Everyday AI Podcast – An AI and ChatGPT Podcast - EP 275: Be prepared to ChatGPT your competition before they ChatGPT you

Episode Date: May 17, 2024

If you're not gonna use AI, your competition is. And they might crush you. Or, they might ChatGPT you. Barak Turovsky, VP of AI at Cisco, gives us the best ways to think about Generative AI and how to implement it.  Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Barak questions on ChatGPTRelated Episodes: Ep 197: 5 Simple Steps to Start Using GenAI at Your Business TodayEp 246: No that’s not how ChatGPT works. A guide on who to trust around LLMsUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1. Large Language Models (LLMs) and Business Competitiveness2. Understanding LLMs for Small to Medium-Sized Businesses3. Use Cases and Misconceptions of AI4. Data Security and PrivacyTimestamps:01:35 About Barak and Cisco05:44 AI innovation concentrated in big tech companies.07:14 Large language models can revolutionize customer interactions.12:01 ChatGPT fluency doesn't guarantee accurate information.13:41 Considering use cases over two dimensions18:16 OLM is good fit for specific industries.21:17 Emphasizing the importance of large language models.23:20 Maintaining control over unique AI model elements.28:50 Questioning the data use in large models.31:27 Barak discusses leveraging AI for various use cases.33:50 Industry leader shared great insights on AI.Keywords:AI, Large Language Models, Jordan Wilson, Barak Turovsky, Cisco, Google Translate, Transformer Technology, Generative AI, Democratization of Access, Customer Satisfaction, Business Productivity, Business Disruption, Internet Search, Sales Decks, Scalable Businesses, Fluency-Accuracy Misconception, AI Use Cases, Data Privacy, Data Security, Model Distillation, Domain-Specific AI Models, Small AI Models, Gargantuan AI Models, Data Leverage, AI for Enterprises, Data Selling, Entertainment Use Case, Business Growth, Professional Upskilling, AI Newsletter.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

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It's something that I say a lot here on the everyday AI show that, you know, hey, it's no longer 2023. There's no longer time to be experimenting. If you're not implementing generative AI in 2024, you are asking for your competition to pass you. or maybe you're asking for your competition to chat GPT you, right? So I'm extremely excited for today's conversation where we're talking to, and we're saying literally this, to be prepared to chat GPT your competition before your competition chat GPT is you.

So extremely excited for today's show before we bring on today's guests, just as a reminder, as always, if you're listening, whether you're in the car, on the treadmill, walking your dog, wherever you are, make sure afterwards or right now to go to your everyday AI. sign up for our free daily newsletter i already know today's conversation is going to be full of so much insightful information where you're going to want to be taking notes don't worry we got that it's going to be in the newsletter that we're recapping and hey as a reminder i'll still be in the comments live answering any questions you have today's uh you know we're debuting this show live but it's technically pre-recorded so uh with that i hope you're excited i am and please help

me welcome let's bring on our guests to today for today. There we go. We have Barack Tarovsky, who is the VP of AI at Cisco. Barack, thank you so much for joining the Everyday AI show. Hi, Jordan. It's a pleasure to be here. I'm very excited. All right. So, you know, I'm sure mostly everyone in the world knows Cisco, you know, a Fortune 100 company, one of the biggest, you know, companies in kind of the tech and cybersecurity space and technology in general, right? But, Brock, tell us a little bit about what you do in your role of VP of AI at Cisco.

Yeah, I want to start a little bit about my background and talk about how I got into that space. So I kind of have this funny way to introduce myself that I work on quote-unquote those esoteric things like AI in large language models long before they became the hottest thing on earth. On a more serious note, my to large extent claim to fame was that I was working on a leading product that was the first product that productized LLMs at scale. It was called Google Translate.

I spent 10 years at Google leading the language as AI product team. In 2015, 2016, we basically did multiple technological breakthroughs across software side and hardware side, to basically to be able to run what's called deep neural networks on a huge corpus of data, which is a prerequisite for what we now call LLMs. Then it transformed, actually transformed to a time. transformer research papers that in many cases, as you know, it's basically a baseline for Chach GPT and everything else. Ironically enough, most of the researchers that work with us on translation and sub shape or form,

if you read this paper, they mostly talk about translation as a use case, because obviously that was a use case that at that time was most exciting. Then I worked on productizing. The first productization of transformer technology was called birth, bidirectional transformers, Bidirectional embeddings based on transformers, also done at Google. We used it initially for Google search and that created a huge jump in search quality and understanding Google user intent or queries. And finally worked on products like Google Ads, where we added multi-billion dollars

lift in Google revenue based on better AI-based targeting, Google Cloud, etc. And now in my current, in addition to that, in between Google and Cisco, I led product engineering, and AI teams as chief product and technology officer at a large late stage startup focused on computer vision AI. And now I'm at Cisco working on applying cutting edge technology to networking domain at Cisco. Wow. So, you know, maybe Brock won't say this outright, but if you, if any of that went over your head,

just know he is one of, I'd say a leading expert, right? So more than 25 years in the artificial intelligence. and in related fields and, you know, working on AI teams at Google. You know, with that, Brock, I'm curious, you know, because, yes, a lot of people maybe don't understand, yeah, AI has been around for many decades, right? It's not new. But what is new is kind of, you know, what you talked about is now these transformers and the GPT technology and in large language models, you know, now being available and accessible

to everyone. So I'm curious from your, you know, vantage point, especially when we're, you know, out there talking maybe about or if we're talking to business leaders and when uh you know can you talk a little bit about how impactful large language models are even you know in the course of someone like yourself who has decades of experience in the space yeah as i mentioned i consider myself extremely lucky because technically in the second wave of i or maybe you can even call it second hypervai because the first wave of i i mentioned was 2015-2016 when google or google translate showed that you can actually run deep neural networks.

So now what we call large language models on a huge source of data. We actually believe that it will be such a revolution. I actually encourage people to read an article called Great AI Awakening from New York Times Magazine that you can probably even find in podcast description. It's a really good article to the history of AI. But I think that hype was pretty short-lived

because very quickly a lot of enterprises understood. It's pretty expensive and actually need an amazing amount of talent. to do it. I think, and that's why a lot of the CIA innovation was limited to companies like Google, Microsoft, Meta, et cetera, because they need a concentration of talent, compute, et cetera. What I believe ChachyPT did this very clever UI and also building a lot of amazing technologies that Google and others developed, and they democratized access to it, right? And now a lot of people suddenly discovered the beauty of LLMs. One thing I would caution everyone is that it's, if democratized access to try it, but productizing it at scale, it's a skill.

and still, it's way easier than before. It's not a rocket science or like a nuclear bomb development, as it was before, but it feels pretty complex. And it's complex because almost in every product you have a, in technology, you have this product principle. You spend 80% of the effort on 20% of functionality that actually makes the product work well. So, but as I said, a lot of use cases, this technology makes a lot of use cases way better than it was.

And speaking of use cases, I'm excited to dive into that here, and we're going to be talking about that a little bit. But first, I do want to set the stage a little bit for even the premise of this episode or even the title, right? So I started off the show Barack by saying, hey, 2024, if you're not implementing generative AI and if your business isn't using large language models already, you might be in for an uphill battle. What are your thoughts on that before we dive into use cases in this concept of you got to chat GPT, your competition, before they chat GPTU. Yeah, so we'll talk about the use case. Obviously, not every use case will be immediately available

or served by LLM, but there are many use cases and many areas where it will be pretty, technology is mature enough that could be productized well. So if you take it, if we start on like a genetic statements that any business, sizable business, that sizeable number of customers, I believe you'll have must connect of their internal knowledge sources, databases,

and internal communication channels across emails and chats and speech to be served through large language models. If they do it, I think they will see pretty significant increase both in customer satisfaction and productivity. But if they don't, I believe within three to five years, that will be in a very increased risk to be disrupted by their existing competition or newcomers, that will be able to offer way better level of customer interactions at a fraction of the cost. And I think of something that needs to be taken very seriously. Yeah. And, you know, kind of beside the point, but kind of related is, you know, at least my take on this, I think that, you know, especially small, medium-sized business owners, don't fully understand generative AI and don't fully understand large language models. I tell people, you know,

kind of the lines between large language models in traditional internet search seem to be blurring, right? So when you talk about Google's SGE, you know, and bringing this, you know, more of a large language model type search into Google, you know, perplexity, you know, we just we just saw a new model update GPT4O from, from OpenAI today. Should business owners even start to think of the two kind of the same, right? Just like you wouldn't, you know, put together a big presentation without using the internet. Should they be putting together a big presentation or a big sale deck without using a large language model? Yeah, it obviously, use case dependent. I would argue for some simple use cases, it might be important, but it might be less important. But definitely when you look at scalable business, the sizable number of customer interactions,

I think then where it becomes way more important, again, as I said, on a local level, on a medium to small businesses, it also depends on your competition. But yes, even on a small level, if your competition is starting to use it, if we create a small chatbot that shows, you know, in an individual, interact away what products and services you offer. That's by itself could be very powerful and start taking away share of your business. Yeah, absolutely. So let's go ahead and jump into some of these use cases here.

So for our live stream audience, this is probably going to be pretty straightforward. But for our podcast audience, we essentially have, and we're going to put this, so check the show notes for a link where you can go take a look at this. But we have kind of this four quadrants here in different use cases. for AI. So everything from, you know, low accuracy and low fluency on the lower left-hand corner, cascading up into the right to high fluency and high accuracy. So, you know, Brock, walk us through a little bit about this kind of graph that we have here. And I'm also curious, where did the idea for this come from? Because I love it. It's something very easy

to visualize, you know, use cases for generative AI. Yeah, it was actually very funny. the idea came from the fact that when CHEGPT launched, I was on vacation on Hawaii. And then there was, I don't know if you remember, but it was a classical market panic or market excitement that Google is finally disrupted and Microsoft will take all the share because they now have an open AI. So literally every investor in the world wanted to talk to me to understand because I had no clue what it is and is it true or not? Should we short Google now?

What should we do? I will basically explain to people that actually I believe search is not. the best use case to start with LLMs. And people listened to me and said, oh, wow. And I basically was explaining to them the biggest misconception is actually the fluency, between fluency and accuracy and people like, oh, wow, all those investors told me you just totally publish it because it's so helpful. And I was actually very proud that within three months as people looked and said, oh, Google share is not really moving.

And Microsoft is not disrupting and don't get wrong, Microsoft will do a lot of money on other places like cloud. But definitely there are not disrupting search. And that's probably partly because it's like this framework. So I basically came with the framework that a lot of people find very helpful. And I wanted to dispel some of the misconception because I believe the biggest misconception and this overexcithment about LLM so that people, especially people who are not in the industry like for 10 years like myself, they suddenly discovered that machine can produce content that is in many cases better than average human producers.

You're talking about the what I call fluency, the Polishnessness, the content. confidence of chat GPT answering questions are pretty amazing and it's a major technological advancement that is built on top of clever UI plus all this transform technology that was developed by Google and others. But I think what people tend to forget, that high fluency, creating a very compelling story, Polish charismatic story, doesn't mean providing correct or accurate information. It's a very different dimension. It could be extremely polished, extremely confident and still say complete crap. And my example, my example of, you know, closest human analogy to this behavior, LLN's at the end of the day, answers a question or yes is the next word or next sentence, right?

So think about a person who can confidently and in a very Polish and charismatic manner talk literally about any topic in the world, but they're provided to give you an answer. If you ask them a question, they don't know, they will never say, I don't know. They will make up stuff on the spot. And because are so good at it, their delivery is so polished, you will actually believe in what they say. And it's very dangerous. Human analogy could be con artist who does it on purpose to defraud you. And that's why con artists are so successful, because in many cases, their fluency is amazing. It's a delivery, especially in topic you don't understand. Or it could be very successful entrepreneur.

Good example is Steve Jobs, who basically has a reality distortion field and so things that others didn't. There might be less good examples. I don't know, like FTCS where you speak. and you really believe in what you do, but it's actually not necessarily true, right? So all those examples at the end of the day, the result are not necessarily great. That's why it's really important when you think about the use cases, to look at this over two dimensions and not one and not be only influenced by the fluency. And finally, I believe in other things that I represent by colors in my this two by two grid is

also be very realistic. What is the consequences or what is the risk of getting the answer wrong? Because if it's a low-stake use cases, it's probably okay-ish, and I will give an example. For example, in those who can see it or can, after in the notes, look at my framework, in the low-acuracy, high-fluency quadrant, we have a bunch of green bubbles, which I call low-step use cases that are roughly creativity or productivity use case, like writing science fiction book, writing children's book, to composing music. The beauty of it, there is no objectively right or wrong answer here. It's all about the story.

That's a perfect use key where fluency is so important. If you look on the other side, on the left, in the low, low, low, fluency, high accuracy, high fluency use case, you have a bunch of red bubbles. Those are the use cases that roughly, let's call them Google Search Plus, it thinks like, hey, what is the earnings growth of Google in the last 10 years? You just need an answer, no story, right? Or I want to buy a dishwasher, tell me which one to buy. But why?

Like, give me a recommendation. Or I'm going to pay it so I want to get a recommendation for a hotel. You do need a story. You need an explanation. But if a story will come with an accurate data, that doesn't help. The product is useless, right? And then in between, you actually have very interesting use keys that I call them productivity enhancement use keys.

Those are use keys like writing business memo, email, or like creating a business presentation, or writing a review. The beauty of it, it's not that it's not important. It's important use case. The beauty of it, you start with a draft. And draft, you need both a story and pretty high accuracy. And the beauty of it, if it's a draft, is that unless the people are really dumb,

they will not send it automatically or not publish automatically. They will use it as a draft. And the division of work between machine and human would be, machine, please give me a good story. Because many of us, Jordan, you're probably an exception. You're probably good in stories. But most of people have really good mastery of their facts, but they're struggling to create a story.

It takes effort and time. If you help them create a good story from the fact, it's a huge productivity boost, huge value creation for a lot of people. And if you create the UI right and explain to them, hey, it might be wrong. You need to, it might be inaccurate. Make sure you double check and adjust if needed. That's to me very compelling product to start this. I would not start in use cases where you actually need high stakes. If you make a mistake, it's actually bad.

And also use case like search because we're talking about enormous scale here, right? You're creating millions of presentations. billions of search queries. In search, you cannot really put a person behind every query to validate it. And if you ask users to validate it, that's effectively what search does today. So I would recommend people to start with use kids that are much more grounded in value creation of creating a good story versus let get me the one answer right from the beginning. So that's basically the gist of this. What is more important for the use case?

The more the fluency is important. the more the story is important, the better it's a feat, the use case as a feat for LLMs, at least in the shorter medium term. Yeah. And this is, again, this is one of those if you're on the podcast. We always appreciate your support, but you got to come watch the video of this or, you know, make sure to check out the graphic that will include in the show notes, because I really think that does really just help better understand the framework for using generative AI, right?

and know when it's just, okay, is this just maybe high fluency? Because Brock, just because something is highly fluent, right? Just because a large language model can spit out a bunch of content, doesn't necessarily mean that that might be the best use case just because it is a use case. So speaking of use cases, let's go ahead and talk here. So now we have some more illustrations here on the same graphic, but talking about different actual use cases. So walk goes through here about some of these specific use cases, and then we'll kind of dive into that.

Yeah. So I mentioned it a little bit. But here in this graphic, it's the same two by two, but I just created two clouds, so to speak. Basically, I noticed something very interesting trend. The basically is actually going back here. Yeah. So the trend here is that what I called the area of creator, workplace productivity use case. It's actually a very good fit for LMS because, as I say, it's either.

there is no right or wrong answer. So accuracy to some extent doesn't really matter or you cannot really measure it objectively. But that's for industries like entertainment, et cetera. It's very specific industries that I'm not an expert. I don't want to talk too much about it. Even though I parted with someone and wrote an article about it just to me, it was like a testing round to understand whether my friend were applies.

But if you think about some of the yellow bubbles on the visual, even some of the green bubbles is like writing business memo, email, or creating business presentations. That's to me the cracks of the use case that can start become useful in the enterprise, in the enterprise setting. And if you kind of focus on families of use cases, families of use cases, in my opinion,

in short and medium term, are relevant. One is entertainment that I mentioned. Perfect use case, even though it has other dynamics, you know, it's highly litigious industry. There is a copyright protections and all this kind of stuff. But the use case, technically one is a perfect use case. No right or wrong answer story is important. The next one is what I call WordPress productivity, and I think on a high level, it's too big bucket.

The first part is that I kind of interact interrelated. The first one, I call them roughly customer-facing interactions. What it means is that anything where you have frequent interaction with the customer, where actually story and facts, both are important, but this use case like customer support, technical support, sales, service, etc. In other use case, that in many cases in highly technical domains, for example, in networking domain like Cisco, is interrelated. related to coding.

Coding, by the way, it's a very good use case for LLMs, because to some extent it's a language. There is a reason those models are called large language models, but that's human language. Coding is a human-created language that was created artificially, and it's much more structured. So LLMs are very good in dealing with that. And in some use case, you actually combine those too,

because in many cases, you need to run code to resolve some customer problem, right? So that's a very important use case. And finally, the next, the last one, I call it education slash professional certification. As you probably know, chat GPT passes bar exam, medical exam, science exams with flying colors. One of the reason for that is that it's actually very good in understanding and choosing from closed number of questions. Because it's very good in reasoning of understanding slight nuances and questions. That's what usually do.

It's not an open-ended question. It's actually understanding from a choice of questions. And there is multiple interesting use cases related to professional certification or education that I think will be very relevant to LLN. So that's on a high level. Again, it's not an exhaustive list of use cases, but that's kind of how I basically look at the framework and then try to translate some specific changes in this case. Adobe just introduced an entirely new way to create,

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and creating social variations. Every step the assistant takes is visible so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adopi.com. Yeah, and like I've said multiple times, you've got to just be able to take a look at this. So we're going to include this in our newsletter as well and break it down a little bit more. But, Barack, let's talk a little bit because I think now any business leader, any decision maker, you know, heard what you just said. And if they weren't already, you know, all on board with using large language models for certain use cases, I'm sure they are now. right? Like you can't just listen to someone, you know, with two and a half decades of experience and

break it down this simply and still say, I think my business is going to pass on this whole large language model thing, right? So maybe let's talk about, you know, how you actually choose the right model for the right task. Because I know there's no, you know, blanket answer for that. But how do you go about, right? So, you know, you see all these stats in these, you know, McKinsey studies that say, oh, you know, large language models are going to, you know, automate up to 80% of knowledge work. And, you know, everyone's scrambling to figure it out. How do you find the right model for the right task? And how do you go about making sure that it's actually working for your company?

Yeah. So I believe there is also a bit of a high overhype or maybe, you know, misconception. And again, it's totally understandable because ChachyPT, which I call maybe, you know, first of all, metology. There is no small language models here. They're all large, right? there is like large homongous and gargantuan or something of that sort, right? So those homongous or gargansan largely with models like Gemini or Chachipti four are pretty amazing.

And they can cover wide range of use cases. I mean, to some extent, any use case, right? But I encourage everyone to understand. There are limitations on accuracy, right? And there are other limitations. One limitation is cost. They're expensive. And yes, it's very easy and frankly cheap to try it out.

But when you start scaling it for a big year, use case, it becomes pretty expensive, right? The second one is control, and maybe I mentioned it a little bit on the entertainment side. It's a highly copyright protective, right? Like, there is a lot of, there is huge importance for many enterprise, on maintaining control authors, there are unique elements when utilizing AI models. The unique elements could be proprietary training data, like an entertainment, right? The unique components could be privacy, security, et cetera.

And frankly, I think it's for many enterprises, it's also important to maintain maximum control over the destiny of the model because in some cases you might want to work in a domain that is not a priority for chat gpte or jemani and then you need to wait right and finally the last one is domain specific quality you might find out for your specific domain or for your domain the quality needs to be upgraded it needs to be higher and then you basically need to pay more to customize the model and you would still use a homongous model for a task that may be very specific Because of those three things, I think it's very important to be grounded and realistic and understand. In some cases, homongous foundational generic model works well.

But in some cases, if you have a relatively constrained use case and you want to achieve better accuracy with lower cost, you might actually go and try to create what's called fine-tuning, your custom domain-specific model. And the good news about it until year ago, obviously doing it on your own required a lot of talent, a lot of compute. But now some companies like Mistral or Facebook or meta actually created those pre-trained large language models that are pretty good for generic knowledge. You can take it and fine tune it if you have your own proprietary data and actually create a potentially better combination of cost versus domain specificity and maybe even more control. And as I say, it's very use case-specific. For some use-case generic model works, for a use-case domain-specific model work.

But it's very important to understand there is no one-side fits all here that you can all generic models work forever use-case. Speaking of size and models, this is something I'm curious. And, you know, it's not often I get to talk to someone with this type of experience. You know, my viewpoint,

this is from, you know, an outsider that doesn't know a lot. But, you know, you mentioned, you know, mistrial and metaz llama, right? And it seems like the models that they're coming out with at least are getting smaller, right? You know, we talked about, you know, hey, there's small models, large models, and then these gargantuan models. Are we going to see a future where models are actually going to become smaller and that might actually help drive down costs and drive up, you know, use cases across the business spectrum just as the fine-tuning process becomes better, compute, technically becoming more affordable. Is that a trend that we're going to see as

smaller models for more specific use cases? Yes. So first of all, I think just to repeat, nothing is small. It's like large homongous and gargantuancheon. But yes, if you want to use numbers, the smaller today or large is like 7 billion parameters, right? The homongous maybe 50, 60 billion, and gargantan is like 500 billion parameters. Nothing is small. But just again, as I say a lot of a lot of current experience come from previous experience for example in google translate many years ago to 2016 we had a model of six billion parameters that was gargantuan at that time like it was nothing at that time and we actually had a problem at that time you know cost was not such a big problem for google but uh we had a problem on performance our problem was that those models

were were at that technology and the level of GPU at that time it it was 100x slower than our production to serve what's called inference. That's actually led Google to do two things. A, to develop their own custom hardware, TPUs. That's why Google is so advanced in that space. Believe it or not, was developed. The first use case was Google Translate. But the second thing, we invested in technology that artificially shrank the model. It's called distillation, where we basically effectively did the trade off. Okay, let's shrink the model and it will give us better latency. Yes, we will lose some in quality, but it will not be significant loss. And then maybe we'll kind of custom train or fine tune and get back that. So I think a lot of the things that we need this time now could be applied

in enormous scale here. As I say, that's all use case specific. If you have, if you take this large six billion, a small model just for a sake of argument, relatively small model, and you have sizable proprietary data and maybe even create a program with high quality human data, you might actually get to relatively high quality bar with a fraction of the cost, right? In some cases, you cannot do that. have enough data or if you don't have enough talent to do it, you might need to use a bigger model. So I think it's a range of possibilities, but I think it's very important to understand. It's not only generic models plus Rack or retrieval augmented generations that people think that will solve all problems. Frankly, it can make it even more expensive because now you send a lot of context

to this huge model, right? It's good for Nvidia. It's good for open AI, but it's not as really good for enterprises. So I think there is a lot of, it requires to define the business problem well, meaning like what is the quality bar, what is the, you know, what are your margins of your business? Can you afford to use this models? You obviously don't want to use, you know, a nuclear bone to kill a fly, right? So it's important to understand and use the wide range of tools at our disposal to try to achieve your, you know, to serve the use case. Yeah, that's, that's a great analogy there, right? And I'm just wondering, I'm just wondering the amount of compute. that's gone out the window to, you know, write 50 versions of a, of a haiku or something just for fun of it.

You know, so, so, you know, you did say something in there, Brock, about data, right? And that's how we kind of started this show is, is how, you know, most companies are going to have to start leveraging their data and bringing their data into large language models. Yet, that does seem, you know, maybe the tide is turning a little bit, but that does seem especially in the first kind of year or two of this, popularization, so to speak, of large language models is so many people and companies and business leaders were concerned about the data. And they're like, you know, they go from banning large language models to now all of a sudden they're, you know, building their own and fine-tuning them. But, you know, in your unique position as, you know, the VP of AI, one of the largest security companies in the world at Cisco, what's your thought on these companies that maybe still

are hesitant to put any of their data into an enterprise level, you know, graded security. large language model. What's your thoughts on that? Yeah, so I think it needs to be practical. I think the most important message that I want to bring back how we started. And I apologize that I'm using the fear factor. But like if you don't do it, somebody will disrupt you. So you need to start with that. You have to do it. Now, there are ways to do it, right? It doesn't mean you need to use it and just give all your data to open AI. That's why sometimes creating a custom model and investing in some kind of a small amount of high high quality talent to develop those models.

If you're worried about control, if you're worried about privacy, if you're worried about security, invest in your own custom domain specific models. There are also other range of disability. For example, if you prefer, if you worried about security of open AI, you can use an enterprise-grade version of open AI through Azure. You can also Gemini through GCP, you can use Anthropics through, you know, AWS. That's the beauty of a cloud-based provider that can provide you a way better level of security.

in privacy. But again, it depends on the industry how important the data, how proprietary considers the data. Are you okay? The data will be used potentially to sell to your competitors. All those questions that are very important to address. But the good news is that there is a range of possibilities how to address it, especially for enterprises. And they definitely need to think about it, right? In my opinion, just say, you'll ban it outright. It's not applicable. If you have large number of customers and customer interaction, I think you're setting it yourself for being disrupted, right? All right. So, so, Barack, this has been a conversation that my hands are hurting because I've been typing so many notes because we've talked about, you know,

kind of your very useful framework on the best ways to leverage or to when to use AI for certain situations. We've talked about different use cases, you know, from entertainment and customer-facing interactions. And then we talked about how to find or when to use the right model for the right use case. So, you know, as we wrap up, maybe what's your best piece of advice, you know, for someone that is out there now, again, not saying we're, you know, going the fear factor, but maybe someone is now a little scared of saying, huh, you know, we've been hesitant and I'm scared that some of my competitors are going to be doing this.

What is your one takeaway piece of specific advice for those people in order for them to really leverage generative AI? Yeah, so I think it's embrace the technology, understand the technology. generally your friend. I really like the example of Steve Jobs that he was giving about technology. He basically said if we put, you know, if you do a competition on speed between all type of species on Earth, you know, humans without any technology will be, you know, in like 25, bottom 25 percent of. Yes, faster than a, you know, a turtle, but way, way slower than a cheetah, right? But human on a bike, human on a plane, human on a car will be way faster than cheetah.

So I believe that's a very good analogy of technology. But it requires you to learn new skills, right? If you don't learn how to drive or ride a bike, you will be left behind, right? So you need to look at this technology. I understand it's your friend, at your strategic potential to take your business to the next level. But you need to invest in understanding it. You need to invest upskilling yourself and upskilling your organization to be able to meet this. Yes, it's very exciting, but also frightening challenge.

but we need to embrace technology. You know, planes and fire and trains are all worse scary initially. But we embrace them and learn how to manage them. And that's what we need to do here too. Love to hear. There's nothing more that I love than ending an episode with a beautiful analogy like that. Oh, wow, this is a good one. So, Barack, thank you so much for joining the Everyday AI show

and sharing all of your insights. We really appreciate your time. Thank you. It was a pleasure. And hey, as a reminder, everyone, yeah, that was a lot of great insights from an industry leader, someone that's been around decades doing this thing at a high level. So, you know, like I said, maybe you weren't able to watch live as we are going through kind of this diagram of use cases. So don't worry, make sure if you haven't already to go to your everyday AI.com, sign up for our free daily newsletter. We will be recapping everything there as well as leaving links to more resources that are really going to help.

help you make sure that you don't get chat GPTed by your competition. Thank you for joining us. And we hope to see you back next time for more everyday AI. Thanks y'all. Meet Firefly AI assistant now live in Adobe Firefly, the Allman One Creative AI Studio. Just describe what you want to create in your own words and the assistant handles the rest, orchestrating multi-step workflows across Adobe Creative Cloud apps, including Photoshop, Premiere Express, and more in one conversational interface.

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