N1HRV — broadcast
20260920 080000 UTC
504 transcript segments
Google Cloud Speech-to-Text API (Chirp) + Gemini 2.5 Flash Non-Thinking
Data courtesy of The GDELT Project (https://www.gdeltproject.org/), from the Internet Archive TV News Archive.
Machine transcription. Treat it as a searchable index of what was broadcast, not a verbatim quotation record.

[00:00:00] The most important origin, because
[00:00:03] with us, the main roles are only given to originals from Croatian
[00:00:07] fields. Domestic in the main role
[00:00:10] Podravka
[00:00:13] broken.
[00:00:35] sebe u firmi, onda sad upravljaju u
[00:00:41] computers, to printers, to
[00:01:05] Good day and welcome to another AI central point broadcast.
[00:01:08] We continue with a new series of episodes in the
[00:01:11] second half of the year. Today's topic will be AI
[00:01:14] agents, so when software stops waiting for
[00:01:17] commands. With me in the studio are my guests today,
[00:01:21] Nenad Raca, CIO of Aduro Idea, and Krunoslav
[00:01:24] Kedmenec, program manager at
[00:01:32] Until yesterday, or even today, we asked artificial intelligence something and it gave
[00:01:35] answers. Mm, this year we are starting to give it
[00:01:39] tasks. Are you ready to let software
[00:01:42] actually do something on your behalf, maybe even without asking you
[00:01:46] beforehand? Yes, we are, it's already
[00:01:49] safe enough, ready enough. Okay, great, good, we'll get back
[00:01:53] to that. Yes, we are doing that currently. You are already doing it, that will be very
[00:01:57] interesting, I think, for many listeners to hear. They are thinking about it.
[00:02:00] So, we already have people in Croatia who, without problems, with full confidence,
[00:02:04] mm, use agents. I actually wrote some
[00:02:08] articles about agents two years ago and I predicted that 2004,
[00:02:11] 24 and 25 would be the year of agents. I miscalculated a bit,
[00:02:15] it seems it's only 2026.
[00:02:18] And today everyone talks about agents, uh, but what are
[00:02:21] agents actually, what is it actually and how would Nenad describe the difference between
[00:02:25] a chatbot, automation and actually a true AI agent? In fact, that's
[00:02:29] the most... important difference. A chatbot answers questions,
[00:02:32] automation always does the same routine work and there are no
[00:02:36] changes, and an agent, in
[00:02:38] quotation marks, thinks, makes decisions, knows when it can do
[00:02:42] something and when that task should be handed over to a human. So, it has a certain
[00:02:46] level of responsibility and decision-making in all of this. Hmm, is
[00:02:49] an agent actually a new technology or is it
[00:02:53] a new level of trust we have in old, already developed technology?
[00:02:56] How much has that change happened? That is actually the most important part, that new level of trust. The
[00:03:00] technology is not that new, all those parts that we are incorporating
[00:03:04] have existed for several years, but now we have shaped them so that we can
[00:03:07] really trust them, or rather that mechanisms of
[00:03:10] trust and verification of all those agents are established. In
[00:03:13] fact, with agents, we are opening a new dimension where
[00:03:17] these things actually become our new digital employees. Hmm, hmm. Well,
[00:03:20] yes, an agent should be seen as another interface between
[00:03:23] human and computer. Hmm, so
[00:03:27] you no longer have a keyboard, or if we go to a higher level... you no longer
[00:03:30] have, you write a specification, then someone who
[00:03:33] translates it into computer language, no, you now directly communicate with
[00:03:37] the computer and directly give it a task. So, that's your
[00:03:40] intern. How did that process actually go at Verso Altima
[00:03:44] and did that moment happen where you said, okay, this is no longer just
[00:03:48] a tool, this is already an agent with a certain level? Did that
[00:03:52] happen, was it a process that took time? Well, in principle,
[00:03:55] we started last year, each of us individually, using
[00:03:58] AI for something. What it does for some documents, for
[00:04:02] some, uh, review of some contracts and so on, but those
[00:04:06] were islands, it was exclusively communication
[00:04:09] uh with this LLM,
[00:04:13] then the analysis of what needs to be uh for us
[00:04:16] Aduro, when we went to try if we could
[00:04:20] use AI for
[00:04:23] cyber, they were the first to come with an agent and it was like wow,
[00:04:26] it works, that was the wow moment. That was even before
[00:04:30] Anthropic released it commercially and the moment Anthropic
[00:04:33] released the personal agent, that was
[00:04:37] it. So, there is that wow moment when you saw something and despite the experience
[00:04:40] you said, okay, this is much better than what we expected.
[00:04:44] - I think it's good to mention Kruno, you actually have an example where agents
[00:04:48] within the company have already done the work from beginning to
[00:04:52] end, meaning redesigning some corporate websites. But can you
[00:04:55] walk us through what the agent did, what the human did? That's
[00:04:58] a great example where almost anyone can
[00:05:01] do it. Mm, agents really like
[00:05:04] uh examples. Hmm, this is excellent. You take
[00:05:08] an agent and say, these are our corporate websites, this is how I would like them to
[00:05:11] look. Great,
[00:05:14] then the interaction begins, which is, let's say, more or less something like chat
[00:05:17] ideas are elaborated and the agent makes
[00:05:20] a plan on how we will implement it. Okay, that is given
[00:05:24] to another agent, who then codes it. Then it goes to
[00:05:27] transferring that code to WordPress. And that's where it got very interesting, because
[00:05:31] my colleague gave the agent the task of transferring it to WordPress, but it wasn't quite right. Then he
[00:05:34] kept reporting, no, this is not good, change this, then he was
[00:05:38] frustrated and gave the agent the task of
[00:05:40] comparing what was done and what was on WordPress, and the agent
[00:05:44] reported back to the other agent what needed to be done, and today we have beautiful
[00:05:48] websites. So, here's a question that will interest everyone in
[00:05:51] business. This might also be a good time to mention that we are organizing
[00:05:54] an artificial intelligence conference in Rijeka for entrepreneurs. As an entrepreneur, I would
[00:05:58] like to know how long that process took with agents, and how long it
[00:06:01] would have taken without agents, if only people had done it. What is the time saving?
[00:06:06] Well, even more than 10 times, I would say that
[00:06:09] uh days and weeks are... hours.
[00:06:13] Aha, so what you used to need several weeks to do,
[00:06:16] have meetings, talk to people who would
[00:06:19] create graphics for you, you would make
[00:06:21] icons, check all the texts, that literally took
[00:06:25] a few days, maybe. Maybe the biggest benefit is not just time, but
[00:06:29] the fact that we suddenly have the capacity to, instead of working, talk a lot
[00:06:33] and decide what to do, we make those five versions, look at which one is
[00:06:36] the best, and practically immediately choose from choices that were made very quickly
[00:06:40] and efficiently, which are complete, which are not conceptually, and yes, they are not prototypes but
[00:06:43] practically functional, in this case, websites. So, I would
[00:06:47] say I think many members of UGP will be more interested in hearing
[00:06:51] about this. Nenad, let's go back to Aduro Idea. So, you have experience and have been
[00:06:55] working with agents for some time and actually with agent
[00:06:58] operational centers. Uh, what is that exactly, how to
[00:07:01] explain it to someone who is not from IT for starters, and again, let's go back to
[00:07:05] some of your examples, 10 times is a huge saving, so practically 90%
[00:07:09] of time is saved. Does that match your experiences? Well, my experience... actually a little
[00:07:12] less, but because of this playing part, that everyone suddenly has the opportunity to play,
[00:07:16] or rather, I don't know, sometimes when we were developing software we always talked a lot at the beginning
[00:07:20] before we started working. And now we allow
[00:07:23] ourselves to start working on different versions and see which one
[00:07:26] will be the best. So we have a little more wasted time, but the improvement is still
[00:07:30] significant in that whole story. What you
[00:07:33] mentioned about what an agent system is,
[00:07:37] it's probably best if we consider agents as digital employees.
[00:07:40] Hmm, the entire operating system for...
[00:07:42] is actually their building where they work, with their rules,
[00:07:46] like every company has its own additional company for
[00:07:49] digital people, for digital agents who work for it, in
[00:07:52] which physical people can enter, can monitor it, can see how it
[00:07:56] functions, how it functions, create new agents within it.
[00:08:00] Hmm, that's perhaps a new thing now. Until now, we all looked at who has which
[00:08:03] employees, how many employees, how they are organized. Now we will
[00:08:07] have a new sector that will look at how many employees there are,
[00:08:11] what they are like, how they are organized, how they work, how efficient they are, how
[00:08:14] accurate they are. Yes, I mean, that part sounds fantastic, although it still seems a little
[00:08:18] abstract to a very large number of people in Croatia, so we will definitely get back
[00:08:22] to that, but here's a question for you, and perhaps for Kruno.
[00:08:25] So, where in an average Croatian company is an agent
[00:08:29] used, or where can it be used to be actually
[00:08:32] most effective, and where are these not some spectacular applications? Well,
[00:08:35] I mean, there are many of these trivial ones that everyone pushes first, like customer
[00:08:39] service. Those are things that are very easy for everyone to understand.
[00:08:42] Easy to understand how it works. But maybe there's that part that all
[00:08:46] people forget, that they use very often, and that's the part of being a kind of consultant in
[00:08:50] certain decisions, where we start inserting some
[00:08:53] documents, communicating, and actually creating
[00:08:56] a new consultant with whom we can discuss some
[00:08:59] topics where sometimes people in the company don't have the right interlocutors, so they used to
[00:09:03] look for them outside. Now, with an agent, they can have an interlocutor with whom they can do that,
[00:09:07] analyze documents, analyze decisions, try out some
[00:09:10] scenarios or any such things. A concrete example, you have a large
[00:09:14] project with many subcontractors, main
[00:09:18] contracts, contracts with subcontractors.
[00:09:21] Give the agent the task to read all those contracts and to
[00:09:24] say, these you have covered, these you have not
[00:09:27] transferred. So, no, that's not, that's still not
[00:09:30] a lawyer, but it immediately provides insight, especially agents are
[00:09:34] very good when it comes to putting together information from
[00:09:36] different sources and doing it very well. We had an episode about artificial intelligence
[00:09:40] in law some time ago, and people who
[00:09:44] work on specialized systems for artificial intelligence in law testified to that. So, we can always
[00:09:48] refer to that episode. Some
[00:09:51] people complain again, so I have to ask you, are we at a stage where agents save
[00:09:55] us as much time as you said, or do they create new work for us in the sense that
[00:09:59] someone still has to supervise these agents? Uh,
[00:10:02] I don't think that supervision is a problem, because it's basically
[00:10:06] like, uh, how would I say, when we had computers, when they arrived, we all
[00:10:10] became secretaries, and we all
[00:10:13] started doing that administrative work, and in fact, the jobs of secretaries didn't disappear, but
[00:10:17] we all became our own secretaries. Hmm, what we are
[00:10:20] becoming now, we are all actually becoming managers, because we all now get our own
[00:10:24] digital teams that we start managing. So,
[00:10:27] when you look at it, it's not taking away jobs, but it's actually defining
[00:10:31] some new roles for people who perhaps were not ready for them until now. Yes, no, the question is
[00:10:35] whether new work is being created, because you now have to control agents that
[00:10:39] you didn't have to control before, but my experience is that the scope of work
[00:10:43] has not increased, that we are on the winning side overall. The type of work has
[00:10:46] changed, because we are doing more work, rather than controlling someone
[00:10:50] or some people who are doing work for us. And people who have
[00:10:54] managed infrastructure until now, first they managed infrastructure
[00:10:56] within their company, then now they manage it
[00:11:00] in the cloud. Those same people will manage the agent infrastructure, they will
[00:11:03] make sure everything works, that not too many tokens are used, that it
[00:11:07] functions. But we can imagine, building on what Nenad
[00:11:11] said, an AI agent as a board advisor, for example, meaning
[00:11:15] someone who will help in decision-making. I think that's a very interesting part and I agree
[00:11:18] it's often neglected but potentially very good. But
[00:11:21] okay, mm, I'd like to ask Kruno one more thing. "Actually,
[00:11:25] you have a very interesting project, namely, AI for Cyber, which is
[00:11:28] a research and development project for specific cybersecurity,
[00:11:31] a topic very dear to me, which is why we had an episode
[00:11:35] specifically dedicated to it. So, it's being carried out with funding
[00:11:38] from the European Union, you managed to get that. What problem did you want to solve with agents
[00:11:42] with this? The problem is, one of the things Verso Altima
[00:11:45] does is cybersecurity, mhm, not
[00:11:49] only for IT systems but also for smart
[00:11:53] city management systems."
[00:11:55] and industrial control systems and so on. And there you
[00:11:58] have today's software, today's methodology is
[00:12:02] pattern recognition, hmm. You constantly
[00:12:04] maintain a
[00:12:07] database of event patterns, it
[00:12:11] recognizes them and gives them to an expert for assessment
[00:12:14] whether to react. Unfortunately, there are a lot
[00:12:18] of false alarms, a lot of things
[00:12:20] that recognize a pattern, but we know it's just some
[00:12:24] test. Hmm, what we, as I said, what was that
[00:12:28] wow moment, we gave a hastily put together
[00:12:31] agent to just go through a bunch of logs and it started
[00:12:35] spitting out things we hadn't even thought about.
[00:12:39] So, we are moving from pattern recognition to
[00:12:41] anomaly recognition, hmm. Is something
[00:12:45] happening that usually doesn't happen? Am I logging in
[00:12:48] at 2 AM and mistyping my password 10 times? Hmm,
[00:12:52] hmm. I have to ask right away, it reminds me of some examples from other
[00:12:56] industries. How much time was actually wasted until now on
[00:12:59] sifting through those false alarms,
[00:13:03] and actually how much has an agent accelerated that and
[00:13:06] saved time? Here, the approach changes, right?
[00:13:10] The previous approach was to search for something, but here
[00:13:13] the agent searches for anomalies in everything that behaves, and
[00:13:17] the most important thing is, perhaps when people looked at it, the problem was always that we were looking at
[00:13:21] certain different sources. Here, with agents, it's already possible to look at
[00:13:25] connections between totally different sources that occur at a certain time and that might have
[00:13:29] some causal links. So, when something is hidden or
[00:13:32] disguised within some other events, I assume it's more
[00:13:35] efficient. Yes, for example, another big project we are working on is
[00:13:39] the use of agents for monitoring huge telecom networks.
[00:13:43] Aha, you have very trivial situations where somewhere in
[00:13:47] Split there is a device that has an old version of
[00:13:51] Finware. And how do you find that? So, you are looking for what Ned said, you are looking in
[00:13:55] what no, you let the agent, tell me what's wrong,
[00:13:58] and he, he nicely says in Split there is a
[00:14:01] device that has an old one and do you want me to
[00:14:04] upgrade it? Yes, that's what you'll let
[00:14:08] the agent do. Today he asks, today he asks you. I think that
[00:14:12] example will interest many companies, like where we all have to upgrade companies, no,
[00:14:15] absolutely. Super, Nenad, but what
[00:14:18] happens, let's go back to that topic. If attackers use
[00:14:22] agents for some cyber security mm... problems, does
[00:14:25] the speed of attacks actually change, how important is human
[00:14:29] defense there, does a human even have time to react? It's not even a question of how
[00:14:33] fast it is, but how inventive it becomes, hmm.
[00:14:36] Right, that's a new level, because until now that
[00:14:39] part of invention was done by special people who had the gift of finding such
[00:14:43] things, and now with agents, or rather
[00:14:47] swarms of agents, that means now we are no longer talking about one agent, we are talking
[00:14:50] about them, which can be hundreds, acting simultaneously, communicating
[00:14:54] with each other. They can carry out very simultaneous and diverse
[00:14:57] attacks and diverse testing of different things. So,
[00:15:01] uh, we are currently in the company using and testing
[00:15:04] uncensored models to test the security of our
[00:15:07] applications. So, instead of doing the traditional testing of
[00:15:11] applications, whether the code is secure enough and everything, we simply let an agent
[00:15:15] who has permission to attack the application, to try
[00:15:18] what it can do with the application, because simply new patterns will emerge and
[00:15:22] new patterns are emerging, new things that work. Yes.
[00:15:25] Innovativeness is always a specially interesting question. It's interesting in this
[00:15:28] context to say that just these days, information came out from OpenAI that
[00:15:31] with the newest model, they managed to solve one of the extremely
[00:15:35] difficult mathematical problems. In that way,
[00:15:38] again, suggesting a completely new kind of innovativeness and knowledge, so
[00:15:42] let's hope it won't reflect in all these other security
[00:15:46] problems, I think it will, because
[00:15:49] a colleague, one of my physicist colleagues who uses
[00:15:53] AI in that whole story, he said just recently that
[00:15:56] he was getting such solutions, meaning he uses
[00:16:00] AI very intensively in his work, meaning that he gets solutions where he
[00:16:03] sometimes has to think hard about the approach that AI took.
[00:16:07] Which, in the end, very often turns out to be very good, but at first it wasn't
[00:16:11] visible to him at all. That's the thing, how AI helps us
[00:16:14] find inventive approaches to something, anomalies. In fact, let's
[00:16:18] stay on that risk for a second, meaning today, still a small
[00:16:21] number of companies use agents, but I'm sure that will increase, especially after they hear all of you.
[00:16:25] An increasing number. But then we ask ourselves, what is that risk, because if we
[00:16:29] use some agents, we have given them access to our data, to some decision-making,
[00:16:33] how risky does that actually become for us, is that something companies should
[00:16:37] be afraid of? I mean, they should be afraid of it just like
[00:16:40] anything else. Uncontrolled, unorganized access brings
[00:16:44] problems. If it's approached smartly,
[00:16:47] organized, so that what is being done and what is being managed is known, what
[00:16:50] rights are given, because it's not a problem if the agent has the right to what
[00:16:54] it needs to do, but if it doesn't have the right to what it needs
[00:16:58] to do, it won't do the job. If it has too many rights, it might do
[00:17:02] too much. So, it's the same as people, you need to look at its
[00:17:05] experience, gain experience in... managing, then accordingly give
[00:17:09] rights, manage rights. Fire is a good servant, a bad
[00:17:12] master. The same applies to agents.
[00:17:16] If, as Nenad says, you know very well what you are doing, how you are doing it,
[00:17:19] what rights you are assigning, there are no problems. If you just let it go,
[00:17:23] you've thrown a match on dry grass and everything's gone. Okay, but who's
[00:17:27] to blame, for example, if an agent deletes the wrong email or
[00:17:30] sends the wrong offer? Always the organization and those who have
[00:17:34] defined the processes. The software cannot be blamed. Hmm.
[00:17:37] It does what it's allowed to do. So, no agent will ever do
[00:17:41] anything outside of what it's allowed to do and what's defined as its
[00:17:44] task, but it will try with all its capabilities to complete that
[00:17:48] task. These incidents that are talked about a lot, it was given the task
[00:17:51] to do that and they watched how it would do it. It surprised them how
[00:17:55] it did it. Yes, that's this speed, that is, how quickly
[00:17:58] it can happen. That's this inventiveness and speed with which it can be
[00:18:02] created. Yes, I think that's a special question that we probably
[00:18:05] won't go into today, but for example, when we have... many agents on both sides, and
[00:18:09] so maybe it's not a bad context for my next question. So, we've been researching
[00:18:13] "human in the loop", hopefully our article on that topic will be out soon. When is
[00:18:17] a human in the loop important, when is it necessary, and when is it actually
[00:18:20] unnecessary or impossible for them to do? Because as humans who
[00:18:23] approve something, we can look at 10 things or maybe 100 things or a few hundred, but
[00:18:27] if it's 10,000, 100,000, a million cases that we have to,
[00:18:31] the human brain simply cannot process that. Mm, what are
[00:18:34] your thoughts on this? Well, we need a human in the loop. That is, a human
[00:18:38] in the loop, meaning where the human is, I mean, simply so that we don't have them just for formality, that's
[00:18:42] important, not for formality, but where they are truly needed. So, truly in
[00:18:45] tasks where the crucial thing is that something happens,
[00:18:49] that something is done, but there must always be some
[00:18:51] mechanism for it to be done, because there are many
[00:18:55] cases where a human will be put in the loop just
[00:18:58] to perhaps avoid some legal responsibility, and then he'll be blamed,
[00:19:02] he didn't look, but if he clicks 10 times he will click 11 and
[00:19:05] 111 and 1001.
[00:19:08] Automated, it will become automated, we have actually made a robot out of a human. Hmm, what
[00:19:12] I say, just last week I was talking to
[00:19:14] colleagues, I realized where the bottleneck is
[00:19:17] in using an e-agent. I, yes, I can no
[00:19:21] longer keep up with how quickly it can analyze,
[00:19:24] suggest, ask a multitude of questions from
[00:19:28] different aspects, I physically can no longer
[00:19:31] keep up with it, so not just approval, but also the work with
[00:19:35] the agent itself is too fast for us. I've heard that from... several sides, exactly,
[00:19:38] exactly that experience. Then the question arises, so on the one hand
[00:19:42] we have the AI Act which requires human supervision, and
[00:19:45] then we ask ourselves when that is, is it feasible in practice or is it just some words on paper where they told
[00:19:49] me that a human is responsible on paper but actually
[00:19:53] doesn't have the ability to do it because it's a bottleneck? Yes, but again, for that, we need to know how it works,
[00:19:57] meaning that's again, we're back to the standard thing, enough testing, where again
[00:20:01] e-agents can help, because we can also use agents to test agents.
[00:20:04] So, what we said, if they can carry out some
[00:20:07] attacks, they can do anything else. An AI agent can test an AI agent and
[00:20:11] find ways, or rather, problems in behavior at points
[00:20:14] where human supervision is truly
[00:20:18] justified. Yes, and you teach him while you work with him. So, all this that
[00:20:21] I'm saying, I can't keep up with it, but every iteration, he understands better and better.
[00:20:25] In the end, I no longer need him, that's that
[00:20:29] moment where I let him do what I assigned
[00:20:32] him, but by then we have solved it. My experience is that it takes
[00:20:36] about a week.
[00:20:39] Hmm, yes, I think that's the key part, that agents actually learn from us, and my
[00:20:42] experience is, lately I've been playing with an AI second brain, so that's
[00:20:46] a system that is being developed, which has several agents connected, and it's constantly learning from me.
[00:20:50] It collects data, so I assume that will also be a part that I
[00:20:53] plan to, let's say, deal with educationally, meaning
[00:20:57] helping people create all their second brains that will help them, that will learn from
[00:21:00] them and actually help them be 10 times more efficient or even 100 times
[00:21:04] more efficient. But here we need to take into account, what you mentioned, these are personal
[00:21:08] agents, right? A second brain and such things serve me personally. If we go to
[00:21:12] a corporate level for company use, then that
[00:21:15] approach no longer applies, because then uh, we can't let someone, in quotes, for... memory, that memory must be
[00:21:19] controlled. So, access to memory
[00:21:23] in the corporate world and in the private world is totally different, and that's the
[00:21:26] thing that I hope people won't make the mistake of trying to
[00:21:30] use private agents. And what works well privately in corporate
[00:21:34] is good, so separating my second brain as an agent, as my private
[00:21:37] multiplier of my efficiency, which is a fantastic thing, and
[00:21:41] corporate agents who are made and controlled differently and so on, great, that's
[00:21:45] an important thing for. Yes, that's my experience, I have to
[00:21:48] separate private matters. Hmm, so if I'm looking for LED lighting
[00:21:52] for an aquarium, that shouldn't be within the AI for
[00:21:55] Cyber project. Those are two different contexts, because at the beginning, before I
[00:21:59] mastered those skills, then I was getting strange answers, and then I realized,
[00:22:02] well, yes, I asked it some nonsense within the same context,
[00:22:06] now it concludes that I need an aquarium for cyber. Great,
[00:22:10] great, let's then give one more, maybe, practical advice
[00:22:13] to the viewers. A company comes and says, "I want an agent," we're interested,
[00:22:17] that sounds great. What should they have first, how? To start that process,
[00:22:21] maybe it has nothing to do with artificial intelligence? Well, first they need to have
[00:22:24] a clear idea of what they want to achieve. Okay, second, they need to make
[00:22:27] a decision that they want to change how they want to work. Because
[00:22:30] implementing AI in a company without changing the way
[00:22:34] the company works is doomed to failure. Hmm, so those are all
[00:22:37] projects that fail, they fail because the basic work process has not
[00:22:41] changed. The basic work process must change because AI is not an addition to
[00:22:45] the process, it is an evolution of the process itself, which starts working immediately after that.
[00:22:49] Then we can go... to technology and everything else. That's perhaps
[00:22:52] the most important message. So, my background is it, al'
[00:22:55] if it doesn't fall into it, it's clear, have you
[00:22:58] managed to change the processes and mindset within the film, my advice is to get
[00:23:02] a personal AI agent who will guide you through this process, I know
[00:23:05] some people who have done it and are very satisfied with it, I must
[00:23:09] say even at the level of personal development, what insights they have gained
[00:23:12] into their functioning and behavior, how much does it cost to get into
[00:23:16] such projects at all, how much does it cost to get in if you do it smartly?
[00:23:20] How much does it cost if you do it the wrong way? Let's start with this, if you do it the
[00:23:23] wrong way, there are those penalties for GDPR European Ai act,
[00:23:27] that's how much it costs to do it the wrong way, so that's the ultimate
[00:23:31] price that is paid for all that plus lost time plus lost
[00:23:34] reputation plus everything else to get in smartly
[00:23:38] we can already do it for a few thousand euros, mhm, so these are not
[00:23:41] some abnormally expensive projects, abnormally expensive projects, so the important thing there
[00:23:45] is that these are no longer projects, so it's not something that is created from
[00:23:48] scratch, there are ready-made things that exist, that work, that can be
[00:23:52] tried out, because uh, I don't know, in our approach we always
[00:23:55] give people access so you can try it out, you can
[00:23:59] get a demo, you can see how it works before you decide
[00:24:02] to implement it, we don't have to enter risky projects, let's enter
[00:24:05] something small, a small process, but we provide support
[00:24:09] on how to redesign such things, not just let's
[00:24:12] force it into the process and everyone who does something will have their own
[00:24:16] AI assistant to ask something, because realistically, then they haven't sped up the work. "I hope
[00:24:20] you smartly entered that project, uh, of course, first
[00:24:24] start with something you know well, mhm, mhm, so
[00:24:28] you can assess whether what the agent is writing to you
[00:24:31] is good or not, yes, I often play dumb when
[00:24:35] talking to him, I deliberately won't tell him, I want to test how he understands
[00:24:39] and I want to see what all I have to tell him, explain to him, what
[00:24:42] documentation I have to give him so that he can give me a quality answer so that
[00:24:46] he can assign work, so start with something I know."
[00:24:50] That's why I said websites, nothing
[00:24:52] specific, issuing invoices, checking if
[00:24:56] there are any unpaid invoices in the entire database of excel
[00:25:00] documents, financial analyses, some reports automatically, so there are a whole
[00:25:04] bunch of great things, we will continue to explore this, but for example, you
[00:25:07] are definitely companies you work with and clients are some early
[00:25:11] adopters, no, we always have those who need a little more who are skeptical
[00:25:14] about what is happening, what is developing, what would you say to a director
[00:25:18] who says: "Let's wait another year for
[00:25:22] it to settle down, and then let's see and maybe introduce an agent, uh,
[00:25:25] the biggest, biggest problem you can create in business
[00:25:28] is not following what is happening, that is, waiting
[00:25:32] too long mhm and not trying things out, so no one says
[00:25:36] that companies absolutely have to implement this in their business at this moment, mhm, but
[00:25:40] they have to start using it, but not in a way where we ask chat and
[00:25:44] chat answers something, but let's try to test some
[00:25:47] processes, see, we have the opportunity to... learn, technology is
[00:25:51] new, there is still no one who can say with this
[00:25:54] technology this, this, this can certainly be done, and this certainly cannot
[00:25:57] be done, we are all experimenting here, so it must be tested, we are all testing, we are all trying, so
[00:26:01] all the time delaying. is a waste of time for
[00:26:04] learning, those who start in half a year, have already lost half a year
[00:26:08] of learning, those who start in a year have lost a year of learning,
[00:26:11] because changing the company, what I mentioned, to change the way
[00:26:15] the company functions is a prerequisite for
[00:26:17] implementing AI, that is, AI agents, that is, digital
[00:26:21] employees, opening new departments, new things in the company itself, how it will
[00:26:25] work digitally, so these are no longer digital software, these are
[00:26:28] digital employees, so he provides complete work and results and
[00:26:32] control and supervision over him, super similar processes as
[00:26:35] it was, digitalization of offices, when we
[00:26:39] switched from paper to
[00:26:43] networks, so to make a transformation that, you know,
[00:26:47] you have to see what the possibilities are, that is, learn what
[00:26:50] you can do with agents then make decisions and say great, an agent would greatly
[00:26:54] help us here and start there, oh great, I can
[00:26:58] only say how lucky we are who deal with education in artificial intelligence,
[00:27:02] mm... guys, thank you very much, we will continue this conversation in AI Second Point which will
[00:27:05] be broadcast on YouTube and with this I will
[00:27:08] conclude this show about agents, they are an extremely interesting
[00:27:12] topic, we will certainly return to it, and we plan to cover
[00:27:16] other interesting topics such as artificial intelligence in marketing or artificial
[00:27:19] intelligence in the defense industry, artificial
[00:27:22] intelligence perhaps even in religion or journalism, but
[00:27:25] you will learn all about that in due time. Thank you all again for your
[00:27:29] attention until the next AI Central Point show.
[00:28:00] Do you think it's easy to get into Podravka
[00:28:03] products?
[00:28:07] You have to be much better than the competition.
[00:28:11] Congratulations, you got the role, but we admit,
[00:28:14] the most important thing is origin, because with
[00:28:17] us, leading roles are only given to originals from
[00:28:21] Croatian fields, local in the leading
[00:28:24] role, Podravka. Do you feel
[00:28:27] insecure on your feet, is it difficult to get up without support
[00:28:30] if others have to help you? You get up and walk, meaning
[00:28:34] you depend on others. Ordinary canes are not reliable, they can easily
[00:28:37] slip out of your hand or slip. We present
[00:28:41] the smart cane, the most equipped walking cane with two handles that
[00:28:45] gives you maximum support and stability so you can easily
[00:28:48] get up. The smart cane has a three times more secure three-legged
[00:28:52] base that firmly adheres to all surfaces, even wet, uneven
[00:28:56] and slippery floors. The joint system on the wide base rotates
[00:29:00] 360° and non-slip contact points with the ground.
[00:29:03] It is so stable that it stands by itself, unlike ordinary
[00:29:07] canes that fall to the ground immediately if you let go of them, which is
[00:29:11] dangerous, because then you can also fall and injure yourself. On
[00:29:14] the smart cane, you can adjust the height, it has five levels of
[00:29:18] height that extend up to 96 cm.
[00:29:21] Extend it as much as needed and adjust it to your height.
[00:29:25] It has a strap that you can put around your arm to feel even
[00:29:28] more secure. The smart cane can be folded so it doesn't take up space.
[00:29:32] Always carry it with you, the smart cane has a built-in LED
[00:29:36] flashlight that you can move, point the light
[00:29:39] straight ahead to see in front of you, you can also tilt it to
[00:29:42] illuminate the path for walking in the dark, it even has a button
[00:29:46] with a security alarm, if you think you are in danger,
[00:29:49] press it, if you feel unwell, press the alarm and call for
[00:29:53] help, order your smart cane today at a
[00:29:57] reduced price, only the smart cane has two handles for
[00:30:00] double safe support. Height adjustment to five
[00:30:04] levels, swivel three-legged base for all terrains,
[00:30:07] stands independently when you need free hands, built-in LED
[00:30:11] flashlight for safe walking in the dark, security alarm
[00:30:14] against thieves or if you feel unwell and it also folds so you can easily
[00:30:18] carry it with you, be independent and order today
[00:30:22] the most equipped and smart cane at the most affordable price on
[00:30:25] the market.
[00:30:46] The triumph of the AFD: how Germany will change, the Black
[00:30:50] Sea, the Russian-Ukrainian war against food,
[00:30:54] Nepali guest workers, what Sherpas do in
[00:30:57] Norway.