Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeartRadio. And how the tech
Speaker 1: are you. I'm here to tell you something. You write
Speaker 1: like a robot. But that's okay because I do too.
Speaker 1: One of the founding fathers of the United States, James Madison,
Speaker 1: wrote like a robot. Robots weren't even a thing when
Speaker 1: he was writing back in the eighteenth century, all right,
Speaker 1: so really, I guess it's more fair to say that
Speaker 1: robots write like us. And while I'm having a little
Speaker 1: bit of fun using the word robots, what I'm really
Speaker 1: talking about is generative AI. You know, stuff like chat
Speaker 1: GPT and Google Bard, that kind of thing, These AI
Speaker 1: powered chat bots right like humans. Right, That's one of
Speaker 1: the big suff features of the chatbots. One that they
Speaker 1: can understand a prompt that we give them, That they
Speaker 1: can understand what we mean when we give them a prompt,
Speaker 1: and two that they then generate a response as if
Speaker 1: it had been written by an actual person. But obviously
Speaker 1: this also creates some challenges, some issues. So you might
Speaker 1: remember that since chat GPT became publicly available last year
Speaker 1: when OpenAI opened it up and let people start playing
Speaker 1: with chat GPT, there were people in education, teachers and
Speaker 1: administrators that sort of thing, who raise the alarm about
Speaker 1: the possibility that students could use chat GPT and similar
Speaker 1: tools to auto generate essays and stuff and thus bypass
Speaker 1: school assignments. My robot wrote it for me. Beyond the
Speaker 1: education sector, there are plenty of arenas where people are
Speaker 1: worried that the less scrupulous folks out there will attempt
Speaker 1: to pass off AI generated text as their own writing,
Speaker 1: whether this is creative writing or business writing, whatever it
Speaker 1: may be. So this then leads us to the concept
Speaker 1: of AI writing detection tools, you know, some sort of
Speaker 1: tool to determine if a piece of text originated from
Speaker 1: a real human being or from that character that Haley
Speaker 1: Joel Osmon played in that film about artificial intelligence. I
Speaker 1: forget what that movie was called. Subsequent to the release
Speaker 1: of these detection tools, we started hearing reports of teachers
Speaker 1: failing students, sometimes an entire class of students, because the
Speaker 1: detection tool indicated that the real source of the works
Speaker 1: that were being turned in by the students it wasn't
Speaker 1: from the students, but from AI. Now a lot of
Speaker 1: students have actually come forward to argue that no, no,
Speaker 1: they actually wrote those pieces themselves, that they authored that work,
Speaker 1: they didn't use AI to do it, and that they
Speaker 1: are the victim of false positives, that these writing detection
Speaker 1: tools made a mistake, and as it turns out, at
Speaker 1: least some of them, and likely a lot of them
Speaker 1: were telling the truth. And we can say that because
Speaker 1: these AI writing detection tools have abysmal accuracy rates, they
Speaker 1: are worse than chance. That's how bad these tools can be.
Speaker 1: So the success rate for an AI writing detector can
Speaker 1: be so low that it has led some of the
Speaker 1: companies to shut them down, and it led to a
Speaker 1: lot of critics to just dismiss the concept of an
Speaker 1: AI writing tool entirely. In fact, there are quite a
Speaker 1: few who have argued that AI writing detection tools are
Speaker 1: essentially snake oil. That there are companies that are making
Speaker 1: what they say are reliable tools that can tell the
Speaker 1: difference between text that was written by person and text
Speaker 1: that was written by AI, but really they're just peddling
Speaker 1: a hoax or a scam, and they're trying to make
Speaker 1: money selling these tools to various organizations like schools and such,
Speaker 1: but in fact those tools don't work, or at least
Speaker 1: they don't work very well. Even open Ai, which is
Speaker 1: the company that is responsible for chat GPT, they had
Speaker 1: a tool that was meant to be a detection tool
Speaker 1: to tell whether or not something was written by AI.
Speaker 1: It was called AI Classifier, but they shut it down
Speaker 1: earlier this year. Why because its accuracy rate was twenty
Speaker 1: six percent. Twenty six percent accurate, that is bonkers. That
Speaker 1: means nearly three quarters of the time that detection tool
Speaker 1: came up with the wrong answer. Either it gave a
Speaker 1: pass to an AI generated piece, or it accused a
Speaker 1: work that a human being actually wrote, like definitively wrote,
Speaker 1: as being the product of AI. This brings us to
Speaker 1: James Madison. James Madison wrote the US Constitution, and folks
Speaker 1: have fed the US Constitution into these AI writing detection
Speaker 1: tools and received a notification that this piece was very
Speaker 1: likely written by AI, which obviously led to lots of
Speaker 1: jocularity on the Internet, as people said, I knew it.
Speaker 1: I knew that the founding fathers of the United States
Speaker 1: of America were really robots from the future sent back
Speaker 1: in time to create a ultra capitalist society that preys
Speaker 1: upon the disenfranchised or something like. There are a lot
Speaker 1: of jokes about it, but the fact is no, it's
Speaker 1: just that this writing detection tool is completely unreliable. So
Speaker 1: you certainly cannot use these kinds of tools to justify
Speaker 1: flunking an entire class of students when you know that
Speaker 1: the reliability is so low. Now, I decided to do
Speaker 1: this short episode about AI writing detection tools after reading
Speaker 1: a couple of great pieces in Ours Technico. Those of
Speaker 1: y'all who listen to my show frequently know that I
Speaker 1: often reference Ours Technica because the folks there reliably post
Speaker 1: great articles. So in this case, the author of both
Speaker 1: pieces I read was BENJ. Edwards b E and J. Edwards,
Speaker 1: And at some point I probably should reach out to
Speaker 1: them and ask if they would like to join tech
Speaker 1: stuff for an episode to talk about something like generative AI,
Speaker 1: because Edwards has done some really good work. Anyways, as
Speaker 1: we think about the issue about how this generative AI works,
Speaker 1: the underlying technology that powers generative AI, we start to
Speaker 1: see why there's this big reliability problem. Why are we
Speaker 1: having such issues with an automated detection tool? Really determining
Speaker 1: if something was written by a person or AI. And
Speaker 1: it's because the tools like chat GPT are built on
Speaker 1: top of large language models, also known as llms, And
Speaker 1: if we take a moment to really understand llms, then
Speaker 1: we start to get a handle on why these detector
Speaker 1: tools are so unreliable. So first off, let's actually talk
Speaker 1: about a precursor to large language models. This would be
Speaker 1: recurrent neural networks or r ends. Now I've talked a
Speaker 1: lot about neural networks on this show, but just as
Speaker 1: a refresher. Neural network is an attempt to create a
Speaker 1: computer system or computer model that processes information in a
Speaker 1: way that is similar to how our brains process information.
Speaker 1: So you have layers of artificial neurons, or you can
Speaker 1: think of them as nodes. These layers connect to other
Speaker 1: artificial neurons. You have multiple connections from neuron to other neurons,
Speaker 1: and you have layers that go from top to bottom.
Speaker 1: You can think of it like at the top that's
Speaker 1: where you put input and at the bottom that's where
Speaker 1: you get output. So essentially, you feed information into the
Speaker 1: model and then the information goes through a series of
Speaker 1: operations in which data passes through these different nodes, and
Speaker 1: the nodes make decisions based upon the input, and then
Speaker 1: they send output to different nodes and eventually you get
Speaker 1: the ultimate output. And sometimes that output is correct. It
Speaker 1: gives you the answer that is correct. Sometimes it's wrong.
Speaker 1: And typically what that means is that you then have
Speaker 1: to adjust how those artificial neurons are making decisions. Those
Speaker 1: neurons apply a sort of bias to input, we call
Speaker 1: it a weight, so they will favor some types of
Speaker 1: input over others in an effort to make a decision.
Speaker 1: If they didn't, then the data would never go anywhere.
Speaker 1: You would never be able to have it processed through
Speaker 1: the system. So the weighting affects how the neuron actually
Speaker 1: processes the data, where does it pass it on to.
Speaker 1: So it may say, if value is greater than X,
Speaker 1: send to node A. If value is less than x,
Speaker 1: send to node B. That could be a very basic weight.
Speaker 1: X would be the weight in that case, and maybe
Speaker 1: that would lead you to a correct outcome. So by
Speaker 1: adjusting the weighting, you can change how these neurons make decisions.
Speaker 1: And if you build a neural network for the purposes,
Speaker 1: let's give it a hypothetical. Let's say it's identifying pictures
Speaker 1: of cats. It's always my go to. And you start
Speaker 1: looking at the output and you see that it is
Speaker 1: mistakenly saying that pictures of flowers are pictures of cats.
Speaker 1: You would say, all right, these artificial neural networks, the
Speaker 1: nodes in this artificial neural network are making the wrong decisions.
Speaker 1: The waiting is wrong in these nodes. I need to
Speaker 1: go and start adjusting things so that I can start
Speaker 1: to get back to this correctly saying whether or not
Speaker 1: an image has a cat in it or doesn't. And
Speaker 1: your goal is to train this model over and over
Speaker 1: and over again until it gets better and better at
Speaker 1: this task, so that then you can just send it
Speaker 1: any raw data you like and not have to worry
Speaker 1: about checking up on it afterward because its accuracy level
Speaker 1: will be high enough to be reliable. That's your ultimate goal,
Speaker 1: But there's a whole process of learning of training that
Speaker 1: you have to go through first. Now, a recurrent neural network,
Speaker 1: it's a little more specific than just artificial neural network.
Speaker 1: Recurrent neural networks use sequential data. These networks can and
Speaker 1: do take information from earlier inputs into consideration when processing
Speaker 1: a new input, so there's a different model, the convolutional
Speaker 1: neural network CNN, not the news channel. This is the
Speaker 1: other big type of neural network where every time data
Speaker 1: goes into an input, it's like a blank slate. It's
Speaker 1: its own thing, it has nothing about That decision is
Speaker 1: based upon any past decision. It's an instance by instance
Speaker 1: kind of case. So you're starting from scratch. But with
Speaker 1: recurrent neural networks, the network can actually incorporate past inputs
Speaker 1: as part of how it processes a current input. But
Speaker 1: one issue with these types of networks, the recurrent neural
Speaker 1: networks is that they need a full sequence before they
Speaker 1: can process the information. So when we're talking about text,
Speaker 1: like if we wanted to process text through a recurrent
Speaker 1: neural network, it would need to work over the entire
Speaker 1: text before producing a result in order to understand things
Speaker 1: like context. Sometimes this approach can lead to errors because
Speaker 1: the model essentially forgets the stuff that was at the
Speaker 1: beginning of the text by the time it gets to
Speaker 1: the end, which sounds a lot like me honestly, where
Speaker 1: I will finish a book and then I'll think, like
Speaker 1: I'll have a discussion with someone about a book that
Speaker 1: we've both read and they'll be like, Oh, I like
Speaker 1: that part where in early in the book blah blah
Speaker 1: blah blah blah, and it pays off much later, and meanwhile,
Speaker 1: I'm thinking, I totally forgot that that happened earlier in
Speaker 1: the book. I remember where we ended up, but I
Speaker 1: don't remember how we got there. Recurrent neural networks can
Speaker 1: fall into the same sort of trap, and so that
Speaker 1: creates a bit of a hurdle when it comes to
Speaker 1: things like analyzing text for the purposes of building natural
Speaker 1: language systems. But I'll explain how that all started to
Speaker 1: change in twenty seventeen. First, however, we need to take
Speaker 1: a quick break to thank our sponsors. Okay, before the break,
Speaker 1: I was talking about recurrent neural networks and how those
Speaker 1: have certain limitations when it comes to the way they
Speaker 1: process data because it has to be sequential. Well, in
Speaker 1: twenty seventeen, a group of AI researchers who were working
Speaker 1: specifically over at Google were coming up with an alternative
Speaker 1: to this approach, and they published a paper, and the
Speaker 1: paper's title was Attention is All You Need, in which
Speaker 1: they suggested that you could do something differently from the
Speaker 1: recurrent neural network approach for the purposes of analyzing stuff
Speaker 1: like text. Their approach was what they called a transformer model.
Speaker 1: While you're old, RNN would analyze text essentially a character
Speaker 1: at a time, not even a word at a time,
Speaker 1: but a character at a time, and thus that's sequential, right.
Speaker 1: The sequential data is character by character. It builds this
Speaker 1: up and then analyzes the whole thing. The transformer model
Speaker 1: instead would tackle a sentence as a unit as opposed
Speaker 1: to a character or even an entire passage of text
Speaker 1: would be a single unit, and so it would analyze
Speaker 1: this to understand the context of what was being said,
Speaker 1: and that's a huge benefit you. Getting a handle on
Speaker 1: context is absolutely critical to understanding what someone means, because
Speaker 1: words can have multiple meanings, right, and without context, we
Speaker 1: can't really be sure which meaning someone intended. So here's
Speaker 1: an example. The English word late. That can mean a
Speaker 1: lot of things if you're an English speaker. So if
Speaker 1: you're talking about the time of day, if you say
Speaker 1: it's late, you usually mean it's getting close to night time.
Speaker 1: You could say it's late at night, which means it's
Speaker 1: actually close to morning time, or maybe it even is
Speaker 1: the morning because it's still dark. And so you think
Speaker 1: of it as night, but technically speaking, it's morning and
Speaker 1: you're just saying it's late at night. If you're saying
Speaker 1: somebody is late, you could either mean they are not
Speaker 1: on time for some appointment, or tragically, you could mean
Speaker 1: that this is a person who has passed away. They
Speaker 1: are late. But you need the rest of the sentence.
Speaker 1: You need that context to understand what meaning of late
Speaker 1: was actually intended. So you need that contextual vision to
Speaker 1: be able to understand the whole thing. So transformer models
Speaker 1: began to revolutionize certain types of AI applications, specifically in
Speaker 1: the realm of natural language processing and generative AI, and
Speaker 1: it's what led to the development of large language models
Speaker 1: the lms. Essentially, a large language model is just a
Speaker 1: huge transformer model. And to make a large language model,
Speaker 1: you need a lot of text to train your model,
Speaker 1: like a lot a lot. Open AI trained its large
Speaker 1: language model known as GPT, which stands for Generative pre
Speaker 1: Trained Transformer. They trained it on countless documents, millions and
Speaker 1: millions of documents found across the web. Some authors allege
Speaker 1: that the training material included copyrighted material and that the
Speaker 1: authors did not give permission for their works to be
Speaker 1: part of the information that fed into this model. That
Speaker 1: leads into its own set of problems that are a
Speaker 1: little bit beyond the scope of what I'm talking about today,
Speaker 1: but they are big problems and they're ongoing now. Stephen
Speaker 1: King argued that his works were clearly used to train
Speaker 1: up large language models. A dead giveaway is if you
Speaker 1: ask a chatbot built on top of a large language
Speaker 1: model to recite passages from specific authors works, and if
Speaker 1: it can do that like accurate, like it's really giving
Speaker 1: you an accurate representation of that text. Yeah, there's no
Speaker 1: way could have received that information without having trained on
Speaker 1: the original text at least somewhere. Now, if it's just
Speaker 1: making stuff up, that's different. That falls into the category
Speaker 1: of hallucinations, which we might touch upon again before we
Speaker 1: finish shut this episode. Anyway, the benefit of feeding so
Speaker 1: much information to a transformer model is that the transformer model,
Speaker 1: the large language model, gets pretty darn good at sussing
Speaker 1: out context. Even stuff that you would expect would trip
Speaker 1: up an AI chatbot can become a breeze. You know,
Speaker 1: you might think that slang or idioms could trip up
Speaker 1: an AI tool, but then you have to remember that
Speaker 1: these tools rely on essentially all the stuff that's on
Speaker 1: the Internet, at least all the stuff that's publicly available
Speaker 1: that's not locked behind something, and maybe even some stuff
Speaker 1: that is locked behind stuff. As it turns out, and
Speaker 1: as such, that means that these models have trained with
Speaker 1: data sets that originate from the same communities that are
Speaker 1: creating the culture that generates certain slang and idioms in
Speaker 1: the first place. So if your AI model is using
Speaker 1: the same source material where these turns of phrase and
Speaker 1: certain slang terms are are originating from, well, of course
Speaker 1: it's going to understand it because that was part of
Speaker 1: its training, so it has that grounding. It's not like me,
Speaker 1: where I am old. I don't understand slang that the
Speaker 1: kids use these days because I'm not in those communities.
Speaker 1: You wouldn't expect me to understand. I am definitely the
Speaker 1: stereotypical out of touch old dude. So when I hear
Speaker 1: people about, you know, people rizing up, I'm like, wait what?
Speaker 1: And I have to look things up. And as we
Speaker 1: all know, urban dictionary is not the most reliable of resources.
Speaker 1: It is frequently entertaining, usually in a way that is
Speaker 1: incredibly offensive, but it's not always accurate anyway. This ultimately
Speaker 1: starts to lead us to why these AI writing detection
Speaker 1: tools are not very good. The material that AI generates
Speaker 1: is built upon how we communicate. It's a built on
Speaker 1: how we write. That's how it was trained. So it's
Speaker 1: not like AI or robots, as I was facetiously saying
Speaker 1: earlier in the episode. It's not like AI has a
Speaker 1: different path toward writing than we do. The AI is
Speaker 1: not following an established set of rules that's unique to AI. Right,
Speaker 1: They're not saying, write this like artificial intelligence. So the
Speaker 1: stuff that AI produces can come across as very human
Speaker 1: and vice versa. Now, this does not mean that it
Speaker 1: is absolutely impossible for someone like a teacher to tell
Speaker 1: if something was written by AI or a student. If
Speaker 1: the teacher is actually really familiar with the writing style
Speaker 1: of that student or students in question, it's entirely possible
Speaker 1: that the teacher might notice if that writing style were
Speaker 1: to suddenly and maybe significantly change between assignments. This can
Speaker 1: be a big ask, by the way, for certain teachers,
Speaker 1: because class sizes can get huge depending on where you are,
Speaker 1: and if you're talking about an overworked English teacher who's
Speaker 1: teaching multiple classes and each class has got, you know,
Speaker 1: thirty kids in it. It can be hard to really
Speaker 1: build up a working knowledge and memory of the writing
Speaker 1: styles of every single person in every single class. But
Speaker 1: that is one way that teachers can tell. If teachers
Speaker 1: read an essay and think, wow, you know, Robert didn't
Speaker 1: write like this in the essay we did last month,
Speaker 1: this is a very different approach to writing and per
Speaker 1: perhaps that's an indicator that someone else wrote the piece,
Speaker 1: whether that was AI or maybe you know, another human being,
Speaker 1: and that can be an indication something hinky is going on. Also,
Speaker 1: I mean, obviously some people get sloppy. This happens a
Speaker 1: lot too when people just aren't paying attention as they're
Speaker 1: using AI to generate either you know, an educational assignment
Speaker 1: or business or whatever. There have been so many examples
Speaker 1: of how people have accidentally copied and pasted not just
Speaker 1: the body of the text, but stuff that's outside the
Speaker 1: body of the text, like it might even be a
Speaker 1: little disclaimer saying it was made by AI, or it
Speaker 1: could be a command like regenerate response. That's something you
Speaker 1: find in certain chat bots, and that is just what
Speaker 1: regenerate response means. It just means, hey, can you create
Speaker 1: a new AI response to the initial prompt I gave you.
Speaker 1: So I wrote a prompt, I had you generate response.
Speaker 1: I want you to create a whole new response based
Speaker 1: on that original prompt. If you have regenerate response written
Speaker 1: at in your essay, that's a dead giveaway that you
Speaker 1: copied and pasted that essay off of an AI chatbot.
Speaker 1: So there are ways that teachers can tell the difference,
Speaker 1: but they aren't. It's not as granular as saying, oh,
Speaker 1: this is clearly something that was written by artificial intelligence
Speaker 1: versus this was written by a human. It's more like
Speaker 1: this is different from what I have received before from
Speaker 1: this particular student, or this contains obvious errors that reveal
Speaker 1: that the student has used AI. Now, the AI writing
Speaker 1: detection tools are at least claiming to use a couple
Speaker 1: of strategies to try and determine if something was written
Speaker 1: by AI or a human. So they're saying, we can
Speaker 1: automate that process, and we can actually analyze a block
Speaker 1: of text and give you a determination as to whether
Speaker 1: or not that was made by AI or a human,
Speaker 1: which suggests that maybe there is some sort of fundamental
Speaker 1: difference between the way AI generates content and the way
Speaker 1: people do. But these strategies that the AI writing detection
Speaker 1: tools are built upon have fundamental flaws, and we know
Speaker 1: that because we know the tools are bad. It was
Speaker 1: bad enough for open ai to shut down its version
Speaker 1: back in June. So this isn't like just us postulating
Speaker 1: that these tools are bad. We know they're bad. We
Speaker 1: know they create things like false positives. So knowing that
Speaker 1: already they are unreliable, you then have to start asking, well,
Speaker 1: why are they unreliable? What are the things that are
Speaker 1: leading these tools to make these wrong determinations? And when
Speaker 1: we come back, I'll talk about how Bene Edwards and
Speaker 1: those OURS Technica articles really kind of digs into two
Speaker 1: main concepts that end up leading to these writing detection
Speaker 1: tools trying to make a determination and why they are
Speaker 1: fundamentally flawed. But first let's take another quick break. So
Speaker 1: before the break, I mentioned that I was going to
Speaker 1: talk about some strategies that Binge Edwards outlines in his
Speaker 1: RS Technica articles, and they fall into two categories. The
Speaker 1: first is called perplexity, and that really means how surprising
Speaker 1: or perplexing are the word choices, how creative are the
Speaker 1: sentences in a given piece of text compared to an
Speaker 1: AI training model. So the thinking behind this is that
Speaker 1: if a block of text seems to conform to the
Speaker 1: same sort of stuff that the language model would produce,
Speaker 1: then AI probably created the text. That's the idea if
Speaker 1: they're saying essentially that if the text is really similar
Speaker 1: to what AI would create, then AI probably created it.
Speaker 1: And let's think about how some tools use autocomplete to
Speaker 1: help you write a text or sentence. Using a purely
Speaker 1: hypothetical scenario to kind of get our minds wrapped around this,
Speaker 1: Let's say that you were typing into something that has
Speaker 1: autocomplete built into it, the sentence or the phrase I'm
Speaker 1: going to go for a and then whatever tool you're
Speaker 1: typing it into suggests the word walk as an autocomplete option. Well,
Speaker 1: that would be because the language model that is powering
Speaker 1: this autocomplete function has a has sampled millions of passages,
Speaker 1: millions and millions and millions of documents, and has found
Speaker 1: that the word walk has been the most common word
Speaker 1: to follow the phrase I'm going to go for a
Speaker 1: and so therefore it offers that as the suggestion, and
Speaker 1: maybe it would even offer you a few options. Maybe
Speaker 1: it would say walk, maybe it'd say swim in the UK,
Speaker 1: maybe it'd say a curry. Who knows so, but you know,
Speaker 1: it would give you maybe a couple of different options,
Speaker 1: but they would be the ones that would most likely
Speaker 1: follow that phrase based upon the training material that that
Speaker 1: large language model had used to build itself up. Right,
Speaker 1: So if you were to measure the perplexity of the
Speaker 1: sentence I'm going to go for a walk, it would
Speaker 1: be very very low, very low perplexity because it's in
Speaker 1: line with what the language model would expect. So the
Speaker 1: thought is, if a passage in general has a very
Speaker 1: low perplexity, these tools tend to suspect that the passage
Speaker 1: as a whole could have come from AI. So let's
Speaker 1: say that it had a very hyperplexity. Let's say that
Speaker 1: instead of saying I'm going to go for a walk,
Speaker 1: you said I'm going to go for a zebra or
Speaker 1: zebra if you're in the UK. Well, that doesn't want
Speaker 1: it doesn't really make any sense. But two, that would
Speaker 1: be very perplexing, right, that would not be something that
Speaker 1: the large language model would expect. And so if it
Speaker 1: has high perplexity, then the writing detection tool is more
Speaker 1: likely to say this was written by a human, because
Speaker 1: what generative chat system would have made that sentence, And
Speaker 1: he's like, no, sane robot would say I'm going to
Speaker 1: go for a zebra. Clearly some human wrote this. Now,
Speaker 1: the problem is these companies are training their large language
Speaker 1: models on enormous amounts of human generated text. And unless
Speaker 1: you're purposefully trying to be really a original in your writing,
Speaker 1: that's a kind way of saying you're being a weirdo
Speaker 1: as you're writing your sentences. Chances are a lot of
Speaker 1: the stuff you're writing is going to have a fairly
Speaker 1: low perplexity, unless you're trying to write in like the
Speaker 1: milieu of humor or absurdity. If unless you're purposely trying
Speaker 1: to do that, then chances are your perplexity is going
Speaker 1: to be pretty low too. Particularly for very structured writing
Speaker 1: like business writing or academic writing, that perplexity is going
Speaker 1: to be very low. So unless you're prone to throwing
Speaker 1: in very odd, random, weird sentences like William Shakespeare's Othello
Speaker 1: is one of the great tragedies of English theater, and
Speaker 1: also I enjoy shoving hot dogs through mail slots. Well,
Speaker 1: there's a pretty good chance that an AI detector tool
Speaker 1: is going to think that your human written, legitimate essay
Speaker 1: was in fact an AI's work, because the perplexity would
Speaker 1: likely be pretty low, again unless you're doing something really
Speaker 1: avant garde, So that there's a fundamental flaw and logic
Speaker 1: of using perplexity as one of your metrics for determining
Speaker 1: if something was written by AI versus a human. Ben
Speaker 1: Jedwards also goes on to explain that another factor that
Speaker 1: AI detection tools will take into consideration is one that's
Speaker 1: called burstiness. Perplexity and burstiness makes me feel like I've
Speaker 1: fallen into a Lewis Carroll novel. But anyway, burstiness really
Speaker 1: has to do with variability, particularly between sentences. So y'all
Speaker 1: probably have noticed I have a tendency toward really long sentences,
Speaker 1: and often with a lot of parentheticals thrown in there. Now,
Speaker 1: if I also incorporate short sentences on occasion, breaking up
Speaker 1: these very long sentences, this creates a lot more variety,
Speaker 1: a lot more dynamic elements between my sentences, because I'm
Speaker 1: switching back and forth between these very long, pontificating sentences
Speaker 1: and then short ones to make a point. Maybe in
Speaker 1: some sentences I use tons of adverbs to describe action.
Speaker 1: Maybe in the next sentence I don't use any adverbs
Speaker 1: at all. This is what creates that variability. The conventional
Speaker 1: wisdom is that AI generated work is more uniform, it's
Speaker 1: more consistent, it has less variability from sentence to sentence.
Speaker 1: Your sentence length and complexity is going to remain more
Speaker 1: or less the same throughout an entire passage. So if
Speaker 1: you're able to qualify how dynamic a writing style is,
Speaker 1: the thinking goes. You could potentially determine if a human
Speaker 1: wrote it or if an AI wrote that specific piece.
Speaker 1: If it's not very dynamic, well that leads more toward AI.
Speaker 1: But that approach depends upon a couple of things that
Speaker 1: are not always reliable. So first up, it assumes that
Speaker 1: AI generated content is going to contain you to show
Speaker 1: more consistency than the stuff that humans. Right, that's going
Speaker 1: to continue to be this very consistent approach to sentence structure.
Speaker 1: But the language models and the generative AI that are
Speaker 1: built on top of the language models are growing more
Speaker 1: sophisticated all the time. A lot of these companies that
Speaker 1: make these language models are mining platforms like x formerly
Speaker 1: known as Twitter or Reddit in order to train their
Speaker 1: language models. They're reading these sort of idiosyncratic messages that
Speaker 1: people write. Sometimes they're writing purposefully in a way that
Speaker 1: is not consistent, and it can get to be a
Speaker 1: little unpredictable. Well, if you're training your language model on
Speaker 1: these things, then over time the language models and the
Speaker 1: tools that are built on top of them begin to
Speaker 1: reflect that training material. It means that we should expect
Speaker 1: generative AI to start increasing variability in sentence because that's
Speaker 1: what we're training it on. You can't expect to train
Speaker 1: it on one thing and it generates something totally different.
Speaker 1: It's going to kind of mimic the material that was
Speaker 1: used to teach it in the first place. So that
Speaker 1: means you're going to see a reduction in the gap
Speaker 1: between how AI creates text and how humans do. But
Speaker 1: on top of that, again, for certain types of writing,
Speaker 1: human authors may take a more structured approach and they
Speaker 1: may purposefully reduce variability between sentences or unconsciously reduce variability.
Speaker 1: That means that their writing is going to start looking
Speaker 1: more like the stuff that these writing detection tools assume.
Speaker 1: Is a marker for AI generated content. If I were
Speaker 1: to write a term paper, I would probably take a
Speaker 1: more consistent, uniform approach to my writing style. That's not
Speaker 1: to suggest that would be the right choice, right, Like,
Speaker 1: I'm not saying that if you write a term paper
Speaker 1: you need to have this very consistent, uniform approach because
Speaker 1: they can get really boring to read papers that are
Speaker 1: written in a style like that. But that would probably
Speaker 1: be my inclination, like thinking in my head, I'd be
Speaker 1: I want to make sure I'm consistent, I'm academic, i
Speaker 1: am thoughtful, I'm methodical. That means that the work I
Speaker 1: would produce would have this low burstiness because I was
Speaker 1: purposefully doing it. Even if that was the wrong decision,
Speaker 1: it probably be the one that I would make because
Speaker 1: I'd be working under the mistaken belief that this is
Speaker 1: somehow more academic. So these AI writing detection tools are
Speaker 1: looking for texts that has low burstiness and low perplexity
Speaker 1: before suggesting that AI had created that particular block of text.
Speaker 1: But as we've talked about, humans right in that kind
Speaker 1: of style too, particularly for formal writing, and so you
Speaker 1: get a lot of false positives, like if you feed
Speaker 1: the US Constitution to a writing detection tool, and it says, well,
Speaker 1: Ai wrote this, Well, a lot of stuff has been
Speaker 1: written about the Constitution, including passages from the content Institution.
Speaker 1: The Constitution itself is clearly available on the web, so
Speaker 1: it's obviously part of these large language models training sets.
Speaker 1: So of course it's going to reflect what's in the
Speaker 1: training set. It was literally incorporated into it. So if
Speaker 1: you're working backward from that logic, then your conclusion, oh
Speaker 1: Ai wrote this because it reflects what the language model
Speaker 1: was trained on. Well, yeah, but that's because the language
Speaker 1: model was literally trained on the material you were analyzing.
Speaker 1: It becomes the sort of catch twenty two sort of situation.
Speaker 1: So we cannot rely on these detection tools in large part. Now,
Speaker 1: this doesn't even touch upon the challenges that non native
Speaker 1: English speakers face with their writing. When they're writing in
Speaker 1: English and these AI detection tools are used on their work,
Speaker 1: they can face disproportionate bias when it comes to these
Speaker 1: detection tools. They get a lot more false positive So
Speaker 1: you're already seeing a lot of false positives anyway, because
Speaker 1: as we've discussed, the criteria being used by these AI
Speaker 1: writing detection tools are faulty because it's making assumptions that
Speaker 1: humans are not writing in those styles when in fact
Speaker 1: they are, and that AI is writing in one specific style,
Speaker 1: when in fact, at least over time, it migrates away
Speaker 1: from that. So you got a double whammy here. Now,
Speaker 1: there are some applications of AI detection tools where it
Speaker 1: works and it makes sense, just not in writing, but
Speaker 1: for stuff like photo or video manipulation. AI detection tools
Speaker 1: can still look for telltale signs that can indicate that
Speaker 1: maybe what you're looking at has at least in some
Speaker 1: part been created by a generative AI tool, right like
Speaker 1: an image creation tool. Obviously, there are examples of this
Speaker 1: where you take one look and you know immediately that
Speaker 1: this was made by AI, because you look at it
Speaker 1: and you're like, no one has that many fingers on
Speaker 1: one hand, but there are other cases where it may not.
Speaker 1: It may be far more subtle to a human perception,
Speaker 1: but if you were to actually analyze the image deeply
Speaker 1: with a very well trained AI detection tool, it could
Speaker 1: indicate this was made by AI because of little subtle things.
Speaker 1: Maybe it's inconsistent lighting, Maybe it's a blinking pattern of
Speaker 1: a person in a video, things like that, Little things
Speaker 1: that would be hard for us to spot as human beings,
Speaker 1: but easy for a detection tool to spot. These AI
Speaker 1: detection tools make sense. They're not necessarily foolproof or flawless,
Speaker 1: but they have a better success rate than when it
Speaker 1: comes to writing, because it's just not that clear cut
Speaker 1: when we're talking about writing. This is unfortunate when teachers
Speaker 1: may rely heavily on AI writing detection tools in order
Speaker 1: to determine if their students are actually doing their own
Speaker 1: work or not. If the teachers are unaware that these
Speaker 1: detection tools are unreliable, they can make some really drastic
Speaker 1: decisions that will have a huge negative impact on their students'
Speaker 1: work and lives, and that's not really fair. Hopefully, the
Speaker 1: educators out there are themselves educating themselves to be repetitive
Speaker 1: about these tools and their unreliability, because otherwise they're going
Speaker 1: to be punishing students and they can't justify it because
Speaker 1: it's all based on a tool that has proven to
Speaker 1: be unreliable at the get go, unless, of course, we're
Speaker 1: talking about instances where someone has copy and pasted some
Speaker 1: ridiculous part of an AI generated response that just gives
Speaker 1: it away. That's a different case. Entirely obviously, But yeah,
Speaker 1: I think it's important to understand the limitations of these
Speaker 1: As we explore generative AI, and we look at the
Speaker 1: pros and the cons and we consider the impact the
Speaker 1: generative AI has on multiple segments of our lives, we
Speaker 1: also have to really think about how do we know
Speaker 1: when it's in use, and how do we know that
Speaker 1: the tools we're using to make those determinations are actually
Speaker 1: good tools. In the case of these AI writing detection tools,
Speaker 1: it looks to me like you might as well not
Speaker 1: even look at them. You are more likely than not
Speaker 1: to get an incorrect answer, because again, we train these
Speaker 1: generative tools to communicate very much the way humans do,
Speaker 1: at least in certain use cases, and those use cases
Speaker 1: typically are the ones where we're most concerned about whether
Speaker 1: or not AI was put to use in the first place.
Speaker 1: So really interesting articles over on Ours Technica. It leads
Speaker 1: to this really deep discussion about generative AI, the limitations
Speaker 1: that we have in detecting it, And obviously there are
Speaker 1: a lot of other things we could touch on. I
Speaker 1: mentioned copyright. That's a big one, because if AI can
Speaker 1: regurgitate copyrighted works with no flaws, then that can be
Speaker 1: a huge blow to authors, for example, or we talked
Speaker 1: a little bit about hallucinations. Hallucinations are when an AI
Speaker 1: tool does not have the information to be able to
Speaker 1: determine what should come next in a sentence. You have
Speaker 1: to remember when you really boil it down these AI
Speaker 1: generative tools, what they're doing is they're following a very
Speaker 1: sophisticated statistical model to determine what should come next in
Speaker 1: its answer. So you give it a prompt and it's
Speaker 1: referencing this incredibly complicated statistical model to say, all right,
Speaker 1: what should I put as a response. Some of the
Speaker 1: information involves things like the actual answers to questions, but
Speaker 1: there are cases where the AI model may be unable
Speaker 1: to identify what the answer to the question is, but
Speaker 1: it still needs to answer your query. It doesn't have
Speaker 1: the answer, so it makes it up, but following this
Speaker 1: very sophisticated statistical model so that the answer it generates
Speaker 1: appears to be valid even though it's just completely made up.
Speaker 1: This is what we call hallucinations in AI. It's when
Speaker 1: AI generates an answer in order to respond to a query,
Speaker 1: but that answer is fabricated. It's a confabulation. That's another
Speaker 1: word that some people are using rather than hallucination, and
Speaker 1: it comes across as being very much legitimate because again,
Speaker 1: these very sophisticated statistical models make it seem authoritative and knowledge.
Speaker 1: The way the sentences are structured, it doesn't come across
Speaker 1: wishy washy. It's not like maybe it's blah blah blah.
Speaker 1: It ends up being it's blah blah blah and presented
Speaker 1: in such a way that you feel like it's reliable,
Speaker 1: even though ultimately it's not. That's another issue. It's related
Speaker 1: to what we're talking about. And it's also means that
Speaker 1: as a student, or as a business writer or as
Speaker 1: a lawyer, as one person found out earlier this year,
Speaker 1: you should not rely on generative AI as your one
Speaker 1: and only source for anything AI. Generative AI has even
Speaker 1: been found to fabricate quotations from people. Obviously that's not
Speaker 1: good either. There are lots of issues here. Anyway. I
Speaker 1: hope that was some food for thought for y'all. I
Speaker 1: hope you're doing well. I will talk to you again
Speaker 1: really soon. Tech Stuff is an eye Heart Radio production.
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