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 iHeart Podcasts and how the
Speaker 1: tech are you? So? In the late eighteenth century, there
Speaker 1: was a man named Wolfgang von Kimpland, and he had
Speaker 1: a clever idea. He really wanted to knock the proverbial
Speaker 1: socks off of Maria Theresa, the Impress of Austria. Moreover,
Speaker 1: he wanted to make a more spectacular display than an
Speaker 1: illusionist named Francois Pelletier who had performed for the Impress
Speaker 1: to great renown, and Kimpland was not impressed. He was like, huh,
Speaker 1: I'm gonna show Frank up. I'm gonna make something that's
Speaker 1: really gonna rub his face in it, and the Impress
Speaker 1: is gonna think I'm her favorite. So, fueled by a
Speaker 1: competitive and perhaps petty spirit, Kimpland came up with an
Speaker 1: invention that some would call the mechanical Turk. Now the
Speaker 1: machine hesitate to call it that, but the machine consisted
Speaker 1: of a large table like cabinet, and on the top
Speaker 1: of this cabinet was a chessboard and standing behind this
Speaker 1: cabinet was a mechanical man dressed in the Western European
Speaker 1: concept of traditional Turkish attire. If you were to open
Speaker 1: the cabinet doors, you would reveal a mass of gears
Speaker 1: and cogs and such, so it looked as though everything
Speaker 1: was mechanical. Kimplan claimed that this machine could play an
Speaker 1: expert game of chess against any opponent, and as it
Speaker 1: turned out, the machine performed very well and won more
Speaker 1: games than it lost. But it was all a trick.
Speaker 1: The machine wasn't really a machine, or at least it
Speaker 1: wasn't a machine that did any work. Instead, hidden inside
Speaker 1: the cabinet, concealed from exposure, hidden by these gears and cogs,
Speaker 1: was a cramped human chess player, and the player could
Speaker 1: manipulate the Turkish figure and was able to play chess
Speaker 1: from below the chess board. So it was an actual
Speaker 1: human being who was actually playing these games against people.
Speaker 1: It wasn't some mechanical construct. The Turk only seemed to
Speaker 1: be a chess playing machine. Now, this was way back
Speaker 1: in seventeen seventy. Today, in twenty twenty four, we still
Speaker 1: have to deal with companies and entrepreneurs peddling artificial intelligence
Speaker 1: that when you look at it more closely, is really
Speaker 1: relying on plain, old, reliable human intelligence. Why, Well, the
Speaker 1: short answer for that is money. It seems like, you know,
Speaker 1: not a day goes by in twenty twenty that doesn't
Speaker 1: include at least one news story about how artificial intelligence
Speaker 1: is going to completely change our lives. And the stories
Speaker 1: run the gamut of hyperbole, from doomsday prophecies about weaponized
Speaker 1: AI making battlefield decisions, to company executives as saying that
Speaker 1: AI programs are a viable alternative to hiring actual human beings,
Speaker 1: to optimists who describe a star trek like utopia in
Speaker 1: which AI handles all the dull stuff and it leaves
Speaker 1: us to experience the world as a never ending series
Speaker 1: of adventures. I'm not sure if any of those scenarios
Speaker 1: are what's actually in store for us, but I do
Speaker 1: know things are going to be messy for a good
Speaker 1: long while. But AI is such a buzzy term, and
Speaker 1: with big companies like Google, Microsoft, Amazon, Apple, Meta and
Speaker 1: more all stomping relentlessly forward to make AI the next
Speaker 1: big thing, there are literally billions of dollars pouring into
Speaker 1: various AI pursuits. Now, with that much money, and enthusiasm
Speaker 1: at play. It's no wonder that dozens of startups attempting
Speaker 1: to cash in on the gold rush have cropped up
Speaker 1: in recent years. And some of those companies might actually
Speaker 1: be making genuine strides toward advancing AI or implementing it
Speaker 1: in a useful way. Some might just be jumping on
Speaker 1: the opportunity to get some of that sweet, sweet VC cash.
Speaker 1: Since AI is the new metaverse slash NFT slash virtual
Speaker 1: reality slash three D technology thing, what I'm saying is
Speaker 1: that we've been through this hype cycle many many times before.
Speaker 1: The term AI itself is incredibly useful if you want to,
Speaker 1: you know, sell some snake oil, because AI as a
Speaker 1: term is still a bit vague. Like the term AI
Speaker 1: is seventy years old at this point, and yet we
Speaker 1: don't have an easy definition for what really is artificial intelligence.
Speaker 1: It's kind of like our definition for actual intelligence. We
Speaker 1: don't have a super great explanation for that either. We
Speaker 1: have ways of describing parts of it, but we don't
Speaker 1: really have a holistic, perfect encapsulation of what is intelligence.
Speaker 1: So how could we do that? For artificial intelligence? You
Speaker 1: don't even have to make an AI application or implementation.
Speaker 1: To take advantage of the opportunities that this vague state
Speaker 1: of affairs creates, you just call whatever thing you're trying
Speaker 1: to sell AI, and you let the hype do the
Speaker 1: work for you. Because people don't understand it fully, you
Speaker 1: probably aren't going to get called out on it unless
Speaker 1: you're really sloppy, which means you can make hay while
Speaker 1: the sun shines and then get up out of town
Speaker 1: when the clouds roll in. So today I thought we
Speaker 1: would talk a bit about fake artificial intelligence, or perhaps
Speaker 1: we should call it artificial artificial intelligence, which in a
Speaker 1: way comes back round to just plain old intelligence, because
Speaker 1: we're going to chat about some cases in which a
Speaker 1: person or group of people passed off stuff that isn't
Speaker 1: really AI, but was rather powered by human intelligence onto
Speaker 1: unsuspecting targets. First, however, let's do a quick refresher on AI,
Speaker 1: because I find that the term is so broad and
Speaker 1: it is so overused that it's really starting to lose
Speaker 1: its meaning. These days, as consumers, you and I, we
Speaker 1: are most likely to encounter AI as applied to generative AI.
Speaker 1: That's the hotness right now, And I know I'm old.
Speaker 1: I use phrases like the hotness. Sorry, but this is
Speaker 1: artificial intelligence that's capable of generating something. Thus you have
Speaker 1: generative AI. Now the something might be written text, it
Speaker 1: might be spoken words, it might be music, it might
Speaker 1: be a sketch or a painting. And there's no denying
Speaker 1: that generative AI can be really impressive when it works well.
Speaker 1: It seems to be able to do the same sort
Speaker 1: of things we humans can, though there remain questions regarding
Speaker 1: how much of that is thanks to the various AI
Speaker 1: programs cribbing from actual human work. Because you'll often hear
Speaker 1: human artists argue that generative AI borrows liberally or outright
Speaker 1: steals if we're being more forthright about all this, and
Speaker 1: does so from human artists. You know, AI doesn't magically
Speaker 1: know how to paint something in a specific style or
Speaker 1: maybe even more specifically, to mimic a particular artists style
Speaker 1: and technique. The AI quote unquote knows how to do
Speaker 1: this because it has been trained to do it on
Speaker 1: countless examples of actual human generated art. That's a real
Speaker 1: problem because it could mean that the AI is lifting
Speaker 1: from real artists and thus potentially putting real artists livelihoods
Speaker 1: at stake. But there are tons of other implementations that
Speaker 1: have nothing or at least little to do with generative AI. So,
Speaker 1: for example, facial recognition technology is a discipline under artificial intelligence.
Speaker 1: The basic task is to compare an incoming signal with
Speaker 1: a database of image records. This is relatively trivial if
Speaker 1: the incoming signal is one that matches the database record precisely.
Speaker 1: In other words, let's say that you've got an incoming
Speaker 1: signal where the camera angle, the lighting, the distance from
Speaker 1: the subject, all of that stuff is the same as
Speaker 1: the reference image that you have in your database. Then
Speaker 1: the computer can very quickly say, yes, these two are
Speaker 1: a match. Typically, it does get trickier if you are
Speaker 1: moving away from whatever types of faces the AI had
Speaker 1: been trained upon. But it gets even trickier if conditions
Speaker 1: are different between the incoming signal and the reference. So
Speaker 1: an example I often give, and this isn't facial recognition,
Speaker 1: this is image recognition. So imagine you have a coffee mug,
Speaker 1: and let's say that we first we have a picture
Speaker 1: of a coffee mug. It's sitting on a table. The
Speaker 1: mug's handle is pointing to the right, you know, to
Speaker 1: our right. As we look at the picture. The mug
Speaker 1: is dark red in color. The body of the mug
Speaker 1: is essentially just a you know, a simple cylinder. There's
Speaker 1: no writing on it or anything. This is the reference
Speaker 1: image that we are using. It's the one that's in
Speaker 1: our database. Now imagine that you've pointed a camera at
Speaker 1: a coffee mug, and this mug is an oversized coffee mug,
Speaker 1: and it's off white in color, and it has the
Speaker 1: words World's Greatest Podcaster on the mud ug and the
Speaker 1: handle's pointed to the left, not the right. And this
Speaker 1: mug actually isn't a perfect cylinder. Let's say that it
Speaker 1: kind of curves outward from the base just about, you know,
Speaker 1: sort of like a bowl more than a cylinder. And
Speaker 1: if I ask you what is this thing, you would
Speaker 1: quickly say, oh, that's a mug, or maybe that's a
Speaker 1: coffee mug. You'd say that right away. You would recognize this.
Speaker 1: But it doesn't match the reference picture in our database perfectly, right, Like,
Speaker 1: it doesn't look exactly like or even close to the
Speaker 1: red reference mug we have in our database. It's got
Speaker 1: features that make it a mug, and you, as a
Speaker 1: human being, can naturally apply your knowledge of those features
Speaker 1: to identify a coffee mug. Even if you've never seen
Speaker 1: that specific mug before, you immediately know, oh, that's a
Speaker 1: coffee mug. Even if the coffee mugs form deviates from
Speaker 1: others you've encountered in the past, You're able to apply
Speaker 1: your intelligence to say that's a coffee mug. But a
Speaker 1: computer cannot do that, not on its own. It has
Speaker 1: to be fed hundreds of thousands, or even millions of
Speaker 1: images of coffee mugs, all in various shapes and sizes
Speaker 1: and colors and orientations to the camera and more. Even then,
Speaker 1: there's no guarantee that the computer will be able to
Speaker 1: identify a new image of a coffee mug that deviates
Speaker 1: from this collection of reference material. So we can help
Speaker 1: computers by applying metadata to information. We might take a
Speaker 1: photo of a new coffee mug and apply metadata labels
Speaker 1: to this image so that a computer can quickly reference
Speaker 1: the metadata and then pull up our new photo of
Speaker 1: a coffee mug when we ask for it. But this
Speaker 1: is not the same thing as quote unquote knowing that
Speaker 1: it's a coffee mug. That would be more like using
Speaker 1: a reference index in order to pull up the matching image.
Speaker 1: It doesn't involve the image itself, It just involves the
Speaker 1: meta data about the image. So facial and image recognition
Speaker 1: are just one of thousands of different aimplmentations that have
Speaker 1: nothing to do or very little to do with generative AI.
Speaker 1: They might ultimately have stuff to do with generative AI,
Speaker 1: but that's because of convergence. It's not that they're the
Speaker 1: same thing. It's that things these different disciplines are converging
Speaker 1: into new implementations. Alan Turing, the great computer scientist, theorized
Speaker 1: that machines might one day be able to take all
Speaker 1: available information in a given situation and apply reasoning to
Speaker 1: that situation in order to reach conclusions similar to how
Speaker 1: we humans operate, and he wrote about it in a
Speaker 1: paper titled Computing, Machinery and Intelligence. It was all hypothetical
Speaker 1: at the time, since computers were still quite primitive back then.
Speaker 1: For one thing, they lacked the capability of persistent memory.
Speaker 1: I'll explain more in just a moment, but first let's
Speaker 1: take a quick break to thank our sponsors. Okay, we're back.
Speaker 1: What was I talking about? All right? Persistence of memory?
Speaker 1: And I should get myself some of that anyway, What
Speaker 1: I meant by that with Alan Turing and the lack
Speaker 1: of persistent memory, is that the computers of Turing's day
Speaker 1: could execute a command, but they couldn't quote unquote remember
Speaker 1: what it was they just did. They would just perform
Speaker 1: an operation, and they would continue to perform that operation
Speaker 1: on new incoming data until you changed all the factors
Speaker 1: of the computer, which often involved physical switches and cables
Speaker 1: and plugs and stuff like. This was a big deal
Speaker 1: to set a computer up to run calculations, so you
Speaker 1: couldn't naturally build upon an outcome and then do a
Speaker 1: new operation. You had to do a lot of work
Speaker 1: in order to make this happen. But Turing thought, there
Speaker 1: will come a day where computers will be able to
Speaker 1: do this. They'll be able to complete the task, create
Speaker 1: an outcome, and then take that outcome and then perform
Speaker 1: new tasks upon that outcome, all with the goal of
Speaker 1: some specific outcome further down the line, like ten or
Speaker 1: twenty steps further along. So it was only a whole
Speaker 1: bunch of different smarty pants is from different disciplines who
Speaker 1: were able to advance the technology of computing that machines
Speaker 1: could actually have something that resembled memory, let alone this
Speaker 1: capability of taking in information and then being able to reason.
Speaker 1: So from transistors to integrated circuits to computer languages, et cetera,
Speaker 1: a lot of different pieces had to come together in
Speaker 1: order to even make this a possibility. So in nineteen
Speaker 1: fifty six, the Dartmouth Summer Research Project and Artificial Intelligence
Speaker 1: saw buffins from across the young discipline of computer science
Speaker 1: gathered to talk about researching concepts relating to machine intelligence.
Speaker 1: This was the conference that serves as the official birthplace
Speaker 1: for the term artificial intelligence. While there were a lot
Speaker 1: of people really excited about the idea and many people
Speaker 1: attending the conference felt pretty sure that machines would one
Speaker 1: day reach a point where they could simulate human intelligence,
Speaker 1: there was no agreement on exactly how this would happen.
Speaker 1: No one proposed any standards or anything like that, and
Speaker 1: because of that, the following decades would see various researchers
Speaker 1: pursue their own pathways toward a common goal. So everyone
Speaker 1: kind of knew where they wanted to get, but they
Speaker 1: weren't in agreement as to how they were going to
Speaker 1: get there, and so there was a lot of different
Speaker 1: work being done in different approaches toward artificial intelligence. In
Speaker 1: the nineteen sixties, a computer scientist and programmer named Joseph
Speaker 1: Weisenbaum created an early chatbot called Eliza. So this chatbot
Speaker 1: is exceedingly primitive by today's standards, and it gave the
Speaker 1: illusion of understanding communications from a human being, But in fact,
Speaker 1: Eliza was really just spouting off responses using some rudimentary
Speaker 1: pattern recognition and substitution strategies. So in a very superficial
Speaker 1: and not particularly useful way, Eliza could chat with humans. Now,
Speaker 1: I think Eliza is a really important early step in
Speaker 1: artificial intelligence. I would also say it's not very intelligent.
Speaker 1: It's following a pretty simple set of rules in an
Speaker 1: effort to simulate conversation, and limited conversation at that And
Speaker 1: while we have much more sophisticated chatbots now, ones that
Speaker 1: can draw on immense libraries of information and use complicated
Speaker 1: statistics to select words and word order, ultimately they're kind
Speaker 1: of doing the same thing. They are using rules to
Speaker 1: create an illusion of intelligence. But the projects I really
Speaker 1: want to talk about don't necessarily even do that much.
Speaker 1: They are creating the illusion of artificial intelligence because us
Speaker 1: that's a field that's getting crazy amounts of investment. Sometimes
Speaker 1: these companies are doing it because they don't yet have
Speaker 1: the money to really dive into AI. So it's not
Speaker 1: that they want to deceive, it's that until they get
Speaker 1: the investment to do the thing they want to do,
Speaker 1: they can't do it. AI is expensive. The processing you
Speaker 1: need in order to run complicated AI implementations is considerable,
Speaker 1: and most people don't have access to that, especially if
Speaker 1: you're just starting up a company. So sometimes an AI
Speaker 1: startup is not using AI, not in an effort to
Speaker 1: deceive investors, but rather as a placeholder with the intent
Speaker 1: of using AI later on when it becomes feasible to
Speaker 1: do so. Sometimes the company just needs to do a
Speaker 1: lot of early work before it can launch whatever AI
Speaker 1: tool it has in mind, and that this early work
Speaker 1: needs to be done by humans. There might be a
Speaker 1: lot of generating training data or that kind of thing,
Speaker 1: and for that you might employ a bunch of people
Speaker 1: to do it, and eventually you will develop your AI tool.
Speaker 1: But again, it does not like AI tools just spring
Speaker 1: fully formed and you can make use of them. So
Speaker 1: again there are cases where an quote unquote AI startup
Speaker 1: isn't using AI, but it's not necessarily an attempt to
Speaker 1: mislead people, but sometimes it might just be a scam.
Speaker 1: It might just be an effort to tap into people's
Speaker 1: enthusiasm and excitement around a buzzy term, but have no
Speaker 1: intent on ever doing any significant work within the AI field.
Speaker 1: And this has been going on for quite a few
Speaker 1: years now. Back in twenty eighteen, a writer named Kian
Speaker 1: jen Cheng, and I apologize for my pronunciation. I am
Speaker 1: notoriously terrible about this way Xichang wrote a piece for
Speaker 1: sixth tone dot com and it's called AI company accused
Speaker 1: of using humans to fake its AI. So the company
Speaker 1: in question was iFly Tech that's little I, big f
Speaker 1: l y te k, and among other things, I fly
Speaker 1: Tech was offering AI powered interpretation services, or at least
Speaker 1: that seems to be what the claim was. So the
Speaker 1: product was supposed to provide real time interpretation and translation
Speaker 1: services and was demonstrated at you know, things like international events.
Speaker 1: But then a man named Belle Wong came forward and
Speaker 1: claimed that he was part of a group of interpreters
Speaker 1: who did the actual work and essentially they were posing
Speaker 1: as the interpretation software. So Wang's accusations centered around a
Speaker 1: symposium called the twenty eighteen International Forum on Innovation and
Speaker 1: Emerging Industries Development Catchy. At this forum, there was a
Speaker 1: professor from Japan who gave a presentation, and his presentation
Speaker 1: was in English, and his words were being transcribed in
Speaker 1: real time by a text to speech program from iFly
Speaker 1: Tech and displayed on a screen behind him. So as
Speaker 1: he spoke, his words in English were showing up behind him,
Speaker 1: but next to the English transcription, his words appeared as
Speaker 1: a Chinese transcription written in Chinese characters, also supposedly handled
Speaker 1: by iFly Tech's incredible technology. Now this is amazing, not
Speaker 1: just because you're talking about translation. I mean we have
Speaker 1: translation apps out there right, We've got translation tools where
Speaker 1: you can speak into a app and have it generate
Speaker 1: an actual response in another language. This was incredible because
Speaker 1: it's not just translation but in interpretation, meaning that the
Speaker 1: turns of phrase that the speaker used were being interpreted
Speaker 1: and then translated into Chinese so that the Chinese translation
Speaker 1: would make sense. Because obviously, like the sayings, the idioms
Speaker 1: that we use in one language do not necessarily translate
Speaker 1: to another. If I say it's raining cats and dogs,
Speaker 1: English speakers know what I mean is that it's raining
Speaker 1: really hard. Non English speakers, if they saw that translation,
Speaker 1: would wonder why are animals falling from the sky when
Speaker 1: that's not what I literally mean when I say it's
Speaker 1: raining cats and dogs. So interpretation requires an extra step.
Speaker 1: It's not just translating word for word, and in fact,
Speaker 1: what was appearing behind the speaker was an interpretation of
Speaker 1: the speaker's words. The problem was Belle Wong says it
Speaker 1: was really him and his colleagues who were doing the
Speaker 1: work of that interpretation and translation. So Wong pointed to
Speaker 1: the fact that the professor's accent was fairly strong. He
Speaker 1: had a Japanese accent as he was giving his English presentation,
Speaker 1: and the real time English text to speech program from
Speaker 1: iFly Tech got into some issues with this. The program
Speaker 1: would sometimes misinterpret what the professor was saying, and so
Speaker 1: the transcript had errors in it. If you were reading
Speaker 1: along while the professor was speaking, you would see, oh,
Speaker 1: the program thought he said this one thing, but in
Speaker 1: fact he was saying this other thing. But the Chinese
Speaker 1: interpretation of the professor's words did not include these mistakes.
Speaker 1: The Chinese translation was accurate, and that's because Wang and
Speaker 1: his colleagues were translating accurately. They were listening to what
Speaker 1: he was saying, interpreting it, translating it, and then putting
Speaker 1: it up in Chinese text, so they had the appropriate
Speaker 1: Chinese interpretations displaying not the mistaken text to speech stuff.
Speaker 1: Wong said that I fly Tech never really acknowledged the
Speaker 1: use of human interpreters at that event, and that the
Speaker 1: implication was the technology was doing all the heavy lifting.
Speaker 1: So Wang said this made him feel very uncomfortable to
Speaker 1: be part of what he felt was a deceptive presentation.
Speaker 1: And it's interesting because in that same piece in sixth tone,
Speaker 1: the piece quotes I fly Tech executives who have essentially
Speaker 1: said the machines are not a suitable replacement for human
Speaker 1: interpreters and that it's far more likely that the future
Speaker 1: of interpreting will involve humans and machines working together, rather
Speaker 1: the machines replacing humans. Outright now, perhaps the ie fly
Speaker 1: Tech representatives at the International Forum were a bit over
Speaker 1: zealous in promoting the work of their company. But it
Speaker 1: feels a lot like the mechanical turk. You know, at
Speaker 1: a casual glance, you have a machine that's doing this
Speaker 1: incredibly complex action but if you take a closer look,
Speaker 1: you see that humans are powering the real process behind
Speaker 1: the scenes. Then there's Olivia Salon's twenty eighteen piece in
Speaker 1: The Guardian titled the Rise of Pseudo AI and how
Speaker 1: tech firms quietly use humans to do bots work. Now.
Speaker 1: I love how Salon frames her piece by saying, quote,
Speaker 1: some startups have worked out it's cheaper and easier to
Speaker 1: get humans to behave like robots than it is to
Speaker 1: get machines to behave like humans. End quote that I
Speaker 1: feel is bang on the money. She did a great
Speaker 1: job with this article. We humans are really versatile. We
Speaker 1: have evolved to be that way like It's not that
Speaker 1: we're special. We have millions of years of evolution behind
Speaker 1: us that have shaped us to be like this. But
Speaker 1: we have to put that same work into machines in
Speaker 1: order to make machines perform in versatile ways, and that
Speaker 1: is a considerable amount of work. We haven't been working
Speaker 1: with computers for millions of years. We've only been doing
Speaker 1: it for a few decades. So companies like open Ai
Speaker 1: and Google and such are spending billions of dollars to
Speaker 1: achieve that goal. It is not at all easy and
Speaker 1: it doesn't always go smoothly. So some startups use humans
Speaker 1: in the early days almost as a way to show
Speaker 1: the proof of concept for their end product. So sure,
Speaker 1: right now, humans are the ones doing whatever it is,
Speaker 1: like the coding or the translating or whatever the startup
Speaker 1: AI is focused on. But further down the line, well
Speaker 1: that's going to be bots. Maybe in fact, it'll have
Speaker 1: to be bots because if the startup were to take
Speaker 1: off and become a big company, then it could become
Speaker 1: too expensive to rely on humans to do all the
Speaker 1: work that needs to be done when you're operating at scale.
Speaker 1: So there's a danger of doing this as a startup,
Speaker 1: right Like, if you're doing it early on, you're saying,
Speaker 1: I'm going to be transparent with you. Right now, we
Speaker 1: have human beings doing this work, but what we're working
Speaker 1: on is developing AI to do the work instead. And
Speaker 1: this is how we're presenting it to you, and we
Speaker 1: want you to be aware of our goals and our strategy.
Speaker 1: If it turns out that whatever they want to do
Speaker 1: is too hard to do by AI, like it's just
Speaker 1: too hard to develop the AI to accomplish this goal,
Speaker 1: and the company is getting big because people value whatever
Speaker 1: the process is, you've kind of shut yourself in the foot, like, yeah,
Speaker 1: you might become successful, but you might not be profitable
Speaker 1: because you can't switch to AI. You never figured that
Speaker 1: part out, and scaling up means that you're employing so
Speaker 1: many humans to do the work that you're not being
Speaker 1: efficient and you're not really making profit. That's a real issue,
Speaker 1: and especially if people are still associating your company with
Speaker 1: AI and you're still not doing AI stuff. So it's
Speaker 1: a dangerous path to go down, even if you're being
Speaker 1: sincere at the beginning. Now so low One also mentions
Speaker 1: a piece in the Wall Street Journal that uncovered how
Speaker 1: Google would work with third party companies and allow them
Speaker 1: access to user email inboxes. So the identities of the
Speaker 1: people who own those emails would be masked, but it
Speaker 1: would mean that these third party companies could essentially read
Speaker 1: emails and stuff, which seems like a bad idea, right,
Speaker 1: Why would Google let that happen? Well, the research was
Speaker 1: largely focused on the field of AI generated responses, you know,
Speaker 1: like using AI to fire off a quick reply to
Speaker 1: someone rather than having to compose a message yourself. But
Speaker 1: in order to train AI to be able to do this,
Speaker 1: humans have to do it first, and that meant other
Speaker 1: human beings were reading like Gmail user emails, so maybe
Speaker 1: they would read the email to make sure that the
Speaker 1: AI generated response was appropriate based upon the email it
Speaker 1: was responding to. Even if you mask the identity of
Speaker 1: the people who are sending and receiving these emails, that
Speaker 1: still seems a bit sketchy, doesn't it. Because I don't
Speaker 1: know about you, but I typically assume other folks aren't
Speaker 1: allowed to read messages that were sent to me. I mean,
Speaker 1: we have rules about that with physical mail. You would
Speaker 1: imagine the same thing applies to electronic mail. Those messages
Speaker 1: being sent might include really sensitive information. So let me
Speaker 1: give you a personal example. This year, as I'm sure
Speaker 1: many of you know, I've been dealing with a lot
Speaker 1: of medical issues and I don't mind sharing that if
Speaker 1: I do so on my own terms, but I don't
Speaker 1: want people to be able to read the messages that
Speaker 1: are coming to me from my various doctors. And sure,
Speaker 1: the actual identities of users was redacted, so my identity
Speaker 1: would be masked in such an email. But I'm sure
Speaker 1: you're all aware it does not take that many points
Speaker 1: of data to be able to identify someone. It's pretty
Speaker 1: easy to do. Actually, there was a famous case, this
Speaker 1: was like more than a decade ago now, where a
Speaker 1: researcher showed that she could use three points of data
Speaker 1: and identify like eighty percent of the people in the
Speaker 1: United States based upon those three data points. Now, those
Speaker 1: were specific, it was like zip code and things like that.
Speaker 1: But my point stance, it does not take a lot
Speaker 1: of information for you to be able to identify specific person,
Speaker 1: So having your ID mast is not that big of
Speaker 1: a comfort to me. Solon also cites an older example
Speaker 1: in her article one from two thousand and eight, and
Speaker 1: this was of a company in the UK called SpinVox
Speaker 1: that claimed to use technology to convert speech so that
Speaker 1: customers could have their voicemails converted into text messages. But
Speaker 1: a BBC reporter named Rory celenne Jones said SpinVox actually
Speaker 1: was sending these voicemail recordings to call centers in Africa,
Speaker 1: which was already questionable under UK and EU law at
Speaker 1: the time of the UK was still in the EU,
Speaker 1: and that humans were actually transcribing the voicemails into text.
Speaker 1: She also cited Bloomberg reports made in twenty sixteen of
Speaker 1: companies like x dot Ai using humans posing as chatbo
Speaker 1: for the purposes of calendar scheduling services. And she mentioned
Speaker 1: a company called Expensify, which made a business expense management
Speaker 1: tool reportedly using AI scanning technology to handle receipts. But
Speaker 1: it turned out that at least some of those receipts
Speaker 1: were transcribed not by a machine but by humans working
Speaker 1: for Amazon's crowdsourced labor business. That's a business which has
Speaker 1: the appropriate name, Amazon's Mechanical Turk. I kid you, not
Speaker 1: all right, We've got more to talk about, but I'm
Speaker 1: running a bit long, so let's take another quick break
Speaker 1: and we'll be back to chat about some more fake AI. Okay,
Speaker 1: we're back. Next up, I want to talk about an
Speaker 1: article written by James Vincent for The Verge. This was
Speaker 1: in twenty nineteen and the article is titled forty percent
Speaker 1: of AI startups in Europe don't actually use AI claims report.
Speaker 1: So the report that is mentioned in that headline came
Speaker 1: from a venture capital firm in London called MMC, and
Speaker 1: MMC looked into nearly three thousand AI startups across thirteen
Speaker 1: different EU member states and found that forty percent of
Speaker 1: them weren't actually using AI in a way that was
Speaker 1: quote unquote material to their business. In fact, the guy
Speaker 1: who wrote the report, a man named David Kellner, went
Speaker 1: even further. He said that in those cases, quote we
Speaker 1: could find no mention of evidence of AI end quote jauza.
Speaker 1: Not just no evidence of AI, no evidence of no
Speaker 1: mention of evidence of AI. That's that's not good. So
Speaker 1: the piece does go on to give at least some
Speaker 1: slack to some of the companies that were included in
Speaker 1: this study because they point out that, you know, the
Speaker 1: AI designation didn't necessarily come from the star ups themselves. Rather,
Speaker 1: independent industry analysts may have categorized some of these startups
Speaker 1: as falling into the AI bucket, but it wasn't coming
Speaker 1: from the company. It was coming from these independent analysts. So,
Speaker 1: in other words, it wouldn't be fair to like walk
Speaker 1: up to an executive from one of those startups and say, hey,
Speaker 1: your company doesn't even use AI. The executive might just
Speaker 1: look a little confused and then say, uh, we never
Speaker 1: claimed it did. So I don't want to paint with
Speaker 1: too broad a brush here. I don't want to suggest
Speaker 1: that forty percent of these twenty eight hundred and some
Speaker 1: odd companies are purposefully trying to trick people. Some of
Speaker 1: them are, I'm sure, but not all of them. Sometimes
Speaker 1: it's literally because some other Yahoo said, oh, that startup
Speaker 1: that belongs in AI. So this same venture capital firm
Speaker 1: MMC gets a shout out in another article I read
Speaker 1: while researching this episode. This article is by Lauren Hamer
Speaker 1: in twenty twenty one and she wrote it for Chip.
Speaker 1: The article is titled how to spot when a company
Speaker 1: is trying to pedal you fake AI, and Hamer cites
Speaker 1: MMC Ventures just like the Verge piece did, And in
Speaker 1: this article, MMC Ventures says that startups that are in
Speaker 1: the AI space tend to attract up to fifty percent
Speaker 1: more investment dollars than startups that are not in the
Speaker 1: AI space. So again we see this is where the
Speaker 1: money is. Like, if you know that AI companies are
Speaker 1: getting fifty percent more in at least sometimes getting fifty
Speaker 1: percent more investment than non AI companies, You're probably gonna
Speaker 1: start scrambling to figure out how can I shove the
Speaker 1: AI into my business idea? Because I want to be
Speaker 1: able to get it funded, and there's only so much
Speaker 1: funding money that's out there. You're fighting for a pool.
Speaker 1: It's a big pool, but it's a pool of investment dollars.
Speaker 1: And if you know that people are more likely to
Speaker 1: invest in companies that are related to AI, then you
Speaker 1: are incentivized to make sure your company is positioned to
Speaker 1: at least appear to be AI related. And I imagine
Speaker 1: that this number has actually grown since twenty twenty one.
Speaker 1: I don't think that this has diminished at all, as
Speaker 1: we saw other hype trains derail over the last few years.
Speaker 1: Like I mentioned at the top of the show, NFTs
Speaker 1: that was a big thing briefly, but it totally and
Speaker 1: spectacularly failed. And then the Metaverse that was a really
Speaker 1: big thing for like a few months, and lots of
Speaker 1: investors got really excited in that. Not to say that
Speaker 1: metaverse development has stopped. It's still going on, but it's
Speaker 1: nowhere close to the level of hype that it was
Speaker 1: a couple of years ago. That means that since then,
Speaker 1: a lot of people have shifted over to AI as
Speaker 1: the next money ticket. And I'm curious what if any
Speaker 1: gap exists between startups that claim to be in the
Speaker 1: AI space and those that don't. As far as funding goes.
Speaker 1: I would imagine that it's more dramatic than it was
Speaker 1: in twenty twenty one. Well, in twenty twenty three, the
Speaker 1: US government began to weigh in on startups making AI claims,
Speaker 1: not just startups companies in general making AI claims. Specifically,
Speaker 1: the Federal Trade Commission or FTC posted a blog post
Speaker 1: titled keep your AI Claims in Check. This is it
Speaker 1: again in twenty twenty three, and the blog post is
Speaker 1: a warning to companies that are attempted to fake it
Speaker 1: until they make it in the AI space. The FTC
Speaker 1: post reads quote, when you talk about AI in your advertising,
Speaker 1: the FTC may be wondering, among other things, are you
Speaker 1: exaggerating what your AI product can do? And then also
Speaker 1: it says, are you promising that your AI product does
Speaker 1: something better than a non AI product? And then on
Speaker 1: top of that, it says are you aware of the risks?
Speaker 1: And finally it says does the product actually use AI
Speaker 1: at all? So the implication here is that the FTC
Speaker 1: might call on an AI startup or other company to
Speaker 1: prove its claims, and if the companies unable to do this,
Speaker 1: the FTC might impose penalties on that company. The FTC
Speaker 1: is also not the only government agency in the United
Speaker 1: States getting involved. The Securities and Exchange Commission, or SEC,
Speaker 1: brought charges against two different investment firms, one called Delphia
Speaker 1: Incorporated and another called Global Predictions Incorporated. This was a
Speaker 1: matter that was just settled earlier this year in March
Speaker 1: twenty twenty four. So the charges stated that both of
Speaker 1: these companies had made quote false and misleading statements about
Speaker 1: their purported use of artificial intelligence end quote. So as
Speaker 1: I said, the two companies each settled with the SEC
Speaker 1: just this past March, and in total they paid four
Speaker 1: hundred thousand dollars in civil penalties. So obviously, with the
Speaker 1: recent explosion of AI startups, there are lots of similar
Speaker 1: articles that are coming out to about be wary of
Speaker 1: AI claims. One is by Pauline Tomaer. I believe that's
Speaker 1: how you say Pauline's name. It's a twenty twenty three piece.
Speaker 1: It's in a blog called be Cool, but Be Cool.
Speaker 1: It's spelled b qool. The post is titled how to
Speaker 1: spot the Fake AI Claims. That's a good one. And
Speaker 1: then sheikar Quatra has an article titled the AI hype Machine.
Speaker 1: When companies fake it till they make it. I found
Speaker 1: that one on LinkedIn. Actually it's also where I first
Speaker 1: saw the term AI washing, which once I saw that term,
Speaker 1: I thought, oh, well, of course that's a perfect phrase,
Speaker 1: because we're already familiar with stuff like greenwashing. That's when
Speaker 1: a company claims to follow eco friendly processes, but in
Speaker 1: fact it fails to live up to those promises. AI
Speaker 1: washing is similar. A company uses AI to drive interest
Speaker 1: in support for the business, even if the company itself
Speaker 1: has little, if anything to do with AI. Now, Quatra's
Speaker 1: piece is large a warning to potential investors that it
Speaker 1: behooves you to examine a company's claims closely and to
Speaker 1: employ critical thinking before handing over a sizable chunk of change.
Speaker 1: Of course, that's true no matter what business a startup
Speaker 1: might be in. But the frenzy around AI creates the
Speaker 1: sense that if you do not act now, you're going
Speaker 1: to be left behind, and you'll sit there while your
Speaker 1: neighbors and co workers all make millions of dollars and
Speaker 1: they move out to live in solid gold yachts or something,
Speaker 1: and you're stuck at home doom scrolling through your various
Speaker 1: social media accounts. So don't give in to the fomo, y'all.
Speaker 1: But my warning goes beyond investors. My warning is for
Speaker 1: all of us out there. We always need to remember
Speaker 1: to use critical thinking. I say that as someone who
Speaker 1: often I will forget to use critical thinking. It's terrible.
Speaker 1: I say it all the time. When I do use
Speaker 1: critical thinking, I'm always thankful for it. But the point is, like,
Speaker 1: this is a skill you exercise. It's not something that
Speaker 1: just passively happens in the background. You've got to employ it,
Speaker 1: and we have to remember to do that. We need
Speaker 1: to remember to ask questions, and we have to examine
Speaker 1: the answers that we receive. And we need to do
Speaker 1: this for a lot of reasons. So top reason is
Speaker 1: probably just you don't want to get tricked, you know,
Speaker 1: unless you're at a magic show, in which case that's
Speaker 1: exactly what you want. But typically getting tricked means someone
Speaker 1: is taking advantage of you, and that's not cool. But
Speaker 1: another good reason is that we need to look into
Speaker 1: how a company is actually doing its business. For example,
Speaker 1: if that business involves relying on call centers or data
Speaker 1: centers located in developing countries, and it all depends upon
Speaker 1: severely underpaid staff working insane hours to do the things
Speaker 1: that a company claims AI is doing well. That comes
Speaker 1: across as mightily unethical to me. I've seen far too
Speaker 1: many stories about people enduring terrible working conditions while the
Speaker 1: companies that are exploiting those people are posting record profits
Speaker 1: and shareholder returns, all while claiming that AI is the
Speaker 1: cornerstone of their business. That just strikes me as inherently
Speaker 1: unethical and really downright evil if we're getting honest about it. So,
Speaker 1: I feel like critical thinking is important, not just for
Speaker 1: our own welfare, but those of people who live in
Speaker 1: other countries. Like I do want them to find gainful employment,
Speaker 1: but I want to find them to find employment that's
Speaker 1: not you know, exploiting them to the point where they're
Speaker 1: falling apart, and meanwhile these companies are posting record profits.
Speaker 1: It's good to remember that AI can be dangerous, not
Speaker 1: just through misuse or weaponization, or through having AI replace
Speaker 1: real folks at their jobs, though those are real dangers.
Speaker 1: I mean, my old colleagues at the editorial department of
Speaker 1: HowStuffWorks dot com found themselves out of a job when
Speaker 1: the site shifted to AI generated articles. If I had
Speaker 1: still been there, I would have been one of them.
Speaker 1: But yeah, AI can be dangerous even when the AI
Speaker 1: itself isn't real or I guess that really just points
Speaker 1: out that humans can be dangerous and deceptive. But we
Speaker 1: kind of knew that already, didn't we. Anyway, I hope
Speaker 1: you learned something in this episode. I hope you go
Speaker 1: and read some of those articles I mentioned, because they
Speaker 1: are really well done and they illustrate some specific examples
Speaker 1: of what I'm talking about and might help you spot
Speaker 1: when it happens again, so that you ask the tough
Speaker 1: questions and you do examine those answers. In the meantime,
Speaker 1: I hope all of you out there are doing well,
Speaker 1: and I'll talk to you again really soon. Tech Stuff
Speaker 1: is an iHeartRadio production. For more podcasts from iHeartRadio, visit
Speaker 1: the iHeartRadio app, Apple Podcasts, or wherever you listen to
Speaker 1: your favorite shows.