Not Just the A-Word – Making AI Actually Work at Work with Emily Heavner

MichelleToday, I'm so excited to get to talk to Emily Heavner, who is one of the most brilliant and also delightful humans that I get to work with. She is currently a product manager at aspect who carries a PhD in mathematics. I know you can correct me if I'm wrong here. I don't think she would call herself an expert, but I am going to call her an expert even if she protests.

MichelleAnd I'm saying this right now, so I'm just going to say that.

EmilyNice.

MichelleSo I think Emily is is so knowledgeable when it comes to creating actually helpful AI solutions. So before your eyes glaze over and you skip to your next podcast. This isn't like we're just going to talk about AI taking our jobs, or that AI is going to save the world. I my goal here today is to take a nuanced look at how I can and will support actual work, and whether that's parsing large volumes of data into usable information so that it can support decision making done by leaders and doers in their workplaces.

MichelleEmily is at the forefront of all that and aspect, taking an approach to AI that is secure, intentional and human aware and honestly, I just feel really privileged to get to like, sit and talk about it with you and also get to work with you on this while like.

EmilyYou know, conversation over can't be that you.

EmilyJust.

MichelleTake no pressure.

EmilyYeah, really. Hyphen me up. Michelle.

MichelleYes, yes I do. I did just want to like, tell you how privileged I feel that we get to have this conversation because I am absolutely one of those people who is like I. It exists, and I don't really know a lot more than that, honestly. So I'm excited to, to to get into this. So I do want to start talking about a little bit about you, and how you ended up here.

MichelleSo will you just take us through, like the pieces of your story that you would like to share from, you know, with that includes your PhD and, and how you ended up here doing this at prospect?

EmilyYeah.

EmilyI, I'm really interested. You know, if I were to describe, like, what do you want to be when you grow up? I really just like learning new things and solving problems, which I think really kind of go hand in hand. But I think really go hand in hand here aspect. Obviously very easy to like, you know, lean in to a PhD when you're like, I just want to learn new things and solve problems.

EmilyI never heard about workforce until I was on the job market. And, I started here at aspect, doing, more algorithm development work. And I met with, my then, manager, Andy Cornell, who's now very high up research development at the company. And we just had such great conversations. You know, he was like, this is where I really see our company growing when it comes to algorithm development.

EmilyWe didn't really talk necessarily about AI at the time. We were just like, let's, you know, have these conversations about what workforce looks like, how, you know, how data plays such a huge role in this from just these nuanced decisions that people have to make. How does sometimes a split real time time. And I, I don't know, I just really loved like the culture itself here.

EmilyAs well as the cool things that I would get to learn, the cool problems that I would get to solve. And then, moving into my product management role is really been like, I get to do really cool research and play around with these models with, you know, test data with like, customer approved data, things like that, which I get a lot of joy in, and being like, wow, I think, like, you know, this particular model will work and it and it does or, you know, sometimes it doesn't.

EmilyAnd then I'm like, okay, well, my hypothesis was wrong. Like, why didn't it work?

EmilyKind of thing.

EmilyYeah. So that's kind of my very short career journey.

MichelleSo okay, so I think, will you give a little bit of a definition on when you talk about algorithm development? Because I think probably a lot of people's experience with the algorithm. Sorry about the air quotes. Is, you know, like the internet is like social media and, you know, the all powerful algorithm. So I'm assuming that's not what you're working on.

MichelleSo what, you kind of just share like, like what? What is that as you talk about it?

EmilyYeah.

EmilyI think of an algorithm as a recipe. Right. It, you know, follow these steps to get this result. So it could be as simple as in a work first world, you know, writing a forecasting algorithm. Follow these steps to get a forecast for tomorrow, you know, whatever that forecast might be. Or the all knowing algorithm in social media is follow this algorithm to get these recommendations for these people or these posts or whatever.

EmilyRight. It's a it's a very broad term that just means here's a to do list that some somebody coded up or wrote up or whatever that you follow.

EmilyThrough.

MichelleWith such a great.

EmilyDefinition on the set.

MichelleDo you feel like that work led you into sort of what you're working on in AI? Like, did that feel like a natural transition?

EmilyYeah, I'm.

EmilyReally.

EmilyIn.

EmilyIt about something called math modeling, which is a fancy way of saying I like to describe the real world using equations. So if you've ever heard of the equation like e equals MC squared, I feel like that's like something, you know, people have potentially heard about that. That is by definition a mathematical model. It's just saying I'm going to describe something using an equation.

EmilyAnd you know, obviously you can't do everything perfect in an equation. But what's really cool is you have to know that system so well to come up with an equation that describes that. And that's really what like I get so excited because you learn something new when you solve it problem. Right? It hits those like really big things that I'm interested in.

EmilySo for me, the idea of of AI and machine learning like feeds really well into that. And I get really excited when I talk about kind of what people define as like the blackbox in AI and machine learning, because I'm like, well, that's the cool stuff. Like, yeah, it is really cool that you can feed it information, a question, data and then get an output.

EmilyBut I'm like, I want to know what's going on inside.

MichelleI feel like there's so much to learn when it comes to like what? What makes that tick. So and I'm guessing there are a lot of listeners who are like that as well. So probably the question how does AI work is too broad, but can you talk us through like, what's a good understanding, of AI for us to kind of go forward in this conversation?

EmilyYeah.

EmilySo I guess kind of starting a little bit before I what maybe isn't really, if we throw back.

EmilyTo.

EmilyOur I don't know when you learned this high school, middle school. You have some sort of equation, I don't know, like y equals mx x plus b, or even just some equation that's like x plus two. The idea is that you have that equation. You have that form, you input something, you know what you're inputting it into and then you output something.

EmilyRight. It's like if you going back to that recipe example, it's like you know, the recipe when you start baking or cooking or whatever, right? The whole thing about machine learning AI models is you don't start with that recipe or that equation. You start with the data and you say, for AI, it's all.

EmilyAbout.

EmilyData. It thrives on data. If the data is not there, then the algorithm isn't there, right? Or the the AI isn't there. So the idea behind AI is that we're replicating how humans learn, which sounds like like when you think about it like that, it sounds so sci fi and so fancy, and you're like, that sounds really scary, right?

EmilyWhat it boils down to very, very simple five year old level is you are figuring out the equation that's specific to that data, right? So if we started with that x plus two and we say this is kind of the black box, I'm going to input data in add two to it and then output data. And now it's like well I have this data but I don't know what the equation is.

EmilyAnd so I am going to figure that out by having the computer really learn so much about that data. For people who like, like concrete examples and a little bit more visuals, if you said, I want to create a, AI that will tell me if I have a picture of a cow, that's it. It'll just say cat or not, cat.

EmilySo you say, cool. Here's a bunch of pictures of cats faces and you say cool. You know, here it is. And what it's doing under the hood is it's saying, oh, I've noticed that all the pictures you've given me have pointy ears and small little noses, and maybe this is the structure of their faces, and it will assign almost like weight and say, oh, it's really important for cats to have like, pointy ears, right?

EmilyAnd then you send it a picture of a dog that's really floppy. You say, oh no, no, no, that doesn't match with my understanding of a. Right. And so what's going on is it's just basically almost like prioritizing and saying, this is the important thing about this data. This is the pattern that I'm picking up so that if I see this again I can identify it kind of like a low level does that does that make sense?

MichelleThere's such a good explanation. And I think, you know, as you were saying, it sort of replicates the way that humans learn. I have a four year old, and so that totally tracks with the way that she is sort of approaching the world. So like, I especially see it in her language where we watching a lot of K-pop demon hunters.

MichelleI don't know if you had a reason to watch that, but, it's really wonderful. But there is a bit at the end. I won't spoil and say what happens, but there is a bit where she doesn't have the word for what's happening, and she's like, like it. It breaks like she's so she has like, she's like, this is the only concept I have that's even close.

MichelleAnd I'm like, okay, yeah. But that is, you know, if you sort of like pare back what's happening? Like, yes, that word does make sense there. I know exactly what you're talking about. Where like, I have a six year old and an eight year old and they, you know, watching them like the six year old has a little bit more of that vocabulary.

MichelleAnd the eight year old has a lot more of that, like, oh, but I am not only seeing what's happening. I understand why that's happening and why it's important for the story. So I see kind of, you know, what you're saying, like, okay, I've got this cat and it's got pointy ears, but also here's like, an elf that has pointy ears.

MichelleLike what other things are important than just the pointy ears anyway, so that was a great explanation.

EmilyOh, yeah. And you're, like.

EmilyKind of expanding on that. We, you know, we say, cool, here's all this data. Did you learn anything or did you just memorize it? Right. And I think this kind of goes to, you know, obviously not your kids, Michelle. But some kids, when they're learning how to read, they just like, memorize their favorite book and they're like, well, I can read.

EmilyAnd so then we have to be like, okay, but did you just memorize all these pictures of the camera, or can you like, identify a new camera from a new image? And so there's also that aspect of.

EmilyOf needing to like nice notes, of needing to, to make sure that the kind of black box this function where you're putting these parameters are not, too focused on specific aspects of that image, so that if you put like candidate a brand new picture of a cat, it could say it to count or this is how you know, if it's your function, the inside, the black box, all those kind of parameters and weights are not set up correctly is when you handed an elf and it says it's a cat and you say, oh no, right.

EmilyAnd this is where you'll see things like just, you know, general public people are like talking about, oh, but I told it this and it gave me this, right. And that's really just how it learned. And as you, you know, develop your machine learning model, the hope is that that doesn't happen.

MichelleWe, we were like a science thing for my daughter's school, and they had that. There's like a Google game. I can't remember what it's called, but it's trying to guess what you're drawing. So it gives you, like some options to draw. And then you, you're drawing one of the options. I'm pretty sure that's how it works.

MichelleAnd it's trying to guess what you're drawing based on the way you start it. And it's essentially.

EmilyTeaching.

MichelleTeaching this model like, oh, here's how people draw strawberries. This is the way people draw a race car or whatever. And so it was really interesting because at the end it like kind of hones in, like, here's why I guessed that that was a race car, even though you were actually drawing, that a bed like this is what other people drew when they were trying.

MichelleSo anyway, super interesting. All right. So coming back to the workplace, what are some common misconceptions that maybe we all have about AI when it comes to the workforce? I will say, besides the obvious, like I say, you know, jobs.

EmilyThat.

EmilyI noted people talking about AI is very like in a case like correlation.

EmilyFor.

EmilyAutomation, right? So and I think it makes sense. Right. You ask an AI something and you like immediately get an answer. But I think something that that I've noticed is that you can have automation without adding any AI. Right. And you can have AI that may not be exactly automatic. And so I think that something that I've noticed is if you're like, wow, I just really want something to be automatic.

EmilyMaybe it doesn't need to be some fancy tool that you, you know, spend a lot of time developing or spend a lot of time investing in. Right. Maybe it's just you have an automated.

MichelleMaybe that email just schedule. You could just do a schedule send. Right. Like that's an automation not AI.

EmilyYeah. Right. But then if you say, well, I want to email 500 people as opposed to just like one person and you know, I do this if if a colleague is out of office, I'm like, well, I'll just schedule this for later on, right? They don't need to see it right now, but I have the time to write it right now versus I'm going to email five different people, completely different emails with different contacts.

EmilyRight. Maybe that's like, okay, I need some help with that.

MichelleYou thing. And obviously this is just kind of maybe in your experience. So it's been like a bit of our small world announcement. Do you feel like I has really entered the workforce, or do you think that there's still like a bit of, like a trust issue there where it's like, well, when I put this in AI or is just, you know, to.

EmilySit in, oh.

EmilyYeah. I mean, I.

EmilyThink.

EmilyFor our day to day, like we have an internal AI tool, that for example, I just heard was able to like pull in a bunch of data and fill that, like some of the missing columns in, I think they said there is like 37,000 rows of data. And it took our tool, a couple of hours to do, but that's a couple hours that somebody didn't have to manually fill in these, you know, particular pieces of data that they were looking for.

EmilyYou know, a lot of people are using some form of note taking when it comes to, like AI tools, which I think is really great because it gives you the chance to have that interaction, especially when somebody is, like fully remote and they're like, oh, cool. Well, let me like also make these notes. But now you can be really engaged.

EmilyFocus in the conversation. You know, maybe if you need to write down an action item because you're like really, really don't want to forget this or something. And then you can, you know, hold on to those notes and not necessarily like have them hanging around. I think it really depends on like the use case of you yourself. You know, some people say I want to have like a draft of an email and, you know, have some sort of tool start where you say, I'm emailing this person and then I'm just going to edit it, right.

EmilyIt saves me a little bit of time. And I think that's the best use case for A is to make it, to have it as a tool that makes your life efficient and time saving. And depending on who you are and your preference, you know, people have different sizes of of tools and tool needs and, you know, carpenters and people who are really, really AI, passionate and hungry, you know, might have a whole shed of their tools.

EmilyAnd some people who rent a small studio apartment may just have a hammer and that's okay. Right? Because they have other ways that they can get this information. It may not be their needs. So I think day to day there are probably just within our company people who are using an internal tool, and there are people who are not.

EmilyAnd I think there's really no right or wrong. It's just what you're comfortable with, how you want to spend your time, and your energy and resources. And I, I will say when it comes to security, like when you ask, you know, should I trust this? I think, depending on, like, you know, if you're just a, not necessarily somebody in like security or it or somebody who implemented this tool to add on, you can always verify with the person who's like the owner of that tool at your company to say, hey, have you guys done any sort of, privacy policies, data governance, like, like, what are we doing to ensure that our internal

Emilyinformation that's running through this tool doesn't get out? So you can definitely do that. Or you can trust that those people have done it for you. And also, if your company says don't specifically use this AI tool, don't use it because they haven't gone through to verify that that data is safe.

MichelleSo I appreciate your your metaphor.

EmilyI'm letting you know I'm not a whole.

MichelleTool shed or I just am using and hammer and not, you know, it's not to be the right tool for the right set. And you also have to know how to use it, right. Like for me, I starting from blank in an email is no problem. I just like knock it out like blah blah blah. You know, like my brain is always thinking that way.

MichelleBut when it comes to something like, I need to reformat this whole page or like, you know, in a and I need this Excel doc to look a certain way, like, I can't do that.

EmilyIt would be.

MichelleWhere it's so much easier for me to say, like, hey, you know, in notion, like, hey, can you just reformat this page to be a table? And it's like, yeah, done it. Did it for you.

EmilyYeah, I think that's what that's done.

EmilyIt's a really powerful tool in the sense of not everybody has the same strength. Right. And and for some people, it really does take a lot of time and energy to do. One of the things you mentioned and they say, cool, let's just have this assist, help me out versus, okay, I don't want to do this task. I may be putting it off because I don't want to do this task or to become so overwhelming.

EmilyNow it's such a big deal just to send this one email, right? Yeah, I, I really appreciate that, Michel. Yeah, I think that's like, exactly perfect.

EmilyThat we.

MichelleShouldn't we should not be relying on AI.

EmilyFor, I think if.

EmilyYou're if it doesn't align with your core values in the sense of if you're like a call center and it's so important that you have that human interaction, you shouldn't have a, an AI, you know, answering your calls, your emails. If it's still important for you to like your data security, maybe due to, like, legal reasons or who you are as a company to have, you know, I don't know, credit card numbers going through an AI or something like that.

EmilyIf that doesn't align with your priorities, don't you?

EmilyIt's I. Right.

EmilyI think that yeah, that's to me, I think the really important thing is think about what would be a huge issue, a negative impact if people found out, oh my gosh, I can't believe you're using AI to talk to me when I'm, you know, this is the chart. Like the fees that I'm paying for these human interactions.

EmilyRight. Whatever. Your kind of luxury brands are things like that.

EmilyI think that.

MichelleThat's a wise call out because it it always comes back to that, isn't it? Like, okay, what should I be doing? It needs to come and line up. Like, okay, do I know the core value of what it means and like what I'm trying to do? And and AI isn't any different. Like the tool needs to still line up that you know, value statement that's at the center.

EmilyYeah I know you.

EmilyMentioned like notion earlier right. This is a productivity tool. So it makes sense that it would have AI that affect and boost somebody's productivity. You know. And so they've made their I use case to somebody that aligns with their values in their course.

MichelleSo maybe the flip side of that where are you seeing like oh like that's such a great use. Maybe that's the best way that like AI is being used. You know, we've talked about kind of that improvement in productivity, but are there other places like maybe you can just best described how you're seeing I used well.

EmilyWell that's that I feel like that's.

EmilySo hard because like I think really the best like like a good AI besides just like a well built model is really only as good as its use case, right? It's like everything. If you say, I built this grand thing, you know, like this beautiful looking piece of pie, you know, and then you eat it. It's not great.

EmilyIt doesn't taste great. So it's like, well, why why did you spend all that time doing that? You know, it's the same kind of thing with, with a and it's just like, well, it's great if this is good, but if it doesn't, if I'm not going to use it, why build it? So I think it depends on the use case, but I really think something that because I love soft loves data and we'll just gobble it up if it's not helping you make decisions based on data, then I don't like I guess I want to say that that is really where it shines.

EmilyI don't want to say that it's like poor if it's not, but I, I think that's really where it shines is being able to say, hey, I've, you know, had other emails read through me and now I can make a new email from scratch, right. And so.

EmilyHow.

EmilyIt just like, loves that data and says, cool, I can make a decision. I've noticed, you know, your, your volume and coming in looks like this. And the last time it looked like this, you had to call in three new people, you know, you know, based on that, things like that. And so sending a message of, hey, I've noticed this, would you like to make this decision or a really great example when you're driving on a road trip?

EmilyHey, there's a speed. I'm down ahead. Would you like to take an alternate route? Right. Those kind of. I'm looking at data, and I want to help you make a decision.

MichelleJust today had you look at a big policy document for like one tiny question and I'm like, I have to sift through all this legalese for like one. This one question is like, oh no, this is in my like approved productivity tool. And I have like an AI that can burn through this whole thing for me. Hey, does this document address this question?

MichelleIt's like, yes, he had here's where it is. Like, like I, I think that that like not having to like prime my brain to read through, you know, like 30 pages of legalese that is not my native language. And being able to just say, like, this is the information I'm looking for. Can you help me find it?

EmilyLike, you know.

EmilyIn terms of the use case, right. So we just talked a little bit about, I use cases. Are we able to, you know, what are really good use cases thinking about things that are efficient save us time. Right. So when we think about making dinner, right, of of any variety, sometimes it's as easy as speeding up food and microwave.

EmilyRight? And sometimes that's a great use case for an AI, right? It's just being like, cool, I'm going to save time. I'm not going to make this on the stove. I'm, you know, going to have microwave pizza instead of oven pizza, whatever might be like, everyone's lives are busy, everyone has different priorities. And if you want to microwave your food to save you time, you can't, right?

EmilyBut there are some cases where somebody might say, man, I cannot do microwave pizza. That's a no go for me. And that's a use case where you say, okay, great, I don't want to use I want to take the time to, you know, run out, pay the price to maybe get pizza, make it at home, whatever it is.

EmilyRight. And that tool of a microwave originally just meant to save you time and energy. And it's the same idea with this. When it's done well, it's really just meant to save you time and energy. And if you want to put your energy and not using it, that's totally fine, right? You don't have to. It is one of the tools in your kitchen, not the tool in your kitchen.

MichelleMy my dad would also say like he could never have microwave pizza.

EmilyYeah, but some.

EmilyPeople are so passionate, right? I could never start an email from the scratch. Right. And other people like Michelle. No big deal. I can start an email from scratch.

MichelleThat's I like the you know, I feel like it's really easy because AI is at the forefront of it feels like every conversation, any blog ever that has been written in the last like couple of years, it's like it like I it's like it's clickbait almost. And so I like that. I, you know, you've sort of like okay, no, no, no, that's not really what it is.

MichelleIt is a tool that exists in your kitchen, but it is not the kitchen.

EmilyYeah, I think that I.

EmilyMean, that's really what we're doing it aspect is we're using it as a tool to help you, but we also understand I mean, if you talk to people in the work force field, they have so much knowledge. They have been in this field and in this domain for years and years and years. I feel like I have conversations with people all the time.

EmilyAnd I'm like, that is like like I'm just yes, let me osmosis through you, you know, and so they I don't know, they've just seen things they've, they've lived in those like really intense high call volume where they didn't expect it and they had to move things around. Right. And they, they know their patterns really well. They know like what to expect, what you know, how they can shift when they don't know what to expect.

EmilyAnd so I don't want to like invalidate that. Right. And we an aspect don't want to invalidate that because there's really nothing that can replace that. There's no way that we can put everybody's 30 plus years of knowledge into this one model and say, go, be free, right. But we can help support.

EmilyYou in the best way. Yeah, I love.

MichelleThat. I like that picture. So as we sort of wrap things, I, we always try to end with kind of like three main things that we should all take action. So as we're thinking about leaders in the workplace, what are some green flags or red flags that leaders should be looking for? For an AI system that is like well designed or, you know, being used thoughtfully?

EmilyYeah, I think.

EmilyYou know, I've mentioned this, but what could I apart from just any other mathematical equation, is that the AI model is shaped and defined and constructed on data, right? You mathematically, if you don't use AI, you just say, cool, this is what I'm going to use and hopefully it works well with the data. And so I think for leaders like being able to understand what like data quality they have.

EmilySo what they're feeding into the model, is that really good. What is if it's something they're developing or something they're using third party. What's the privacy policy? For some people it's not a big deal that the data that they're feeding in gets used later on to train the model. Some people it is right. And so making sure that privacy policy if they have if they're developing something in-house, that that data is set up to where they can easily, it always cycles and flows through that model.

EmilyRight. So you say cool, here's data, here's another batch. But you want to make sure that your data kind of is the foundation for everything. So it's clean. It's set up well. It's being fed in. Well also looking at besides data privacy policy, any sort of compliant or data governance, if you're not familiar with that, I don't know if that's like a common, term, but it's basically like a companies policy, and like best practices for how they use your data, how they store your data, how they manage your data, things like that.

EmilyI think those are things. Yeah. If you want to use any I it's it's just all about the data. If you feed a bad data and you say, show me pictures of cats and you only show them, you know, feed it dogs, it's probably not going to identify the cat.

EmilyThey will be.

MichelleMaking sure you have good data. And I think you were saying like and that it's it's well set up.

EmilyYeah. And I think also I mean if this is a tool that you want to keep using, you want it to kind of like feed through to be.

EmilyCyclical.

EmilyIf you will. And so taking the time to front load the kind of data read in the data quality is nothing but helpful to you in the long run.

EmilyYou can say more.

MichelleAbout that, like the idea that it's cyclical.

EmilyUsually when you create a very much like a child, right? You start it for kind of going back to your example, Michelle, your four year old, was able to or let's, like your eight year old was able to comprehend something a little bit more than your four year old because of their knowledge. Right? They've spent more time, they've learned more things, their context, all that good stuff.

EmilyRight? And it's nothing against your four year old. It's just that your eight year old has had a lot more data.

MichelleRight? It's not double the data.

EmilyYes.

EmilyIt's the same kind of idea for a model is that if you want it to improve, you have to give it more data, right. And so you you don't necessarily want to just say, cool, I've given I've dumped all of this data in three years. You know, it's okay. Cool. I've given you all of this. We've kind of tweaked all these different parameters inside and we have an AI model.

EmilyWoo hoo! Okay, great. And then we say, you know, the next day, the next month, the next quarter whatever. Okay. Here is a huge other batch of data. And we want you to learn on that now. Right. We don't want you to forget the other stuff, but you now have a bunch of cool new things to learn. And so that's really where it's not just a one process where it says, okay, bam, dump all the data.

EmilyHere we go. It's consistently using that data. And so if you don't have that like infrastructure set up, then it's really hard to make that work flow.

EmilyClean.

EmilyAnd consistent and easy.

MichelleYour phone is always trying to say like this is a picture of your daughter. But my daughter has been growing for her whole life, so they're like, this is a picture of your daughter, and it's her when she's like two. But without telling it, this is also a picture of my daughter, who is now eight. Like it does.

MichelleIt can't like make the jump to like it hasn't made that cycle where it's like, oh, that's the same person you have to keep telling it like, yes, we've grown now as an organization. So here's more data. Is that a good.

EmilyYeah. And even, you know, thinking about things like a seed data coming in. Right. It's okay. Well you've gotten so much more in the last week. If we want something that will distinguish patterns and give you you know, some sort of notification like, oh, something's going on, maybe you want to check it out, right? Then you need to be able to pull that data in so that it has the most up to date knowledge.

EmilyWhat would you.

MichelleSay for someone who wants to learn a bit more about.

EmilyLike, how could.

MichelleI make the best use of, of AI in my workplace, where's somewhere that they could look like in the next five minutes? Like, this is a good starting place to go get a little bit more education.

EmilyMaybe using.

EmilyA tool to give you never once use an AI tool. Open it up, ask it. I don't know, like what? What's the history of lasagna? What's the average temperature of Jupiter like? Whatever. You're just something really curious about. So if you've never once use a tool, give it a go. One of the things like my husband and I just did was we gave date, like, time frame.

EmilySo this is where we're going to be on vacation. Can you give us, like, itinerary? And then we say, oh, cool. We want to go there. We don't want to go there. We even included this is exactly where we want to go this time of day. And it, like, fit it in, right. So I think education.

EmilyGive it a try it out. If there's a tool that your company's thinking about using Skype, you can, you know, send an email to ask for a demo, quick five minute thing. And then you can learn more about it. You can say, here is my data. Can I test on your your AI tool?

EmilyYou know.

MichelleI love that. And maybe, you know, you'll learn a little more about the history of the lasagna.

EmilyYeah.

EmilyAnd then maybe share it because I don't actually know the history of lasagna.

EmilyI'm just like, look, I wonder when. Yeah, you.

EmilyHad no idea I would be interested in learning.

MichelleIt. Exactly. Well, thank you so much. I feel like I learned a lot, and you just gave me a lot of, I don't know, I feel like just more settled, around the idea of I just like, as a concept and using it, and it just feels a little more like, okay, but this isn't like some crazy new thing that is impossible to understand.

MichelleInstead, like, it's still this thing where, like, we're going to find our core values, we're going to stick to it and then we can use this as a tool, just like other tools that we have always used in our tool belts, to kind of do this thing more efficiently or, you know, just in a different way.

EmilyYeah. Yeah. And I don't I.

EmilyDon't want to ramble, but I think.

EmilyIn case.

EmilyYou're curious about some of like the history of AI, I mean, it was probably like 40s and 50s like 1940s and 50s when it kind of first was this idea that, researchers were really into and then had its kind of big experiment and like ebbs and flows of research. And then in 2017, there is prior to that, there's kind of this point of, well, there's a big scaling issue, like there's only so much data that we can use to train on.

EmilyAnd I think it was Google who originally basically discovered something to fix this. And this was really big in academia. This was like, whoa, what you're doing is super cool. And then ChatGPT, the company who created that use that a couple of years go by, and then 2020, they released the first like ChatGPT. And that's really where it it blew up.

EmilyBut it's been in the background for, for a while.

MichelleYes. I was just listening to another podcast about like, this isn't like a new concept. There was there was for so long, like no one could picture using the internet. Like how could you possibly, like, go on and just like, ask a question and find someone who's written about the answer and now like that is, you know, second nature.

MichelleLike, I don't know who the person was who is in that movie. I'll just look it up. Right. But that it just and the idea of it like, you know, I not know, it will eventually kind of be just like a part of what we do instead of this. Honestly, this like Clickbaity kind of thing that's like, feel scary.

EmilyYeah, yeah. And it goes back to.

EmilyWho you are as a person. Do you want a shred full of like, do you need that? Or is you just need the hammer? It's up to you and how you want to use AI. Like there are people who have smartphones and look things up all the time and people who don't have smartphones and don't, you know, search and Google and.

EmilyIt's.

EmilyIt's up to you. And nothing's wrong with that.

MichelleYes. And and and both live full, complete lives. Yes.

EmilyYeah.

MichelleYeah yeah. Well thank you so much Emily. This was amazing. And I'm so thankful that I get to not only have this conversation with you, but also work with you.

EmilyYeah, yeah, I know, it's it's always lovely to talk to you, Michelle.

MichelleThank you so much for listening to future works. If this gave you something to think about, please pass it on. Share it with, someone that you know that might be interested in this topic and then, you know, go get coffee with them and chat about it in real life. You can find Emily at E heavener. net.

EmilyE h e.

MichelleA v and e rcom and follow along at aspect.com As the AI strategy aspect continues to take shape and grow, and you can be.

EmilyOn the forefront with.

MichelleThat. Be sure you subscribe as well so you don't miss our next conversation here on YouTube. It's.

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