Speaker 1: Welcome to Tech Stuff, a production of I Heart Radios
Speaker 1: How Stuff Works. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer with
Speaker 1: I Heart Radio and I love all things tech, and guys,
Speaker 1: stick with me. I am fighting off a cold. You'll
Speaker 1: be able to hear it in my voice. I have
Speaker 1: no doubt. But you know, I wanted to get you
Speaker 1: guys a brand new episode. So we're gonna fight on
Speaker 1: because the show must keep going. I think I think
Speaker 1: this is saying, oh no, this cold medicine is good though.
Speaker 1: All right, Anyway, I thought that we would do an
Speaker 1: episode about smart speakers because I wanted to kind of
Speaker 1: start this whole episode off with with an old man observation,
Speaker 1: you know, get off my lawn kind of thing. And
Speaker 1: this is from our resident old man, old man Strickland.
Speaker 1: That meaning meaning me, So, when I was young, speakers
Speaker 1: were dumb. Now I don't. I don't mean that speakers
Speaker 1: were useless, or that they were terrible, or that they
Speaker 1: were incapable of replicating certain frequencies or volumes of sound,
Speaker 1: or that they were limited in some other way other
Speaker 1: than they didn't quote unquote think they didn't connect to
Speaker 1: any sort of computational engine in a meaningful way. You
Speaker 1: might have a set of speakers plugged into a computer,
Speaker 1: but that was just a one way communications tool, right.
Speaker 1: It was just a way to provide an outlet for
Speaker 1: sound that your computer was generating, nothing more than that.
Speaker 1: But contrast that with today, when we have numerous smart
Speaker 1: speakers on the market. These speakers act as a user
Speaker 1: interface between us and the Internet at large, often facilitated
Speaker 1: by a virtual assistant of some kind. Now with these speakers,
Speaker 1: we don't just listen to stuff like music and podcasts
Speaker 1: and the radio and you know, other traditional audio content.
Speaker 1: We use them to find out information. We might link
Speaker 1: them to our calendars so that we can get reminders
Speaker 1: for upcoming appointments. We probably use them to ask about
Speaker 1: the weather report. I use mine at home for that
Speaker 1: all the time, or even more often than that, if
Speaker 1: you're at my house, you'll hear us use it to
Speaker 1: find out which foods are safe for us to feed
Speaker 1: to our dog. My doggie, Tibolt, absolutely loves our smart
Speaker 1: speaker because it frequently gives us permission to spoil him
Speaker 1: with a carrot or a piece of banana. But how
Speaker 1: do these smart speakers work, How are they able to
Speaker 1: respond to our requests? And what are their limitations? How
Speaker 1: safe are they? That's the sort of stuff we're gonna
Speaker 1: be looking into in this episode of tech Stuff, and
Speaker 1: we'll start off with the basics, which means we have
Speaker 1: to start off with how speakers work in general. Now,
Speaker 1: this is something that I've covered before on tech Stuff,
Speaker 1: but I want to go over it again from a
Speaker 1: high level because well, I just find it fascinating that
Speaker 1: people figured out how to harness electricity to drive a
Speaker 1: motor so that it could in turn cause components to
Speaker 1: replicate a recorded or transmitted sound. And really motors being
Speaker 1: too generous, but to drive an element to create vibrations
Speaker 1: that could replicate a sound that was made into another component,
Speaker 1: that whole thing just boggles my mind that people are
Speaker 1: smart enough to figure that out. Okay, So to understand
Speaker 1: how speakers work, it first helps to understand how sound
Speaker 1: itself works. Sound is a physical phenomenon. Do do do do?
Speaker 1: Sound is all about vibrations, and typically we experience sound
Speaker 1: when we pick up on changes in air pressure that
Speaker 1: enter through our ear canal and then affect the tympanic
Speaker 1: membrane or ear drum. So it's all about these changes
Speaker 1: of of of air pressure, all about air molecules transmitting
Speaker 1: vibrations from a source outward in a radiating pattern from
Speaker 1: from that source. So let's think of someone knocking on
Speaker 1: a door. For example, you're inside a house, someone's knocking
Speaker 1: on your door. When that person's hand hits the door,
Speaker 1: it causes the door to vibrate, and that vibration transmits
Speaker 1: to the surrounding air molecules on the other side of
Speaker 1: the door. They get pushed through that vibration and then
Speaker 1: pulled when the the wood is vibrating back towards its
Speaker 1: original position. So the air molecules vibrate, those air molecules
Speaker 1: cause the next surrounding layer of air molecules to vibrate
Speaker 1: as well, and so on and so forth. It's like
Speaker 1: a cascade or domino effect. You get these little pockets
Speaker 1: of high and low air pressure that travel outward from
Speaker 1: that door. It spreads further as it goes towards you know,
Speaker 1: any distance, and if you are close enough so that
Speaker 1: you can still detect those changes in air pressure. You
Speaker 1: experience this by hearing the knocking on the door. Those
Speaker 1: vibrating air molecules lose a bit of energy as they
Speaker 1: move outward. Right, as they vibrate to the next layer,
Speaker 1: you start to lo use a bit of energy with
Speaker 1: each transmission of that So the sound gets quieter the
Speaker 1: further away you are because there's not as many air
Speaker 1: molecules vibrating, its amplitude as decreased. So if you are
Speaker 1: in hearing range, you can pick up on those changes
Speaker 1: of air pressure they encounter the tympanic membrane in your
Speaker 1: ear canal. Those changes in pressure will cause a reaction
Speaker 1: in your middle and inner ear set that will ultimately
Speaker 1: get picked up by your brain that interprets it as sound. Now,
Speaker 1: the frequency at which those fluctuations occur relate to the
Speaker 1: pitch that we hear, so faster vibrations are higher pitches,
Speaker 1: higher frequencies, higher notes. If you think of a musical scale,
Speaker 1: we perceive the force of the changes as volume, so
Speaker 1: lower forces lower volume right, and higher forces higher volume.
Speaker 1: The human ear can hear a pretty decent range of
Speaker 1: frequencies from twenty hurts, which means twenty cycles or twenty
Speaker 1: waves per second past a given point of reference, to
Speaker 1: twenty killer hurts. That's twenty thousand cycles or waves per second.
Speaker 1: So yeah, the cycle refers to the frequency of the
Speaker 1: wavelength of sound. The lower the frequency, the lower the sound.
Speaker 1: All right, and then our brain has to make meaning
Speaker 1: of all this, Right, it's not just that it's picking
Speaker 1: up on it. Our brain interprets this and we experience
Speaker 1: it as a sound we have heard. So it either
Speaker 1: matches this perceived sound with one we've encountered before, and
Speaker 1: then we say, oh, I know what that is. That's
Speaker 1: someone knocking at the door, or they might be Holy Cala,
Speaker 1: I've never heard that sound in my life. I have
Speaker 1: no idea what it is. If the sound is language,
Speaker 1: then our brains have to derive the meaning from the
Speaker 1: perceived sound. We've heard someone say words such as you're
Speaker 1: hearing me say this. Then our brains have to take
Speaker 1: that collection of sounds and say, what does that actually mean?
Speaker 1: What is the the context, what is the the intent?
Speaker 1: What is the message here? Otherwise it would just be
Speaker 1: you know, random noises that I'm making with my mouth. Alright,
Speaker 1: so we have a basic understanding behind the physics of sound.
Speaker 1: Now to talk about speakers and microphones and the reason
Speaker 1: I'm going to talk about both of them is that
Speaker 1: the devices complement one another. You can think of one
Speaker 1: as being the other in reverse. Plus smart speakers we
Speaker 1: have to talk about microphones anyway, because smart speakers have
Speaker 1: microphones as well as the speaker element. So you can
Speaker 1: think of this as one long process of taking the
Speaker 1: physical phenomena of sound waves, transforming that physical phenomena into
Speaker 1: an electrical signal, taking the electrical signal, and changing it
Speaker 1: back into something that can produce the sound waves that
Speaker 1: started the whole thing. So you're replicating the original sound
Speaker 1: waves with this end device, which in this case is
Speaker 1: allowed speaker. So the microphone is the part of the
Speaker 1: process where you take the sound and you turn it
Speaker 1: into an electrical signal, and the speakers where you take
Speaker 1: the electrical signal and you turn it back into actual sound.
Speaker 1: That's the simple way. But what's actually happening, Well, let's
Speaker 1: talk about on a physical level. Sound waves go into
Speaker 1: a microphone. So you've got these fluctuations and air pressure
Speaker 1: that encounter a microphone. I'm speaking into a microphone right now,
Speaker 1: so this is happening right now. Inside the microphone is
Speaker 1: a very thin diaphragm, typically made out of a very
Speaker 1: flexible plastic, and it's sort of like the skin of
Speaker 1: a drum. So as the changes in air pressure encounter
Speaker 1: the diaphragm, they cause the diaphragm to move back and forth. Well.
Speaker 1: Attached to the diaphragm is a coil of conductive wire,
Speaker 1: and that coil wraps either around or near a permanent magnet.
Speaker 1: Magnets have magnetic fields. They have a north pole and
Speaker 1: a south pole, and there's a magnetic field that surrounds
Speaker 1: the magnet. And the electro magnetic effect means that if
Speaker 1: you move a coil of conductive wire through a magnetic field,
Speaker 1: it will produce a change in voltage in that coil,
Speaker 1: otherwise known as electromotive force, and that means electrical current
Speaker 1: will flow through the coil. Now, if you have the
Speaker 1: end of that coil attached to a wire, a conductive
Speaker 1: wire for that current to flow through, you can send
Speaker 1: that current onto other components. So for our purposes, the
Speaker 1: component in question would be an amplifier, and I'll get
Speaker 1: to explaining why that is in just a moment, but
Speaker 1: first let's talk about loud speakers, and the way allowed
Speaker 1: speaker works is essentially the reverse of a microphone. You've
Speaker 1: got your permanent magnet around or near which is a
Speaker 1: coil of conductive wire. The wire is connected to a diaphragm,
Speaker 1: one much larger and typically made out of stiffer material
Speaker 1: that the plastic you'd find in a microphone. This is
Speaker 1: the element inside a speaker that will vibrate, that will
Speaker 1: push air and pull air as it moves either outward
Speaker 1: or inward. The electrical signal comes from a source such
Speaker 1: as the microphone we were just using a second ago
Speaker 1: that comes into the loudspeaker and it flows through the coil. Now,
Speaker 1: when you have an electrical current flowing through a conductive coil,
Speaker 1: you generate a magnetic field because the laws of electromagnetism.
Speaker 1: You've got the electro magnetic field generated as a result.
Speaker 1: Now that field will interact with the magnetic field of
Speaker 1: the permanent magnet. That the permnet magnet always has a
Speaker 1: magnetic field. The coil only has one when electric current
Speaker 1: is flowing through it. And as I said, we have
Speaker 1: magnets to have a north pole and a south pole.
Speaker 1: And we also know that when we bring two magnets
Speaker 1: with their north poles together, they'll push against each other,
Speaker 1: right because like repels like, But if we turn one
Speaker 1: of those magnets around so that now it's a south
Speaker 1: pole and a north pole, they attract one another, you know,
Speaker 1: opposites attract. So by having the this magnetic field being
Speaker 1: generated by the coil, uh, it starts to generate interactions
Speaker 1: with the magnetic field of the permanent magnet, so they
Speaker 1: start to push and pull against each other. Well, the
Speaker 1: coil is attached to that diaphragm, so it in turn
Speaker 1: drives the diaphragm to either push outward or pull inward.
Speaker 1: That causes air molecules to vibrate, just as it would
Speaker 1: with any other you know, source of sound, and it
Speaker 1: emanates outward from the loudspeaker, so you get a representation
Speaker 1: of the same sound that was going into the microphone
Speaker 1: got converted into an electrical current. The electrical current then
Speaker 1: was passed through a coil and next to a permanent
Speaker 1: magnet to create the same sort of movement. It replicates
Speaker 1: the movement of the original diaphragm in the microphone and
Speaker 1: generates the sound. So you get the replication of the
Speaker 1: sound that was made in the other location. It's pretty cool.
Speaker 1: I think now I did mention earlier that you would
Speaker 1: need an amplifier. And the reason you need an amplifier
Speaker 1: is that the electrical signal generated by a microphone is
Speaker 1: far too weak to drive allowed speakers diaphragm. You just
Speaker 1: wouldn't have the juice to do it. It would be
Speaker 1: much much less, uh powerful than what the speaker would need.
Speaker 1: So chances are the diaphragm would either not move at
Speaker 1: all because it would just be too stiff, it would
Speaker 1: resist the movement too much, or it would move so
Speaker 1: weakly as to generate little to no sound, so it
Speaker 1: wouldn't do you any good. So the signal from the
Speaker 1: microphone has to first pass through an amplifier, which, as
Speaker 1: the name implies, takes an incoming signal and increases the
Speaker 1: amplitude of that signal the volume. In other words, uh so,
Speaker 1: it doesn't affect pitch, but it does affect the signal
Speaker 1: strength and consequently the volume. And I've done episodes about amplifiers,
Speaker 1: including explaining the difference between amplifiers that use vacuum tubes
Speaker 1: and ones that use transistors, so I'm not going to
Speaker 1: go into that here. Besides, it doesn't really factor into
Speaker 1: our conversation about smart speakers anyway. It's just important for
Speaker 1: it to work with a microphone and speaker setting. Now,
Speaker 1: over the years, engineers have paired microphones and speakers and
Speaker 1: lots of stuff. You've got telephones, you've got intercom systems,
Speaker 1: public address systems, handheld radios, all sorts of things, so
Speaker 1: that technology was well and truly mature. Before we ever
Speaker 1: got our first smart speaker, there wasn't much call to
Speaker 1: incorporate microphones into home speaker systems for many years. I mean,
Speaker 1: what would you actually use a microphone embedded in a
Speaker 1: speaker for? Before smart speakers, Typically you would have your
Speaker 1: speakers like I'm talking about, like like sound system speakers.
Speaker 1: You would have them hooked up to some other dumb
Speaker 1: as in, not connected to a network technology. So it
Speaker 1: might be a sound system or home entertainment set up
Speaker 1: with a television as the focal point, or maybe even
Speaker 1: you know, a computer for the purposes of playing more
Speaker 1: dynamic sounds for like video games and and things like that. Um.
Speaker 1: But for a very long time, these were all thought
Speaker 1: of as one way communications applications, right, Like, the sound
Speaker 1: was coming from a source and it would get to
Speaker 1: us through the speakers, but we weren't meant to send
Speaker 1: sound back through those same channels. The information was just
Speaker 1: coming to you. You weren't sending anything back, But that
Speaker 1: would all change in time. Now. One thing to keep
Speaker 1: in mind about smart speakers is that they are the
Speaker 1: product of several different technologies and lines of innovation and
Speaker 1: development that all converged together. The microphone and speaker technology
Speaker 1: is one of the oldest ones that we can point
Speaker 1: to as far as the fundamental underlying technology is concerned,
Speaker 1: the stuff that's been around since the late nineties century.
Speaker 1: Now there is one other we'll talk about that's even older.
Speaker 1: But I don't want to spoil things. I'll just mention
Speaker 1: there is an even older line of development that goes
Speaker 1: into smart speakers than the microphone speaker stuff of the
Speaker 1: nineteenth century. Most of the other components, however, are much
Speaker 1: younger than that. One big one is speech or voice recognition.
Speaker 1: Creating computer systems that could detect noise was relatively simple. Right.
Speaker 1: You could have a computer connected to microphones and they
Speaker 1: could monitor the input from those microphones and any incoming
Speaker 1: signal could be registered. Right, they could record an incoming
Speaker 1: signal that would indicate the microphone had detected a noise.
Speaker 1: That's child's play. That's easy to do. But teaching computers
Speaker 1: how to analyze those signals and decipher them so that
Speaker 1: the computer could display in text or otherwise act upon
Speaker 1: that that sound in a meaningful way that was much
Speaker 1: more difficult. There was an IBM engineer named William C.
Speaker 1: Dirsh of the Advanced System Development Division who created an
Speaker 1: early implementation of voice recognition. It was a very limited application,
Speaker 1: but it proved that the ability to interact with computers
Speaker 1: by voice was more than just science fiction. Within IBM.
Speaker 1: It was called the Shoebox. Dirsh worked on this project
Speaker 1: in the early nineteen sixties and what he produced was
Speaker 1: a machine that had a microphone attached to it. The
Speaker 1: machine could detect sixteen spoken words, which included the digits
Speaker 1: of zero to nine plus some command indicators like plus
Speaker 1: minus total, sub total. You get the idea. So you
Speaker 1: could speak a string of numbers and then commands to
Speaker 1: this device, then ask it to total everything and it
Speaker 1: would do so. So it was more or less a
Speaker 1: basic calculator with some voice interpretation incorporated into it. Now
Speaker 1: there's a great newsreel piece about this shoebox. There's a
Speaker 1: demonstration of it, and it came out in nineteen one,
Speaker 1: and I love that newsreel because it has that great
Speaker 1: music you would hear in the background of those old
Speaker 1: industrial and business films. Anyway, there's also a helpful chart
Speaker 1: that hangs in the background of that video where Dersh
Speaker 1: is actually explaining how it works. You can see a
Speaker 1: little bit behind him what the what is actually being
Speaker 1: analyzed and uh he broke the words down into phonemes
Speaker 1: and syllables, so phonemes being specific sounds that make up words. So,
Speaker 1: for example, the digit one is a single syllable word
Speaker 1: with a vowel sound right at the front. But you
Speaker 1: also have the word eight that's another single syllable word
Speaker 1: as a vowel sound right at the front, but it's
Speaker 1: different from one phonetically in that eight also has a
Speaker 1: plosive and has that hard t at the end. So
Speaker 1: the shoebox was limited not just in what words it
Speaker 1: could recognize, but also the types of voices it could recognize.
Speaker 1: Get someone who has a different dialect or manner of speech,
Speaker 1: and the machine might not be able to understand them
Speaker 1: because they're not pronouncing the words the same way that
Speaker 1: drsh did. This would be a big challenge in speech
Speaker 1: recognition moving forward, and it's also an example of where
Speaker 1: we find bias creeping into technology. And it's not necessarily
Speaker 1: a conscious thing, but if you have people designing a
Speaker 1: system and they're designing it based off their own uh,
Speaker 1: you know, speech patterns, their own pronunciations, their own dialects,
Speaker 1: then it may be that the system they create works
Speaker 1: really well for them and less well for anyone who
Speaker 1: isn't them, And the further away you are from their
Speaker 1: manner of speaking, the more frustration you will encounter as
Speaker 1: you try to interact with that technology. That's an example
Speaker 1: of s and in fact, if you read the histories
Speaker 1: of speech recognition and as we'll get too later natural
Speaker 1: language processing, you'll see a lot of people say it
Speaker 1: works great if you happen to be a white man,
Speaker 1: because the manner of speech was being or the people
Speaker 1: who were designing it were primarily white men who were
Speaker 1: uh typically aiming for a a what is considered a
Speaker 1: non accented American dialect somewhere in you know, the Eastern
Speaker 1: seaboard side. But that meant that if you did have
Speaker 1: an accent or a dialect, or you had a different vernacular,
Speaker 1: that it was harder for the systems to actually understand
Speaker 1: what you were saying. That's an example of bias. Well.
Speaker 1: The general strategy was again to break up speech and
Speaker 1: too constituent sound units, you know, those phonemes, and then
Speaker 1: to susse out which words were being spoken based on
Speaker 1: those phonemes, and that was done by digitizing the voice train,
Speaker 1: forming it from sound into data that represented stuff like
Speaker 1: the sounds frequency or pitch, and then matching up specific
Speaker 1: signal signal signatures with specific phone nmes. So generally the
Speaker 1: idea was that the computer system would monitor incoming sound,
Speaker 1: convert the sound into digital data, compare that data that
Speaker 1: had received with information stored in a database, and effort
Speaker 1: to look for matches. Uh. The shoebox database was just
Speaker 1: sixteen words and size. Later ones would be much larger,
Speaker 1: but pretty quickly people realized this was not an efficient
Speaker 1: way of doing speech recognition because the bigger the vocabulary,
Speaker 1: the more work intens of it was to build out
Speaker 1: those databases. So it wasn't something that people thought would
Speaker 1: be sustainable for very large vocabularies. But the Shoebox marked
Speaker 1: the beginning of a serious effort to create machines that
Speaker 1: could accept audio cues as actual input, and as we'll see,
Speaker 1: that's one important component for these smart speaker systems. I've
Speaker 1: got a lot more to say, but before I get
Speaker 1: into the next part, let's take a quick break. Now,
Speaker 1: obviously we didn't jump right into full voice recognition right
Speaker 1: after IBM S Shoebus innovation. The challenges related to building
Speaker 1: automated speech recognition systems were numerous, even for just a
Speaker 1: single language, because, as I said, you can have accents
Speaker 1: and dialects. One voice can have a very different tonal
Speaker 1: quality from another, people speak at different speeds. Teaching machines
Speaker 1: how to recognize speech when the phonemes and pacing of
Speaker 1: that speech aren't consistent from speaker to speaker, that's really hard.
Speaker 1: This kind of gets back to the same sort of
Speaker 1: challenges you have when you're teaching machines how to recognize images.
Speaker 1: You know, you teach a human what a coffee mug is.
Speaker 1: I always use this example, but you teach a human
Speaker 1: what a coffee mug is, and pretty soon they can
Speaker 1: extrapolate from that example and understand that coffee mugs can
Speaker 1: them in all different sizes and colors, and you know
Speaker 1: different designs and textures. We get it. Like you you
Speaker 1: see a couple of coffee mugs, you understand machines though
Speaker 1: they aren't able to do that. Machines, you know, you
Speaker 1: have to give them lots and lots and lots of
Speaker 1: different examples before they can start to pick up on
Speaker 1: what things actually make a coffee mug. Same sort of
Speaker 1: thing with speech, right, So if you don't have consistency
Speaker 1: between speakers, it makes it very hard for machines to
Speaker 1: learn what people are saying. Now, it didn't take long
Speaker 1: for the tech industry at large to really dive into
Speaker 1: trying to solve this problem. In ninete, DARPA, that's the
Speaker 1: Research and Development division of the United States Department of Defense,
Speaker 1: got behind speech recognition in a big way. Now, remember
Speaker 1: darp it self doesn't do research. The organization's purpose is
Speaker 1: to invite organizations to pitch projects that align with whatever
Speaker 1: darpest goals are and and DARBA would provide funding to
Speaker 1: the winning organizations to see these projects to completion if possible.
Speaker 1: So DARK is really more of a vetting and funding
Speaker 1: organization anyway. In n DARPA created a five year program
Speaker 1: called Speech Understanding Research or s u are. The initial
Speaker 1: goal was pretty darn ambitious considering the capabilities of the
Speaker 1: technology at the time. The project director, Larry Roberts, wanted
Speaker 1: a system that would be capable of recognizing a vocabulary
Speaker 1: of ten thousand words with less than ten percent error.
Speaker 1: After holding a few meetings with some of the leading
Speaker 1: computer engineers of the day, Roberts suggusted that goal significantly.
Speaker 1: After that adjustment, the target was going to be a
Speaker 1: system capable of recognizing one thousand words, not ten thousand.
Speaker 1: Nearror levels still had to be less than ten percent,
Speaker 1: and the goal was for the system to be able
Speaker 1: to accept continuous speech, as opposed to very deliberate speech
Speaker 1: with pauses between each pair of words that would not
Speaker 1: be really that useful. One person who was skeptical about
Speaker 1: the potential success of this project was John R. Pierce
Speaker 1: of Bell Labs. He argued that any success would be
Speaker 1: limited so long as machines remained incapable of understanding the words,
Speaker 1: not just recognizing a word based on phone names, but
Speaker 1: understanding what the word is. That is. Pierce felt that
Speaker 1: the machines needed some way to parse the language to
Speaker 1: get to the meaning of what was being said. That's
Speaker 1: an important idea that we will come back to in
Speaker 1: just a bit now. Among the companies and organizations that
Speaker 1: landed contracts with DARPA were a Carnegie Melon University BBN,
Speaker 1: which actually played a big part in developing our ponette,
Speaker 1: the predecessor to the Internet, Lincoln Laboratory, and several more
Speaker 1: and very smart people began to create systems intended to
Speaker 1: recognize speech and meaningful ways. The names of the programs
Speaker 1: were a lot of fun. There was h W I
Speaker 1: M that stood for hear what I mean as in
Speaker 1: here as in listen hear what I mean. That one
Speaker 1: was from BBN. CMU introduced hearsay, which was later designated
Speaker 1: as Hearsay one, and then they came out with Hearsay two.
Speaker 1: They also would demonstrate another one called harpy. Oh, and
Speaker 1: there was a professor at CMU named Dr James Baker
Speaker 1: who would design a system called Dragon in nineteen seventy
Speaker 1: five that he would later leverage into a company with
Speaker 1: his wife, Dr Janet M. Baker in the nineteen eighties,
Speaker 1: and they had a very successful business with speech recognition software. Now,
Speaker 1: I'm not going to go into each of those programs
Speaker 1: in deep detail, but rather just mentioned that they all
Speaker 1: helped advance the cause of creating systems that can recognize speech.
Speaker 1: One of the big developments that came out of all
Speaker 1: that work was a shift to probabilistic models, which would
Speaker 1: also play a really important part in another phase of
Speaker 1: developing the smart speaker. So what do I mean when
Speaker 1: I say probabilistic? Well, as the name indicates, it all
Speaker 1: has to do with probabilities. Essentially, systems would analyze incoming
Speaker 1: phonemes and make guesses as to what was being said
Speaker 1: based on the probability of it being a given word
Speaker 1: or part of a word. The systems typically go with
Speaker 1: whatever word has the highest probability of being the correct one.
Speaker 1: Even with that approach, there are nuances to language that
Speaker 1: are difficult to account for with a machine. So, for example,
Speaker 1: you have homonyms and which you have two words that
Speaker 1: sound the same but have very different meanings and potentially
Speaker 1: spellings like right as in to write a sentence, or
Speaker 1: right as in am I right? Or am I wrong?
Speaker 1: Or you could have a pair of words that sound
Speaker 1: like a single word and have confusion there, such as
Speaker 1: a door. You can say a door you mean you're
Speaker 1: meaning a single door a door to go into a building,
Speaker 1: or you might say a dore as an I adore
Speaker 1: this podcast you're doing, Jonathan. That's sweet of you, Thank
Speaker 1: you for saying that. So computer scientists were hard at
Speaker 1: work advancing both the capability of machines to make correct
Speaker 1: guesses at individual phone names and then full words, as
Speaker 1: well as figuring out a way to teach machines to
Speaker 1: adjust guesses based on context. That requires a deeper understanding
Speaker 1: of the language within which you're working. If you're aware
Speaker 1: of certain idioms, you can make a good guess at
Speaker 1: a word or phrase even if you didn't get a
Speaker 1: clean pass at it right. So, for example, the phrase
Speaker 1: it's raining cats and dogs just means it's raining a lot.
Speaker 1: And if a system included a database that indicated the
Speaker 1: phrase cats and dogs sometimes follows the phrase it's raining,
Speaker 1: then the system is more likely to guess the correct
Speaker 1: sequence of words instead of guessing something that sounded similar
Speaker 1: but it's wrong. For example, if it said, oh, they
Speaker 1: must have said it's raining bats and hogs, that would
Speaker 1: not makes sense. So the systems estimate the probability that
Speaker 1: any given sequence of sounds within the database matches what
Speaker 1: the systems have just quote unquote heard progress in this
Speaker 1: area was steady, but slow, and I'd argue that it
Speaker 1: was also a reminder that concepts like Moore's law do
Speaker 1: not apply universally across technology. Rapid development in one particular
Speaker 1: domain of technology is not necessarily an indicator that the
Speaker 1: same sort of progress will be observed in all other
Speaker 1: areas of tech. We often get into the mistaken habit
Speaker 1: of believing that Moore's law applies to everything. Alright. So
Speaker 1: a related concept to voice recognition is something called natural
Speaker 1: language processing, and this relates back to how we humans
Speaker 1: tend to process information compared to the way machines tend
Speaker 1: to do it. So we humans formulate ideas, we shape
Speaker 1: those ideas into words and sentences. We communicate them in
Speaker 1: some way to other people through that language. It may
Speaker 1: be through speed you maybe through text. It may even
Speaker 1: be through a nonverbal or non literary way, but we
Speaker 1: communicate those ideas. Machines typically accept input, they perform some
Speaker 1: process or sequence of processes on that input, and then
Speaker 1: they supply an output of some sort. Machines do this
Speaker 1: in machine language. That's a code that's far too difficult
Speaker 1: for humans to process. Easily. Binary is an example of
Speaker 1: machine language. Binary is represented as zeros and ones, which
Speaker 1: would group together can represent all sorts of stuff. But
Speaker 1: if you just looked at a big block of zeros
Speaker 1: and ones, it would mean nothing to you. It's not
Speaker 1: easy for humans to use, and then machines in turn
Speaker 1: are not natively able to understand human language, so there's
Speaker 1: a language barrier there. Because of that, people created different
Speaker 1: programming languages. These languages provide layers of abstraction from the
Speaker 1: machine language. They make it easier to create programs or
Speaker 1: directions that the computer should fall low. So the person
Speaker 1: who's doing the programming is using a programming language that's
Speaker 1: easy for humans to use that then gets converted into
Speaker 1: machine language that the computers understand. But what if you
Speaker 1: could send commands to a computer using natural language, not
Speaker 1: even programming language. You could just speak in Plaine vernacular,
Speaker 1: whether it's English or any other language, the way humans
Speaker 1: communicate with one another. What if a computer could extract
Speaker 1: meaning from a sentence, understand what it was you wanted
Speaker 1: the computer to do, and then respond appropriately. So imagine
Speaker 1: how much time you could save if you could just
Speaker 1: tell your computer what you wanted it to do, and
Speaker 1: it took care of the rest. If you had a
Speaker 1: powerful enough computer system with strong enough AI, maybe you
Speaker 1: could even potentially do something like describe a game that
Speaker 1: you would love to be able to play, like not
Speaker 1: not a game that exists, a game in your head,
Speaker 1: and you could describe it to a computer and the
Speaker 1: computer could actually program that game. Well, we're we're definitely
Speaker 1: not anywhere close to that yet, but we made enormous
Speaker 1: progress with natural language processing. Now, the history of natural
Speaker 1: language processing isn't exactly an extension of voice recognition. It's
Speaker 1: actually more like a parallel line of investigation. And that's
Speaker 1: because natural language processing doesn't require voice recognition. You can
Speaker 1: have an implementation in which you just right commands in
Speaker 1: natural language, you know, you type them out on a
Speaker 1: keyboard and the machine then carries out those those instructions.
Speaker 1: So much of the early work in natural language processing
Speaker 1: was in text based communication rather than in speech. The
Speaker 1: history of natural language processing includes stuff like the Turing test,
Speaker 1: named after Alan Turing. So the most common interpretation of
Speaker 1: the Turing test these days is that you've got a
Speaker 1: scenario in which a person is alone in a room
Speaker 1: with a computer terminal, they can type whatever they like
Speaker 1: into the computer terminal, and someone or something is responding
Speaker 1: to them in real time. Now it might be another person,
Speaker 1: or it might be a computer system that's responding to
Speaker 1: that person. You run a whole bunch of test subjects
Speaker 1: through this process, and if the computer system is able
Speaker 1: to fool a certain percentage of those test subjects, like
Speaker 1: say thirty percent of them, that it is in fact
Speaker 1: another human and not a computer, it is said to
Speaker 1: have passed the Turing test, And typically we use that
Speaker 1: to mean the machine has given off the appearance of
Speaker 1: possessing intelligence similar to the one that we humans possess.
Speaker 1: That gets beyond our scope for this episode, but it
Speaker 1: helps point out that stuff like speech recognition and natural
Speaker 1: language processing are both closely related to the field of
Speaker 1: artificial intelligence. In fact, they really belong within the artificial
Speaker 1: intelligence domain. The Turing test was more of a hypothetical.
Speaker 1: It was a bit of a cheeky way of saying, Hey,
Speaker 1: if you can't tell whether or not something is intelligent,
Speaker 1: it makes sense to treat it as if it actually
Speaker 1: is intelligent. After all, we assume that every human with
Speaker 1: whom we interact possesses some level of intelligence. Based on
Speaker 1: those interactions, so why should we not extend the same
Speaker 1: courtesy to machines. Now, natural language processing would prove to
Speaker 1: be another super challenging problem to solve. In computer science.
Speaker 1: Early work was done in translation algorithms, and these were
Speaker 1: programs that attempted to take phrases written in one language
Speaker 1: and translate those automatically into a second language. At first,
Speaker 1: that seemed pretty straightforward, but you realize that's also pretty tricky. Really.
Speaker 1: For one thing, you can't just translate word for word
Speaker 1: and keep the same order from one language to another.
Speaker 1: The syntax or the rules that the language follow uh,
Speaker 1: they could be different from language to language. In one language,
Speaker 1: you might use an infinitive such as to record, in
Speaker 1: the middle of a sentence, while another language might put
Speaker 1: all the infinitives at the end of a sentence. So
Speaker 1: in one language, I might say I'm going to record
Speaker 1: a podcast in the studio right now, but in another
Speaker 1: language it might come out as I'm going a podcast
Speaker 1: in the studio right now to record. It starts to
Speaker 1: sound like yoda. There was initial excitement around machine translation,
Speaker 1: but once computer scientists and linguists began to see the
Speaker 1: scope of this challenge, their excitement faded a bit. Also,
Speaker 1: there was a lot of other stuff going on in
Speaker 1: the nineteen sixties and seventies that was demanding a lot
Speaker 1: of attention, such as the Space race. So for a while,
Speaker 1: this branch of computer science was given less attention than
Speaker 1: other branches, and by less attention, I really mean funding. Now,
Speaker 1: when we come back, we'll talk a bit more about
Speaker 1: the advances that were necessary to support natural language processing,
Speaker 1: and we'll move on to how this would be another
Speaker 1: important component in smart speakers. But first, let's take another
Speaker 1: quick break. Okay, So early enthusiasm for an natural language
Speaker 1: processing created a bit of a hype cycle that ultimately
Speaker 1: crashed into the telephone poll of unmet expectations. That was
Speaker 1: a really bad metaphor. Anyway, natural language processing went through
Speaker 1: something similar to what we saw with virtual reality in
Speaker 1: the nineteen nineties. You know, people saw what was actually achievable,
Speaker 1: and then they compared that to what they thought they
Speaker 1: were going to get, and those two things didn't match
Speaker 1: up at all, and that really pulled the rug out
Speaker 1: of funding for natural language processing, which men of course,
Speaker 1: that progress slowed way down. It kept going, but it
Speaker 1: was definitely on the back burner for a lot of projects.
Speaker 1: When interest renewed in the nineteen eighties, there had been
Speaker 1: a shift in thinking around natural language processing. Computer scientists
Speaker 1: were starting to look at statistical approaches similar to what
Speaker 1: was going on with speech recognition, building up probabilistic models
Speaker 1: in which a computer can start making what amounts to
Speaker 1: educated guesses at the meaning of a command or a phrase.
Speaker 1: Machine learning became an important component on the back end
Speaker 1: of these systems, and later artificial neural networks became an
Speaker 1: important part as well. A neural network processes information in
Speaker 1: a way that's sort of analogous to how our brains
Speaker 1: do it. You have nodes or neurons that connect to
Speaker 1: other nodes, and each node affects incoming data in a
Speaker 1: certain way, performing some sort of operation on it, and
Speaker 1: the degree to which they do that in one way
Speaker 1: versus another is called the weight of that node. Computer
Speaker 1: scientists apply weights across the nodes in an effort to
Speaker 1: get a specific result in order to train these models.
Speaker 1: So you might feed a specific command into such a system,
Speaker 1: and you let it go through the computational process from
Speaker 1: the beginning of the neural network through to the end,
Speaker 1: and then you look at the result, and if the
Speaker 1: result is correct, well, that just means the system is
Speaker 1: already working as you intended it, which honestly is not
Speaker 1: likely to happen early on. But if it's not correct,
Speaker 1: then you start adjusting the weights on those nodes in
Speaker 1: order to affect the outcome. I almost think of it
Speaker 1: as like Plinko or pachinko, where you've got the little
Speaker 1: coin and you drop it down and it bounces on
Speaker 1: all the pegs and sometimes you're like you might think,
Speaker 1: all right, well, this time it's going to go right
Speaker 1: for that center slot, but it doesn't, and you think, well,
Speaker 1: maybe if I remove some of these pegs or I
Speaker 1: shift these pegs over a little bit, I can drop
Speaker 1: it in that same spot and get hit the center.
Speaker 1: It's kind of like that, except you're talking about data,
Speaker 1: not physical moving parts. So you have to do this
Speaker 1: a lot, like up to like millions of times in
Speaker 1: order to try and train a system so that responds
Speaker 1: appropriately to commands. And once it's trained, you can then
Speaker 1: test new commands on the system to see if it
Speaker 1: can parse them and respond appropriately. And in this way,
Speaker 1: the system quote unquote learns over time how to respond
Speaker 1: to commands. And then we have another component that's important
Speaker 1: with smart speakers, and that's speech generation. So it's one
Speaker 1: thing to have a machine either broadcast or play back
Speaker 1: a recording of speech. It's another thing for a machine
Speaker 1: to generate brand new speech. In computer science, we call
Speaker 1: it speech synthesis. Now, this is the really old technology
Speaker 1: I was alluding to at the beginning of this episode,
Speaker 1: speech synthesis. If you want to be really, you know,
Speaker 1: kind of technical about it, it actually predates every other
Speaker 1: technology I've mentioned up to this point, at least in
Speaker 1: its most rudimentary implementations. You have to go way back
Speaker 1: to the eighteenth century the seventeen seventies, as when a
Speaker 1: Russian smarty pants named Christian Kradsenstein was building a device
Speaker 1: that used acoustic resonators. These these reads that would vibrate,
Speaker 1: and it was in an attempt to replicate basic vowel sounds. Now,
Speaker 1: even with such a working device, it would be really
Speaker 1: difficult to communicate anything meaningful unless you were, i guess,
Speaker 1: speaking whale like Dory and finding Nemo. But it would
Speaker 1: be an early example of how people tried to create
Speaker 1: mechanical systems that could replicate speech or elements of speech.
Speaker 1: Another inventor named Wolfgang von Kimberland built an acoustic mechanical
Speaker 1: speech machine and that used reads and tubes and a
Speaker 1: pressure chamber, and it was all meant to replicate various
Speaker 1: speech sounds. He had other elements to create sounds like plosives,
Speaker 1: those hard sounds that I mentioned earlier in the episode.
Speaker 1: So he had all these different elements that, working together,
Speaker 1: could create parts of the sounds that we humans make
Speaker 1: when we speak. He also built a supposed chess playing machine,
Speaker 1: and it turned out that the chess playing part was
Speaker 1: a hoax. So unfortunately, because that device was a hoax,
Speaker 1: a lot of people dismiss his other work, which was legitimate.
Speaker 1: So by fudging on one thing, he kind of cast
Speaker 1: doubt on everything he had ever done. Skipping ahead quite
Speaker 1: a bit, we get to Homer Dudley, which is a
Speaker 1: fantastic name. He unveiled the voter or voice Operating Demonstrator
Speaker 1: device at the New York World's Fair in nineteen thirty nine.
Speaker 1: It consisted of a complex series of controls and it
Speaker 1: sort of reminds me of something like a musical instrument,
Speaker 1: kind of like a synthesizer, but with extra controlling units.
Speaker 1: Like there was like a wrist element, there was a pedal.
Speaker 1: There's a lot of stuff that made it very complex,
Speaker 1: and with a lot of practice, you could create specific
Speaker 1: sounds from this synthesizer. You could even create words or
Speaker 1: full sentences, though from what I understand, it was incredibly
Speaker 1: challenging to do. It was a very high learning curve,
Speaker 1: but it demonstrate the possibility of a like tronic synthesized speech. Now.
Speaker 1: There was a lot of work done in this field
Speaker 1: by lots of different talented scientists and engineers, and someday
Speaker 1: I'll have to do a full episode on the history
Speaker 1: of speech synthesis. It's really fascinating, but it's far too
Speaker 1: big a topic to cover in its entirety in this episode.
Speaker 1: By the late nineteen sixties we had our first text
Speaker 1: to speech system, and by the late nineteen seventies and
Speaker 1: early nineteen eighties, the state of the art had progressed
Speaker 1: quite a bit and we were starting to get to
Speaker 1: a point where we could create very understandable computer voices.
Speaker 1: They weren't natural, they didn't sound like people, but you
Speaker 1: could understand what they were saying. And finally, something else
Speaker 1: that would enable smart speakers and virtual assistance was the
Speaker 1: pairing of improved network connectivity and cloud computing. That removes
Speaker 1: the need for the device that you're interacting with to
Speaker 1: do all the processing on its own. So, if you
Speaker 1: think about or the history of computing, we used to
Speaker 1: do main frames with dumb terminals that attached the main frame,
Speaker 1: so the terminal wasn't doing any computing. It was just
Speaker 1: tapping into the mainframe computer, which was sending results back
Speaker 1: to the terminal. Then you get to the era of
Speaker 1: personal computers, where you had a device sitting on your
Speaker 1: desk that did all the computing and it didn't connect
Speaker 1: to anything else. Then we get up to networking and
Speaker 1: the Internet, where we suddenly had the capability of having
Speaker 1: really powerful computers or grids of computers that were able
Speaker 1: to take on processing power. Uh, and you just you
Speaker 1: send the request out to the Internet and you get
Speaker 1: the response back. That's the basis of cloud computing. So
Speaker 1: your your command or message or whatever relays back to
Speaker 1: servers on the cloud that then process it and send
Speaker 1: the proper response to whatever device you're interacting with, and
Speaker 1: then you get the result. So with the case of
Speaker 1: the smart speaker, it might be playing a specific so
Speaker 1: long or giving you a weather report or whatever it
Speaker 1: might be. Now, if the speakers were doing some of
Speaker 1: that computation themselves, that would be an example of edge computing,
Speaker 1: where the processing takes place at least in part, at
Speaker 1: the edge of a network at those end points. But
Speaker 1: for now, most of the implementations we see send data
Speaker 1: back to the cloud to get the right response, so
Speaker 1: you have to have a persistent Internet connection. These devices
Speaker 1: are not useful without that connection. You do have some
Speaker 1: smart speakers that can connect to another device like a
Speaker 1: smartphone via Bluetooth, so you could do things that way,
Speaker 1: but without those connections, the smart speaker turns into, you know,
Speaker 1: just a dumb speaker, or sometimes just a paperweight. Now,
Speaker 1: this collection of technologies and disciplines are what enabled Apple
Speaker 1: to introduce Sirie in two thousand and eleven, and Syria
Speaker 1: is a virtual assistant. Series origins actually trace back to
Speaker 1: the Stanford Research Institute and a group of guys Grouber,
Speaker 1: Adamshire and dog kit Louse who had been working on
Speaker 1: the concept since the nineteen nineties, and when Apple launched
Speaker 1: the iPhone in two thousand seven, they saw the iPhone
Speaker 1: as a potential platform for this virtual assistant that they
Speaker 1: had been building, and they thought, well, this is perfect
Speaker 1: because the iPhone has a microphone, so the assistant can
Speaker 1: respond to voice commands as a speaker, so it could
Speaker 1: communicate back to the user, it could do all sorts
Speaker 1: of stuff. We can tap into the interoperability of apps
Speaker 1: on the device. It's a perfect platform for us to
Speaker 1: deploy this. So they developed an app once the opportunity
Speaker 1: arose because apps were not available for development immediately when
Speaker 1: Apple launched the iPhone, and once they did launch that app,
Speaker 1: uh within a month, less than a month, Steve Jobs
Speaker 1: was on the phone calling them up and offering to
Speaker 1: buy the technology, which of course they would agree to
Speaker 1: and it would become an integrated component in Apple's iPhone
Speaker 1: line afterward. And that's where voice assistants kind of lived
Speaker 1: for a few years. They mostly lived on smartphones like
Speaker 1: the iPhone. But in November two thousand fourteen, Amazon introduced
Speaker 1: the Amazon Echo smart speaker, which was originally only available
Speaker 1: for Prime members, and it had its own virtual assistant
Speaker 1: named Alexa, and thus the smart speaker era officially began. Now,
Speaker 1: there are plenty of other smart speakers that are on
Speaker 1: the market these days. There are products from Google like
Speaker 1: Google Home. Uh, there are so no speakers that can
Speaker 1: connect to services like Amazon's Alexa or Google's Assistant, and
Speaker 1: we're probably going to see a ton more, both from
Speaker 1: companies that piggyback onto services from the big providers like
Speaker 1: Google and Amazon, and maybe some that are trying to
Speaker 1: make a go of it with their own branded virtual
Speaker 1: assistants and services. Smart speakers respond to commands after they
Speaker 1: quote unquote here a wake up word or phrase. Now,
Speaker 1: I'm gonna make up a wake up phrase right now
Speaker 1: so that I don't set off anyone's smart speaker or
Speaker 1: smart watch or smartphone or smart car or whatever it
Speaker 1: might be. So this is just a fictional example of
Speaker 1: a wake up phrase. So let's say I have a
Speaker 1: smart speaker and the wake up phrase for my smart
Speaker 1: speaker happens to be hey, they're Genie. Well, my smart
Speaker 1: speaker has a microphone, so it can detect when I
Speaker 1: say that, but really it's constantly detecting all sounds in
Speaker 1: its environment. The microphone is always active. It has to
Speaker 1: be in order to be able to pick up on
Speaker 1: when I say the wake up phrase. So the microphone
Speaker 1: is always active on most smart speakers. There's somewhere you
Speaker 1: can program it so that it will only activate if
Speaker 1: you first touch the speaker and that wakes it up.
Speaker 1: There's some that you can do that with, But for
Speaker 1: the most part, they're always listening. While the speaker can
Speaker 1: quote unquote here everything, it's not listening to everything. In
Speaker 1: other words, it's not mon of during the specific things
Speaker 1: being said. At least that's what we've been told. And honestly,
Speaker 1: that makes a ton of sense from an operational standpoint.
Speaker 1: And the reason I say that is that the sheer
Speaker 1: amount of information that would be flooding in from all
Speaker 1: the microphones on all the smart devices from any one
Speaker 1: provider that happened to be deployed all over the world,
Speaker 1: that would be an astounding amount of data. And sifting
Speaker 1: through all that data to find stuff that's useful would
Speaker 1: take an enormous amount of effort and time and and
Speaker 1: processing power. So while you could have all the microphones
Speaker 1: listening in all over the place, finding out who to
Speaker 1: listen to at what time would be a lot trickier
Speaker 1: and probably not worth the effort it would take to
Speaker 1: pull something like that off. So what these speakers and
Speaker 1: other devices are actually doing is looking for a signal
Speaker 1: that matches the one that represents the wake phrase. So
Speaker 1: when I say, hey, they're Genie, the microphone picks up
Speaker 1: my voice, which the mic then try inslates into an
Speaker 1: electrical signal which gets digitized and compared against the digital
Speaker 1: fingerprint of the predesignated wake up phrase. And in this case,
Speaker 1: the two phrases match. It's like a fingerprint matching something
Speaker 1: that was left at a site. So that turns the
Speaker 1: speaker into an active listener rather than a passive one.
Speaker 1: It's ready to accept a command or a question and
Speaker 1: to respond to me. But if I didn't say, hey,
Speaker 1: they're Genie, then the speaker would remain in passive mode
Speaker 1: because it wouldn't have a digital fingerprint that matches the
Speaker 1: one of the wake up phrase. Everything stays at the
Speaker 1: local level, and none of my sweet secret speech gets
Speaker 1: transmitt related across the internet. It's all staying right there.
Speaker 1: At least that's what we've been told. And again I
Speaker 1: don't have any reason to disbelieve this, but it is
Speaker 1: something to keep in mind. You are talking about devices
Speaker 1: that have microphones. Of course, if you have a smartphone,
Speaker 1: you've already got one of those or a cell phone.
Speaker 1: In general, you've got a device with a microphone on
Speaker 1: it neck near you pretty much all the time. Now,
Speaker 1: once I do make a request with my smart speaker,
Speaker 1: the speaker then sends that request up to the cloud
Speaker 1: where it gets processed, It's analyzed, uh, and then a
Speaker 1: proper response is returned to me, whether that is playing
Speaker 1: a song or giving me information I've asked for, or
Speaker 1: maybe even interacting with some other smart device in my home,
Speaker 1: such as adjusting the brightness of my smart lights in
Speaker 1: my house. Now, if the system is not sure about
Speaker 1: whatever it was I just said, it will probably return
Speaker 1: an error phrase. So maybe maybe I'm too far away
Speaker 1: from the speaker, so it's it couldn't quote unquote hear
Speaker 1: me really well. Or maybe I've got a mouthful of
Speaker 1: peanut butter or something as I want to do. Then
Speaker 1: I'm going to get something like I'm sorry, I don't
Speaker 1: know how to do that, or I'm sorry I didn't
Speaker 1: understand you, and then I'd have to repeat it. Now,
Speaker 1: smart speakers are pretty cool. However, they do represent another
Speaker 1: piece of technology that you have to network to other devices,
Speaker 1: including your own home network, and as such that means
Speaker 1: that they represent a potential vulnerability in a network. It
Speaker 1: doesn't mean they're automatically vulnerable, but it means that every
Speaker 1: time you are connecting something to your network, then you're
Speaker 1: creating another potential attack vector for a hacker. Right now,
Speaker 1: if everything is super strong, it it doesn't really effectively
Speaker 1: change your safety in any meaningful way. But if one
Speaker 1: of those things that you connect to your network is
Speaker 1: less strong than the others, you're looking at the weakest
Speaker 1: link situation where a hacker with the right know how
Speaker 1: in tools could potentially target that part of your network
Speaker 1: to get entry into everything else. And when you're talking
Speaker 1: about a smart speaker, you're talking about device that has
Speaker 1: an active microphone on it. So potentially, if someone were
Speaker 1: able to compromise a smart speaker, they would be able
Speaker 1: to listening on anything that was within range of that
Speaker 1: smart speakers microphone. So that's why you have to at
Speaker 1: least be cognizant of that, do your research, make sure
Speaker 1: the devices you're connecting to your network are rated well
Speaker 1: as from a security standpoint, when you're setting things up
Speaker 1: and you have to create passwords, create strong passwords that
Speaker 1: are not used anywhere else. The harder you make things
Speaker 1: the more likely hackers will just pass you by, not
Speaker 1: because you're too tough to crack. Never get your into
Speaker 1: your head that you're too strong to to be hacked,
Speaker 1: but rather if there's someone who's weaker than the hackers
Speaker 1: are going to go after that person instead. So just
Speaker 1: don't be the weak person. Practice really good security behaviors,
Speaker 1: and you're more likely to discourage attackers and they'll they'll
Speaker 1: go on to someone else. Um, especially if you're talking
Speaker 1: about newbies who don't really know their way around their
Speaker 1: just using tools that other people have designed. They get
Speaker 1: discouraged very quickly. They'll move on to someone else because
Speaker 1: there's always another potential target. I'm curious about you guys,
Speaker 1: whether or not you have any smart speakers in your life,
Speaker 1: and uh if you find them useful. I find mine
Speaker 1: pretty useful. I use it for a very narrow range
Speaker 1: of things. I don't tend to use it. I definitely
Speaker 1: don't use it to its full potential. I know that
Speaker 1: because what's in the blue moon. I'll just try something
Speaker 1: and I'm amazed at what happens when when I get
Speaker 1: a response. But for the most part, I'm asking about
Speaker 1: whether what I can feed my dog whether or not
Speaker 1: it can turn on the lights and uh and and
Speaker 1: that's about it. Are occasionally playing a song. Um, but
Speaker 1: I'm curious what you guys are using them for. Reach
Speaker 1: Out to me on social networks on Facebook and I'm
Speaker 1: on Twitter, and the handle for both of those is
Speaker 1: text stuff. H s W also use that those handles
Speaker 1: if you have suggestions for future episodes. If you've got,
Speaker 1: you know, an idea for either a company or a
Speaker 1: technology or a theme in tech you'd really like me
Speaker 1: to tackle, let me know there and I'll talk to
Speaker 1: you again really soon. Text Stuff is a production of
Speaker 1: I Heart Radio's How Stuff Works. For more podcasts from
Speaker 1: my heart Radio, visit the i heart Radio app, Apple Podcasts,
Speaker 1: or wherever you listen to your favorite shows.