The Chris Voss Show - The Chris Voss Show Podcast – Artificial Intelligence and Machine Learning in Human Resources: A Concise Guide by Dr. C. Rasmussen

Episode Date: November 28, 2025

Artificial Intelligence and Machine Learning in Human Resources: A Concise Guide by Dr. C. Rasmussen https://www.amazon.com/Artificial-Intelligence-Machine-Learning-Resources/dp/B0FWZQXHMG What if a computer could help find the perfect employee or predict who might leave a job? This exciting idea opens the door to a new way of working. Overview This guide explains how artificial intelligence (AI) and machine learning (ML) are transforming human resources (HR). Smart computer programs can quickly review thousands of job applications to find the best candidates, suggest training tailored to employees’ needs, and predict which workers might quit, helping managers take action to keep them. The book includes real-world examples, like how large companies use AI to save time, and covers benefits, such as improved hiring, as well as key concerns, like protecting personal information. At just 61 pages, it’s concise by design, following Richard Feynman’s wisdom: “If you can’t explain something simply, you don’t understand it well enough.” More pages don’t equal more value; in fact, lengthy texts can bury useful insights. Since every organization is unique, this book equips HR professionals and managers with the right questions to ask rather than a rigid roadmap, making it a practical tool for anyone curious about the future of work. About the author Dr. Curtis “Curt” Rasmussen is a leading expert in industrial-organizational psychology with a Ph.D. from Walden University. He specializes in blending human skills with artificial intelligence (AI) and machine learning (ML) to make workplaces better and more efficient. With years of experience in research, consulting, and government roles, he helps businesses use data and tech wisely. His career highlights include owning Cyber-Human Performance Tech, LLC, where he advises small and mid-sized companies on adding AI to hiring and daily tasks while keeping things ethical. He also guides students in George Mason University’s Data Engineering program, focusing on AI tools like natural language processing and computer vision. At the Cybersecurity and Infrastructure Security Agency (CISA), he led workforce planning as a senior I/O psychologist, creating surveys and frameworks that improved employee satisfaction by 45% and helped with smarter hiring. Earlier, he reviewed AI and data science proposals for the Department of Commerce, National Academy of Medicine, and the Office of the Director of National Intelligence, making sure projects were strong and fair. Dr. Rasmussen has invented patent-pending tools like the Multidimensional Algorithm Structure (MAS), which picks the best AI methods by checking data and company needs, and the eXplainable Artificial Intelligence Construct (XAIC), which makes AI easy to understand and trust by involving people in decisions. These ideas help fix common AI problems, like failures or hidden biases.

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We have an amazing young man who's joining us today on the show. We're going to be getting into it with him. His book is, entitled Artificial Intelligence and Machine Learning in Human Resources, A Concise Guide. Today, we are joined by Dr. Rasmussen. We're going to get into it with him and talk to him. His first name is Kurt. He goes by C. Rasmussen, if you look up his books on Amazon, just so you can find him easy there, folks. And we're going to talk to him about his experience and everything else. Welcome to the show, Dr. Rasmussen. How are you? Thanks. I'm doing great. By the way, I love your

intro. And I was just wondering what happens if I stick my arms outside the, you know, confines. I get cut off. Yeah. It's a roller coaster, you know, that's what they tell you. So you are a leading expert in industrial organizational psychiatry with a PhD from Walden University. You specialize in blending human skills with artificial intelligence. Is that what they call simulation? And machine learning to make workplaces better and more efficient. Years of experience, research, consulting government roles. He helps businesses use data and tech wisely. Because why should we use it wisely when we can just use it recklessly? Kurt, what do you think about that? You know, I guess if we want to get the benefit wisely is the best. No, let's look up AI to nuclear systems and just see what

happens. I think what could go wrong, right? What could go right? That's true too. Yeah, I mean, that may actually be the thing. It's like something could go right, and then that disappoints a lot of people because, you know, profit and whatnot. Well, it would solve a lot of human problems, nuclear war. People always ask me, they're like, we should have more peace on Earth. You know how you get peace on Earth? Kill all the humans. Anyway, so give us a dot com.

Where do you want people to find you on the interwebs? So they can look me up on the focal point coaching.com website. under Kurt Rasmussen. I'm also on LinkedIn. Those are generally the places that I inhabit, and then my books are on Amazon. So give us a 30,000 overview. What's inside this book?

So if I give you a 30,000-foot overview, it's only 61 pages, hence the title of being concise. Oh. So really, this book really gives you, you know, what I should say, It arms you with the questions and some of the thoughts you should have if you're going to implement artificial intelligence in a human resource process. So, for example, like, oh, I'm going to use AI to, you know, select candidates because there's a lot of companies out there saying, hey, we use AI and we can select the best candidates. the issue then becomes is it legal and the general answer is yeah not so much so

the and there's there's companies doing it nowadays there's been a lot of news reports of companies that are doing the that are doing the AI is filtering or maybe doing all of the interview there I think there were some people that were saying recently that they figured out that their interview that they gave to HR was being run by an AI bot? You know, it's very possible.

You know, kind of the bottom line is if you go and you look up, and I have all these references in the book, but if you go and you look up and you go, okay, well, it's not strictly illegal to do it. You have to really think about the bottom line and what

court cases say. And what it really comes down to is any selection process. The case law that sits out there right now shows that you have to have what's called a validation study. And most places don't do a validation study. Now, the thing is, it's very difficult to bring a lawsuit. You know, if you didn't get hired and you feel it's AI bias, it's extremely difficult and extremely expensive. But here's the thing. If the company didn't do a validation study, they will go to court and they will lose. Really?

That's, it's that simple. They will go to court and they will lose. I can hear a whole section of, of, uh, of, uh, a freaking attorneys, uh, chasing the sand was, so. Oh, absolutely. And then, you know, and so I also, uh, in the book, I also cover some of the, the different laws across the U.S. Um, one of the, one of the, one of the.

One of the key laws that's out there is actually the Virginia Consumer Data Protection Act. The reason I say that is because there are eight other states that have either enacted similar legislation or in the process. But one of the key elements is that data can only be used for the purpose it was collected for. Criminal background checks. You know, criminal data isn't, isn't, captured so you can do a background check for a job. So just think, chew on that. I mean, it's very common to use a service to, you know, check out somebody's background,

make sure they don't have a felony. But, but that's not a use. That wasn't the intended use for that data. And we haven't had a law, you know, lawsuits with that. yet, but these are things that somebody could, if you integrate that into your business and now this is your business model and now that you get rid of your HR because now you have this wonderful AI and somebody goes and sues you and you haven't done these steps, it might cost you your company.

Oh, well, that's a good, that's good advisement to know because people need to know what's going on with all this stuff and whether or not what the impacts are going to be of it. And, yeah, I think it's really interesting. I mean, can AI really, like, yeah, I don't know. There's a human element to a team. Right. And you can, when you interview people, you can kind of feel if you have a groove or not or you jive or whatever the word is you want to use, but you want to, you know,

you preview of, I guess, someone's going on the team. And I don't know that AI is good at, I mean, I don't know, what do you think? Well, here's the bottom line. So interviews are a terrible way to select people, but as humans, we think we know how to do things. So you don't get, you don't get past that. You know, we have over a century of evidence that supports that there are some ways that are great ways to select people and interviews really aren't them. Now, with that being said, if you're not doing. doing it good as a human, how are you going to do it better as AI? Because the humans have to

program the AI to do what somebody's doing not well. They're doing it poorly. So how does that, how does that work out? So, and you know, I would say not well. Not well in the long term. no well long term i'm being very very kind to the so now this this book here in particular this is targeted towards human resources utilizing AI then um it's really any business person or yeah HR but anybody that's like i'm going to go i'm thinking about putting an AI you know i'm having an AI service design, bots, I may have a platform where it does automated selection testing. This is really kind of the book that goes, okay, here's some things that you need to be aware of before you go down that road. Because a lot of these companies, you know, and I respect them, but, you know, at the end of the day, you're going to get sales.

Sales isn't somebody that's telling you exactly how the AI works. Sales is also not somebody that typically has a background in selection like I do where they're going to go, okay, here are some things you need to be aware of. You know, such as, hey, you know, if you don't have a validation study, and oh, by the way, here's a really cool thing about AI, you can't actually conduct a standard validation study with AI because the math behind the AI changes as it's exposed to different data. So you can't, you can't do the statistics.

Oh. So how do you do the statistics then? With AI, you don't. Yeah. Otherwise, I mean, to not dive too far into geeky statistics stuff, essentially what we're looking at is what's called criterion validity or. or construct validity. So in other words, we go out and we go, okay, what do you do for a job, Chris? Okay, tell me about the tasks you do.

You know, tell me about the things that you need to know. And then somebody like myself sits down there and we capture all this. And then we develop an assessment, a test, based off what you've told us. and then we have an incumbent like yourself. You take the test. We'd have multiple people take the test. And then we go and we make sure that those items are predicting your potential for success. So in other words, it's a very involved process.

But at the end of the day, what we're trying to do is get you the best return on investment. That's R.O.I. that you can possibly get. So do you, do you officially prove those AIs being used to interview people and analyze, I guess, resumes and stuff? Interview people, that would be a non-starter for me. Although, I will tell you that the best type of interviewing is called a structured interview. and AI is going to do a structured interview way better than a human well. But this still goes back to validation. You still have to validate.

Now, could you validate AI doing an interview? Absolutely. Would it be that hard? That is actually something that wouldn't be that hard because the questions aren't going to change. Now, there's ways to wrangle it in, but one of the ways is, doing what's called inter-rater reliability statistics on the AI itself. You should be doing it on the humans. If the humans are doing interviews, essentially what that says is how consistent they are.

So in that fact, an AI interview actually is better. Now, it's not, that doesn't mean it's good. It just means it's better. If you do all the steps, that is a big if. Because if you don't know to do this, then you just, put something in, you could be doing something that is going to damage you in your company or, you know, whatever organization you have. Now, if it's reviewing resumes, there's already been several EEOC cases with large settlements because some company used AI to review

resumes. In one case, in one case, there was a 800,000, and it may not seem like a lot, but, you know, it's all relative to the size of the organization, the business, but there was an $800,000 judgment against company that used AI screening of resumes because it was rejecting resumes of people that were over 50, and particularly women. Wow, it's discriminated. Yep. Wow. And what was the reason it did it?

The functionality was bad, or did it just decided, how did it come up? with that, did it? So it did what it was supposed to do because it's math. So two plus two plus two equals four, right? So it's math. But the way you have to look at this is this gets in and this is a problem with the way AI is often designed and trained is the people doing it don't understand some of the factors they really need to understand. So if I'm going to a population, so I'm going, I'm going to do this to screen resumes. And I can have anybody between the ages of 18 and 80, then I need to make sure that the data set I train the AI on

has an equal representation of all the attributes that could come out on that resume. So you're going to need 50-50 male-female. You're going to need an equal split between if you do ethnicity, you're going to need, you know, an equal split between different age groups. You have to be very careful. And the other thing is standardization is a great thing for AI. You know, people love large language models. They're like, oh, you know, I got rock and it can figure all these things out.

The issue with that then becomes what the probability is of an outlier. So really what you got to do is you got to have, you got to really look at that and have a standard form. Every time I feed the AI form that is a unique form. So you get to write your own resume. I have to compensate on my side for that. So I have to make sure that it can read letters in a ribbon. I have to make sure that it. it recognizes different types of font.

So every time you complicate something for yourself, you know, in the training, you know, you got to compensate for it when you do the programming. So if I just say, okay, here's the standard format, fill out your resume, you know, plug and play, and places are doing this, right? It'll extract the information. Then that's going to give you better results, but you still have. have to be conscious that you know what it's being exposed to is going to have maybe a very low representation of of women versus men and you have to pay attention to this because over time that

algorithm will pick up on the patterns and the pattern is mostly men you know i don't see a lot of women out there you know dumping trash and trash trucks right so if if you just use the people that apply, eventually it's going to say, ah, well, this woman applied, but only men are the ones that are really scoring high, so only a man's going to get the job. So, you know, it's not a fire and forget thing. It's something that has to be continuously monitored. Wow. Otherwise, you break fair, fair job hires, right? The law for that. Yeah. And what are some other ways human resources are using machine learning and AI? One of the big ways, and a lot of places are using this, they're using chatbots.

Because, you know, frankly, a lot of questions that come in are very standard questions. Like, I'm thinking about changing my health care provider. How do I do that? But again, the challenge becomes, did you integrate that properly with the people that are your customers in this case? And a lot of times, the answer is no. I was talking to a gentleman a few months ago, and the credit union he works for came out with a chatbot. They spent months, and I'm sure thousands and thousands of dollars. and it was up for less than a week.

That's funny, man. That's funny. Well, you know, it's a wild world we live in. Are you surprised at how fast artificial intelligence is advancing? I mean, it's just whipping fast. It seems, at least someone like me. Not really. It's brute force. So arguably,

One of the first references to artificial intelligence was in the 1,200s for a thesis and mathematical thesis that was written. Even if you look in Gulliver's travels, there is a point when Gulliver comes to a land where everybody does what the machine tells them to do, which the machine's essentially AI, but it's mechanical AI. So he's amazed when he's walking through and people are building houses from the top down. on, not the foundation up. So really what it's come down to is we have better microchips, but they're not improving like they were, say, in the 80s and 90s. And we just have more of them. So it's a brute force thing.

So you have these large data centers that are sucking up a lot of energy. I mean, for example, the training of chat GPT, I'm going to say, I think, version three. I did the calculations on it, the approximate calculations of how much energy it used. It took three months to train it approximately. And the electrical power that it consumed in those three months was the equivalent that 120, 100, no, 162 average American homes used in a year. Wow.

That is wild, man. And energy usage hasn't gotten down, gone down. I was speaking at a workshop for Pacific Northwest National Labs. They're Mathematic and Artificial Reasoning Workshop. And the key theme there was, we need to reduce power consumption by, well, we need to improve efficiency, I should say, by a thousand percent. We're not even anywhere close to a thousand percent, and we're building, we're building more and more data centers. I think the state of North Dakota has got $50 billion worth of data centers going up in North Dakota. Yeah.

I mean, of course, you know, they'll stay cool during the winter there. Got that. Yeah. There's the, here in Utah, they're talking about building. a nuclear plant up in some rural area of our state yeah to do data centers and I'm like okay nuclear nuclear powers back and and you know that's a good thing that's that's a benefit yeah so one of the things and I don't want to diverge too much but you know when you start talking about wind and solar that has to be electronically converted from DC to AC that is

So all the equipment that is required to do that is actually pretty fragile, whereas if you have your standard generation, turbines, right? So with nuclear power, steam turbines, you actually have, you know, something physically turning that has a resistance to slowing down and can regulate the frequency for the electricity, which is required on the grid. So nuclear power is actually a good thing. Oh, who knew? Is that the reason people's bills are going up? There's lots of people that seem to think that their electricity bills have been going up lately because of all these data centers and the need for stuff. It's almost like they feel like they're paying for, you know, this other stuff here.

Well, you know, in a lot of ways they are. Oh, wow. So the thing is, data centers, along with some other, utilities, you know, hospitals, things like that, get priority power. So if power is going to go out, they're going to be one of the last, last groups to get power cut out and they're going to be one of the first groups to get power cut in. Now you're dealing, you're dealing with, we don't put up a lot of power plants in the U.S. And we haven't for a long time, you know, coal-fired power plants are dying, going out. You do have

gas-fired power plants, nuclear power plants have been in regulatory hell for decades. Yeah. So what you get is you wind and solar, well, one of the issues with wind and solar is how far it has to be transmitted. So the farther way you have to transmit the power from wind and solar, the more expensive it becomes to the point it actually surpasses what it would cost for just, you know, you know, conventional generation. Wow.

That's interesting to know. Yeah, it's kind of wild. I mean, all the different data centers they've set up. I think even the Trump administration's been trying to get coal back online. I think they've done some things for their coal buddies or whatever in different areas. Is coal, like, coming back in them? I'm going to say it's, so some of it's not going away as fast. Yeah.

But here's the thing, too, on, the power side of things, you know, a conventional power generation facility, its lifespan is 50, 75 years, solar panels and wind generation, lifespan's 25. So nuclear is also 50 to 75 because we have ships in the Navy that have been running nuclear power aircraft carriers for upwards of 50 years you know no accidents or anything else you know it's not that we don't have experience doing this stuff it's just people got to you know they didn't like the feeling of nuclear oh my god we're going to all glow green not in my backyard that's sort of bit exactly exactly and if you look at the if you actually look at physical waste you know the volume of waste from a

nuclear power plant compared to because that's a big thing with coal right is your emissions and how many tons of pollutants and blah, blah, blah. If you look at what, you know, the physical dimensions of nuclear power rods and how often they have to be changed out, you're talking, it's a fraction of what a conventional power plant's actually going to pump out into the atmosphere. So, you know, again, people are like, where are we going to stick all this nuclear material? It's like, do you realize it's not that much? It's not physically that much.

You know, you got more trash probably and generated out of your house in a year than that would be in 25. Oh, really? Oh, yeah. Yeah. Oh. I know, did they keep the nuclear thing open

in the nuclear mountain open in Nevada? Are they closed and sealed that thing? I don't remember. The Yucca Mountain facility was open for all of a day, which was supposed to be the place we store all the way. I mean, it got certain. it was they had a ribbon cutting and literally like the next day they went up well we're shutting this down because nobody could agree on how to transport it because again

everybody's that trend that's what it was the transport on trains and the risks to different states is they well and that there again you know it's like oh my god it's nuclear well the chances of having an issue with that I mean this is something that probably amaze a lot of people but, you know, isopropyl alcohol rolls through populated areas. Yeah. That is deadly stuff. If you had a train wreck and it's happened, just the fumes alone, and I'm not talking fire, just the fumes alone would kill people. Wow.

You would die of alcohol poisoning, literally. Wow. That's a whole way to die. So some of the things you talk about, too, you talk about, too, you talk about. something called a multi-dimensional algorithm structure, a mass. Tell us about what this is, and how's it being utilized? So I came up with the multidimensional algorithm structure, partly because up until I came up with that,

there was no taxonomy for AI algorithms. So, you know, like there's a taxonomy for a lot of things. You use this hammer for this kind of project with these kind of nails. What's happened in the world of AI is there's a lot of different algorithms. There's about 60-some-odd families algorithms based on the math. But it's what I've been exposed to. That's what I pick. That's not a good way of doing things because some algorithms work really good for certain things

and not so good for other things. A large language model is really good for unstructured, language data. It does not do math well. It does not math. There are other things that do math well. So with mass, the multidimensional algorithm structure, that's designed to identify the different factors and help select the algorithm that meets your need.

So that's really where that starts coming in, which is starts coming in with the integration of AI into an environment. So let's say that you want to do, I don't know, you want to predict the stock market. Okay. Then you don't want a neural network, which is typically what an LLMs based on. There are other algorithms that are much better for that.

But, you know, maybe you don't have the right data. So this is all the structures, all designed to identify those critical attributes that'll either increase or decrease the potential for success with the AI algorithm, integrating it in. So that's kind of the short of it. Hmm. Well, and here's an interesting thing, and I haven't talked about the explainable artificial intelligence construct yet, but, and they.

actually go together but did a head-to-head comparison with with mass and x-aIC with other other structures for implementing AI including like NIST and mass and XAIC beat out all of them like hands down hands down if you want if you want something that is actually going to really reduce your risk when you're trying to implement AI that right now now that's really that's the only way to do it is with mass and x-a-I-C. Hmm, there you go. And we talked about, and you explained what the X-AIC is then, the explainable artificial intelligence construct.

Yeah. Good. Oh, I was just going to say, so to touch on that really quickly, so X-A-I-C, so mass is for the algorithms. X-A-I-C is really for how you're going to integrate it into the, human environment. So, like, for example, an analogy I like using is, you know, a lot of us use, you know, some sort of map program, Google Maps.

How many people go, oh, what kind of algorithm is driving Google Maps? Do we don't care. We just use it. We just use it. Yeah. That's good. Right. So if you're going to have something like that that people are just going to use,

Do you really need to explain how it functions? Because there's this big kick, like, oh, I need to train people on this, so they'll trust it. It's like, do they care? Now, you know, there's several different traits that you actually have to identify factors. You have to identify whether you do need to train somebody. But here's the other thing. Can you train somebody? For example, I'm, I have somebody that's.

a cashier and not to bang on cashiers or anything or individuals but you know on average high school diploma average high school math do you think that we could actually reasonably train that that group of people now we're not talking to individuals because there's always somebody that's going to be smart and you're going to be able to train them but as a group do you think we can train that group on a neural network, you know, do you think that we're going to be able to jump in and start explaining quadratic equations to them or, you know, any kind of advanced mathematics? Probably not.

I mean, it sounds pretty complex to me. Yeah. The answer is no. And so at that point, why are we integrating this tool in that we now have to explain to people that probably aren't, they're not going to grasp the explanation? That's a risk, right? So you want to make sure that you're balancing all this out. And then the other aspect of that too is, you know, if you integrate something into your business, your organization, and the people don't understand it, also probably don't trust it, what are they going to do? They're going to do the human thing. They're going to do workarounds. So you just wasted a bunch of money. And you might have fired people, too. I mean, this has happened.

It's like, oh, well, you know, we put this AI in there. So they're going to be twice as efficient. So I only need half the staff. And then you find out, yeah, well, your AI doesn't work. You fired all these people. And now look at where you're at. Yeah. There's a lot of people doing layoffs, too, with AI and thinking they're fine.

So that'll be interesting if it turns out that way. So let's talk about some of the services you are. offer on the website. You, uh, uh, you talk about, uh, helping people with, uh, their decision making and, and planning, I guess, and execution of AI. So when it, because, because really the thing is, is the human machine teaming side of things. You know, the machine side of things is engineered. We often don't know the human side of things. That's where I come in is to make sure the two are matching up. Because a lot of times, you know, in this, goes back to when we were talking about using AI to select job candidates, you know,

if you want it to do a really good job, you really have to understand what those candidates are doing, what the people doing the job are really doing. Because a lot of times we miss something. You know, we think we understand what somebody's doing. So that's, that's part of the services too. So, you know, let's say that you haven't done an actual real job analysis and really gotten down to figure out what folks do, that's a service I provide. I can go in there and I can go, okay, here's the knowledge skills and abilities. And if you're going to integrate AI into this, this is kind of how you need to do this. So you can augment, even replace, although that's something I would not highly recommend for a lot of things. But you want to augment the people. So how do you best

do that. And then there's other considerations too is like what's the long-term effect, you know, are we going to lose, you know, knowledgeable workers because they're used to pushing the easy button. That's something. I also do business coaching and executive coaching where we look at things like management. So it's kind of a holistic deal. You know, people will go in and they think AI is going to solve a bunch of problems while I'm here with a pallet. of things to say, okay, well, you know, maybe you can save some money on AI and we have a better solution for you that's more cost effective. And, you know, one thing that a lot of people don't realize is, you know, just picture this that you have a business. And now you've put AI in

place. All right. Somebody came in. They designed this tool for you for Chris Voss. Awesome. that means that you have one provider what if they what if they double their price what if they go bankrupt no that would suck and you lose access to everything yeah how does your work as an organizational psychologist how does that integrate with some of the consulting you do and stuff so really what it does is it gives me you know insights into the human condition and, you know, the culture, things like that. You know, and I'm not going to say soft scales, because I'm more of the eye, the industrial side. You know, I'm more the measurement kind of guy.

But really what it does is like, okay, this is how humans react to this. This is the probability. This is going to occur. And this is the best way to approach these topics, you know, the integration of AI and humans. Or you want, let's say your company is scaling, you know, you're, you're, you're, you're growing. And you go, man, we got a great culture. I know it'll change, but I want to, I want to maintain a great culture. Now, that's where I come in and I do some of these assessments and I look at it and go, okay, here's, here's what you want to do. Here's what you don't want

to do. Here's some things that you need to pay attention to. And I'm the kind of guy that I come in, I build stuff. I like saying, you know, as an industrial organizational psychologist, I'm like, I'm like a contractor to build your house. I'm going to build your house. I'm not going to run your house. I'm not going to live in your house. You're going to tell me what you want. I will tell you if it's possible and how much it costs.

And then if you have some problems down the line, I can come in and help you again. You know, most places don't need somebody like me on staff. I mean, that's just a waste of money. But, you know, And you can benefit greatly from me coming in.

Hopefully, I kind of hit what you were asking. Yeah. And you can help people avoid some of the legal pitfalls, it sounds like, and some of the other pitfalls that come with it as well. Absolutely. Absolutely. And, you know, here's the thing. Because I wind up talking to folks about the legal side of things,

because legal has a punch, right? We watch law and order because, you know, law, right? It has a punch. Yeah. But the reality is at the end of the day, you probably will not get sued. You probably won't run into legal problems. What you will encounter, though, is your return on your investment. And one of the things that I can help ensure is that you're getting the most bang for your buck

from, say, a selection method or from any of these other things. Because there's a lot of things in life that sound great. They are not. They're just a money sink. you know, and these are things I know. It's like, for example, it's great talking about right brain and left brain. But there's no such, you know, there's no such thing. There's no difference really between the two hemispheres.

So, you know, what are you really trying to get out? Are you just trying to communicate to somebody and get something grounded? Well, there's better ways to do it than that. You know, and not falling into some of these traps where it's like, you know, AI trap even. Oh, it's a shiny. It's going to do this for me and then find out now. Not really. Kind of like back in the day when you used to get toys in a cereal box and you think, oh, wow, you know, I'm getting this super thing out of a honeycombs box and you find out it's just this piece of plastic that falls apart in about two seconds. Oh, wow.

So now you've got some other books you're working on too as well. Tell us about them, tease them out to us if you would. Sure. So I'm working. working on a series that deals with human machine teaming and best practices. Really, really to, you know, kind of get down to say, look, you know, this is kind of how you want to approach this. Here's things you need to take into account and to really kind of walk people through it. Again, give them, and they're going to be short. You know, I could be the academic and have a 300-page book, but frankly, You know, most people wouldn't read 300 pages, and, you know, that's why you got me.

You know, I'm trying to make some job security for myself by saying, you know, come and talk to me. There you go. Yeah. So as we go out, give people a final pitch out to onboard with you, order up the book, how they can reach out to you and find out how to do business with you and all that good stuff. So the books, my book is on Amazon. It may be a little bit difficult to find because, you know, it's not like a number one bestseller. So, but, you know, persistence pays off.

One of the best places to actually link up with me is on LinkedIn. LinkedIn is probably the place that I inhabit the most. And I have my calendar there and you can schedule an appointment with me. And, you know, we can get to know each other and find out where you're paying. points are. And I can help you with a lot of different things. I'm not trying to be everything, you know, to everyone. But I do tend to focus on veterans. And I also focus on tech. So I'm very much into the artificial intelligence and cybersecurity, having dealt with those topics for years. Well, it's been wonderful to have you on the show, very insightful. And we

learn a lot about AI, I think I, I think I learned. It's still, uh, it's still kind of interesting all the stuff that's going on. I'm just, I'm just like holding on for dear life going, what do I do now? Well, that's your, that's, that's your introduction, right? You know, making, you know, smart people, you know, blowing your mind kind of thing. And just, just as, you know, to throw something out there kind of as a final thought, too, is, you know, Have you ever heard this sigmoid curve? No, I might have, but not, I can recall. Okay, so the sigmoid curve, the basic idea is really quickly, is that in the very beginning,

like you're starting your business, there's a lot of effort, and you see some gain, but it's not real fast. And then you hit a point when you're really starting to hit stride, and you're getting a lot of gains. So your return on investment is really good. You're rocketing up. And then when you get to the top of the sigmoin curve, the S curve, all of a sudden, you're spending more and more to get less and less. So, I mean, we don't have, we don't have, you know, planes that do Mach 12, like, that are commercial airliners, right?

So these are all things where you start spending. gets to data centers too, is you start spending more and more to do less. And functionally, we have not changed how we approach AI. The mathematics has been around for many, many decades. Chip design, while it's improved, it's an improvement. It's not new. It's not radical. And until we actually have something like that, it's just,

brute force. Well, it'll be interesting to see how it all turns out. You know, there's a lot of worries right now about people, you know, losing jobs and a huge amount of joblessness and stuff like that as we change you over to these new things. So it's going to be interesting how it all comes out. Thank you very much, Kurt, for coming the show. We really appreciate it. Oh, thank you.

Thank you. And thanks, thanks for tuning in. Order up his book, wherever fine books are sold. artificial intelligence and machine learning in human resources, a concise guide, and watch for his upcoming releases. That way you can know more. And so there are a lot of legal productions, too. You've got to take a do with yourself.

Thanks to my on us for tuning in. Go to goodreads.com, Fortess, Chris Foss, LinkedIn.com, Fortress Chris Foss, 1 on the TikTokity, and all those crazy places on the Internet. Thanks for being here. We'll see you guys next time. That should have us out. Great job, Kurt.