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Minding Machines is a podcast dedicated to fostering thoughtful, open dialogue about artificial intelligence. The show brings together educators, students, and practitioners to explore how these tools are changing the way we teach, work, and think. Our conversations focus on practical experiences, emerging questions, and diverse perspectives across disciplines rather than establishing rigid "right or wrong" answers or settling debates about AI.
Minding Machines is an independent podcast produced at Lawrence University, Appleton, Wisconsin. The views expressed are those of the participants and do not represent the university.
Background music - Relaxing Light Background (short version) by AudioCoffee (https://freesound.org/s/725615/)
[00:00:00] (music)
[00:00:03] Abhishek: Hello and welcome to Minding Machines, a podcast dedicated to fostering thoughtful, open dialogue about artificial intelligence. The show brings together educators, students, and practitioners to explore how these tools are changing the way we teach, work, and think. Our conversations focus on practical experiences, emerging questions, and diverse perspectives across disciplines, rather than establishing rigid right or wrong answers or settling debates about AI. Thank you for listening. I am your host, Abhishek Chakraborty, and I am an associate professor of statistics at Lawrence University.
[00:00:42] Abhishek: Our guest today is Mark Phelan. Welcome, Mark. Thank you for taking the time. It's great to have you in the show. Before we begin, Mark, could you please tell us about yourself, your role at Lawrence, the focus of your work, and anything exciting going on in your life right now?
[00:00:59] Mark: Sure. Thank you, Abhishek. I'm so delighted that you invited me here and happy to talk with you today. So I am a professor of philosophy at Lawrence. I also direct the program in cognitive science, and I'm a co-director of the AI Task Force, the faculty AI Task Force at Lawrence.
[00:01:21] Mark: So right now I'm getting ready for classes, thinking a lot about my courses for the coming fall term at Lawrence, also trying to get the task force organized and working on some other projects related to AI that we can talk about today.
[00:01:37] Abhishek: All right, sounds cool. Thank you, Mark. Are you ready for our icebreaker question then?
[00:01:42] Mark: Sure.
[00:01:43] Abhishek: Mark, what's one thing you would happily automate away forever, and one thing that you would never automate, no matter how good technology gets?
[00:01:54] Mark: Okay. So I think one thing that I would happily automate away is searching through manuals to figure out how to operate the various devices in my life. And I may mean by this like a manual telling me how to reset the clock in my car, incredibly complicated in my Subaru Outback for some reason, or even, you know, the menus that we engage with. Well, especially the menus that we engage with in different computer programs. So, for example, figuring out how to format text in Microsoft Word. I remember the days of searching through menu after menu to find how to do this. And now you can simply ask Claude Cowork to help you figure that out. And I think that's a great advance that AI is making possible.
[00:02:47] Mark: Something that I would not automate away is I think, you know, I, like all people who spend a fair amount of time writing for a living, really appreciate how that exercise of thinking through the written word, thinking, getting my thoughts down on paper, I really appreciate how that helps me crystallize and form my thoughts better. And I hope we don't get to a stage where we learn that kind of interaction with the written word, because I do think that it is very productive in terms of what we're able to think. So that's something that I hope doesn't get automated away.
[00:03:29] Abhishek: All right, cool. And as we know that AI can write fairly well, I would say, and we can talk about what's the impact of that on our students and what we do for a living. I want to begin our conversation by asking you what's going on in the world of philosophy regarding AI and large language models? If you could elaborate on that.
[00:03:58] Mark: Sure. Well, it's probably helpful to say a bit, first of all, about my individual interests within philosophy. Philosophy is a very large field with lots of different research programs and questions within it. My own work is at the intersection of philosophy of mind and philosophy of language. And also I have a kind of empirical dimension to my work. So I've published in psychology journals. I've worked really at the intersection as well of philosophy and psychology. So those are my interests, basically. And I really think that AI, if you're working in the areas that I'm working in, artificial intelligence, large language models have really been probably the largest development within the field of philosophy of mind in my lifetime I think.
[00:04:54] Mark: I think that these systems that are able to speak fluently, that are able to solve problems, to understand quote unquote what we are asking with our questions, they really upset the apple cart in terms of what people. You know, a lot of theories that had been promulgated about how people understand how language works in general, they're really causing us to rethink a lot of that. I've heard them described, I've heard these LLMs described as thought experiments made real. And I think that's a reasonable take on LLMs. It really is bringing a lot of the ideas that we talked about in the past, when I was being trained in philosophy of mind, into the real world and causing us to rethink how things are going. I can tell you a bit more about specific ways this is developed if you want.
[00:05:46] Abhishek: Yeah, absolutely. Go ahead.
[00:05:47] Mark: Okay. So I think within philosophy of mind, specifically, and philosophy of language, there are a variety of questions that are really coming to the fore in relation to LLMs. So one set of questions bears on meaning and reference. So, for example, do LLM utterances, do the sentences that they produce mean anything? Or another way to think of it is, do LLMs refer to the things we refer to by using the words we use? And if so, how do they refer? There's a number of thought experiments going back a long time suggesting that a computer that doesn't have interaction with the real world could never really mean anything by its utterances. An example due to the linguist Emily Bender suggests that AI is like an octopus under the ocean that has a lot of exposure, maybe through some underwater telecommunications cable, to how people communicate with one another and constructs a theory of what word comes after the previous word. But would such an octopus really understand, for example, what a coconut is if it's never interacted with one? Or would it just in some way know how to use the word coconut?
[00:07:12] Mark: Is there anything to understanding what the word coconut means beyond knowing how to use the word? So that's one open question that people are very interested in. Closely related to this is, are questions related to mental states. So what mental states, if any, does it make sense to attribute to LLMs? So for example, can LLMs be said to intend to express the things that their utterances seem to mean? So I've written a paper on this topic. Essentially this paper argues that a lot of reasons for thinking LLMs can't have intentions fail. A lot of the arguments people have offered that they can't, I think are bad arguments. For example, some people have suggested that LLMs lack self knowledge, the kind of self knowledge humans have, and that this bars them from, from having intentions. But in fact I've argued that LLMs can be thought to have the same kind of, or could potentially have the same kind of self knowledge that we have. We may not be there, they may not be there yet, but I don't think there's anything that will necessarily bar them from having self knowledge.
[00:08:28] Mark: Some people have suggested that LLMs are too short lived. They don't have enduring intentions or they don't have enduring goals that allow them to have rich intentions. But I think there are counter examples to this too. There are famous cases of humans with memory deficit issues, severe memory deficit issues, who seem themselves not to persist beyond a few seconds. And yet they seem to be intentional agents, they seem to intend things and we treat them as such. So I don't think, so, I think a lot of the arguments have been offered to suggest that LLMs lack intentions are not actually great arguments when we get down to it. That's not to assert the positive idea that they do have intentions, but just to say that this is more of an open question.
[00:09:17] Mark: Other people are working on the question of whether LLMs can be conscious, whether they can have conscious states, whether they can have feelings. And I think this opens up a lot of ethical questions about if they could be conscious, how should we treat them, if they could have feelings? And then there are just a variety of other questions within ethics about, you know, if we're relying on these opaque systems to supply us with information, in what sense are we still justified in our assertions? In what sense can we still be said to have expertise? And then there are questions about what sorts of social relations we can have with these systems. And then, of course, lots of broader ethical questions about the impact of development of these systems on society and so on.
[00:10:12] Abhishek: Great. Thank you. Thank you for telling us about what's happening in the world of philosophy. I want to come back to something that you mentioned about knowledge and I wanted to ask you about the social structure of knowledge production. And you can correct me if I'm wrong, but I feel like, I think that before LLMs made their way into our lives, knowledge was mainly produced by human beings, by human researchers. And there, of course, are debates about this. But it seems like knowledge can now be produced by machines or bots in the sense that LLMs nowadays, or agentic AI, has solved, let's say, or proved some mathematical conjectures which have been alive for decades. So where does that stand in terms of the social structure of knowledge production?
[00:11:26] Abhishek: Is the world we are looking forward to now, in the world of knowledge production, do you think humans are going to be working with AI? Should we even think of AI as, or should we include AI LLMs within our world of knowledge production? Or should it just be, you know, just, just be humans, like, like we have done in the past?
[00:11:51] Mark: Yeah. This is a question that I've been thinking about a lot. It's one that's of current interest within philosophy. Recently, the philosophy journal Public Affairs and Philosophy, Philosophy and Public Affairs, I think is the name of it, published a journal article that had been largely written by AI. So it had a human author as well, but he essentially directed the AI system to produce this work. It went through blind review. It was approved by the journal. He had disclosed that this was the way it was produced beforehand, and they went ahead and published it to open some debate. So it was a good enough paper essentially to be published in a respectable journal within philosophy. And this was taken up on the philosophy blogs, and there's been a very heated discussion within philosophy itself about whether this is okay, to what extent it's permissible to use these systems in producing philosophical knowledge. And really, you know, about the question of why we write in the first place.
[00:13:04] Mark: As near as I can tell, there are kind of two camps on this. So one camp says, well, one purpose of philosophical writing is to acquire new knowledge about the world, so to find out truths that we didn't previously know about the world. And that's one of the major goals of philosophy. And if we can use these systems to acquire more useful truths about the world, then that's totally legitimate. Other people have the viewpoint that the point of philosophy is kind of the things I was mentioning earlier to get clearer on our own ideas, to get a kind of sense of how we understand things ourselves. And so it's problematic to bring these systems in. Another idea would be that the reason I read philosophy, some people have suggested, is to engage with another mind. Right. And arguably I'm not doing that when it comes to AI. So I think there's a legitimate question here. Maybe I'm not doing a good job of answering the specific question you asked about what this does about the social structure of knowledge, but I think these are all issues to be negotiated.
[00:14:21] Abhishek: And are these topics that researchers are, like, working on right now? And of course, I believe there are going to be debates because if anything, AI has some sort of, like a polarizing effect, you like it or probably don't. Yeah, yeah.
[00:14:43] Mark: So are there philosophers working on these questions specifically? I don't know of anyone who's written a argument that it is morally unjust. But there are certainly people working on. There are probably people working on the kind of social dimensions of knowledge more. I'm not so familiar with that work myself, but I will say just recently. So last week I heard about a competition within philosophy, an AI philosophy competition. So the competition invites people to submit up to three original essays on any philosophical topic alongside a kind of methodology report. And these are supposed to be produced using AI, so largely AI written papers. So there is a kind of active research project underway within philosophy to think about how these systems can be used to produce new philosophy if there are, you know, important shortcomings that emerge from the work they produce relative to how humans produce that work. I'm actually working on a project within, for this competition as well myself right now, so.
[00:16:01] Abhishek: All right, that's cool. It seems like the more we discuss, the more we have questions. You know, I wanted to ask you something that educators have been talking about a lot these days, and I want to hear what you, as from your vantage point of a philosopher, think about it. So educators are worried about cognitive offloading, and, where people could ask the AI to do most of the work. But there is, of course, there is a danger to it. I want to ask you, like, where do you think? Or is there an idea of the tipping point? Like, where do we, like, at what stage should we stop using an LLM and be like, okay, this is something I need to do and I need to think about in order to learn, like, from the perspective of learning.
[00:17:14] Abhishek: Because I fear that the popularity of LLMs right now, or AI, you know, offloading cognitive effort is probably the most concerning thing for me. And I could see down the line, and I hope it never happens, of, you know, cognitive atrophy. So from your perspective, where should somebody, at what point should somebody be, make that decision, like, draw the line? That this is something that I want the LLM to do, but of course there is something that I don't want it to do. For example, you mentioned about writing. Can you just talk a little bit about that?
[00:17:59] Mark: Yeah, I'll talk about this. So, first of all, I think that the danger of cognitive offloading is very real. I'm pretty sure that the inappropriate response or an inappropriate response to that danger is to say, okay, so we should never engage with LLMs at all. I think there are certain tasks that engage cognition that can be offloaded unproblematically. So, for example, my wife is recovering from a broken leg right now, and she has a lot of physical therapy appointments that she's having to go to.
[00:18:41] Abhishek: I'm sorry to hear about that.
[00:18:42] Mark: Thank you. Her PT sent a schedule with all of her appointments in it. It was a full page, maybe 30, 30 scheduled appointments. I took a picture of it. I had Claude add these all to my calendar. You know, add them all to our shared calendar. So if I had done that by hand, it would have taken me, you know, upwards of an hour, probably. Well, maybe not that much, but I would have had to do a lot of repeating.
[00:19:10] Abhishek: It's a tedious task.
[00:19:11] Mark: Yeah, it's tedious task, but it certainly engages cognition. Right. Is there any danger in my offloading that task to AI? I don't think so. And in fact, I think it can be cognitively detrimental to hang on to every task that engages cognition and not cede any of these to automated systems, because I freed up time to think more deeply about questions that are much more interesting by offloading that little bit of work that wouldn't have helped me develop in any way as an individual.
[00:19:44] Mark: But I do think that there are certain tasks that I would never offload. I mean, again, so the writing example, it's very important to me to have my own voice as a writer. I spent a lot of time when I was younger writing and thinking about how to express my ideas. I developed that over many hard years of working on different written work. And I do think that there's a danger, I see it in other people that I know who are more engaged with AI than I am. Sometimes they tend to embrace phrases and mannerisms of speaking that AI might embrace. Claude, for example, is very addicted to the word, "honestly", I notice. And employing this in different situations. I've noticed this creeping into emails from people I know are heavily engaged with AI, for example, other sorts of mannerisms like that.
[00:20:48] Mark: So certainly I think offloading that, you know, my writing into an AI system, you know, if I'm not trying to design an AI system that can write its own philosophy papers, I think that's very problematic. And I think, so you asked where to draw the line. I don't know that there is a like set place. I think.
[00:21:14] Abhishek: It probably depends.
[00:21:15] Mark: Yeah, it probably changes too, over the course of our lifetime. Like there are probably. I mean, I do often use AI to gather research resources. Right. I don't want my students doing that because they need to think about how to acquire research themselves, think about how to judge research as to whether it's good or not. I mean, I've spent a lot of years developing that skill before AI ever existed. And I think it's somewhat less problematic for me, having developed that skill to use it to help me in my research than it is for students to use it to help them in their research because they're still building those skills.
[00:21:58] Abhishek: I see. Yes, absolutely. And, well, as I mentioned, the more we discuss, the more questions keep coming up in my mind. I'm already planning to invite you to more rounds of sitting with me and chatting. In the interest of time, I want to take a different direction.
[00:22:21] Mark: Okay.
[00:22:22] Abhishek: Which I think would be helpful for our Lawrence students.
[00:22:26] Mark: Okay.
[00:22:27] Abhishek: I know that you teach a class, Philosophy 111. Right.
[00:22:34] Mark: That's right.
[00:22:35] Abhishek: Computation and Cognition.
[00:22:37] Mark: Yes.
[00:22:38] Abhishek: I have virtually sat in your course. I was part of the canvas course. I do think that this is one of the best places currently that exists at Lawrence where students could actually interact with an LLM and learn from that and about it. I want you to share more about the course. You know, what do you do, like, what sort of things have you been doing? Are there any things that have worked? Are you planning to change something? And what has been your experience and what have you heard from students?
[00:23:23] Mark: Yes. Okay. So this class, Philosophy 111, it's kind of a joke there. So it's, yes, yes, yes, and binary is the idea. So this course, it's called computation and cognition. And this is really, you know, in sort of my wheelhouse of philosophy of mind, cognitive science is the angle that the class is coming from. I also teach Introduction to Cognitive Science at Lawrence and that's really a term long investigation of human cognition with some comparative pieces built in there, primarily comparing human cognition to non human animals.
[00:24:05] Mark: But computation and cognition is really looking at what LLMs are doing and comparing that to what humans are doing. So the course has kind of like three components in it. So the first part is just two weeks, kind of like a crash course, a kind of reduced version of my cognitive science course. So we investigate aspects of how humans think in those first two weeks. We get some of the kind of fundamental moving pieces that I talk about in cognitive science on board, the importance of ancestral environments to human cognition. The way in which we use subtle aspects of psychology and our reasoning without oftentimes even knowing it. So for example, the way in which we use heuristics, which are ways of replacing a difficult question with an easy question. We do this all the time, we don't even know it and it causes us to be biased in different kinds of ways. So we spend two weeks just thinking about human cognition.
[00:25:14] Mark: Then we really turn to exploring how generative AI systems work. And, there in the past iterations of the class. So that's the middle five weeks of the class on our 10 week term system. In those five weeks it's really been very, very hands on with AI systems in the past. So students have a subscription to, in the past it's been ChatGPT, I may change to Claude this next year and they go through some different tasks of engaging with the systems. So seeing the ways in which, you know, it can fall short on perceptual tasks if you want it to like it can look at a picture and make certain determinations about that. But it systematically fails in certain surprising ways that humans don't. Thinking about the ways in which we can build in reasoning structure to get these systems to reason better. Right. You know, right now this middle part, it changes radically every time I teach the class just because the models are changing so much over time.
[00:26:24] Mark: I think I need to spend a lot of time talking about agents in the next iteration of the class because it really feels like systems are going. And then in the last several weeks, the last three weeks of the class, it's really philosophical reflections on similarities and differences between human and AI cognition and students in that last part of the class. So sort of through each of these sections there's a major assignment that they complete for the first two weeks. It's to say something essential about human cognition that we've left out of our crash course. Right. They've got to write a short paper on that. The middle section, they actually have to design an AI system of their own, like a chatbot, to do a certain kind of task. So that really gets them in there thinking about how the systems work. And there's a kind of iterative process.
[00:27:21] Abhishek: More hands on work.
[00:27:22] Mark: Yeah. And then the last assignment is to answer the question whether an LLM does think or could think. And I think some of these are more ambitious than others. For example, if you want to argue that generative AI, generative AI could never get to the level where it could be considered a thinking thing, that's a kind of high risk paper. So students earn more points for arguing that. Or if you want to argue, it's thinking right now that I consider to be difficult too. And then the more middle ground is it's not thinking right now or it will someday think. And so they take a stand on one of these papers using more philosophical reflections on what it is to think.
[00:28:13] Abhishek: I see. And I would definitely encourage students to take this course. This is very cool. Mark, can you tell us a little bit about when are you going to be teaching this course again?
[00:28:27] Mark: Yes. This course will be offered in the winter term and it's on a Tuesday, Thursday schedule.
[00:28:32] Abhishek: Okay, thank you. And we will put details about this course on our shownotes.
[00:28:38] Mark: Thank you.
[00:28:39] Abhishek: I wanted to ask you, how, how have your students reacted? And specifically have, one of the things that we as instructors kind of struggle with whenever we plan to incorporate AI into our course, we might have students who ethically oppose AI and they might not want to work with any. Have you had that kind of an experience in your course?
[00:29:08] Mark: Yes. So this course, it does require you to work with an AI. So if you're absolutely dead set against that, it's probably not the right course for you, because just the central component really does involve that exploration of AI. Some students have said in the past that they are very against AI, but they have gone ahead and taken the course. And I think in those cases, sometimes those students have come around maybe liking AI a little bit more. Oftentimes they've ended up in the same position, but I think their arguments are better informed after having gone through the class. So, you know, there is definitely that kind of negative reaction. I think an important thing for us as instructors in using AI is to be honest with our students about how we're using it. And so, you know, they produce these, these bots in the middle of the class. And it's done fairly well in terms of enrollment, the class has in the past. And so, you know, there these systems I have to try to break in various kinds of ways. It's very time consuming to grade those bots. So I have myself made bots that help me grade them.
[00:30:25] Abhishek: Okay.
[00:30:26] Mark: And I've used those to engage with the systems, to help write up a report to the students and things like that.
[00:30:34] Abhishek: And you have been open to the students?
[00:30:35] Mark: I told the students about this, yes. And we have a very healthy discussion about whether this is appropriate or not. In what ways it's okay. In what ways it's not okay for professors to use LLMs. And you know, I'm not using this to assess students or to assign their grade or anything, but I do think that these systems can turn up a lot of good things that an instructor often doesn't have the time to turn up. It's always an addition to my comments on their paper, but I think it can be helpful. Or not their paper, but their project. But I think it can be helpful for the AI to be involved in it as well. Some students feel it's a slippery slope, it shouldn't be involved at all.
[00:31:21] Abhishek: And I do respect all voices. I'm not personally strongly in favor of AI or against AI, but I do feel strongly that in order to have an opinion about this new development that has come across our worlds, it's probably better to interact with it and have an informed opinion, like an engaged opinion, whether your opinion is for or against. But I think your course lets students to engage and then have an opinion.
[00:31:55] Mark: Right.
[00:31:55] Abhishek: So that is one thing I really like, like about your course.
[00:31:58] Mark: Thank you.
[00:32:00] Abhishek: I wanted to ask you about, as we are liberal arts educators, I wanted to ask you, and starting from the assumption that AI is here to stay, it's not going away anytime soon. The reason why I say that, because when AI came in, ChatGPT was launched in November of 2022. I myself felt that this was just a hype and it was going to go away. But it turned out to be more than a hype. Anyways, as AI is here to stay and of course it's improving, better and better models are coming out. I wanted to know what would be your suggestion, advice, message to two different audiences? (A) What would be your suggestion advice message to our Lawrence colleagues and (B), what would you say to our students?
[00:33:07] Mark: Well, I think one thing that I'll start with the students. I think one thing that I've been really pleased with about our students when it comes to AI is the way in which I feel Lawrence students are very protective of their own cognitive development. I know that there are students who use AI for in ways they're not sanctioned to use it in classes or there's cheating that goes on. But I think in general, Lawrence students really care about the skills that they're developing at college. And I think a lot of times the reticence about AI, aside from environmental concerns, comes from a worry about it damaging their own cognitive development.
[00:33:58] Mark: And of course there are also economic considerations, thoughts about employment in the future and things like that. But I really am impressed with the students that they're already giving this much thought to what's going to be best for them and developing their own cognition. So I think that's great. I think at the same time it's important not to take a knee-jerk relationship to it to realize that LLMs and certainly machine learning is a much larger field than just the chatbots that I think we tend to think of.
[00:34:35] Abhishek: There is a lot going on behind the scenes than just, you know.
[00:34:39] Mark: Yeah, yeah. And so I think a lot of times people paint with a broad brush here and this is a topic that could repay a little bit of nuance in thinking about it I think. That's probably the main piece of advice I would give to my colleagues as well.
[00:34:57] Mark: I think a lot of times, you know, the arguments that I hear from colleagues against AI, you know, sometimes. So sometimes they come from an educational point of view and I think they're very often well placed in that regard. I think sometimes they come from a more environmental perspective also, certainly coming from the right place. But I think that a lot of times people haven't really given the appropriate level of consideration to these systems and I would just encourage my colleagues to actually engage with this technology to actually think about it a little bit deeper than maybe political slogans might end up suggesting we take it.
[00:35:49] Abhishek: And getting back to what you mentioned, what would you say like in, as you have gone through your personal journey of interacting and playing around with LLMs, if you had to suggest what would you say is a very low hanging fruit. If somebody wants to use or interact with an LLM, how would you suggest they do it so that it's beneficial to them as well?
[00:36:22] Mark: Yeah. Well, let me say actually one more thing about like considerations about faculty's perspectives on it. I think a lot of times people who are anti AI have in my experience really not engaged with it at all. And I think a lot of times, this leads to bad arguments about it. So this idea that it's always hallucinating, that whatever it reports is false, that might have been, you know, it had a lot more hallucinations three years ago than it does now. It's gotten a lot better. And so, you know, you can't just say it's a bullshit machine and it's always making stuff up. It actually gets a lot of things right. It's actually good at dealing with large amounts of information. If you have lots of data that you need to think about, it can help you approach that data in a useful way.
[00:37:19] Mark: And so what is low hanging fruit? I would say probably that some low hanging fruit that people can think about is one thing is if you're like me, I keep coming back to calendars, but I have a couple of different calendars. The Outlook calendar and the Apple iOS calendar. I think if you're using Cowork you can sync those two very easily.
[00:37:44] Abhishek: Claude Cowork.
[00:37:45] Mark: Claude Cowork. Yeah, Thinking about how you can. Another really low hanging piece of fruit I think. If you're using Claude Cowork I used it this summer to organize my files in my document area. I've spent years simply moving things into document, not putting them into the appropriate folders that I had made for them. And, you know, within a matter of 30 minutes or an hour, I was able to build a new organizational system. It sorted through all my documents, gave me suggestions about where stuff goes. That was wonderful. That has been something I've been thinking about doing for years.
[00:38:28] Abhishek: Like managing logistically, logistical parts of your job.
[00:38:32] Mark: Yeah, yeah, that organizational stuff. I've been thinking about organizing my files for years, but just the lift was too great.
[00:38:39] Abhishek: I see. Thank you, Mark. We definitely have to have you back, and thank you so much for your time for sharing your thoughts. To our listeners, thank you for tuning into Minding Machines. Full shownotes, resources mentioned in this episode and a complete written transcript will be available on our podcast page. If you have an idea for a future topic, a question you would like us to explore, or if you have suggestions for guests, whether they are educator, colleagues or students, please reach out through our feedback form linked in the shownotes. Until next time, stay curious and keep prompting thoughtful questions.
[00:39:21] Abhishek: Minding Machines is an independent podcast produced at Lawrence University. The views expressed are those of the participants and do not represent the university. This podcast is also separate from my teaching and research roles at Lawrence University.