The Potential of Gen AI with Faizaan Charania from LinkedIn

Smart Products

I’m excited to share this conversation with Faizaan Charania. Faizaan is an AI product lead at LinkedIn. During this conversation, Faizzan discussed the potential of Generative AI and its applications, the importance of keeping GenAI solutions simple, and how to think about trust, transparency, and managing costs as a product manager working in Gen AI.

Links

Faizaan On LinkedIn

Transcript

[00:00:00] Faizaan Charania: I love the analogy with cloud because cloud can make experimentation so easy. And you're just like trying to set up something new. Test it out. See, see if it works. Will I get product market fit? What are my users thinking about this feature? All of these things are also possible with GenAI. So for any PM who's thinking about GenAI, my recommendation would be test it out.

[00:00:24] Hima: I'm, Himakara Pieris. You're listening to smart products. A show where we, recognize, celebrate and learn from industry leaders who are solving real world problems. Using AI.

[00:00:34]

[00:00:35] Himakara Pieris: My guest today is Faizan Charania. Faizan, welcome to the show.

[00:00:40] Faizaan Charania: Thank you so much for inviting me, Hima.

[00:00:43] Himakara Pieris: To start things off, could you tell us a bit about your background

[00:00:46] Faizaan Charania: Yes. I am a product manager at LinkedIn. My main focus is around machine learning and artificial intelligence.

[00:00:53] Faizaan Charania: And obviously these days I've been looking into gen AI as well. I've been in the machine [00:01:00] learning field for around Eight, over eight years now started on the research side, worked with startups, uh, was a machine learning engineer for a bit. And then I switched to product management.

[00:01:12] Himakara Pieris: There is a lot of attention on generative AI at the moment. Could you tell me a bit about the way you see it? What is generative AI and how it's different from all the various other types of AI that we have seen so far?

[00:01:24] Faizaan Charania: Yeah, definitely. There is so much hype around gen AI. Uh, one thing, uh, one code that I've heard multiple times is. Uh, this is like the iPhone moment or this is the desktop to mobile moment of technology again. To answer your second question around, uh, how is it different from all other kinds of AI?

[00:01:46] Faizaan Charania: Because it's like so many things that we can qualify as AI, right? So a simple explanation that I try to go with is. Differentiate these two types of AIs, analytical AI and generative AI. [00:02:00] So analytical AI is where you, where you have some specific features or data points or like historical input, and you're trying to make one single decision based on that.

[00:02:12] Faizaan Charania: So the decision can be, Hey, is this email spam or not spam, spam classifiers? It can be a ranking decision. So say you log into Facebook or Instagram or like any of these applications and what post should appear first? What should be first? What should be second? What should be third? And this is based on the text in the post, the images.

[00:02:35] Faizaan Charania: It's based on what you like, what you don't like. So this is like a ranking problem. So ranking, decision making, all of these are a part of analytical AI and generative AI. As the name says, it's about taking, uh, generating new content. So if it's about post completion and everyone has heard about child GPD, so I'll just like [00:03:00] use that as one of the examples.

[00:03:02] Faizaan Charania: Like, Hey, I asked you a question and give me a response in natural language format. So natural language generation is generative AI generating new images, images that did not exist before is generative AI. So like even for images, if you were to classify an image, Hey, is this. Safe for children or not safe for children, that's analytical, but if you want to generate a cartoon image, that's generative.

[00:03:30] Himakara Pieris: From an overall landscape standpoint. So we have a ton of startups that are out there and then there are a couple of, in a way, key gatekeepers, Microsoft slash open AI. Um, I would say one of them, and then there is an emerging, emerging rivalry with, with Google, um, or refresh rivalry with Google on this front.

[00:03:53] Himakara Pieris: And then there are also chip makers. How do you. So if a map out this landscape, [00:04:00]

[00:04:00] Faizaan Charania: yeah, so, uh, when you're thinking about landscape, yes, Google and Microsoft are big players, but then there's like so many more important players over there. So if you're just thinking about the flow of generative AI at the base layer, you will have the infrastructure companies, these chip companies, and they are the ones who actually make gen AI possible.

[00:04:25] Faizaan Charania: So that's one thing then at the top level, you will have applications that are using generative AI. And in the middle, you would find all of these other players who are building new features and new utilities to even make gen AI, um, efficient. So to give you one example for prompt engineering, there's new companies that are just focused on prompt engineering, making prompt engineering easy.

[00:04:52] Faizaan Charania: Versioning of it, iteration, structures of it. Um, there's a prompt engineering [00:05:00] marketplace now. So people can sell prompts and people can buy prompts. So, I, yes, Microsoft and Google are the popular ones because they're like big players so there's like more Um, media limelight around them, but I think they're, they're just like one of the initial pioneers and there's just so many players and there's so much scope for everyone to be a part of this.

[00:05:24]

[00:05:24] Himakara Pieris: So I think what we're talking about is there is the foundational layer, right? Which Microsoft and Google's of the world are going to provide similarly to how they provide cloud computing today. And there's going to be a huge ecosystem that is getting built on top of it. And prompt engineering sounds like.

[00:05:42] Himakara Pieris: One big part of it prompt prompt engineering and everything that's that goes around prompt engineering Are there any other ecosystem participants at that layer

[00:05:54] Himakara Pieris: in your view

[00:05:56] Faizaan Charania: In the initial days The market is going to evolve a lot [00:06:00] So when these new models were launched and again, I'm talking about November and December you You might have seen, um, a large list of startups that just like came about.

[00:06:13] Faizaan Charania: So those are the ones who are early adopters and who are just making these things, uh, making like new applications possible. I think that's just the spur and that's the wide net that we are casting. But as time progresses, this is going to become business as usual. Gen AI won't be exciting anymore. Then the problems to solve are, hey.

[00:06:34] Faizaan Charania: How do I scale this? How is it going to be efficient? How do I do it for cheaper? And there are many different players who are playing in the infrastructure side of this. There are many new startups. I, there's this one startup that I. Sort of from, I can't remember their name, but, um, they've been working on making Gen AI more efficient for like three years now.

[00:06:59] Faizaan Charania: So Gen AI for the [00:07:00] public, it's, it seems like a new word and all of us are talking about it right now, but the early seeds were sown in 2017. And actually even before that, everyone has been building on the top of giants that came before them. But yeah, the concept has been around for a while and there are new marketplaces.

[00:07:19] Faizaan Charania: There are no new ecosystem players that are just going to solidify even more as time passes.

[00:07:25] Himakara Pieris: Let's say you are a product manager, , for a product that exists in the market today. Where do you see opportunities and threats and challenges, , someone should look out for, , as a PM?

[00:07:39] Faizaan Charania: . My approach to Gen AI is to just think of it as a tool as it is. I've been doing this for like AI for a while and now Gen AI is just a flavor of it, right? So think of it as a tool and see how this tool can help me or my customers.

[00:07:54] Faizaan Charania: Solve their, solve for their opportunities or solve the challenges that they're facing, more easily. [00:08:00] And that is the core of how we should approach all kinds of product solutions. And then see where can Gen AI come in? How can we solve problems using Gen AI? Is there some flow or some funnel that my user is going through right now?

[00:08:15] Faizaan Charania: Where's the friction? Can Gen AI solve that? Can Gen AI make something possible which would make my users happy? But it was too difficult to do in the past. So there are many ways to think about this. The core of all of this should be the jobs to be done, the user needs, and then see where the unique capabilities of Gen AI are going to be useful for them.

[00:08:41] Himakara Pieris: What I'm seeing is that you can use. Generative AI for summarization, , expansion, style translation, I think I can put. Graphic stuff for diffusion into into one of those three buckets as well.

[00:08:56] Himakara Pieris: Am I missing something here?

[00:08:58] Faizaan Charania: Summarization, [00:09:00] expansion, style translation. There's obviously all kinds of like generation. When you say style transformation, this could be just text style transformation.

[00:09:09] Himakara Pieris: It could be anything from turning Drake's voice into JC's voice. I think I see all those as some kind of a, , a transfer operation, right? It's essentially any kind of transductive problem you can, approach with, , generative AI in, in some ways.

[00:09:25] Faizaan Charania: Yes. Yes. And, , to go one step deeper into summarization, even summarization can be done on, uh, like in a very basic manner where you're just like summarizing one document or it could just 10x the speed of research. So a doctor who's looking at new symptoms and trying to find out a diagnosis or a prognosis, or a lawyer who's working on a case and wants to find, um, or precedent, like other cases that are similar to theirs, what happened and consuming all of this information and coming up with [00:10:00] takeaways and next steps.

[00:10:02] Faizaan Charania: These places are also where I see ChargPT and I like ChargPT has just become like a pseudonym for All Gen AI these days. But Gen AI play a very big role.

[00:10:13] Himakara Pieris: What would be a framework or rubric PMs could use, when they approach Gen AI?

[00:10:21] Faizaan Charania: I really like this topic. So the framework is actually almost independent of Gen AI, and then we have to adapt it to Gen AI.

[00:10:31] Faizaan Charania: So, whenever we are building any new product, your product has to be rooted in, again, what the member, what the customer wants, what the user needs, but also some principles. Like how do you want to build things? So if I were to come up with some principles for generative AI products, how I would put them, I would put them in like three categories.

[00:10:54] Faizaan Charania: So the first thing, because Gen AI can do so many things and it's possible to, [00:11:00] uh, it's possible to be like used in many different ways. One core aspect that I would want to obsess over is keep it simple because not every user is going to be an advanced user. Not every user is going to be able to use Gen AI to the full of its capabilities if you just like give the model to them.

[00:11:20] Faizaan Charania: So your product or my product, whatever product you're building should keep it simple for the user. So that's one thing. Um, the second would be is create unique value, create new value. This goes to the look at the members needs and opportunities thing. If, if someone doesn't need Gen AI to solve their problems, we shouldn't just force Gen AI into a product just because it's a new cool thing to do.

[00:11:48] Faizaan Charania: And we've seen this happen with some other technologies in the past. So, again, this is just one thing to be very mindful of when we are thinking about, okay, this is a new technology, GenAI, and how we do [00:12:00] this. So that's two. And I can go into depth of like these two as well. But the third one is going to be build with trust.

[00:12:08] Faizaan Charania: And I'm focusing on trust over here because GenAI, at the end of the day, it is AI. And this is generating new content. So now people are going to read this, people are going to consume this, and for, for a lot of instances. It becomes a black box and we don't know why it is saying whatever it is saying.

[00:12:28] Faizaan Charania: Yes, it depends on all of the training data. Why one particular output came out in this particular instance? We, we can't really answer that. So we have to build with trust. We have to, um, make sure that we proactively think about avoiding bias. inclusion, diversity in our data sets. Um, whenever we do go wrong and we will go wrong, it's AI.

[00:12:53] Faizaan Charania: It makes mistakes. All kinds of AI do have a feedback mechanism. Let your users interact with your [00:13:00] products see where it's going wrong And then as a PM or like whatever company we are so that we can actively work on it And this is going to build trust with GenEA, build trust with your company, build trust with your product Otherwise, it's just a black box and people can be apprehensive towards technology as well Maybe there's yeah, a lot of things can go wrong.

[00:13:23] Faizaan Charania: So having a Continuous conversation is going to be very useful. Okay to summarize keep it simple meet the users where they are keep it Um easy to use second create unique value create new value Only use genii when it is actually useful for your members users customers Whoever they are and third build with trust Avoid bias.

[00:13:48] Faizaan Charania: Think about inclusion in your data set and build for feedback because this is new. You're going to make mistakes. You just need to keep improving.

[00:13:58] Himakara Pieris: You talked about [00:14:00] removing complexity, can we break it down a bit more

[00:14:02] Faizaan Charania: To break it down, let's look at particular examples, like example products that are already out there so that we can contextualize these understandings.

[00:14:10] Faizaan Charania: Okay. So one product that I truly love is Notions AI Enhanced Editor. They were one of the early adopters of Gen AI and launching it in production. So I don't know if anyone listening has used Notion's AI product before or even Notion. So just to give you some context, Notion is for note taking, but it does like so much more than note taking.

[00:14:37] Faizaan Charania: And the AI enhanced editor. Can help you use all of these genii capabilities that uh, he might just mention like summarize translate, uh Transform like change the tone of things so there are many features available, but I love how they keep it simple So the first thing is They have fixed [00:15:00] entry points, like, Hey, if you want to translate, click over here.

[00:15:02] Faizaan Charania: If you want to change, don't click over here. Everything is just one click away. I don't have to worry about talking to a gen AI model and asking for asking in detail what I want from them. I just click and it's done on the backend. It's still using gen AI. But as a user, I don't have to worry about what's happening on the backend.

[00:15:24] Faizaan Charania: I just say, Hey, make it more conversational, make it more professional, make it more polite. And it happens. So that's one thing. Remove barriers. The second, uh, I had mentioned the, I had mentioned abstract the complexity again, by putting everything on the backend, you abstract the complexity. Uh, one thing is the, you could give your, uh, users just.

[00:15:51] Faizaan Charania: What you enable using one click, but then if you expose a Uh, gen ai model and give the user all kinds of hyper parameter [00:16:00] options like hey adjust the temperature adjust the max tokens adjust Uh frequency penalty repetition, but there's like so many things That you can do with, uh, even like GPT 3, the base model and like the new models are have like more capabilities.

[00:16:16] Faizaan Charania: But if you give all of this to the user, like, um, a separate form that where you keep editing things, editing hyperparameters, it's just going to confuse them. So abstract the complexity of it. And in the UI, I don't have to leave my editor for anything. I can just press forward slash. It gives me a section to enter what I want, whatever AI command I want to enter.

[00:16:40] Faizaan Charania: And the output comes in my, comes in my document where I was already writing things. So meet the user where they are. This again, if anyone hasn't used Notions AI Enhanced Editor, check it out. It's a great example of how you can build a simple UI.

[00:16:57] Faizaan Charania: While still doing a lot of powerful things.[00:17:00]

[00:17:00] Himakara Pieris: Let's build on the Notion example then. How do you think Notion created new value, , using this AI powered editor?

[00:17:09] Faizaan Charania: The new value over here is we are reducing a lot of friction that writers have to go through when they are creating any new content.

[00:17:19] Faizaan Charania: Okay. When I come up with ideas, it also has a brainstorm section, by the way. Say, if I want to come up with ideas. Right now, without GenAI, how would I do it? Hey, I would look at, I would search it online, search the topic that I'm writing about, read a few things, or even look at what the industry trends are, what people in the field are talking about, and then get all of that information.

[00:17:42] Faizaan Charania: But with this notion, AI, I just go in the brainstorm section, enter what I want to enter. Um, say I want to talk about how people can transition to product management. Again, this is just a very specific topic, but how people can [00:18:00] transfer to product management as a career. And there could be a 10 other ideas that are similar to this.

[00:18:06] Faizaan Charania: So, okay. Advice for early career product managers or what transferable skills are useful when you want to transfer into product management and like eight other ideas. So that's new value. When I want to expand my blog posts to from like English to Spanish, French, German, there's a translation option.

[00:18:27] Faizaan Charania: Right there. Now I don't have to go to some other product and do it over there. So that's new value. And yeah, new value comes with all of these new features that I as a Notion user couldn't use before, but now I can.

[00:18:42] Himakara Pieris: There are a number of challenges I could imagine right off the bat in the case of Notion, everything from inappropriate suggestions to incorrect, , outputs to hallucination and all that. How do you think Notion is handling the concept of build with trust? [00:19:00]

[00:19:00] Himakara Pieris: Okay.

[00:19:01] Faizaan Charania: A few things that I did notice were one, there's like always a disclaimer that like, Hey, this is AI generated. If anything seems off, please provide feedback.

[00:19:11] Faizaan Charania: So that shows me that Notion is willing to, is. has potentially thought about where the AI could go wrong. So there's like good intentions and they're collecting feedback and are willing to improve. So, um, for, for one of the things I was trying to like write, what I was working on was like write a blog post with a particular topic and I was stress testing it.

[00:19:37] Faizaan Charania: So as a new user, as a PM who works in AI, I was interested in how they did things. So I was just trying to play around with it. And I tried to make it say, um, some not so appropriate things. I, I have done similar things with like chat GPD as well. So, uh, one thing that I've seen is they have content policies [00:20:00] in place.

[00:20:00] Faizaan Charania: So if an output is, um, is against the content policies, it will get flagged. So this is, these are extra layers on top of the Gen AI, uh, model. So that is something that's very important. Um, Fairness, Inclusion, Bias, I didn't see any of those issues in the Notion AI outputs. So maybe they have cleaned all of their data already.

[00:20:26] Faizaan Charania: Maybe my prompts weren't, um, scandalous enough for them to break, but in my experience, they were doing fine. So maybe they've thought about those things as well. The third thing that I won't have much information on, like how notion did is data collection and security. So whenever people. Are talking to gen ai models.

[00:20:47] Faizaan Charania: They are providing a lot of input. So we should actively think about hey Where is this data stored? Is it is it in a secure spot? Is it unsecured? Is it encrypted? There are government regulations [00:21:00] around what data you can keep what data can you can you not keep like based on california law ccpa gdpr So I have to think about those things And, God forbid, if your company gets hacked, that data is going to leak, that sensitive data about members.

[00:21:15] Faizaan Charania: So, you have to be very careful about, uh, what you store, what data you store.

[00:21:26] Himakara Pieris: If you are looking into adopting Gen AI or exploring Gen AI. It's not a cheap thing to do especially if you're adopting OpenAI's, , API. Those, token based pricing could add up very quickly. How do you think, as a PME, you should, you should plan for, , cost and scale?

[00:21:45] Faizaan Charania: This is a very interesting problem that even I am thinking about, um, during my day job because Gen AI is new. It can do a lot of exciting things for our users, but at LinkedIn we have [00:22:00] millions and millions of users. I guess the last number that we had released was monthly active users around 800, 900 million.

[00:22:07] Faizaan Charania: So that's a huge number of people that you have to build a solution for. Okay. So when you're thinking about cost and scale, you have to first think about what the problem is. Like what is the problem for the user that you're trying to solve and in a lot of use cases you won't have to Create an extremely personalized solution for each and every member Members users like whatever your product is at linkedin.

[00:22:38] Faizaan Charania: We like to call them members but Members can be a part of a cluster. They can be similar users. So if I am a PM, the things that I would value are going to be slightly similar to what other PMs value. So whether it's a job application or whether [00:23:00] it's a post that I would want to write. A topic that I would find interesting if there's like, so I'm taking gen AI aside right now.

[00:23:08] Faizaan Charania: I'm just thinking of solutions. So it doesn't have to be extremely personalized. Now if you can put me in a small cohort of 10 users who are extremely similar to me and have very similar attributes and create a solution for them and you do this for everyone at LinkedIn. Now your cost is reduced 10 X.

[00:23:29] Faizaan Charania: So that's one way of going about it. So this is one thing. The sec, again, cluster users and you don't have to go the extremely personalized route. So that's one. The second way you can bring down costs is not everything will need G P T for, or like whatever the biggest bad model is out there. A lot of problems that we generally attempt to solve are going to be, uh, solvable with much smaller models.

[00:23:57] Faizaan Charania: And again, this is how machine learning research goes [00:24:00] in general. If there's a big new model out there, it's the state of the art, it's performing best on all benchmarks. Six months later, it doesn't even take a year generally, but six months to one year later, you'll have models half the size, one fifth the size, one tenth the size doing the same thing now.

[00:24:19] Faizaan Charania: So GPT 3 launched in 2020. ChatGPT launched last year. We have so, like once ChatGPT gained all the hype, there have been so many new models that are out there. We have the Facebook model at 13 billion parameters compared to 370 billion. So cost can be adjusted based on what model you're using. And I know you had alluded to this a while ago, people can train their own models as well, like fine tune their own models.

[00:24:52] Faizaan Charania: And once you fine tune, that's going to be a fixed cost and upfront cost, but then it becomes very cheap because you can host it [00:25:00] yourself. You don't have to pay someone else to host it. So these are some ways like model selections, how you're doing your targeting. All of this can really, really help with the cost and how you can scale your solution.

[00:25:11] Himakara Pieris: You know, when, , cloud computing was new, everyone was trying very hard to estimate costs. I feel like that's going to be a similar exercise with as well. , especially if you're working on a free product, if you have a free component, um, it's going to be. A process to figure out how much it's going to, cost

[00:25:29] Faizaan Charania: I love the analogy with cloud because cloud can make experimentation so easy. And you're just like trying to set up something new. Test it out. See, see if it works. Will I get product market fit? What are my users thinking about this feature? All of these things are also possible with GenAI. So for any PM who's thinking about GenAI, my recommendation would be test it out.

[00:25:54] Faizaan Charania: At a small scale, only release it to 1 percent of your users. See how it works. If it works, then you can [00:26:00] think about, okay, how do I make the scale? How do I get the cost down?

[00:26:05] Himakara Pieris: It would be similar to any other product launch process. I presume in, in that way, if you create enough value, you can charge enough money and then you have the spread there.

[00:26:14] Himakara Pieris: So it becomes a viable product. It's, it really comes down to finding a big enough pain point to solve with this new, hammer,

[00:26:23] Faizaan Charania: Absolutely. There's, um, there's one thing different. Like for Specifically machine learning models, uh, before gen AI, we had to build our own machine learning models.

[00:26:34] Faizaan Charania: That would mean get data, clean data, build a model, iterate, release something, gather information, gather feedback, and then do something. So this was a very big pipeline and that has been cut short because these large models already have a lot of, , data and they're kind of pseudo intelligent.

[00:26:55] Himakara Pieris: Faizan, thank you so much for coming on Spot Products today. Is there anything else you'd like to [00:27:00] share with the audience?

[00:27:02] Faizaan Charania: Yes. , one thing I'd love to share is just my excitement around generative AI. But, um, anyway, so I write about product management. I write about generative AI on LinkedIn. You can find me as like Faan Nia on LinkedIn and if you have anything interesting that you're working on, if you find some interesting products that you want to talk about, just reach out.

[00:27:25] Himakara Pieris: Great. Thank you very much. And we'll share those links in show notes as well.