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Now onto the show.
Daniel: Welcome to another episode of the Practical AI Podcast. This is Daniel Wightnack. I am CEO at Prediction Guard, and I'm joined as always by my cohost, Chris Benson, who is a principal AI research engineer at Lockheed Martin. How are doing, Chris?
Chris: Hey. Doing very well today, Daniel. How's it going?
Daniel: It's going really well because I have a close friend joining us on the podcast today and a previous guest. We went through the Intel Ignite accelerator program together in in different companies. And, yeah, just really excited to have with us, Ramin Mohammadi, with us who is an adjunct professor at Northeastern University and also lead principal AI engineer at iBaseT. Welcome, Rameen. It's good to good to see you again.
Ramin: Yeah. Thanks then, Chris. It's always, great to be back.
Daniel: Yeah. Yeah. I I've been excited to talk through these things. And even before the show, obviously you're kind of living in two worlds. You're living in the industry world and you're living in the academic world.
And you've kind of been living in those two worlds for quite some time, which is interesting because you have a perspective on how, for example, data scientists or AI people or machine learning people are being trained and what those people are actually doing in industry, which I find really intriguing, especially because so much has changed. I guess maybe that's a good initial question is, is my perception right that the role of an AI person or a data scientist or a machine learning person in industry, the day to day life of that person has really changed dramatically over the past even few years. And I'm curious if the academic side has kept up with that.
Ramin: Yeah. So I think that's an interesting question. I think we need to break it down into multiple sections. Yeah. I mean, let's just start first, do a quick review of what has happened, you know, because we're talking about the complete transformation of the AI and data science job market, You know?
I mean, if you remember, and it was about, like, a decade decade ago back in 2012, our business review, they called data scientists the sexiest job of twenty first century.
Daniel: Yeah. That's why I got into it because obviously that describes what I wanted to be.
Ramin: That's right. And if you think about it, that one phrase, it kicked off a massive gold rush. Everyone wanted it. Universities were spinning up the new master programs overnight, and the promise was pretty simple: get a degree and learn a little bit of machine learning, and you're instantly employable. That promise feels like almost like a myth now, you know?
I mean, if you talk with any new graduate today, especially someone looking for their first role, the feeling is totally different. It's brutal. The market is absolutely brutal. We see job posting for entry level, you know, that job requires about three years of experience. The demand has changed.
It's shifted fundamentally. It's not about what do you know about it from the textbook anymore. It's about what can you build? Can you deploy and maintain a real like, a scalable AI system? It's kind of like that's the new currency of hiring.
Daniel: I think one time, Chris, I don't know if this was us that came up with this discussion, but I remember quite a while ago we talked about full stack data scientists or something like that. The idea being like, you could figure out what kind of modeling you needed to do. You could do the prototyping and POC, but you could also like deploy something to actual cloud environments or something like that. I mean, that seems like quite a tall order, Ramin, because you're basically saying be a software a proficient software engineer, but also be an infrastructure person and also I don't know, I've heard a lot of people say there's not really a full stack engineer doesn't really exist. So yeah, I guess from that perspective, how much of what a data scientist or machine learning or AI person fits into those different buckets at this point, whether it's software engineering or infrastructure work or actual knowledge of differential equations or statistics or something.
Ramin: I think that's also a great point. So if you think about back to Data Science Job, the idea of Data Science Job was that your job is kind of done once you got a good score in the notebook. That the classic, my model has 95% accuracy on the test data, you're good, you pass it to someone else. And then you remember, I think it's around 2020s with some resources like Google Cloud rules of MLOps, it laid out these new realities that successful ML needs a whole suite of real engineering escapes. The things like containerization with Docker, CICD pipeline automation, monitoring, and, you know, you have to know if that your model actually works in the real life, and then you need to monitor it.
And after you deploy it, you need to basically look for the drifts, you know? So industry made it really clear that job wasn't just build the model anymore. It's kind of like you need to own the pipeline. So and then if you think about it, all of sudden, the analysts or data scientists went from just being a simple analyst to being engineers who build and maintain the intelligent systems. And so just as that engineering bar was being raised by MLOps, along comes the second, maybe even bigger tidal wave, the generative AI.
And it becomes like around 2023 explosion that you can see in the Stanford AI Index, basically, they mentioned that this was not just a cool new tool. This was an automation event. I immediately attacked the entry point in the field that they could do those jobs, basically. This shift was drastic from the data scientists to ML ops engineers and all of a sudden AI, basically.
Chris: In addition to that, there's so much more diversity in you know, we as we were talking a moment ago about the the notion of the full stack engineer, especially at the entry level trying to fit into this. And the notion of, like, what is full stack is changing fairly rapidly. There are a lot of different options out there. And not only do you have to try with that entry level student have to try to fit in to the notion of what, you know, an organization is looking for, but there's all these variations on that. And if they're not in the right variation of what that organization is looking for, in terms of this abundance of skills that are required for that given position, they're still out of luck.
I mean, it's it's really a crapshoot for students today in terms of trying to find the right fit and represent that cell represent their own ability to fit to the organization that's looking to hire. It's I I'm I'm really glad that I'm not out there in the job market in that way right now. Would be brutal.
Ramin: Yeah. So I think I think that's that's true. It's like, if you think about it, as this AI wave comes in and this series of automation task, basically this AI made certain things simpler. Those are the types of tasks, like a bullet per task that you always used to give to the new hire, basically. It's kind of like the groundwork.
And for someone who's an early hire, a recent graduate, those type of jobs were kind of like the first step on the ladder. How to, for example, you write a complex SQL query to get the data, make simple Python, and get your hand dirty with the company's data. You learn about it, and also you show your skills, you know? But now it's no longer like that, so you need to basically find the correct fit, what they exactly want, what they want to build. So I show that I can build that.
And there was this study from OpenAI and University of Pennsylvania that they look at this task exposure to large language model, and the take takeaway that they had was pretty simple. Any repeatable task that used to be given to juniors are highly vulnerable to AI, basically, and innovations. So if a junior analyst used to take all this afternoon, write the SQL queries and make the dashboard, now AI can just write it with a great prompt, right? So basically, economy case for hiring a big group of trainees and have them to do the work has evaporated. There's kind of like a change.
For example, I used to hire lots of interns to basically help with the development and speed up the process. And since AI shift, to be honest, I just took I use AI for all of those tasks, you know. So this has been this big change. And of course, you know, we are seeing this shift in hiring strategy kind of everywhere in big tech or even in startups. They just stop hiring for potential and they are starting hiring for proven capabilities.
It's kind of like that. The paradigm has changed. New companies these days basically afford to bring in 50 juniors or spending a couple of years to train them. They're rather to hire five or maybe 10 people that already have built or developed some complete system from day one. So it's kind of like fitting about it, that new entry level jobs is technically what we would call mid level engineers a couple of years back.
This shift is really bad. With this new bar, it's not like that you don't need knowledge. All this deep statistical knowledge, Python skills, they're all essential, but they are just at this point, they are kind of prerequisites. They are the ticket to the game. They are not how to win it.
It's of it's has here. You need to prove that you can build. The company wants what you built, and then, you know, you go for hiring.
Daniel: I'm wondering because that bar has been raised, like you say, the kind of mid level positions that we used to call mid level or maybe the entry level ones now, how does that change? Because I mean, maybe this is a negative view that I'm about to give, but I'm very pro higher education. But I also think like even whether you look at computer science or data science sort of education, a lot of that does not, even before the recent shift that you talk about, it didn't always connect to what you were actually going to do in your day to day work, right? So now not only does it not connect to that entry level kind of day to day work, but does it now even increase that divide where like how could we possibly train people to come in as mid level kind of data science folks? Because I think if I'm interpreting what you're saying correctly, it's not that AI is making data scientists no longer relevant or AI or machine learning people no longer relevant.
It's still very relevant. It's just the stuff that entry level data scientists or machine learning people used to do and kind of level up on, that's no longer available. So where are they going to do that? And is it even reasonable for us to think that universities could help get them up to that level, I guess?
Ramin: Yeah, I think So I would answer to that question in two sections. I think one part is about where is academia stands right now. And then the second part would be talking about the industry versus academia right now. So let's just start with where does academia stands. If you think about it, and I kind of call this I don't want to be negative I call it educational bottleneck.
And to be clear, the first thing is that the faculties that we have in CSML, data science department, they are all brilliant. They are world class at teaching the fundamentals, the math, theory, history, the research. That foundation is non negotiable. You need it. But the curriculums often just stop there.
And it used to be also kind of like that. And it's some of the theory and leaves basically this huge gap between what the student learns and what employees actually need for them to do on the first day. As an example, a student might spend the whole semester learning about the math and all sorts of optimization, back replications, and stuff like that, which is necessary. But as soon as they graduate, see this job market that wants them to deploy on the Kubernetes or they know how to work with all different cloud resources. So they know exactly how the engine works, but they actually never tried to drive a car into traffic.
And that you know, there was this new post by Andrew Ng recently that he argued this urgent shift in education. I'm going to paraphrase in what he said. He said, Knowledge is great, but skills are greater. Meaning that in the field that's moving this fast, you have to teach the practical skills to get the work done. You you need to give the capacity to get meaningful work done by having a proper knowledge and proper training.
So this is exactly what the job market is selecting for now. So that's the view that I have on education at the moment. And the second part that we can basically talk about is like a comparison between where it is like a risk industry versus academia. And there is a really good, basically, study by MIT, a recent study, basically, that the stats are staggering. Basically, they say that right now, about 70% of the AI PhDs are just skipping academia and go to job market, basically industry directly.
And that's a huge brain drain for the universities, you know? And the second is that, which is the real killer risk, and probably you I'm sure you know all this, like 96% of the major state of art systems comes from industry labs, not from universities anymore. So your university is already falling behind, and then companies like Google, Meta, OpenAI, they are the one that defining the frontier now. They are building the tools. They are setting their standards.
And that's the absolute core of the bottleneck. Academy curriculums moves on a cycle of years. Getting a new course approved, like updating a textbook, it's slow. By the time a university approves one new course to be, like, let's say, for example, LLM application course to be added to curriculums, the tools have already changed three times, you know? So the entire framework is really different because, you know, it took a while.
And that has happened to me also. Like, developed a course and take years to get approval to teach that course. And then you need to go back and update everything that you were planning to teach because, you know, the industry has changed already.
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Daniel: So Ramin, I love how you highlighted this kind of divide between academia industry, like what that is in reality. Anecdotally, I remember actually last, I think it was last year or maybe a year and a half ago, I lived by Purdue University. I was like walking through campus and they were just finishing their, They had this new building, right? And so this was '20, whatever, 2024, right? And it said like, Hall Of Data Science, right?
And I thought My immediate thought in my mind is like, in 2017, you could have created a Hall Of Data Science. Now you need a Hall Of AI. You're building the wrong hall. To their credit, I think they actually I just looked this up while we were talking. They did rename it Hall Of Data Science And AI.
To their credit, they at least caught up with the name. Yeah, I guess, obviously you're an educator and so you see that there is value in trying to have these formal education serves a purpose and is different from maybe on the job training. What do you think, or have you seen examples where this sort of practical skills are built up in an academic environment rather than just the theory or the knowledge as you were drawing the distinction there?
Ramin: Yeah. Actually, that's something that we have been doing for almost the last three years. So I basically developed this course, this MLOps course at Northeastern University almost two years ago that we have been ongoing. So the idea was this. This is like about three, four years ago, I was this hiring manager, and I used to do lots of interviews for our team.
And I always basically interviewed smart, motivated, good to school, basically, candidates, but most of them struggled with the same thing. They understood the theory, but they couldn't build anything, they couldn't ship anything, you know. And that's when it clicked for me that, okay, if the industry, I personally as someone who was in the industry and academy, expect these students or these basically candidates to build a real system from day one. And then I know in the industry we don't teach them that. Could we do something about it?
So I started working on this course. I built this MLOps course that every semester right now, we have about 150 to 170 students within one class, like a huge classroom. And instead of just learning the concept, they start by choosing a domain that they actually care about, healthcare, finance, sport, robotic and whatnot. Then as a team, they spend the entire semester on building one real product. And this real product, it's not just homework assignment.
It's not a toy example. It's a real working system with deadlines, milestones, deliverable, just like a real, an actual ML and software team. And the best part of that is we wrap up this semester. You know, the way that we wrap up the semester is that the students basically present their product at our MLOps Expo, which is a full industry partner event we have been holding over the last, I think, two years now. This year, for example, we partnered with Google.
So we are hosting on in two weeks, December 12, at Google Main campus in Boston, and where our students are pretty hired to come there. But they will basically what they do, they show they demo the actual product that they have built. And so the whole course is simple. You don't just learn ML anymore. We teach you how to build with it, you know?
And the idea for me was to give students this hands on experience that companies are looking for right now. And honestly watching the students go from, I have never deployed anything before to me and my team, we build a real product this semester. That's kind of like the best part for me.
Daniel: One at least hypothesis that I have here, which I would love your opinion on, Ramin, is on one side you have highlighted how this kind of gap is widening even, like the between the theory and like where you need to come into a job, like at a mid level. At the same time, this revolution of Gen AI has been happening, which in some ways, to your point, some of those things are the things that are being automated by AI, but it's also enabling maybe this like younger generation of software engineers, AI people to actually perform at a higher level out of the gate, but in a different way. So not like there's kind of a burden on maybe us as prior generation data scientists and machine learning people to understand that students and new hires need to from the start be doing their data science work differently. So just by way of anecdote, we were talking about this a little bit before the show that my wife owns a e commerce business, Black Friday, Cyber Monday just happened. Day to day in my company, I'm not doing as much kind of hands on work on the product as I was given my role as CEO, but it was nice to go back.
So for like four days I helped them during the sale and I just sat in a room doing customer lifetime modeling and updated forecasts for 2026 and looking at churn and analyzing customer journey and all this stuff. And number one, it was a ton of fun, but I was kind of coming at it from that perspective and kind of reentering some of those things that maybe I hadn't done as much for a little while or even, you know, maybe since the previous year when I helped them with forecasting, like I was able to get tons of that done so quickly because I was having AI honestly write most of the code for me. The thing though was I still had to play the data scientists to get from point A to point B. There was no way that I could have just said to any AI system, Hey, I want write a three sentence prompt and get out all of the lifetime modeling and forecasting and all of this stuff. I still had to play that kind of data science orchestrator and know what the things were, know what modeling techniques were relevant, know maybe what trade offs were and other things.
So do you think on the one hand it's maybe depressing that the academic kind of industry gap is widening, but on the other hand, maybe there's Am I right that there's an opportunity to actually lean in for these students in terms of different ways of working to get to a higher level faster?
Ramin: I'm not sure about the higher getting to the higher level faster part, but just I I saw a a new talk recently by Neil Ahoyne over at Google, and he made a great point about this data science job. And he basically was saying that the data science job is not gone, but AI is just forcing them to change dramatically. It's no longer it's about analyzing the data or building certain, you know, sort of dashboards or stuff like that. As we say, you can just with the knowledge, just prompt it properly, and just having the data and just build that quickly, You know? So there are certain types of tasks that you used to do for trying to climb the ladder to learn more and more, but that they are not the same anymore.
And the expectation is not for you also to do the same task because, you know, if this company is hiring, you probably at this stage they want more. But I think it is a really great point that for hiring managers or for someone that's when you hire someone on your team or have someone new juniors on your team, you need to also account for helping them to like, mentoring them properly to to be sure that they can evolve and learn. Otherwise, we basically take this cognitive ability from them because they everyone if you just ask everyone to just build, build, and they just use AI, they don't They're never going to learn basically how to build. So we take that cognitive ability away from them to just build new, faster products.
Chris: Yeah, think you're really onto something there in terms of one of the things that that I have done for the last few years is, is I'm a capstone sponsor for capstone projects at Georgia Tech, in the in the College of Computing. And so as and I'm doing that from my nonprofit role as opposed to my day job. When I work with different teams there, I think one of the challenges is they're kind of bringing what they know. Certainly, Gen AI capabilities have helped them you know, step up a little bit along the way in terms of figuring out. I think the areas that I've noticed that they're still struggling, the students, are there's, you know, going back to to Dan being a data scientist over the weekend instead of a CEO in that moment, is he's bringing all that business knowledge, you know, years and years and years of business knowledge and understanding about what's really needed in that.
And I think that's, you know, that's one of those things that is is part of the struggle with junior level is is there's the the kind of concept of I've learned tools in university, and I'm trying to bring them to bear, and they're not always the right tools for the organization they've joined. And they don't necessarily know how to combine that with all the other all the other tie ins that that organization may need, that were not necessarily something accommodated in their in their academic development. And so, you know, that's kind of exacerbated by the fact that now with Gen AI kind of replacing a lot of those junior roles coming in and and you know, how do you how do you ramp up? It does seem to your point like things are actually getting like even though we have new amazing tools in in the form of Gen AI capabilities, it seems like things are getting harder to bridge that gap. And and I'm not sure how you do that.
Because it's a combination of both kind of the the experience of being in the real world, along with fast moving, you know, a fast moving technical landscape to navigate. Are you seeing that from your side with students? And how are you tackling some of those subtleties that are there?
Ramin: Yeah, actually, definitely. So two weeks ago, I sent out a survey to my students and I asked them basically to take a couple of questions. And I specifically did this for Albert Talk. And so as part of the survey, basically, there were some questions. And one question was which is 60%, basically, of the students, they say that they are taking online courses on top of what they are taking in in the school.
In another question, 82% of the students say that they're participating in hackathons in order to learn to how to quickly to build. And about 46% of the time, they are attending workshops, you know? So they are building their own parallel curriculums through side project, open source contributions, or certification through AWS, Google, you know? And that's exactly it. You know, the portfolio kind of has become a new credential.
It's no longer about your grade. It's like about what you have as a portfolio. And this is also important for us to it's kind of like a dose of reality that this self learning path isn't easy and isn't equitable. You know, it takes tons of time and costs lots of money. And if you want to practice building a real production grade system, working with a cloud service that always costs money, you know, as those commercials.
And how many students like, if you think about students already paying thousand intuitions, they cannot also afford hundreds of dollars per month for cloud computing, you know, to practice. So it's kind of like a huge change. It creates this resource divide. And at this point, I think the bar isn't just higher. It's kind of also financially more expensive for the students to learn.
And right now, for example, shout out to our friends at Google. They give us lots of credits for our ML ops course every semester because our students, they can't otherwise build anything in the real world. I personally reach out to lots of providers in the industry and say that, hey, you know what? We train these students to use your tools. Give us some cloud credits so they can basically learn and build a phone.
Yeah, that's my take on that.
Daniel: Well, Ramin, I am kind of intrigued because, well, on the one side you're thinking very in an innovative way about how to bring this kind of skill or reducing the skill gap, being creative in the academic setting to get people these skills, but also, you're a practicing AI engineer. What have you seen kind of personally? Because you're already operating at a higher level. Are there also changes, any like significant changes that you've noticed in your day to day work over the kind of past few years that have caused you to think about your day to day tasks differently, like more so than the entry level type of folks, but actually ways that you're fundamentally thinking about your workflows or how you're doing those kind of higher maybe higher skill or higher level kind of data science AI stuff. I'm wondering if anything stands out for you.
Ramin: Yeah, definitely. I mean, I personally have been part of this shift. I started my career as a data scientist. Then in 2018, I started as an ML engineer, and it just went up. Then last year, I started as an AI engineer.
So I also have been part of this chain myself.
Daniel: Data, ML, AI.
Ramin: Exactly, the same pattern. And for me, when I look at them, they are kind of similar. If we put the data science aside, because that was kind of like There was no production. There were lots of research, especially around it. But when you go to ML and AI, just the terminology is different.
They're technically kind of similar. I think the main difference that I personally felt is that I need to, in my day to day work, to work a lot with LLMs because it's a requirement for certain things and work a lot with the larger models, which requires you to have a better understanding on, you know, like a GPU optimization, how to break your models and basically ensure that they're optimal, basically. And those changes, you know, it wasn't something that you do maybe a couple of years ago. So I ended up more personally trying to read a lot, you know, spend summertime just read different books to learn to advance my own career. And I always talk about this with my students.
When I learn something new, I bring it to the class. I was like, okay. I was recently basically reading about this, and this was really interesting. This is the link, and maybe I sometimes give them a small lecture also on it. But I think yeah.
So it's like the change is there for everyone, not just for junior. It's like it doesn't matter if you are a principal or a junior technically. But who's getting being more impacted, I think that's the part that's kind of, like, unfair, you know, to to the for to the juniors technically or or recent graduates.
Chris: I'm curious to extend this out a little bit, you know, as we kind of went from the challenge of juniors, and Dan introduced, you know, the challenge of kind of us, you know, as as as people who are past that point in their life. But like, we have fast coming, you know, fast changes are coming even more in the sense of like, we're hitting that point where physical AI is really on the rise now, you know, not just in certain industries as it has been historically, but in many industries, it's, know, it's exploding outward at this point. And we all have challenges in terms of incorporating these new realities into what we're doing and how we're going to learn about it. What does that imply at the university level? When you're getting back to students and and they're already you know, you're already trying to bridge the gap into the corporate world or the startup world or wherever they're gonna be productive, But you also have this explosion in terms of the places that AI is touching in new and different ways.
What are the what are the implications on the curriculum and on the the burden that professors have to try to get their students ready for that next thing, is steamrolling over us already?
Ramin: I think it depends. So let let me just I know some other schools are doing that, but I'm going to speak with respect to Northeastern. For example, Northeastern Curry College of Computer Science, as of this year, basically 2026, they're updating their curriculums finally. Not everything is going to be a small shift, but gradual, basically. So they are introducing some more practical courses into the curriculums.
And they also, for example, they are weaving their ethics directly into the coding part of the curriculum. But this is going to be kind of like a slower shift on the curriculum side. But on the other end, from the teaching perspective, this is kind of like AI is kind of like a double edged sword at this point because students, they all use AI. They are using degenerative AI, which is great. I would tell my students, use it, but don't lose it.
Know, kind of like you need to use it, don't lose it. So it's kind of like you need to be sure that you can learn, move faster with this type of thing, not to just give away all the autonomy and you just basically you just use them for everything. And so and then further from the other end, from the teacher's perspective, it's it's kinda difficult because when you give, for example, homeworks or labs to students, it's just especially on coding. I'm not talking about writing an essay. Like, coding perspective, you don't know.
You can't even tell that if they wrote the code or not. Everyone returned great codes these days, and then there's a homework. There's no way for you to just say that if it's written by AI or not. They're really smart in how to change the temperature to ensure that the result is not being detected. So the the so again, this is like a double edged sword, but also from the other end, it's like because there are lots of information, lots of changes in the market, in the industry, in the domain every day.
Like, every day you read the news, there's a new article, there's something coming out, and it's hard for basically academia to keep up with that. You know, it's like you academia is falling far behind the industry and it's going to go into this this gap is going to just expand the way that it is. And I think at some point, industry need to help academia. It shouldn't be just academia need to keep up with the industry. If the industry needs new talent to come later, you need to step forward.
And I say, okay, let me also help them. Let me start some program. Let me participate in some of the courses that we have. So otherwise, it's kind of like a chasing a ball, a academy just constantly trying to keep up and that's not going to win.
Chris: That's fair. And I think that's a it's a good notion that I think inner industry really needs to consider as an investment back. I I agree. I think it's been largely a one way street there. I would like to flip a little bit the timeline around to to the students that are coming in.
So and I'm asking this selfishly, I have a 13 year old daughter in eighth grade. She is we're we've been applying to magnet schools and things like that and getting her ready for her high school experience. And she has never been someone interested in AI that was dad's thing and all that. But as she has started looking at what she wants to do, she's starting to recognize that whatever that is, AI will impact her in a significant way going forward. So it's not just the kids that are are focused on technology at this point, but all of the kids.
And as she does that and they're entering into high school, what advice do you have for what high schools need to do before they come to you? Before you're getting those students and you're trying to prepare them for industry and a career and and moving through their lives, You have students coming to you. What would you like to see from high schools in terms of how they prepare these kids to be better or more ready to come into your care as a professor so that you can do the thing that you do?
Ramin: Yeah, so I think that's a great point. And are already two shifts. I have been spoken by neighbors, similar question that, Hey, my kids, should they go to college for computer science anymore? Should they study this anymore? And I think the answer is that yes, you know, that there will be shifts in the market.
And it's it's not just computer science. It's not just AI. AI is going to impact so many things. Some some areas, like, slower, but some areas much faster. And at some point, all of us basically become somehow we need to learn how to work with AI.
And I think it's really good if from high school, you understand the concept, not not maybe the master theory behind AI, but just to learn, okay. In general, but how does AI work? There are lots of AI capabilities that you don't technically need the math behind them. You can just build a system just by knowing how to put the components together. So if they could, like, from high school, to part of the workshops or participate in some sort of, like, a training set, build something simple, you know, that automatically opens lots of doors, like a thinking for you for the future.
As you go to after high school, then you want to go, basically, to universities and you learn in different courses, different concepts, you're like, oh, I know. Maybe I can build something around this. I always think that everyone can be an entrepreneur. It's kinda like as long as they have the correct mindset and the energy for it. So if if they already have been trained from high school and they have not not trained in bigger way, just in this easier way of training, like teaching, they could potentially advance more in university compared to students that they just want to learn it during the university.
Daniel: Well, I know that we've talked a lot about kind of a lot of perspectives, both from the industry side, from the academic side. I think all of us on the call though are generally excited about kind of certain parts of the ecosystem, way that they're developing. From that side of things, as we get closer to the end here, Ramin, what as you look at the ecosystem, because you're, again, you have multiple views of this ecosystem from the industry side, from the academic side. What's most exciting for you as you're kind of entering into this next year? And maybe it's something like, oh, I can't wait personally to have the time on a weekend to explore this, or maybe it's something you're already getting into.
Ramin: Definitely. Actually, I recently purchased the Riichi Mini by
Chris: Hot Yeah,
Daniel: yeah, yeah. The robot, right? The little it's kind of a desk type robot, Yeah.
Ramin: So I'm pretty excited and waiting for that to be delivered. I think the delivery is going to be early January, hopefully, finger crossed. And I'm pretty excited to work with that and build some capabilities I have in mind. And when I think about all these changes, like, oh, if you would put me back a couple of years ago, I would have never gone for robotic. Oh my god.
No. You know what? It's not my thing. But now with this AI change and I already went through the, you know, contents of on Hugging Face, which is these guys are great. Reading it through the documentation, I was like, wow, that's pretty straightforward.
So think about how much AI changed it, feel that I can easily go buy a robot, like a small robot, and I'm planning already ahead of time. You also have this simulator, so you don't need to wait for it to deliver. You built ahead of time the apps and simulate it that it will work on the robots and the robot comes with deploy it. So that's my go to, like what I'm excited for in 2026.
Daniel: Yeah, it's kind of crazy. I feel like when we started in this field, it was like hard enough to get the dependencies installed for TensorFlow and just be able to run any model. Just like that in and of itself was like
Chris: trying Are to give us PTSD? Is that the goal mean,
Ramin: TensorFlow and CUDA. Oh.
Daniel: Yes. Yeah. It's like regardless, that was the hardest problem. And now you can have a whole digital twin of a robot and do all that. It is pretty spectacular.
Yeah. Well, I'm also excited for that. I think we do have one coming here to our offices as well. So, I'm excited to see what that's like. I've never done any robotics, really other than maybe those, like what what are those?
Lego robotics sort of things. But but, yeah, excited to excited to see where things are going. Thanks for sharing some of your insights with us, Ramin. It's been, it's been a real pleasure, and hope to have you, on the show, third time to let us know how the robotics went.
Ramin: Yeah. I appreciate that. Thanks for having me again, and, it was great.
Jerod: Alright. That's our show for this week. If you haven't checked out our website, head to practicalai.fm, and be sure to connect with us on LinkedIn, X, or Blue Sky. You'll see us posting insights related to the latest AI developments, and we would love for you to join the conversation. Thanks to our partner, Prediction Guard, for providing operational support for the show.
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