Speaker 1: Welcome to tech Stuff, a production from I Heart Radio.
Speaker 1: Hey there, and welcome to tech Stuff. I'm your host,
Speaker 1: Jonathan Strickland. I'm an executive producer with I Heart Radio
Speaker 1: and how the tech are you? We're gonna do a
Speaker 1: tech stuff tidbits episode for those of y'all who are
Speaker 1: not familiar wants in a while, I try and do
Speaker 1: kind of a shorter, more concentrated episode on a tech
Speaker 1: topic that doesn't merit a full episode. Now, it's not
Speaker 1: to say that this particular topic doesn't merit a full episode,
Speaker 1: but it will actually require a lot more in depth discussion,
Speaker 1: and I wanted to kind of give an overview just
Speaker 1: to give y'all something to think about. So let's get
Speaker 1: to it. Late last November, the Open Ai organization launched
Speaker 1: chat GPT, which if you haven't used it, I recommend
Speaker 1: you go play with it. Try it out. You can
Speaker 1: make a little account for free and interact with this
Speaker 1: chat bot. So the GPT for chat GPT stands for
Speaker 1: Generative pre Trained Transformer. But it is a chat bot. Now,
Speaker 1: it's a chat bot that can respond to prompts and
Speaker 1: queries with sometimes astonishing results. Uh. This is the tool
Speaker 1: that has certain teachers worried that their students will be
Speaker 1: hoisting all of their homework onto this chat bot and
Speaker 1: make it, you know, write essays for them, and it
Speaker 1: may not always be easy to spot when that happens.
Speaker 1: There are people who are actually offering tools that purport
Speaker 1: to be able to detect if it was generated by
Speaker 1: a chat bot as opposed to written by a student.
Speaker 1: So there's already this kind of seesaw struggle between the
Speaker 1: chat bots and the detectors, which is kind of interesting.
Speaker 1: Some people argue that Pandora's box has been opened and
Speaker 1: chat GPT came roaring out, and it's going to make
Speaker 1: teaching even more difficult and challenging that already is. By
Speaker 1: the way, if you're a teacher, my hat's off to you.
Speaker 1: Both my parents were teachers, they're both retired now. I
Speaker 1: have nothing but the utmost respect for teachers. Y'all have
Speaker 1: a incredibly important, incredibly challenging, and criminally undervalued job. Anyway,
Speaker 1: for chat GPT to work as well as it does,
Speaker 1: there are a ton of different elements that had to
Speaker 1: be implemented. Open Ai had to develop a sophisticated machine
Speaker 1: learning process to train chat GPT, so that it can
Speaker 1: formulate relevant and convincing but not always accurate or correct
Speaker 1: responses to prompts, and relevant is really important. Right. If
Speaker 1: you ask something a question and you get an answer
Speaker 1: that's about something entirely different, that's a really frustrating experience.
Speaker 1: So one thing that the app needs to be able
Speaker 1: to do is parse human language. And this is a
Speaker 1: non trivial engineering problem, and we're going to talk about
Speaker 1: that briefly in this Tech Stuff Tidbits episode. So while
Speaker 1: chat GPT is the launching off point, this is not
Speaker 1: exclusively applicable to chat GPT. It's actually applicable to a
Speaker 1: wide range of applications. So when you hear the phrase
Speaker 1: natural language, that means it's how we humans tend to
Speaker 1: communicate with one another with languages that naturally evolved out
Speaker 1: of culture and society. There are manufactured languages like Esperanto,
Speaker 1: where people created a language with the purpose of creating
Speaker 1: a language, but generally natural language just refers to a
Speaker 1: language naturally evolving over time, and we've developed these over millennia, right,
Speaker 1: And a language includes things like the rules we use
Speaker 1: to form phrases and sentences, as well as the vocabulary
Speaker 1: we use to populate those phrases and sentences. So we've
Speaker 1: got syntax and grammar, and we've got vocabulary. All of
Speaker 1: these elements come together to form language, and you can't
Speaker 1: just throw them in willy nilly, or you will make
Speaker 1: no sense. That's why we have the structure and rules.
Speaker 1: It also includes the quirks we develop over time, you know,
Speaker 1: exceptions to established rules and things like puns and jokes
Speaker 1: and idioms and that kind of thing. Right, we have
Speaker 1: phrases that we use that means something, but they don't
Speaker 1: necessarily mean the surface level. If I say it's raining
Speaker 1: cats and dogs, I probably do not mean that it
Speaker 1: literally is raining small furry animals. If it is, I
Speaker 1: will be inconsolable. I am saying instead that it's raining
Speaker 1: really hard, but I'm using this idiom to express that. Right, So,
Speaker 1: for humans, it's not that difficult to understand what someone
Speaker 1: is saying, assuming you both speak the same language, even
Speaker 1: if that other person is using some unfamiliar words, like
Speaker 1: you could be sitting in on I think back to
Speaker 1: when I was in a class in English class and
Speaker 1: I had a professor who insisted on using the word paradigm,
Speaker 1: and I had never encountered the word paradigm. I it
Speaker 1: was not in my vocabulary, and I picked up in
Speaker 1: context that the way she was specifically using paradigm was
Speaker 1: essentially as a stand in for the word example. Uh.
Speaker 1: This taught me two things. It taught me one of
Speaker 1: the meetings of the word paradigm, and it taught me
Speaker 1: that my professor was incredibly pretentious. Anyway, context really can
Speaker 1: clue us in. Even if we don't know what a
Speaker 1: specific word or phrase means on the surface, from context,
Speaker 1: we can derive at least some meaning. We only still
Speaker 1: miss the full meaning. Or on occasion, you might pull
Speaker 1: a malapropism and you might use the wrong word in
Speaker 1: place of one you intended to use. This has happened
Speaker 1: to me many times. If you want a recent example
Speaker 1: of a character who uses malapropisms, you should watch the
Speaker 1: movie Glass Onion. There is a character in that who
Speaker 1: frequently uses the wrong word to stand in for something
Speaker 1: that he intends to say. Also a fun side note,
Speaker 1: the word malapropism comes from the world of theater. Richard
Speaker 1: Brinsley Sheridan wrote a play called The Rivals. This was
Speaker 1: back in the eighteenth century, so the late seventeen hundreds
Speaker 1: and the rivals includes a character h a a a
Speaker 1: caretaker sort of or a chaperone almost, and her name
Speaker 1: is Mrs Malaprop, and she often employs the wrong words
Speaker 1: to comedic effects. She means one thing, but she says
Speaker 1: another because she's using the incorrect words, which makes the
Speaker 1: meaning of what she's actually saying change and that's where
Speaker 1: the humor is right. And I think this is a
Speaker 1: fun bit of history to know where Malaprop comes from,
Speaker 1: because there's actually another famous character from theater, from English
Speaker 1: theater who predated Mrs Malaprop by a couple of hundred years.
Speaker 1: That character is Ugberry from Billy Shakespeare's Much Ado About Nothing,
Speaker 1: and dog Berry also frequently uses the wrong words in
Speaker 1: an intended comedic effect. The difference is Mrs Malaprop is
Speaker 1: actually funny and dog Berry more often than not isn't
Speaker 1: and mostly being a snobby tease. Here up, please keep
Speaker 1: in mind that back in college I majored in English
Speaker 1: Lit with a focus on Shakespeare. I love Shakespeare dearly,
Speaker 1: but I have issues with some of his comedy. Although
Speaker 1: I guess you could argue the the broad gap in
Speaker 1: time between his time and mind could play a part
Speaker 1: in that anyway. My point is it's not too hard
Speaker 1: for humans to communicate the same thought in lots of
Speaker 1: different ways, and if we encounter a new turn of
Speaker 1: phrase or a new word, we can pick it up
Speaker 1: without too much problem. This stands in stark contrast to
Speaker 1: computers and machines. Now often in the show I talk
Speaker 1: about binary information, about bits and bytes, because when you
Speaker 1: dig way down into how many machines process information, you're
Speaker 1: looking at circuits that run various mathematical operations upon strings
Speaker 1: of data, and that that data is grouped into zeros
Speaker 1: and ones. This is binary or machine language. Computers can
Speaker 1: interpret this quickly. The computers are effectively looking at a
Speaker 1: series of off or on indicators. I often say a
Speaker 1: binary digit or bit is a lot like a light switch.
Speaker 1: It's either off or on. It's zero or one. When
Speaker 1: you put this through logic gates that are have specific
Speaker 1: designs to them, you run them through various operations, these
Speaker 1: zeros and ones can almost magically become complicated processes that
Speaker 1: let you do anything from type out the script for
Speaker 1: a podcast to playing the latest video game. Now make
Speaker 1: sure you keep in mind I said, almost magically in fact,
Speaker 1: it's not magical at all. It's just that we humans
Speaker 1: can't really process huge banks of zeros and ones and
Speaker 1: really make much sense of it. While on a similar note,
Speaker 1: a machine without the proper programming can make no sense
Speaker 1: of our human languages. It is complete gibberish. It's meaningless
Speaker 1: to a computer. Now, it would be exceedingly difficult to
Speaker 1: do any significant kind of programming if programmers had to
Speaker 1: depend upon hard coding zeros and ones while composing their work. Fortunately,
Speaker 1: computer scientists came up with solutions, namely compilers and computer languages,
Speaker 1: and we're gonna start with computer languages. A computer language
Speaker 1: creates levels of abstraction that make it a bit less
Speaker 1: daunting to write software for computers. So the idea is
Speaker 1: that each computer language has its own set of rules,
Speaker 1: like its own syntax, its own vocabulary, and as long
Speaker 1: as you work within those rules, and you do so
Speaker 1: precisely and with as few errors as you possibly can,
Speaker 1: you can create a program to make the computer do
Speaker 1: whatever it is you want it to do. Because the
Speaker 1: computer language itself has built into it the ability to
Speaker 1: be converted into machine language. That will get us two compilers,
Speaker 1: which we will chat about in the minute. In fact,
Speaker 1: we'll talk more about computer languages and compilers after we
Speaker 1: come back from this quick break. Okay, before the break,
Speaker 1: I had introduced this idea of computer languages. Now, not
Speaker 1: all computer languages are equal, and there are dozens of them.
Speaker 1: In fact, there are some notable differences between computer languages.
Speaker 1: Some of them are very similar, some of them share
Speaker 1: a common root computer language, and some of them could
Speaker 1: not be more different. Now, in general, we can separate
Speaker 1: them into very two, very broad categories. There are a
Speaker 1: lot of different ways to categorize programming languages, but we're
Speaker 1: looking at one of the most basic, which is low
Speaker 1: level languages and high level languages. So a low level
Speaker 1: language is relatively close to machine language. It only provides
Speaker 1: a thin layer of abstraction, and as such it can
Speaker 1: still be very challenging for programmers to work with these
Speaker 1: languages because they're not that far off from the basic
Speaker 1: machine languages. Uh, it's a little easier. It's it's designed
Speaker 1: so that humans can interact with it a little more
Speaker 1: naturally than they would with pure machine language, but it's
Speaker 1: not that easy. A high level programming language, on the
Speaker 1: other hand, has a great deal of abstraction, and it's
Speaker 1: much closer to a human language in that way. So
Speaker 1: these languages are far easier for humans to work with
Speaker 1: on a day to day basis, And by humans I
Speaker 1: mean programmers. Like, if you had never seen a computer
Speaker 1: programming language and one day you just sat down to
Speaker 1: work on one, it would not seem easy or intuitive
Speaker 1: to you, probably, But both low level and high level
Speaker 1: languages have their own sets of rules. If you break
Speaker 1: those rules, your program is not going to behave the
Speaker 1: way you intended to. It might not run at all.
Speaker 1: You might just get error messages. But yeah, they have rules,
Speaker 1: and if you follow them, then programs can potentially work. Now,
Speaker 1: if computers process information in zeros and ones, and programming
Speaker 1: languages provide levels of abstraction that approach human language at
Speaker 1: the higher levels, how do we reconcile that? How does
Speaker 1: a computer interpret a program written in uh language like Python?
Speaker 1: For example? Python is much closer to a natural language
Speaker 1: than it is a machine language. So how do we
Speaker 1: get to the point where a computer can take that
Speaker 1: information and actually execute a program? Well, the program I'm
Speaker 1: once composed has to go through what we call a compiler,
Speaker 1: and the compiler's job is to take this program and
Speaker 1: according to the rules of that programming language, convert the
Speaker 1: program from the programming language into machine code so that
Speaker 1: a computer can actually do something with it. So the
Speaker 1: compiler is kind of like a translator. And the compiler
Speaker 1: actually has to do a few jobs to make this happen,
Speaker 1: and has to scan the program's source code for recognizable
Speaker 1: commands and terms. That has to analyze the syntax a
Speaker 1: gave the structure of the code to understand the order
Speaker 1: of operations. That has to break all that down into
Speaker 1: machine code that follows what the language says. And if
Speaker 1: the programmer made a mistake, well that ends up getting
Speaker 1: translated to and then you don't find out un till
Speaker 1: you try and run the program, and then it gets
Speaker 1: back to debugging figuring out where did you make that
Speaker 1: mistake in the actual program. Now here's the thing. The
Speaker 1: programming languages, while they can create high levels of abstraction,
Speaker 1: are still not necessarily accessible to the average person. Like
Speaker 1: I had mentioned before the break, you know, I'm talking
Speaker 1: about the average person who has little to know experience
Speaker 1: with computer languages or programming. So to people like that,
Speaker 1: and I'll include myself here, a sheet of code written,
Speaker 1: and even a very high level computer language might end
Speaker 1: up being indecipherable. You could look at and say, I
Speaker 1: don't know what this program is supposed to do because
Speaker 1: I don't know enough about this programming language to understand
Speaker 1: what any of this means. As such, for folks such
Speaker 1: as myself, programming a computer is a daunting task because
Speaker 1: we lack the basic knowledge of the programming languages we
Speaker 1: would need to use to make an effective program. Now
Speaker 1: you you can teach yourself these things lots of people have.
Speaker 1: In fact, a lot of the most famous hackers and
Speaker 1: even like leaders in in tech business are self taught programmers.
Speaker 1: So there's nothing stopping you from doing this, even if
Speaker 1: you never took a class in computer science or programming.
Speaker 1: In my case, it's literally that I haven't sat down
Speaker 1: to do any programming since the days when I did
Speaker 1: it an Apple Basic, and I only vaguely remember those days.
Speaker 1: But you know, what if you could interact with a
Speaker 1: machine through the use of natural language, not a programming language.
Speaker 1: What if the computer we're able to take your queries
Speaker 1: and commands that were either written or spoken in everyday
Speaker 1: human language and then suss out what it was you
Speaker 1: wanted and then give it to you. That's the goal
Speaker 1: of natural language processing, and we see it in different implementations.
Speaker 1: Right with chat GBT it's a text based interaction, but
Speaker 1: with smart speakers it's through speaking it into a microphone
Speaker 1: and giving a response. But the basic idea is still
Speaker 1: the same, and at a shallow level, it appears that
Speaker 1: a computer is able to understand you and can respond
Speaker 1: in kind, but in reality, what is going on in
Speaker 1: the background is a very complex analysis to determine what
Speaker 1: it as you are saying or asking or typing or whatever.
Speaker 1: And like a compiler, a natural language processor has to
Speaker 1: identify all the components of a query and to analyze
Speaker 1: the syntax and then respond in a way that's most
Speaker 1: likely to be relevant. For example, if I were to
Speaker 1: ask a smart speaker what's the weather in Walt Disney
Speaker 1: World today, it would have to understand that I've got
Speaker 1: a location I've given It's not my location. I've asked
Speaker 1: for Walt Disney World, I've asked a specific set of
Speaker 1: data what is the weather, and I've given a timeframe
Speaker 1: of today. It would have to understand all that, analyze
Speaker 1: all that, and then get the correct response and present
Speaker 1: it to me, and that's that's incredible, Like, that's an
Speaker 1: incredible amount of work going on in the back end.
Speaker 1: It happens almost instantly when whenever we interact with these systems,
Speaker 1: but it's a lot of stuff that has to happen
Speaker 1: or over that to work. Otherwise we would end up
Speaker 1: with devices that just do irritating things like I imagine
Speaker 1: and telling my smart hub to dim the lights, and
Speaker 1: instead it tells me what the weather is, that the
Speaker 1: Magic Kingdom, and I'm thinking, well, that's nice, but the
Speaker 1: lights are still too bright. Please dim them so I
Speaker 1: don't have to get up off my couch, walk across
Speaker 1: the room and turn a dial, because you know, lazy. Anyway,
Speaker 1: As for natural language processing, that's a simple phrase that
Speaker 1: hides how insanely complicated the actual processes. In reality, natural
Speaker 1: language processing is a multidisciplinary area of development. It incorporates
Speaker 1: elements of artificial intelligence, machine learning, human linguistics. There's a
Speaker 1: bit of psychology that goes in there too. And the
Speaker 1: evolution of natural language processing is a little difficult to trace,
Speaker 1: particularly if you're looking at it from the perspective of
Speaker 1: a user. So way back in the day, kiddos a
Speaker 1: lot of computer games didn't have graphics. They were text
Speaker 1: based games, kind of like a choose your own adventure novel.
Speaker 1: To say, do they still have those? If you're familiar
Speaker 1: with them. You read these books and at the end
Speaker 1: of certain pages you are presented with a choice, and
Speaker 1: it gives you two different page numbers to go to
Speaker 1: depending upon whichever choice you make, and then you continue
Speaker 1: the story from there. Well, a text based adventure was
Speaker 1: very similar to that. There were fewer overt prompts in
Speaker 1: your typical text based adventure. You could theoretically you could
Speaker 1: choose to type in whatever you wanted, so you could
Speaker 1: type commands into a prompt line and then the computer
Speaker 1: game would produce a response. So you might type something
Speaker 1: like look and that would prompt the game to produce
Speaker 1: a description of the environment that you were in at
Speaker 1: the moment. Or you might type inventory to find out
Speaker 1: what stuff you happen to be carrying on your character.
Speaker 1: Or you might type put bit in T in order
Speaker 1: to get the dag nabbed and probability drive working. I
Speaker 1: am still traumatized by the text adventure The Hitchhecker's Guide
Speaker 1: to the Galaxy. Decades later, that game was fiendishly hard
Speaker 1: and non intuitive anyway, then the program would respond appropriately.
Speaker 1: It would give you the response based upon the command
Speaker 1: you typed in, and on a surface level, it looked
Speaker 1: like the computer game understood what you were saying, except
Speaker 1: as soon as you typed in a phrase that the
Speaker 1: programmers hadn't accounted for or just didn't support for whatever reason,
Speaker 1: you would get a pretty standardized message saying something along
Speaker 1: the lines of I'm sorry, I don't understand, and so
Speaker 1: it turned out the game didn't understand you at all. Instead,
Speaker 1: the game had a list of inputs that mapped to
Speaker 1: specific outcomes, and if you provided the input while, you'd
Speaker 1: get the outcome. But anything outside of that list was
Speaker 1: not something the game could handle, and it had to
Speaker 1: give you a response saying I'm sorry, I can't do that,
Speaker 1: or something along those lines. So it gave the illusion
Speaker 1: of understanding, but the player would quickly come to learned
Speaker 1: that there was no such thing actually going on in
Speaker 1: the background. On a similar note, we have chat bots,
Speaker 1: and these have been around for ages, and developers have
Speaker 1: worked for a long time to make chatbots sophisticated enough
Speaker 1: so that you might start to think that maybe the
Speaker 1: chat bot actually understands what you're saying, or maybe there's
Speaker 1: a real human on the other side posing as a
Speaker 1: chat bot. We'll talk about that again in just a second,
Speaker 1: but first let's take another quick break. Okay, so you've
Speaker 1: likely heard of the Turing test, which is this mythical
Speaker 1: test for artificial intelligence and to determine whether or not
Speaker 1: a computer may or may not have sentience or consciousness
Speaker 1: or whatever. Uh That that is really kind of snowballed
Speaker 1: from what it originally was, but the basic idea is
Speaker 1: that it's a take on a game called the imitation game.
Speaker 1: And in this game, a person an interrogator, sits down
Speaker 1: at a computer terminal and they compose questions and they
Speaker 1: get answers displayed on a computer display in front of them,
Speaker 1: and their job is to determine whether or not the
Speaker 1: entity that's creating the responses is another human, or in fact,
Speaker 1: it's a machine that's attempting to pose as a human.
Speaker 1: And if you get to a certain percentage of interrogators
Speaker 1: who cannot be certain or they mistakenly misidentify a machine
Speaker 1: as a human, you would say that machine passes the
Speaker 1: Turing test, and it can convincingly pose as a human. Well,
Speaker 1: we've seen lots of different examples of chat bots that
Speaker 1: have supposedly passed the Turing test. But again, this isn't
Speaker 1: like a solid tests. It's not it's not like the
Speaker 1: S A T S or something. There's not like a
Speaker 1: solid grading structure. It's more it's more interpretive than that.
Speaker 1: But some of the early chatbots we saw do this
Speaker 1: kind of thing by selecting a subset of human behaviors.
Speaker 1: So an example, there were early chatbots or meant to
Speaker 1: simulate someone who had paranoid schizophrenia, or it would pose
Speaker 1: as a therapist, which mostly involved taking whatever it was
Speaker 1: you last said and then turning it into a question,
Speaker 1: which is, why do you think that your coworkers don't
Speaker 1: like you? I gotta say, dr spate, so give me
Speaker 1: a lot of complicated feelings. Anyway, by selecting this subset
Speaker 1: of human behaviors, the programmers are limiting the sort of
Speaker 1: things that the chat bought would be expected to chat about,
Speaker 1: and they would also plant an expectation in the part
Speaker 1: of the human interrogator. It lowers expectations. In other words,
Speaker 1: if you are told that, hey, you're gonna be chatting
Speaker 1: with someone, and it might be a young boy from
Speaker 1: another country who only has a passing understand ending of
Speaker 1: say English, and uh, they are like fifteen. Well, that's
Speaker 1: gonna set your expectations right. You're no longer gonna think, oh,
Speaker 1: this is someone who's going to have a very deep
Speaker 1: knowledge of, say the Vietnam War. That's not gonna happen,
Speaker 1: So there's some leeway there. Uh. Anyway, that's that's one
Speaker 1: of the tricks of creating chat bots that are convincing.
Speaker 1: But these days we're actually seeing much more sophisticated chat
Speaker 1: bots like chat GPT, and they seem to actually understand
Speaker 1: what it is we want, and we can give chat
Speaker 1: GPT a complicated prompt and the program is capable of
Speaker 1: providing a response. So, for example, I actually did this.
Speaker 1: I wrote in the prompt, compose a high KU about
Speaker 1: g p U s as graphics processing units. And this
Speaker 1: is what chat GPT created for me. GPUs speed up
Speaker 1: my code, processing data, lightning, fast, silent, powerful. For now,
Speaker 1: I could point out that this poem does not strictly
Speaker 1: adhere to the structure of hiku because a typical hiku,
Speaker 1: the first and third lines have five syllables, the middle
Speaker 1: line has seven syllables. The hiku quote unquote that chat
Speaker 1: gpt produced had seven syllables, eight syllables, then six syllables.
Speaker 1: So this is not a hiku in the structural sense.
Speaker 1: But you you do see how chat gpt is trying
Speaker 1: to comply with my request. It it's giving something that
Speaker 1: has the feel of a hiku, even though it's not
Speaker 1: strictly speaking a hiku. Now, I've talked in the past
Speaker 1: about how chat gpt pulls data from a huge library
Speaker 1: of information. It is not actively connected to the Internet,
Speaker 1: but instead has this massive repository of information that it
Speaker 1: can pull from, kind of like having a really big encyclopedia,
Speaker 1: like horror coded encyclopedia. Think of something that you would
Speaker 1: have in a home library. Actual books. The information in
Speaker 1: those books is not going to change, not frequently, like
Speaker 1: once a year you might get an updated volume that
Speaker 1: gives information about different things that have developed over the year,
Speaker 1: but otherwise, no, it doesn't change. So chat gpt is
Speaker 1: not pulling the most recent information and then serving that up.
Speaker 1: It's it's going to this big library. Now, Unfortunately, that
Speaker 1: library doesn't guarantee that the responses you get are going
Speaker 1: to be accurate. They will appear to at least be
Speaker 1: relevant because chat gpt s programmers were really good at
Speaker 1: having it analyzed queries and to really hone in on
Speaker 1: what was being asked so that chat GPTs response, it's
Speaker 1: generative response would relate to the query. It is one
Speaker 1: common complaint with chat gpt that it presents information in
Speaker 1: such a way as to authoritative though in fact it
Speaker 1: may not quote unquote know what it's talking about. So
Speaker 1: it's working on this very complicated system to parce language
Speaker 1: infirm meaning based upon the words and syntax provided by users,
Speaker 1: and then generate a response following the basic rules of
Speaker 1: grammar and vocabulary well probabilistically picking the most likely response
Speaker 1: to be correct and to be relevant. Again, this is
Speaker 1: something we humans do pretty naturally, but for computers it
Speaker 1: is anything but natural. It required a ton of work
Speaker 1: and evolution to get there. Now, beyond the surface level,
Speaker 1: I feel it's important to say that chat gpt does
Speaker 1: not truly understand what we're saying to it, or what
Speaker 1: it's saying to us. Non the level of deriving meaning
Speaker 1: from it, it's not able to associate different ideas, it's
Speaker 1: not able to come up with something new. It's not thinking.
Speaker 1: It's analyzing and it's responding, and it's doing so in
Speaker 1: a very cool way, but it's not sentient or anything
Speaker 1: like that. Now I will probably do a full episode
Speaker 1: about what's going on behind the curtain with natural language processing.
Speaker 1: It's a pretty challenging topic to cover. It is incredibly complex,
Speaker 1: it is incredibly sophisticated. It requires an interdisciplinary approach that
Speaker 1: is hard to describe easily, and it also typically involves
Speaker 1: several different machine learning strategies that are somewhat challenging to describe,
Speaker 1: particularly without the benefit of visual aids. But I think
Speaker 1: it's worth diving into, and I think we can do it.
Speaker 1: I think we can at least get an appreciation for
Speaker 1: how these systems are working. If nothing else, it can
Speaker 1: remind us that the magic we're experiencing when we tell
Speaker 1: chat GPT to compose, say a punk rock song about
Speaker 1: the iPhone, well we understand that what we see is
Speaker 1: the result of complex sesses and not you know, some
Speaker 1: sort of mystical event. Now. I say that not to
Speaker 1: take anything away from the phenomenal achievements of the hundreds
Speaker 1: of folks who have worked on natural language processing projects,
Speaker 1: but rather to prevent the rest of us from bringing
Speaker 1: meaning where maybe there is no meaning. We don't want
Speaker 1: to project onto this thing. We don't want to make
Speaker 1: assumptions because that could lead us down pathways where we
Speaker 1: start to trust things that are inherently not totally trustworthy.
Speaker 1: One of the issues we've heard multiple times with chat
Speaker 1: GPT is that it's kind of a black box and
Speaker 1: that you ask a question, it gives you an answer,
Speaker 1: but you don't see the process that chat GPT went
Speaker 1: through in order to understand what you were asking and
Speaker 1: then generate the answer that it gives you. And because
Speaker 1: of that, you can't double check its work right. You
Speaker 1: can't check to see what sources did you pull your
Speaker 1: information from to generate your answer, because the sources may
Speaker 1: or may not be reliable. And chat GPT may be
Speaker 1: in a phenomenal tool, but if it's pulling from unreliable resources,
Speaker 1: while the answer you get is still going to be wrong.
Speaker 1: But because chat GPT doesn't really do that, doesn't really
Speaker 1: show its work. Uh. That's where you start to run
Speaker 1: into these problems. And the more dependence you put upon
Speaker 1: these kinds of systems, the more important it is to
Speaker 1: understand how these systems are actually generating the responses. This
Speaker 1: goes beyond chat bots. Obviously, this applies to AI across
Speaker 1: the board. It's an ongoing issue within AI in general.
Speaker 1: Is this desire to make certain that the results that
Speaker 1: AI generates where whatever it may be. Maybe it's facial
Speaker 1: recognition technology, um, maybe it's a robot deciding how to
Speaker 1: open a door. Being able to see that process and
Speaker 1: understand what steps the system went through in order to
Speaker 1: get to its decision are critical in order to be
Speaker 1: able to uh, to judge how well that's that overall
Speaker 1: system works or doesn't work, or if it's reliable or unreliable. Uh.
Speaker 1: That transparency is absolutely necessary for that sort of thing.
Speaker 1: And frankly, a lot of the systems we encounter today
Speaker 1: have a lack of transparency and that makes it kind
Speaker 1: of scary. However, that being said, I think chat GPT
Speaker 1: is a really really cool project. I do share the
Speaker 1: concerns of people relying upon it to do work that
Speaker 1: they should be doing. Um, I think that's I think
Speaker 1: they're cheating themselves. If you aren't doing the work, then
Speaker 1: you're not learning how to think. Which is the most
Speaker 1: important lesson you can learn in your education is learning
Speaker 1: how to actually think and to think critically, and if
Speaker 1: you deny yourself that, then you just set yourself up
Speaker 1: to be led around by the nose by anyone who
Speaker 1: has a convincing enough story, and that rarely turns out well.
Speaker 1: At least it doesn't turn out well except for the
Speaker 1: person who's doing the leading, and even they tend to
Speaker 1: come to a bad ending once it's all said and done.
Speaker 1: All right, that's it. Hope you enjoyed this tech stuff
Speaker 1: tidbits about natural language processing, specifically within the context of
Speaker 1: chat GPT. Like I said, we'll have to do a
Speaker 1: much deeper dive because this isn't even really scratching the
Speaker 1: surface like that. This is such a deep dense topic
Speaker 1: that we could do a couple of episodes about it
Speaker 1: and really kind of explore it. Uh. I would even
Speaker 1: reach out to experts to have on the show to
Speaker 1: talk with them about it. The only issue there is
Speaker 1: that I would worry very quickly that experts would use
Speaker 1: terminology and jargon that I'm not familiar with, let alone
Speaker 1: some of my listeners, some of y'all are way ahead
Speaker 1: of me on this stuff, and that's awesome. Some of
Speaker 1: you all are probably in the same position that I'm in,
Speaker 1: where you know, you could hear someone spelled off a
Speaker 1: lot of jargon and not know what the heck they
Speaker 1: were saying. Context be darned, it would just not be
Speaker 1: be accessible to you, uh or to me. And so yeah,
Speaker 1: I just got to make sure that if I pick
Speaker 1: someone who is an expert, they're also a great communicator
Speaker 1: for that kind of stuff. Uh. Some engineers are phenomenal communicators,
Speaker 1: and some are phenomenal engineers who can make machines sing,
Speaker 1: but may not be able to uh talk with other
Speaker 1: human beings in a way that the other human beings
Speaker 1: can follow. It's it's more that, you know, I lack
Speaker 1: that experience and understanding to do it confidently and accurately.
Speaker 1: But yeah, we'll do more about natural language processing. I
Speaker 1: didn't even really touch on the spoken language processing stuff,
Speaker 1: you know, like things like speech to text and that
Speaker 1: kind of thing. That stuff is also incredibly complex. It
Speaker 1: adds other layers of complexity upon this system. UM. I
Speaker 1: have talked about those in the past. I've done episodes
Speaker 1: where I've talked a bit about, you know, like things
Speaker 1: like like voice recognition and those sorts of things, and
Speaker 1: speech to text but yeah, that's also important to remember
Speaker 1: that that adds yet another layer. Well that's it. I
Speaker 1: hope you're having a great three so far. I mean
Speaker 1: we're four days into it. I hope things haven't gone
Speaker 1: pear shaped already. And if you have any suggestions for
Speaker 1: topics A should cover in future episodes of tech Stuff,
Speaker 1: there a couple of ways you can reach out to me.
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Speaker 1: s W and I'll talk to you again really soon, y.
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