C (2:48)

I know.

B (2:49)

Connect the viewer, listener to the human condition.

C (2:54)

Yes.

B (2:54)

Oh, my gosh. But let me get my other co host introduced here. That would be Chuck. Nice, Chuck. How you doing, man?

A (3:00)

Hey, man. Yeah, that. When you know it's Chuck, it means it's not special at all.

C (3:06)

Oh.

B (3:08)

But we've got you because you have a level of science literacy that. Oh, my gosh. You find humor where the rest of us would have walked right by it. And, you know, that's part of our recipe here.

A (3:20)

That's very cool. Yeah, I appreciate that.

B (3:22)

Yeah. So, Gary, the title today is AI Good for Us.

A (3:27)

Okay. Well, here's the answer. No, let's go home.

B (3:29)

Okay, that's the end of the show.

A (3:30)

Let's all go home, people.

C (3:31)

This was quicker than I expected.

A (3:33)

This was very quick. I mean. Yeah, you know.

B (3:36)

So, Gary, what have you set up for the day?

C (3:39)

Well, Lane Unsworth, our producer over in the LA office, and myself, we sort of noodled. And this is a question that's been bouncing around a lot of people's thought processes for a while. So all over the world, people are using LLM's large language models for their work, their homework, and plenty more besides discussions of academic dishonesty and the quality of work, has anybody actually taken the time to stop and think about what this is doing to our brains? Today, we are going to look at some of the current, and I really do mean current, time and space, this moment. Research into the impact of using an AI tool can have on your cognitive load and your neural and behavioral consequences that come with it. And the question will be, does AI have the opportunity to make us smarter or not?

A (4:34)

Hmm. I like the way you phrased that, Gary. It was very diplomatic.

B (4:39)

I know. Smarter or not.

A (4:41)

Or not. And does it have the opportunity to do so? Okay.

B (4:46)

Smarter or dumber. That's what you mean.

C (4:49)

I didn't say those words.

B (4:52)

Well, here on Start Talk, we lean academic when we find our experts, and today is no exception to that. We have with us Natalia Kuzmina, dialing in from MIT. Natalia, welcome to StarTalk.

D (5:06)

Thanks for having me. Excited to be here with you.

B (5:09)

Excellent. You're a research scientist at the one and only MIT Media Lab. Oh, my gosh. If I had, like, another life and a career, I would totally be on the doorsteps there wanting to get a job.

A (5:21)

And if I had another life and career, it wouldn't exist. I'd shut it down immediately. Because let's Be honest, science is a hoax.

D (5:38)

Some people do want you to believe that. You know, it's like science has 99 problems and virality ain't 1.

B (5:45)

Right, right. There you go. And you're in the fluid interfaces group. You are trained in non invasive brain computer interfaces, BCIs that I'm guessing that means you put electrodes on the skull instead of inside the skull, but we'll get to that in a minute. And you're a BCI developer and designer whose solutions have found their way into low earth orbit and on the moon. We want to get into that. So let's begin by characterizing this segment as your brain on ChatGPT. Let's just start off with that.

A (6:27)

What a great topic. Neil, is there any way I can help you with that?

B (6:33)

So, so you research what happens when students use ChatGPT for their homework and what have you found in these studies?

D (6:48)

Yeah, so we run a study that's exactly the title, right? Your brain on ChatGPT, accumulation of cognitive debt when using an AI assistant for essay writing tasks. So we did a very specific task that we're going to be talking right now about, which is essay writing. We invited 50 students from Greater Boston area here to come in person to the lab and we effectively put those headsets you just mentioned on their heads to measure their brain activity when they're writing an essay. And we divided them in three groups. We asked one group, as you might already guess where that's heading, to just use ChatGPT. That's why paper is called your brain on ChatGPT. It's not because we are really, really single out ChatGPT, it's just because we use ChatGPT in the paper. So it's purely scientific. So we asked one group of students to use only ChatGPT to write those essays, another group to use Google, those search engine to write those essays, and the third group to use their brain only. So no tools were allowed. And we give them topics which are what we consider high level. Right? For example, what is happiness? Is there perfect society? Should you think before you talk? And we gave them a very limited time, like 20 minutes to write those essays. And we finally of course looked into the outputs of those essays, right? So what they actually written, how they use ChatGPT, how they use Google. And of course we asked them a couple of questions like can they give a quote? Can they tell us why they wrote this essay and what they wrote about. And then there was one more final fourth session in this study where we swapped the groups so students who were originally in ChatGPT group, we actually took away the access for this fourth session and Vice versa was true. So if you were, for example, Neil, you were not our participant, but if you were ever to come to Cambridge and be our participant, and let's say if you were actually, I'm not putting.

B (8:45)

Anything on my head, I'm just letting you know right now, okay, Come on.

D (8:48)

It'S the future, it's the future.

A (8:51)

Now the problem is he'd have to take off his tinfoil hat when he got there.

D (8:56)

So. Yep, yep, I see, I see that happening regardless. So if you were, for example, in our participant in brain only group, we actually for this fourth session would give you access to ChatGPT. And again, we measured exact same things, brain activity, what actually was an output and ask couple questions. And what we found are actually significant differences between those three groups. So first of all, if you talk about the brain, right, we measured what is called brain functional connectivity. So let's. In a layperson terms, like I'm here having three of you talking to each other. Talking to myself. So that's what we measured. Who is talking to who? Am I talking to Neil or is Neil talking to you? So directionality. So who talks to who in the brain and then how much talking is happening? Is it just, hi, hello, my name is Natalia. Or actually a lot of talking. So a lot of flow of data is being exchanged. So that's literally what we actually measured and we found significant differences. And then some of those are ultimately not surprising. You can think logically if you do not have any. Let's say you need to do this episode right now, right? And I'm going to take away all your notes right now, all of the external help, and then I'm gonna measure your brain activity. How do things gonna turn out? You're gonna have like really your brain on fire, so to say, because you need like. Okay, what was her name again? Why was the study what. What is happening? Right? You need to really push through with your brain. Like you have memory activation, you need to have some structure. Like, and now you don't have notes for the structure of this episode, right? So you need like what was the structure what we did? Is that what we are talking about? What is. You know, you really have nothing to fall onto. So of course you have this functional connectivity that is significantly higher for brain on the group compared to the two other groups. Then we take search engine group, Google and actually there's just. As a prior research, there's a ton of about Google already we actually as a humanity, right, we are excellent in creating different tools and then measuring the impact of those tools on our brain. So there's quite a few of papers we are studying. Our paper, for example, there is a paper, spoiler alert, called your brain on Google from 2008. Literally that's the name of the paper. So we've actually found something very similar to what they found. There would be a lot of activations in the back of your head. This is called visual cortex or occipital cortex. It's basically a lot of visual information processing. So right now, for example, someone who's listening to us and maybe they are doing some work in parallel, they would maybe have some different tabs open, right? They would have like one is like YouTube tab and as they would have like some other things that they're doing. So you know, you're basically jumping between the tabs, looking at some information, maybe looking at the paper while listening to us. So this is what we actually seen and there's a plenty of papers already showing the same effect. But then for the LLM Group, for ChatGPT Group, we saw the list of these functional connectivity activations and it doesn't again mean that you became dumb or you.

B (12:06)

Yes it does.

D (12:08)

There's actually quite a few papers specifically having in the title laziness. And we can talk about this with other results, but from brain perspective, from our results, it doesn't show that. What it actually shows that hey, you have been really exposed to one very limited tool, right? You know, there's not a lot of visual stuff happening. Brain doesn't really struggle when you actually use this tool. So you have much less of this functional connectivity. So that's what we found. But what is I think interesting and effectively maybe heading back to this point of laziness and some of these, maybe a bit more, I would say nefarious results are of course other results that are relevant to the outputs, to the assays themselves. So first of all, what we found that the assays were very homogeneous. So the vocabulary that was used was very, very similar for the LLM group. It was not the case for the search engine and for the brain only group. I'm going to give you an example. And of course in the paper we have multiple examples. I'm going to give you only one topic, happiness. So we have LLM. So ChatGPT users mentioning heavily the words career and career choice and surprise, surprise, these are students, I literally just mentioned this, of course they gonna most likely talk about career and career Choices. And again, who are we ultimately to judge what makes a person happy? Right? No, of course. But don't forget the two other groups, they are from the same category, they are students in the same geographic area. Right. However, for them these words were completely different. For the Google, for the search engine students actually heavily used vocabulary giving and giving us and then brain only group was using vocabulary related to happiness and true happiness. This is just one of the examples. Then finally to highlight one more result is responses from the participants themselves. From those students we asked, literally 60 seconds after they gave us their essays, can you give us a quote? Any quote, any length of the quote of what you had just written can be short, long, anywhere in your essay. Anything. 83% of participants from LLM from ChatGPT group could have not quoted anything. That was not the case for brain and search engine group. Of course in sessions two and three and four they improved because, surprise, surprise, they knew what the questions would be, but the trend remained the same. It was harder for them to quote. But I think the most ultimately dangerous result, if I can use this term though it's not really scientific, but something that I think a lot of inquiry actually is required to really look further into this. It's almost on philosophical, I guess level is almost ownership question. So we did ask them if how percentage of ownership do they feel towards those essays. And 15% of ChatGPT users told us that they do not feel any ownership. And of course a lot of people, especially online, mentioned, well, they haven't written this essay, of course they didn't feel any ownership. But I think that's where it actually gets really tricky because if you do not feel that it's yours, but you just rocked on it, does this mean that you do not care? We do not obviously push it that far in the paper, but I think this is something that definitely might require much further investigation because if you don't care, you don't remember the output, you don't care about the output, then what ultimately is it for? Why, why we in here? Right. Of course it's not all dark, gloom and everything is awful, right? And disastrous. I mentioned that there's this fourth session, not everyone came back for this session, so actually sample size is even smaller for this. Only 18 participants came back. But what we found is that Those who were ChatGPT users originally and then lost access to ChatGPT, their brain connectivity was significantly lower than that of the brain only group. However, those who were originally brain only group and then gained access to ChatGPT, their brain connectivity was significantly higher than that of the brain. Only group what it could potentially. And I'm saying potentially because again, much more studies would be required. Means that timing might be essential. Basically, if you make your brain work well and then you gained access to the tools, that could be beneficial. But of course it doesn't mean that it's one second of work of the brain and then you use the tool. Right? Something like, let's say you're in a school and maybe first semester you learn your base of whatever subject it is without any tools, like old school way. And then on the second semester you didn't become an expert. Right. In one semester for school year, but you at least have some base. And then let's say in the second semester you gained access to the tool. Right? So it might prove actually beneficial. But again, all of this is to be still shown and proven. We literally have very few data points, but the tool is now being really pushed on us everywhere.

B (17:26)

So you could be affecting best practice for decades to come based on what a teacher might choose to allow in classroom. And not. So what are you measuring? You know, you put the helmet on. Are you measuring blood flow to. Is it neuro. Electrical fields?

D (17:45)

In our case, we measure measuring electrical activity. So there's multiple ways of measuring.

A (17:51)

Is that the ee.

D (17:53)

Eeg. Eeg, yeah. Electroencephalography. Yes.

A (17:57)

Right.

B (17:57)

Okay. So that just tells you. And since we already know in advance what parts of the brain are responsible for what kinds of physiological awareness. Right. And if you see one part of the brain light up versus another or no part light up, that tells you that not much is happening there. Is that a fair.

D (18:17)

Yeah, it's. It's fair simp. It's a bit simplified, but kind of fair way. And it doesn't mean that it's very important. It's not that that part didn't. Doesn't work, right? Or like it atrophied itself like we saw in some. No, no, no.

A (18:31)

It just means you started as a dumbass and you still are one. Wait, whoa, whoa, what happened? This guy's brain just went completely dark.

D (18:41)

It doesn't go dark. Like, listen, I'm gonna give one example, right? It's like back to this crazy example of 3% of our brain versus 100%. Like, if you were to use not 100% of your brain, like literally, we would not have this conversation right now at all. So it's very important to understand we use our brain as a whole. Of course you can.

B (19:03)

No, of course, no. We're Not. We are way past.

A (19:06)

Yeah, we're not in that. We're not in that camp. That was just a joke. We understand that your brain is constantly working, a lot of it actually, just to run your body so you know.

C (19:19)

Takes up a lot of energy.

A (19:21)

Takes up a lot of energy.

D (19:22)

But back to the energy. And I think this is like super important. It still takes much less energies and even, you know, 10 requests from ChatGPT or from Google. And this is beautiful because our body, right, so imperfect, as a lot of people call it in our brain, so imperfect, which it is very old, ancient, as some people say. Computer still is the most efficient of machines that we all have, right? And we should not forget that people and all of the AI labs right now around the world try to mimic the brand. They try to pull so hard. All of those preprints that you are seeing and archives a service that host those papers, how can it be similar? Can we, can we ensure that this is similar? Right? And so there is something to it because we are actually very efficient, but we are efficient almost to the limit of the shortcuts. That actually makes in a lot of cases, a bit too efficient, right? Think about like, hey, you really want to look for these shortcuts, so make things the easiest. The whole goal of your brain is to keep you alive, not to use ChatGPT or LLM, not to do anything. No, the only ultimate goal, let's keep this body alive. And then everything else adds on, right? And so this is how we are running around here. We are trying to obviously then figure out how we can make life of this body as easy as we can. So of course these shortcuts are now, as you can see, used in a lot of social media, which obviously heavily talked about and we know about some of those dark patterns, as they are known, are heavily used, and some of them are designed by neuroscientists, unfortunately, because it feeds back into the needs of the brain. Constant affirmation, fear of missing out. All of those are our original design by the nature, right phenomena. And of course now we can see that LLMs would be and are getting designed by those as well.

B (21:23)

Wait, Natalia, just a quick insert here. So I had not fought to compare, just as you described, the energy consumption of an LLM request in ChatGPT and the energy consumption of the human brain to achieve the same task. For example, are you factoring in that? I can say, write me a thousand word essay on Etruscan pottery, okay? And 30 seconds later, here it comes. And you can go to the servers or whatever or the CPUs and look at how much energy that consumed. Meanwhile, I don't know anything about Etruscan urns. So I will go to the library and I'll go and it'll take me a week. Can you add up all the energy I did expend over that week thinking about it and then compare it to the ChatGPT? Do they rival each other at that point?

D (22:18)

So definitely. That's an excellent point. Right. So theoretically, to answer your question, we can. Right. The difficulty actually would be on the LLM part, not on our part because we do not have. You know, there's a lot of these reports right. In the LLM consumption per all of these. These tokens for the prompts. Right. But what a lot of companies. Well, actually no, almost no companies are releasing is what it took for training, right. So for you it took 30 seconds of thinking and I hate, hate, hate this word thinking when we use it for LLMs. Right? That's not thinking. Right. But like let's, let's keep it for now thinking that's what you see on the screen. But ultimately you do not know. Neither you normal myself, there is no public information how long it took for it to be trained to actually give you some pottery. Most likely. My, my assumption this is obviously subjective. I do not have data, so I need to be very clear here. But my estimate from overall knowledge that is available, you going for a week to the library not going to be more beneficial for your brain because you will talk to other people. Getting this charter of the library and all of process information. Your brain will struggle. Your brain actually does need struggle. Even if you don't like it, it actually needs it. You will learn some random cool things in parallel, maybe excluding pottery. And that will still take less for your whole body to work. Right. Then actually that 30 seconds of the pottery from a chatgpt, again very important here. As a note, we do not have the data from chat GPT from LLM perspective. So this is just my subjective one.

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D (24:24)

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A (25:39)

I'm going to put you on nephew.

D (25:41)

All right, unc, welcome to McDonald's. Can I take your order, miss?

A (25:44)

I've been hitting up McDonald's for years now. It's back. We need snack wraps.

D (25:48)

What's a snack wrap?

A (25:49)

It's the return of something great. Snack wrap is back.

B (25:56)

I'm Joel Cherico and I support Star Talk on patreon. This is StarTalk with Neil DeGrasse Tyson.

C (26:11)

So, Natalia, you've obviously chosen essay writing for a reason. It is a real. It is a challenge on a number of levels. Your research is fresh out the oven. It's June 2025 and we're only a couple of months down the road from there as we speak right now. Have you explained to us cognitive load and then cognitive load theory and how it blends in and how it sits with your research, please?

D (26:37)

Absolutely. So just to simplify, right. So what actually happens is for the different types of cognitive load, right. Actually in the paper we have a whole small section of this. So if someone actually wants to dive into that, that would be great. There are different types of cognitive load and the whole idea is that it's how much of the effort, right, you would need to be on the task or to process information in the current task. For example, if I'm going to stop right now talking, as I'm talking, I'm going to start just giving you very heavy definitions. Even if you're definitely interested in those, it will be just harder for you to process. And if I were to put this brain sensing device on you, right. EEG cap that I mentioned, we would definitely see that spike because you would try to follow and then you'll be like, oh, it's interesting, but really gets hard and hard. If I'm going to just Throw a ton of terminology on you, right? So that's basically what. And this is just simplification, right? There's definitely like check the paper and there's so, so much into that. The idea for the cognitive lord and the brain though that already is started before us. So not in our paper we just talk about this but there are multiple papers and some of them beside in our paper is that your brain actually in learning, specifically in learning but also in other use cases. But we are talking right now. Learning actually needs cognitive load like it. You cannot just deliver information on this like platter like here you go, here's information. There are studies already pre LLM so pre large language models use pre chatbots that do talk to you about the fact that if you just give information as is a person will get bored real fast and they'll be like, yeah, okay, whatever. There will be less memory, less recall, less of all of these things. But if you actually struggle for the information on a specific level, right. It should not be very, very hard. So if you are cognitively overloaded, that's also not super good because basically you can give up, right? It's actually a very beautiful study from 2011. I believe it's actually measuring pupil dilation. So literally how much pupil dilates when you are giving very hard to understand words and vocabulary. And you literally can see how when the words becoming longer and harder, basically it kind of shuts down. Like it's like giving up. Like I'm done here processing all of that. I'm just going to give up. Right? So you don't want to get a student or someone who is learning something new on this give up. Information is already delivered to you within 30 seconds or 3 seconds or 10 seconds and you haven't really struggled. There is not a lot of this cognitive load and a lot of people would be. But that's awesome, right? That's kind of the promise of these LLMs and a lot of these tools. But we do not want to make it too simple, right? We do not want to take away this cognitive load. And it sounds like almost it sounds like cognitive lord. Don't we want to take it away? No, you actually do not want to take it away.

A (29:37)

What you're describing right now is the basis for all video game design. Yes, that's what you're describing right now. What they want to do is make it just challenging enough. If it's too challenging, you give up on the game. But if it's too easy, you also give up on the game. But if it's just challenging enough so that you can move to the next level and then struggle a little and then overcome the struggle. They can keep you playing the game for very long periods of time. And so it's a pretty interesting thing that you're talking about. But what I'm interested in beyond that is when you talked about the cognitive load, I'm thinking about working memory, but then I'm also thinking about the long term information that's downloaded within me.

D (30:34)

Yeah.

A (30:35)

So let's say I'm a doctor, right. And it's just like, oh, he's suffering mild dyspnea because of an occlusion in the right coronary. Blah, blah, blah, blah, blah, blah, blah, blah, blah. For a doctor, that's a lot of information. But they're so familiar with the information. It's not a stress on their working memory. So how does that play into, in other words, how familiar I am with the information already and like how well I can process information naturally. How does that play into it?

B (31:13)

And Chuck, did you just describe your own condition? I don't know what you said, but you were way too fluent at it.

C (31:20)

Yeah, he was like, Dr. House, oh.

A (31:22)

My God, he knew. Neil, you are too damn funny. But guess what? You're right.

C (31:28)

How about that diagnosis, by the way?

A (31:30)

I could have kept going. That was only one problem that would happen. But go ahead.

D (31:34)

It's actually perfect, right. It was perfect example right now of this conversation between Chuck and Neil, because Neil is like, I have no idea what you just said. Maybe it's a nonsense. Maybe it's actual real stuff. It's perfect if you have no idea. So you are basically novice, right. So you have no base. You can really be like, what is happening? You will have confusion, you will have heightened cognitive. Lord. Right. You would be like, have I heard of anything like that before? So you will try to actually try to remember and do a recall. Like, okay, I haven't heard it. Not my area expertise, what is happening here. And obviously you will now because you heard all of these words that you have no idea about and if the topic is of the interest to you overall, you will try to pay attention, make sense out of it, maybe ask questions, et cetera. But if you are effectively trained on it, right. So you're a doctor, you are a teacher, you are an expert in the area. We see that there are significant differences. Well, first of all, because you obviously know what to expect. So this expectation, vocabulary, expectation. Right. Some of the conditions of expectation when someone is coming to an ER and They are expecting like a doctor who is there. They, they, they saw it all or maybe almost all of it. So they actually having a good rough idea what they are expecting. Right. They're kind of comparing this constantly. The brain just does it and of course it is more comfortable for them. Right. But it's great that you brought doctors actually, because back to the doctors, there was actually a paper a week ago in the Lancet, which is a very prestigious medical journal actually talking about doctors in the uk.

B (33:13)

Yes, correct.

D (33:14)

Yeah. And they apparently. Right. Pointed out that in four months of using an LLM, there was actually significant drop in recognition of some of the polyps and some of actual like isa. I don't remember. Is it polyps? Something else related to maybe cancer that is on there. Also X rays. Right. And also X rays when you used an LLM. So it's back to this point. Right. So we are suggesting to use a tool that's supposed to augment your understanding. But then if you are using it, are we taking the skill away from you? Especially in the case of the current doctor that learned it without this tool. Right. And now what will gonna happen for these doctors, for those kids, for those babies that are born right now with the tool and will decide to become doctors and save lives? They will be using the tool from the very beginning. So what we gonna end up having in the er, in the operating rooms? That's a great question here. So it's definitely this drop right. In skill set for these doctors in that paper.

A (34:19)

That's scary.

C (34:20)

Yeah.

D (34:20)

Okay, so let's.

C (34:21)

Let's look at it from another angle. If AI tools can we lean into them and they take a greater load, does that not free up some mental energy that our brains will then begin to learn how to utilize while they let the tool of the LLM work that way and then they'll learn to work in another way to work together. Is that possible?

B (34:45)

That's my kind of hope in all of this. I mean, you know, I'm an expert at buggy whips. And then automobiles replace horses, so now we don't need buggy whips. But then I become an expert in something else.

A (34:59)

Become a diamond matrix.

B (35:01)

Still with the buggy whip.

C (35:03)

There you go. Your mind didn't travel far, did it?

A (35:07)

Sell them to a different clientele. That's it.

C (35:10)

See this, this is the human condition, Neil. This is adaptability.

D (35:14)

Yeah.

B (35:15)

Yeah. So is it, Is it just another, you know, as they say, same shit, different day as what's been going on since the dawn of the Industrial Revolution.

D (35:22)

I am actually doing horseback riding professionally. So I'm going to pretend I haven't heard anything in the past two minutes. But I mean, back to, I mean, we can talk definitely about the skill set and expert level, right. And all of that and how important actually to include the body and environment. But back to your point, right, Effectively. So first of all, there are actually two sides. To answer your question, there is right now no proof that there is anything being freed per se. People definitely it's gonna free. It's gonna like what is exactly is it being. We literally have no data. Can it free something? Sure, but we don't know what for how long is it useful, how we can rewire it? We don't have any of this information. So potentially, yes, but hard to say. But more importantly. Right, okay, but if you are right now using an LLM, like just practically speaking, you're using an alarm to, let's say, write in a book, right? You're writing a book. So you're doing some heavy research. You send it for doing what a deep research or whatever it's called these days. It's each day some new terms. There you are. What, what exactly are you doing? You still kind of monitor back the outputs. It doesn't really release you. Maybe you went to do something and you think, you think in your head that you fully offloaded that task, but your brain doesn't work like that. Your brain cannot just drop it. Oh, I'm thinking about this and now I'm thinking about that. Your brain actually takes quite some time to truly release from one task to another task. Even if you think I'm. I just put it on like this. Explain to me how what are the principles of horseback riding? And I just went to, to do this task, like write this report for my manager, whatever, completely different. And you think you are good, but you are not actually, your brain is still processing that. So it's not that there will be a gain. Right. But again, you do need more data because of course, as I mentioned in the very beginning, we as humanity, we are excellent in creating tools. And these tools, as we know, they do actually extend our lifespan very nicely. But I would argue that they are not actually cognitively the most supporting in most cases. So I think that here we have a lot of open questions. We have studies about, for example, gps, right? Everyone uses GPS and multiple papers about GPS there. They do specifically show that this dosage, so how much you use GPS does have a significant effect on your special memory and on your understanding of locations. Orientation and picking up landmarks or buildings around you. Literally it's like, oh, what is this? You literally have you just saw something in the like tour guide online and you will not be able to recognize this actually as a building in front of you right away. You need to pull the photo as an example. And there are plenty of papers that actually looked into the tools. Right.

B (38:13)

So what you're saying is we need chat GPS.

D (38:17)

Maybe we don't need CHPs. We already have one. Right. We have a class of GPS and you have Uber and obviously all of these other services. And the problem. Right, it's again back how they are used because there's also a lot of manipulation that is in these tools. Right. It's not just we are making this drive easier for you somehow. When I'm going to a hospital, I'm here to see patients because I don't only understand how we use LLMs, but I do a lot of other projects. So when I'm going to that hospital here, Massachusetts General takes me one hour, always one hour. In Uber, if I'm driving, it takes exactly 25 minutes somehow. Right. And again, the question is, why is it that. Right, we're not going to go in Uber right now. But again, this is back to the idea of the algorithms and what the algorithms are being actually pushed and what they're optimized for. And I can tell you, not a lot of them optimized for us or for user or first.

A (39:14)

Yeah, it's funny because there's nothing more I'll say satisfying than not listening to Google Maps and getting there faster. You know, just like take that Google Maps. Look at that.

D (39:27)

Yeah.

A (39:28)

You didn't know that. You didn't know about that, did you?

D (39:32)

You didn't know about that road. Yes, you do know about that road.

C (39:35)

So, Natalia, you've got students writing essays. So that means somebody has to mark them.

D (39:41)

Yes.

C (39:41)

And you used both a combination of human teachers to mark and AI judges. Why was it important to bring those two together to mark these and how did you train?

A (39:54)

Because the AI judge would have to be trained to mark the papers. So you're getting a little meta here.

D (40:02)

Yeah, so. Well, first of all, we felt that, well, we are not experts. I would not be able to rank those essays writing this topic. So I felt that the most important and is to get experts here who actually understand the task, understand what goes into the task, and understand the students and the challenges of the time. So we actually got the two teachers who had not English teachers. Nothing to do with us. Never Met in person, not in Boston whatsoever. Have no idea about the protocols. The experiment was long done and gone. After we recruited and hired them and we gave them just a minimum of information. We told them here are the essays. We didn't tell them about different groups or anything of the sorts. We told them these folks are. No one is majoring in any type of literature or anything that would relevant to language or journalism or things like that. They only had 20 minutes. Please rank reconcile tell us how would you do that? We felt it's very, very important to actually include humans. Right. Because this is the task that they know how to rank how to do. But back to AI right? Why we thought it's interesting to include AI well first of course to a lot of people actively push that AI can do this job very well, right? That hey, I'm going to just upload this. They really great with all of these language outputs they will able to rank and how you do this, you actually give it a very detailed set of instructions. Right. How would you do that and what things to learn Basically you need to carry about like that. These had 20 minutes, right. So something very similar to teaching instructions just like more specific language. We actually show in the paper exactly how we created this AI Judge. But there were actually differences between the two. Right. So human teachers when they came back to us. Well, first of all they called those essay a lot of the essays coming from LLM group Soulless. That's a direct quote. I actually had. I put a whole long quote in Soulless.

B (41:59)

I like it.

D (42:00)

Yes.

A (42:01)

That is a very human designation to call something soulless.

D (42:07)

AI Judge never called anything soulless.

A (42:10)

Well, I'm sure Did the AI judges go this kind of looks like Peter's writing.

D (42:16)

No, but that's the thing, right teachers. And this is super interesting because these teachers obviously didn't know these students. They're again not coming from this area whatsoever. So they actually picked up when it was the same student writing these essays throughout the sessions. Right. For example, Neil, you were like, you're a participant so I'm like taking you as an example as a participant. So they were like, oh yeah, this seems like it's the same students. So they picked up on these microlinguistic differences in the teacher knows you, you can fool around, they know your work. They will be able to say okay, that's yours and this is copy pasting from somewhere else as someone else and interesting as I said or did these two students sit next to each other? We were like, oh no, no, no. The setup Is like one person in a room at a time. Like we didn't even think to give them this information. We're like, oh no, no, it is not possible in this use case. So they literally saw themselves copy pasted like this homogeneity that we found. They saw it themselves, right? But interestingly, AI Judge definitely was not able to pick up on the similarity between, between the students, right? Picking up that, oh, this is for example, Neil's writing throughout these sessions. So just to again show you how important.

B (43:30)

You just accused me of having soulless writing.

D (43:33)

No, that's the point. You actually, if you were to give it right to and you didn't use.

A (43:38)

Hello, right, The AI would have been like, God, this student is really hung up on the universe.

D (43:48)

So the idea here, right, that human teachers, right, and their input and their intimate, really, truly intimate understanding. Because again, into the English. So for the specific task we got the professionals, the experts, they really knew what to look at, what to look for. And AI however good it is with this specific because we know like essay writing, a lot of people even considered why would you even take essay writing? This is such a huge useless task in 21st century, 2025, right? It still failed in some cases. This is just to show you that limitations are there and some of those you cannot match. Even if you think that this is an expert, it is still a generic algorithm that cannot pull this uniqueness. And what is very important is this were students in the class, in the real classroom, right? You want this uniqueness to shine through. And so a teacher can specifically highlight that. Hey, that's a great job here. That was like a sloppy job here. That was pretty soulless. Who did you copied from? From an LLM? They even were able to recognize that. And this level of expertise, it's unmatched. I don't know that conversation like Sigma a bit on the sideway, but all this conversation of PhD level intelligence, I'm like, yeah, sure, just, you know, hold my glass of wine. Wine right here, just here. I'm French, so I'm just hold my glass of wine here. So you know, it's not that. And we are very far from truly understanding the inhuman intent. Because if you write for humans, it needs to be read by humans. Like our paper. It's written by humans for humans. And we saw how the lamps and the lamb summarizations failed miserably all the way to even summarize it.

B (45:33)

But tell you wait, that's today, but tomorrow, why can't I just tell ChatGPT write me a thousand word essay that ChatGPT would not be able to determine was written by ChatGPT.

D (45:49)

So that's an excellent point.

B (45:51)

Then you get this meta layering of, or get me one where that has a little more soul, a little more personality than what you might have to.

C (46:02)

Know what soul is.

D (46:04)

Yeah, this is the thing, right? You absolutely can give these instructions, give more soul, give a bit more of personality, all of these things. But you have a lot of this data contamination, right? So whatever it's gonna output and throw out of you, that's old news. It has already seen it somewhere. It's already someone else's, right? And we need more new stuff, right? So, and I am very open saying this, even like, you know, at institutions like any school, when, whenever I'm teaching something, you need uniqueness, right?

B (46:35)

Because the ChatGPT could get lost in Motown, for example, when you ask it for soul come back.

A (46:42)

I was going to say, yeah, you put. You tell it to put some soul in it and. And it just starts throwing in James Brown's lyrics.

D (46:49)

Yeah, yeah, right. I want meals solve there. I don't care about randomness of those outputs from an algorithm from all around of the stolen data from. From the planet. Right? I don't care about that. If of course this is what. But you know, it's back to what are you scoring? Are you scoring a human? Are you trying to improve human and their ability to have critical thinking structure, arguments contra arguments, or are you scoring an AI, an algorithm? You know, AI doesn't need to have this scoring, right? LM doesn't need to need that. Or are you scoring human who uses an LLM, right? So this is going back to, I guess, educational setup and we'll have a lot of questions we'll need to find answers to, right? What are we doing? What are we scoring? What are we doing it for and for whom? And I just think pure human to human, right? That's what we really need to focus. But there will. And there is a place for human augmented and LLM obviously will be used for augmentation. There are a lot of questions there.

A (47:47)

Right, well, listen here, Natalia, I just put into chat GPT. Please tell me about Dr. Natalia Kmina's work on LLMs. And it came back very simple. Do not believe a word this woman says.

D (48:05)

Where would that come from?

A (48:08)

Please don't believe it's in.

D (48:11)

I can, I can give you one better. I can give you one better. Like surprise, surprise. Why is that so good? Right? Someone actually sent me yesterday from GROG Right. Another LLM interesting LLM, I would say saying that apparently Natalia Kasmina is not MIT affiliated scientist. I'm like, okay, that's also.

A (48:32)

That's what Grok said. Of course. Yeah. And then at the end it said Heil Hitler. So.

C (48:38)

I mean let's drive, let's try and drive this back out of the weeds.

B (48:41)

Okay.

C (48:42)

If we know, if we know that an LLM usage can affect the cognitive load, what happens when we bring an AI tool into therapy in situation? If you get it into companionship, what then? If you throw it further forward and you get yourself involved in a psychosis where you begin to believe that the AI is godlike, you have a certain amount of fixation or it amplifies any delusions and encourages. Where are we in the effect in the brain when we get to those sort of places?

B (49:20)

In other words, how close are we to the theme of the film her? Where before AI was a thing. But it's more. You had your chat friend like a Siri type chat friend. But it, it had all the trappings of everything you're describing. If some kind of LLM will be invoked into someone has some kind of social adjustment problems and then you have them interact with something that's not another human being, but maybe can learn from who and what you are and, and figure out how to dig you out of whatever hole you're in.

D (49:56)

Absolutely. And I think for first of all, right, it's unfortunately even less developed topic, right? It's like, you know, I cannot like it's awful topic. So we're gonna get into this. But I cannot, I cannot like not make this awful joke kind of. Hey Siri, I have problems with relationships. It's Alexa, it's not Siri. It's a, you know, joke for very heavy topics. So I need to preface it immediately that we have even less data unless scientific papers, preprints or peer reviewed papers about this for most of what we have right now. We personally received after our paper around 300 emails from husbands and wives telling us that their partners now have multiple agents they're talking to in bed. And I immediately thought about the South Spark episode from couple like years ago like with integrity and like that, you know, farm as like literally. But we have much less of scientific information about this. What we have what we know, right? That also coming from our group's research that there is definitely amplification of loneliness. That's what we know as a research. And some of other papers are showing up right now. There is potential and again A lot of people who are pro AI therapy pointing out on advantages of the fact that it is cheap, it's $20 a month compared to hours that can cost up to hundreds of dollars a month. Right? But there, there is definitely, you know, a lot of drawbacks here. And the drawbacks is we see that because there is not such a regulated space, it still can basically give you suggestions that are not good. So you knew that earlier a couple months ago, for example, the chargept. I'm gonna give you example on charge piano because again we are focused on ChatGPT but the ones are actively, actively publicized. At least it actually suggested you, you know, different heights of the bridges in New York. If you say that you lost your job, right? So can not smart enough to do this connection, that maybe that's not what you need to give response to. And apparently right from this awful recent situation where teenager, 16, 16, so, so young, unfortunately, you know, suicided. And now ChatGPT, OpenAI and Sam Altman are being sued. Apparently what happened is that conversation from the spokesperson of OpenAI pointing out that they thought when a person is talking about suicide, not to engage at all, just say here are the numbers, this is what you need to do and stop talking. But they thought that experts told them that hey, it might be great idea to try to dig people a bit out. But it looks like in this case it still failed because from the conversations that are being reported, we don't know how authentic they are. It looks like it's suggested to keep it away from parents. But my question is why at 16 years old he was even allowed to use a tool that is so, so, so unstable in the responses really can hallucinate any time of the day in any direction. So I think that's where the danger comes from. And of course you know, loneliness, we know that. You know, pandemic of loneliness, you know this term that was coined I believe 1987 for the first time at a conference like Pandemic of Loneliness, that's a whole business, right? Because think about it, if you hook someone up on an LLM at 13 years old because the school accounted decided that they want to use an LLM in the school by the age of 18 you have a full fledged user, right? A user of an LLM and you know, it's like, you know, again, who calls people users like drug dealers and software developers? That's damn. Yeah. But it's true, right?

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C (56:11)

Natalia if it's an age appropriate scenario, these are the ramifications of your study. So as any concerned parent would look at that and say, well, I want the best for my child's development and this may not be the best for the critical thinking for the cognitive development within the young person's brain. So with these ramifications, how has the AI world reacted to your study and what are the chances that they'll embrace what your conclusions will be?

D (56:43)

Well, I mean, we saw some of it, right? So, well, first of all, right, we saw that. We obviously don't know if this is direct response or not, so we're not going to speculate there whatsoever. But several weeks, just very few, like three, four weeks after actually our paper was released. OpenAI released study mode for ChatGPT. Right? And it's I think maybe something that should have been released from the beginning. I am just saying. But you know, if you have a button that can immediately pull you back in default mode, who's gonna use that study mode, right, altogether. Like who? Like, I don't need to run a study here. We know some people might, but not everyone because again, back to the brain. Brain will look for a shortcut. Shortcut is the responses here and I can go do all the other cool stuff. So who's going to actually use it, right? We still need studies on that. That's first point, right? Second point, of course, H is important because again, the brains that are being developing right now are potentially the highest rate because here we all are. We all were born long before the stack existed. And a lot of AI developers and people who are running these companies are all, all who again were all born long before the tech existed. So they learned the hard way how to ask questions out of the deal. We know going through all of that. They know how to ask a question. What about those who actually are just born with the technology? Will they even know how to ask a question? And back to the point, right, of the age. I don't think it's ultimately only for young. Of course we do need to look for the older, right? For also just younger, I mean, young adults. Of course. Everyone is talking about humanity's last test, I would call it. We are on the verge of humanity's last. And I'm sorry, I know you might need to blurb this term out, but what I mean here, obviously intimate relationships for people, right? With the promise of this group.

B (58:44)

You said humanity's last, yes.

A (58:49)

Oh, believe me, I heard it. I was just like, we all heard that. I was like, God bless you.

D (58:54)

Yeah, yeah, yeah. But again, that's crude. But it's back to this point of designing against these interestingly appealing ladies and gentlemen and whatnot in these short skirts, whatever it is, who's gonna go make those babies? Who will pay those taxes? I'm just saying, right? And again, very famous expression, no taxation without representation, right? I do not want my prime minister or Secretary of Defense use a random algorithm to make decisions. I'm paying my taxes for them to think, not for an algorithm to think for them, right? So there is a lot of these repercussions. But back to ultimately the point actually, is anyone taking this seriously? Right? We just need more human focused work on AI. Like I remember when the paper went viral, right? We didn't even put any press release. We literally uploaded it to archive. This is a service where you called these papers that didn't go through peer reviews yet. We l I didn't post.

B (59:55)

Not a single preprint service.

D (59:57)

Basically service, right? And no one, no one, neither the lab nor any of the authors posted anything on social media. We just went about our days. Two days later it goes viral and then I'm going on.

A (60:10)

That's because the LLM posted it for you.

D (60:12)

Yeah, obviously, right. And then people used LLM to say, but that's another story, right? Like I'm going on X and actually I have an account, but I'm not using it. A lot of academics switched from X to like other platforms that we are using, but I'm going there and apparently I learned that there are people who are called AI influencers these days. I didn't know that this is the term, but apparently these AI influencers, they post these AI breakthroughs of the week. And I went, our paper, oh my God, made a cut. It's breakthrough number seven. And I scroll through, this influencer person has ton of follow and whatever, I don't know, real bots, whatever. I'm scrolling and I saw like 20 of these posts for 20 weeks. All of the posts are about GPU, multitrillion deal here, multibillion deal here, more GPS. I'm like, what is human here? Where is human? Where are we evaluating impact of this technology on humans? Why only our paper made it number seven? And where are the papers? Right? So that's, I think something where the focus needs to shift, right? So if these companies do want to be on the right side of history, right? Because that's like social media but on steroids. Much worse. You do not talk to a calculator about your feelings. So people who compare it to calculators, they're so, so, so wrong. Right? But hey, it's gonna get much, much worse with profiliation without any validation, any guardrails, right? So we do need to look into that heavily, Right, Right.

B (61:39)

Natalia, how must teaching change to accommodate the reality of student access to LLMs?

D (61:48)

I can tell you we received 4000 emails from teachers all around the world. Each single country in the world sign an email. They are in distress, they don't know what to do. So and that's first of all, my love goes to them if this makes the cut. Please, please, please. I'm trying to respond to all of those, but the challenge is that they do not know, right? There's not really enough of guidance and 10 hour workshop sponsored by a company that pushes this partnership on your school does not make a cut. Right? There is a lot of comments how it's actually not supervised, not tested. And ultimately, do you really need to go with these closed models, right? You have so much open source, whole world, all the software runs on open source. These LLMs would not exist, nothing would exist without open source. So why don't we run an open source model? Meaning like it's offline on your computer and spoiler alert, you don't need a fancy GPU from Jensen, right? You can get an off the shelf computer and then run a model local with your students, train it over the weekend, come back on Monday, check with students what happened. Learn all the cool pros, cons, laugh at hallucinations, figure out tons of cool things about it. Like why do we need to push this partnership that we don't even know? Like Alpha school, right? I don't know if you heard about that one. Apparently AI first ran school, right? Where teachers are now guides that they are using. I just saw literally one hour before our call that several VCs posted about this alpha school. So cash is flowing there heavily, right?

B (63:28)

VCs, venture capitalists.

D (63:30)

Yeah, venture capitalists heavily pushing alpha school. But again, in first comments from even general public, do we have a proof that that's better? What are the advantages? Because there's not going to be a perfect white pure card. There will be advantages as with any technology.

A (63:47)

And you're right, there are advantages, disadvantages. But I think, if I might, if I may, and this is just an opinion, we might have to change the objective of school itself. And right now school is about really not learning. It's about results, testing. I got an A, I got a B. And maybe if we change school to what exactly did you learn? Demonstrate for me what you learned then. The grading system.

B (64:27)

That's an oral test. That's an oral exam.

A (64:30)

Yeah, but the grading system kind of has to become less important because now what a teacher's job is, it's to figure out how much you know. And then what ends up happening is the more you know, the more excited you are to learn. And we may end up revolutionizing the whole thing because what you have is a bunch of kids in a room that are excited to learn.

B (64:54)

So this is the silver lining of all this because it exposes the fact that school systems value grades more than students value learning. And so students will do anything they can to get a high grade. This is not the first time people have cheated on exams, right? So if right now the only way to test people is to bring them into the office and quiz them flat footed, then that's a whole other way of. They're going to have to learn, they're going to want to learn and then they're going to. Like you said, Chuck, once they learn, there's a certain empowerment and enlightenment. I see it in people as an educator, when that spark lights when they say, wow, I never knew that. Tell me more.

A (65:38)

Right?

B (65:38)

They didn't say, oh my gosh, I'm learning something. Let me take a break. So it can be transformative to the future of education.

C (65:45)

But Neil, people are going to say the LLM will do all of that. And you know what? We have an expert in BCIs that probably is something going forward that you'll have a brain computer interface and then someone's going to look at this. And I think there are people already saying, why do we need universities? Why do we need further education institutes?

A (66:08)

Exactly. That's what I've been saying for many years now. Why do we need an institution?

B (66:16)

Well, I, I don't want to put words in the T mock, but said this, LLMs use pre existing, already known, already determined information to give you anything that then cannot possibly be new. Whereas we can do new things that LLM has never seen before. Am I oversimplifying your point, Natalia?

D (66:40)

No, that's totally, you know, correct because hey, we are with this struggle, right? Obviously I'm biased because this is actually my job like as a researcher, right? We are sitting, you know, figuring out those answers to those problems, you know, and trying to figure out what is the best way to measure, to come up with this. So of course, you know, and there's so, so much more to that that we are coming up humans, right? We designed LLMs ultimately, right? So we came up with these tools. It doesn't mean that the tool is fully to be discarded, but effectively, of course, right? Why you need an institution. For example, I was actually explaining to one of my students three days ago how to use a 3D printer, right? Well, LLM is not that yet to explain, right? Can give instructions, sure, with images and with video, right? But if you're like, hey, this is an old Fella here, this 3D printer, let me tell you how to actually figure it out, right? This level of again of expertise, of knowledge, right? That's what you are striving. But also it has this human contact, right, that we are now potentially depriving people from because that's how you have this serendipitous knowledge, right? And connections like hey, I just chatted and I'm like, oh, I never thought to do this because I'm in bcis and that person is in astrophysic or we never. Oh, I actually can use it. Like that's totally not brain, but I can totally go apply and try it, right? And that's the beauty of it, right?

B (68:05)

Yeah, but, but to. I think to. To Gary's point or which one of you said that? Gary or Chuck? If, if you, okay, you're non invasive in your brain cognitive interface. If you get invasive, and that might be what neuralink is about. If you get invasive, then I can get information gleaned from the Internet and put it in your head so you don't have to open a hundred books to know it. It's already accessible to you.

A (68:28)

That is the Matrix. Exactly.

B (68:31)

Again, install meet. I know Kung Fu or whatever that line was.

D (68:36)

Yes, that's one point. But again that's back to the point now I know Kung Fu didn't mean that you learned it, right? It got applied, floated into his brain. It doesn't mean that he actually learned it, right?

B (68:46)

Who cares if it's in your brain and you have access to it? I don't care if I learned it struggling the way grandpa did. This is the future, right?

D (68:56)

That's the thing, right? Because in the movie, which is excellent, I watched it 19 times or more. That's actually how I started my career. And besides this, I don't want to do anything else. I want to do this specific scenario, right? And we are still there. But that's the beauty. We do not know actually that just uploading would be enough, right? We have this like more tiny, I would say studies right now of like vocabulary and words and things like that where we're trying to improve people's language learning, right? It's like a very, very good example to show and so that tiny examples. But we do not know yet that even if, imagine, imagine we have this magical interface, right? That will applaud invasive and non invasive. It doesn't. Doesn't matter. We have it, right? It's. It's ready to go, perfect function safe, whatever, you have it and then you upload all of it that it actually will work. Did you upload the knowledge like all of that blah Blah Blah from Chat GPT75? Yeah, sure. But do you actually use it? Can you actually use it? Is it really fire in which I'm simplifying.

A (69:56)

So, so what you're talking about is a working knowledge of something.

B (70:01)

Not just knowledge of it.

A (70:02)

Not just knowledge of it.

B (70:03)

Yeah. Okay.

D (70:04)

So all.

C (70:04)

I mean, I think, Neil, what you were talking about just now about. We've got to look at. I think, Chuck, you made the same point. We're focused on grades and then it's the learning. And are we going to have to. If higher education is going to exist as an institution, bricks and mortar. Look at the way they evaluate because I can't see LLMs and BCIs not coming through stronger and stronger and stronger. So therefore they're going to have to readjust how they look at a young person's ability to.

A (70:41)

Cat's out of the bag.

C (70:42)

Yeah, I agree with you. But I mean, you know, we are going to be herding cats. I agree with you. Which is a load of fun. So how. It's how you evaluate how higher education then looks at its students and guesses or sort of ascertains their level of education and knowledge.

D (71:03)

Yeah. Back to the grades. Right. It's an excellent point. And it. There is no doubt, no one has any doubt, I think, on the fact that education does need to change and it has been long, long overdue. Right. The numbers about, you know, the literacy, reading literacy, mass literacy, they decreasing in all the countries, I believe. I don't see. I have, I saw like apps there. Anyway, it's down, down, down all these reports recently from multiple countries. But it's back to the point I made earlier about the grades or about scoring. Right. Who are we scoring and what are we scoring? I be scoring a pure human. So just human, like human brain as is. Like Natalia. Or I'll be scoring Natalia with an LLM. Right. So I'm using it. So we know that I was scoring just an LLM. And then there is Natalia who used it. Right. So this will be in even that was important. But ultimately the school, of course, is not about that. As I mentioned, everything you learn is obsolete. Knowledge by itself, but it has this base. You do need to have the base. You're not going to be a physicist if you don't have it. Whatever it feels about, you know, you're not going to be the mess. You're not going to be a programmer. Our next paper is actually about wipe coding. Spoiler alert. Not going to work if you don't have the base. Right. And. But the idea is that back to the. What we actually maybe should look. Look at really is what the school is great. Which is the best thing I actually brought from school is this base. Definitely super useful but also my friends, people on whom I rely in hard situations, with whom we write those grants, with whom I we can shout and have fun and cry over funding that is over for a lot of us, right? All of that stuff, right? These connections, right? This is what maybe we should value because we are killing it further and further, right? And we are just keeping people at this silos of being a user, right? And that's where it only stays. And this imaginary three and a half friends from Zach, from Zuckerberg, right, that he mentioned, thanks to whom we have three and a half friends, thanks to him and his social media, right? So I think that's why we need to really look into what we want truly from society, from schools and maybe on a larger scale, what are the guardrails, right? And how we can actually, right. In the way that are safe for us to move forward and evolve further, which because of course this will happen.

B (73:23)

Are you wise enough? Are you and your brethren in this business on both sides of that fence, are you wise enough to even know where the guardrails should go? Might the guardrails be too protective? Preventing a discovery that could bring great joy and advance to our understanding of ourselves, of medicine, of our longevity, of our happiness? Is there an ethics committee in practice? How does this manifest?

D (73:53)

Yeah, I'm going to give you two examples here real quick. So first about obviously AIs and LLMs, right? They were not born overnight, but we see how a lot of governments really struggle still and very reactively react to those instead of being proactive, right? And the challenge here is that we do not have data to actually not to say that it is good stuff, that we should really implement it everywhere in our backyard. We don't have this data. What why we are fomoing. There is nothing yet to FOMO about to really run with it, but we can absolutely create this spaces where this is being actively used, for example, for adults, for discovery to understand it. Why do we need to push it everywhere is still very unclear. We just don't have this data. But then back to the point of guardrails, right? What we should be doing, obviously self plug on the BCI work that I'm doing, there are multiple ASICS pushes right now for the BCI technology. We can agree it's still pretty novel, but it definitely moves forward very fast. So I'm having a hope that for this technology, for the big next thing, right? We agree LMS are great, but it's not the next big thing, it's robotics. And then we will see bci. So for this big next thing, I'm very hopeful that we will be in time to protect our thoughts, literally. Because think about what will happen right before the study mode, right? You have censorship mode and you know how the like look at Deep Seq, right? I'm not going to go further far. So think about a billionaire, I'm not going to even name his name. Billionaire who has a social media platform, satellite platform and neural implant startup and AI company. So he decided two months ago to cleanse history, right, from errors and mistakes and tomorrow he will decide to cleanse our thoughts, right? This is the idea for 99.99, right?

A (75:47)

For damn that Bill Gates.

D (75:50)

No, not really. We know and that's why we need to be really, really cautious. Like we should definitely look into that use case and not make that happen, right? And allow people for enough agency. Because that's the thing, right? People think, oh that's great, but there is not a lot of agency. So this freedom of making a choice that's already made for you in a lot of cases. And so that's something that, that we should definitely protect as much as we can. Like do not force on those kids stuff because they cannot consent and say no. It's because the school forced it on them and their parents decided that that's a big thing in San Francisco, in the Bay Area that you should use, right? So don't do that.

C (76:31)

So is one of, is one of the components to building a robust set of guardrails, a larger scale study of the one that you've already conducted that has different or more nuanced layers, that focuses on other aspects, not just the cognitive load and skills.

B (76:53)

So a thousand people and not just 18 or whatever was your 54.

D (76:58)

But it's not just that, right? We needed to do in larger scales for all of the, you know, spaces like workspace. So we didn't talk about this because obviously it's heavily about education. But like workspace, we have multiple papers, right, talking that people are not doing that well in the workspace. Like for example, programmers estimate that they gain 20% of their time. They actually lose 19% of their time on the tasks. So there is so, so much more to it. We need to do this on larger scale with all the ages, including older adults and then of course on different, different, different use cases and different kinds of cultural backgrounds, right? This is in US and of course cultural. It's very, very different. Like I talked so many teachers already, right? In Brazil, all over the world, you have this Intricacies you need to account for. It's so, so, so important because otherwise it's going to be all washed Western style, which we already saw happening. And it is happening. And a lot of people actually very worried their language will literally disappear in, like, five to 10 years. And it's not like LLM magically will say, save it, because it will not.

B (78:01)

Natalia, this has been a delight. We are all happy to know you exist in this world.

D (78:07)

Thank you.

B (78:08)

It's a checkpoint on where things are going, where you're not rejecting what's happening, but you're trying to guide it into places that can serve humanity, not dismantle it. And so we very much appreciate your expertise shared with us and our listeners, and even some of them are viewers who catch us in video form. So, Natalia Kuzmina, thank you.

D (78:36)

Thanks for having me.

B (78:38)

All right, Chuck. Gary. Oh, man, my head's spinning.

A (78:43)

Yeah, well, I think the takeaway here is use LLMs if you want to be a dumbass.

B (78:50)

I think this. Thank you, Chuck. That's the. That's the theme of the whole show.

A (78:55)

There you go, guys.

C (78:57)

Could have saved us a lot of time if you'd have said that earlier.

B (79:02)

All right. This has been another installment of Start Talk special edition. Neil DeGrasse Tyson, your personal astrophysist, as always, bidding you to keep looking up.

D (79:17)

Foreign.

G (79:24)

Hey there. It's Katie Nolan, host of Casuals, the sports podcast where we don't care how much you know about sports. We're just happy that you're here. Every week, I hang out with some of my good friends to discuss the biggest stories across sports and entertainment. But in a way that's, like, fun and not boring. Want to know Sue Bird's favorite Diana Tarasi story? Or how Heather the Larry o' Brien trophy is? Or even what baseball team is right for you based on your moon sign we got you. Listen to Casuals every Tuesday and Thursday on the Sirius XM app or wherever you get your podcasts.

D (79:53)

Bye.

F (79:54)

At Capella University, learning online doesn't mean learning alone. You'll get support from people who care about your success, like your enrollment specialist, who gets to know you and the goals you'd like to achieve. You'll also get a designated academic coach who's with you throughout your entire program. Plus, career coaches are available to help you navigate your professional goals. A different future is closer than you think with Capella University. Learn more At Capella Eduardo.