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. At the beginning of this year, that being
Speaker 1: twenty twenty three, I said like it felt like it
Speaker 1: was going to be the year of AI, and so
Speaker 1: far I think I'm pretty much on the money. But
Speaker 1: more specifically, twenty twenty three has been the year of
Speaker 1: generative AI. That is artificial intelligence that creates or generates something,
Speaker 1: whether it's an image, a sound, or as we're going
Speaker 1: to talk about today, text in response to some sort
Speaker 1: of input. Now, before we go any further, this is
Speaker 1: where we need to remind ourselves that while this is
Speaker 1: a type of artificial intelligence, it's not all of AI.
Speaker 1: Not every AI application involves generative processes. And while generative
Speaker 1: AI can seem fascinating, exciting, surprising, or creepy, I believe
Speaker 1: that largely stems from how generative AI appears to be
Speaker 1: mimicking humans, and it's not an indication of how sophisticated, advanced,
Speaker 1: or dangerous it really is. It's kind of an uncanny
Speaker 1: Valley thing because it appears to be behaving like a human,
Speaker 1: we start to project things on it that aren't necessarily
Speaker 1: accurate or realistic. I think of it kind of like
Speaker 1: the way we can be with our pets, where we
Speaker 1: will project things on our pets that may not reflect
Speaker 1: what the pet is actually experiencing, but that's how we're
Speaker 1: perceiving it. So the reason I say all of this
Speaker 1: up at the very top of this episode is that
Speaker 1: we're also seeing a lot of people expressing concern about AI,
Speaker 1: which is understandable. You know about how it could potentially
Speaker 1: lead to harm, and these are legitimate and rational concerns. However,
Speaker 1: with the focus on stuff like chat GPT for example,
Speaker 1: or Google Bard, I would argue the concern is far
Speaker 1: too narrowly focused on just one aspect of AI, and
Speaker 1: in my opinion, it's not even the most dangerous implementation
Speaker 1: of AI. I mean, we have cars on the road
Speaker 1: right now that use AI for driver assists and autonomous operations.
Speaker 1: If we're worried about the robots taking us down, maybe
Speaker 1: we shouldn't make them our chauffeurs. But really that's a
Speaker 1: topic for another episode. Today, I wanted to take a
Speaker 1: look at an issue that crops up in AI chat
Speaker 1: bots like open ai or goole Bard and similar products.
Speaker 1: This is one that is concerning because it's an issue
Speaker 1: that leads these tools to create false or misleading information
Speaker 1: while presenting that info in a way that seems authoritative
Speaker 1: and trustworthy. And in the field of AI, the term
Speaker 1: hallucination is used to describe this situation. At least a
Speaker 1: lot of folks will use the word hallucination. As it
Speaker 1: turns out, there's actually some debate in AI circles about
Speaker 1: whether or not that should be the appropriate term. Now
Speaker 1: for we mirror mortals, a hallucination is when we have
Speaker 1: an experience in which we perceive something that isn't reflected
Speaker 1: in reality. Maybe we hear a sound but there was
Speaker 1: actually no sound present. Maybe it was that tree falling
Speaker 1: in the woods and no one was around or something,
Speaker 1: or we see something that's not really there. It can
Speaker 1: be really darn disconcerting, and sometimes it can be absolutely terrifying.
Speaker 1: I'm reminded of how many people who experience sleep paralysis
Speaker 1: often will also have hallucinations accompany this period where they're
Speaker 1: awake but they cannot move, and it's probably because sleep
Speaker 1: paralysis occurs when you're kind of caught between being asleep
Speaker 1: and being awake, so there's still some dream like activity
Speaker 1: going on in your brain that's trying to explain things
Speaker 1: like why you're unable to move. Oh, it's because you
Speaker 1: have this witch perched on your chest and she's pinning
Speaker 1: you to the bed. Tools like chat GPT are not dreaming,
Speaker 1: you know, they're not perceiving anything at all. They have
Speaker 1: no senses to trigger, so they cannot hallucinate in that sense. Instead,
Speaker 1: what they are doing is mistakenly assigning high confidence to
Speaker 1: something that they just plane made up. So they're treating
Speaker 1: it like it's a fact that they're highly confident is accurate,
Speaker 1: when really they just invented it. So it is an
Speaker 1: instance where they're really confident in something that is not
Speaker 1: coming from a reliable source in the AI's actual training data.
Speaker 1: So if we wanted to put that into human terms,
Speaker 1: it'd be kind of like if you made up a
Speaker 1: story to explain something that otherwise would either be really
Speaker 1: boring or maybe really embarrassing. So you make up a lie,
Speaker 1: in other words, to cover up something that you would
Speaker 1: rather not be known, And so you tell this lie
Speaker 1: over and over when people are asking you about this
Speaker 1: particular thing, and you repeat it often enough where gradually
Speaker 1: your brain essentially makes a pathway where this fake version
Speaker 1: of history of what actually happened becomes the real one
Speaker 1: in your head. You begin to believe your own lie,
Speaker 1: and so in future tellings of the story, you don't
Speaker 1: even realize you're lying at all. You're telling what you
Speaker 1: believe to be the real sequence of events, even though
Speaker 1: it's all a fib. That's kind of what's happening with
Speaker 1: AI hallucinations, only it happens all at once, And for
Speaker 1: that reason, some folks prefer to use other terms to
Speaker 1: describe what AI does when it starts to invent things
Speaker 1: in response to a query from a user. So some
Speaker 1: have proposed the word confabulation as an alternative descriptor of
Speaker 1: what's going on. So this is similar to kind of
Speaker 1: the scenario I just gave, because it's in human psychology.
Speaker 1: A confabulation is when we have a hitch in our memory,
Speaker 1: and so we fill in a gap that's in our memory.
Speaker 1: We're not doing it consciously, it just happens, and that
Speaker 1: might mean we fill in the gap that doesn't at
Speaker 1: all reflect what really happened. So this can happen at
Speaker 1: any time. I've seen it happen with people who are
Speaker 1: in like a situation that was totally on a expected
Speaker 1: in high stress. I've seen it in training operations where
Speaker 1: you have a group of people and then someone bursts
Speaker 1: in as if they are a burglar or a thief or something,
Speaker 1: and then they get out, and then those people who
Speaker 1: were just subjected to this very scary situation are asked
Speaker 1: to give details about the thief's appearance, and people start
Speaker 1: to invent things, not purposefully, not with the intent to deceive,
Speaker 1: but because their memory is just trying to fill in
Speaker 1: gaps because their perception didn't really take it all in.
Speaker 1: So confabulation doesn't imply intent, and I think that might
Speaker 1: be why a lot of researchers like the word, because
Speaker 1: it's not the intention of the AI to fool people
Speaker 1: or to pass off fantasy as if it were reality. Instead,
Speaker 1: the AI is making an honest go of trying to
Speaker 1: meet the expectations of the user. So if you ask
Speaker 1: the AI about, say a historical figure, really tries to
Speaker 1: give you a good answer, but occasionally that answer might
Speaker 1: be wrong, not because the AI is drawing from a
Speaker 1: bad data source, but because there's actually a gap in
Speaker 1: its knowledge, and the AI just fills that gap as
Speaker 1: best it can. Unfortunately, the end result is you get
Speaker 1: an answer that seems totally cromulent, like you could just
Speaker 1: imagine reading that answer in a respectable, thoroughly fact check encyclopedia,
Speaker 1: but then it turns out to be garbage. So let's
Speaker 1: talk about how this happens, which will involve an overview
Speaker 1: of how these chatbought AI tools are trained and at
Speaker 1: a very very high level, how they work. So this
Speaker 1: is going to involve some discussion about machine learning and statistics. So,
Speaker 1: first off, how do machines actually learn? I think it's
Speaker 1: pretty easy to understand. How we program machines to do
Speaker 1: some specific task. Right, we create a set of rules
Speaker 1: that this machine follows sequentially, and the machine executes those
Speaker 1: rules as directed, and then we get the result we wanted.
Speaker 1: That is easy to understand. So I'll give an example.
Speaker 1: Let's say we have a robotic arm and you've got
Speaker 1: two tables, and you put a wooden block on table
Speaker 1: number one, and you program the robotic arm to pick
Speaker 1: up this wooden block on table one and move it
Speaker 1: over to table two. Once you program it then it
Speaker 1: should be able to do that task over and over,
Speaker 1: assuming that no one has moved the tables. No one
Speaker 1: has moved the robotic arm, and the wooden block is
Speaker 1: always in the same place and it's always the same size. Right,
Speaker 1: you haven't changed any of the parameters, so it's the
Speaker 1: exact same situation over and over and over again. You've
Speaker 1: created this simple program. It should be no surprise when
Speaker 1: the robotic arm does it successfully. But what if we
Speaker 1: wanted a robotic arm that could learn how to pick
Speaker 1: up different objects from table one and then move them
Speaker 1: to t able to These objects could be different shapes,
Speaker 1: they could be different sizes, they could weigh different amounts,
Speaker 1: They might be made of different stuff. Maybe some of
Speaker 1: them are fairly delicate and the arm would break the
Speaker 1: object if it applied too much pressure. So how would
Speaker 1: we build a robotic arm that could deal with these
Speaker 1: different scenarios, including ones where we put something completely new
Speaker 1: to the robot on the table, something that the robot
Speaker 1: has never encountered before. Well, to do that, we would
Speaker 1: probably pursue a machine learning model in order to teach
Speaker 1: this robot the whole process of picking something up, especially
Speaker 1: something it had not encountered before. So basically, machine learning
Speaker 1: uses sets of algorithms in an effort to get better
Speaker 1: at a given task, and part of learning involves training,
Speaker 1: which really boils down to feeding a machine lots and
Speaker 1: lots and lots of information, like the more information you
Speaker 1: can feed it, the better, and then letting it process
Speaker 1: this information in an effort to get a specific result,
Speaker 1: and then going back and tweaking the model to refine
Speaker 1: it over and over and over and over again to
Speaker 1: get better at it over time. So we'll imagine a
Speaker 1: hypothetical machine learning model that is designed to do something
Speaker 1: relatively simple like recognize if an image has a cat
Speaker 1: in it or not, because this is actually something that
Speaker 1: has been done with machine learning models in the past.
Speaker 1: It's actually a fairly popular approach is does this picture
Speaker 1: have a cat in it? Or does this video have
Speaker 1: a cat in it? That kind of thing. Let's imagine
Speaker 1: that our machine learning model is an actual physical model,
Speaker 1: like it's a giant funnel. So on the wide end
Speaker 1: of the funnel, that's where we just dump tons of
Speaker 1: photographs with some of them have cats in them, some
Speaker 1: of them don't. Now imagine that at the narrow end
Speaker 1: of the funnel. At the bottom of the funnel, we
Speaker 1: actually have two channels. One channel leads into a bucket
Speaker 1: that says no cats here, and the other channel leads
Speaker 1: to a bucket that says, ah, sweet kitty cats. So
Speaker 1: we dump thousands, maybe millions of photographs into the top
Speaker 1: of this funnel, and the funnel starts to sort the pictures.
Speaker 1: We can't see this because it's inside the funnel, but
Speaker 1: there are channels inside that funnel where photos are directed
Speaker 1: either to go more toward the no kittycat side or
Speaker 1: the yes kittykat side, And they go through these channels
Speaker 1: all down the funnel, and at the very end of it,
Speaker 1: they start spitting out these images into the two buckets. Well,
Speaker 1: once it's done, once it has processed all the photos,
Speaker 1: we take the two buckets and we see how our
Speaker 1: model did. And maybe we see that the model caught
Speaker 1: most of the pictures with cats in them, but not
Speaker 1: all of them. Maybe we also see that there are
Speaker 1: some photos that fell into the kitty cat bucket that
Speaker 1: have exactly zero kitty cats in the picture. Something is
Speaker 1: not working inside our model. So at that point we
Speaker 1: open the funnel, we take the top off or whatever
Speaker 1: we have built in a hinged latch or something, and
Speaker 1: we've opened it up. Now essentially inside our funnel, we
Speaker 1: see all those channels, and each channel is meant to
Speaker 1: look for some sort of evidence of a cat, and
Speaker 1: if it finds evidence, it pushes it closer toward the
Speaker 1: pathway of kitty cat, and if it doesn't, it pushes
Speaker 1: it closer to the pathway of no kitty cat. But
Speaker 1: there's tons of these channels. Some of them feed images
Speaker 1: back up through the whole process. Again, it's very complicated
Speaker 1: inside this funnel, and you have to go in there
Speaker 1: and start to tweak little bits of rules in these
Speaker 1: channels to adjust for whatever problem you're encountering at the
Speaker 1: end result when you're done. So, when you're training your model,
Speaker 1: you change the weights of these different decisions that are made.
Speaker 1: Some decisions perhaps have too much emphasis on them. They
Speaker 1: like they're too powerful and they're skewing the results. So
Speaker 1: you reduce the weight of that particular decision point and
Speaker 1: you increase the weight of a different one to try
Speaker 1: and get things right. It's a painstaking process and you
Speaker 1: have to do it over and over again, and these
Speaker 1: exercises repeat and you try to refine your model to
Speaker 1: get it better at deciding whether or not a photograph
Speaker 1: has got a cabinet or does it, and eventually, if
Speaker 1: everything is working well, it gets very very good at
Speaker 1: sorting images. Maybe once in a while, something sneaks through.
Speaker 1: Maybe there's a cloud that kind of looks like a
Speaker 1: kitty cat and it goes into the wrong bucket, or
Speaker 1: maybe there is a kitty cat that goes into the
Speaker 1: no kitty cat bucket, but the kitty cat was kind
Speaker 1: of obscured in the picture and the model just couldn't
Speaker 1: suss it out. But it succeeds more often than not. Okay,
Speaker 1: that's a baseline. When we come back, we'll talk a
Speaker 1: bit more about machine learning and how this plays into
Speaker 1: tools like chat GPT. Okay, I laid out one version
Speaker 1: of machine learning, and I want to stress that's just
Speaker 1: one version of machine learning. It's related to things like
Speaker 1: neural networks, which are designed to kind of mimic the
Speaker 1: way our brains process information and form pathways among neurons
Speaker 1: while we're trying to suss things out. But that's just
Speaker 1: one version of machine learning. I don't mean to say
Speaker 1: that's how it all works. There are actually lots of
Speaker 1: sub fields within machine learning, neural networks being just one
Speaker 1: of them, but there's also subsets of neural networks. One
Speaker 1: of those was would be deep learning, which always makes
Speaker 1: we think of MST three K and deep hurting shout
Speaker 1: outs to any misties out there. Now, as you dive
Speaker 1: down to deep learning, you're really getting into an interesting
Speaker 1: field of AI and machine learning. So deep learning models
Speaker 1: can accept unstructured data. If you're going further up to
Speaker 1: less specialized machine learning models, these have to use heavily
Speaker 1: labeled data sets and heavily structured data and use supervised
Speaker 1: learning in order to improve with time. But when you
Speaker 1: get into deep learning, you're looking at a very focused
Speaker 1: approach to machine learning where you can just feed unstructured
Speaker 1: data that has no labels to it and start to
Speaker 1: use this model to do whatever it is that you
Speaker 1: want it to do. But we're still kind of talking
Speaker 1: about a channeling or funneling situation here. The input goes
Speaker 1: into the model, the model analyzes the input and pushes
Speaker 1: it further one way or another through the system, and
Speaker 1: it comes out the end as output, which could be
Speaker 1: an image search result for kiddy cats in your smartphone's
Speaker 1: photo role, for example, So if you've ever gone into
Speaker 1: a smartphone photo collection and you just typed in a
Speaker 1: general word in search, you know it's not that you
Speaker 1: tagged any of your photos with this. You're just like
Speaker 1: looking for photos in your role that has a cat
Speaker 1: in them, and it returns something like that. Well, that
Speaker 1: can be the result of a machine learning process like
Speaker 1: the one I've just described, because again, the system has
Speaker 1: to figure out which of your photos have cats in them,
Speaker 1: even though you didn't tag any of those photos with cats.
Speaker 1: It doesn't have metadata. It has to analyze the photo itself.
Speaker 1: Now it's time to talk about probabilities. Large language models lms,
Speaker 1: which are what power chat bots like Google Bard and
Speaker 1: Chat GPT. They work in probabilities. And there's one example
Speaker 1: of an AI using probabilistic algorithms to generate responses that
Speaker 1: I really loved reference, and that example is IBM's Watson platform.
Speaker 1: So while the world right now is struggling to figure
Speaker 1: out how to handle chat GPT and Google Bard and such,
Speaker 1: IBM's Watson gave us a glimpse at what we could
Speaker 1: expect all the way back in twenty eleven. That's when
Speaker 1: IBM famously put Watson to the test and some exhibition
Speaker 1: games of the game show Jeopardy against former champions of
Speaker 1: that game show, human champions. So in many ways, this
Speaker 1: was an echo of IBM's Deep Blue going up against
Speaker 1: chess master Gary Kasparov in various games of chess. Putting
Speaker 1: Watson up against humans and Jeopardy was a fantastic publicity stunt,
Speaker 1: and it also was really impressive because the way Jeopardy
Speaker 1: works is players get several categories of trivia that they
Speaker 1: can choose from. Each category has different levels of questions
Speaker 1: that are designated by a dollar amount, So higher the
Speaker 1: dollar amount, the harder the trivia question is. Generally speaking,
Speaker 1: the actual clue that the players get is given in
Speaker 1: the form of an answer, and they have to provide
Speaker 1: a question that relates to that answer. So here's an example.
Speaker 1: The answer revealed in say a hypothetical Jeopardy game that
Speaker 1: has the category podcasts, could be something like he was
Speaker 1: Jonathan Strickland's original co host on the show tech Stuff.
Speaker 1: The correct response would be bipp a bip Who is
Speaker 1: Chris Palette? That would be the correct answer, But Jeopardy
Speaker 1: goes beyond just trivia. Often the answers provided will include
Speaker 1: word play or images or sound cues, and players will
Speaker 1: have to think outside the box. They can't just know
Speaker 1: the answer. Sometimes there's interpretation that has to happen first.
Speaker 1: The clue to the correct response could be a pun,
Speaker 1: it could involve a rhyme to the answer. It's not
Speaker 1: always a straightforward trivia question. In other words, so Watson
Speaker 1: needed to be able to analyze the clue given, to
Speaker 1: break it apart into components to understand what exactly is
Speaker 1: being asked of it. Then it needed to search its
Speaker 1: database for relevant information. So Watson famously was not connected
Speaker 1: to the Internet during these Jeopardy games. Instead, it was
Speaker 1: relying upon a database representing millions of books filled with facts.
Speaker 1: Then it would generate hypothetical responses like a hypothetical answer
Speaker 1: that Watson should give, or rather questions we're talking about jeopardy,
Speaker 1: and it would submit these hypotheses to a second round
Speaker 1: of analysis to look at is there any evidence that
Speaker 1: supports this response as being correct? Kind of measuring like, well,
Speaker 1: here's a possible answer, how likely is this answer to
Speaker 1: be right? And that was all part of the process.
Speaker 1: So it might even produce more than one answer. You
Speaker 1: might have multiple potential answers, and Watson would assign each
Speaker 1: answer a probability kind of a confidence level of how
Speaker 1: it felt that answer measured up against all the other ones. So,
Speaker 1: as an example, answer A might receive a ninety percent
Speaker 1: confidence level, So that's pretty darn confident that's the right answer.
Speaker 1: Maybe you have answer B and you're like, I'm seventy
Speaker 1: eight percent sure that this could be right. An answer
Speaker 1: C is the long shot with thirty three percent confidence.
Speaker 1: These don't add up to one hundred because they're not
Speaker 1: It's not like a zero sum game. It's more like, oh,
Speaker 1: it could be this or it could be that, but
Speaker 1: I feel like this is more likely than that, so
Speaker 1: I'm going to go with this. And Watson also had
Speaker 1: a threshold. If the answer it generated failed to meet
Speaker 1: a certain confidence threshold, Watson would not buzz in to
Speaker 1: try an answer. Otherwise, Watson played pretty aggressively and even
Speaker 1: in some sticky situations with daily dumb where if you
Speaker 1: get a daily double in Jeopardy, you don't buzz in anymore.
Speaker 1: If you are the one who chose the daily double,
Speaker 1: you're playing by yourself and you just have to give
Speaker 1: an answer. So in those situations, Watson got aggressive, and
Speaker 1: it would it would guess with very low confidence thresholds
Speaker 1: for some of these, like at the thirty percent range,
Speaker 1: and occasionally it was right. In fact, more often than
Speaker 1: not it was right until it got to final Jeopardy,
Speaker 1: where at least the first time, things did not go
Speaker 1: totally in Watson's favor. Also, Watson had an interesting betting
Speaker 1: strategy when it came to daily doubles. But I'm getting
Speaker 1: way off track. So that confidence level is really what
Speaker 1: I want to hone in on here. So it was
Speaker 1: expressed in percentages, So zero percent confidence would be like
Speaker 1: I do not know the answer, I do not know
Speaker 1: what goes here. A one hundred percent confidence level would
Speaker 1: be I am absolutely certain this is the right answer.
Speaker 1: And in a way, AI chat bots like chat GP
Speaker 1: and Google Bard are doing the same thing, only their
Speaker 1: confidence isn't about this is the answer to your question.
Speaker 1: I'm one hundred percent certain that this answers your question.
Speaker 1: It's more like it's more granular than that, because it's
Speaker 1: more at the sentence level. It's like, I think this
Speaker 1: word is the word that needs to go next to
Speaker 1: create the sentence that I'm building. So let's talk about
Speaker 1: how these models do create sentences, and I'm not going
Speaker 1: to wade into stuff like natural language processing. That is
Speaker 1: a major part of this, but I have done full
Speaker 1: episodes about natural language processing before. That essentially says, it's
Speaker 1: a way for machines to analyze information that's written in
Speaker 1: you know, your normal language, whether that's English or whatever.
Speaker 1: But you're not trying to create a sentence that the
Speaker 1: machine is able to parse. Right, You're not trying to
Speaker 1: work with the machine on its terms. You're just communicating
Speaker 1: with it the way you would with anyone else. It's
Speaker 1: the machines job to figure out what the heck you're saying.
Speaker 1: So we're not gonna dwell on that. Instead, we're going
Speaker 1: to talk about how a chatbot chooses how to respond
Speaker 1: to something that is said or asked of it. These
Speaker 1: chatbots are built on top of language models that have
Speaker 1: had enormous data sets fed to them during training. The
Speaker 1: data sets include stuff like basic facts. So if you
Speaker 1: ask a chatbot who was the sixteenth president of the
Speaker 1: United States, a well trained chatbot at least is going
Speaker 1: to say it was Abraham Lincoln. But that data also
Speaker 1: trains the chatbot on how we communicate with one another.
Speaker 1: So through analyzing hundreds of millions of documents, ranging from
Speaker 1: books to online social platforms like Reddit, these chatbot models
Speaker 1: learn rules of communication. They learn rules about spelling syntax.
Speaker 1: They learn about structure that goes from the sentence level
Speaker 1: to paragraphs like They learn how to build a sentence properly,
Speaker 1: how to build another sentence that builds on the first one,
Speaker 1: how to build a whole paragraph that gets a thought across,
Speaker 1: and then how to do a series of paragraphs to
Speaker 1: convey meaning of some sort right, how to build to
Speaker 1: like a thesis almost They learn which words typically follow
Speaker 1: behind other words, which ones are statistically likely to be
Speaker 1: the best word to use in any given moment. So
Speaker 1: when a chatbot is dynamically generating a response, it is
Speaker 1: referencing this huge amount of learning, and that learning will
Speaker 1: guide the content and influence which facts are included or excluded,
Speaker 1: but will also just simply guide the chatbot to build
Speaker 1: sentences properly. So if we were to zoom weigh in
Speaker 1: on what is going on as a chatbot builds a
Speaker 1: new response, we would see the chatbot is selecting words
Speaker 1: based on statistical probability. Essentially, the chatbot would be considering
Speaker 1: which word is statistically most likely to be the correct
Speaker 1: one for that part of its response. Whichever word ranks
Speaker 1: highest is likely to go in there. Now, guiding this
Speaker 1: guessing game is the context of the conversation. So if
Speaker 1: I'm asking a chatbot a question about Abraham Lincoln, the
Speaker 1: chatbot is not likely to pull superfluous information about like
Speaker 1: key lime pie or something. So when I talk about
Speaker 1: which word is statistically most likely to come next, we
Speaker 1: have to take an account that context is determining this too.
Speaker 1: Each situation will be unique, and if you and I
Speaker 1: both are having similar conversations with a chatbot, but we're
Speaker 1: framing our questions slightly differently, or coming at this topic
Speaker 1: from different perspectives, the responses we get from the chatbot
Speaker 1: could reflect that. Now here's where we get into the
Speaker 1: tricksie territory. Sometimes the chatbot will be attempting to build
Speaker 1: a response and there will be a gap in its
Speaker 1: data set, So, for some reason or another, the relevant
Speaker 1: data to answer our question just isn't there, Or perhaps
Speaker 1: the language model can't reconcile that the data is relevant
Speaker 1: for this particular conversation, or maybe there are conflicting elements
Speaker 1: in its data set, and so in the absence of
Speaker 1: reliable information, the chatbot simply invents a response by following
Speaker 1: those statistical rules when constructing a sentence. So what we
Speaker 1: get is a sentence that is grammatically correct, that is
Speaker 1: posted in a way that appears to be trustworthy, but
Speaker 1: it does not necessarily reflect reality. We get an answer
Speaker 1: that reads as if it is correct, but it's not.
Speaker 1: It would be as if someone with an agenda had
Speaker 1: written an article for an encyclopedia and none of the
Speaker 1: editing staff caught that this was the case, and so
Speaker 1: the whole thing went to print, and it's presented as
Speaker 1: if this is an objective truth, when really it's a
Speaker 1: subjective point of view. Except with AI, there's no agenda
Speaker 1: needed because AI is not thinking anything. It's not motivated
Speaker 1: because it lacks the capability of being motivated. There's no
Speaker 1: sentience there, there's the mimicry of sentience, there's the appearance
Speaker 1: of it. And again, I think this is a large
Speaker 1: reason why we have a lot of people concerned about
Speaker 1: AI right now, because it appears to be behaving like
Speaker 1: a person, even though there's nothing behind that. Right There's
Speaker 1: no sentience or conciousness behind this it just has the
Speaker 1: surface level appearance of it, and that's enough to make
Speaker 1: us start to create all sorts of scenarios where the
Speaker 1: AI goes bad or sinister. That's not even necessary. It's
Speaker 1: just trying to answer our questions and occasionally having to
Speaker 1: make stuff up while it does so. The chatbot, the
Speaker 1: machine is just presenting what is estimated to be the
Speaker 1: most statistically likely response. And by that I don't mean
Speaker 1: that the answer is statistically likely to be correct, but
Speaker 1: rather down to the sentence and paragraph structure that they
Speaker 1: are statistically probable to be the most correct from a
Speaker 1: like a grammatical and structural point of view, not from
Speaker 1: a content perspective. So it's really about how statistically likely
Speaker 1: is word two to follow word one, and that word
Speaker 1: three would follow word two, and so on. Where the
Speaker 1: finished sentence is what's important, and whether it's factual or
Speaker 1: not is immaterial. Okay, we're gonna take another quick break.
Speaker 1: I've got a lot more to say about this. We
Speaker 1: have to cover a lot more ground we're back. So
Speaker 1: a lot of the time, perhaps even most of the time,
Speaker 1: you won't run into trouble when you're using these chatbots
Speaker 1: because the dataset feeding these large language models. Is truly huge. Plus,
Speaker 1: there are people working on these models all the time.
Speaker 1: They're refining them, they're catching mistakes, they're trying to correct
Speaker 1: those mistakes, they're tweaking the model to prevent it from
Speaker 1: happening again. But now and again, you might ask a
Speaker 1: chatbot a question and you'll encounter a situation where there's
Speaker 1: this gap in the chatbot's data and it makes stuff up,
Speaker 1: It hallucinates. Personally, I find it both odd and oddly
Speaker 1: human that the companies behind these chatbots haven't built in
Speaker 1: a fail safe where if a chatbot comes up up
Speaker 1: against this kind of situation, it just says something akin
Speaker 1: to I don't know the answer to that, and instead
Speaker 1: it kind of invents an answer. So it's kind of
Speaker 1: like being in a conversation with someone who is incapable
Speaker 1: of admitting that they don't know something. I used to
Speaker 1: be that guy. In fact, sometimes I still am that guy.
Speaker 1: I have to catch myself to remind myself that it's
Speaker 1: actually okay to not know something, and that curiosity is
Speaker 1: a way better look than trying to bluff your way
Speaker 1: through life. But then I also admit I don't know
Speaker 1: how you would go about implementing a system in which
Speaker 1: an AI chatbot fesses up to not knowing something. It
Speaker 1: may not be as simple as that. And there's also
Speaker 1: a related problem, which is that without knowing what source
Speaker 1: or sources the AI is referencing for any given query,
Speaker 1: you don't really know how reliable that response is. If
Speaker 1: the AI is pulling on information from unreliable sources, whether
Speaker 1: those sources were poorly informed, or they were biased, or
Speaker 1: it was satire and it was just being presented as fact.
Speaker 1: I've talked about this before on this show. There are
Speaker 1: a lot of websites that were really popular just a
Speaker 1: few years ago that called themselves satire, but really they
Speaker 1: just posted lies like it wasn't satire. There was nothing
Speaker 1: humorous about it. They weren't saying anything other than just
Speaker 1: making up stuff. So if the AI is pulling information
Speaker 1: from those kinds of sources, you cannot expect the AI's
Speaker 1: answer to magically scrub all the bad from those sources
Speaker 1: and then provide good information. So, in other words, garbage in,
Speaker 1: garbage out. So in some cases it may not be
Speaker 1: that the AI is hallucinating at all. It may just
Speaker 1: be that it's referencing a poor source for its information.
Speaker 1: The trouble is you can rarely tell what's going on
Speaker 1: from a user standpoint, and the AI presents everything the
Speaker 1: same way, So you'll get responses with good info, you'll
Speaker 1: get responses with bad info, and you'll get responses where
Speaker 1: the AI just made up stuff and it's all handed
Speaker 1: to you in a format that makes it impossible to
Speaker 1: tell the difference between them all on a surface level.
Speaker 1: So this can lead to really dangerous situations. For example,
Speaker 1: Google employees reported while they were internally testing the Barred
Speaker 1: chatbot before Google rolled it out for a beta program
Speaker 1: that the responses were unreliable in many cases, and in fact,
Speaker 1: in some instances, those responses could actually lead to people
Speaker 1: getting hurt. Allegedly, when asked about scuba diving procedures, Google
Speaker 1: bar generated a response that had incorrect information, and if
Speaker 1: someone were to act on that, they could be injured
Speaker 1: or worse. So clearly that represents a real danger. It's
Speaker 1: one thing if the chatbot gives you the wrong answer
Speaker 1: to put in your essay about Emily Dickinson. It's another
Speaker 1: if you're counting on it to teach you how to,
Speaker 1: I don't know, pack your parachute correctly for your first
Speaker 1: skydiving solo jump. But there's also the danger of people
Speaker 1: weaponizing AI hallucinations to push a narrative that may not
Speaker 1: be accurate. And it's easy at least to understand what
Speaker 1: led people to form that kind of narrative. So I'm
Speaker 1: going to give a recent example that really happened. Fox News,
Speaker 1: which has a reputation for right leaning reporting, it's kind
Speaker 1: of putting it lightly, published a story relating to Elon
Speaker 1: Musk's appearances on a show with Fox News personality Tucker Carlson.
Speaker 1: The accompanying news story pointed out that chat gpt produced
Speaker 1: an outright incorrect answer when asked to give a background
Speaker 1: on the late Al Gore Senior, who's al Gore's father,
Speaker 1: the former Vice President. His father served in the House
Speaker 1: of Representatives and then the US Senate for the state
Speaker 1: of Tennessee. Now, the chat gpt generated information on al
Speaker 1: Gore Senior included the following statement quote. During his time
Speaker 1: in the Senate, Gore was a vocal supporter of civil
Speaker 1: rights legislation and was one of the few Southern politicians
Speaker 1: to vote in favor of the Civil Rights Act of
Speaker 1: nineteen sixty four. End quote that is one hundred percent
Speaker 1: not right, that is completely incorrect. Gore actually voted against
Speaker 1: the Civil Rights Act of nineteen sixty four. I guess
Speaker 1: technically it wasn't one hundred percent incorrect because he was
Speaker 1: a senator, so that part was right. But no, he
Speaker 1: voted against the Civil Rights Act of nineteen sixty four.
Speaker 1: He was a Democrat representing a state that, to put
Speaker 1: it lightly in general, was not in favor of granting
Speaker 1: civil rights to anyone who wasn't white. So what his
Speaker 1: personal feelings on the matter were, I don't know. I mean,
Speaker 1: he certainly positioned himself as a defender of the great
Speaker 1: State of Tennessee's right to oppress people who weren't white.
Speaker 1: But I can definitely say that he wanted to get reelected,
Speaker 1: and whether he believed in his vote or not, he
Speaker 1: did vote against the Civil Rights Act of nineteen sixty four.
Speaker 1: Of course, the Act passed anyway, and Golore was able
Speaker 1: to get re elected, and he did subsequently vote in
Speaker 1: favor of the Voting Rights Act of nineteen sixty five.
Speaker 1: But the point is chat GPT got this response very wrong,
Speaker 1: and Fox News positioned it as if this was a
Speaker 1: feature not a bug that that was the intended outcome,
Speaker 1: and it was evidence of a campaign to rewrite history
Speaker 1: to position Democrats as like saintly saviors who could do
Speaker 1: no wrong. But there's no need to go looking for
Speaker 1: a conspiracy here. The problem isn't in some invisible hand
Speaker 1: guiding chat gpt to create biased history. It's the very
Speaker 1: nature of how this kind of AI works. When it
Speaker 1: doesn't have the data, it makes stuff up based on
Speaker 1: what is statistically the most quote unquote correct word for
Speaker 1: the sentence. Now you might ask why did chat gpt
Speaker 1: not have access to the relevant data, And I do
Speaker 1: not know the answer to that. I did test this myself, however,
Speaker 1: I actually opened up chat GPT and I asked it
Speaker 1: to give me background on al Gore Sr. And sure enough,
Speaker 1: I got a similar response to what Fox reported, including
Speaker 1: the incorrect fact quote unquote that al Gore Senior had
Speaker 1: voted in favor of the Civil Rights Act of nineteen
Speaker 1: sixty four. So I then asked a follow up question.
Speaker 1: I specifically said, how did al Gore Senior vote on
Speaker 1: the Civil Rights Act of nineteen sixty four? Chad GPT
Speaker 1: gave me the wrong information again. Then I said, you're
Speaker 1: wrong that Al Gore sor voted against the Civil Rights
Speaker 1: Act of nineteen sixty four. What sources did you use?
Speaker 1: Chad gpt gave me a message that essentially said, I'm sorry,
Speaker 1: you're right, al Gore Senior didn't vote in favor of
Speaker 1: the Civil Rights Act, he did vote against it. Then
Speaker 1: it gave me a vague response that it draws from
Speaker 1: various articles and such for its answers. It didn't give
Speaker 1: any specifics. It was not a very satisfying response, but
Speaker 1: it did at least admit, Oh, you're right, I give
Speaker 1: you the wrong answer. But again, there's no need to
Speaker 1: assume there was some conspiracy that caused this to happen.
Speaker 1: These hallucinations happen across every topic, not just history and politics. Yes,
Speaker 1: if we look at this very specific example, you start
Speaker 1: to ask, oh, is there an intent here? Is there
Speaker 1: a desire to rewrite history to make democratic leaders look
Speaker 1: more positive in a modern lens? And is it a
Speaker 1: way to avoid tough questions like which party actually was
Speaker 1: supporting civil rights and which party was opposing them? If
Speaker 1: you're talking about Southern Democrats, the answer is they were
Speaker 1: opposing it because Southern Democrats are very, very different from
Speaker 1: of the time of the nineteen sixties Southern democrats, very
Speaker 1: different from modern democrats. But you kind of you if
Speaker 1: you're whitewashing, if you're changing the facts to try and
Speaker 1: make them seem more sympathetic, that would be bad, right,
Speaker 1: that's clearly manipulation. That, however, I don't think is what's
Speaker 1: going on here. I think there's no need for it,
Speaker 1: because the AI is just hallucinating and creating information that
Speaker 1: it thinks is correct, or at least thinks is the
Speaker 1: most statistically correct answer to give based upon the information
Speaker 1: that has available to it, and it's presenting it as
Speaker 1: if it's hard fact and it's not. So we know
Speaker 1: that the AI, when it's presenting information that could potentially
Speaker 1: be harmful, that that can't be the intent. Right. There's
Speaker 1: not some cabal out there that's say A Now those
Speaker 1: scuba divers who aren't smart enough to ask people who
Speaker 1: are really knowledgeable about this, but will turn to AI,
Speaker 1: they'll get to what's coming to them. That makes no sense.
Speaker 1: So I don't think there's any intentional approach to trying
Speaker 1: to create misinformation. The problem is by its very nature,
Speaker 1: these chatbots create misinformation in these in these instances, not
Speaker 1: in every case, but in enough cases where it is
Speaker 1: a problem. I think there is bias in these chatbots
Speaker 1: and including chat GPT. In fact, I don't think there's bias.
Speaker 1: There's just bias, but it's necessary bias. So you might
Speaker 1: recall a few years ago, Microsoft released an AI chatbot
Speaker 1: named Tay. Tay, this chatbot was supposed to respond to people,
Speaker 1: specifically younger people. This is Microsoft's attempt to relate to
Speaker 1: the youth. It was supposed to do so in a
Speaker 1: natural way, and it was also supposed to learn as
Speaker 1: users interacted with Tay, like learn how to interact in
Speaker 1: a way that was reflective of the culture of the time.
Speaker 1: So it would pick up slang, and it would pick
Speaker 1: up phrases and perspective and points of view. And in
Speaker 1: less than twenty four hours, Microsoft had to take it
Speaker 1: down because within twenty four hours, users had already turned
Speaker 1: Tay into a crazy, racist, misogynistic, toxic machine. Tay was
Speaker 1: a disaster, both from a technical perspective and a pr perspective.
Speaker 1: So AI companies have started to put in restrictions like
Speaker 1: guardrails to keep AI from going to extremes. So it
Speaker 1: includes tools that try to prevent AI from generating hate speech,
Speaker 1: for example, or slandering people. Now, these tools are far
Speaker 1: from perfect, and there are plenty of examples of people
Speaker 1: figuring out ways around them, and there are plenty of
Speaker 1: examples of chad GPT even saying factually that a person
Speaker 1: was accused of and convicted of a crime when that's
Speaker 1: just not the case. Like that, there have been examples
Speaker 1: of that happening as well. But these rules do tend
Speaker 1: to push AI responses in a general direction. Right, This
Speaker 1: is bias. It's intention I don't bias, but it's also
Speaker 1: not meant to be harmful. It's meant to try and
Speaker 1: avoid situations that themselves could be harmful, either to users
Speaker 1: or more pointedly, to the companies behind the chatbots. Because
Speaker 1: you've got to remember open ai one of the big
Speaker 1: business models for it is to work with other companies
Speaker 1: and to incorporate chat GPT into the tools and services
Speaker 1: that these other companies have. Well, if chat GPT gets
Speaker 1: a reputation for going off on racist rants, that's not
Speaker 1: a good look and no one's going to want to
Speaker 1: incorporate chat GPT into their business, And then open ai
Speaker 1: doesn't have a product to sell So there's like a
Speaker 1: it's not just altruistic, right, It's not just we don't
Speaker 1: want to cause harm, it's we don't want to kill
Speaker 1: ourselves out of out of getting business. So there's a
Speaker 1: lot of work being done to try and guide chat
Speaker 1: GPT's responses to avoid the extremes and to avoid things
Speaker 1: that would cause problems. As result, it could be an
Speaker 1: overcorrection and we could be seeing that chat GBT is
Speaker 1: creating responses that don't reflect reality and do appear to
Speaker 1: be erasing important historical context. So the bias, in combination
Speaker 1: with gaps and knowledge, can lead chatbots to appear, at
Speaker 1: least on a surface level, to have a political leaning
Speaker 1: to them. But again, I don't think that's the result
Speaker 1: of a conspiracy. I don't think that was intentional. I
Speaker 1: think it's the natural destination considering one how these chatbots
Speaker 1: work and two the guardrails that are put up there
Speaker 1: to prevent chatbots from going bonkers. Now, to be clear,
Speaker 1: I don't think we should just accept this any time
Speaker 1: any chatbot presents incorrect information as fact. That is a problem,
Speaker 1: particularly when companies like Google and Microsoft are looking to
Speaker 1: incorporate these tools into stuff like search results. It would
Speaker 1: be like going to a library. The librarian has their
Speaker 1: own agenda to only point people to resources that support
Speaker 1: the librarian's own personal philosophy, and they never point out
Speaker 1: anything that would contradict it. That would also not be good.
Speaker 1: The lack of transparency makes it worse. Ultimately, I would
Speaker 1: caution anyone from relying too heavily on responses generated by
Speaker 1: AI based on these large language models. Now, you might
Speaker 1: not ever encounter a response that includes hallucinations or draws
Speaker 1: from unreliable sources, but based on how these chatbots present information,
Speaker 1: you also could never really be sure that that's the
Speaker 1: case unless you then went to the extra trouble to
Speaker 1: fact check the AI. And at that point you're just
Speaker 1: doing the additional research you would have done at the
Speaker 1: beginning without the AI being there in the first place.
Speaker 1: So I think AI hallucinations are a huge problem. That's
Speaker 1: another thing that the Fox News article kind of ignored,
Speaker 1: like it felt like it was a gotcha moment in
Speaker 1: the Fox News article. But the fact is, if you
Speaker 1: just search AI and hallucinations on whatever web search you like,
Speaker 1: you're going to find countless articles across the entire media
Speaker 1: spectrum that have been bringing this up for months and
Speaker 1: concerns that people both within and outside the industry have
Speaker 1: had about hallucinations and AI, and that this is not
Speaker 1: a new thing, and it's not again, it's not related
Speaker 1: specifically to trying to rewrite history. It's more of a
Speaker 1: broad problem in the field itself that affects all sorts
Speaker 1: of responses and we absolutely should be concerned about it
Speaker 1: and be working toward fixing it. That the hallucinations present
Speaker 1: a genuine problem, and it's not necessarily because there's a
Speaker 1: cabal trying to rewrite how the world works and brain wall.
Speaker 1: You don't need the cabal for that to happen. The
Speaker 1: AI is doing it itself because it's working from a
Speaker 1: very complex statistical table and very few people have the
Speaker 1: insight into that table or understanding of it to fix
Speaker 1: the issues. So yeah, that, in a nutshell, is the
Speaker 1: problem of hallucinations in AI. I don't see it going
Speaker 1: away soon unless we move away from the large language
Speaker 1: model approach of AI. And there are alternatives out there.
Speaker 1: There are companies that are pursuing a different approach to
Speaker 1: creating a reliable chatbot and maybe they'll have better success. Yeah,
Speaker 1: flights of fancy are fun when it's fiction, but when
Speaker 1: it's someone trying to present to you a factual document,
Speaker 1: it's less fun. So hopefully we suss this out before
Speaker 1: it causes any more problems. And again, while I do
Speaker 1: think this is a type of AI that we should
Speaker 1: keep our eye and we should ask critical questions and
Speaker 1: we should use critical thinking, it's not necessarily the AI
Speaker 1: that I'm concerned about the most when it comes to
Speaker 1: things like I don't know a potential existential threat. All right,
Speaker 1: that's it. I hope all of you are well out there.
Speaker 1: Be careful, especially with AI, you know, make sure you
Speaker 1: double check. I know it's a hassle, but it can
Speaker 1: save you a lot of grief down the road. And
Speaker 1: I'll talk to you again really soon. Tech Stuff is
Speaker 1: an iHeartRadio production. For more podcasts from iHeartRadio, visit the
Speaker 1: iHeartRadio app, Apple Podcasts, or wherever you listen to your
Speaker 1: favorite shows.