --> 00:38:55.880
<v Speaker 2>they said, no, you can't change that, said, well, the

716
00:38:55.920 --> 00:38:58.440
<v Speaker 2>bot told me, and then he was forced to buy

717
00:38:58.480 --> 00:39:00.480
<v Speaker 2>the ticket the last minute spend even more so forth.

718
00:39:00.559 --> 00:39:03.880
<v Speaker 2>But he's now gone through the courts and the court ruled, hey,

719
00:39:03.960 --> 00:39:07.519
<v Speaker 2>that you're using that chatbot as if it was an employee.

720
00:39:07.880 --> 00:39:12.119
<v Speaker 2>You have the same liabilities to that employee. So if

721
00:39:12.119 --> 00:39:15.039
<v Speaker 2>your employee gave incorrect information, you'd be held accountable for it.

722
00:39:15.199 --> 00:39:16.880
<v Speaker 2>Your software, you're also held accountable.

723
00:39:16.920 --> 00:39:20.119
<v Speaker 1>This is a great time to be alive in the

724
00:39:20.199 --> 00:39:23.960
<v Speaker 1>you know, seeing the real genesis of AI taking off

725
00:39:23.960 --> 00:39:26.159
<v Speaker 1>and getting more and more accurate in the in these

726
00:39:26.239 --> 00:39:28.880
<v Speaker 1>kinds of stories really shape it well.

727
00:39:28.880 --> 00:39:31.880
<v Speaker 2>And it's I'm happy to have those case laws in

728
00:39:31.920 --> 00:39:35.400
<v Speaker 2>place because it's for me as the architects, sitting down

729
00:39:35.440 --> 00:39:37.719
<v Speaker 2>with leadership saying we want to use this technologies, like

730
00:39:38.039 --> 00:39:40.840
<v Speaker 2>here is the risk we are assuming, right, right, And

731
00:39:40.920 --> 00:39:44.280
<v Speaker 2>so you know that's just why you spend money on testing.

732
00:39:44.679 --> 00:39:47.719
<v Speaker 1>And that was a yes or no answer, right, It was.

733
00:39:47.719 --> 00:39:48.960
<v Speaker 2>A little more complicated than.

734
00:39:48.840 --> 00:39:51.440
<v Speaker 1>That, Can I get a refund? Yes or something along

735
00:39:51.440 --> 00:39:53.199
<v Speaker 1>those lines or something along those lines.

736
00:39:53.039 --> 00:39:54.760
<v Speaker 2>And yeah, you're right. It pretty much breaks down it

737
00:39:54.800 --> 00:39:55.599
<v Speaker 2>exactly that, Carl.

738
00:39:55.679 --> 00:39:57.840
<v Speaker 3>Yeah, and that's the reason. Like some of the customers

739
00:39:57.840 --> 00:40:00.559
<v Speaker 3>I work with, they are pretty large customers, and one

740
00:40:00.599 --> 00:40:03.760
<v Speaker 3>thing they're doing is hey, EI is great, but nobody

741
00:40:03.760 --> 00:40:06.800
<v Speaker 3>should be launching anything because it going through some governance

742
00:40:06.800 --> 00:40:10.639
<v Speaker 3>committee where they will be evaluating what exactly you're doing.

743
00:40:11.039 --> 00:40:13.679
<v Speaker 3>You know, what technology you're using, whether it's going to

744
00:40:13.679 --> 00:40:17.320
<v Speaker 3>be for internal users or for your customers, because if

745
00:40:17.320 --> 00:40:19.639
<v Speaker 3>you do any mistake, and let's say if it's for

746
00:40:19.760 --> 00:40:22.840
<v Speaker 3>your outside customers, you know, the implication of that can

747
00:40:22.880 --> 00:40:23.559
<v Speaker 3>be tremendous.

748
00:40:23.800 --> 00:40:27.599
<v Speaker 2>Yes, now, definitely the motion at this moment here we are,

749
00:40:27.719 --> 00:40:29.719
<v Speaker 2>you know to a latter half of twenty twenty four

750
00:40:31.000 --> 00:40:34.920
<v Speaker 2>is all internal apps. It's an hr bot, Yes right.

751
00:40:34.920 --> 00:40:37.920
<v Speaker 2>It's those kinds of things where you're only upsetting the

752
00:40:37.960 --> 00:40:40.079
<v Speaker 2>employee when you lie to them about how many vacation

753
00:40:40.159 --> 00:40:40.679
<v Speaker 2>days they have.

754
00:40:41.320 --> 00:40:43.960
<v Speaker 3>And again that's a good point. You got what, Richard,

755
00:40:44.000 --> 00:40:46.840
<v Speaker 3>because vacation days is something that you shouldn't be relying

756
00:40:46.880 --> 00:40:49.239
<v Speaker 3>on general to AI. You tell you, it should be

757
00:40:49.280 --> 00:40:53.039
<v Speaker 3>coming straight from your employee handbook, where where you know

758
00:40:53.079 --> 00:40:55.599
<v Speaker 3>you have full control over what information is getting from

759
00:40:55.599 --> 00:40:57.559
<v Speaker 3>there and what information is surfaced.

760
00:40:57.199 --> 00:40:59.599
<v Speaker 2>If this isn't a project that makes sense Pshan, what

761
00:40:59.679 --> 00:41:02.760
<v Speaker 2>prout checks do like that seems like exactly, I'm not

762
00:41:02.800 --> 00:41:05.280
<v Speaker 2>going to read the handbook. Yes, I'm going to pester

763
00:41:05.559 --> 00:41:07.840
<v Speaker 2>HR for an answer, yes, and they're going to yell

764
00:41:07.880 --> 00:41:09.960
<v Speaker 2>at me. So we make the bot to solve that

765
00:41:10.119 --> 00:41:11.719
<v Speaker 2>problem and you.

766
00:41:11.679 --> 00:41:14.519
<v Speaker 3>Can solve it easily. Now with Copilot Studio along with

767
00:41:14.599 --> 00:41:18.159
<v Speaker 3>Azure Ai Studio as well, but co Pilot Studio, a

768
00:41:18.159 --> 00:41:21.119
<v Speaker 3>lot of companies are also using it for customer centric

769
00:41:21.159 --> 00:41:25.039
<v Speaker 3>BOTE outward bots like Business to Customer as well. And

770
00:41:25.159 --> 00:41:28.320
<v Speaker 3>one of the reasons they are doing it because some

771
00:41:28.360 --> 00:41:32.440
<v Speaker 3>of the red teaming and other stuff the co Pilot

772
00:41:32.480 --> 00:41:35.880
<v Speaker 3>Studio team has done because not peny people understand when

773
00:41:35.880 --> 00:41:39.119
<v Speaker 3>you launch anything external, especially at childbot like Microsoft learn

774
00:41:39.199 --> 00:41:42.320
<v Speaker 3>It in twenty sixteen, where people will try to get

775
00:41:42.320 --> 00:41:44.719
<v Speaker 3>some nasty stuff from it. And if you launch a

776
00:41:44.719 --> 00:41:47.880
<v Speaker 3>public facing website chadbot, you know, anyone can start interacting

777
00:41:47.880 --> 00:41:51.159
<v Speaker 3>with it and start asking some stuff that they shouldn't

778
00:41:51.159 --> 00:41:53.559
<v Speaker 3>be asking or your company shouldn't be giving responsor to that.

779
00:41:53.800 --> 00:41:56.760
<v Speaker 2>So business bring us back to outward facing is not

780
00:41:57.000 --> 00:41:58.519
<v Speaker 2>there yet. It's too high.

781
00:41:58.400 --> 00:42:01.400
<v Speaker 3>Risk y's high risk. Yes, but if you know, if

782
00:42:01.400 --> 00:42:03.599
<v Speaker 3>you use the right tools, or if you know you

783
00:42:03.639 --> 00:42:06.159
<v Speaker 3>know you have a good team in house, you can

784
00:42:06.199 --> 00:42:08.960
<v Speaker 3>definitely use some of these tools where you can have

785
00:42:09.000 --> 00:42:11.920
<v Speaker 3>a more confidence, you know, launching this outward facing application

786
00:42:12.000 --> 00:42:12.320
<v Speaker 3>as well.

787
00:42:12.320 --> 00:42:14.559
<v Speaker 2>I just debate that anyone has a good team at

788
00:42:14.559 --> 00:42:15.079
<v Speaker 2>this point.

789
00:42:15.400 --> 00:42:18.440
<v Speaker 1>What are the things that I think would help And

790
00:42:18.519 --> 00:42:23.280
<v Speaker 1>it's definitely almost impossible with large language models that are

791
00:42:23.320 --> 00:42:26.000
<v Speaker 1>trained on language right and try to predict the next

792
00:42:26.039 --> 00:42:30.559
<v Speaker 1>word in their answer, but citing references. Right, if that

793
00:42:32.239 --> 00:42:35.599
<v Speaker 1>Air Canada bot, you know, when it said yes you

794
00:42:35.639 --> 00:42:38.119
<v Speaker 1>can get a refund and here's a link to the

795
00:42:38.159 --> 00:42:43.280
<v Speaker 1>policy that you can read and decide for yourself, right,

796
00:42:44.800 --> 00:42:48.199
<v Speaker 1>then it might have been a bit more accurate, and

797
00:42:48.400 --> 00:42:51.079
<v Speaker 1>not not necessarily accurate, but it would have allowed the

798
00:42:51.199 --> 00:42:53.960
<v Speaker 1>user of that bot to make a more informed decision.

799
00:42:54.079 --> 00:42:54.320
<v Speaker 2>Yep.

800
00:42:54.480 --> 00:42:58.199
<v Speaker 3>And I think KYL, like Microsoft co Pilot studior team

801
00:42:58.239 --> 00:43:01.360
<v Speaker 3>should hire KRL because now he's letting all their good

802
00:43:01.360 --> 00:43:04.880
<v Speaker 3>features indirectly via all these success stories. But yes, that's

803
00:43:04.880 --> 00:43:08.559
<v Speaker 3>one of the advantage where this co pilor studio giates

804
00:43:08.639 --> 00:43:11.119
<v Speaker 3>is if you are going with the rat pattern and

805
00:43:11.199 --> 00:43:13.719
<v Speaker 3>let's say you're using your knowledge based then while it

806
00:43:13.800 --> 00:43:18.360
<v Speaker 3>generate the information, it does give you information about the citation,

807
00:43:18.519 --> 00:43:21.480
<v Speaker 3>like what are the source of this information and so

808
00:43:21.519 --> 00:43:23.079
<v Speaker 3>that you can click on it and then you can

809
00:43:23.199 --> 00:43:25.599
<v Speaker 3>verify Okay, this information looks great or I can then

810
00:43:25.639 --> 00:43:28.400
<v Speaker 3>you know, use it with more confidence and then I'll

811
00:43:28.440 --> 00:43:29.480
<v Speaker 3>go on with the next stuff.

812
00:43:29.559 --> 00:43:33.159
<v Speaker 1>That's great. It's so good to hear. Yeah, ask chat

813
00:43:33.199 --> 00:43:38.400
<v Speaker 1>GPT for a reference. Sometimes get that and on lessly.

814
00:43:38.480 --> 00:43:40.800
<v Speaker 1>It doesn't know the way, it's bunchet. It's a very

815
00:43:40.840 --> 00:43:42.079
<v Speaker 1>difficult thing to deal with. Yah.

816
00:43:43.280 --> 00:43:45.400
<v Speaker 2>Yeah, I think this is where they it's just where

817
00:43:45.400 --> 00:43:47.480
<v Speaker 2>they use the term grounding. I don't know why this

818
00:43:47.559 --> 00:43:48.639
<v Speaker 2>term was necessary.

819
00:43:48.880 --> 00:43:51.199
<v Speaker 3>Yeah, they use the grounding. Yes, Like, okay, we use

820
00:43:51.239 --> 00:43:54.159
<v Speaker 3>this information from grounding. It's not like something the lllum

821
00:43:54.199 --> 00:43:56.800
<v Speaker 3>made up and this is the source of it or

822
00:43:56.840 --> 00:43:57.320
<v Speaker 3>proof of it.

823
00:43:57.400 --> 00:44:00.840
<v Speaker 2>Can we just always be grounded like thing? Right?

824
00:44:00.920 --> 00:44:02.960
<v Speaker 3>Like really like the people who have kids, you know,

825
00:44:03.159 --> 00:44:05.239
<v Speaker 3>grounding me is totally different in their context.

826
00:44:05.280 --> 00:44:07.440
<v Speaker 1>Yeah right, true enough. Or the people who like to

827
00:44:07.440 --> 00:44:09.599
<v Speaker 1>walk barefoot out in the lawn every day because it

828
00:44:09.639 --> 00:44:12.079
<v Speaker 1>helps their magnetism or something.

829
00:44:12.960 --> 00:44:15.199
<v Speaker 2>Walking barefoot and grass is pretty good. Man. You don't

830
00:44:15.199 --> 00:44:16.159
<v Speaker 2>have nice.

831
00:44:16.719 --> 00:44:19.880
<v Speaker 1>It's nice. It's a nice feeling, but it's all it

832
00:44:19.920 --> 00:44:23.559
<v Speaker 1>needs to be. There's some weird science that is associated

833
00:44:23.559 --> 00:44:28.639
<v Speaker 1>with it would say not soccer, but okay, pseudoscience anyway.

834
00:44:29.239 --> 00:44:34.440
<v Speaker 2>Now I appreciate that. I don't want software speculating on

835
00:44:34.480 --> 00:44:37.559
<v Speaker 2>my van. I prefer it to say I don't know.

836
00:44:37.760 --> 00:44:40.400
<v Speaker 3>Yes, and those kinds of things you can configure in

837
00:44:40.440 --> 00:44:43.519
<v Speaker 3>co pilot studio, like you can have Okay, I'm launching

838
00:44:43.519 --> 00:44:45.920
<v Speaker 3>this copilot and it's going to take care of only

839
00:44:45.960 --> 00:44:48.960
<v Speaker 3>four or five things. Anything extra, people like, anyone ask

840
00:44:49.320 --> 00:44:50.920
<v Speaker 3>it can just come back with I don't know or

841
00:44:51.119 --> 00:44:53.119
<v Speaker 3>I'm not allowed to talk about that. Go through this

842
00:44:53.239 --> 00:44:55.360
<v Speaker 3>link or talk to this person and then and do this.

843
00:44:56.039 --> 00:44:59.840
<v Speaker 1>In chat GPT's defense, it does that, you know, when

844
00:44:59.840 --> 00:45:02.199
<v Speaker 1>it doesn't have an answer, it says you should call

845
00:45:02.239 --> 00:45:05.920
<v Speaker 1>the manufacturer or you know, contact the whatever the authority.

846
00:45:06.159 --> 00:45:10.280
<v Speaker 3>One thing people don't understand is chigibt is a custom experience,

847
00:45:10.400 --> 00:45:13.639
<v Speaker 3>Like you're not interacting with an LM directly like as

848
00:45:13.679 --> 00:45:17.239
<v Speaker 3>an API. It's a customer application. A lot of guard rails,

849
00:45:17.320 --> 00:45:20.639
<v Speaker 3>like Opena has put into it. Now they're launching a

850
00:45:20.679 --> 00:45:22.519
<v Speaker 3>memory as well, so a lot of things are going.

851
00:45:22.559 --> 00:45:24.599
<v Speaker 3>It's a custom app, so yes, if you have a

852
00:45:24.599 --> 00:45:27.000
<v Speaker 3>team that can build a custom app like that, then

853
00:45:27.079 --> 00:45:29.119
<v Speaker 3>yes you will get similar answer. But let's say if

854
00:45:29.119 --> 00:45:32.159
<v Speaker 3>I'm a developer, I'm calling this LLM directly. You know

855
00:45:32.320 --> 00:45:34.920
<v Speaker 3>most of this LM don't have a memory as well,

856
00:45:34.960 --> 00:45:37.719
<v Speaker 3>Like the calls are stateless, so those kinds of things

857
00:45:37.760 --> 00:45:39.039
<v Speaker 3>it may not say, and it may just give you

858
00:45:39.159 --> 00:45:41.840
<v Speaker 3>completely like completely incorrect answer as well.

859
00:45:41.920 --> 00:45:44.400
<v Speaker 2>Yeah, and I guess that that brings me back to

860
00:45:44.440 --> 00:45:46.519
<v Speaker 2>this whole. Like the HR bot makes sense is if

861
00:45:46.559 --> 00:45:48.800
<v Speaker 2>you can ground it on. I know you're not going

862
00:45:48.840 --> 00:45:51.719
<v Speaker 2>to read the user they employee hand books, so I'm

863
00:45:51.719 --> 00:45:53.639
<v Speaker 2>going to turn it into the ability for you to

864
00:45:53.679 --> 00:45:56.519
<v Speaker 2>ask questions and it's going to reference accurately. And when

865
00:45:56.559 --> 00:45:59.920
<v Speaker 2>I'm not when my certainty is low in the SAW

866
00:46:00.159 --> 00:46:02.840
<v Speaker 2>where let software certainty low, it's like you should talk

867
00:46:02.880 --> 00:46:05.000
<v Speaker 2>to HR about that. Because from an HR perspective, if

868
00:46:05.000 --> 00:46:07.199
<v Speaker 2>I can take eighty percent of the stupid questions and

869
00:46:07.320 --> 00:46:10.000
<v Speaker 2>just answer them so I'm only dealing with the twenty

870
00:46:10.039 --> 00:46:12.599
<v Speaker 2>percent that are more complex, I'm pretty happy. Like that's

871
00:46:12.639 --> 00:46:13.320
<v Speaker 2>a good outcome.

872
00:46:13.360 --> 00:46:15.039
<v Speaker 3>You can use for HR, you can use it for

873
00:46:15.079 --> 00:46:17.840
<v Speaker 3>your IT support as well, because it has connectors where

874
00:46:18.239 --> 00:46:20.320
<v Speaker 3>it can talk to your own knowledge base and that

875
00:46:20.360 --> 00:46:21.639
<v Speaker 3>knowledge base can be in service.

876
00:46:21.719 --> 00:46:24.199
<v Speaker 2>Now yeah, no, I just put I have an automated

877
00:46:24.199 --> 00:46:26.000
<v Speaker 2>responder just says you try turning it off and turning

878
00:46:26.000 --> 00:46:26.599
<v Speaker 2>it back on again.

879
00:46:26.599 --> 00:46:32.000
<v Speaker 3>Like said, yeah, my favorite code is be smart restart.

880
00:46:32.159 --> 00:46:36.679
<v Speaker 1>You know there you go. Nice? Nice works for phones too. Yep, yes,

881
00:46:37.239 --> 00:46:41.239
<v Speaker 1>all the time. And Pat and Dwayne Patrick Kines Douain

882
00:46:41.320 --> 00:46:44.400
<v Speaker 1>Laflat from security this week say you should be restarting

883
00:46:44.400 --> 00:46:49.039
<v Speaker 1>your phone at least once a week at least if not,

884
00:46:49.280 --> 00:46:53.320
<v Speaker 1>you know, three or four times a week. Yeah, all right,

885
00:46:53.400 --> 00:46:55.519
<v Speaker 1>So where do we go from here? Is there more

886
00:46:55.639 --> 00:46:58.639
<v Speaker 1>that we haven't discussed? Like how is it expensive? How

887
00:46:58.639 --> 00:46:59.440
<v Speaker 1>do we get started?

888
00:46:59.519 --> 00:47:03.559
<v Speaker 3>Okay, so cost wise, even though it's a part of

889
00:47:03.639 --> 00:47:07.159
<v Speaker 3>Power Platform, its cost is a little bit different. It's

890
00:47:07.239 --> 00:47:10.920
<v Speaker 3>just like ash. Everything is based on the consumption. So

891
00:47:10.960 --> 00:47:13.840
<v Speaker 3>the minimum pricing point is you had to bot. You

892
00:47:13.880 --> 00:47:17.400
<v Speaker 3>had to buy a package for twenty five thousand messages,

893
00:47:17.679 --> 00:47:21.360
<v Speaker 3>which cost two hundred dollars. And again this is a

894
00:47:21.400 --> 00:47:24.039
<v Speaker 3>retail price, like if you have an enterprise agreement, you

895
00:47:24.039 --> 00:47:26.679
<v Speaker 3>may be getting some discount on that, but that brings

896
00:47:26.760 --> 00:47:28.840
<v Speaker 3>up a question is what exactly a message is? Right?

897
00:47:28.960 --> 00:47:33.039
<v Speaker 3>A message is basically anything like when I ask anything

898
00:47:33.519 --> 00:47:36.320
<v Speaker 3>to my copilot, if that's going to trigger any kind

899
00:47:36.320 --> 00:47:39.320
<v Speaker 3>of response or any kind of AI agent a call

900
00:47:39.400 --> 00:47:42.880
<v Speaker 3>behind the scene, that constitute as a one message. And

901
00:47:42.920 --> 00:47:46.880
<v Speaker 3>if I'm using generative AI features, then just for the

902
00:47:46.920 --> 00:47:49.880
<v Speaker 3>sake of simplicity, what Microsoft has done is any answer

903
00:47:49.920 --> 00:47:51.880
<v Speaker 3>that has generative AI in it, it is just going

904
00:47:51.880 --> 00:47:54.480
<v Speaker 3>to count as the two messages. So if I ask

905
00:47:54.519 --> 00:47:57.599
<v Speaker 3>a question you know how many PTOs are there, and

906
00:47:57.639 --> 00:48:00.519
<v Speaker 3>I'm using some of the generative EI features and then

907
00:48:00.559 --> 00:48:03.519
<v Speaker 3>that come back, then it just counts as the two messages.

908
00:48:03.719 --> 00:48:06.079
<v Speaker 1>Now it's two hundred dollars a month for twenty five

909
00:48:06.119 --> 00:48:08.679
<v Speaker 1>thousand messages a month. Does that mean that if I

910
00:48:08.719 --> 00:48:12.440
<v Speaker 1>only use one thousand messages or you know, less than that,

911
00:48:12.559 --> 00:48:14.639
<v Speaker 1>am I only going to get charge point zero zero

912
00:48:14.760 --> 00:48:16.599
<v Speaker 1>eight cents per message?

913
00:48:16.719 --> 00:48:19.719
<v Speaker 3>No? No, you have to, Like the minimum is two hundred.

914
00:48:19.760 --> 00:48:21.239
<v Speaker 1>That's what you have, whether you use it or not.

915
00:48:21.480 --> 00:48:26.320
<v Speaker 3>Yes, And that's one story which is really not good

916
00:48:26.679 --> 00:48:30.519
<v Speaker 3>in my opinion right now, because you buy that license

917
00:48:30.519 --> 00:48:33.639
<v Speaker 3>for per tenant. Okay, but what if I'm working for

918
00:48:33.719 --> 00:48:36.480
<v Speaker 3>a really big organization where I have thousands and thousands

919
00:48:36.480 --> 00:48:40.440
<v Speaker 3>of employee and ten departments are there. Every department has

920
00:48:40.440 --> 00:48:42.880
<v Speaker 3>their own copilot and how exactly we're going to manage

921
00:48:42.880 --> 00:48:46.239
<v Speaker 3>the pricing because those twenty five thousand message I can

922
00:48:46.280 --> 00:48:47.960
<v Speaker 3>be exhausting in one hour or two hours.

923
00:48:48.039 --> 00:48:51.199
<v Speaker 2>Yeah sure, yeah, yeah, it feels like it's beta pricing,

924
00:48:51.360 --> 00:48:54.000
<v Speaker 2>Like it's just pricing because you're doing a beta test project,

925
00:48:54.039 --> 00:48:57.920
<v Speaker 2>not actually deployment level yet. And so now I'm starting

926
00:48:57.920 --> 00:49:01.920
<v Speaker 2>to feel like the relationship between copile studio and as

927
00:49:01.960 --> 00:49:05.119
<v Speaker 2>your AI studio is the same relationship between power platform

928
00:49:05.239 --> 00:49:06.639
<v Speaker 2>and visual studio.

929
00:49:06.360 --> 00:49:08.960
<v Speaker 1>Correct, right, Yeah, that makes sense to me, Richard.

930
00:49:09.159 --> 00:49:12.880
<v Speaker 2>So if you're the front end stuff, the place to experiment,

931
00:49:13.280 --> 00:49:16.760
<v Speaker 2>the price to prototype copilot studio when you want to

932
00:49:16.800 --> 00:49:21.079
<v Speaker 2>get into precision and tuning and more skilled people AI

933
00:49:21.199 --> 00:49:23.960
<v Speaker 2>studio knowing that anything you build an AI studio you

934
00:49:24.039 --> 00:49:27.920
<v Speaker 2>can surface in copilot. Yeah, and all right, I dig this.

935
00:49:27.920 --> 00:49:30.400
<v Speaker 2>This is huge in development. Like you said, we did

936
00:49:30.400 --> 00:49:34.000
<v Speaker 2>that show with vishuas earlier this year where we talked

937
00:49:34.000 --> 00:49:37.400
<v Speaker 2>about that same dynamic between power platform and dot net right.

938
00:49:37.239 --> 00:49:39.440
<v Speaker 3>That the and I know yeah, which Fus is a

939
00:49:39.519 --> 00:49:42.719
<v Speaker 3>huge advocate of future development. In fact, he's the one

940
00:49:42.719 --> 00:49:45.119
<v Speaker 3>who taught me all this concept, and I agree with

941
00:49:45.159 --> 00:49:48.280
<v Speaker 3>it because the more and more I work with enterprise customers,

942
00:49:48.480 --> 00:49:52.840
<v Speaker 3>like there's always the case where developers, like good developers

943
00:49:52.880 --> 00:49:56.320
<v Speaker 3>are a rare commodity. Yeah, and you'll agree that, you know,

944
00:49:56.440 --> 00:49:58.599
<v Speaker 3>it's very hard to find good developers, Like how about

945
00:49:58.639 --> 00:50:02.000
<v Speaker 3>then we create some tools where you need developers only

946
00:50:02.039 --> 00:50:04.519
<v Speaker 3>when that tool cannot do certain things for you, right.

947
00:50:04.559 --> 00:50:07.119
<v Speaker 2>Right, But the other part is like I never look

948
00:50:07.159 --> 00:50:09.000
<v Speaker 2>at the power platform folks, and I guess it's the

949
00:50:09.039 --> 00:50:12.079
<v Speaker 2>copilot studio folks. As these are the lessers, these are

950
00:50:12.119 --> 00:50:15.159
<v Speaker 2>the domain experts. They're actually going to save you time

951
00:50:15.519 --> 00:50:19.000
<v Speaker 2>because they know more about the problem space than you do. Right, Like,

952
00:50:19.239 --> 00:50:22.119
<v Speaker 2>my struggle with really great developers is how much time

953
00:50:22.159 --> 00:50:25.199
<v Speaker 2>they spend having to learn the domain. Where if you

954
00:50:25.320 --> 00:50:28.800
<v Speaker 2>have a good domain expert relationship going there, you can

955
00:50:28.880 --> 00:50:31.039
<v Speaker 2>focus more on breaking those bits down and go, okay,

956
00:50:31.039 --> 00:50:33.559
<v Speaker 2>well that's where some custom code needs to live. That's

957
00:50:33.599 --> 00:50:35.719
<v Speaker 2>there is no easy solution there. We need to build something,

958
00:50:35.800 --> 00:50:37.840
<v Speaker 2>take a spike, you know, those kinds of things. Like

959
00:50:37.920 --> 00:50:41.119
<v Speaker 2>that's a really constructive way to work quickly.

960
00:50:41.320 --> 00:50:44.960
<v Speaker 3>And I'm seeing like so I'm talking about this conversation

961
00:50:45.039 --> 00:50:49.280
<v Speaker 3>AI or Microsoft Board framework since twenty seventeen and since

962
00:50:49.400 --> 00:50:52.960
<v Speaker 3>last Ignite. I'm saying like just a huge interest from

963
00:50:52.960 --> 00:50:56.599
<v Speaker 3>developers as well, like pro developers thinking about or are

964
00:50:56.679 --> 00:50:59.599
<v Speaker 3>eager to learn about co pilot studio. Like a couple

965
00:50:59.639 --> 00:51:02.159
<v Speaker 3>of weeks before, I was at dev Intersection or next

966
00:51:02.239 --> 00:51:05.800
<v Speaker 3>Gen EI conference in Vegas and I did session and

967
00:51:05.840 --> 00:51:10.280
<v Speaker 3>a workshop on copilot Studio and its attendance was more

968
00:51:10.360 --> 00:51:12.840
<v Speaker 3>than some of the popular you know, pro developer sessions

969
00:51:12.960 --> 00:51:14.760
<v Speaker 3>or confront or workshops were there.

970
00:51:15.920 --> 00:51:20.360
<v Speaker 2>Yeah, that's great, but any Yeah, I appreciate this because

971
00:51:20.360 --> 00:51:22.920
<v Speaker 2>you start now, you start thinking about rethinking the UX

972
00:51:22.960 --> 00:51:26.880
<v Speaker 2>of an application, a lot less buttons and sliders and

973
00:51:26.960 --> 00:51:30.639
<v Speaker 2>a lot more texts and most of the screen taken

974
00:51:30.719 --> 00:51:35.599
<v Speaker 2>up with it presenting information to you based on your requests.

975
00:51:35.679 --> 00:51:37.599
<v Speaker 3>Right, And it doesn't have to be only this. It

976
00:51:37.639 --> 00:51:40.239
<v Speaker 3>can be. Yes, for some things you'll be using this,

977
00:51:40.440 --> 00:51:44.440
<v Speaker 3>but for some more traditional or more things where you

978
00:51:44.519 --> 00:51:47.119
<v Speaker 3>definite need to have you like proper UI with multiple

979
00:51:47.119 --> 00:51:49.920
<v Speaker 3>buttons and multiple sliders, yes you'll have that, but this

980
00:51:50.000 --> 00:51:52.559
<v Speaker 3>can be your beginning point where I'm every day I'm

981
00:51:52.559 --> 00:51:54.960
<v Speaker 3>starting my work. I'm just coming here, you know, doing

982
00:51:55.000 --> 00:51:57.159
<v Speaker 3>my stuff. I asking copilot to do a bunch of

983
00:51:57.199 --> 00:52:00.559
<v Speaker 3>stuff for me. And I always give the example, you know,

984
00:52:00.639 --> 00:52:04.400
<v Speaker 3>Javis in Iron Man. That's a good example. Hey, there

985
00:52:04.400 --> 00:52:07.039
<v Speaker 3>are certain things if Javis can do. Let Javis handle that.

986
00:52:07.119 --> 00:52:09.400
<v Speaker 3>And I'm going to focus more on the complicated stuff

987
00:52:09.400 --> 00:52:10.039
<v Speaker 3>behind the scene.

988
00:52:11.559 --> 00:52:16.840
<v Speaker 2>Yeah, I'm waiting for office to bet on this tap. Right,

989
00:52:16.920 --> 00:52:19.559
<v Speaker 2>Like you think about immediately think in terms of the

990
00:52:19.599 --> 00:52:22.599
<v Speaker 2>old Alan Cooper of the Sovereign app. Right, what's the

991
00:52:22.639 --> 00:52:25.719
<v Speaker 2>first app you go to? Now? For me? It's still Outlook,

992
00:52:26.079 --> 00:52:27.239
<v Speaker 2>you know. That's why I'm sad.

993
00:52:29.880 --> 00:52:32.639
<v Speaker 1>Six five threads one for me.

994
00:52:35.559 --> 00:52:37.639
<v Speaker 2>There's also teams, like a lot of folks, that's their

995
00:52:37.679 --> 00:52:41.840
<v Speaker 2>starting point. And you start thinking about the LLM interface

996
00:52:41.880 --> 00:52:44.119
<v Speaker 2>to this, how you would start your day? I mean

997
00:52:44.599 --> 00:52:48.559
<v Speaker 2>right away, your first step in either app is triage. Yes, right,

998
00:52:48.639 --> 00:52:51.880
<v Speaker 2>what's the most important thing? Right? What do I need

999
00:52:51.920 --> 00:52:52.400
<v Speaker 2>to deal with?

1000
00:52:52.480 --> 00:52:52.760
<v Speaker 1>Now?

1001
00:52:53.079 --> 00:52:54.800
<v Speaker 2>You know, where are the crises and so forth? Like

1002
00:52:54.880 --> 00:52:58.719
<v Speaker 2>you could see a good agentic type tooling in this,

1003
00:52:59.400 --> 00:53:02.360
<v Speaker 2>helping this or through those priorities. I mean, I would

1004
00:53:02.440 --> 00:53:04.800
<v Speaker 2>argue the first triage is how many of these has

1005
00:53:04.840 --> 00:53:06.639
<v Speaker 2>the bot already responded to?

1006
00:53:07.000 --> 00:53:07.159
<v Speaker 3>MM?

1007
00:53:07.239 --> 00:53:10.079
<v Speaker 2>Hmm right, I've not only sorted these out. Here's the

1008
00:53:10.079 --> 00:53:12.039
<v Speaker 2>ones I think. Here the answers are, are you okay

1009
00:53:12.079 --> 00:53:14.280
<v Speaker 2>with this? It's like yeah, yes, and that that that

1010
00:53:14.280 --> 00:53:16.000
<v Speaker 2>that that that okay? What are the ones I need

1011
00:53:16.039 --> 00:53:18.800
<v Speaker 2>a trioge like my wife could my life could be

1012
00:53:18.840 --> 00:53:19.320
<v Speaker 2>way better?

1013
00:53:19.519 --> 00:53:24.119
<v Speaker 3>Yeah. I think that story is still evolving with Copilot

1014
00:53:24.159 --> 00:53:27.960
<v Speaker 3>for Microsoft sixty five. Uh, and they do have one

1015
00:53:28.840 --> 00:53:31.440
<v Speaker 3>capability there where in case you would like to extend

1016
00:53:31.440 --> 00:53:33.639
<v Speaker 3>the functionality of out of the box co pilot there

1017
00:53:34.039 --> 00:53:37.039
<v Speaker 3>you can use Copilot Studio and its license is already

1018
00:53:37.079 --> 00:53:41.119
<v Speaker 3>covered as part of your Microsoft Pay sixty five Copilot license,

1019
00:53:41.719 --> 00:53:43.039
<v Speaker 3>so I can listen.

1020
00:53:43.159 --> 00:53:46.880
<v Speaker 2>Prashan, I'm an old grizzled Microsoft observer. At this point,

1021
00:53:47.320 --> 00:53:52.280
<v Speaker 2>you're not grizzled. I've come to appreciate disper he said

1022
00:53:52.320 --> 00:53:56.480
<v Speaker 2>on a podcast. You will know a technology is being

1023
00:53:56.519 --> 00:54:00.239
<v Speaker 2>taken serious by Microsoft when they bet their mark key

1024
00:54:00.280 --> 00:54:06.760
<v Speaker 2>products on it. Yes, right, when that shows up in Outlook. Okay,

1025
00:54:06.760 --> 00:54:10.599
<v Speaker 2>they're committed, like, no two ways about it. That's why

1026
00:54:10.639 --> 00:54:13.920
<v Speaker 2>we bought into Active Acts or you know that whole

1027
00:54:13.960 --> 00:54:17.760
<v Speaker 2>control surface. That's why we bought into Ola, and it's

1028
00:54:17.760 --> 00:54:22.119
<v Speaker 2>also why we were suspect of WPF when Office never

1029
00:54:22.199 --> 00:54:25.360
<v Speaker 2>took on WPF. It wasn't until Visual Studio took out WPF.

1030
00:54:25.400 --> 00:54:29.239
<v Speaker 2>They're like, okay, right, Like that's there's a reason to

1031
00:54:29.280 --> 00:54:31.599
<v Speaker 2>take the cues this way as to how far down

1032
00:54:31.639 --> 00:54:34.199
<v Speaker 2>the path these products really are. Like I feel like

1033
00:54:34.239 --> 00:54:38.239
<v Speaker 2>these are still experimental products because the Microsoft seems to

1034
00:54:38.280 --> 00:54:40.360
<v Speaker 2>feel these things are still expound products.

1035
00:54:40.360 --> 00:54:42.320
<v Speaker 1>Well, they may be working on them, but they haven't

1036
00:54:42.320 --> 00:54:45.440
<v Speaker 1>surfaced yet. Yeah, I have a question for you, totally

1037
00:54:45.480 --> 00:54:49.119
<v Speaker 1>off topic, well not really. Apple's coming out very soon

1038
00:54:49.239 --> 00:54:51.880
<v Speaker 1>with probably out by now, the new iPhone that has

1039
00:54:52.079 --> 00:54:59.360
<v Speaker 1>AI Apple Intelligence not artificial at all, and I kind

1040
00:54:59.360 --> 00:55:04.239
<v Speaker 1>of I feel like I get it. They're basically taking

1041
00:55:04.320 --> 00:55:09.559
<v Speaker 1>the SII experience and exposing you know, GPT four oh

1042
00:55:09.679 --> 00:55:13.840
<v Speaker 1>on the back end so that Siri will become more intelligent.

1043
00:55:14.880 --> 00:55:17.280
<v Speaker 1>And I just wonder if you know anything about that,

1044
00:55:17.400 --> 00:55:20.400
<v Speaker 1>because I, you know, if that is as easy as

1045
00:55:20.400 --> 00:55:22.239
<v Speaker 1>it sounds. Man, I'm going to get in my car,

1046
00:55:22.320 --> 00:55:25.119
<v Speaker 1>turn on car Play, and just start talking to my

1047
00:55:25.280 --> 00:55:27.440
<v Speaker 1>car and it's going to be night rider.

1048
00:55:28.599 --> 00:55:30.880
<v Speaker 2>You know, what do you think for Shadow? I have thoughts.

1049
00:55:31.199 --> 00:55:33.760
<v Speaker 3>So I'm not an Apple person. I use most of

1050
00:55:33.800 --> 00:55:37.400
<v Speaker 3>the Android stuff. But after saying, like some of the

1051
00:55:37.440 --> 00:55:42.800
<v Speaker 3>demos from open Ei recently, the demo ware, the latest

1052
00:55:42.880 --> 00:55:45.280
<v Speaker 3>version of GPT four oh is multimodel, and then we

1053
00:55:45.360 --> 00:55:48.159
<v Speaker 3>can have multiple conversation and we can also change the

1054
00:55:48.199 --> 00:55:51.519
<v Speaker 3>context in between. I think we are getting there as

1055
00:55:51.559 --> 00:55:55.119
<v Speaker 3>long as they figure out how to run this LLLMS

1056
00:55:55.239 --> 00:55:58.039
<v Speaker 3>or all these models cheaply on the devices. Yeah, I

1057
00:55:58.039 --> 00:56:00.400
<v Speaker 3>think we are there, like maybe one or two years.

1058
00:56:00.599 --> 00:56:03.239
<v Speaker 3>Once they put some SA safeguards in it. And let's

1059
00:56:03.239 --> 00:56:05.519
<v Speaker 3>say I'm driving a car and if that LM can

1060
00:56:05.599 --> 00:56:08.239
<v Speaker 3>execute on my car's hardware, we should be there.

1061
00:56:08.559 --> 00:56:12.199
<v Speaker 1>Are you saying that the LLLM stuff is on the phone,

1062
00:56:12.239 --> 00:56:14.960
<v Speaker 1>they're not just calling an API out to GPT to

1063
00:56:16.159 --> 00:56:16.840
<v Speaker 1>do the work.

1064
00:56:17.039 --> 00:56:20.000
<v Speaker 3>Yeah, Like once that gets mainstream, like where we will

1065
00:56:20.000 --> 00:56:22.920
<v Speaker 3>have more and more this small language model is doing

1066
00:56:22.920 --> 00:56:26.360
<v Speaker 3>all this stuff, but they're actually executing on the local stuff,

1067
00:56:26.440 --> 00:56:28.920
<v Speaker 3>we'll have like much better experience, and we'll also see

1068
00:56:28.960 --> 00:56:32.079
<v Speaker 3>more and more push from these companies as well, because.

1069
00:56:32.079 --> 00:56:34.519
<v Speaker 1>I don't know if that's the case now. It seems

1070
00:56:34.559 --> 00:56:37.239
<v Speaker 1>to me that they're just calling out too with an API,

1071
00:56:37.360 --> 00:56:38.000
<v Speaker 1>but that.

1072
00:56:38.159 --> 00:56:39.920
<v Speaker 3>Is a very expensive right now, Like if you look

1073
00:56:39.960 --> 00:56:44.440
<v Speaker 3>at yes, they can do it for a few months

1074
00:56:44.480 --> 00:56:47.679
<v Speaker 3>or a few years, but that's not really long sustainable

1075
00:56:48.239 --> 00:56:52.800
<v Speaker 3>business model because all these lllms require really bf infrastructure.

1076
00:56:53.599 --> 00:56:56.679
<v Speaker 3>That's the reason why I think. I don't please don't

1077
00:56:56.760 --> 00:56:58.840
<v Speaker 3>quote me on this, but I heard even though openan

1078
00:56:58.880 --> 00:57:01.119
<v Speaker 3>AA is making so much money, like billions of dollars,

1079
00:57:01.639 --> 00:57:03.800
<v Speaker 3>they're still not profitable because they had to spend a

1080
00:57:03.800 --> 00:57:06.559
<v Speaker 3>lot of money behind the scene on the infrastructure to support.

1081
00:57:06.159 --> 00:57:07.679
<v Speaker 2>That far and far more.

1082
00:57:07.920 --> 00:57:09.639
<v Speaker 1>Yes, what was your thoughts, Richard?

1083
00:57:09.760 --> 00:57:11.880
<v Speaker 2>I think Apple is late to the AI game and

1084
00:57:11.920 --> 00:57:13.880
<v Speaker 2>needed to announce something, and the fact that they keep

1085
00:57:13.920 --> 00:57:15.639
<v Speaker 2>pushing back the release of it is proof they don't

1086
00:57:15.639 --> 00:57:19.800
<v Speaker 2>know what they just committed to. Ooh, and they're panicking.

1087
00:57:20.960 --> 00:57:26.280
<v Speaker 2>You know, we expect Apple to come late with a

1088
00:57:26.320 --> 00:57:29.480
<v Speaker 2>refined product that has clearly not happened. The fact that

1089
00:57:29.480 --> 00:57:34.119
<v Speaker 2>they've already pushed the dates back shows they they said

1090
00:57:34.119 --> 00:57:36.960
<v Speaker 2>they were going to jump before they jumped, and now

1091
00:57:37.079 --> 00:57:40.519
<v Speaker 2>they're struggling because it is a very unrefined product. It

1092
00:57:40.599 --> 00:57:43.119
<v Speaker 2>is not asked Apple asque at all, but they could

1093
00:57:43.159 --> 00:57:45.920
<v Speaker 2>not afford to not say something good way to hear,

1094
00:57:46.280 --> 00:57:48.039
<v Speaker 2>so they had You know, they only do one show

1095
00:57:48.039 --> 00:57:50.559
<v Speaker 2>a year. They do that show in the WWDCS in June.

1096
00:57:51.039 --> 00:57:52.679
<v Speaker 2>Were you really going to wait till twenty twenty five?

1097
00:57:52.760 --> 00:57:55.440
<v Speaker 2>You couldn't, so you said it, and now you're trying

1098
00:57:55.440 --> 00:57:58.360
<v Speaker 2>to make it come true. That's not very apple asque,

1099
00:57:58.559 --> 00:58:01.360
<v Speaker 2>but all the hallmarks are there. The only reason we

1100
00:58:01.440 --> 00:58:03.760
<v Speaker 2>might give a pass is because Zappli you don't do that.

1101
00:58:04.199 --> 00:58:05.400
<v Speaker 2>It looks like they did this time.

1102
00:58:05.639 --> 00:58:08.320
<v Speaker 1>Well that feels like the end of our show, Prashant.

1103
00:58:08.400 --> 00:58:12.280
<v Speaker 1>This has been very enlightening and I appreciate you putting

1104
00:58:12.360 --> 00:58:16.840
<v Speaker 1>up with my digressions and you know, little voyages into

1105
00:58:16.960 --> 00:58:20.199
<v Speaker 1>stories and things. But man, this is just chocolate block

1106
00:58:20.280 --> 00:58:22.360
<v Speaker 1>full of great meaty goodness.

1107
00:58:22.519 --> 00:58:24.920
<v Speaker 3>So thank you, thank you, thanks for having me. And

1108
00:58:25.239 --> 00:58:28.559
<v Speaker 3>it was really it was my pleasure, you know, pleasure

1109
00:58:28.599 --> 00:58:29.920
<v Speaker 3>on all of my side for.

1110
00:58:30.000 --> 00:58:33.800
<v Speaker 1>This fantastic all right, and we'll talk to you next

1111
00:58:33.840 --> 00:58:57.840
<v Speaker 1>time on dot net rocks. Dot net Rocks is brought

1112
00:58:57.840 --> 00:59:01.280
<v Speaker 1>to you by Franklin's Net and produce by Pop Studios,

1113
00:59:01.679 --> 00:59:05.719
<v Speaker 1>a full service audio, video and post production facility located

1114
00:59:05.719 --> 00:59:08.639
<v Speaker 1>physically in New London, Connecticut, and of course in the

1115
00:59:08.719 --> 00:59:12.599
<v Speaker 1>cloud online at pwop dot com.

1116
00:59:12.800 --> 00:59:14.920
<v Speaker 4>Visit our website at d O T N E t

1117
00:59:15.159 --> 00:59:19.199
<v Speaker 4>R O c k s dot com for RSS feeds, downloads,

1118
00:59:19.320 --> 00:59:22.960
<v Speaker 4>mobile apps, comments, and access to the full archives going

1119
00:59:23.039 --> 00:59:26.280
<v Speaker 4>back to show number one, recorded in September two.

1120
00:59:26.119 --> 00:59:29.119
<v Speaker 1>Thousand and two. And make sure you check out our sponsors.

1121
00:59:29.280 --> 00:59:32.320
<v Speaker 1>They keep us in business. Now go write some code,

1122
00:59:32.639 --> 00:59:33.360
<v Speaker 1>See you next time.

1123
00:59:34.320 --> 00:59:36.079
<v Speaker 3>You got Jamal Vans

1124
00:59:38.199 --> 00:59:42.920
<v Speaker 2>And