3 Decisions You Should Never Give to AI w/ Marinela Profi

The outsourcing that we're doing with machines, nobody gives us the certainty that they are better than human. And so we are choosing to outsource something, but no one is asking what are we actually losing by outsourcing?

If you say this on stage at a major AI conference, what opinion will get you in trouble?

Large language models don't have reasoning!

This is Marinela Profi, with a hybrid background in machine learning, marketing and economics, and a passion for human-centered innovation. Marinela is an TEDx speaker, thought leader, and trusted advisor to enterprises navigating the next era of intelligent systems - from generative AI to AI agents.

The first time I saw an AI agent make a decision, I couldn't explain, I felt very unease. I didn't feel excited.

Marinela you just said, that large language models don't have reasoning. Can you please dive into that?

The number one thing that fascinated people the most about this technology was the realism and how similar it was to people, to the way in replying in the way that we reply. And so the belief being established and being commonly accepted is that large language models are these magic systems that are able to reason. They understand exactly what you are telling them and before they reply to you there is actually a reasoning that happens behind the scenes. That is absolutely not true. Because large language models, they are probabilistic systems. What that means is that they are very good at doing something which is so basic, but we as humans are not as good as doing that. They're very good at doing that, which is. Predicting the next best word or the next best series of words. So every time that you give a sentence or a prompt to a large language model, whatever it is.

What happens behind the scenes is that those words are taken by the model and that there is a huge, gigantic artificial intelligence algorithm that turns those into numbers. There are called tokens and so then the artificial intelligence algorithm, it basically, understands the patterns that there is between each of the words.

Based on that, it predicts what is the next best word based on the training that it has. Now, obviously, unlike the human brain, these artificial brains, they are trained on every possible. Text, video image, word conversation, that it's out there in the world, whether it's on the internet, whether it's on books, newspapers.

Think about everything that humans have built for the past centuries. They are constantly trained on that. So think if you have access to all that awareness and all that knowledge, then you get really good at predicting the next best word. That's why they sound so like us, but it's just because they're very good at mimicking, the answers that a human would most likely or most give to a similar prompt, to a similar question, to a similar request, but there is no reasoning.

They don't really understand, like there is no. emotion or, or reasoning that they do to understand what it's better or what it's.

Fascinating is the fact that even that I use AI daily, I literally find about this two days ago while preparing for this, interview, I had no idea how it worked behind the scenes. And I, I consider myself a technical person, but I was like, no way. No way. Basically, it's a predictability algorithm there and still people consider them thought partners.

They consider them.

Therapists, some like, and it's scary in the same time because often we assume that everything these tools are saying back to us is true. How often we are wrong with that?

It depends on how we define truth and what we choose to believe in, right? So the systems are very good at validating your response unless you challenge them. Unless you tell them, like, challenge me. They're never gonna do that. They're always gonna be validating. And the more you talk to them, the more they have these thing called internal memory where they memorize and they remember obviously all the past conversations that, and all the past informations that we've given them about you.

And that's why they're so good, because the more information you give them about you, the better they are at making connections. And so the more truth. Human, they sound because sometimes they might give you an answer remembering, something about you that you yourself had forgotten, or you might go like, how did this thing know about this, about me?

And that's when you start thinking, oh my God, this thing knows me. You start believing it. Like they, it knows me, actually understands me. And, and that's how you build that trust. And you were right, like more people are starting to use this technology as, friends as therapy. And, this is a research that, I have commissioned myself and I am about to release, and reveal the results of this, at my upcoming TEDx talk where we have analyzed, How people are, if people are trusting more, human advice or an advice that comes from a machine. And how that changes across different generations. So if there is some sort of pattern, whether you are a millennial, whether you are a Gen Z, whether you are an elder person, that are just getting familiar with technology, how that changes across, situations that you are in your life and how the fact that we are starting to trust. Or believe or prefer to get advice from machines than humans, more from machines than humans. How is that redefining what we choose to trust and what is even trust, the definition of trust? So this is a topic that I'm really passionate about and I decided to commission research on my own to, to, share this at the upcoming TEDx Harbor Square.

And can you tease us a bit, can you give us at least a bit, on a glimpse on that research,

I think that the research is gonna be very surprising there is some big differences between generations and we have found out the why behind it.

So why there is differences between how, and why somebody that it's a certain age range believes AI or goes to AI first, then somebody who is not a certain age range. And so the, the results are surprising for sure, and the only thing I can say for now is that there is a lot of differences between generations.

Yeah.

Speaking of that, differences between generation, what I found that, obviously, for example, my parents, they never touch AI so far, so, but I have, I have as well clients that are close in age with them and they use AI daily. Kind of validate decisions based on what AI shares. And that's bit scary in a way that we rely too much on our decision making towards ai.

I, just listened to Ray Dalio recently on, uh,Diary of a CEO O and. He was mentioning the fact that he basically outsourced to algorithms the decision making for more than a decade, even before lms. And now obviously with ai he's using that because he mentioned that we overcome the limits of human biology by doing so, and at the end of the day, if you train it well, it'll make better decision than you as a human.

What do you think on that?

I do think that when we go to a human for asking advice or making a decision, we are taking some risks. Their face going like, what the hell are you talking about? Or I think this is not a nice thing, or, I don't like what you're saying, or I don't agree with you. so every time we go to a human for advice, we are taking some risks of being misunderstood, the risk of not, being accepted, the risk of being hurt, when we are going to ai.

So I agree with Ray Dalio that this is an outsourcing. Now, the, the fact is that when we talk about outsourcing, if you think about the definition of the term outsourcing, you outsource something when something or somebody else can do it better, right? If you're an enterprise, if you're a human, like you outsource something, you go to an agency, you hire somebody externally and external consultant because they can do it better than you, or at least that's your assumption right now.

The outsourcing that we're doing with machines, nobody gives us the certainty that they are better than human, and so we are choosing to outsource something, but no one is asking what are we actually losing by outsourcing? Our emotions, our vulnerabilities, our decision making. when we go to humans, we take risks again, of being misunderstood, of being heard, but it's in that discomfort.

It is in that risk that you grow and you learn, and you heal and you change. If you eliminate that, if you have a frictionless decision making process. Then how is growth being impacted? How is, you know, everything that makes us human actually being impacted? Because you are really removing any friction, any barrier to your decision making process.

And most importantly, you might live an entire life where you get. Old. You look back and you think, how many decisions did I actually make about my life versus a machine So how much of my decisions were actually mine?

That's an interesting question to, to ask yourself if you're listening or watching this. 'cause what, how I see this basically is that some of the decisions, personally struggle with, because I'm a perfectionist and. I will overthink probably even the simplest decisions, but when it comes to life changing decisions or what I think are life changing decision, personally, I never outsourced to to ai.

a practical example that I use is prioritizing things based on predefined, set of restrictions. Priorities, and so on. So that's one way. I used to, to make decision for me, but for example, if someone is in a situation like they have to choose, right? If I let AI decide everything, obviously as you mentioned, you'll prevent me from growth, from like having some sort of. Live experience into making decisions that I know that I am the one that it, it's risking, let's say, to go on a less than ideal path than, than the other. But there is a situation in which they're like, alright, I have this repeated decision that I do every single day and probably I can help there.

Should we balance or should we be like straight from the beginning decision making? It's something we don't. Push away to, to algorithms and, some we just doing the My cell, it was the filter there. Or if there is any filter.

I think so. To me, there is absolutely filter. I don't think that we should not go to AI for every decision. I think that AI is really good at some things. I think that AI is really bad at some things. I think what it's missing is the literacy from people of knowing for what they can actually use AI for.

What kind of decisions is good to go to AI, to, and for word not, I, I hear people that go to AI to ask, how can I hit on a girl? How can I make peace with my friend? Or, I have had a fight with, my best friend. help me understand if it's my fault or their fault, or I don't like this shirt. What do you think?

Which shirt looked better on me? I'll upload pictures of my outfit. so I think those kinds of decision, if you outsource them and if you let another system, a machine to influence you in those decisions, I think there, it's where we start losing, Control over our lives, and it's fascinating because what my research showing is that people go to AI for those questions because they actually feel they are in control.

It makes them feel more in control, but what they're not realizing is that they're actually losing completely control of their emotions, of their lives, of their decisions. Right. If you instead are talking about a decision of a process, right? Like email automation or data days, highly automated tasks.

Auto, yeah, automatable. Then it's a different kind of decision, right? And then we enter a completely different space. Like is the decision that I'm letting AI make explainable, is it ethical? Is it bias? Is it, you know, to what level of autonomy I can give to this system to make the decision?

For me, there are different levels of autonomy that AI should or should not have with a human in the loop, without a human in the loop. So there is a lot of like ethical considerations that you then enter, But, and obviously it's different if you're an enterprise or if you are an individual, but I do think that artificial intelligence, it is great for certain things and it is great at automating, processes that are highly repetitive in nature. So something that you do every week and AI can. Help you free up that time. Why not? Right? at the end of the day, artificial intelligence is not about replacing people.

I do really believe this. It's about reimagining what we're capable of. When free ourselves from repetitive work and lean into what only humans can do, which is connect, which is empathize, which is decide with care. And those are things that machines cannot do. And so that's where that distinction comes in.

What decisions can I, should I rely to AI on versus what I should keep just between humans?

When you make decision what to outsource and what to do yourself, do you use some sort of, decision making f framework or process that you go through to make sure that you just outsource the right

Yeah. So, there are several things that, and several checkpoints that I typically use to understand. If a decision needs something that should go to an AI or not, or leave to humans, and it's a combination of different factors. first of all, it's the, the, how many times this decision is repeated, so the repeat repeatability of the decision, if it's a low frequency decision versus a high frequency decision, then.

If it's a high frequency decision that you want, you might want to explore artificial intelligence being applied into it versus a low frequency decision. You might want to keep it as a human because it means that it's highly dependent on variables that can change many times. if it's a low frequency, then is, can I explain every decision the system makes even six months later. I think explainability, it's something that we don't talk enough about in general with with ai. The first time I saw an AI agent make a decision, I couldn't explain. I really couldn't understand, like it had, it was, I was building a, approval for loan detection system, like a loan, artificial for, for loan approval or denial obviously.

So, and I was obviously in an experimental environment, so there was just data being trained. There was no one actually being impacted by this decision. I saw this agent and it had rejected someone, and I could not explain why, and it, it was, I felt very unease. I didn't feel excited. Even though, you know, as a statistician, I have tools and methodologies that mathematics gives me for traditional artificial intelligence systems that are pure math in statistics to explain.

Or try to explain how a model got to a decision. But for agents now while we're entering this agentic AI world and the era of this new type of artificial intelligence, right, that is agentic, which means that has agency, which means that can take actions for us and takes decisions and acts on those decisions.

you cannot use traditional statistics or methodologies to explain the model. And so when the first time that I saw I need to make a decision, I couldn't explain. I felt so unease. I didn't feel excited. And that's when It clicked for me. I was like, power without explainability, without traceability is a risk, especially when you are in regulated industries like you are banks, you are healthcare, life sciences, institutions, or making decisions that affect people's life in general.

These are like bombs because, and these are bombs that can have an effect later because they might not be dangerous today, but they will be in the future. Right? And they will have a scale effect. so I use a mental model, which is no autonomy. Then you keep the human in the loop. Partial autonomy. AI proposes human approves. Full autonomy. AI acts with tightly defined govern, governed spaces. So if you wanna give full autonomy, you need to give AI a lot of boundaries. Like, if this happens, then escalate it to a human. Or if this happens, kills process, kill the process, kill like stop. create an alert, send an email. So very boundaries.

So what's coming back to become very sexy actually are deterministic guardrails. So things like if then else thinks rules, business rules, those are becoming again, the most important thing.

Then we had neural networks, then we had deep learning and recognition. and then now we're going back to where we have these powerful systems that if there are, lets alone in full autonomy, there are so dangerous that we need to compare and, and buy, like, combine them with deterministic guardrails.

So that's where we start from.

Yeah, and for example. What, what's the lifecycle of an agent? If you want? Like how do, how do you actually use it to evaluate to whether something, it's, it's real or hype, let's say, or if, when you build those gar guardrails, like can you use AI as well to maybe it be, be responsible for the other ais that are working behind the scenes.

Like if you want an AI manager. Or should be always having some sort of guardrails that are moving towards humans.

So, I mean, I am, maybe I'm controversial in this, but to me it's moving towards humans should be the rule of thumb. And, like you should move towards no matter what. That's for me. and that's because I'm biased, probably because I work with banks, I work with financial services. I work with like highly regulated industries where it's not like a, a customer support routing system.

Where the worst case can happen is that a customer is very unhappy because they get a wrong email from a chat bot. To me, it is all about it. It, I don't see a world, to be honest, where for some use cases we get ai, we let AI to make decisions without human in the loop. I don't believe in AI managers.

I don't think those are going to be successful because if you follow the reasoning, quote unquote reasoning of an agent, you know how when you kick off an agent. They always show this, like now I'm opening this folder, now I am initiating this process. Right? And in our industry, that's called reasoning, even if it's not actually reasoning how we define human reasoning.

But if you follow that, if you read that very carefully, and if you push that, like if you have the agent irate and self irate for a lot, a lot of times. It gets to a point where it's, it's counterintuitive. It does not produce value anymore because it's, it's almost like it has only enough context then it can act on, right?

Because agents, all that makes a successful agent is the types of tools that it has access to and the level of context that he has. That's it. So the more, context you give to an agent and the better quality tools, you give it access to then every time that you give the agent an objective or a goal, it's gonna iterate and it's gonna reason based on the context it has, and it's gonna access the tools to achieve that goal.

Right. But it's very limited within that context, within those tools. And that's it. So like, if something goes wrong, another AI agent manager, it will be limited as well, only within that context and within those boundaries. So always have human escalations in the process.

Always. That's my recommendation.

Yeah. And it's a valid one because indeed, and for my own implementation, so I, I started using Claude Code to build some agents and have, train on certain skills and predefine comments for those listening, are basically processes that, that are like. Predefined. And indeed, apart from that, obviously I, I connected with some MCPs to have access to, to the internet, to my notion account, to my, Google Workspace.

But outside of that is just, I, I analyze that's how, how it goes into the process and. It doesn't have something that they need, they'll just, I train them to get back to me. And maybe that's my way of, adding myself in the loop. Plus, what I try to do is to never submit something as a deliverable, as a final thing.

Like I need to look at it first, because no matter how good is the context, they'll still do mistakes that I cannot put out there.

But speak of implementation, like in a day-to-day world in your work, how do you really use it? And if you use certain tools and if you avoid others, because I dunno, some restrictions and so.

So, what I use in my day-to-day job, it's a lot of co-pilots. So it's just for, I mean you, this is probably gonna make people laugh. 'cause people assume that because I work in AI, I will, I use AI tools for everything. I always have the answers. And I wake up in the morning and AI makes coffee and breakfast for me.

The truth is that, that is, that is not the case. It's really because when you work in ai. You realize, that the most valuable part and the most important thing is not building something that is capable of, but it's building something that you can explain, right? And you can explain how, you know, to the people who need that.

And so for me, the majority of my work is about building models that. I can then explain to other people how it got to a decision, right? And so. I, I use AI for very basic stuff. they use it to help me, to beautify emails.

I'm not. English native. So I, I was raised, I grew up in Italy, born from Albanian family. So English is not my first language, so I, I use it a lot to help me for copy editing. I use it to give me ideas, to brainstorm ideas for presentations, for, proposals that I have to put together for, for clients or for, internally at work with my, the team that I lead.

So it's a lot for like brainstorming in this thing. So it's a way of using it where I'm always the one that reacts to the answer. Like, oh, do I like it? Do I not like it? I don't have like this magic unicorns. The only thing that I build is I'm big on news and you know this better than me.

Probably. Like, it's so hard to keep up with all the news and newsletters and things that happen, and scientific research, which is something that's. Sometimes people don't care about, like all the scientific research and papers that get published. so the agents that I've built is like the, where I can say that I use, you know, a Gen TKI, To give you an example is this, that based on some keywords just tracks articles for me and posts it to me for me in an Excel and summarizes some of them. And so I then go, I read a quick summary every morning and I just decide which ones I wanna deep dive. More where I wanna look at, and I have them cat categorized by, whether it's a podcast, whether it's a white paper, whether it's a video, whether it's an article or a scientific paper.

So I get to choose which type of format or type of content I wanna consume. and that's really the only thing. And then the majority, the bigger way that we, I use AI is really for my clients. So when I train models, for on their data. Whether I'm building a fraud detection model, whether I'm building a optimization model or like it's, it's there that I really dive into building artificial intelligence.

Nice and yeah, that that's the thing, right? Like, because often people assume if you're in a certain industry, you are like using some super advanced systems that is doing everything for you.

But the reality is, and speaking of the industry like you, you move from Italy to us basically building a career in a male dominated

Oh yeah.

What was harder than you expected

there, there were a lot of things that were harder. first of all, being a woman in technology is, Is, interesting. I would say, as women, we have this huge creativity and innovative mindset, and I see this not just just because I'm a woman, but because I loop people.

In my work, and I see how women tend to process, tasks or when we are in a brainstorming session versus man. And so the most innovative and the most creative and the most crazy ideas, sometimes they come from women. Right? And in technology, we need that because we need to be creative. We need to be innovative.

And I grew up in u in college and university, all my friends thinking I was a nerd and I would do only like boring things. But then the reality is that, saying this, I don't know if you have students that listen to your podcast, but if you are a young woman, like get into ai. Get into technology because it's not boring.

It's so innovative, it's so curious. It's so creative and, and I think women can bring so much, potential to it. so I've, I, the role of women that I've seen in my career is that. First of all, you need to work harder to prove yourself. I have examples where in the beginning of my career, I would enter the room and the client would automatically look at my male card apart as the person who is going to present, but I was going to present and lead the, the conversation.

so there are those situations where you feel like you don't belong And you start having some imposter syndrome, you start questioning yourself if you're good enough, because sometimes you see male counterparts that they are so ease that they're much better at you and just, just like.

Going with it, right? But I was always been a perfectionist, for example, myself. So for me, everything that I had to prepare, every model that I had to build needed to be perfect, or at least what was perfect according to me. growing up you learn that perfection doesn't exist and you stop chasing it.

But at the beginning of your career, especially if you're a woman, I mean, I was 25 when I moved by myself from Italy to United States and I was so young, I didn't have, you know, kids or families. It was just me and, and you are. In this world where customers are this man with in tie and suits and, they can be scary, right?

it's very important for you to, for women to continue to find purpose in what they're doing, continuing to believe in themselves, and most importantly, continuing, I would say to rely on their male. colleagues. So I don't know if this is gonna sound controversial, but personally, the people that have helped me the most in my career, they were men and the people that have challenged me the most in my career were also men.

So I always invite women to, you know, use or leverage man to get help on how to deal with man. that has helped me a lot because it's easy to say or easy to let those, those 10 people, that 10 guys that you meet, they're, they're bad with you, right? And then challenge you. It's easy to let that make you believe that all men are like that, but that's not true.

You're gonna find a lot of amazing. Man in your life, they're gonna help you and that they're gonna advocate for you. Of course, women as well, right? But a lot of times when a man find, an, an encounter and meet a woman that is capable woman, that they were staed, they're never going to, not help you.

So always, you know, lean on on the man that's. You can encounter in your journey because those have helped me a lot. This is on the professional side.

Then there is the women in tech conversations on the AI side because that's a whole other story. So the majority of the data that we use, it's not because we use them, it's because the majority of the data there are available and there they're produced are from man.

And so there is a lot of bias in algorithms out there that are built not just on based on gender, right? You have algorithms bias based on race, based on a lot of other things and all about variables. But on women, this has a lot of impact on their lives to, to effects. There can be deadly. So women have 47% more chances to die to get hurt and injured than a man in a car crash.

And they have 17% more chances to die on a car accident than a man. and that's simply because how gender bias can affect women, can impact women. This is just simply because how. Safety gears are designed on which body, on the more traditional body or of a man or of a woman.

And so recent research is showing this is from the last year, that women are using AI tools way less than men. And that is valid across every gen, like every age group. Especially for Gen Zs, so especially younger generations. And what the research is finding out, found out is the reason why men or women are using AI less than man is because they feel less capable. They think that they're not good enough. Women think that ai, it's something just for, man, it's very nerdy. It's very difficult. It's not for them, so they don't even jump into it. On the other side, men are, they tend to be more, let me just try it. If I fail, who cares? I'll learn. Women are, I don't know how to use it.

I'm not even gonna try it. It's too much for me. And so this is creating a power gap because AI will decide who will have the power to lead in the next decade. And if we as women are not part of that conversation, we already lost the financial war because we did. Completely. We cannot lose the AI wave as well.

And what you'll tell, to those that are on that fence and they know, they, they are quote unquote left behind. If they don't use ai, would be their first step?

just go on any search engine and type. AI tools that I can use. Open ChatGPT, Open Gemini. Ask it a question, like really just start somewhere. Ask your friends how they're using it. Ask your, uh, uh, man friends, how they're using, ask your women friends how they're using it. Just, just, just start, take a picture of your fridge and ask what recipe you can cook with it.

Or, take a picture of your car or of your living room and ask how you can remodel it. Like use it somewhere. Just start. That's what I would say to start and be always curious. I mean that's what everybody like, that's how everybody starts with a new thing. Like nobody, nobody has figured AI out.

And if anyone tells you that they have is because they're just better at. Being louder about it, and I'm one of those people myself, like I publish contents about AI, but that's just documenting my journey. It's not because I have figured it out. It's not because I'm smarter than others. Like nobody has figured AI out.

That's the only certainty and the only truth that we know today. And so don't feel like you are not smart enough for this. don't feel you cannot start setting it. just start somewhere. Try open chat, GPT, open Gemini, ask it a question and go from there.

I love that example, and especially the fact that, so practical ones that you give, like give it a picture. Reimagine that, I dunno, living room or whatever. 'cause often we have that aha moment afterwards you see the tool in action making something that you, you didn't know it's possible. And from that moment on, the curiosity sparks in, the creativity sparks in, and you try using daily and you arrive a point image like, why can I live my life without it?

But speaking of that, do you imagine your life without ai.

Sometimes I wish I didn't have AI in my life, to be honest. because, you know, it tends to, I love artificial intelligence. I, I, you know, it's my, it's my work and I've seen it, I've seen its ability. I've see, I see firsthand it's ability to do so much good and to do so much, value, positive value for, for people even just thinking about what. AI is allowing the innovations that it's allowing to achieve in healthcare, the discoveries that we're making. Like a hundreds years ago, people, the average life expectations was 50 years old. Now it's what? 80, 90? It's gonna be a more than a hundred. We're gonna get there thanks to artificial intelligence as well, paired with the incredible human knowledge that we have in our doctors, right?

But like the life expectancy is gonna increase a lot. And that's one thing that I'm super excited about, artificial intelligence, right? It's gonna make us live longer. It's gonna make us live better, healthier for those of us who get on board again. So we need to get on board. But in the other side, I think it's gonna create a lot of other.

Things like isolation, loneliness, we're gonna start losing our ability to communicate among ourselves our. Our ability to feel vulnerable, to open with each other because we're gonna have these tools that we can just open to, to them and ask them how we can process, somebody's loss or how we can process something right in our lives.

And so that's what scares me the most. Uh, that's the part that I don't wanna have in my life. Um. Do I manage my life with artificial intelligence? Yes, of course I do. But I also am super excited about seeing how AI is gonna change my life and, others, in a positive way.

So it comes with pros and cons, but if you look at the positive side of things, greatly overcome the the negative part. And if we're mindful about it, if we're responsible users, then we can truly leverage to, to our own sake. And please tell those watching or listening where they can connect with you.

I am very active on LinkedIn. just search for Marinela Profi I have a website where they can reach out. I have, there is my email and contact there. So I would say LinkedIn is, or my website is my main, my main channels.

Awesome. And, uh, for those, uh, tuning in, make sure to check the description because the links will be there. And I'd like to conclude with one thing, like if someone is on the fence of start using AI and they're like, alright, I will just. I dunno, Google a tool and start using it, but they, they don't know where to start.

What really a practical way of doing it for, for day-to-day work that they can implement in less than one day.

Well, obviously it depends on the tasks and it depends what type of job that you, everybody has, right? But what I would do is, I would just look on the internet and search for how can artificial intelligence be used in X, Y, and Z? And just to my role, like their, their, their job, right? and just start from there.

Like, there's a lot of great courses, free training courses online, that they can take. so what I would do is just start from searching online. How can I use artificial intelligence? For this role. And I get that question a lot from from friends or people like, oh, I work in marketing. How can I use it?

I'm like, just go online and search for it like that. That's where you start. And then you're gonna find forums. You're gonna find training, you're gonna find courses. Subscribe to a forum. I always like that. I think it's a great way to have a virtual, if you cannot have physical, like a virtual community where you can type questions and say, Hey, I would like to learn about AI in this field.

I found out this course. Do you, does anybody know it? Have you taken it before? Do you like it? what do you recommend? So that's what I would, suggest and obviously they can reach out to me if they have any questions. I'm happy to help.

That was Marinela Profi. If this conversation may tick differently about AI and tech careers, share it with someone that needs to hear it. If there is, are your kind of conversations, you know what to do. Subscribe to not miss out the next episode. I'm Gabe Marusca and Siri in the Wild.