Rebel Human Resources Podcast

RHR 124: AI for HR with Sameer Maskey

November 01, 2022 Kyle Roed, The HR Guy Season 3 Episode 124
Rebel Human Resources Podcast
RHR 124: AI for HR with Sameer Maskey
Show Notes Transcript Chapter Markers

 Sameer Maskey is the Founder and CEO of Fusemachines Inc, a company that makes Artificial Intelligence accessible to everyone through education, software and services. Dr. Maskey has more than 18 years of experience in artificial intelligence, natural language processing, machine learning, and data science. After completing his PhD in Computer Science from Columbia University, he joined IBM Watson Research Center where he invented various statistical algorithms to improve speech-to-speech translation and question answering systems. 

Sameer currently serves as an Adjunct Assistant Professor at Columbia University where he teaches several courses including “Statistical Methods for Natural Language Processing” and “Programming for Entrepreneurs”. He has published more than 20 peer-reviewed articles and served as Session Chair, Program Committee member, and Review Committee member at ACL, HLT, ICASSP, NAACL and COLING. Sameer is an inventor of 15 United States patents and has authored over 20 publications. 

https://fusemachines.com/
https://www.linkedin.com/in/sameer-maskey-92680232/
https://twitter.com/sameermaskey

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Sameer Maskey:

So from the future perspective, I think the right balance is not thinking of as complete automation of or now we just have machine do end to end, and you'll be hired just talking to machine but finding machine assisted processes where human and machine is working together to will improve efficiency and reduce bias, but he's also able to provide the human touch.

Kyle Roed:

This is the rebel HR Podcast, the podcast where we talk to HR innovators about all things people leadership. If you're looking for places to find about new ways to think about the world of work, this is the podcast for you. Please subscribe from your favorite podcast listening platform today. And leave us a review revelon HR rebels. Welcome back, rebel HR listeners extremely excited for the conversation today, we are going to be talking about AI and HR and how those two things can work together. With us today, we have Samir maski, he is the CEO of fuse machines, and an AI professor at Columbia University. Welcome to the show, Samir.

Sameer Maskey:

Thank you, thank you for having me

Kyle Roed:

here. Well, I'm really excited for this, you know, I think this is one of those topics where, you know, I, I saw your background, and I and I saw some of the work you're doing. And I just had to talk to you because Admittedly, this is an area that I am very under educated in. And so

Molly Burdess:

the same thing, Kyle.

Kyle Roed:

So I'm just I'm just fascinated. And Molly, thankfully, is here to ask the good questions. So but before we get into that, I want to understand, you know, what got you into the world of AI and ultimately, into how AI and HR can work together.

Sameer Maskey:

Um, my interest in AI, I think, started with some of the sci fi movies on the, you know, computer talking to humans, you know, natural language dialogue systems. So in college, I studied physics and math. And during that time, I started doing research on speech synthesis. So I build the first Nepali speech synthesizer. I grew up in Nepal. So I speak in the paddy as well. And we I ended up building Nepali speech synthesizer, so AI for language, and that's how my research interest in AI, started, and I started doing more and more research on language and AI, and did my PhD in natural language processing, and speech processing, for summarization, and so forth. And that's, that's how I got started in the world of AI. Oh, and then the second part of the question, how, how I started doing work, and particularly, AI in HR world, right. So as I just mentioned, I came from an academic background of just using AI for language, how do we build a natural language processing system like question answering systems, dialogue systems, and so forth. And with that expertise of building machines, to be able to read text, extract information, doing information extraction from Texas or forth, has led on to much better understanding of how we can apply some of the same technologies like information extraction from documents, where documents is actually resumes, right. And that's how we've ended up now doing one more research on trying to figure out all possible applications of AI in particular from both NLP and speech processing. On the end to end process of somebody applying to a job to getting hired, be it skills, extraction from resumes, be it automatically scoring, you know, interviews, trying to figure it out the possible matching score between applicants and job descriptions, and so forth. So we've been doing more and more research on this. And as the company we build more and more technologies around it, find a bunch of patterns around this as well, which now has transformed the company into these set of product lines on talent management platform and education platform with large AI features.

Kyle Roed:

I think it's really fascinating. And it does, you know, I'm a big sci fi fan as well. So, in fact, the, you know, the rebel HR thing, rebel can mean a lot of different things, but in my nerd brain, it's like the Star Wars Rebels, you know, it's like the people who are trying to like, you know, change the world and just a small band of rebels anyways, but so, so like, I totally get that. i One of the questions I have on AI is kind of where we're at in the journey of AI because it always seems like when you when You read about it or you hear about, it always seems like it's like some far off distant thing in the future. But there's actually been a lot of work. So as you look at, like the kind of the lifecycle of AI and kind of where we're at in the journey, you know, where are we? And where do you see this really going as it relates to our world, specifically in human resources?

Sameer Maskey:

I think, from overall development of AI, and how useful it could be for real business use cases, now is the time finally we are at the stage where it's a combination of the accuracy of algorithm, the cost of stories in, like, you know, cost stories in GPUs, and compute power. And the amount of data that is available in the world, particularly the amount of data businesses themselves have about the products and customers, it's at this point to where it is trying to make a real ROI for companies. And that's why I think we're suddenly seeing this explosion of applications of AI, along product and services in companies because they see real, real returns on investing on the application. So because of that, I think we are now in this even more explosive growth cycle, where we will see more and more use cases of AI across many, many different industries and applications.

Molly Burdess:

Yeah, it's came real fast. Like, I think we're at the point where it's here. But our laws and our policies aren't quite up to speed yet, which is something I think we're all going to deal with in HR. So you mentioned return on investment with what what are what are some of those that you that your organizations that you're working with are actually seen.

Sameer Maskey:

So to give you an example, for example, a company that we worked with, were building pricing prediction engines, right, they were trying to figure out the the sale the product, across multiple geographies. And when they sell the same exact products, such as games, and you're trying to sell across multiple different geographies, you need to price them differently, but you still want to price it so that you're not leaving money on the table. Right. And they were able to build a very good machine learning algorithm to figure out the optimal price across different geographies and increase their revenue. Another use case is a hospital trying to reduce cost inventory by doing better implant prediction. So that they have to, they don't have to store all variations of implants for each surgery, but fewer implant sizes for each surgery. So many different use cases are quite different industries, a lot of them doing a lot of them to do with predicting something that a lot of times either reduces cost or increases revenue.

Kyle Roed:

So I think it's, you know, it's fair to say, like, this is going to be the future, right, this is where computing is going. You know, I think, especially in the HR realm, there's, there's like, there's like a counter movement to AI, you know, and it's like, you know, put the human back in HR, you know, these types of movements, which I think, I think in many cases are correct and right. But as it relates to AI, and as it relates to the future of technology, what's your vision of how those two things come to gather that, you know, that we don't lose the kind of the humanity of, of work, or the the work that we do, but we also embrace AI? How do you see that in the future somewhere?

Sameer Maskey:

It's about finding that right balance, right? I think with with AI, you could automate a lot of stuff. And some of you have very good applications, such as I think AI could be used with a lot less bias. If we build a model, right, then there could be a lot less bias in screening step. Right? Humans, a lot of I mean, in humans in generally, depending on the background, all of that. We know, several cases that we have seen in the news that there are these biases that humans have when they're screening resumes. But if we're able to build machines, with a lot of unbiased data to model it, we could have a very unbiased machines that allows people to go through the screening processes. Right. But having said that, as you know, there might be questions that the candidates also want to ask to understand the nuance sense of the culture of the company and so forth, in which case machines may have a hard time providing really good answers, because machine's not working alongside all the other people in the company, it will just spit back with answers that, you know, whoever model the data saying, This is what the culture looks like. But it's very hard to relate, it's very hard for the machine to relate to the culture and be able to provide that very nuanced answer to the candidate that an HR person could be able to do with that human to human touch. So I think it's finding the right balance. And I think definitely, there's advantages of using machines to automate. And then they're offered definitely advantages of human being in the loop. So from the future perspective, I think the right balance is not thinking of as complete automation of, or now we just have machine do end to end. And you'll be hired just talking to machine. But finding human assisted system, machine assisted processes, where human and machine is working together, to will improve efficiency and reduce bias, but he's also been able to provide the human touch.

Molly Burdess:

Yeah, when I think of AI and HR, I, you know, that's one of the first things that goes to my mind, as well as if we can eliminate some of those biases, that that's a positive. Also, if it saves me time on some of the manual stuff, and I can focus on some of the bigger strategic HR stuff. And I think for me, it's also about just getting that data to become a better strategic partner partner. I think that's one thing that HR can do better. And I think it's hard when we don't have the data. So I think AI can really help with that piece.

Sameer Maskey:

Yes. But we do have to worry about the quality of data and the bias in the data itself as well. Right. So you probably had seen this news where one of the banks used an AI system to screen for engineers, right, and they use historical data. And historically, especially over the last, whatever number of years, a lot of the engineers that got hired in the bank, were male. So the machines automatically the, the model, builders didn't do anything. To reduce that bias, just build the model, based on the historical data. And the machines were just predicting and giving higher score for in the screening steps to interview a lot of these male candidates. And so, so while it could be useful for reducing bias, we still need to be also aware that, especially in HR, like with most machine learning system, a lot of historical data gets used, right. But especially in HR, when you use storica data, you need to do a deep dive to see if the data itself is very biased, because of how you just collected and what the process looked like in hiring.

Molly Burdess:

Yeah, so I was actually just reading this and the advice in this article is that, you know, when you're looking at these, these technologies, and these companies that HR, you really have to do your due diligence and ask the right questions about the program you're using, specifically regarding the biases and all of that stuff. But I think for me anyway, that is not my skill set, I wouldn't even know what questions to ask. So I guess for those HR folks like me, who that's not my skill set, what advice would you have for us when we are evaluating these companies? And these, these tools?

Sameer Maskey:

Yeah. So I was, I would say, one of the, one of the big, like points I tried to make across to the company that are trying to deploy AI, across different departments be data and be somewhere somewhere else is collaboration, right? And like we are here for in the HR, HR talking about HR. So for that it applies the same thing, which is like, there are engineers who are building the models, and they are the end users were actually going to use it right. So their HR officers, HR administrators, HR managers who may not know much about AI, but they know all about HR processes, the biases that creep in all the nuances of our HR workflow. While there are engineers who know nothing about HR, but they just know how to build the models. And when a company just tries to take off the shelf to not have this collaboration across engineers and HR managers and just build a process. That's when something like that would pop up. Right. The news just talked about this, probably the model builders built it. And our manager said, Oh, this is ready. We'll use it. It's automating now we just need to look at 5% of resumes 95% is automated, everybody's happy. But then use then later on after a lot of people are hired and used for many months, you see, oh, there's something something's off here, right. So the biggest advice I would say is for any company when they're trying to implement AI in HR, or be it in other departments, but particularly even in HR, you need collaboration of HR managers with AI engineers and model builders, to make sure that engineers understand the nuances of HR process and workflow. At the same time, the HR managers are at least understanding the basics of how the machinery behind the scene is working. So they are able to ask interesting questions to engineers to make sure that engineers are taking account of the nuances of the workflow and building the model.

Kyle Roed:

I think it's, it's so interesting, and it it reminds me of going through, you know, an implementation of a new, like an HR system or some sort of a, you know, some sort of a system protocol where you know, it, the system is only as smart as how you program it, right. So, so if you, if you don't think about all of these, the nuance and the context of a system, and you you take something that's just off the shelf, you're gonna miss something, but I do think, you know, to that point, HR does need to get educated on this, that so we can ask those right questions, right, you know, like, I would not have thought, Okay, if it let's say an applicant tracking system came to me and said, Hey, we have this AI solution, to screen all your resumes, if you're looking for, you know, an engineer, just put in all these skills that you want, and we'll give you will top grade all the resumes. But I wouldn't think to ask the question, well, how do you specifically make sure that bias that gender bias is not a part of that? You know, that that solution? You know, now I'm going to ask that question. Right, but you need to be thinking about that.

Molly Burdess:

Right? Yeah, you know, I'm so glad that you said that. Because I think one of the concerns that a lot of us, HR professionals have is that AI is going to eliminate our positions and just eliminate our a lot of our job responsibilities. And what I just heard is that no HR will actually play a big part in it, or they should play a big part in it. So really, they can't do it without us.

Sameer Maskey:

Yes, absolutely. I think this tight coupling between the HR managers is or officers, along with the model builders from engineering side, is super important to be able to create a lot of value out of applying AI in HR. But having said that, I would also like to say it's not like there wouldn't be any job losses, I would say, as AI systems get more sophisticated and is able to, you know, automate a big chunk of the process, there will be then we need for probably slightly smaller sized teams. But that does not mean, you know, there will be no humans involved.

Molly Burdess:

I think that's a great opportunity for all of us in HR to level up to a higher level.

Kyle Roed:

I agree. You know, I think it's interesting. So, you know, obviously, no, Molly and I have spent our career staring at the job market, and trying to figure out where are people? And how are, you know, how do we match the right skills with this job and that job and, and, you know, in my experience, whenever I've heard that the comment that or the fear that something is going to come and take our jobs away, in general, that's a really great opportunity to take a look at what is the future going to look like? Because a lot of times, there's some truth to that, you know, for instance, there will be some job loss with AI, it's natural. That's the whole point. It's, it's deflationary. It's making things easier and Systemising things and that ultimately, reduces the amount of manual workload that needs to occur, right. But I want to talk a little bit about this because I think this is really important. It's not about taking jobs away necessarily from people. It's about empowering people to do things that are different and the the amount of opportunity within the realm of AI is massive, like this is going to be a huge job market it already is. And right now, there's a there's a huge talent gap. So Samira, can you talk a little bit about that and kind of what you see as it relates to the the labor market and kind of AI as a as a labor force?

Sameer Maskey:

Yeah, um, AI labor market is absolutely crazy right now, you probably already know that right? I would say like engineering talent. In general, the demand for that is as high as it could ever be. But even within engineering, I think, yeah, engineers even harder to find, right? I mean, it requires more additional skill sets and maths besides programming and so forth. And there's just not enough hours in the world. So time to market is crazy. And but I think one way to address the talent market is upscaling I think more and more companies and organizations need to think about how they upskill engineers, be it other types of engineers, and give them real opportunities to learn AI and bi engineers. But this upskilling, I think, is not only it's not only applicable for engineers to get upskill to become AI engineers, but as we were talking before, how do we upskill it's our managers, it's our officers, right, and the other other types of managers across other departments to learn about AI and really upskill their skill sets around applications of AI, and so forth.

Kyle Roed:

I think it's really funny, because it's, it's, it's exact coral, or corollary to what Molly and I are trying to do in which is retain employees, and, and put them on a path for a long and stable career. And, you know, by investing in in upskilling, you know, not only your employees, but potentially you're helping, you know, your, your vendors or suppliers upskill, you know, being open to AI and kind of that, you know, as opposed to AI taking or stealing somebody's job, you know, AI empowering somebody to do something different. I mean, I think that speaks to the human experience, right? It's all about growth and learning. And that's, I mean, we should all be aspiring to do that, and to have our employees be doing that. So is your is your vision that, you know, that that AI that there's going to be somebody in almost every enterprise that has a job in AI? Or, or is or at least is at least working directly with AI as it relates to that enterprise?

Sameer Maskey:

Yeah, I would say, I mean, more and more companies are creating Center of Excellence for AI, where the the bring about all of the engineers in one place, and they collaborate across multiple departments. So I think AI as AI touches pretty much every industry, and many different service and product lines, it is I would say it is natural to think that there will be employees, that has to do with something with a in pretty much most of the large enterprises and medium sized companies.

Kyle Roed:

Yeah, it's just going to be fascinating, I think, you know, it's my, you know, my experience right now, it's kind of like, you know, I feel like, our organization could use AI, but I wouldn't even know where to where to start. But but my guess is that it's probably already, it's already being leveraged, it's just I don't have necessarily have the visibility to or maybe I just don't understand how an algorithm is working in the background as it relates to a back office procedure or something like that. So I mean, it's already kind of here, isn't it?

Sameer Maskey:

It is it is. And even though you might not see it, as you just mentioned, just using the product lines across many different use cases, be back office, you know, documentation document storage system, like there's more and more data document storage system coming where it automatically tags a document, so that it's easy to search later on, and it puts them in the right folders and so forth. And you might be using it without even really thinking about it. Like a lot of you probably I mean, many people use, for example, Google's email system across organizations, and it has this autocomplete that people just use it naturally on creating these sentences and so forth. Now, we are probably not even thinking too much about it while we read the emails, but it is here and it's already making a difference in you know, day to day workflow for a lot of people

Kyle Roed:

you know, how many times that saved me from you know, putting the wrong grammar in a in a you know, company wide email, you know, for sure, thankfully that exists. Let me know. Sorry, go ahead. Well,

Molly Burdess:

I was just I really wish I would have paid more attention to this stuff in college and now I feel like I'm having to like oh my gosh, you know, things like cyber security and yeah, how to what questions to ask and what am I looking at and is is all coming full circle into the HR realm and I just, I need to do better.

Kyle Roed:

Hey, I'm still struggling with I don't have to double space anymore. You know, that's I'm still working on that. I do think it's, it's fascinating. I mean, it's there's just there's so many applications and it already exists in our world. But I also think that there's an opportunity for us to get much more educated ourselves. And so, you know, as we think about that, just, you know, maybe it's not our core function, but it's an area that we're very interested in, you know, I think about actually think HR, my theory is, AI is becoming going to become more prevalent in HR than we think, because we're getting to a point where we have so much data, we have too much, we don't know what to do with it. And the reality is that the data that we choose to do something with a lot of time has, has just has so much bias in it, because it's something that we think is important, but we we might be looking at a, you know, a correlation versus causation type of an issue with with some of this data, you know, and, and sometimes it's just, it's just very difficult. So I, I think there's gonna be a lot of applications in our space.

Molly Burdess:

Well, and it's not like you said, if you're interested in it, this is something that I am not interested in at all. But it is something that I feel like I have to learn, and I just have to dive in.

Sameer Maskey:

Yeah, I think all of us should learn more and more. More and more topics related to AI, be it AI for HR VDI for other applications. Uh, one thing I would, I would say is, you know, as a lot of companies tries to create these upskilling programs for AI. And I think more companies are realizing that, especially for learning AI, just providing an access to online videos is not enough. Because of the complexity of topics, like the company's really needs to make an effort, and providing high quality training programs, and so forth to be able to teach AI to as many people as possible, who may not be core programmers.

Kyle Roed:

Yeah, I think what's so interesting, so, you know, you know, I'm reflecting on an engineering position, as an example, you know, this is something where it might take two years before I have, you know, a specific type of engineer that's actually competent, and able to do their job function, you know, autonomously, without having to seek the advice of a mentor or a support person, or somebody that has done the job for 30 or 40 years, you know, that's just kind of the industry that I'm in. And it's really, really challenging to find the right individual, that's actually willing to spend the time to learn and that's, you know, that's just kind of a nuance, but I think about, you know, the application of AI, and, and the opportunity that that can present us to think about upskilling shortening a time to competency, putting in a process that that means that somebody doesn't have to remember 50 different steps to take before they can actually start to put the theoretical application of what they're thinking about on, you know, into that document or, or, you know, whatever their process is, I just think there's, there's so much there. For organizations that are struggling with where to start, is there typically a natural kind of an entry point to start to look at at AI? And where to upskill? You know, automatically, I assume, do you start with it? Do you start with you know, do you start with your your product experts? What do you see they're out in the marketplace? What are companies doing that are trying to solve this problem?

Sameer Maskey:

I think a lot of companies are trying to upskill from IT Engineering Department, but I think the biggest bang for the buck you get and quick ROI you get is actually provide a very short Training Program B couple days training programs for the C suite. I think what ends up happening is you know, there's usually a bigger mandate to start applying AI in the business because, you know, board may have seen other companies, similar companies using AI and you know, then it trickles down saying oh, that the as the engineers that the engineering team gets out, like, figuring out something to do with AI and start applying the product line, right. And they start to do it but usually a lot of the junior employees in the engineering back in the engineering team, they don't have enough a high level overview and insight into where the businesses end, what's the high level strategic decisions that have been made in product and service offering and the evolution of that in a year out to two years out, and so forth. So that level of vision and, you know, Insight is with the C suite. But a lot of times what I've seen is the C suite is not trained enough on AI. And they're just relying completely on engineer to come up with the problems come up with the solutions, and so forth. So I think there's a big, big value of progress, providing even 3d training for the full C suite on AI for business. And I know, it's a lot of time for a full C suite to be spending three days, right, it's like, everybody's super busy. But if you take our three days, for many years ahead on the use of AI, it's absolutely worth it. And that could actually have much bigger ROI on the business from upskilling. perspective.

Molly Burdess:

I think that's great advice. And I think HR can add a lot of value and some of those pain points that you were talking about, you know, I always tell people, I love HR, because you're kind of in the middle of it. All right, you see each different department, you understand a little bit of everything, and you can really help be that connector to make the business run more smoothly, or I just think it's a really good position to be in to add value. So I think HR can take that advice and help connect some of these departments and really show and be that true business partner. Exactly.

Kyle Roed:

Absolutely. And I think about it, you know, in the context of, you know, what does your learning development program look like? You know, and is it? Are you using a, are you using a structure and a strategy, that's something that you saw 20 years ago, and it's all the same training, or training topics, you know, how many l&d programs have an AI curriculum, but how many employees would love to go through an AI curriculum, and you know, maybe they're not going to be the next leader, but maybe they want to be that individual contributor, and they want to be the AI expert, and they could be leading your next innovation division, you know, you just, it's an opportunity for HR to step up and say, Hey, what about this, you know, ask the right question. And, and, and potentially structure something that could be pretty cool.

Molly Burdess:

I would imagine the younger generations to when they come into the workforce, that's kind of what they're going to expect.

Sameer Maskey:

Yeah, I think newer generation, a lot of times they are coming in expecting a lot of learning opportunities, across across topics, and fields, they may not be even core part, right, like, so like somebody would joining as a junior officer, they might not be doing AI, but they probably would be expecting that HR will start employing AI, and they would want to start learning about AI right off the bat. And if the organization doesn't provide any upskilling opportunities, and learning opportunities, and AI and its application HR. There might be issues with retention, for example.

Kyle Roed:

Absolutely. I mean, I think, you know, an example here in my organization. So, you know, I, we've been trying to systemize administrative processes since, you know, forever. And it's just one of those areas where it's just, it's always a component of somebody else's job to do that. And, and, you know, you know, what happens is, your day job gets in the way, somebody comes in into your office, and inevitably, you know, slows down any sort of progress. So I ended up hiring an HR system specialist, and her function is to systemize HR. Right. But that's a great opportunity, where, you know, I guarantee you, she never took an AI class in college. But I bet she'd love this, you know, and she would probably go through this course and come up with 25 ideas I would never think of, and then figure out how to help us deploy them within within our function. So yeah, I just wrote that down on my to do list. So with that being said, that I think some really great things to think about. This is an area that we really haven't explored much on the podcast. So Samir, I really appreciate you taking some time. I do want to shift gears and get into the rebel HR flash round because you think your perspective is going to be pretty different than than many of our guests. So question number one, where does HR need to rebel?

Sameer Maskey:

I would say technology and innovation for some of the steps within the HR processes, I think, overall HR processes and the technology involved in it is hasn't evolved too much. Still, there's still new applicants apply I would like upload their resumes, they get screened, they get interviews and so forth, right. And even, for example, in the screening process, so many people apply for very few jobs, right, like when we post a job opening, we get hundreds and hundreds of applicants. And there's only enough resources to interview maybe five or 10. And the condensation of overall personality and skill set in one piece of paper on one piece is not enough to really show how good a person might be. So for example, there could be an innovation of AI combined with virtual reality, where 95% of people get to get interviewed by an AI HR recruiting interviewer, where the machine is able to really get the nuance of the personality of the person, the applicant beyond just what's in the listed in the resume. Right. So I think there needs to be more technological innovation in HR processes. And it could even start with, like I said, a screening process, where you know, it ever avatar, along with virtual reality could be enjoying 90% of the candidate.

Kyle Roed:

Absolutely, I think there's so much opportunity there. And that's something that absolutely, you know, HR has to be, you know, at the forefront of that decision making process and owning some of that. So, question number two, who should we be listening to?

Sameer Maskey:

We should mean thing to our employees, the company should be listening to the employees, and especially, you know, this with a COVID. In the new year, new generation, a lot of employees have a certain level of expectation on what company does for the employees? And what is the expectation beat, like we talked about before on the training programs, within the company from learning opportunities, and upscaling opportunities. And I think companies need to spend a lot more time listening to the employees on what they're asking for, and what would make them happy and how they are thinking about other ways to contribute towards the company. And I think many organizations don't do enough besides sending surveys once in a while. There should be a way a systematic way of really, really listening to the employees.

Kyle Roed:

I think that's a great example. That's a whole nother application we haven't even talked about with AI, right? It's how do you allow people to surface surface questions, concerns, what have you, and put that into systematic protocols so that you can actually address the root cause of some of the concerns and find, you know, common themes, right? Yeah. So yeah, yeah. Sorry, I'm, I'm nerding out again, I'm going back to the Star Wars reference again. All right, last question here. How can our listeners connect with you and learn more about some of the work you're doing?

Sameer Maskey:

Um, they can connect with me through LinkedIn, semi maski, linkedin.com/i, mean ASCII or Twitter? Somebody mask is my handle, or my email, SMS key at Fuse? Calm? Any of those channels would work. And I will respond right back.

Kyle Roed:

Absolutely. And we'll have that in the show notes. We'll also have a link to fuse machines.com There's a bunch of really great curriculum there if you are thinking about, you know, an l&d program or maybe you just want to learn yourself so that you become an you know, an educated HR professional for for the future of what this looks like, for yourself or for your organization, check it out. some pretty cool stuff out there. I already know, you know, double of what I knew before this discussion just by just just in researching for, for this. So I appreciate that Samir and and I also I do want to thank you, I know a part of what you know, your goal your your function is at Fuse machines is really to democratize AI education, and to really truly, you know, embrace AI for the future to make the world a better place. So I thank you for that, for that mission and the work you're doing there.

Sameer Maskey:

Thank you. Thank you. Thank you so much.

Kyle Roed:

That being said, that's all we have for you today. Thank you so much for joining us and have a great rest of your day. Thanks. All right. That does it for the rebel HR podcast. Big thank you to our guests. Follow us on Facebook at rebel HR podcast, Twitter, at rebel HR guy, or see our website at rebel human resources.com. The views and opinions expressed by rebel HR podcast are those the authors and do not necessarily reflect the official policy or position of any of the organizations that we represent. No animals were harmed during the filming of this podcast. Maybe You

(Cont.) RHR 124: AI for HR with Sameer Maskey