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Hello everybody, welcome to the Fire Science Show.
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Today's episode is going to be a little easier than usual and instead of presenting you fire science facts or an interview with an eminent guest, I'm going to sit here and present some of my opinions, opinions about the use of AI, artificial intelligence and machine learning technologies in the world of fire science.
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And what do I think about their future use in our discipline.
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How prevalent will they be?
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Will they make big change in some aspects of fire safety engineering?
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That's what I've been thinking about a lot recently and I felt it's about time that I share those opinions with you and I hope that it's going to trigger some discussions.
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I wonder what are your opinions?
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So reason to do an opinion analysis episode.
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It's an inspiration from other podcasts that I listen.
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I've heard that being done in some other projects and I've kind of enjoyed those episodes.
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So I wondered if the Fire Science Show audience will also enjoy an episode of that kind in this podcast.
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I hope you will.
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And the topic, the theme of the episode the artificial intelligence is kind of inspired by the recent Nobel Prizes.
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As you may know, the Nobel Prize in physics was given for the development of neural networks, the fundamental thing of machine learning and AI technology itself.
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This award was given in physics to John Hopfield and Geoffrey Hinton, and those gentlemen have enabled us this massive, massive revolution that's changing the world as we see it today.
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Also, there was another Nobel Prize in chemistry given for the understanding the structure of proteins, and that was done through computational modeling and also through a massive, massive use of machine learning.
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That helped us understand the most complicated structures of proteins.
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That fueled an insane development in biology and in medicine.
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Think about it we are at the level where we are using machine learning, ai and computational methods to create new medicines, to create new pathways to solve medical problems in our organism.
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That's how confident we can be with those solutions in some aspects of the modern world and science.
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And the question is are we at the same level in fire science?
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Are we at the same level at civil engineering?
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Another reason for doing this episode I was recently invited by Warsaw Technical University that's the biggest technical university in Poland to give an inaugural lecture for civil engineering department and in that lecture I was sharing with the first year students my predictions about the use of AI and how AI will change their discipline as they finish their school in some years.
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So I've been predicting a lot recently.
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I'm not sure if I feel super comfortable with that, but I guess it's a fun thing to do.
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It's a fun exercise.
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It pushes people to think a little bit, it pushes people outside of the box and I think it's just an interesting exercise.
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In this episode we're going to narrow things down to some areas that I've came up with.
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That's going to be use of AI in supporting fire engineers in their modeling tasks in fire modeling.
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Use of AI in supporting fire experts in their everyday job.
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So how can AI support a fire expert?
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And that will transition into use of all kinds of large language models, such as chatbots in fire engineering.
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We're going to talk about use of AI in working with data that we have in fire science.
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We're going to talk about the use of AI to predict fire phenomena or explain fire phenomena.
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We're going to touch a bit of fire testing and then we'll perhaps move into some more crazy uses of AI, as incorporating AI in our codes and frameworks and how could the world look like when or where we would rely on it very heavily in fire engineering.
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A lot to go through.
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I'm going to give my opinion on a scale of one to five for each of them and provide you an explanation, one being something I find pretty useless and I don't see much potential in it.
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Five, something that I believe could be a groundbreaking, paradigm-shifting change in fire safety engineering.
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I wonder if you agree or disagree with me, but anyway, let's get going with that.
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Welcome to the Firesize Show.
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My name is Wojciech Wigrzyński and I will be your host.
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This podcast is brought to you in collaboration with OFR Consultants.
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Ofr is the UK's leading fire risk consultancy.
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Its globally established team has developed a reputation for preeminent fire engineering expertise, with colleagues working across the world to help protect people, property and environment.
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Established in the UK in 2016 as a startup business of two highly experienced fire engineering consultants, the business has grown phenomenally in just seven years, with offices across the country in seven locations, from Edinburgh to Bath, and now employing more than 100 professionals.
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Colleagues are on a mission to continually explore the challenges that fire creates for clients and society, applying the best research experience and diligence for effective, tailored fire safety solutions.
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In 2024, ofr will grow its team once more and is always keen to hear from industry professionals who would like to collaborate on fire safety futures this year.
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Get in touch at ofrconsultantscom.
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Okay, so about the time to clean my crystal ball and look into depths of that to see what the future of AI and fire safety engineering looks like.
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I'm kidding, it's just some of my opinions and predictions, though I must say the theme of AI and machine learning has appeared very early in the fire science shows in the first episodes.
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I think it was episode four where I had Matt Bonner and we already talked about use of AI in the field of fires and facades.
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Then I had episode with Sina Huang, also within the first 10 episodes, where we've talked about AI as a supportive tool for firefighters, and we've recently revisited that episode with Sina as well to see three years later what were the real implementations from their project.
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See three years later what were the real implementations from their project Very early, somewhere around episode 25,.
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I've had MZino, Sir, and we've talked about how to learn artificial intelligence and how to apply that, and we've also revisited that episode some episodes ago.
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Later on, I've been here with you when the chatbot revolution happened and we had an episode with Mike Kinsey about how to use chatbots in fire engineering and how to perhaps make a fire.
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Chatbot revolution happened and we had an episode with Mike Kinsey about how to use chatbots in fire engineering and how to perhaps make a fire chatbot.
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And not long later I had an episode with Mike Strongren where we were brainstorming some of the future technologies in fire safety engineering.
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So the theme of AI, machine learning and new technological developments has been in the Fire Science Show present for a long time, since the very beginning, and it's not going to change.
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And you know, having all those conversations with those people who are much better than me in the world of AI, who are the real experts, it gives me some confidence in being able to give my opinions on how the future may look like.
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So I've promised you some topics.
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Let's perhaps go outside of the list I've presented and just start with a very general question Can we predict an AI, a general AI model that could just do the entire fire safety engineering job?
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And I would say in the next five to 10 years?
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No, not really.
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We'll always be necessary.
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The fire safety engineers will always be necessary at the job.
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It's just too complicated, too much interaction with other stakeholders, too much risk in that, too much uncertainties involved with the use of AI as a general tool.
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So if you ask me a question, will AI replace fire safety engineers?
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I would say no.
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If you ask me a question, will AI replace fire safety engineers?
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I would say no, at least not in any foreseeable future, because in 20, 30 years, who knows how big the breakthroughs will be In machine learning?
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Perhaps we'll all be replaced by some sort of artificial intelligence.
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But I would say at the current level of the complexity of the fire engineers' work, it's highly unlikely that the entire field could be replaced by some sort of general AI.
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I think there are other fields that would be replaced first, such as podcasting, for example.
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I'm now being challenged by my friend, grunder Jumas, who's creating AI-based podcasts for his newsletter Burning Matters.
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Actually, you should subscribe to that one if you have not.
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It's a great read on Monday morning.
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But Grunde has recently been using technology called Notebook LM that pretty much takes the text that you supply it with and it creates a podcast episode.
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This is crazy.
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I'm gonna get out of job soon if this technology improves at this pace.
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So yeah, in terms of replacing fire safety engineers, I think we'll replace fire podcasters earlier and if that happens, you'll know.
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You will know.
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Now, going into specific topics that I've mentioned.
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So first, use of ai as something that supports fire safety engineer in their CFD calculations, and I would split that into three groups.
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One is use of AI in the preparations of CFD use of AI in helping with the calculations, and use of the AI in post-processing CFD results.
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In terms of preparing your CFD projects, I think in next five to ten years there's not going to be a big breakthrough in that.
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It's not going to be that helpful.
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I would rank it two out of five.
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I'm not sure if we'll see that much use in that, perhaps in specific applications like splitting your models into meshes or just helping you do some mundane repetitive tasks.
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Then, yes, you could use that.
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You can use chatbot to write FDS code but to build a complex model of a building for your analysis.
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That's just so much responsibility, so much work and you need so much interaction with other stakeholders in the process that a simple machine learning algorithm will not be able to do it at the level we would need.
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Perhaps later in the future, if we got really really good BIM integration, if we got really powerful processing technologies, standardized design, that would just streamline the process.
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Perhaps then we could use some AI-based technologies to prep our models quicker, but I think it's pretty far from now For supporting in calculations.
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I'm actually quite positive in that, so I would give it 4 out of 5.
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The technologies already exist and they're getting better and better.
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What I mean about speeding CFD is that you could make a hybrid in which you use your computational fluid dynamics to solve the region of interest in your model like the fire plume, like the jet fan locations, like the inlets, outlets of your model, so the places where most interesting physical phenomena take place that define the outcome of your simulation and then you use machine learning algorithms to solve the other parts of your model, the ones in which the interesting physics is not happening, and you can speed up the simulations tremendously.
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I've seen applications like that and it's just brilliant how much quicker you can solve your CFD while not losing the accuracy of your solution.
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But that's only for the parts of the model where the interesting physics is not happening.
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If you're asking me about just replacing CFD with AI as a black box tool, I know there are applications made like that.
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I would give it somewhere like one, two out of five.
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It's just too complicated.
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It's very unlikely that we could actually teach machine learning models on big enough data sets to give us a generalized solver for smoke and fire in buildings.
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It's just very unlikely, maybe for some repetitive geometries, but the more repetitive geometry is, the less reliant you are on the CFD prediction anyway.
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So I'm not sure that's going to be that useful.
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I would not trust such a tool because simply the complexities coming out from architecture or details of your building were going to be the drivers for the smoke flow and the outcomes of your simulation, and I don't think we could teach AI to a level where it would capture all those intricacies.
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Now the last part, post-processing, and I'm quite optimistic in this.
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So I would give it three out of five.
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I think it's going to be quite useful in our everyday job, but probably not a breakthrough.
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It's just going to help us streamline some processes.
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So I can see tools being created that will help you post-process your simulation results, that will help you go through those simulation results, turn them into a report that you can just prepare it quicker and supply it to your client in a more clear way.
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Perhaps you could also use chatbots to check the clarity of your reporting, to check the clarity of your communications.
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I think it's just going to be useful, but not something I would consider a massive breakthrough, just something that makes our lives a little bit easier as fire safety engineers.
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It's also something that I'm talking about recently in conferences about the post-processing of simulation results and how, with the growing power of computers and this AI support at CFD solving how we're getting access to faster and faster CFD and this support will be necessary to process the amounts of data that we will be creating in future CFD analysis.
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So overall, ai for CFD modeling and just modeling in FIERS I would give it overall three out of 5.
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I think it's going to be a good companion.
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Nothing that changes the game, but also quite positive that it's going to have a good, positive outcome on the work of Firesafe Engineer in this space.
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Next on my list AI for the design work, and here, similar as in preparation of CFD models, the incorporation of AI directly into engineering framework is just difficult.
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It's too much interaction with other people, it's too much soft knowledge that a generalized AI model could handle.
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If your work is very specific, you work on a specific type of projects, specific type of code you could probably get something out of it.
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You could perhaps teach a neural network to work in the specific projects that you're working with, but the more general you get, the less useful AI will be.
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I saw some applications like optimizing the design of sprinklers, for example, or perhaps calculating the evacuation distances and optimizing some footprints of the buildings for evacuation.
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There's a lot of happening in generative design, where you can build entire layouts with AI in split second.
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I think those technologies are very interesting and possibly will be used in the future, but I think in the next five to 10 years it's not going to be something that's going to be a big breakthrough in our discipline.
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I mean, it's an interesting gadget and perhaps could find some use in some specific aspects of the design, but I don't think it's going to have a big impact overall.
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Our job is just simply too complicated for the artificial intelligence at its level.
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Another thing is chatbots.
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So here I'm also not that excited for the future.
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The more I work with the chatbot, the less excited I am actually.
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I mean it's quite impressive in what it can do in terms of assisting me in programming and assisting in some tasks, but at the same time, many times the outcomes are just purely stupid and it's very difficult to actually tell this stupid answer from the smart answer when you don't have enough knowledge to distinguish between two of them Because they are as confident they look the same.
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You have to have good knowledge about what you're asking about to be able to tell if the answer is correct or not.
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So in a way, it's helping me more with automating the stuff that I would be doing, rather than replacing me at the stuff that I'm doing, and I view this technology only through this lens.
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So if I had to rank it on a scale of one to five, I would give it a solid two, maybe three in some specific spaces.
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I don't think it's going to be that much a breakthrough.
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If someone made their own chatbot like we discussed that in episode with Mike Kinsey a very specific code that has a very specific knowledge.
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It could be a good support tool, but still just a support knowledge.
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It could be a good support tool, but still just a support, not something that could replace or do job for a fire engineer, and I think this is a big trap.
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I think a lot of people will try to use chatbots to do engineering design for them because it looks good, it sounds real, and those people will get into a lot of trouble, lot of trouble.
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Don't do that.
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Don't use chatbot in your engineering design.
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Don't use large language models unless you build them yourselves and you're very, very familiar with the input that was used to teach them and you can evaluate the output of them.
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Only in that I would rely on such a technology.
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One more aspect, a side topic of that I think it's quite interesting to use AI in teaching and in onboarding professionals in your companies, and you know CPD quizzes.
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I think there are many supporting roles that we could use AI to just be sharper file safety engineers, you know, test our abilities to some extent, cross-check our work, perhaps use the chatbots to make it clearer and just communicate better.
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In that case, yes, this is a good technology, but still just a supporting tool at most, not something that will change our work tremendously.
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So, overall use of large language models and general AI in helping fire engineering in the fire engineers everyday work I would give it two out of five, and since this is a fire science show project, I'm also going to give a prediction for fellow fire scientists, and here I'm less excited than for fire engineering.
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I really think we should not be using AI in our research work in terms of analyzing the data, preparing the research outcomes.
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I still think you can use AI for some of your processing work to clean out the data, which we'll be talking in a few seconds but to rely on AI on on, for example, writing a scientific paper, this is just horrible.
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This is a misconduct.
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You should not do that.
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I see people do that and you can immediately tell.
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I mean, the moment we will be not able to tell a real researcher work from a chat, gpt generated work.
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That's that not going to be funny and I understand that technology is improving, but you can tell it's creating a lot of bullshit.
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It's just not able to process and give reasonable outcomes.
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It's basically it's learned on what's already been known and science is about finding stuff that we do not know.
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I think it's a horrible application to just rely on AI in creating research.
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I think it's a serious misconduct.
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Yes, you can use AI to check your paper for clarity, to correct the English grammar, to rephrase things.
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I think that's a very nice use of AI.
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You can use AI to help understand if there's more to the topic that you've covered.
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There are some AI-based tools that help you conduct literature review.
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That's actually quite interesting.
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We are using a tool called Research Rabbit and it is very interesting and very helpful in going through literature, but those are just very basic supportive tools.
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The science must be done by the scientist, not the AI itself.
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So the use of AI in research, I can give it the most two out of five.
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And if you think about using AI to write the research, I would give it one out of five.
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It's a horrible, horrible use.
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However, that's for, let's say, five-year horizon.
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If we think long-term, it actually could be that when the networks get better, we perhaps could use AI in science to change the way how science is done or science is communicated.
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We're in a space where there are way too many research papers published worldwide.
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It's hard to keep track with what's being published, with what's being done Definitely too much of that and perhaps AI could help us replace some parts of those papers.
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If you think about the paper, the method section is just a description of the method that has been used.
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You could replace that section with just telling the of the method that has been used.
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You could replace that section with just telling the novelty, what's new being used in your model, and then you could imagine having a link in the paper that would just get you into a chatbox sort of technology that would explain what has been done in the paper.
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I had this interesting discussion with some friends Mike Spearpoint, lucas Arnold and Arnaud Trouvet about this in the ESFS conference and it was quite interesting.
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Like, if you think about papers and what goes into a paper, sometimes we put too much stuff into there, and if information can be processed at blink of an eye, perhaps instead of the researchers spending their time writing some repetitive parts of the papers, they can just focus more on the science.
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Perhaps AI could be used to make those papers more concise, more clear, more focused on the problem they're solving and thus becoming better communication tools.
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If that's a new use of AI, I would support that, but I'm not sure if the current technology is at the level that I would support that, but I'm not sure if the current technology is at the level that would allow for that.
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So I'm mildly optimistic for a future.
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So far, maximum two out of five.
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Perhaps in future it will improve.
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Anyway, if you're using AI to write papers for you, that's like zero out of five.
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The next part is using AI in data simulation.
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So some people say that data is the new gold.
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I disagree with that.
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I mean, if you look at the gold, it has some technical functionality.
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It's a great conductor for electricity and heat.
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You can process it into extremely thin foils of the material and that has various uses extremely thin foils of the material and that has various uses.
00:23:12.962 --> 00:23:20.002
But you know, the value of gold comes from the market speculation, not from its inherent technical value, and I don't think data is something speculative.
00:23:20.002 --> 00:23:21.626
I think we have real value in data.
00:23:21.626 --> 00:23:31.267
For me, the data is the new coal that could fuel the new industrial revolution in the world, and it is already doing that, and with that I completely agree.
00:23:31.768 --> 00:23:47.950
Having data in the modern world to give you better predictions, better outputs in engineering as well, is fundamental, and following on that, we can use AI to help us work with the data.
00:23:47.950 --> 00:24:05.786
First of all, that's an interesting dynamic there, because if you want to do some really good machine learning or you want to use the most out of the data you have, the clearer the structure of the data you have, the better organized your data is, the more points you have, the better outcomes you will have.
00:24:05.786 --> 00:24:09.385
But then again, to collect the data from multiple stakeholders.
00:24:09.385 --> 00:24:37.853
For example, if you want to do analysis across countries from different statistical regimes that were analyzed like, think about the EU Firestat project that we've also discussed in the podcast If you want to gather data sets that big coming from this many spaces, it's actually quite challenging to work with that data and here you can use the AI to work through the data, organize, structure it and actually help you work with it better.
00:24:37.853 --> 00:24:56.011
I think that's a really good application of AI because you have quite good control over the process and you can validate the outcomes from the process with some well-established methods that we know for machine learning, and it's something that can be very powerful.
00:24:56.440 --> 00:24:59.351
I can refer you to an interesting paper some music.
00:24:59.351 --> 00:25:03.767
It was published in Fire Technology Journal by a Polish scientist, marcin Mironczuk.
00:25:03.767 --> 00:25:07.859
He did analyze the reporting from firefighters.
00:25:07.859 --> 00:25:22.711
So in Poland after every fire incident the firefighters would issue a report and Marcin did process those reports with language model many years ago, long before chatbots, and he was able to extract a lot of interesting data from those reports.
00:25:22.711 --> 00:25:36.093
So he was taking a random pieces of text that firefighters filled on site and turned those into databases where you could browse through how many fires happened, what size of the fire, what did ignite, and so on and so forth.
00:25:36.093 --> 00:25:47.605
So using AI to create really good and valuable database from a very chaotic dataset, that's a really really powerful use of AI.
00:25:47.605 --> 00:25:58.664
And if we can find users like that in fire safety engineering and fire science and I'm very sure we will, because data is something we always struggle with we always need more data.
00:25:58.664 --> 00:26:01.209
We always need data to feed our decisions.
00:26:01.209 --> 00:26:04.509
So I would give it three, maybe four, out of five.
00:26:04.509 --> 00:26:14.534
I think this is a really really good application and I hope more people start working on incorporating AI into working with data in fire engineering.
00:26:14.534 --> 00:26:16.969
I think there's a great future in this application.
00:26:17.880 --> 00:26:25.770
The next on my list perhaps this one is going to be a little controversial, but let's talk about fire phenomena and predicting fire phenomena.
00:26:25.770 --> 00:26:40.872
So, looking at the models that we currently have having myself play a little bit with some algorithms to predict phenomena that we see in our laboratory, to work with the data we have in our laboratory I would give it one out of five.
00:26:40.872 --> 00:26:59.584
On one hand, yes, those are powerful tools that, if you teach them correctly, they can quite well capture the fire phenomena or the outcomes of fire phenomena that are happening within this very specific range of variables that you've used to teach your machine learning algorithm.
00:26:59.584 --> 00:27:03.894
But these algorithms cannot predict outside of that.
00:27:03.894 --> 00:27:38.334
They are absolutely unable to predict anything beyond the space at which you have taught them and this is extremely challenging of proteins, with machine learning having access to, I think, 100,000 of three-dimensional structures of proteins and then being able to extrapolate that into 100 billion types of protein.
00:27:38.334 --> 00:27:40.827
I mean, they've certainly achieved that.
00:27:40.827 --> 00:27:51.568
They certainly did achieve the predictive capability of AI in a field that perhaps is 100 times more complicated than fire science.
00:27:51.568 --> 00:28:06.634
So I'm not saying it's physically impossible that you capture extremely complicated physical phenomena with AI, but I think it's out of the reach of the fire community at this point in time.
00:28:07.180 --> 00:28:09.484
The people who did that they work for Google.
00:28:09.484 --> 00:28:15.182
They have one of the best machine learning teams, if not the best machine learning team in the world.
00:28:15.182 --> 00:28:26.750
They have unlimited access to resources and people and they were working on a problem that's multi-billion dollar in impact.
00:28:26.750 --> 00:28:33.268
If you can predict the structure of protein in impact, like if you can predict the structure of protein, you can save billions in your resurgent development.
00:28:33.268 --> 00:28:56.980
So the scale at which they have been working is something completely different than the scale that most fire scientists are at and I think fire phenomena I think it was Hoyt Hotel who said that the fire and life are two most complicated problems to solve in the world and we certainly do not appreciate how complicated, how multivariable the fire phenomena are.
00:28:57.462 --> 00:29:08.752
We're talking about hundreds of chemical reactions happening at the same time, creating different energy and product outcomes in every millisecond of the combustion.
00:29:08.752 --> 00:29:21.126
We're talking about turbulent mixing at all of the scales, from the level of turbulent diffusion into the flame up to entrainment phenomena and flow around structures.
00:29:21.126 --> 00:29:34.084
We're talking about flame spread across complicated three-dimensional materials with their thermomechanical properties that change with temperature and time.
00:29:34.084 --> 00:29:41.585
We're talking about humans involved in the process and all the interaction between the human and the fire.
00:29:41.585 --> 00:29:46.094
It's really the world of fire is so, so, so complicated.
00:29:47.181 --> 00:30:03.311
We have some success in simplifying it enough, but to capture the real thing as you have in the laboratory, you can put the world's best fire scientists to an experiment in a fire lab and I can guarantee that some of them will still be surprised by the outcomes.
00:30:03.311 --> 00:30:04.605
We get that every day.
00:30:04.605 --> 00:30:06.646
That's kind of the beauty of our job.
00:30:06.646 --> 00:30:19.329
And now you take those limited outcomes that you have, limited data points that we have from fire experiments and you're trying to teach an algorithm, you try to give predictive capabilities to that algorithm.
00:30:19.329 --> 00:30:21.080
It's simply not going to happen.
00:30:21.080 --> 00:30:28.261
My trust to such applications is very minimal and in the literature we see a lot of people try.
00:30:28.804 --> 00:30:33.528
Okay, there are some spaces in which people actually try and quite succeed with that.
00:30:33.528 --> 00:30:36.117
For example, prediction of spalling.
00:30:36.117 --> 00:30:45.722
That's an interesting one where the ai could get very good results compared to our ability to model spalling, which we do not have today.
00:30:45.722 --> 00:30:52.388
Predicting flashoashover, predicting backdrafts research like that has been done and shown good success.
00:30:52.388 --> 00:30:59.125
Predicting the size of fire based on the images of the fires very interesting technology.
00:30:59.125 --> 00:31:01.648
We've just talked about that with Xin Huang in the podcast.
00:31:01.648 --> 00:31:13.525
So there are some applications in which it works, but it's very narrow and they are only able to feed you within the space of variables that they've been taught about.
00:31:14.248 --> 00:31:23.073
And the most exciting thing about science is where you discover something that does not fit to the model, where you are outside of the current scope of knowledge.
00:31:23.073 --> 00:31:25.224
These are the exciting parts of science.