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? You know, I'm sure you've all seen movies
Speaker 1: which someone has to get past a security system by
Speaker 1: having an eye scan, and maybe they have an unconscious
Speaker 1: or potentially unalive security guard laying nearby and they hold
Speaker 1: the person's head up to the scanner to gain access
Speaker 1: to the security door. Or maybe the main character has
Speaker 1: undergone some sort of incredibly disturbing surgical procedure to have
Speaker 1: their own eyes altered to give them access. But you
Speaker 1: get the idea. The eye scan verifies that the person
Speaker 1: trying to open the door has the authorization to do that.
Speaker 1: This is a form a biometric and I'm sure we're
Speaker 1: all familiar with biometrics. You probably use them regularly. If
Speaker 1: you have a phone that has a fingerprint scanner or
Speaker 1: a face scanner to unlock the phone, that's biometrics. I
Speaker 1: used to use a laptop that had a fingerprint scanner,
Speaker 1: which was kind of cool. It made it really easy
Speaker 1: for me to log in. Not necessarily the most secure technology. However,
Speaker 1: as I remember reading about how that particular fingerprint scanner
Speaker 1: had some issues, but it's still sure made logging into
Speaker 1: my computer easy back in those days. My current one
Speaker 1: does not have that. And there are actually a couple
Speaker 1: of different definitions for biometrics, but the one we're concerned
Speaker 1: about involves body and behavioral measurements for the purposes of
Speaker 1: authentication and verification or identification, because these are all related
Speaker 1: but different things. So for this to work, the stuff
Speaker 1: what we're measuring has to be unique to an individual.
Speaker 1: It needs to be something that cannot be easily replicated.
Speaker 1: That's the whole point, right. If it could be replicated,
Speaker 1: then it's no good for verifying someone or identifying someone
Speaker 1: because it could be one of many people. So you
Speaker 1: need to have something that is unique to that person.
Speaker 1: Spoiler alert. While biometrics can be used for the purposes
Speaker 1: of security, for verification or identification, it's also pretty darn
Speaker 1: handy if you just want to keep track of someone, right,
Speaker 1: so you get enough biometric data on a person, it
Speaker 1: becomes easier to track that person down to keep track
Speaker 1: of what they're doing, assuming that they're not like hold
Speaker 1: up in some off grid shack in the middle of
Speaker 1: nowhere and they never venture out of there, in which case, yeah,
Speaker 1: they can be under the radar then, but otherwise it
Speaker 1: gets pretty tricky. But before we get into all that
Speaker 1: and the concerns around biometrics, let's talk a little bit
Speaker 1: about the history of biometrics. This history is varied, It
Speaker 1: goes all over the place. It has lots of starts
Speaker 1: and stops and stutters, and that's because biometrics focuses on
Speaker 1: lots of different things, right Like, it's not all just
Speaker 1: one area of study. Typically we're talking about things that
Speaker 1: people were interested in studying or advancing that collectively we
Speaker 1: would categorize under biometrics, but they were looking at a
Speaker 1: very specific version of it. So you could argue that
Speaker 1: biometrics traces its history to more than twenty five hundred
Speaker 1: years ago. Because Babylonians back in the day, which I'm
Speaker 1: told was a Tuesday, would sign off on business transactions
Speaker 1: by placing a thumb print on a clay tablet. Right
Speaker 1: that the clay tablet would be pliable, you would PLoP
Speaker 1: your thumb down and make an impression. The tablet would
Speaker 1: dry and your print would be there to authenticate that
Speaker 1: you had, in fact completed whatever the transaction was. Bo
Speaker 1: that's biometrics, and Tom Cruise wasn't even involved a little bit.
Speaker 1: I always think of Tom Cruise in movies like the
Speaker 1: Mission Impossible series, or even in Minority Report. When I
Speaker 1: think about biometrics, those are the ones that pop up
Speaker 1: in my head. Of course, it's not like there was
Speaker 1: a direct line of evolution from the ancient Babylonians to
Speaker 1: Tom Cruise, at least not that I am aware of.
Speaker 1: Biometrics weren't always a common means of marking documents or
Speaker 1: anything like that. But by the late nineteenth century, some
Speaker 1: folks started to figure out that the Babylonians could have
Speaker 1: really been onto something. Actually, I'm kidding. They probably didn't
Speaker 1: even know about the Babylonian stuff. They just thought they
Speaker 1: came up with it themselves. Because typically that's how we
Speaker 1: see the smarty pants of the eighteenth and nineteenth centuries.
Speaker 1: Some of them were a little more humble than that,
Speaker 1: but not all of them. Anyway, Folks in the late
Speaker 1: nineteenth century, so we're talking the eighteen hundreds here, they
Speaker 1: observe that fingerprints appeared to be unique to the individual,
Speaker 1: and if you got two different people to make prints
Speaker 1: of their fingertips. You would see the differences in the
Speaker 1: patterns of those fingerprints, and you would be able to
Speaker 1: tell which person made which set of prints, assuming that
Speaker 1: you had a reference right, as long as you're able
Speaker 1: to refer it back to something on record. This logically
Speaker 1: led to the concept of using fingerprints as a way
Speaker 1: to identify if a suspect had been present at the
Speaker 1: scene of a crime. Of course, folks had to figure
Speaker 1: out how to detect and lift prints at the scene. Typically,
Speaker 1: they would use a very fine powder or dust which
Speaker 1: would adhere to the oils left behind from the fingertips
Speaker 1: on different surfaces, and then they would lift those prints
Speaker 1: and then compare that to a record of fingerprints so
Speaker 1: that they could reference the prints that were lifted at
Speaker 1: the scene with ones that were in their database the
Speaker 1: reference system, or if they had the suspect in custody,
Speaker 1: they would have the suspect, you know, get printed, and
Speaker 1: they would compare the prints against whatever was found at
Speaker 1: the scene and then try to determine if the suspect
Speaker 1: was actually present at the scene of the crime. You
Speaker 1: know how this works, But the science of that and
Speaker 1: the systems based around that took a while to develop.
Speaker 1: Folks had to identify the elements and fingerprints that are
Speaker 1: definable and identifiable. They had to make classifications and taxonomies.
Speaker 1: You know, you had to have a way to communicate
Speaker 1: the qualities of a print when talking with someone else,
Speaker 1: or to be able to look for a match or
Speaker 1: even just a partial match. And it took a couple
Speaker 1: of decades to really catch on. And while fingerprints are
Speaker 1: the most famous example of early biometrics and law enforcement,
Speaker 1: police were trying different stuff too. In France, around the
Speaker 1: same time as fingerprints coming into vogue, there was a
Speaker 1: system called the Bertilline process or Bertillion process. The police
Speaker 1: would measure suspects and they would write down their measurements
Speaker 1: on a card and keep that in an index. And
Speaker 1: I'm talking all sorts of measurements. You know. It's kind
Speaker 1: of like if you were to go and get fitted
Speaker 1: for custom clothing, except you're being fitted for crimes, I guess.
Speaker 1: So the police would take down stuff like how tall
Speaker 1: you were, you know, how wide were your shoulders, how
Speaker 1: long are your arms, all that kind of stuff. This
Speaker 1: approach had some big drawbacks, however, the biggest one being
Speaker 1: that there was a lack of standardization in technique and metrics,
Speaker 1: which meant it wasn't terribly useful. Because if one police
Speaker 1: officer measures arm length one way, let's say, like they
Speaker 1: measure the arm just hanging down from the shoulder, and
Speaker 1: someone else measures it in a different way, like you're
Speaker 1: holding your arm up at an angle, then you might
Speaker 1: end up getting very different you know, metrics measurements. And
Speaker 1: that meant that you would look at the index card
Speaker 1: and say, oh, well, this person's not a match. That's
Speaker 1: not who who this person is or whatever, And so
Speaker 1: it was not terribly useful, and you could get false
Speaker 1: positives or false negatives. So this particular method wouldn't stand
Speaker 1: the test of time. Fingerprints definitely did. In the nineteen thirties,
Speaker 1: a guy named Frank Birch had another idea for using
Speaker 1: biometrics as a way to identify people. He was an ophthalmologist, that,
Speaker 1: by the way, is a great word that trips me
Speaker 1: up in spelling bees, and you might imagine. His idea
Speaker 1: was that an individual's iris contains complex patterns, and those
Speaker 1: patterns are unique to that person. It's like a fingerprint,
Speaker 1: but it's the pattern that's in the iris of your eye,
Speaker 1: and so like a fingerprint, a person's iris could serve
Speaker 1: as a way to identify that person and subsequently verify
Speaker 1: their identity in the future. You just needed a way
Speaker 1: to capture those patterns and then a technology capable of
Speaker 1: scanning and matching patterns against those that are stored in
Speaker 1: a database. So in twenty six when Frank Birch came
Speaker 1: up with this idea, none of that was really practical
Speaker 1: or possible, but his basic concept using the eye as
Speaker 1: a way to verify identity, that was solid. Now, it
Speaker 1: would take more than fifty years before someone would come
Speaker 1: up with a computer algorithm that could identify iris patterns
Speaker 1: and moreover, match an IRIS pattern that is collected to
Speaker 1: one in a database. That came to us courtesy of
Speaker 1: Cambridge University professor John Dogman in nineteen eighty seven, and
Speaker 1: it took a few more years to actually capitalize on
Speaker 1: that development. It wasn't until the mid nineteen nineties that
Speaker 1: a company called Iridium Technologies became the first to commercialize
Speaker 1: iris scanning. Now there are lots of companies that do this,
Speaker 1: many with their own proprietary approach to doing it, but
Speaker 1: it's all rooted in Frank Birch's suggestions back in the
Speaker 1: nineteen thirties. Okay, we've got a lot more to talk
Speaker 1: about with biometrics before we get into that. Let's take
Speaker 1: a quick break. You know, not long ago I talked
Speaker 1: a little bit about the early days of researchers trying
Speaker 1: to use computers to develop facial recognition technology, another example
Speaker 1: of biometrics. So the pioneers in that space included Woody Bledsoe,
Speaker 1: a mathematician and computer scientist, Helen chan Wolf, a scientist
Speaker 1: specializing in early artificial intelligence work, and Charles Bisson, another
Speaker 1: computer scientist and the three word for panoramic research in California.
Speaker 1: And they set about trying to figure out how to
Speaker 1: break down facial features into data points that a computer
Speaker 1: could analyze and match. So, you know, you imagined that
Speaker 1: at first, this just looks and involves looking at pictures
Speaker 1: of people and saying, well, where are some ways that
Speaker 1: we can we can point out, like landmarks that a
Speaker 1: computer would be able to recognize and measure against, right,
Speaker 1: like the corners of eyes compared to the bridge of
Speaker 1: the nose, that kind of stuff. And there are all
Speaker 1: these different images of pictures of people with all these
Speaker 1: different lines geometric lines drawn on the faces in an
Speaker 1: effort to kind of establish these standards. And as you
Speaker 1: might imagine, in the early days, this technology hit some
Speaker 1: pretty tough limitations. A computer couldn't necessarily match two different
Speaker 1: pictures of the same person's face. If the lighting and
Speaker 1: the shadows or the angle of the picture were different enough,
Speaker 1: the computer system would have trouble determining that both images
Speaker 1: were of the same face. Yes, if you had two
Speaker 1: pictures of the same person where it was under identical
Speaker 1: lighting conditions and the camera was at the same distance
Speaker 1: in the same angle, then it was easier, But with
Speaker 1: any deviation from that it became much more difficult. It
Speaker 1: would take many years for the algorithms, the computer technology,
Speaker 1: camera technologies to improve to a point that made facial
Speaker 1: recognition a possibility. So we're going to skip way up
Speaker 1: to the present day, and we've talked extensively on this
Speaker 1: show about how facial recognition technology often has major issues,
Speaker 1: particularly when it comes to false positives and false negatives
Speaker 1: with certain populations. Now, I'm not going to dive into
Speaker 1: that whole can of worms yet again, because I'm sure
Speaker 1: most of you have heard me talk about it a lot,
Speaker 1: but it is an inescapable fact that most facial recognition
Speaker 1: systems are prone to making errors due to biases that
Speaker 1: are in the system. And just to be clear, I
Speaker 1: don't mean to imply that there was ever any intentional bias,
Speaker 1: but intentional or not, that bias still has an effect. Meanwhile,
Speaker 1: other computer scientists were working in the mid twentieth century
Speaker 1: on the challenge of developing speech recognition technologies. Now, like
Speaker 1: facial recognition, this tech had a steep barrier of entry.
Speaker 1: Voices come in all sorts of timbers, volumes, pitches, dialects,
Speaker 1: or accents. So two people might say the very same
Speaker 1: word in very different ways. You know, it may sound
Speaker 1: very different, and yet it's the same word, And that
Speaker 1: means the system needs to be capable of recognizing that
Speaker 1: word no matter who says it or how they say it.
Speaker 1: Otherwise you're not going to have a satisfying experience. Well,
Speaker 1: folks in Bell Labs were working on speech recognition tech
Speaker 1: as early as the nineteen fifties, but that work focused
Speaker 1: on training a machine to recognize when someone was speaking
Speaker 1: out numbers. It took a decade of hard work to
Speaker 1: get to a point where the computer systems could recognize
Speaker 1: words of a certain complexity. The nineteen seventies would see
Speaker 1: the technology advanced significantly, helped in large part by a
Speaker 1: DARPA initiative. Y'all remember DARPA, right, that's the division within
Speaker 1: the US Department of Defense that awards contracts to companies
Speaker 1: that engage in R and D, and it's all in
Speaker 1: the purpose of trying to fund projects that could potentially
Speaker 1: benefit US defense initiatives in the future. DARPA has funded
Speaker 1: research into everything from autonomous vehicles to robots capable of
Speaker 1: performing half a dozen different tasks, and in the seventies,
Speaker 1: the division funded work in speech recognition. But speech recognition
Speaker 1: is really related to using voice technology for the purposes
Speaker 1: of a computer understanding what someone is saying. There's also
Speaker 1: voice recognition, or some people prefer speaker recognition, because that's
Speaker 1: all about recognizing a specific person. So speech recognition is
Speaker 1: really more about computer systems that can parse what you're
Speaker 1: saying and glean instructions from that and respond properly so
Speaker 1: that when you ask your smart speaker about the weather,
Speaker 1: it can give you the information you want instead of
Speaker 1: I don't knowtaneously turning off all the lights in your
Speaker 1: house or whatever, not that I've had that experience. Voice
Speaker 1: recognition or speaker recognition involves breaking a specific voice into
Speaker 1: data points for the purposes of identification, just like with
Speaker 1: facial recognition. Right. So, the idea of developing a system
Speaker 1: to do serious analysis on voices dates back to the
Speaker 1: early twentieth century. However, a lot of that early work
Speaker 1: lacked scientific value or rigor. So while you had people saying, yes,
Speaker 1: I can get a print out of what someone's voices
Speaker 1: and thus be able to compare two print outs and
Speaker 1: tell you if it's the same person speaking or not,
Speaker 1: in truth, it was a bit more complicated than that.
Speaker 1: I mean, you know, it required more than just having
Speaker 1: a long strip of paper tape pulled across a pencil that,
Speaker 1: in turn was connected to a diaphragm that would cause
Speaker 1: vibrations to move through when sound would go through like
Speaker 1: a horn or a microphone. Yeah, you could create a
Speaker 1: visual representation of someone's voice that way, but it wasn't
Speaker 1: something that was specific enough for you to be able
Speaker 1: to differentiate that voice from another voice or even sometimes
Speaker 1: other noises entirely, like you might get a record of
Speaker 1: noise that is not a voice at all, but because
Speaker 1: it looks kind of like what someone else produced when
Speaker 1: they were speaking into it, you might think, oh, it
Speaker 1: was this person. It would take years to get to
Speaker 1: a more sophisticated approach to speech analysis and voice analysis
Speaker 1: to approach the possibility of identifying someone based on their voice.
Speaker 1: In the nineteen forties, folks learned about a sound spectrograph technology.
Speaker 1: So this is a device that would create a visual
Speaker 1: representation of a signal. Sound spectrograph being sound. We're talking
Speaker 1: about audio in this case, but you can have spectrographs
Speaker 1: that create a visual representation of lots of different types
Speaker 1: of signals. This is just specifically about audio. Some people
Speaker 1: would refer to these as voice prints. They would compare
Speaker 1: this to a fingerprint, saying, oh, this representation shows the
Speaker 1: quality of this person's voice. If we find a match,
Speaker 1: then that's the same person. It's just like a fingerprint.
Speaker 1: The people who were actually working in the field really
Speaker 1: didn't use the term voice print very much, if at all,
Speaker 1: but it was a term that was used a lot
Speaker 1: in the media, and I think a lot of folks
Speaker 1: who were working in the field were frustrated because they
Speaker 1: felt it was a bit reductive and an oversimplification of
Speaker 1: what they were doing. So a slightly more acceptable term
Speaker 1: is what I've referenced earlier, speaker recognition. This implies the
Speaker 1: technology isn't necessarily trying to understand what someone is saying.
Speaker 1: Instead is trying to identify the person who is saying it,
Speaker 1: or verifying that the person who is saying it is
Speaker 1: who they claimed to be. You might use this technology
Speaker 1: to have someone get access to something secret, right, like
Speaker 1: the voice print analysis. You've seen this in movies too,
Speaker 1: where someone walks up to a door and they say
Speaker 1: a phrase and then apparently the computer inside the door
Speaker 1: and analyzes the voice and then either allows entry or
Speaker 1: denies it. Or you might use it to try and
Speaker 1: identify somebody, right, maybe you've got a recording of someone.
Speaker 1: Maybe someone calls into a television station and makes a threat.
Speaker 1: This has happened, and then what you're trying to do
Speaker 1: is identify a suspect you have found and to determine
Speaker 1: whether or not they were the person who made the
Speaker 1: phone call, and you're trying to match the voices together.
Speaker 1: This is something that's been going on in law enforcement
Speaker 1: for decades and for a very long time. Courts would
Speaker 1: reject the evidence presented because there was a lack of
Speaker 1: scientific studies showing the accuracy and reliability of this kind
Speaker 1: of approach, and you needed to show that there was
Speaker 1: a scientific basis for this and not just a claim
Speaker 1: that these two voices must be identical. The technology involves
Speaker 1: sophisticated pattern analysis and it is really tricky. So those
Speaker 1: early court cases, it's probably a good idea to throw
Speaker 1: those cases out. Now, maybe some guilty people went free.
Speaker 1: It's hard to say, but the fact is we just
Speaker 1: weren't at a level of sophistication a pattern analysis to
Speaker 1: be able to have really reliable identification. These days, there
Speaker 1: are examples of speaker recognition technology built into consumer products.
Speaker 1: My smart speaker at home supposedly recognizes my voice, for example,
Speaker 1: and this should make it possible for me to ask
Speaker 1: about my daily schedule, and because the speaker recognizes that
Speaker 1: the user is yours, truly, it could then cross reference
Speaker 1: my calendar and then tell me what my schedule is,
Speaker 1: or at least it's supposed to be able to do that.
Speaker 1: I don't know. I mean, it's probably because I never
Speaker 1: put anything in my calendar. I'm really bad about doing that.
Speaker 1: So really, my speaker just gets fed up with me
Speaker 1: because I'm asking it to do something it really cannot do.
Speaker 1: All right, we're gonna take another quick break. When we
Speaker 1: come back, I'm going to talk a little bit more
Speaker 1: about some other biometric approaches and also about a current
Speaker 1: story that really inspired this entire episode and the different
Speaker 1: sides to that story. But first let's take another quick break.
Speaker 1: Before the break, I alluded to the fact that there
Speaker 1: are other approaches to biometric verification or identification. So another
Speaker 1: one is gate recognition GAI. This is the way someone
Speaker 1: moves through a space, like things like the length of
Speaker 1: their stride, the position of their body as they are walking,
Speaker 1: the location of various body parts in relation to one another,
Speaker 1: like how far are your hips from your knees or
Speaker 1: your knees from your ankles, that sort of thing, what
Speaker 1: sort of how much do you bend when you're moving.
Speaker 1: So it is possible to analyze how a person naturally
Speaker 1: walks or moves through space and then to use that
Speaker 1: information to identify someone. So if you have reference data
Speaker 1: where you know how a person typically walks, then you
Speaker 1: might be able to use that in when you're searching
Speaker 1: for somebody. A person could be trying to evade surveillance,
Speaker 1: for example, through the use of disguises, but gate analysis
Speaker 1: might reveal who they really are, assuming you've got reference
Speaker 1: data as well. I've seen movies that do this where
Speaker 1: it's a person who's just watching like a video feed.
Speaker 1: They're saying, ah, I see they have a limp, and
Speaker 1: you know, we know so and so favors their right legs,
Speaker 1: so I think we've got the person here. Like we've
Speaker 1: seen that sort of thing. It's the same basic idea,
Speaker 1: except you don't have to have a limp. It's really
Speaker 1: just all the basic movements you make that end up
Speaker 1: kind of being unique to you. You can try and
Speaker 1: disguise that. Obviously, you can purposefully give yourself like a
Speaker 1: limp or something in an effort to throw off any
Speaker 1: surveillance techniques that would be looking to match your GIT
Speaker 1: with you. And that's a possibility, but this is an
Speaker 1: actual thing that's being used, this gate analysis and gait verification. Really,
Speaker 1: when it comes to biometrics, again, as I said at
Speaker 1: the beginning, the important quality is that whatever thing you
Speaker 1: are measuring needs to be unique to individuals. So it
Speaker 1: could be physical, it could be behavioral. It could be
Speaker 1: a combination of the two, but it has to be
Speaker 1: unique or else it doesn't do you any good whether
Speaker 1: you're using this technology to verify someone's identity before or
Speaker 1: letting them through a secure checkpoint, or if you're just
Speaker 1: trying to use it to determine an unknown person's identity
Speaker 1: by comparing it against the database of known individuals. Now,
Speaker 1: there are a lot of reasonable concerns about biometrics. It's
Speaker 1: totally understandable, right, like, this is information that is unique
Speaker 1: to you, that is a very private info. And we're
Speaker 1: seeing more and more implementations of biometric systems around us.
Speaker 1: Like I mentioned before, your very phone may rely on it.
Speaker 1: Mine has a fingerprint sensor for example. A lot of
Speaker 1: iPhones use facial recognition to activate, to turn on without
Speaker 1: you having to put in some sort of code. So
Speaker 1: we're seeing biometrics rolled out very in very wide deployments
Speaker 1: in all sorts of different applications. We also have seen
Speaker 1: it for things that are very official, not just use
Speaker 1: of consumer products, but things like you know, maybe getting
Speaker 1: an iris scan as part of identification like in a
Speaker 1: past or or something, or you might have a biometric
Speaker 1: scan that allows you to bypass the normal process of
Speaker 1: getting on a plane. I've had that happen a couple
Speaker 1: of times, and it freaked me out the first time
Speaker 1: because I don't remember ever submitting to the initial scan,
Speaker 1: but it knew who I was when I walked up,
Speaker 1: and I thought, well, that's weird because I didn't actually
Speaker 1: I didn't do I didn't sit down or agree to
Speaker 1: do a process I didn't knowingly agree. I probably did agree,
Speaker 1: I just didn't read the fine print, because nobody ever does, right,
Speaker 1: But that I walked up to the gate and it
Speaker 1: recognized who I was and I didn't even have to
Speaker 1: present a boarding pass or anything. I just got waved in.
Speaker 1: And that's a little creepy, and I can see why
Speaker 1: people would be hesitant about it. And you think about
Speaker 1: the possibilities of this technology and the possibilities of abusing
Speaker 1: that technology, and it quickly does get into that dis
Speaker 1: stopian kind of a vibe. But we're still seeing it
Speaker 1: rolled out every place. There are a lot of sports
Speaker 1: stadiums out there that now use biometric systems in order
Speaker 1: to scan someone, and that way you have a ticketless approach.
Speaker 1: Right you walk up to the stadium, you scan your
Speaker 1: palm and it identifies who you are and the fact
Speaker 1: that you already have tickets to that event and you're
Speaker 1: just you're waved in. This is being deployed more and
Speaker 1: more around the world. And as I record this, Amazon
Speaker 1: is rolling out It's Amazon One technology. This is it's
Speaker 1: palm scanning technology, which was already being used in things
Speaker 1: like Amazon Go storefronts. It's now rolling those out to
Speaker 1: Whole Foods grocery stores here in the United States, and
Speaker 1: it's already in several hundred of them. It's going to
Speaker 1: be put into the rest of them before the end
Speaker 1: of the year. And the value proposition that Amazon is
Speaker 1: giving customers is this, It's mainly one of convenience that
Speaker 1: customers can opt to scan their palm, which then will
Speaker 1: create a unique identifier associated with that person. That identifier
Speaker 1: then in turn must be linked to the customer's Amazon
Speaker 1: account and then whatever payment options they have linked to
Speaker 1: that Amazon account. So you go to Whole food so
Speaker 1: you do your grocery shopping, you scan all your items
Speaker 1: at the self scan cashier area, and then you scan
Speaker 1: your palm and it automatically deducts however much the groceries
Speaker 1: were from your account and you're done. You don't have
Speaker 1: to carry credit cards. You don't even need a smartphone
Speaker 1: with a digital card on it. You literally have your
Speaker 1: payment on hand. It is your hand. The scanner, meanwhile,
Speaker 1: is looking not just at the skin of the palm.
Speaker 1: It's actually using wavelengths of light that let it see
Speaker 1: below the skin level to the pattern of veins in
Speaker 1: your palm. That ends up being part of the information
Speaker 1: that ultimately generates this identifier that it's associated with you.
Speaker 1: Amazon says they're not actually saving the palm scans themselves,
Speaker 1: so it's not like there's some According to Amazon, it's
Speaker 1: not like there's some sort of massive database that has
Speaker 1: all these different palm scans in it. Instead, what they're
Speaker 1: saying is that when you scan your palm, the scanner
Speaker 1: essentially reduces all the features of your palm into data points,
Speaker 1: which then end up generating this unique identifier. So really
Speaker 1: what you're doing is verifying every single time right you
Speaker 1: hold your palm up, it scans it, it generates this number,
Speaker 1: it compares that to the number on file, and if
Speaker 1: the two numbers match, yay, you're who you say you are,
Speaker 1: and then you can be charged for your very expensive
Speaker 1: groceries and then you can go on your merry way.
Speaker 1: What they what they're saying is not happening is that
Speaker 1: it's scanning your hand and then comparing the physical scan
Speaker 1: again and stay previous physical scan that has to be
Speaker 1: saved somewhere. That's important because all the information that Amazon
Speaker 1: is storing is in the cloud. They say that they've
Speaker 1: put a lot of security on this because people are
Speaker 1: understandably concerned about having very personal and personally identifiable information
Speaker 1: stored on the company systems, and if it's on the cloud,
Speaker 1: that means that potentially you could have hackers target it.
Speaker 1: Maybe you could have law enforcement agencies try to force
Speaker 1: Amazon to share information as they go through some form
Speaker 1: of investigation. So people are understandably concerned. If Amazon's telling
Speaker 1: the truth and the only information it's storing is a
Speaker 1: unique identifier, then if hackers were to get access to that,
Speaker 1: then it arguably wouldn't be very useful. It'd be kind
Speaker 1: of like you got the answer to a math question,
Speaker 1: but you don't know what the math question was, or
Speaker 1: in the case of hitching Guide to the Galaxy. They
Speaker 1: the part of the story is that these people build
Speaker 1: this massively powerful computer to give the answer to life,
Speaker 1: the universe, and everything, and the answer ends up being
Speaker 1: forty two, and they say, how's that? What does that
Speaker 1: mean forty two? Well, you need to know the question.
Speaker 1: You need to know what the question is for forty
Speaker 1: two to make sense. You just said the answer to life,
Speaker 1: the universe, and everything. And so you see that you
Speaker 1: need to know both the question and the answer. If
Speaker 1: you only have the answer, you don't know what the
Speaker 1: question was. So if Amazon's just storing these numbers and
Speaker 1: hackers got access to it, they wouldn't be able to
Speaker 1: backtrack and figure out your scan What they could do, however, potentially,
Speaker 1: is find out every single time you used that technology,
Speaker 1: whether it was when you were grocery shopping or going
Speaker 1: to a sporting event, or going to any other place
Speaker 1: that has opted to use AMI Amazon's palm scanning technology
Speaker 1: in their business. It would become a way of tracking
Speaker 1: your movements and potentially also seeing what it was you
Speaker 1: were using the scanner for, you know, maybe getting access
Speaker 1: to stuff or whatever, like going to a sporting event
Speaker 1: or a concert. And that's where you start to see
Speaker 1: real security and privacy issues. Even if Amazon's super super
Speaker 1: careful with this, Amazon itself still has access to all
Speaker 1: of that right, So of course, if you go to
Speaker 1: Amazon dot com and buy something, Amazon knows what you bought,
Speaker 1: and it can use that information to try and target
Speaker 1: you for advertising and to give suggestions for products you
Speaker 1: might find useful based upon your past purchases. If you're
Speaker 1: using Amazon's palm scanning system out in the wild, then
Speaker 1: Amazon also knows all that information out in the real world,
Speaker 1: not just on Amazon dot Com. So you go to
Speaker 1: Whole Foods and you buy a whole bunch of groceries,
Speaker 1: you scan your palm. Now Amazon knows exactly what things
Speaker 1: you bought, and they know it's you. They've got the
Speaker 1: identifier it's connected to your Amazon account that could be
Speaker 1: used for the purposes of targeted advertising. Amazon has said
Speaker 1: they're not sharing the palm data with advertisers, which is fine,
Speaker 1: but they didn't say anything like all the stuff I read.
Speaker 1: They very carefully did not say they weren't sharing purchase
Speaker 1: history or use history. And sure they might keep your
Speaker 1: actual palm scan private, but if they're sharing with advertisers
Speaker 1: what it is you're buying or where you are going,
Speaker 1: the palm scan thing ends up being kind of moot, Like,
Speaker 1: that's not that important. Your activities are telling the advertisers
Speaker 1: and Amazon a lot about you beyond just what your
Speaker 1: palm is like it. Yeah, knowing where the veins are
Speaker 1: in your palm is potentially disastrous if someone's trying to
Speaker 1: somehow replicate you and run up a bunch of charges
Speaker 1: on your account. But to me, the more disturbing thing
Speaker 1: is that every time you use a point of sale
Speaker 1: or point of access that relies on this technology, it's
Speaker 1: another data point that associates you with a specific action,
Speaker 1: and collectively that ends up really mattering a whole lot,
Speaker 1: both to Amazon and to potential advertisers. So I have
Speaker 1: real concerns about using this kind of technology. I mean,
Speaker 1: you could argue the same thing is true if you're
Speaker 1: using the same credit cards or the same payment systems.
Speaker 1: It's the same issue, right, Like, Honestly, the big issue
Speaker 1: I have, and I'm not like a conspiracy minded person,
Speaker 1: but the big issue I have is that we are
Speaker 1: past the days where you would do things like cash
Speaker 1: transactions for a lot of stuff, and cash transactions could
Speaker 1: be really useful if you need to make a transaction
Speaker 1: that you didn't want to be associated with you for
Speaker 1: the rest of your life. Right if you need to
Speaker 1: buy something, Maybe you need to buy some medication and
Speaker 1: it's nobody businesses but your own that you need this medication,
Speaker 1: and you normally would use cash to do it, but
Speaker 1: now you're using some other method that is linked to you,
Speaker 1: which means other people know that you're having to get
Speaker 1: this medication beyond you and the pharmacist and your doctor,
Speaker 1: and that becomes a potential privacy issue, and you could
Speaker 1: apply that to all sorts of different stuff. So to me,
Speaker 1: the biometrics approach, especially in a consumer use for things
Speaker 1: like access to events or a way of paying, doesn't
Speaker 1: appeal to me. But you could argue that it's really
Speaker 1: just an extension of how we already interact in the world.
Speaker 1: I guess the more I talk about this, the more
Speaker 1: I can see why some cryptocurrency enthusiasts really like cryptocurrency.
Speaker 1: They like the idea of being able to use currency
Speaker 1: in a way that doesn't immediately associate them as a
Speaker 1: person with their purchases or the way they're spending their money.
Speaker 1: I kind of get that. I don't think cryptocurrency is
Speaker 1: still the solution for that personally. That's my own personal belief,
Speaker 1: but I can understand the tendency because I feel the
Speaker 1: same way about biometrics and other forms of credit and
Speaker 1: debit cards and stuff too. But really, biometrics just takes
Speaker 1: it to a degree that's so personal that I think
Speaker 1: it's impossible to deny. So anyway, that's just a quick
Speaker 1: overview of biometrics. There's clearly tons more we can talk about,
Speaker 1: and lots of different applications that have really valid uses,
Speaker 1: including ones where you could argue, yeah, I understand that,
Speaker 1: you know, I'm giving up my privacy, but in return,
Speaker 1: I'm getting this additional convenience and maybe even some other
Speaker 1: features that normally wouldn't be available to me if I
Speaker 1: were using other methods of payments. So I'm willing to
Speaker 1: make that trade. That's valid if that's who you are.
Speaker 1: There is nothing invalid about that. I think the important
Speaker 1: thing is just making the decision with as much information
Speaker 1: as possible so that you feel good about it. And
Speaker 1: even though I don't feel good about that for myself,
Speaker 1: I wouldn't fault someone else for opting into it. If
Speaker 1: to them they're like, no, this is what matters most
Speaker 1: to me. That to me is a perfectly cromulent thing
Speaker 1: to say. Okay, I've rambled enough. Like I said, we'll
Speaker 1: probably do episodes where we focus more on specific types
Speaker 1: of biometrics in the future. I have done one on
Speaker 1: ones on things like fingerprints, and I think I've even
Speaker 1: done some on voice recognition. All they think I was
Speaker 1: focusing more on speech recognition as opposed to speaker recognition.
Speaker 1: But maybe I'll do some more specifics and talk about
Speaker 1: the actual development of the technologies and the algorith needed
Speaker 1: to make that possible. Because you know, I summarized it
Speaker 1: in this episode, but you have to understand it took
Speaker 1: decades of work and tons of very smart people and
Speaker 1: a lot of advancements in technology to make it a
Speaker 1: really feasible possibility. So very interesting stuff. Lots of concerns
Speaker 1: around it, not necessarily about the technology itself but how
Speaker 1: we apply it. And yeah, there's plenty more to talk about,
Speaker 1: but for now, let's wrap up. I hope you are
Speaker 1: all well. This Tech Stuff Tidbits ended up being sub
Speaker 1: forty minutes, so I'm going to call that a win,
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
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