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0:01Who are the characters behind science? This is Science Behind the Glass, illuminating the human side. In this podcast, we unveil the human side of laboratories and science. Scientists share personal experiences and we discuss their meanings from different perspectives and fields of research. Welcome to Science Behind the Glass, illuminating the human side. Today we are diving into the world of artificial intelligence, a realm that’s both revered and feared. In the scientific arena, AI has become an indispensable tool, revolutionizing fields like medicine, conservation, and modeling.

0:45Our special guest today is Ryan Takhani, a skilled software engineer who harnesses the power of AI for image processing tasks. Ryan holds a bachelor in electrical and computer system engineering from Monash, Malaysia. and a Master of Engineering of Internet of Things and Data Engineering from James Cook University, Cairns, Australia. His expertise shines through AI projects where he delved into the face detection and rural road extraction from satellite images. But that’s not all. Ryan also is the co -director of Quizland AI. former director and also have an startup and we are going to delve more in his experience and his prospects. Ryan, welcome to Science Behind the Glass. Thanks for joining us today and to start this episode. could you please explain a little bit about your background a little bit about you oh thank you so much yeah uh you explained it quite well uh from my career point of point of view it was uh electrical and computer systems engineering at monash university and like funnily enough that’s where i first got my taste of ai i made a project for them back in 2017 for my t -scenes which was uh identifying faces even when half of the face was covered or part of the face was covered So this was back then when AI couldn’t detect them. So that was my project.

2:18So that is where I got my first ever taste of AI. Yeah, and this was in Malaysia. So I grew up in Saudi Arabia. So as soon as I finished my schooling in Saudi Arabia, I moved to Malaysia for my university. I wanted to do IT, but it was back then electrical and computer systems. Engineering was offering more opportunities. So I did that. At that moment, I went back to India. I did my internship in electrical engineering.

2:47And then I started looking into other opportunities. And I was looking all over the place. Canada, Malaysia again. And I landed in Australia. And I think by far the best choice out of the options I had at the time. But yeah, I got to do my Internet of Things and Data Engineering Master’s or that. That is where I got really into AI, because I had my master’s thesis in this field, where I had to create an AI that could look at satellite images on rural and remote roads, identify which ones are not basically on the Google map, and just draw it out. So that was a lot of the math they are missing out on rural and remote roads, right? So to bridge this gap, they wanted an AI that could do that. Right now, it was Jayden, who was manually drawing out roads for his entire thesis. And he did such a good job as well. Jayden, they even had some volunteers, but mostly Jayden and Yoko. And they managed to map out over 2 million kilometers of road manually by hand.

4:00And it was by manually looking through Google. Google Earth, Satellite Cube, and Matt, yeah. So they didn’t even know where to, yeah, so they identified where to look for it with their research. And for this research, right? Wow, that sounds painful. It is. You know how it is, like collecting data properly. So this is where the first ever, my first ever approach in like how AI can be used for science came into being. Yeah. Can you explain us a little bit, what is the Internet of Things? Internet of Things. Alright, back in 2020, 2019, there was a big hype around these devices. So, Internet of Things is basically smart connected devices. And they do have one smart component of it. For example, if you have the DAB connected to internet, that is just a massive product connected to internet. That doesn’t mean anything. But Internet of Things is basically…

5:02machine to machine communication which is if you had a fridge which could look at what is there in your fridge and update your grocery list now that is internet of things it is machines communicate uh communicating with different components or different devices and components within itself or outside itself looking at what data is given to them and updating their data or updating uh making decisions based on the data they have given so the fact is So that was Internet of Things. And this is, again, comes in really handy in sites, especially who are going out in the field, deploying sensors, monitoring different regions. They don’t just need data to be collected.

5:47They need decisions to be made by the device. Technology of internet device is monitoring, for example, controlling the glucose or the levels of sugar in blood and telling the person… communicating with the phone of the person and telling like, oh, it’s too high or too low. That would be an example? That would be an example of IoT device. Yeah, that’s a really good example. Oh, that’s great. And can you explain us in simple words, like for general public, what is AI? Oh, AI is artificial intelligence. People can look at things, they can make decisions, they can understand like when my hands show up, right? For me. It is like, oh, this is my hand, this is me. But for computers, it is just colors of pixels. It doesn’t mean much to them. So up until a couple of years ago, the majority of the computers and the majority of the computer software was where people were manually hard -coding the programs, manually writing out all the instructions which the computer has to perform in certain different positions. They had to manually identify what they needed to do, and they had to encode all of the scenarios. Now, a couple of years back, artificial intelligence came in. Now, this was where computers could actually make decisions based on the data. They could interpret data the way people could, to a certain extent. Now, they did that by, instead of manually coding

7:20everything right, all they did was, okay, computer, here is the data. Here is what. what I’m studying. Here are the parameters I’m looking at. And these are the responses which we need for certain values of these parameters. So you give the computer a list of those parameters or values. And once you give back to the computer, you have already programmed the computer to look at the data and learn a certain way. So instead of manually encoding the responses, or manually encoding the response data you are actually programming the computer to learn from the data and adjust its decision making so once the computer learns right it can then look at the data and it can refer back to its training or its learnings instead of referring back to what the person has coded AI is really important for scientific work because the AI can help analyze data at a larger scale A lot of scientists write when, if it is an individual person or a team of people that are looking at data, they can only look at certain small scope of data before they make the generalizations, before they make their statements, or before they do their research. Regardless of how big the team is, you are only capable of looking at limited amount of data. Now what happens is

8:51all these researchers, all these people who hire as data analysts are They certainly have to, you need to pay them. You need to fund the research. You need to get the resources for them. So it costs a lot of money. Now, AI can do a lot of these, well -trained AI can do a lot of these processes of what people take days or months to do. It can do it in minutes or hours. It can process a large amount of data based on how many resources you give it. And it can process the data and come to a conclusion or make. uh to an analysis of data within a matter of minutes instead of days or weeks and this is and it can do the it can do the analysis and it can work as if it is working in a team of people or a group of people so it has so much power when it is plus a simple data ai is really good tool for researchers to use because whatever data they collect they can progress faster within their research something which a person a master’s student does his entire thesis on now could be a scale of a one subject or one means of work for a student or a researcher so you you see like how fast the progress in science will be if they’re allowed to process a higher scale data at a larger scale or large capacity so it’s it’s optimizing the all the process to science,

10:24for example. Yes, and not just optimize, but also processing it at the largest speed at which processes are made and done, and conclusions. Kill and conclusion. Because it can copy the… This is where I know it is controversial to say. To a certain degree, it can copy the decision -making skills of a skilled individual based on the data you provided to a certain extent.

10:51So this is where having that type of skill within your research is going to be really important for scientists than just using computers. That sounds exciting. And what ignited your belief in the pivotal role of AI in science? What drew you into this fascinating convergence of technology and research or other potential field works?

11:18Oh, okay. So for me, my background was in engineering, but I’ve always been a fan of science and always been a fan of research. I always enjoyed looking at what other people are doing, other research people are doing in biology, marine biology. And I wanted to be more involved in these types of research. So that’s what got me into working with AI for researchers. Now, the other thing with AI, what… The researchers I worked with, they either didn’t have data or they had really limited data. So how do we work with that? So that was the other fascinating thing, the other challenge AI is facing now. It’s basically, how do you work when you have a limited amount of data? And how can you train AI models? How can you make inference with this? So that really fascinated me as a researcher, and that got me involved in the scientific process. because they are always at the edge of understanding of what they believe about certain things, what they believe about certain medicine, certain concepts, and how they can change their belief. And being there with my tools present for the researchers to use, that was a different kind of challenge which I really found. Yeah, it was like I was assisting people make important discoveries in their field. So that’s what brought me. Yes, and especially when you, for example, as you said, having enough data to get some conclusions

12:51from the data. Sometimes we don’t have enough number of animals or sample size. And using AI helps to draw more insightful conclusions from that without going and getting more animals or if there are not more. We can process the data in a different way. That’s amazing. And now, scientists might not be engineers, but AI provides tools that can optimize scientific endeavors across different fields. How can scientists leverage these tools without in -depth and specialized knowledge of an engineer? Oh, okay. I think 2023 has been a really good year for AI -related tools.

13:35What has happened is that strategy, BTE, Bing, brings so many other systems, especially large language markets, which are generated with AI search engine, which uses GPT -4. I think scientists and researchers, there’s already tools which are easy to use, which are easy to understand, which are quick to adapt, which people can quickly adapt. And the user interface is truly easy for these students. And scientists can start looking at using these tools to further understand what other people are doing with their research. So one of the most important thing which comes when it comes to any of the research, it is using AI for doing literature review. So this used to be something which took six months or six months, 11 months. Now this is something that can be done within seconds using GPD or using Yeah, using Bing’s GPT -4 because what it does is it goes through all of the search results and Google’s bar, you feed any document into it, it goes through it and it understands what all you can do with it. It understands what’s happening, it summarizes it for you, and it looks at what other people are doing in similar fields.

14:51Now, because I have been enrolled in other search fields, I need to be doing search equals in different fields. which i’m not an expert in but i still need to come to meetings without meetings uh with the knowledge of what each of the researchers are doing so linda if i come and have a meeting with you i’d really understand what your meeting is about because you are doing something related to drug discovery but oh and then the next day i have to meet somebody in the field of dentistry so they have different challenges they have different should really take challenges. Now these are two completely different things. How do I prepare for it? I use AI to help me understand the research papers in this field, problem statements in this field, and because AI is trained on so many specialized knowledge, we need specialized tools and so many specialized knowledge, it is also really good at dumbing it down for me to understand. So whenever I complete video meetings, right? I go through the research papers, but I also have a co -pilot, big co -pilot, right next to me, which I use to explain certain concepts within those research papers or certain concepts within them. So I think if you, for example, give kids an AI to work on, honestly, do it on supervision, but if you give kids an AI to work, it will be the same as scientists because they are going to ask. questions which not all adults can answer.

16:23Why is the sky blue? Why is the ocean blue? So for that case, if you look at it like a literature review where you’re looking at research what other people have already been doing it, I think that would be a more patient and more easier way for a kid to understand, same as adults, to understand difficult thoughts. So what are the risks that we have to be mindful? of integrating AI into the scientific world. To be honest, it’s unique. AI is a really good tool. AI is really simple to use and you don’t need any skills.

16:59Even the user interface that I’m working on, you don’t really need much in the way of understanding how AI works. You can actually understand how AI works, right, basically. Now, the thing is that you shouldn’t… worry about ai as a tool but you need to worry about who is deploying that tool because ai is only trained on producing a certain result right but you also need to give it feed it something for it to produce a search results it could be something in your private life it could be some secret discovery or reaching it could be something uh yeah so it could be something which is for example, proprietary code, proprietary gene coding, or like something which are trade secrets, or you might not want to enter it into an AI platform because AI is consistently interlinked. AI is not a set -and -go sort of thing. You need to constantly improve your AI methods. So when you do that, right, when you actually do this, AI is going to be, you need to be more careful about where you’re uploading your scientific data. That would be the number one risk because it could be used for training the next iteration of AI and that could result in somebody else being rediscovered by just entering a couple of forms rather than, yeah, so that could happen. That is one of the risks. So it’s like a misleading information or depending on who is feeding the AI or? Yes, yes. What other

18:31people can do, whether your data is being used for training data, that is one of the risks you need to look into. The other risk, you need to look into is the ai is there’s a lot of privacy concerns with ai so again this is only going to come with where you are uploading your data this is one of the problems i had when i was building my general purpose ai platform i’ll just be posting it in the background soon uh so the only way i could mitigate this problem for researchers and scientists was okay you can train your own ai models with this but once you delete the data even i won’t have a copy of the data so uh so that is yes exactly i don’t own any of that data i don’t own any of this your post processing network it is yours once it is done it is done it is not going outside of my privacy uh outside outside the so so that is one way of one risk with the ai uh number two the risk is over reliance on ai now there’s a new uh the new tools generated by is are really good at researchers doing like i said literature right but this is one skill which researchers and students need to have knowing how to do literature, knowing how to look at research papers, understanding the data. You can use AI to help you understand the data better or help improve your skills of using AI to understand your research papers.

19:59But there shouldn’t be an over -reliance on AI where you just forget certain skills, forget how to do certain things. And that is going to be another risk for researchers. The over -reliance on AI, that you forget certain skills. See, I told you it was a controversial topic. I know. Handle it properly. Use it properly. It can be amazing. But misusing it and believing all that shit. Yeah. And the third most important thing is biases. Now, the risk of AI, right? Yeah, it is a good tool and it overaligns its power.

20:36But also, it is a tool which is trained on data which has already been fed to it. Now, that data can be… bias now if the data which it has been trained on is biased right all of your research all of your analytics are going to be inclined towards a certain will lean towards a certain direction than the other direction now it is all going to be based on the storage data and if historically people have been done things which are wrong or which are bad it is going to be 10 times more prevalent in the research or it what i’m referring to is more like ai not being able to recognize people of different skin color. For example, if I was brown or if I was black, the initial AI mode couldn’t actually recognize it because in the training data, they only used white people’s photos to train the AI mode. So that is a massive bias. So when the AI ethics people looked into this, they discovered that AI was really not good at finding people of black or brown skin color. That was number one. Again, twice based on data. The other one was Amazon at one point used AI to hire people based on their skills. But the AI only ended up hiring men because it was, again, trained on historic data. So the men’s resumes were under no priority historically over women’s resumes, right? So because of that bias, storage bias,

22:10it also went into AI. And then it started, yeah, it started, like, eliminating all the women from the hiring pool. So they mitigate this issue by removing the addresses, removing names, removing, like, certain things which were prominent, any markers which identified as women, and they used to interact with me. That was one way. It’s like a running experiment. You have to try to avoid bias. and randomize everything that we don’t have exactly in the results exactly yeah and then we are also curious to learn more about your startup and your ideas could you give us a glimpse into your business and how your intersect how this intersects with the fascinating world of artificial intelligence so my goal with startup was to create specialized rooms for researchers to you with their own data and with the trade models which are specialized to be used with their own data. So that’s where I got an idea for the startup and I looked into GCU for example.

23:21There were a lot of researchers in here. There are a lot of people who are collecting data at large scale. There are a lot of people who are going into hiking for example. they’re going underwater, they’re going into tropical forests and they’re collecting data. Now I, with my background in AI, I wanted to find ways in which I could help them develop this, help them further understand more about my data, help them understand more about the tools they have on the internet, and help make AI for them to process their own data.

23:56So that’s what my startup was all about to most researchers. it was again my passion my hobby working with researchers working with businessmen identifying what they can do with their data and giving them the tools necessary to work with that data oh that’s amazing because giving a little bit of background to people that doesn’t know that one of the biggest steps in science is analyzing data yes it’s difficult it’s not easy not everybody has the same skills so for example there are no statisticians and it doesn’t mean that they just work with a statistician and they understand everything So that’s a big, big process that they have to go through understanding what type of analysis they are going to carry out, what is meaningful for the collection of the data, what analysis can or can’t not be done.

24:44So that’s a big step. And I think that is amazing what you are doing because you help not only to run the… basic analysis of what the researcher is thinking about also to suggesting other type of analysis that can do or potentially highlight a result that get from those data that is collected that’s some ways that’s so oh thank you and to add to that like my specialty was in images so a lot of these a lot of these researchers right they have a lot of images that have like thousands and thousands of frames which need to be analyzed and in this case they have to be analyzed with an individual person to manually look through it and then label it as whether it is this or whether it is that so they have to constantly manually do that to what i was doing was like okay i’ll make a tool that can process thousands and thousands of images within a matter of minutes rather than yeah rather than having students manually go through it having research yeah and it takes time it just time consuming having a phd student manually go through all that data it will take me and considering that they have a phd scholarship for three years for example with ai you still need to do some quality analysis quality control it’s not like self -driving cars just here you still need to have somebody behind the wheel that’s what what is important to highlight that ai is more like that the tool is like they

26:16are going to replace humans actually it’s a tool it’s a it is a tool a lot of people there’s a lot of scare going on right now where people are talking about ai is going to replace their jobs ai is going to straight up cancel industries and to be honest ai might AI will do that. Let’s be really honest here. AI is going to make changes and it’s going to be jobs which are going to be replaced by computers. But this is also going to give us something which we haven’t been looking towards for a long time. Instead of doing tasks which are repetitive or instead of doing things which are, which is basically the same thing you are doing over and over again, you can actually automate computers right now. now as an as a person instead of sitting in the office all day instead of sitting in the lab all day you can because you have an ai processing all of this for you you can spend more time with your family and spend more time with your people oh with your folks your friends you can have more time to yourself and you can have more time to think and analyze the results rather than processing the data wondering what you’re going to get out of that data now is this going to replace job yes it is going to it is going to take new jobs but but we are only scared because we cannot see in the future how the new jobs are going to evolve now for example you have if you have

27:47the data analysts are going to be replaced by consultants for example now they they will be able to consult in jobs as well because they can analyze the data and give feedback from the person consultants job definitely replaced because AI can do the job better So you don’t really have to hire consultants, expensive consultants to work with certain, for example, people like them. You don’t need to hire consultants to learn about it, to analyze it, to make decisions. You just give AI the data and it can do the consulting for you. Now the consultants, on the other hand, their jobs will evolve into… being business managers, being business assistants, they can take more of the management roles of the people so they can actually take on more responsibility because if their jobs are replaced, they can move into a higher team. They will have to know how to control those tools. Otherwise, adapt or… Yeah, for sure. That sounds a little scary, but to be honest, this is what happens to Steve and Jill. This is what happens. I think… it more yeah it’s a tool that we are developing now we we have to be able to manage and to use it properly and yeah i don’t know and i leverage and always skills yeah yeah and i see like that yeah yeah no but to be honest like a lot of jobs

29:21which ai is going to replace you need to ask yourself are these really the jobs we want to do in the first place it’s repetitive it is like something people are being exploited for yeah that’s true and like it takes a lot of time for example report that if i ai could help for example doctors and with the reports doing the report better spending hours doing administrative work instead of investing that time doing research or checking and the patients so i think and managing your startup alongside your work in ai sounds challenging and do you do you manage your own time like uh how do you balance your personal life and oh because like for me at the moment over here it has only been for uh because this has been my life for a couple of months now and i genuinely enjoy it i genuinely enjoyed working on that i used to go for heights uh once in a while i used to go for yeah i got earlier Yeah, I used to go outside to meet friends more often than I do now. But being an entrepreneur and working as a researcher is a very challenging thing. You need to be really passionate about what you do. You need to be really into what you do because you are going to be working with a lot of difficult data, really a lot of difficult data, a lot of difficult systems. And it will not just be in difficult systems for AI architecture.

30:53but also understanding what people are doing in areas of that kind of difficulty. So you won’t really have much time to do other things. If you are, it’s not something you can just turn off. You have to be on it. What is your opinion about the concerns of these technologies and the reliance of them to solve social and environmental issues? Social and environmental issues. Okay, now this is a really good question. yeah because let’s start with environmental issues now because i know about this using ai is going to be offset it leaves a lot of carbon waste because it it will cost you the whole year because here’s the thing when you’re training ai right you need to use a lot of gpus and using gpus overheats a lot it takes up a lot of energy so a lot of data centers or you will have in between training uh treating air mountains and using self -driving cars uh using electric cars and everything i think the consumption of electricity is going to go up drastically in the next couple of years because intensive computers use ai is really expensive to run so uh i mean it is cheap compared to hiring but it is expensive in a sense that it is that that’s a lot of waste that decimal use that’s the motor and it takes up a lot of energy and using and energies are created using coal, energy is created using renewable resources.

32:23It is going to have multiple impact on the planet and with our drastic increase in the use of AI, we are also drastically increasing the use of electrons. So it is going to have major environmental impact. And also to make those computers, to make those computers, to make those data centers, it is going to require a lot of resources which are sourced, some not in any good way. No, mining. Yeah. mining, but yeah, a lot of unethical ways.

32:51So you need to, AI is going to have a lot of challenges over time as well because certainly there’s such a need for these resources and we need to find, we need to claw out the right way to meet these requirements. And this is not something you can use to solve social problems, but it is going to cause a lot of social problems, because no matter where you are, there will be controversies on how you train an AI to respond to a certain question, which will be controversial. which could be controversial in a certain week of capitalism.

33:21Now, because of the data, it’s streamed on the brain. So for social issues, this is going to have a hundred. It’s going to be more challenging because whatever the AI is echoing, people might echo. And vice versa, whatever people are echoing, AI will echo the loudest. Because of AI, because of biased streaming data, because it is going to be down to get to a certain level of analytics.

33:47what is in your professional personal life oh yes i will be moving to brisbane main goal before i start my job in brisbane is to host my ai platform and just leave it running in the background so i’ll be giving you a link for debugging and testing and debugging stage andrew will be debugging it andrew will be handling everything so yeah so so you’re moving to brisbane for a company Yes, I got, because of all the consulting I did for my company over here, they require the skills investment as well. They need something to actually understand how the data acts in the world, how they can actually, how they can really develop AI related work for people in different forms.

34:33So this is what I’ve been doing for a year. I used to meet you, I used to talk to researchers and everyone, right? And I used to… to help them understand how AI solution can be created, created, made using official products or creating a product for them. This was something I already did. So to help with this, this game is something which they need for the startup. Again, I’m working for. So they need this. It’s called Co -Pilot HQ. They are solutions architectures. What they do is they look at how people are currently working and they find ways to implement AI within them.

35:09That sounds cool. Congratulations. Thank you. Thank you. Like, I’m really excited. I’m really excited about the handovers when my project will go live. And it will be handled, everything will be handled by Andrew. So, yeah, I’ll just be. Andrew is your partner in the company, right? Andrew, going to be. It’s going to be. Going to be. Because, yeah, I need, because this is not something I can manage on my own, right?

35:38Yeah, no. Yes. That’s great. And lastly, tell us an anecdote that you have had during your studies or your career paths. And it can be funny or it can be a challenging one or both. I would say my most favorite moment. It is interesting or it is very defining for me as my career as an AI developer as well. It was back when I was doing my research with Jaden, right? So one of the things that happened was, My AI was not working.

36:11It was worrying for the submission of my thesis. My AI was just not performing. I couldn’t get it to reach the levels or reach the accuracy levels which I needed for it to actually, by individual strength, I could not get it to work. And I was panicking and I was panicking. And there’s this really smart lecture I had. I needed somebody who didn’t know anything about my research so I couldn’t go to my supervisor at the time as well like well I needed somebody with a fresh perspective so I randomly go to this genius I would say he’s having poor guys trying to enjoy his you know engineering part of you you have like one every semester where all the engineers come up the students are you so I just go to him and I’m having my workout and I’m like a high random question i know you’re enjoying your burger but i’m having so so so problems my ai is not reaching the accuracy level what is going wrong with it and then he gives me any like just without hesitation the guy is so so good at the time he just points out like okay do this this your your models are not reaching like a certain threshold your training is insufficient this and it gives me a list of things that are wrong and one week for submission that basically two days later like my training was like i stream it all night anyway it performed twice as well

37:44it just it just happened dr ronson phillip yeah but my supervises were also really helpful but this is not something which we could figure out at the time why it had happened but and i think uh this list is very important because those are some things which i still i still like playing with this and i have yeah and i have wanted to have not my understanding of ai standing on how to train ai models based on the conversation i had with them what an amazing interview i personally like ai as a tool and use it almost daily for a repetitive task However, it needs to be used wisely and not blindly accept everything it provides or suggests. I make it a point to double check citations and concepts whenever I use it. What are your thoughts about Dani? Yes, those are good precautions. This is a controversial hot topic right now, and as usual, it seems to be about taking sides, pro -AI and anti -AI. I think it’s interesting to address the legitimate concerns of people more on the anti -AI side, because there are important discussions that are still scarce. For example, the role of governmental regulation and the importance of building consensus around the limitations that this technology might need. Nevertheless, this is already happening.

39:06And I think there is no turning back. So it is urgent to have these discussions at every level of society. I agree. The ethical concerns need to be addressed. I think that as it evolves, it’s also necessary to guarantee people that it is safe to use. And as Ryan pointed out, depending on the task or field, who is feeding the system needs to be careful with information that provides to the AI, because this can lead to biases and discrimination. Yes, that is right. In the end, I see that this technology, as many others, will probably reflect the creators as they are, for good or for bad. I think we as humans are at a breaking point where we need to take a good self -evaluation and decide to be better, because this technology will probably co -evolve with us. Absolutely. It is important to recognize that just because AI is effective on one specific task doesn’t necessarily mean it can be seamlessly applied to other tasks or fields. Training the tool is crucial. And it’s vital to approach AI with a realistic mindset, steering clear the misconception that it can solve all problems like a fortune teller. Each application requires careful consideration and thoughtful implementation for optimal results. Right. Something similar has happened with different technologies. Maybe in this case, it seems a bit easier to use these chatbots than using, for example, the first computers.

40:26So it might be easier to misusing it or misinterpreting it. So I think that may be the obstacles that scientists can find on using this kind of technology. And with this, we reach the end of this episode. We thank you for listening and our interviews for their time. Tune in again in two weeks for more Science Behind the Glass, Illuminating the Human Side. This episode was inspired by curiosity, personal experiences, and the paper Artificial Intelligence in Science, an Emerging General Method of Invention. We will leave the references to these materials in the episode description.

41:01This podcast is self -funded and self -edited by its host. Find us on our YouTube channel, Spotify, and our official website, Science Behind the Glass. Follow us in our social media on X, Facebook, and Instagram. Original music by our colleague and friend, Bernardo Aguililla.