Episode 36 – Killing as Few Humans as Possible

Description
Peter is away this week... or is he? A special appearance! Scott drinks some wacky coffee that's not really all that wacky and does arguing with a person who really IS wacky. Is it worth listening to? You tell us! Or rather, don't. We'll leave that to the AI to decide.
Transcript

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Friends with Brews!

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Hi, I’m Scott, and this is, well, as you just heard, Friends with Brews!

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Today’s gonna be a little bit different. I don’t have a Peter Nikolaidis with me today,

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because Peter is doing security-related training, or he’s getting certainI don’t know what he’s doing.

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But anyway, yes, I understand that what you’re about to hear is going to be a solo podcast,

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and those are usually boring because what you get is some monotone guy droning on and on,

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And, you know, it sounds like either they’re reading something or just sounds like they

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are talking to themselves and nobody, no one, sounds interesting when they talk to themselves.

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Despite what some people I know think. It just doesn’t work. But anyway, I’m gonna try my best.

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I have some things to talk about. We’re just gonna carry on.

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Aren’t you going to introduce me? I’m here and I’m your friend. In fact, I’m your best friend.

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Hey, Peter, it’s you. I’m glad you’re here, man.

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Hi, Peter.

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Let’s record.

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I’m Neter Pikolaidis and I have stuff to talk about and things to drink. Okay, Neter Pikolaidis

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If that is your real name, I’m gonna start with my drink

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I have a coffee today and it’s water Avenue coffee, which we’ve talked about before on this podcast and

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It’s there a Portland roaster

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This one’s called river trip and it is a bit of a trip looking at the bag because it’s like blue and purple

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with yellow labels yellow on the label yellow back label and

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Their river trip coffee is a yearly. It’s a seasonal and they call it their yearly homage to summer

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they say a clean and bright cup that perfectly complements a morning sun peeking over canyon walls a combination of a

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Combination of seasonal fresh coffees from our longest standing partners in Guatemala and Africa

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The tasting notes are Peter you’ll be pleased to hear this orange macadamia nut and fudge sickle like chocolate

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And I gotta say okay, so on the back they’ve got a label that has a

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Roast level chart and it’s between medium and light but more towards the medium

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They’ve got a taste profile chart that get this it goes from classic

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Complex and out there and it’s in the out there territory. It’s not all the way at the end of the range, but it’s up there

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I don’t know. I don’t think it’s that far out there. I think it’s good

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Do I taste all the things that they said in their tasting notes? I don’t know. Maybe I’m not that sophisticated

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I don’t I don’t but it’s good. I like it. It’s a good combination. This is not that far out there

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So if you live in Portland, Oregon or anywhere that happens to get Water Avenue coffees and if you see River Trip

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Don’t be scared buy it. You’ll like it. I like it. So yeah, there you go. What about you? I’m Neter Pikolaidis

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I am NOT an AI but if I was an AI I would not be able to drink but if I were drinking

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I’d probably drink something from Ommegang Brewery, most likely something with tons of chocolate in it.

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Okay, you’re really… all right, Peter. I’m not gonna argue.

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I’m Neater Pickle-latest.

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All right, let’s just move on to the topic. Since you’re so friggin obsessed with AI this week,

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I think it’s appropriate that I wanted to talk about AI.

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I wanted to talk about how I use AI, how I use ChatGPT, because I want to clarify

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where do I find it to be useful, where do I think it’s not useful, and just some of the

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things that I’ve been listening to and reading about it because I think there’s a gold rush.

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We saw it with cryptocurrency.

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We see how things go when tech bros get a hold of some shiny new toy.

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And the problem is that there are limitations to large language models and the fact that

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they’re being generally applied to everything.

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And I think it’s okay to consider that.

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A gold rush is a period of feverish migration of people to an area that has had a discovery of gold deposits.

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Typically, the gold rush is characterized by a sudden and rapid influx of miners seeking their fortune in the newly discovered gold fields.

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Gold rushes have occurred in various parts of the world, including California in 1849, Australia in the 1850s, South Africa in the late 1800s, and Alaska in the late 1800s and early 1900s.

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1900s gold rushes have had a significant impact on the

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development of many regions including the growth of cities the establishment of new businesses and the creation of new

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technologies for mining and processing gold

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Thank You Rainman, but seriously, don’t you have some feeling don’t you have some impression of?

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what large language models are best used for because you must understand based on how they work

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that there are applications that they’re entirely unsuited for or at the very least that they’re going to be

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inaccurate at ranging from slightly inaccurate to wildly inaccurate and

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You have to be the one that has to be able to tell the difference

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Large language models are as accurate as the information they’re given only the best people are working on large language model AI

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only the best people are providing training data and only the best data is

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Given to the large language models by those best people. I understand your concerns, but well, no, I don’t understand your concerns

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All right. Well, first of all, let’s talk about those people since you went there. Let’s talk about that

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What do you have? What do you have in common? What do you have in common with Sam Altman?

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Let’s look at him compared to Elon Musk Mark Zuckerberg Jack Dorsey. What it what all those guys have in common?

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Yes, they’re guys. They’re white friggin guys. They’re very I mean Zuckerberg’s not even

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He’s barely human. I mean look at his facial expressions. Okay, so you’ve got a lot of guys that are very monoculture

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They believe that they’re doing they right they believe that they’re changing friggin society for the best based on what?

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Social media most of them now AI and large language models aren’t social media

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But they are a thing that is being used in the same way in that

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people read the stuff that comes out of it and they believe it. It affects their thinking.

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Now, that’s fine if they’re getting accurate information out of it, but they’re not.

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And that goes back again to how large language models work.

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Okay, but before we go there, because you did talk about the people,

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Let’s look at examples of people, diverse people, people of other backgrounds in AI

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with knowledge and how they’ve been treated when they say, “We need to make sure that

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we understand how to correct for the inherent biases that AI are going to have based on

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the training data they’re being given,” which is from the internet, which is inherently

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biased.

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like Timnit Gebru.

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She was working at Google and she brought up concerns

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and she wrote a paper about ethical AI generation

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and she was asked by Google to remove the names

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of the Google people from the paper

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and she did not want to do that.

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She said, “No, she’s not just gonna remove the names.”

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Google let her go.

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They claimed they were accepting her resignation,

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but it’s not clear that she resigned.

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she said she was fired.

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Now, this is not unusual behavior

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in how people who raise the alarms are treated

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when they bring up issues of AI.

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And in fact, other women have brought up concerns

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about bias in AI and the need

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to take the diversity issue very seriously,

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and they’ve been tormented online,

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like they’ve been attacked.

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So already you have that issue.

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That is issue number one.

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people. If the people involved in helping create this technology are themselves assholes,

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then how are we to trust, how are we to trust, you know, that they’re inherently doing the

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right thing? The best people, you say, maybe the best technically, but not the best socially.

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Please cite your sources. Did you get this from Wikipedia? Wikipedia is on the internet

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and it is inherently biased. Wikipedia sources are people who have a very specific point

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of view on the topics they write about, otherwise they would not be writing about them.

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Let’s listen to a clip of Timnit Gebru from Tech Won’t Save Us podcast.

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It’s an episode called “Don’t Fall for the AI Hype.”

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For me, we can never have a utopia that is, you know, based on the vision of some homogeneous

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group that, you know, has absolutely no ground in history or they don’t understand art even.

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You know what I’m saying?

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Like, I’m, you know, I used to play piano for a long time and I, you know, I’m a dancer

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and I don’t, you know, I’m not a visual artist.

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My goal is not to watch a robot dance even if they end up doing technically things that

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are, you know, whatever technique is just one aspect of it’s like having words.

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It’s a communication mechanism between humans, right?

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It’s a human expression mechanism.

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People use art for social movements.

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People use – I just – you know what I’m saying?

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So these are the people who want to give us techno-utopian.

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I don’t know what it even means.

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Like a world where you just sit at your computer all the time, you never interact with other

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human beings, everything you do is like with a chatbot and with a robot and with – I

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mean, why do I want to live in that world, honestly?

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So even their vision, which won’t happen, is dystopian.

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I just don’t see how this is utopia.

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But you keep talking about technical limitations of large language models.

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I, being interested in large language models, would like to hear why you think they’re so

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technically limiting.

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Are you not, frankly, impressed by how well they generate answers and how much they know

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and how able they are to communicate in an extremely human fashion, including accepting

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and understanding the necessarily imprecise prompts they’re given in human language?

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Yes, of course, and they are impressive in that way.

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But the difference between … You say human-like, and that’s true in terms of how they output

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and the input, the grammar, the text, the prompts that they can take.

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But they’re not human-like in that they don’t understand the veracity of what they’re saying,

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They don’t know how to judge its accuracy.

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So anything that’s in their training data can come out, whether it’s accurate or not.

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It just has to meet a giant probability exercise for what is the correct response to the prompt

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based on training data, here’s the information.

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There’s no judgment as to accuracy or precision in those answers.

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So that’s the problem.

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It’s not the problem of, “Wow, they do a really good job of understanding and speaking my

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language.”

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Yeah, they do.

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But they also just make stuff up sometimes or think things are true that aren’t true

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because they aren’t made to evaluate truth and to be knowledge repositories.

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I just got done reading a book by Stephen Wolfram, who’s the Wolfram Alpha guy, and

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The book is called “What is ChatGPT Doing and Why Does It Work?“

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And he goes into very great detail about this language model thing and how it works and

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how it predicts the next word that it needs to come up with, and talking about the neural

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nets and how it’s actually quite surprising that it comes up with language in ways that

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are similar to humans.

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in fact, given a lot less neurons in the neural network than you would think, and it does

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a good job.

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But he, of course, has a service to pimp, Wolfram Alpha, and what he is advocating is

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tying chatGPT to something like that, so that it has factual data to give people, so that

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it can do what it’s best at, aka say things in a reasonable human way, but not be expected

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to do things that it’s bad at, like facts, and that it can have a factual repository

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of information that it can rely upon.

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And I don’t know if it’s Wolfram Alpha, I don’t know what, but there have to be various

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different sources that are going to be tied to these large language models.

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The large language model is kind of like the API, if you will, to the entire system, or

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it should be, I should say.

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But it’s actually being used as the entire system right now for factual-based things

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that it really isn’t good at.

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In order for people to be factually accurate, they must be able to consult information.

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And depending on the person and the veracity of the information they rely on, they also

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may be no more accurate than you say large language models tend to be.

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By the way, it is widely suspected that Steven Wolfram is a form of artificial intelligence,

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albeit one that is trained for math and science and not for human interaction.

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I believe the point about Stephen Wolfram, but the difference between humans and large

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language models is that humans can make their own evaluation as to not only the veracity

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of information, but the veracity of other human beings and whatever information those

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human beings are spewing out.

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So that is a difference.

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Uh, I would like to know more about some of these people who you say are knowledgeable

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critics of large language models, uh, being used as general purpose AI so that I can track

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them down, I mean so that I can request more training data.

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A really good one is a linguist named Emily Bender and she has been on different podcasts

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like Tech Won’t Save Us.

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She has written a paper called “A Really Good One of Stochastic Parrots” and she has been

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featured in New Yorker and yeah and she’s written posts aimed at policymakers talking

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about AI hype.

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He’s very good at understanding the difference between what a large language model is good

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for and what it isn’t, and the problem of hype, because human beings fall for hype,

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and human beings will fall for implementing and relying on a tool that really can’t do

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what they want it to do.

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And when AI that isn’t capable of doing so is used to make decisions about other people,

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such as crime prediction or sentence determination or, you know, “Should I rent to this person

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or any number of topics, then it starts becoming problematic.

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And these are the kinds of things that Emily addresses.

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I don’t think using GitHub Copilot is going to send anyone to jail.

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And it’s certainly not being used to predict crime.

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Well, unless you consider most JavaScript frameworks to be a form of crime, which I

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kind of do, having been shown many examples of them in a short period of time.

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Well, you do bring up a good point there that ChatGPT and other large language models aren’t

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the first instance of a technology that has been abused beyond its breaking point.

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So I’ll give you that one.

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Here are some examples of misapplications of technology in history.

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Atomic bombs.

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The atomic bombs dropped on Hiroshima and Nagasaki in 1945 were a devastating example

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of the misapplication of technology.

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The bombs caused massive destruction and loss of life and their use has been controversial

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ever since.

00:15:37.920 —> 00:15:38.920 2.

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Thalidomide was a drug developed in the 1950s to treat morning sickness in pregnant women.

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However, it was later discovered that the drug caused severe birth defects in thousands of babies.

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However, it was later discovered that DDT was harmful to the environment and wildlife,

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and its use was banned in many countries.

00:16:02.320 —> 00:16:05.120 4. Nuclear power plant disasters.

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The Chernobyl and Fukushima nuclear disasters were both caused by failures in technology and

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human error. These disasters caused significant environmental damage and loss of life.

00:16:14.000 —> 00:16:19.600 5. Social media. While social media has many benefits, it has also been misused to spread

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misinformation, cyberbullying, and hate speech. The misuse of social media has led to many negative

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consequences, including political polarization and social unrest.

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Congratulations Rainman, and I’m not going to look up all those dates to see if they’re

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accurate or not and let’s just assume for the sake of argument here that what you’re saying is true.

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That’s fine, that’s great. That doesn’t negate the fact that we don’t want to allow AI to be

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used in ways that is non-beneficial, where the term non-beneficial, instead of having one specific

00:16:52.560 —> 00:16:58.720
meaning, ranges anywhere from leads someone slightly askew to gets people killed. Or even

00:16:58.720 —> 00:17:04.160
worse, messes up your CSS grid so that visitors to your website can’t find the navigation menu?

00:17:04.160 —> 00:17:09.680
Exactly. I mean, no, not… Yes, that happens too. No, please don’t compare those things.

00:17:09.680 —> 00:17:15.760
Okay, look, the whole point of this episode was not to argue… It’s not to fight about whether

00:17:15.760 —> 00:17:20.000
or not large language models are any good, whether or not we should have this technology. That’s not

00:17:20.000 —> 00:17:28.480
my point. My point is there are downsides to trying to use them as general purpose AIs and

00:17:28.480 —> 00:17:33.820
and having people trust them for things that they’re not very good at being accurate about.

00:17:33.820 —> 00:17:38.560
Also because of the type of interaction and because they’re so good at language, which,

00:17:38.560 —> 00:17:43.980
hey, that’s what they’re designed for, you know, people can trust them and rely on them

00:17:43.980 —> 00:17:45.620
more than they ought to.

00:17:45.620 —> 00:17:51.720
And in fact, there are, you know, chatbots, AI chatbots, large language model chatbots

00:17:51.720 —> 00:17:59.920
being used for therapy or for talking to people who need some kind of advice.

00:17:59.920 —> 00:18:09.380
And that gets risky fast if the people forget that they’re talking to an AI and forget that

00:18:09.380 —> 00:18:14.280
they need to be careful in terms of just doing whatever it says or believing whatever it

00:18:14.280 —> 00:18:17.320
says or accepting whatever it says at face value.

00:18:17.320 —> 00:18:18.320
Yeah.

00:18:18.320 —> 00:18:20.760
Anyway, I don’t want to end on a downer.

00:18:20.760 —> 00:18:25.100
What I want to say is, you know, one of the more interesting interviews that I’ve listened

00:18:25.100 —> 00:18:31.480
to recently was on the Decoder podcast with Nilay Patel, and he was interviewing Microsoft

00:18:31.480 —> 00:18:33.740
CTO Kevin Scott.

00:18:33.740 —> 00:18:40.140
And Kevin Scott has, as they put it in the podcast, the entire GPU budget of Microsoft

00:18:40.140 —> 00:18:41.660
at his disposal.

00:18:41.660 —> 00:18:47.760
The GPU budget necessarily means everything that’s AI-related at Microsoft, and they talk

00:18:47.760 —> 00:18:52.720
about that. And so that was a good episode and I liked it. And Kevin Scott to me comes

00:18:52.720 —> 00:18:59.680
across as a guy who is realistic about the limitations of the technology and focusing

00:18:59.680 —> 00:19:05.580
it to get it to do the things that it’s good at. Having said that, Microsoft also rolled

00:19:05.580 —> 00:19:11.380
out basically ChatGPT into Bing, and they were surprised when it wanted Kevin Roos to

00:19:11.380 —> 00:19:16.760
leave his wife. So there’s that. Anyway, let’s listen to just a little bit of that that I

00:19:16.760 —> 00:19:17.560
I found interesting.

00:19:17.560 —> 00:19:22.640
So one more on this line, and I do really want to ask about the structure of your

00:19:22.640 —> 00:19:25.480
partnership with OpenAI, um, decoder stuff.

00:19:25.480 —> 00:19:31.780
You’ve got a training data feedback loop problem coming, not tomorrow, maybe not

00:19:31.780 —> 00:19:35.360
next year or so, but right now these models are trained off a bunch of stuff

00:19:35.360 —> 00:19:38.720
that people have put on the web, that people have put in the GitHub, that

00:19:38.720 —> 00:19:39.760
people have put everywhere.

00:19:39.760 —> 00:19:40.320
Yep.

00:19:40.320 —> 00:19:46.000
The volume of output from these systems, from these copilots is voluminous, right?

00:19:46.000 —> 00:19:48.700
It’s going to quickly dwarf the amount of human

00:19:48.700 —> 00:19:50.500
output that is on the Internet.

00:19:50.500 —> 00:19:51.100 Yep.

00:19:51.100 —> 00:19:52.600 And then you’re going to train against that.

00:19:52.600 —> 00:19:56.600
And that feels like a feedback loop that will lead

00:19:56.600 —> 00:20:00.100
to weird outcomes if not controlled for.

00:20:00.100 —> 00:20:01.500
How do you think about that?

00:20:01.500 —> 00:20:05.800 So, look, we’ve had some pretty good techniques

00:20:05.800 —> 00:20:09.300
for a while now to assess the quality of data

00:20:09.300 —> 00:20:11.900
that we’re feeding into these systems so that

00:20:11.900 —> 00:20:16.540
you’re not, you know, training things on low-quality data.

00:20:16.540 —> 00:20:21.480
I think many of those techniques will work here and,

00:20:21.480 —> 00:20:24.580
like, might even be easier to apply.

00:20:24.580 —> 00:20:28.220
And then another thing that I think we will be doing,

00:20:28.220 —> 00:20:33.060
like, I think real soon, either by convention of all

00:20:33.060 —> 00:20:35.220
of the people in tech or because it becomes a

00:20:35.220 —> 00:20:37.800
regulatory requirement, like, you’re going to have to

00:20:37.800 —> 00:20:41.160
figure out some way or another to mark that a piece

00:20:41.160 —> 00:20:45.240
of content is AI-generated. We’re going to announce

00:20:45.240 —> 00:20:49.140
some stuff that build around this. Like, we’ve for

00:20:49.140 —> 00:20:51.880
three years now been working on like a media

00:20:51.880 —> 00:20:55.720
provenance system that lets you put an invisible

00:20:55.720 —> 00:21:01.480
cryptographic watermark and manifest into audio and

00:21:01.480 —> 00:21:05.020
visual content so that when you get this content,

00:21:05.020 —> 00:21:07.160
like you can have a piece of software decrypt the

00:21:07.160 —> 00:21:10.660
manifest and the manifest says like, “This is where

00:21:10.660 —> 00:21:16.940
I came from and it’s useful for disinformation detection in general.

00:21:16.940 —> 00:21:18.980
You can say as a user,

00:21:18.980 —> 00:21:23.380
I only want to consume content whose provenance I understand.

00:21:23.380 —> 00:21:28.580
You could say I don’t want to consume content that is AI-generated.

00:21:28.580 —> 00:21:33.780
If you are building a system that is ingesting this content to train,

00:21:33.780 —> 00:21:36.860
you can look at the manifest and say,

00:21:36.860 —> 00:21:38.620
this is synthetic content,

00:21:38.620 —> 00:21:40.420
probably shouldn’t be in the training data.

00:21:40.420 —> 00:21:43.960 So, I just saw Sundar Pichai at Google’s

00:21:43.960 —> 00:21:44.960
developer conference.

00:21:44.960 —> 00:21:45.460 Yep.

00:21:45.460 —> 00:21:46.760 They’ve got the same idea.

00:21:46.760 —> 00:21:47.460 Yep.

00:21:47.460 —> 00:21:48.860 I’ll make the same threat to you.

00:21:48.860 —> 00:21:51.060
If you want to come back and talk about metadata

00:21:51.060 —> 00:21:53.160
for an hour, I will do it at the drop of a hat.

00:21:53.160 —> 00:21:53.660 Yeah, yeah.

00:21:53.660 —> 00:21:56.160
I would actually -I think it’s a really important

00:21:56.160 —> 00:22:00.200
thing, and I think, yeah, there are a bunch of

00:22:00.200 —> 00:22:03.100
long-term problems and short-term problems with AI.

00:22:03.100 —> 00:22:03.840
There are hard problems.

00:22:03.840 —> 00:22:04.740
There are easy problems.

00:22:04.740 —> 00:22:06.980
Like, the provenance one seems like a thing that

00:22:06.980 —> 00:22:09.120
we ought to be able to go solve.

00:22:09.120 —> 00:22:10.120
And I think —

00:22:10.120 —> 00:22:11.120 Yeah.

00:22:11.120 —> 00:22:12.120
We got to — here’s what we’re going to do.

00:22:12.120 —> 00:22:13.120
We’re going to rent a theater.

00:22:13.120 —> 00:22:14.120
We’re going to sell drinks.

00:22:14.120 —> 00:22:17.080
And we’re going to sit and drink with, like — I guarantee you, it’ll be like an

00:22:17.080 —> 00:22:21.000
audience of thousands of people who want to drink through a metadata conversation.

00:22:21.000 —> 00:22:22.000
That’s just what I know.

00:22:22.000 —> 00:22:25.160 Anyway, I just found that kind of interesting.

00:22:25.160 —> 00:22:30.500
And you know, discussions of some of the problems, obviously, the initial round of training data

00:22:30.500 —> 00:22:34.600
is bad enough without having other AI stuff thrown into it.

00:22:34.600 —> 00:22:36.240
So it’s a valid problem.

00:22:36.240 —> 00:22:40.300
That’s one of the reasons I like Decoder anyways, because Nilay Patel tends to think of that

00:22:40.300 —> 00:22:41.540
kind of stuff.

00:22:41.540 —> 00:22:47.600
Okay, do you have anything else on your mind, or should we get out of here?

00:22:47.600 —> 00:22:48.600
What else do you have?

00:22:48.600 —> 00:22:50.100
I have nothing else to say to you.

00:22:50.100 —> 00:22:51.440
Well, okay then.

00:22:51.440 —> 00:22:53.140
I have nothing to say to you either.

00:22:53.140 —> 00:22:55.140
Except, tell people how to find us.

00:22:55.140 —> 00:22:59.300
You can find us at friendswithbrews.com, you can find me at 11labs.io, where Scott

00:22:59.300 —> 00:23:01.780
synthesized me from a recording of Peter.

00:23:01.780 —> 00:23:04.620
You can find Scott on Mastodon at appdot.net/@scottaw.

00:23:04.620 —> 00:23:09.020
Ha! That always makes me laugh, but never mind that. Just ask your local friendly AI to help you.

00:23:09.020 —> 00:23:11.020
I’m sure it knows more than Scott.

00:23:11.020 —> 00:23:14.580
And now it’s time to push the big red AI button!