The Real and False Promises of AI & What They Really Ate at the First Thanksgiving

Summary

Detailed Summary of "The Real and False Promises of AI & What They Really Ate at the First Thanksgiving"

Podcast: Something You Should Know
Host: Mike Carruthers | OmniCast Media
Episode Title: The Real and False Promises of AI & What They Really Ate at the First Thanksgiving
Release Date: November 21, 2024

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Introduction

In this enlightening episode of Something You Should Know, host Mike Carruthers delves into two captivating topics: the true capabilities and misconceptions surrounding Artificial Intelligence (AI) and the authentic culinary traditions of the first Thanksgiving. With expert guests Arvind Narayanan, a renowned AI specialist, and Leslie Landrigan, a historian specializing in New England history, Mike uncovers facts that challenge popular narratives and offer deeper insights into both technological advancements and historical events.

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Section 1: The Real and False Promises of AI

Understanding AI: Definition and Types

Mike Carruthers initiates the discussion by demystifying AI, explaining it as an umbrella term encompassing various technologies. He differentiates between generative AI, like ChatGPT, and predictive AI used in sectors such as criminal justice and healthcare.

Mike Carruthers [06:00]: "AI is an umbrella term for a loosely related set of technologies... some types of AI, notably generative AI, are rapidly advancing."

Arvind Narayanan probes deeper into these distinctions, seeking clarity on the functionalities and differences between them.

Capabilities and Limitations of AI

The conversation highlights the impressive feats of generative AI, capable of understanding language patterns and predicting subsequent words based on vast datasets. However, Mike emphasizes that despite their prowess, these systems lack genuine comprehension.

Mike Carruthers [07:00]: "What is happening in ChatGPT is that it's simply a machine... it does have some 'understanding' of language... but I think it is truly remarkable that developers are able to create something useful with this really brute force approach."

When discussing predictive AI, Mike underscores its reliance on historical data to forecast future events, raising ethical concerns about its applications.

Mike Carruthers [10:35]: "It's making decisions about the future based on the past. So no matter how accurately it works, we should think about, is this a just way to treat people?"

Overhyped AI Claims and Snake Oil

Mike critiques the exaggerated promises surrounding AI, particularly the notion that AI might develop independent agency or revolutionize every aspect of human life. He labels such scenarios as "pure sci-fi" with no grounding in current technological realities.

Mike Carruthers [15:25]: "Those ideas about AI developing an agency of its own and deciding to do stuff, these are pure sci-fi scenarios based on the way that AI is currently built today."

Arvind reflects on these statements, questioning the validity of high-profile warnings from figures like Elon Musk.

Ethical Concerns and Future Implications

The discussion shifts to the ethical ramifications of AI misuse, such as deepfakes and biased decision-making in critical systems. Mike expresses particular concern over the erosion of trust in digital media and the potential for powerful individuals to manipulate perceptions.

Mike Carruthers [12:41]: "What does that mean for the erosion of trust in the online environment and how easy that makes it for powerful people... to evade accountability?"

He advocates for stringent regulations to mitigate these risks, emphasizing that many AI fears are "vastly overblown."

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Section 2: What They Really Ate at the First Thanksgiving

Historical Context of the First Thanksgiving

Transitioning from technology to history, Mike introduces Leslie Landrigan, who sheds light on the true nature of the first Thanksgiving. Contrary to the modern portrayal of a harmonious feast, Leslie reveals it was more akin to a state dinner focused on alliance-building between Pilgrims and Native Americans.

Leslie Landrigan [28:45]: "If you're going to call it a Thanksgiving, then the Pilgrims in 1621 were not the first Thanksgiving. The first Thanksgiving would have been in 1607 in Phippsburg, Maine."

Foods Actually Consumed vs Modern Myths

Leslie debunks common misconceptions about the first Thanksgiving menu. While modern traditions center around turkey and pumpkin pie, the original feast included deer, various birds, shellfish, and the "three sisters"—pumpkins, beans, and corn.

Leslie Landrigan [30:31]: "They would have had four deer... birds, deer, probably shellfish, probably corn."

She highlights the significance of pumpkin in New England, noting its prominence long before it became a Thanksgiving staple.

Leslie Landrigan [31:26]: "Pumpkin was hugely important. New England was the pumpkin dominion."

Role of Women in Early Thanksgiving Preparations

The narrative delves into the pivotal roles played by the four adult women in Plymouth Colony during the first Thanksgiving. These women were responsible for preparing meals for approximately 140 people under challenging conditions.

Leslie Landrigan [40:37]: "There were these four women who have to pluck all the birds that the men caught... It's just would have been a nightmare."

Evolution of Thanksgiving Traditions

Leslie explains how Thanksgiving evolved into a national holiday, influenced significantly by Sarah Josepha Hale, who popularized turkey as the centerpiece of the meal through her writings.

Leslie Landrigan [33:06]: "Sarah Josepha Hale... made turkey the centerpiece of the American meal."

She further clarifies that many traditional Thanksgiving foods, like potatoes and celery, became popular in the United States much later, debunking the notion that they were part of the original feast.

Leslie Landrigan [35:52]: "The sweet potato didn't come to America until 1764... the Irish Potato didn't come to the United States until 1718."

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Conclusion

This episode of Something You Should Know offers a profound exploration of both contemporary technological debates and historical narratives. By demystifying AI and challenging long-held Thanksgiving myths, Mike Carruthers provides listeners with a nuanced understanding of complex subjects. The insights from Arvind Narayanan and Leslie Landrigan encourage critical thinking and a reevaluation of commonly accepted truths, embodying the podcast's mission to equip listeners with "fascinating information and advice" to enhance their lives.

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Notable Quotes:

• Mike Carruthers [05:52]: "AI is an umbrella term for a loosely related set of technologies... some types of AI, notably generative AI, are rapidly advancing."

• Leslie Landrigan [28:45]: "If you're going to call it a Thanksgiving, then the Pilgrims in 1621 were not the first Thanksgiving."

• Mike Carruthers [15:25]: "Those ideas about AI developing an agency of its own and deciding to do stuff, these are pure sci-fi scenarios based on the way that AI is currently built today."

• Leslie Landrigan [35:52]: "The sweet potato didn't come to America until 1764... the Irish Potato didn't come to the United States until 1718."

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This comprehensive summary encapsulates the essence of the episode, providing valuable knowledge to both existing listeners and those new to the podcast.

Transcript

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[Arvind Narayanan]

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[Arvind Narayanan]

Now that's a bingo. Today on something you should know. Some fascinating facts you never knew, including one weird one about Wild Bill Hickok. Then a top AI expert on the amazing things AI can do and the false promises, the things AI cannot do.

[Mike Carruthers]

These ideas about AI developing an agency of its own and deciding to do stuff, these are pure sci fi scenarios based on the way that AI is currently built today. Those speculative scenarios really have no basis in reality.

[Arvind Narayanan]

Also the real reason printer ink is so expensive and a look back at what they really ate at the first Thanksgiving.

[Leslie Landrigan]

One of the things that they always ate and ate to excess is pumpkin. Pumpkin was hugely important. New England was the pumpkin dominion and the first American folk song was written in 1620 and it was about how they ate too much pumpkin all the time.

[Arvind Narayanan]

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[Mike Carruthers]

Hi Mike. Thank you for having me.

[Arvind Narayanan]

So, on a very fundamental, simple level, what is AI?

[Mike Carruthers]

AI is an umbrella term for a loosely related set of technologies. You have on the one hand generative AI like ChatGPT. On the other hand you have self driving cars and you have predictive AI AI that's used in the criminal justice System, for instance, to make enormously consequential decisions about people. AI that's used in healthcare, these types of AI generally have very little to do with each other. And it's true that some types of AI, notably generative AI, are rapidly advancing. But we should be careful about, I think, the snake oil salesmen in the AI world who like to just slap the AI label on whatever tech product they're selling to try to get us to think that it is some remarkable technology that's going to solve all our problems for us.

[Arvind Narayanan]

So you mentioned some different kinds of artificial intelligence just now. Can you go through and just explain what each one of them does, or is that just too complicated to do?

[Mike Carruthers]

Oh, not at all. And I would say to listeners that if someone tells you it's too complicated, you should be skeptical. They're probably trying to hide something, but in broad strokes. So let's take a couple of different types of AI. So what's happening in ChatGPT is that it's simply a machine. And some of you may have heard this, a machine for predicting the next word in a sequence of words. What is the most likely next word? And it turns out, and this was largely a surprise to AI researchers as well, that the way for AI to be really good at predicting the next word in a sequence of words is to have some quote, unquote understanding of language, of grammatical rules and patterns, and understanding of facts about the world. Because if you have a sentence like the capital of France is blank, it helps to know what the capital of France is so that you can complete that sentence with a high probability word instead of a low probability word. Right? So it turns out that's really the secret behind it. And it might seem a little bit disappointing to hear that that's all it is. And in a sense, that's all it is. But I think it is truly remarkable that developers are able to create something useful with this really brute force approach.

[Arvind Narayanan]

And so when it's predicting the next word in a sequence, where is it getting the information? Where is it pulling from to come up with that word?

[Mike Carruthers]

Chatbots have been trained on essentially all of the text on the Internet, approximately speaking, and a lot of books and so on. So that's what it's pulling from. Right. So what trading means is that it has learned the statistical patterns that allow it to say, for example, you know something, you should. The bot would know that no is a likely next word, because there are many discussions of this podcast online. And so it has learned that statistical pattern, so that's primarily what it's learning from. To a lesser extent, these bots learn from their conversations with us, but it's not in the way that a person would learn. It's not automatic. There's a cumbersome process by which companies have to filter these chat conversations and feed that back into the training data. But to a first approximation, it's learning from text on the web.

[Arvind Narayanan]

And so now talk about predictive AI and what that is and how it's different and it works and all that.

[Mike Carruthers]

Sure, yeah. So predictive AI, on the other hand, is statistics that we've had for a century almost, that's been rebranded into quote, unquote AI. So this is used, for example, in the criminal justice system to determine if a defendant should be jailed before their trial, you know, which could be months or years away. It's used in healthcare to detect sepsis in a hospital context, for instance, by looking at various indicators. It's often used in hiring to try to predict which employee might be a fit for the role or who's going to be a good employee and that sort of thing. Now what's happening in all of these cases is that the system is just picking up crude statistical patterns. So in criminal justice, the system learns that younger defendants are more likely to reoffend if they're released before their trial, and so recommends treating them more harshly. So these kinds of, again, fairly crude statistical patterns that, you know, we've known how to do for a long time, but it's not the same kind of technology behind ChatGPT. It is not something that's advancing quickly, and it is something that I think we should be pretty skeptical about.

[Arvind Narayanan]

But all it's doing is it's predicting based on the past. Right? I mean, that's pretty much it.

[Mike Carruthers]

Exactly. It's making decisions about the future based on the past. So no matter how accurately it works, I think kind of on a fundamental philosophical level, we should think about, is this a just way to treat people right? Should you deny someone their freedom in the criminal justice system because of the behavior of people like them in the past? So that's something that's deeply questionable as well.

[Arvind Narayanan]

What about this whole idea, though? We hear about AI and people throw that term around so much that AI can know fake things and it can create images of people that aren't real. It can replace, you know, actors in a movie. I don't. I don't get all that.

[Mike Carruthers]

Yeah, image generation. AI has been advancing very quickly, and over the last year or so, companies have been working on video generation AI. So yes, I think to some extent the hype around this is real. I think deep fakes are already a problem. Specifically, the thing I'm most concerned about is deep fake nudes. And this has affected, from what I can tell, you know, hundreds of thousands of people, primarily women, as you can imagine, around the world. And I think we desperately need regulation to curb some of the damage here. Now, in the political sphere, there's also concern that deepfakes can be used to trick voters and that sort of thing. I'm less convinced of that. There has been a lot of alarmism about that. But I think something we should think about is that in a world where we're online and we have no easy way to tell what's real and what's not, what does that mean for the erosion of trust in the online environment and how easy that makes it for powerful people, politicians and others to evade accountability by claiming that even real videos are actually deepfakes? So we see that happening over and over. And that is something I'm worried about.

[Arvind Narayanan]

There have been very prominent people who have sounded the alarm that AI is dangerous. And we've heard things about how, what if it develops a mind of its own? And there's all this stuff that's very scary sounding and I just don't know, is that real? What is the big concern that people like Elon Musk and others have? What are they worried about? What's the problem, first of all, with.

[Mike Carruthers]

Elon Musk and other CEOs of AI companies, it's a very self serving thing to say, way right. So this is incredibly powerful technology. You know, it's going to change the world, either bring about a utopia or destroy humanity. And we're the only ones in a position to ensure that this technology doesn't get out of control. We and many others in the AI community have spent a lot of time looking at the evidence behind these AI fears and we've come up short, basically. So let's take some of the concerns that have been brought up that AI could help bioterrorists, for instance, by finding information about how to create bioweapons. Now the funny thing about the sphere is that finding that information is not the hard part. For the most part, that's readily available on Wikipedia, right? And so if a chatbot, you know, makes it easier and makes it, you know, 10 seconds faster for someone to access that information, that's not the end of the world. And there have been concerns in cybersecurity, for instance, that AI could be used to hack critical infrastructure, and that could bring about catastrophic risk. But here's the funny thing. If the ability to use AI to find bugs in software and attack systems that way, if that is a critical capability for hackers, then we've already lost. Because for the last 10 or 20 years, automated ways to find bugs have been readily available even before AI. Yet the world hasn't ended. And in fact, it's turned out that these methods primarily help defenders over attackers. They help software developers because those developers can use these automated tools, including AI, to find and fix bugs in their software before they ship it out, before hackers even have a chance to take a crack at it. And we think the same thing is happening with AI, and we think some of these fears are vastly overblown. And then these ideas about AI developing an agency of its own and deciding to do stuff, these are pure sci fi scenarios based on the way that AI is currently built today. Those speculative scenarios really have no basis in reality.

[Arvind Narayanan]

Yeah, so quick break here. I'm speaking with one of the smartest guys I've ever met when it comes to AI. His name is Arvind Narayanan. He's one of Time magazine's 100 Most Influential People in AI and he's author of a book called AI Snake Oil. What Artificial Intelligence can do, what it can't and how to Tell the Difference.

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[Arvind Narayanan]

So Arvind, because of your work, you're steeped pretty deep into AI. But for the rest of us, for people who, you know, we understand sort of what it is. But like, how. How do you think I could use AI? Or the average person could use AI in a way that would be really helpful.

[Mike Carruthers]

So it's not this one big thing that AI is going to do for everybody. I mean, that might happen in the future, but so far I don't think there has been this one kill application, but it's 100 little things. So in my work, for instance, it's been enormously useful in helping me write code. Frankly, it's hard for me to imagine going back to a time before I had AI assistance in writing code, because it's just so much faster but also more fun, frankly. And for lawyers, I'm hearing there are so many ways in which legal tech is making their lives better, making it easier to find information. Of course it's not going to replace a lawyer. There have been many overblown claims of companies building a robot lawyer and things like that. I think that sort of stuff is a little bit silly for now at least. But I think in every profession that involves basically dealing with knowledge in Some way, AI can be a creativity enhancer or a way to automate certain mundane tasks in your everyday life. I think it can also be a good learning tool. There are pitfalls here because AI can hallucinate, I.e. generate incorrect information and not even be aware that it is hallucinating. That said again, once one spends a few hours learning to work around these pitfalls, I think it can be a very good learning tool. I use AI a lot for learning about new topics. I haven't stopped using books for learning. But you know, I can't ask a book a question and I can't summarize my understanding of a topic to a book and ask it if I have gotten it right. These are things that I can do with chatbots. So those are a few of the ways in which I've been using it in my own work and in my own life. And I think each person has to figure it out for themselves.

[Arvind Narayanan]

And so when you say that, you know, it would be worthwhile to, to use some of these tools and see what they can do. Like what? Like if I type a question into Google, am I using AI or am, is that a different technology? Like what are the AI tools or are they embedded into everything?

[Mike Carruthers]

There are broadly two different ways to use AI. One is you can use a specific AI app, you know, ChatGPT is the most well known one, or you can use AI that's embedded into the other apps that or other physical products that you use. And I think both of those are interesting and both of those are worth trying out. So specifically, if you do a Google search, there are relatively simple types of AI that have existed even in traditional Google search. But more recently, Google has started creating these AI overviews which can often be wrong. So I think it's caveat mtor to actually verify that information. But I think it can be more enlightening to play with a standalone AI tool like ChatGPT or Gemini or whatever people want to use and explore the kinds of things that it can do as well as learn how it gets things wrong. And I think that's going to give you a much better understanding of AI's powers and limitations.

[Arvind Narayanan]

And so I think when people use like ChatGPT or Gemini, there is an assumption that whatever it tells you is probably right. Is it probably right? What's the accuracy rate?

[Mike Carruthers]

That's a great question. We should not assume that whatever AI tells us is probably right. I think the accuracy rate varies greatly depending on the kind of topic. When I use AI with my kids, you Know, when I ask IT science questions it's, it's very good at explaining those things in a way that a five year old can understand and almost never makes mistakes. But if you ask IT questions on a very specialized topic, there have been papers looking at the accuracy of AI in the legal sphere or medical sphere. Right here it's much more dodgy and obviously these are areas where accuracy is much more important. So one might wonder if it's going to sometimes make mistakes. Should you use this tool at all? I would argue still probably yes, because I think it can still enable you to do things that would otherwise be very hard. So one example of this is when I'm exploring a new topic, I don't even know how to frame my question and if I don't know the right terms to put into Google search, I can't find the authoritative sources on that topic. But with chatbots it's very easy. I just describe it in a fuzzy way in which I think of it in my head and it, you know, it rephrases it for me and then it gives me information about that topic. Sometimes it's reliable, sometimes it's not. So it's just a very different way of interacting with information. It's just really hard to understand it in terms of previous ways of interacting with information. I can't give you a number saying, you know, 90% of the time it's going to be right. It just really varies depending on one's use case. So I think, you know, each of us has to put, put a little bit of trial and error into adapting it for our own specific purposes.

[Arvind Narayanan]

So where is the snake oil? What's the snake oil part of this that has you most concerned.

[Mike Carruthers]

Criminal justice for instance. Right. So I don't think we should be making decisions about people based on these crude statistical formulas. With some caveats. Like I was saying earlier, if it's the judge who is empowered to make that decision, that's a different story. There is a lot of the same coil in hiring. There are companies that claim that by analyzing a 30 second video of a candidate of a job candidate, not even talking about their skills for the job, but about their hobbies or whatever, that they can do video analysis and look at the candidates facial expressions and body language and that sort of thing and use that to drive a personality score, which companies should do their hiring based on. There's so much more. There is AI for detecting which students in a school or college might be at risk of suicide or mental health difficulties. There have been investigations of all these kinds of AI tools, and they barely work better than the flip of a coin. So I think these are the kinds of things we should be very suspicious of. And unfortunately, these are the kinds of things that are often used in order to make very high stakes decisions about people.

[Arvind Narayanan]

Is it just a matter though of over time it will kind of work itself out. It'll get better because the more people use it and the more practice it gets, basically, that the better it gets for ChatGPT.

[Mike Carruthers]

Yes, but here's the difference between ChatGPT and trying to predict if someone will commit a crime. You know, ChatGPT is just trying to do things like, you know, some. A typical thing you might use ChatGPT for is to translate text from one language to another. Right. That's not like a fundamentally impossible task. It's something that humans can do. And AI over time is learning to do it better. Right. Or write code or whatever it is. On the other hand, predicting what's going to happen in the future, no one knows. The universe doesn't know. It doesn't matter how much data you can throw at it. What we're seeing is that these technologies are not really getting better. They haven't got better in decades. And it should be common sense that we can't really predict the future, or at least not with anything close to perfect accuracy. And yet a lot of companies are telling us to suspend our common sense because AI. Right. And that's what we're trying to push back on.

[Arvind Narayanan]

Well, I appreciate all you've said. It's helped me get a better understanding of what AI is and what it does and doesn't do. And I'm sure other people listening feel the same way. My guest has been Arvind Narayanan. He is a professor of computer science at Princeton and director of its center for Information Technology Policy. And he is author of a book called AI Snake Oil, what Artificial Intelligence can do what it can't and how to Tell the Difference. And there's a link to that book at Amazon in the show notes. And thank you again for coming. Arvin.

[Unknown]

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[Leslie Landrigan]

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[Arvind Narayanan]

Most of us learned in school about the first Thanksgiving, how Pilgrims and Native Americans came together for this big feast. And they ate turkey and pumpkin something or other and they gave thanks. And I have to admit, I don't remember too much of what I learned about the first Thanksgiving. And in fact, I wonder how much of what I did learn was in fact, fact or fiction? Here to talk about what really went on at the first Thanksgiving and how some of our customs around this holiday actually came later is Leslie Landrigan. She's been writing about New England history for over 10 years and she's author of a book called Historic Thanksgiving Foods and the People who cook them, 1607-1955. Hi Leslie, welcome to something you should know.

[Leslie Landrigan]

Thanks, Mike. I'm happy to be on so it.

[Arvind Narayanan]

Seems like there's always been this fascination about what they ate at the first Thanksgiving. I'm not sure why that is, but is it a mystery? Is it a theory? Do we really know what they ate?

[Leslie Landrigan]

We know two things. We know that they had four deer, that the natives brought the 90 natives. And we know that the men went out shooting birds with the with the natives and the Englishmen. So birds, deer, probably shellfish, probably corn. That's what we know for sure. Lobster maybe.

[Arvind Narayanan]

And do we know why that first Thanksgiving, like how these people came together and did they call it Thanksgiving? And like, what's as briefly as you can, what's the quick story of why these people came together?

[Leslie Landrigan]

What's interesting to me, if you call the meeting of indigenous people and English colonists in the early 17th century to eat food in autumn, if you're going to call that a Thanksgiving, then the pilgrims in 1621 were not the first Thanksgiving. The first Thanksgiving would have been in 1607 in Phippsburg, Maine, where a failed colony was established for about a year. But the circumstances were very, very similar. The two groups came together. Basically, it was more of a state dinner than it was at Thanksgiving. They were negotiating alliances. They would trade with each other and they would defend each other against common enemies.

[Arvind Narayanan]

The food that they ate, which we'll get into very soon here, but is it the food that they always ate, or was this some real special kind of food?

[Leslie Landrigan]

It was the food they usually ate. They may have dressed it up a little bit, and it would have been plentiful because of the time of year, but it was pretty much what they ate. I was going to say one of the things that they. They always ate, and they ate to excess, and they have eaten it since 1620, and they're still eating it, is pumpkin. Pumpkin was hugely important. And you know how we call people in Wisconsin cheese heads? People used to call New Englanders pumpkinheads. New England was the pumpkin dominion. And the first folk song was written in. The first American folk song was written in 1620. And it was about how they ate too much pumpkin all the time.

[Arvind Narayanan]

And what was the magic of pumpkin. Just because there were so many. I mean, that wasn't something that came over from England, right?

[Leslie Landrigan]

Actually, they did know of pumpkin in England, and pumpkin pie was really popular. The Spanish had brought it over, and then it kind of fell out of favor. But it grew. Well, it was more resistant to deer and insects and fungus and things like that. So I think it was just its hardiness and it kept for a while.

[Arvind Narayanan]

In addition to a pie, what do you make out of pumpkin?

[Leslie Landrigan]

They tended to stew it. They would do a lot with it, but mostly they'd chop it up and stew it and mix it up with other stuff. I don't know that it was terribly appetizing.

[Arvind Narayanan]

Well, if you ever eat pumpkin, because we feed our dog pumpkin on recommendations of the vet, and it isn't much. I mean, without spicing it up, it doesn't really. No, it's pretty bland.

[Leslie Landrigan]

It's pretty nutritious, though, right?

[Arvind Narayanan]

That's why the dog eats it.

[Leslie Landrigan]

Well, you know, the natives, they grew what was called the three sisters, the pumpkin or squash, beans and corn, which for some reason, having to do with amino acids or carbohydrates or something, I don't know, makes for a very nutritious diet.

[Arvind Narayanan]

At the center of today's Thanksgiving dinner is typically a turkey. Was it their center of the table?

[Leslie Landrigan]

No, it wasn't for a long time. They may have had turkey at the first Thanksgiving. Turkeys are wild. Turkeys are really stupid birds. They roost in the same place all the time. So, you know, if you want dinner, you just go get yourself a turkey. But in fact, they were so easy to kill that they were obliterated from New England probably by the Civil War. Turkey, it was a part of the meal and it was something they ate. But chicken pie was the big thing for a long time. And it was a woman named Sarah Josepha Hale, who was a Widow with five kids and needed money. So she wrote a book in 1827. It was a novel. I can't think of the name of it. But she described a Thanksgiving dinner in New England, a classic New England Thanksgiving, which was really, at the time, only celebrated in New England. And the book sold well. And she got a job as the editor of what became Godey's Lady's Book, which was this tremendously influential magazine. It was way more influential than Martha Stewart. And she was an American influencer, and she was the one who made turkey the centerpiece of the American meal. And she was also the one. She lobbied for a long time to make Thanksgiving a national holiday. And finally, Abraham Lincoln was the one who said, yeah, okay, so can you.

[Arvind Narayanan]

Run down without going into. You don't have to stop at any of them and go into any detail. We can do that later. But just like, what's the menu look like at these early Thanksgivingy kind of dinners? What's on the menu?

[Leslie Landrigan]

Well, for the Pilgrims, it would have been something called n'samp, which was a native kind of a porridge made with cornmeal and nuts, berries and maybe a sweetener. They probably would have had striped bass, which was a fish that was easy to catch and that was also sustaining them. Probably would have had shellfish. They would have had deer, probably, and I'm guessing a lot of different kinds of wild fowl. They. I don't know that they would have had dessert, but they did develop this thing called Indian pudding, which was cornmeal with milk and a sweetener.

[Arvind Narayanan]

What about potatoes, Stuffing and gravy?

[Leslie Landrigan]

Oh, potatoes. Well, in 1620, we're talking about that first Thanksgiving, that first alleged Thanksgiving, they would have known about potatoes, but the potato they would have known about was the sweet potato, which the Spanish had brought to Europe. And it was highly prized because it was believed to be an aphrodisiac, and it was a luxury item. So some of the pilgrims, who were of the gentry, would have been familiar with the sweet potato. But the sweet potato didn't come to America, I think, until 1764. The Irish Potato didn't come to the United States until 1718, when a bunch of. There were five shiploads of Scots Irish who came to Boston. And the Boston Puritans didn't want to have anything to do with them, so they sent them to the New Hampshire frontier. And in what is now Derry, New Hampshire, they planted the first potato, the first Irish potato. And it was viewed as a food for the poor, for pigs and for the Irish, you just didn't eat the potato. And the French hated the white potato even more. They banned its harvesting, or they banned the planting of the potato because they thought that it caused leprosy. But then during the Seven years war, around 1755 or so, there was a French pharmacist who was captured by the Germans. And while he was imprisoned, they made him eat potatoes. So after he got released, he got really interested in nutrition, and he rehabilitated the potato, and the French came to embrace the noble spud. And they served Thomas Jefferson french fries in Paris when he was minister to France. And Thomas Jefferson liked the french fries, so he served them at the White House when he was president. And that's how the white potato became a popular menu item at Thanksgiving.

[Arvind Narayanan]

You said the sweet potato didn't come here until the 1700s, but I thought you said that they was. It was at the first Thanksgiving, which would have been before then, so help me.

[Leslie Landrigan]

No, no, no. They would have known about the sweet potato, but they wouldn't have had them here. It was. It was something, you know, it was like a really fancy food.

[Arvind Narayanan]

So there are some foods that. That I think of newing as New England foods that are often associated with Thanksgiving. Were they. And those would be cranberries, apples, things like that. Were those there or not?

[Leslie Landrigan]

Oh, they would have had cranberries, definitely. The natives revered the cranberry. In fact, there is a. There are some Wampanoag people who live on Martha's Vineyard, and their Thanksgiving is the second Thursday, I think, in October, and it's cranberry day, and the kids get out of school and they eat cranberries. It was very, very useful. Was used as a dye, it was used as a sweetener. It had medicinal properties.

[Arvind Narayanan]

Were the early settlers here, the Pilgrims, were they big on vegetables? Meaning did they have, like, peas and celery and carrots and things like that?

[Leslie Landrigan]

They would have eaten the three sisters, the pumpkins, the beans, and the squash. Celery is kind of an interesting vegetable because it didn't really come to America until the American Revolution, the 1770s. And it was. It was kind of a fancy food. But think about it. You're celebrating Thanksgiving in late fall, and vegetables are mushy, but there's this nice, green, crisp vegetable. And for many years, it was the most popular item on US restaurant menus next to coffee and tea.

[Arvind Narayanan]

So talk about the people. Because you mentioned this, the one woman who was kind of the Martha Stewart of her, or bigger than Martha Stewart, but I imagine that there are other people in this story that kind of steer the menu a bit, or the legend of the menu. Yes.

[Leslie Landrigan]

Well, the people who stick in my mind are the first four women who cook Thanksgiving, because after that first winter, there were only four adult women left in Plymouth Colony. And there would have been some 48 others who survived and 90 Native Americans. So that's cooking for 140 people. Here are these four women who have to pluck all the birds that the men caught. They probably have to cut up the deer. They have no running water. They've got to cook outside. It just would have been a nightmare. I can't even imagine it. But I can tell you who they were. There was Mary Brewster, who was older, she was in her 50s, and she was the wife of William Brewster, the spiritual guide. There was Susanna Winslow, who was the wife of Edward Winslow, who was one of the leaders. And those two were saints, which means they were the Puritans who came for religious reasons. So the other two women were Elizabeth Hopkins and Elizabeth Billington. And the Billingtons were bad news. Her husband, John Billington, was hanged for murder. Her son was a troublemaker who got lost and nearly started a war between the Pilgrims and the natives. And she was whipped for slander. But the one who really interests me is Elizabeth Hopkins. Her husband was Stephen Hopkins, who was in a Shakespeare play. He had come over to North America one time previously as an indentured servant and his ship got wrecked. And they lived on Bermuda for nine months and rebuilt the ship and went to Jamestown. And Shakespeare heard the story and wrote the Tempest. And so Stephen Hopkins, who came back to North America after returning to England, he was Stefano in the Tempest. He was the power mad butler.

[Arvind Narayanan]

So you have this image that we got in school of, you know, the Native Americans and the Pilgrims coming together as some sort of like community dinner and that they're all getting together and sharing their food. Is that what this was? Was there a lot of let me help you cook that, or here's how we do it here as Native Americans, or was there that kind of relationship?

[Leslie Landrigan]

I think there would have been. One thing I'm really unclear about is whether the native women came because, you know, they might have brought some Nasam or some cornbread or something. There were servants and there were children. And so I think everybody would have been pressed into service. They'd been working together for over a year. You know, the Pilgrims had things that the Indians wanted, guns, for example, or, you know, trade goods, pots. And the natives had something that the Pilgrims wanted, which was fur. There was a huge, huge market for beaver fur in Europe. And the natives taught the Pilgrims how to fish. So I think it would have been a cooperative effort.

[Arvind Narayanan]

So the natives and the Pilgrims have this big meal together. But was this like a special occasion? They came together, had this meal, and then they went their separate ways, or did these people mingle together all the time?

[Leslie Landrigan]

No, they intermingled a lot. As a matter of fact, Edward Winslow, who was the husband of Susannah Winslow, who cooked that dinner, he saved the chief's life at one point. Massasoit had some illness, and Edward Winslow came, and I think, honestly, I think he fed him something like chicken soup and did something to save his life. So, yes, and of course, Squanto, the native who breeded them, taught them how to grow corn. So they were. They were. They mingled a lot.

[Arvind Narayanan]

What else about this holiday or the. Or the first Thanksgiving anyway, or the early traditions of Thanksgiving do you find people still don't understand or maybe is a bit of a myth or anything like that?

[Leslie Landrigan]

It wasn't really Thanksgiving until the 19th century. It was kind of forgotten. And the Thanksgiving was something that the English celebrated in England. And here wasn't a harvest meal. A real Thanksgiving was getting the community together because you were thankful for something that could be rain after a drought or a military victory. So after the Battle of Saratoga and the Revolution, Sam Adams in Massachusetts declared a day of Thanksgiving. You could have Thanksgiving in April. You could have. Your town could have a Thanksgiving. Thomas Jefferson actually declared Thanksgiving when he was governor of Virginia, and it didn't really become a national holiday until Abraham Lincoln declared it.

[Arvind Narayanan]

But the idea of Thanksgiving, as you say, came later. So what did they view it as when they came together? They're coming together saying, hey, thanks for coming to our.

[Leslie Landrigan]

What I think it would have been like a state dinner. You know, they didn't sign any treaties, but that would have been the point of it.

[Arvind Narayanan]

Well, it sounds like the Thanksgiving we have today that we celebrate in our homes with our family and friends is very different than those early Thanksgivings and frankly, seems a lot tastier. But it is fun to hear you talk about what those real Thanksgiving meals were like. I've been speaking with Leslie Landrigan. She is author of a book called Historic Thanksgiving Foods and the People who cook them 1607-1955. And there's a link to her book at Amazon in the show notes. Leslie, thank you.

[Leslie Landrigan]

Terrific. Thanks so much, Mike.

[Arvind Narayanan]

Why is printer ink so expensive? A lot of people ask that question and according to Consumer Reports, there are actually some good answers to that question. You might not like them, but they're good answers. For one thing, the engineering that goes into printer ink today is really expensive. Today's printers have to fire thousands of drops of ink per second, representing four different colors with tremendous accuracy. Accuracy. And that ink needs to be quick drying water and smear resistant and avoid making the page curl up. It also has to prevent the tiny little ink jets from getting clogged. All of that costs a lot of money. Turns out that a lot of ink gets wasted. Well, it's not wasted, but it's not for printing. Printers use ink in two ways. First, of course, the ink is used to print documents and images. But the inkjets also use ink just to clean the printheads. According to one expert, it's typical for an inkjet printer to waste as much ink on maintenance as it does on printing documents. Another reason is, in essence, you're paying off the printer. Think of the price of the printer as a down payment. It's theorized that some printers cost more to make than the price they sell for. But the printer companies make up the difference by marking up the ink. And that is something you should know for a successful podcast like this to stay successful, we always need new listeners because, as you can imagine, listeners come, listeners go. And so we constantly need to attract new listeners. And you can help by telling people you know about this podcast and suggesting they give a listen. I'm Micah Ruthers. Thanks for listening today to something you should know.

[Unknown]

Hey.

[Mike Carruthers]

Hey.

[Unknown]

Are you ready for some real talk and some fantastic laughs? Join me, Megan Rinks and me, Melissa D. Montz, for Don't Blame Me, But Am I Wrong? We're serving up four hilarious shows every week designed to entertain and engage and, you know, possibly enrage you. And don't blame Me.

[Leslie Landrigan]

We dive deep into listeners questions, offering advice that's funny, relatable, and real.

[Unknown]

Whether you're dealing with relationship drama or you just need a friend's perspective, we've got you.

[Leslie Landrigan]

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[Unknown]

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[Leslie Landrigan]

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[Unknown]

Plus, we share our hot takes on current events and present situations that we might even be wrong in our lives. Spoiler alert. We are actually, quite literally never wrong.

[Leslie Landrigan]

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[Unknown]

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[Leslie Landrigan]

See you next Tuesday, where we reveal.

[Unknown]

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[Leslie Landrigan]

From But Am I Wrong?

[Unknown]

And don't miss fisting Friday, where we Catch up chat about pop culture, TV and movies. It's the perfect way to kick off your weekend. So if you're looking for a podcast that feels like a chat with your besties, listen to don't blame Me, But Am I Wrong On Apple Podcasts, Spotify.

[Leslie Landrigan]

Or wherever you get your podcasts.

[Unknown]

New episodes every Monday, Tuesday, Thursday and Friday. Do you love Disney? Do you love top 10 lists? Then you are going to love our hit podcast, Disney Countdown. I'm Megan, the magical millennial. And I'm the dapper Danielle. On every episode of our fun and family friendly show, we count down our top 10 lists of all things Disney. The parks, the movies, the music, the food, the lore. There is nothing we don't cover. On our show, we are famous for rabbit holes, Disney themed games, and fun.

[Mike Carruthers]

Facts you didn't know you needed. I had Danielle and Megan record some answers to seemingly meaningless questions. I asked Danielle what insect song is typically higher pitched and hotter temperatures and lower pitched and cooler temperatures.

[Unknown]

You got this.

[Mike Carruthers]

No, I didn't believe that about a wish coming true.

[Unknown]

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[Arvind Narayanan]

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[Leslie Landrigan]

I'm suicidal. You win that one.

[Unknown]

So if you're looking for a healthy dose of Disney magic, check out Disney Countdown. Wherever you get your podcasts.