Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeart Podcasts. And how the
Speaker 1: tech are you? Y'all? Were getting up to the time
Speaker 1: of year when I like to look back on, you know,
Speaker 1: the months that have passed and reflect on all that's
Speaker 1: happened in the tech space, and for twenty twenty four,
Speaker 1: it's been a heck of a lot. But I thought
Speaker 1: today I would talk about three startup companies in the
Speaker 1: AI tech space that are really hoping to make it
Speaker 1: big in the coming years. I'm sure I'll talk a
Speaker 1: lot about AI when we do our year in review
Speaker 1: episode because this year has been largely about AI and
Speaker 1: different aspects, a lot of them kind of scary, right,
Speaker 1: But I wanted to talk about some startups because that's
Speaker 1: something we don't focus on that much in the news episodes.
Speaker 1: Most of the talk has been around companies like open
Speaker 1: AI and Microsoft and Meta and Google and Amazon. I
Speaker 1: thought maybe we could take a look at some startups
Speaker 1: in the space because there are lots of those two.
Speaker 1: There are tons of AI startups, some of which are
Speaker 1: doing all right right now, some of which may be struggling.
Speaker 1: And honestly, like, there's this growing concern in the AI
Speaker 1: field that perhaps some versions of AI are starting to
Speaker 1: hit like a wall when it comes to advancements, like
Speaker 1: not that they're not continuing to evolve, but that that
Speaker 1: evolution is happening on a slower time frame than what
Speaker 1: we saw previously. Which typically that's what happens, right, Like,
Speaker 1: usually you have lots of early gains and then it
Speaker 1: starts getting harder and harder. Those of y'all who work
Speaker 1: out and know what I'm talking about. Anyway, I thought
Speaker 1: I would give a shout out to the true team
Speaker 1: tru I see over at startups Savant. That website has
Speaker 1: put together a list that they called the one hundred
Speaker 1: top startups to watch in twenty twenty four. Now, that's
Speaker 1: a huge number of startups, right and I'm not gonna
Speaker 1: go through all of that. I'm going to look at
Speaker 1: just a few of them, and I want to come
Speaker 1: in a bit on what's been going on. So artificial
Speaker 1: intelligence is still something of a boom period right now.
Speaker 1: You know, Yes, there are these these areas where AI
Speaker 1: could potentially be brushing up against some limitations to certain approaches,
Speaker 1: particularly in the large language model space. But AI, as
Speaker 1: I've said many times, is a very complicated topic. There're
Speaker 1: lots of nuance to AI. It's not just one big,
Speaker 1: monolithic discipline. It's lots of much smaller, subtle disciplines that
Speaker 1: collectively make up artificial intelligence. To that end, I want
Speaker 1: to talk about some of the startups mentioned in this
Speaker 1: piece in Startup Savant. One I would like to chat
Speaker 1: about is Suno. Suno because simultaneously puts on a really
Speaker 1: darn impressive display while also being indicative of some of
Speaker 1: the more challenging aspects of generative AI in particular. So
Speaker 1: Suno is based out of Cambridge, Massachusetts. Technically it's a
Speaker 1: twenty twenty three startup, and it released its first build
Speaker 1: of the generative AI tool that they created back in
Speaker 1: December twenty twenty three. But the first stable release of
Speaker 1: that application came out just this past November. So I
Speaker 1: think Suno really qualifies as a twenty twenty four AI
Speaker 1: company if you ask me. I actually downloaded the Suno
Speaker 1: app to give it a try, and I have to
Speaker 1: admit it is pretty impressive. So Suno uses generative AI
Speaker 1: to create music based off user prompts, so those prompts
Speaker 1: can be really specific or really vague. So, for example,
Speaker 1: you might write a prompt like create a high energy
Speaker 1: dance track with lots of synthesizers and drums about going
Speaker 1: out on the town with a group of friends, sung
Speaker 1: by a female vocalist right, Or you might give it
Speaker 1: much more broad direction, like create an Appalachian folk tune
Speaker 1: about witches. Suno can compose music including lyrics and synthesized vocals,
Speaker 1: which on a phone speaker sound pretty darn convincing. Like
Speaker 1: when I made it make folk tunes. You could even
Speaker 1: hear like the breathing sounds. I guess someone will go,
Speaker 1: you know, it sounded organic, at least on a phone speaker.
Speaker 1: I'm sure if I were listening on really high end speakers,
Speaker 1: I could probably detect a bit of the artificiality. But
Speaker 1: to my dumb ears, they sounded pretty good. Now, what
Speaker 1: you do with that music track from that point forward
Speaker 1: depends on whether or not you're a paid subscriber to
Speaker 1: the service. If you are using their free basic plan,
Speaker 1: then you can use those tracks for any non commercial purposes.
Speaker 1: The actual ownership of the music tracks itself, those belong
Speaker 1: to Suno, so you don't have ownership of the tracks
Speaker 1: you make if you are a free user of the
Speaker 1: basic service. But let's say that you were running a
Speaker 1: role playing game session, like you're the game master and
Speaker 1: you really need some ruddy mysterious music playing in the
Speaker 1: background as your adventurers walk through a dungeon. Well, you
Speaker 1: could use Suno to generate that music for you if
Speaker 1: you liked like it could be instrumental pieces orchestral pieces,
Speaker 1: you know, synth wave, whatever it may need to be
Speaker 1: to suit the needs of your game. You could do that.
Speaker 1: And because it's non commercial, you know, you're just playing
Speaker 1: with friends, there's no problem there. Now, if you wanted
Speaker 1: to make commercial use of the music generated based off
Speaker 1: your prompts, then you would need to be a subscriber
Speaker 1: of the Pro or Premier level, and then you own
Speaker 1: whatever tracks are generated. You own the intellectual property of
Speaker 1: those songs, and Suno grants such users a commercial license.
Speaker 1: Copyright gets really complicated because, at least here in the
Speaker 1: United States, you cannot copyright a work generated by a
Speaker 1: I And what you can do is you can monetize
Speaker 1: the track, right like if you wanted to make a
Speaker 1: commercial podcast, Like you want to monetize the podcast, and
Speaker 1: you wanted to use Suno to generate the theme song
Speaker 1: for your podcast, you could do that at the pro
Speaker 1: or premier level, right, because that's a commercial use. You'd
Speaker 1: be using it for a commercial podcast. You could then
Speaker 1: do that. This does start to raise some questions, however,
Speaker 1: I mean, you could just start churning out songs, you
Speaker 1: could switch out prompts, you could tweak approaches in an
Speaker 1: effort to make something, you know, anything that resembles a
Speaker 1: catchy hit, and you don't have to hire musicians or
Speaker 1: singers or anything. You're just working with a computer and
Speaker 1: you're you know, if you have lots of time in
Speaker 1: your hands and you have one of those subscription levels
Speaker 1: where you don't have a lot of limitations on how
Speaker 1: many times you can use the app, well you can
Speaker 1: just keep rolling the dice and maybe you get lucky
Speaker 1: and you have a track that ends up getting crazy
Speaker 1: amounts of attention, and you're just flooding streaming services with
Speaker 1: track after track after track of AI generated music. This
Speaker 1: is something that is happening, and it's somewhat concerning because meanwhile,
Speaker 1: you have actual human being musicians who are trying to
Speaker 1: get noticed, and if there's just a glut of AI
Speaker 1: generated material hitting the various streaming platforms, it gets harder
Speaker 1: and harder to get discovered as an artist. And that
Speaker 1: doesn't seem terribly fair, right. But to the person who's
Speaker 1: just using this tool to make track after track, it's
Speaker 1: a licensed print money baby. Of course, it's not as
Speaker 1: simple as that, and there's definitely some discomfort in the
Speaker 1: music industry over the rise of these AI tools. The
Speaker 1: stuff made by Suno sounds retive of the various genres,
Speaker 1: at least to my ears. In other words, if you
Speaker 1: give it the direction to make a song in a
Speaker 1: specific genre or subgenre, what you get sounds like it.
Speaker 1: It fits pretty much. I didn't try things like rockabilly
Speaker 1: I should. I should try and do a rockabilly song
Speaker 1: about something and just see what it sounds like. But
Speaker 1: you know, for very broad categories like classical or folk
Speaker 1: or R and B or funk, the stuff I was
Speaker 1: getting sounded fairly representative of those genres. Not necessarily brilliant,
Speaker 1: but you know, it's not like something that you would
Speaker 1: hear if you were tuned into a radio station that
Speaker 1: catered to that specific genre of music. One thing to
Speaker 1: keep in mind is again, anything generated by AI here
Speaker 1: in the United States is ineligible for copyright protection, because
Speaker 1: in order to qualify for a copyright, a work has
Speaker 1: to be created by a human being, and writing a
Speaker 1: text prompt into a field and then having an AI
Speaker 1: model generate music does not qualify as a work created
Speaker 1: by a human being. That means that if you did
Speaker 1: create this musical track, and if you did have a
Speaker 1: commercial license to make money from it, there's no copyright
Speaker 1: protection that would allow you to go after anyone who
Speaker 1: infringed upon your intellectual property and made copies of your music,
Speaker 1: whether in whole or in part, or sampled it or whatever.
Speaker 1: And I say your music in the sense of you
Speaker 1: own it, not you created it. And that's a real issue.
Speaker 1: Beyond that, there's a bigger concern in the music industry
Speaker 1: over how SUNO trained its models in the first place.
Speaker 1: Like AI doesn't just magically know that a folk song
Speaker 1: sounds like a folk song, or that you know, rhythm
Speaker 1: and blues sounds a specific way, or Chicago blues having
Speaker 1: a different style to New Orleans blues. The models had
Speaker 1: to learn the rules of music theory and the styles
Speaker 1: and the things that go along with the various style.
Speaker 1: It had to have some form of understanding, which is
Speaker 1: a tough word because I don't mean to imply that
Speaker 1: the model has general intelligence. It doesn't understand like humans understand,
Speaker 1: but that it recognizes these qualities that are associated with
Speaker 1: different kinds of music, right like, it has to have
Speaker 1: that reference bace, and it has to be able to
Speaker 1: generate music that we find nice to listen to. Otherwise
Speaker 1: it's just spitting out random noise. So to get to
Speaker 1: that point, Suno presumably trained its models on a data
Speaker 1: set that included lots and lots of songs made by
Speaker 1: real life, living, and breathing human beings, and that also
Speaker 1: raises concerns about the potential for plagiarism. Now, Suno asserts
Speaker 1: that its model has guardrails in place to prevent it
Speaker 1: from just say, lifting chord progressions and melody lines and
Speaker 1: such from existing songs. But the music industry isn't so
Speaker 1: quick to accept that explanation. I mean, we just kind
Speaker 1: of had a pop culture moment in the form of Heredic.
Speaker 1: If you saw the movie Heredic had Hugh Grant in it.
Speaker 1: That brought up the issue of plagiarism a couple of
Speaker 1: different ways, and one of the ways was he plays
Speaker 1: a song by the Hollies called The Air that I Breathe,
Speaker 1: and then he plays Radioheads Creep and shows like, here's
Speaker 1: two songs that have the same chord progression. It's so
Speaker 1: similar that the Hollies sued Radiohead and were ultimately allowed
Speaker 1: to have some writing credit and royalties from Creep because
Speaker 1: of the similarity between the two songs. Hugh Grant's character
Speaker 1: also points out that Lana del Reys get free shares
Speaker 1: that same chord progression and most of the melodic line
Speaker 1: from Creep, and then in fact, Radiohead or Radiohead's agents
Speaker 1: or whatever sued Lana del Rey for plagiarism as well,
Speaker 1: which is wild because Radiohead had already been sued and
Speaker 1: essentially admitted to or at least acknowledged the similarities with
Speaker 1: the Hollies, and then the Radiohead then goes and sues
Speaker 1: Lana del Y. This is a really sticky subject in
Speaker 1: music already, like this issue of how similar can one
Speaker 1: song be to another? Before you start to say, hey,
Speaker 1: wait a minute, I think you actually copied this other
Speaker 1: piece of music, as opposed to you both independently arrived
Speaker 1: at the same structure from different pathways. But imagine how
Speaker 1: much more complicated it gets if a company were to
Speaker 1: argue that an AI model was plagiarizing protected works by
Speaker 1: generating songs that conform to the rules of music in
Speaker 1: a way that's already similar to existing pieces. In June
Speaker 1: of this year, the Recording Industry Association of America the
Speaker 1: RI double A, filed a lawsuit against Suno. The RI
Speaker 1: double A claims that Suno has engaged in copyright infringement
Speaker 1: by training its models on protected works. The lawsuit seeks
Speaker 1: damages in the form of up to one hundred and
Speaker 1: fifty thousand dollars per copyrighted work that was used in training. Now,
Speaker 1: I'm going to guess that would be a huge amount
Speaker 1: of money, but I don't know for sure because we
Speaker 1: can't actually see the data set that Suno used. Notably,
Speaker 1: it is behind closed doors, so we aren't allowed to
Speaker 1: see how many songs or what kinds of songs are
Speaker 1: from what catalogs Suno dipped into in order to train
Speaker 1: up its AI. I mean it had to be a lot.
Speaker 1: When you're training AI, you need lots and lots and
Speaker 1: lots and lots of training material to get your model
Speaker 1: to start to hone in on what it is you
Speaker 1: want it to do. In the case of image recognition,
Speaker 1: that could be millions of images in order to train
Speaker 1: a model to recognize one thing versus another. So presumably
Speaker 1: the training material for Suno was pretty darn extensive. I
Speaker 1: think it's pretty safe to say that every single record
Speaker 1: label out there likely has some music from their catalog
Speaker 1: that was used in the training. I don't know that
Speaker 1: for sure, that's just a guess, because again, you know,
Speaker 1: it's not like you can have the AI scan the
Speaker 1: radio for like a second and then say oh yeah,
Speaker 1: I got this, and then they could just go and
Speaker 1: generate all that. Okay, well, we've got more to say
Speaker 1: about Suno plus some other AI startups Before I get
Speaker 1: into any of that, though, let's take a quick break. Okay,
Speaker 1: we're back. We're still talking about Suno here, the AI
Speaker 1: company that makes a tool that allows you to create
Speaker 1: a song just based off a text prompt. So I
Speaker 1: think Suno really illustrates the pros and cons of generative
Speaker 1: AI in a neat little package. So the pros are
Speaker 1: if you are not musically inclined, but you need to
Speaker 1: create some original music for whatever reason. Suno is incredibly
Speaker 1: easy to use, and you can even continue to work
Speaker 1: on a piece. Like let's say you prompt Suno to
Speaker 1: make something, you listen to it, you're like, well, that's close,
Speaker 1: but it's not really what I want. You can keep
Speaker 1: on refining it by continue to type in and get
Speaker 1: that shaped into a place where you really like it. Now,
Speaker 1: if you don't know any musicians or you can't afford
Speaker 1: to hire musicians, arguably this is a tool that could
Speaker 1: work for you. But personally, I feel really icky about
Speaker 1: the idea of leaning too hard on Ai to create
Speaker 1: art of any kind, or even calling AI generated material
Speaker 1: art in the first place. That doesn't seem right to me.
Speaker 1: I like to think that art has to have some
Speaker 1: sort of human intent behind the piece. There needs to
Speaker 1: be a human motivation beyond just a text prompt for
Speaker 1: it to be art, at least in my opinion. But
Speaker 1: art is a very subjective thing. And also I'm very
Speaker 1: old fashioned, so it's entirely possible that I'm out of
Speaker 1: step here, but it doesn't feel that way to me. Now.
Speaker 1: The cons are that Tsuno could really impact human musicians
Speaker 1: who otherwise could be hired to devote their skill and
Speaker 1: craft toward creating new stuff, thus making it harder for
Speaker 1: people who have honed their art to make a living
Speaker 1: off of that art. Like think of the countless hours
Speaker 1: that songwriters and musicians and vocalists spend to get good
Speaker 1: at what they do. You know, people aren't just naturally
Speaker 1: flawless in their execution, right. They take years of practice
Speaker 1: to get to where they are. And they do this
Speaker 1: not just from a love of the art, although I'm
Speaker 1: sure that's a big part of it, but also with
Speaker 1: the desire to make a living off of all that
Speaker 1: hard work. Well, tools like SUO arguably create a shortcut
Speaker 1: that might be very tempting for some folks out there
Speaker 1: to just bypass all that messy human stuff and the
Speaker 1: costs that come with it and go for you know,
Speaker 1: let's just go with AI if it's popular. Who cares
Speaker 1: if humans didn't make it, because, you know, to the
Speaker 1: producer side, anyway, the goal is to make money off
Speaker 1: of music. You don't really care where the music came
Speaker 1: from or how it was generated. You just care that
Speaker 1: it makes money. You don't even care if it resonates
Speaker 1: with your audience. You just need it to sell well.
Speaker 1: So that makes me feel icky. The balance between commerce
Speaker 1: and art is always a tricky thing. I had a
Speaker 1: really good conversation with an artistic director of a theater
Speaker 1: about this and how that's always a difficult balance, Like
Speaker 1: how do you balance between commerce and art? And it's
Speaker 1: tricky because you know, we don't live in a world
Speaker 1: where we can just create art and not have to
Speaker 1: worry about paying the bills. We have to do both.
Speaker 1: Then there's also the plagiarism issue. So at what point
Speaker 1: do you say an AI tool is creating music the
Speaker 1: way a person might create music. So for example, people
Speaker 1: lean on inspiration from previous works all the time, right,
Speaker 1: Like you might hear something in a musical piece and think, oh,
Speaker 1: that's interesting and you want to build off of it.
Speaker 1: I mean, sampling is built off this. Their entire genres
Speaker 1: of music that are rooted in this idea where you
Speaker 1: take something that was used in one piece and you
Speaker 1: repurpose it to create something brand new and transformative. Doing
Speaker 1: that is okay, right, even copyright losses, that's okay if
Speaker 1: it's transformative to a certain extent. Like you can get
Speaker 1: into trouble if you don't do it correctly, but you
Speaker 1: kind I would get to a point where you say,
Speaker 1: is the AI, you know, taking inspiration from this previous
Speaker 1: corpus of music or is it actually just copying something
Speaker 1: that's already been done because the AI is determined this
Speaker 1: is the best way to do it. That's tricky. Now
Speaker 1: we'll have to see where companies like Suno go in
Speaker 1: the future. I know that media companies are simultaneously curious
Speaker 1: and worried about this technology. If the media companies can
Speaker 1: make a buck off the tech, then it will come
Speaker 1: as a surprise to no one when we're flooded by
Speaker 1: AI generated tunes. But if those media companies determine that
Speaker 1: working with AI puts their relationships with human artists at risk.
Speaker 1: You know, these are artists who could potentially be earning
Speaker 1: media companies billions of dollars every year, then it's a
Speaker 1: little different, right, Like it would be a dumb move
Speaker 1: to experiment with AI if in the process you're alienating
Speaker 1: the superstars you regularly work with. I say this because
Speaker 1: I've listened in on meetings where there have been discussions
Speaker 1: about using AI to create music, and these were with
Speaker 1: people who worked with companies that had tight relationships with
Speaker 1: musicians and artists, and I had to ask, like, what
Speaker 1: do you think that's going to do with your relationships,
Speaker 1: your professional relationships with these other people who clearly have
Speaker 1: a vested interest in not having AI flood the market.
Speaker 1: And that really gave pause to the meeting. I'm known
Speaker 1: as Debbie Downer at those types of meetings because instead of,
Speaker 1: you know, kind of blue skying the whole AI thing,
Speaker 1: I say, let's take this into context. Let's really think
Speaker 1: critically about this, because otherwise we're putting lots of people's
Speaker 1: careers at risk. And not only that, but potentially we
Speaker 1: are suppressing artistic expression because again, I don't think you
Speaker 1: can call it artistic expression if it's AI generated. It
Speaker 1: might be conforming to certain conventions and rules in an
Speaker 1: effort to create music that sounds like, you know, whatever
Speaker 1: the prompt was, But that's not the same thing as
Speaker 1: artistic expression. All right, let's switch to a different startup.
Speaker 1: This is one that is also a couple of years old,
Speaker 1: but it completed its Series A funding just this year,
Speaker 1: and that's web Ai. Now, I guess I should kind
Speaker 1: of cover what Series A funding actually means. So in
Speaker 1: the world of startups, and this is not just in tech,
Speaker 1: this is startups in general. But in the world of startups,
Speaker 1: there are typically multiple rounds of investment funding that are
Speaker 1: necessary for a business to get to a point where
Speaker 1: it can operate like a business. So first up, you've
Speaker 1: got your seed funding, and this is used to get
Speaker 1: a company established in the very early stages. You might
Speaker 1: use seed funding to do things like, you know, incorporate,
Speaker 1: to design a logo, to secure some office early office space,
Speaker 1: which might be like a sharing situation. Early on, there
Speaker 1: are a lot of businesses that started off as taking
Speaker 1: up a corner of an existing business's office, for example.
Speaker 1: And I think of seed money kind of like how
Speaker 1: buskers will put a few dollars in their hat before
Speaker 1: performing on the street. Right, you see that street musician,
Speaker 1: they get their hat out, it's got some money in
Speaker 1: the hat. Often these musicians will put a couple of
Speaker 1: bucks in there to start with, and that money is
Speaker 1: seed money, and you're hoping it's going to grow and
Speaker 1: blossom as more people throw dollars into the hat. Seed
Speaker 1: funding is kind of similar. Seed money typically comes from
Speaker 1: folks like angel investors, so these could be really influential
Speaker 1: people in the sector, maybe people that the startup owners
Speaker 1: already know personally, could be someone they went to college with,
Speaker 1: or advisor or a relative. It could be something like that.
Speaker 1: It could also be from an incubator group. Incubators exist
Speaker 1: in order to foster ideas that could potentially grow into
Speaker 1: viable businesses. The incubator gets a stake in whatever the
Speaker 1: company is and thus profits if the company does well,
Speaker 1: and in return, the startup gets a little bit of
Speaker 1: stability and access to some assets. But seed money only
Speaker 1: goes so far, and typically it's enough to keep a
Speaker 1: company afloat for just a relatively short amount of time.
Speaker 1: What typically comes after that is Series A funding, and
Speaker 1: in this series the startup opens itself up to investments
Speaker 1: beyond that initial group of seed money investors. This could
Speaker 1: then be followed by additional rounds of investment. You can
Speaker 1: have Series B, Series C, et cetera. And it does
Speaker 1: mean that the investment crowd gets more dense as you
Speaker 1: go on because you get more investors, which means you
Speaker 1: have to pay out more shares. In the long run,
Speaker 1: the goal is to either become a scaled operation that
Speaker 1: has sustainable growth, potentially going public at some point and
Speaker 1: everyone makes their money back plus interest, or you get
Speaker 1: swallowed up by some bigger fish for a handsome payout
Speaker 1: and everyone goes home rich. So webai concluded its Series
Speaker 1: A funding this year and saw about sixty million dollars
Speaker 1: flood into the company. Coffers analysts value Webai in the
Speaker 1: seven hundred million dollar range, So yeah, they got sixty
Speaker 1: million in investment, but they're valued at around seven hundred
Speaker 1: million dollars, So they're closing in on that Unicorn status
Speaker 1: where you hit a billion dollar valuation. So what the
Speaker 1: heck does webai do? What's interesting because they're called webai,
Speaker 1: but in fact they're focused on creating on device AI solutions,
Speaker 1: which by that I mean instead of relying on a
Speaker 1: cloud based AI server farm, you do or AI processing
Speaker 1: on devices that are local to you, whether it's an
Speaker 1: individual or a business. Now this is important because most
Speaker 1: businesses aren't necessarily keen on using an off site AI
Speaker 1: solution out of concern that the AI provider could possibly
Speaker 1: train AI models on the company's proprietary data. Right, let's
Speaker 1: say that you are an analysis firm and you're using
Speaker 1: AI to assist in the analysis. If you find out
Speaker 1: that the company you're using that provides these AI tools
Speaker 1: is actually training its AI on your information. Well, that
Speaker 1: brings your information security and privacy into question. If your
Speaker 1: business handles sensitive information, and let's face it, most businesses
Speaker 1: do to at least some extent, then you could have
Speaker 1: legitimate concerns about a third party gaining access to that
Speaker 1: data and further potentially exploiting that access by training its
Speaker 1: own models. You could even get into some real legal
Speaker 1: trouble if you are working with other parties like partners,
Speaker 1: that have agreements that would prevent you from legally being
Speaker 1: able to share their information in the first place. So
Speaker 1: there are a lot of sticky situations around this particular
Speaker 1: approach to business. And this is not a hypothetical issue.
Speaker 1: This concern about AI potentially compromising security and privacy because
Speaker 1: we have seen examples of various AI tools generative AI
Speaker 1: tools pulling information from other user interactions with the AI. Now,
Speaker 1: usually this is because there's been some sort of error
Speaker 1: on the back end of the AI side. So theoretically,
Speaker 1: each customer's interactions should be siloed from everybody else's, but
Speaker 1: now and again, mistakes happen, and you might be in
Speaker 1: a conversation with an AI chat bot and you end
Speaker 1: up starting to see information that was inserted by some
Speaker 1: other customer, right you start to see interactions that they
Speaker 1: had with the AI and that obviously is a huge
Speaker 1: breach of privacy that has happened a few times over
Speaker 1: the last couple of years. Now. Companies obviously don't want
Speaker 1: those sorts of mistakes to include their intellectual property that
Speaker 1: could include things like code for software or business strategies
Speaker 1: or you know, trade secrets, all that kind of stuff.
Speaker 1: You don't want that to suddenly get just dumped into
Speaker 1: an AI large learning model and then someone else is like, hey,
Speaker 1: what does company XYZ think about this? And then you
Speaker 1: find out because those trade secrets have been included in
Speaker 1: the training material that would be bad. So a better solution,
Speaker 1: if you plan on making use of any sort of
Speaker 1: AI process might be to make sure that you can
Speaker 1: handle all that processing yourself. Now, depending on what your
Speaker 1: business does, that may or may not be practical from
Speaker 1: the classic standpoint, like open ai has an enormous enormous
Speaker 1: number of computers running AI processes, and most companies would
Speaker 1: not be able to replicate that. And if you're talking
Speaker 1: about a big company that needs to run some hefty
Speaker 1: functions of AI processing, going that server route might not
Speaker 1: be a viable option. Those server farms for open ai
Speaker 1: are so large and they're so expensive that for a
Speaker 1: while this year it looked like open ai might even
Speaker 1: spend itself out of business just in order to pay
Speaker 1: the bills. But investors ultimately did sweep in and injected
Speaker 1: open Ai with a mega truckload of cash, so bankruptcy
Speaker 1: has been saved off for now. Like you might think
Speaker 1: of open ai as the next too big to fail company,
Speaker 1: even though open ai is spending money at like a
Speaker 1: truly eye popping rate, because AI processing is hard. It
Speaker 1: takes lots of processing power if you're doing it the
Speaker 1: way open ai does it. All right, we're gonna take
Speaker 1: another quick break. When we come back, i'll talk more
Speaker 1: about web ai, and then we'll follow up with a
Speaker 1: third AI startup. Okay, we're back, and we're going back
Speaker 1: to Webai. So webai has developed some products that allow
Speaker 1: for local AI processing. So none of this goes over
Speaker 1: the cloud, none of it goes over the Internet. It's
Speaker 1: all contained on premises. Some of Webai's products that they're
Speaker 1: offering sound pretty nifty. The company claims to have taken
Speaker 1: a tailored approached for every single customer and they optimize
Speaker 1: the strategy to fit whatever that customer's needs happen to be.
Speaker 1: The company also says that no coding is needed to
Speaker 1: use webi or Webai, I should say, to build out functions,
Speaker 1: but the programmers can take advantage of advanced settings if
Speaker 1: they want to stretch themselves a bit. Now. I haven't
Speaker 1: used Webai's tools, so I don't know how webi integrates
Speaker 1: AI into the different applications and processes that businesses have, Like,
Speaker 1: I don't know what that looks like. Like I imagined,
Speaker 1: it's got to be more complicated than just see this,
Speaker 1: do this automatically. It has to be a little more
Speaker 1: complicated than that. I don't know how much more complicated
Speaker 1: because I haven't had hands on time with the tools.
Speaker 1: But some of the applications Webai lists on their web
Speaker 1: page include stuff like airline and airport logistics. You know,
Speaker 1: using webai to help reduce turnaround times and improve efficiency
Speaker 1: at airports so that planes are spending less time at
Speaker 1: gates and you have more flights arriving on time or
Speaker 1: ahead of schedule, and just improving efficiency in general, or
Speaker 1: using Webai to help develop educational applications to customize learning
Speaker 1: approaches for classes or even on student to student levels.
Speaker 1: That's something that's been talked about for a very long time,
Speaker 1: this futuristic vision of imagine a world where every student
Speaker 1: has an education that is catered to their style of learning.
Speaker 1: That's obviously something that teachers cannot do right now. It's impossible.
Speaker 1: If you have a class of thirty students, you don't
Speaker 1: have the time to be able to craft and design
Speaker 1: and maintain a teaching plan for each and every student.
Speaker 1: But with technology, the idea seems closer. It may be
Speaker 1: that we can never really achieve it, but it seems
Speaker 1: like it's possible. Then we ai man I just say
Speaker 1: webi all the time. Webai it also suggests medical applications
Speaker 1: that could improve the quality of patient care. And if
Speaker 1: all of this is sounding vague, that's because webai is
Speaker 1: offering a platform upon which many different services and products
Speaker 1: can be built, so it's impossible to go through every
Speaker 1: single variation that webai could empower. It's more like these
Speaker 1: are some examples of what the tools are able to do.
Speaker 1: They're able to improve the performance of various processes in
Speaker 1: all these different fields. By the way, this is one
Speaker 1: of those approaches in AI that I can really get behind.
Speaker 1: I feel that creating on premises processing capabilities and optimizing
Speaker 1: an approach that makes sense for specific companies, that's the
Speaker 1: way to go. You know, don't do a one size
Speaker 1: fits all approach because that just isn't realistic. And I'm
Speaker 1: not saying that cloud based AI services don't have a place,
Speaker 1: but cloud based AI services concerned me for lots of reasons,
Speaker 1: privacy and security being two of the big ones. Plus
Speaker 1: you know, some AI companies are making some choices that
Speaker 1: I personally find a little questionable. Cough open AI pairing
Speaker 1: up with Lucky Palmer's defense company, cough. But how about
Speaker 1: we cover an AI startup that has a much more
Speaker 1: focused purpose. This is the third and final of the
Speaker 1: three that I wanted to highlight today that brings us
Speaker 1: to Overjet. This is an AI company that caters to
Speaker 1: the world of day dental care. Yep, we're bringing together
Speaker 1: the terminator and dentistry to create an unstoppable tooth scraping supervillain.
Speaker 1: All Right, I'm going a little far. I apologize. I
Speaker 1: recently rewatched Little Shop of Horrors and it has rubbed
Speaker 1: off of me. My apologies to all the dental hygienists
Speaker 1: and dentists out there now. This year, Overjet secured a
Speaker 1: Series C round of funding, so this was the third
Speaker 1: round after seed investment. In that Series C funding round,
Speaker 1: they raised more than fifty three million dollars in the process.
Speaker 1: The company was originally founded back in twenty eighteen, and
Speaker 1: according to a press release, the company's mission is to
Speaker 1: quote make dentistry patient centric by providing dental professionals with
Speaker 1: the AI tools they need to operate efficiently and give
Speaker 1: patients exceptional care in the quote. So how do they
Speaker 1: do this? Well, from what I can gather, one way
Speaker 1: is for overjet to use image analysis tools to help
Speaker 1: dig dental diseases in patients and suggest methods of care
Speaker 1: to help treat or prevent dental issues. So, for example,
Speaker 1: if you were to get X rays done at your
Speaker 1: dental appointment, Overjet could be used to analyze those those
Speaker 1: X rays and diagnose any issues. And maybe it tells you, hey,
Speaker 1: it looks like on this one side of your mouth
Speaker 1: you've got a little bit more damage, Like maybe you've
Speaker 1: got more build up a plaque, or maybe you've got
Speaker 1: a you know, maybe you have the beginnings of cavities.
Speaker 1: Over here, it suggests that perhaps you're not reaching your
Speaker 1: teeth effectively when brushing that part of your mouth, and
Speaker 1: that it's something to be mindful of, or more seriously,
Speaker 1: it might detect early signs of oral disease and give
Speaker 1: your dentist more time to address the problem before it
Speaker 1: becomes much more serious. Moreover, a goal of overjet is
Speaker 1: to create a sort of centralized point of information for
Speaker 1: every patient, and that would give dentists and other doctors,
Speaker 1: plus patients and even insurance companies a common foundation to
Speaker 1: work from. So the goal is to smooth out any
Speaker 1: rough spots or miscommunication that could otherwise crop up between
Speaker 1: these different parties and to make sure everyone has the
Speaker 1: same understanding. And I could definitely see where that could
Speaker 1: be helpful right where you have this tool that dentists
Speaker 1: could use to say to the insurance companies, for example,
Speaker 1: that this is something that absolutely has covered and needs
Speaker 1: to be reimbursed or whatever. Because we have this common
Speaker 1: point of contact where we can have an understanding of
Speaker 1: what's going on with this particular patient. Overjet is actually
Speaker 1: the first artificial intelligence company to receive clearance from the
Speaker 1: US Food and Drug Administration or FDA to make use
Speaker 1: of AI in the detection and diagnosis of oral disease.
Speaker 1: And I think that's an incredible achievement. I mean, you
Speaker 1: have to be able to pass lots of inspections and
Speaker 1: analysis in order to do that. The FDA doesn't just
Speaker 1: rubber stamp stuff, and this is there's definitely an AI
Speaker 1: application that I really like, you know, anything that can
Speaker 1: help give more precise care to patients and improve that
Speaker 1: quality of care. To me, that's a really good thing,
Speaker 1: as long as the technology performs reliably and consistently across
Speaker 1: all patients. Obviously, we have seen cases of AI. I'm
Speaker 1: not talking about medical AI necessarily, but we have seen
Speaker 1: examples of AI that have performed really well with one
Speaker 1: set of people and not so well with other sets
Speaker 1: of people. I'm thinking primarily of stuff like facial recognition
Speaker 1: technology and how it is not as reliable when looking
Speaker 1: at anyone who isn't a white dude. Essentially, if you're
Speaker 1: a white dude, it works pretty well, and then if
Speaker 1: you're not a white dude, the reliability of the tool
Speaker 1: starts to decline. Let's say I want to make sure
Speaker 1: that any medical AI doesn't fall into those same sort
Speaker 1: of biases where just because something might be true for
Speaker 1: one subset of the population doesn't mean it's going to
Speaker 1: be true for everybody. And again, this takes us back,
Speaker 1: sort of like with education, to this futuristic view of
Speaker 1: a world where healthcare is customized down to every single patient.
Speaker 1: And that's the dream, right where every person is given
Speaker 1: individualized healthcare so that they get the optimized approach to
Speaker 1: taking care of themselves, either to prevent illness and disease
Speaker 1: and conditions or to treat the ones that they have.
Speaker 1: Because we don't live in a one size fits all world, you know,
Speaker 1: what works for me might not work for you. In fact,
Speaker 1: I personally experience this the hard way this year. You
Speaker 1: might recall, at the end of twenty twenty three, I
Speaker 1: had what I like to refer to as my little
Speaker 1: medical whoopsie, and I was sent to the emergency room.
Speaker 1: And at the end of that I was prescribed a
Speaker 1: blood pressure medication and turned out that that particular type
Speaker 1: of blood pressure medication was not effective for me. I
Speaker 1: didn't know it at the time until three days later
Speaker 1: when I was so poorly off that I had to
Speaker 1: be admitted to the intensive care unit at the hospital.
Speaker 1: So I upgraded from ER to ICU. And part of
Speaker 1: the reason for that was that my blood pressure medication
Speaker 1: I was prescribed wasn't cutting. It turned out my kidneys
Speaker 1: were really badly damaged. Fun times, they're much better now,
Speaker 1: by the way, just so y'all know. So at that point,
Speaker 1: I was then put on a different kind of medication,
Speaker 1: and then they fine tuned that so that it would
Speaker 1: work best for me, and that way, you know, I
Speaker 1: wouldn't die. And that's kind of how it has to
Speaker 1: go right now for most patients. Like it's not something
Speaker 1: where a doctor can speak with one hundred percent confidence
Speaker 1: that a specific medication at a specific dosage is going
Speaker 1: to do the trick. Often it requires a lot of
Speaker 1: trial and error. The hope is that with AI we
Speaker 1: can get to a future where patients can receive a
Speaker 1: much more individualized approach to care that minimizes the risks
Speaker 1: of complications and hopefully the impact of stuff like side effects,
Speaker 1: Like you're never going to get a rid of side effects,
Speaker 1: but hopefully you'd be able to use these complex technologies
Speaker 1: to design a type of care that gives a patient
Speaker 1: the higher quality of life. That's the goal. Now we
Speaker 1: still have a very long way to go before we
Speaker 1: get there, but I feel like Overjet's story is evidence
Speaker 1: that it's at least a potentially achievable goal, maybe not
Speaker 1: one hundred percent achievable. I don't want to paint AI
Speaker 1: as being a perfect solution that's going to get rid
Speaker 1: of all these problems and that will be magically living
Speaker 1: in a Star Trek universe. I don't want to suggest that.
Speaker 1: I do want to say that I think it can
Speaker 1: help us, assuming it is responsibly and accountably designed and maintained,
Speaker 1: It can help us reach a better future depending on
Speaker 1: how we implement it. So there are lots of ways
Speaker 1: where I think AI is going to be a good
Speaker 1: thing moving forward. I know on this show I can
Speaker 1: get really critical of AI, but that's because it does
Speaker 1: have the potential to do good things. It also has
Speaker 1: the potential to do really bad things, either intentionally or
Speaker 1: as we have often seen, unintentionally intentionally. I worry about
Speaker 1: companies like open Ai pairing up with defense contractors because
Speaker 1: I don't see that ending well. I see that going
Speaker 1: to a place that's very dark and honestly something that
Speaker 1: I thought would only exist in science fiction throughout my life,
Speaker 1: and it turns out I was being naive unintentionally is
Speaker 1: arguably just as bad because it shows a lack of
Speaker 1: oversight on the part of whoever's developing the AI process.
Speaker 1: And often when we have our site set on a
Speaker 1: specific goal, we can have blinders put up to the
Speaker 1: potential consequences our choices, and that can be a really
Speaker 1: bad thing too. But that doesn't mean we should shy
Speaker 1: away from AI. It just means that we have to
Speaker 1: be extremely mindful and careful as we develop and deploy
Speaker 1: AI solutions because the potential for them to really improve
Speaker 1: our lives is definitely there. We just have to make
Speaker 1: sure we're doing the right stuff in order to get there.
Speaker 1: That's it for this episode, just a quick look at
Speaker 1: just three AI startups. I mean, there's obviously lots more
Speaker 1: out there, but I wanted to kind of pick three
Speaker 1: that would be fun to talk about for today's episode.
Speaker 1: We'll be back with more new episodes, including hopefully some
Speaker 1: special guests in the very near future, and I will
Speaker 1: talk to you again really soon. Tech Stuff is an
Speaker 1: iHeartRadio production. For more podcasts from iHeartRadio, visit the iHeartRadio app,
Speaker 1: Apple podcasts, or wherever you listen to your favorite shows.