Speaker 1: Now here's a highlight from Coast to Coast a m
Speaker 1: on iHeartRadio.
Speaker 2: Welcome back George Norri along and Lauren Weinstein. His website
Speaker 2: is Vortex v O R T e X dot com
Speaker 2: linked up at Coast to Coast AM dot com. Lauren,
Speaker 2: when you hear the phrase artificial intelligence with computers, what
Speaker 2: does that mean to you?
Speaker 3: You know, we've talked about this, you know, about AI
Speaker 3: so many times, and there's probably a lot of listeners
Speaker 3: who have gotten you know, tired of my mind.
Speaker 2: Nor nobody's ever tired of you, Lauren.
Speaker 3: But you know, it's it's important I think always when
Speaker 3: talking about AI to be to be clear about the
Speaker 3: definitions that we're using. And and the term artificial intelligence
Speaker 3: gets gets thrown around a lot, and there's a foundational point.
Speaker 3: There is no intelligence in artificial intelligence, right, I mean,
Speaker 3: it's it's a handy, it's a handy, uh, you know,
Speaker 3: turn a phrase, but there's no intelligence there as as
Speaker 3: you or I or most people would define intelligence. And
Speaker 3: so within the the broad sphere of what we call AI,
Speaker 3: there are basically two different categories, and one of them
Speaker 3: is the category of of the type the type of
Speaker 3: AI that's being used for uh, digging into into complex
Speaker 3: calculations and dealing with massive amounts of data. The traditional
Speaker 3: kinds of applications being things like you know, gigantic climate
Speaker 3: models trying to model whether it advanced or Nowadays, there's
Speaker 3: a lot of work being done in medical applications looking
Speaker 3: at X rays and other dignostic data and digging through
Speaker 3: tremendous amounts of data in ways that are difficult for
Speaker 3: humans to do, especially because these AI systems, these of
Speaker 3: this type can have the ability to find patterns pattern
Speaker 3: matching that that aren't necessarily the best things for human
Speaker 3: beings to do, though humans still excel in lots of
Speaker 3: other areas, of course, and certain kinds of patterns humans
Speaker 3: are humans are better at. But that's the really more traditional,
Speaker 3: the good side of AI helping helping to deal with
Speaker 3: lots of data in these kinds of ways. Now then
Speaker 3: we move over to the hype filled side of AI,
Speaker 3: and this is the side that I am very critical of,
Speaker 3: and this is mostly under the bannered generative AI, and
Speaker 3: this is where all the hype is, and this is
Speaker 3: where the big tech players, you know, Google and open
Speaker 3: AI and the others are all hoping praying. Really, I
Speaker 3: think that this investment that they're making in it of
Speaker 3: just untold billions and billions of dollars going into building
Speaker 3: more and more data centers to crunch this data because
Speaker 3: generative AI is very consumptive of computing resources. Uh. And
Speaker 3: and in fact, they're using so much energy that this
Speaker 3: has become a big issue now. The amount of energy
Speaker 3: they need is just extraordinary, to the point where they're
Speaker 3: trying to get permission to build their own nuclear reactors
Speaker 3: and things to power these data centers. And and this
Speaker 3: is this is the realm of the chatbots and and uh,
Speaker 3: uh you know these you know, we're all where all
Speaker 3: the hype is now, all the the the deep fakes
Speaker 3: being used to generate uh you know, fake voices and
Speaker 3: fake videos, and we're all the really controversial aspects of
Speaker 3: AI really are now. And uh, the problem for these
Speaker 3: firms is that they've poured not only have they been
Speaker 3: pouring in all this money, but in many cases they
Speaker 3: have diverted so much of their resources, their core resources,
Speaker 3: uh they're human resources, they're engineers and and other people
Speaker 3: into AI that other aspects of their operations have have
Speaker 3: been many people would say have been neglected, and yet
Speaker 3: there is no clear sense of where the money is
Speaker 3: going to be made back from generative AI, because by
Speaker 3: and large, the evidence so far is that while people
Speaker 3: are willing to play with it when it's free, they
Speaker 3: are much less willing to pay for it when it's
Speaker 3: not free. And so what some of the firms have
Speaker 3: been doing is just kind of bundling it in there
Speaker 3: with other stuff that people need, like Google's doing this
Speaker 3: with some of their their workplace products that are aimed
Speaker 3: at businesses, and and they just say, well, now you
Speaker 3: get now you get the AI too. And by the way,
Speaker 3: the price of the whole packages of everything that has
Speaker 3: just gone up. And this has been very controversial because
Speaker 3: people have found in some cases their corporate information ending
Speaker 3: up in these AI systems where they didn't want it
Speaker 3: to go. And some of the problems with these systems,
Speaker 3: which are basically on what are called large language models loms,
Speaker 3: is that information that goes in can in many cases
Speaker 3: will be in a complicated manner leak out to other users.
Speaker 3: So there's all kinds of security and privacy issues beyond
Speaker 3: the more foundational issues that so much of what these
Speaker 3: systems say tends to be incorrect, and I have a
Speaker 3: whole collection people. You know, people point out all the
Speaker 3: time these simple questions that you could ask some of
Speaker 3: these systems and they just give you completely nonsensical answers. Now,
Speaker 3: when they're nonsensical answers, you know, usually you'll be able
Speaker 3: to recognize it. You know, if it says that three
Speaker 3: inches is equal to five inches, you know that that's wrong.
Speaker 3: And that's the kind of mistakes you actually we'll see.
Speaker 3: But the real risk of these systems is when you
Speaker 3: ask a question and the answer seems plausible, and if
Speaker 3: you're not an expert on a particular topic, you know,
Speaker 3: you know, that looks like a reasonable answer to me.
Speaker 3: But in some cases, many cases, the answer can be
Speaker 3: completely wrong, or even more dangerous, partly wrong. And when
Speaker 3: you get an answer that's partly right and partly wrong,
Speaker 3: that's the recipe for real misinformation disinformation problems. It's sort
Speaker 3: of the heart of propaganda when you look at it
Speaker 3: from that standpoint. And it just seems like many of
Speaker 3: these problems are intrinsic to these large language models, and
Speaker 3: this is why a lot of people express concern when,
Speaker 3: for example, Google has been pushing more and more toward
Speaker 3: using these AI overviews. There's talk now that they're going
Speaker 3: to be having versions of search. In fact, I believe
Speaker 3: they have this in the operation for some classes of
Speaker 3: users now, where the whole search experience is just talking
Speaker 3: to the talking to the AI, and you're not necessarily
Speaker 3: going to see the links the way you did, and
Speaker 3: if the answers are wrong, you're just going to have
Speaker 3: to try to figure it out. I personally, I think
Speaker 3: it's kind of creepy when you get an answer from
Speaker 3: these things, and then in the fine print underneath it says,
Speaker 3: in essence, by the way these answers could be wrong,
Speaker 3: it's up to you to verify them. So I say,
Speaker 3: what's the point of asking a question if you're going
Speaker 3: to get an answer back? And it says it's on
Speaker 3: you to figure out if we're telling you the truth
Speaker 3: or not.
Speaker 2: Truly remarkable, though, I was talking to doing a developer
Speaker 2: of AI this weekend and he admitted that AI does
Speaker 2: not have empathy, and I'm not sure it's ever going.
Speaker 4: To Yeah, well, I I don't think the AI in
Speaker 4: the way we view it now in terms of these
Speaker 4: large language models, is ever going to have any kind
Speaker 4: of human emotion, right, so we can put empathy on
Speaker 4: that list.
Speaker 3: Now, that's not to say that at some point in
Speaker 3: the future, and I don't know if it'll be a
Speaker 3: year from now, or one hundred years from now or
Speaker 3: maybe never, that some methodology for creating what we might
Speaker 3: call genuine artificial intelligence won't come to pass. And when
Speaker 3: that happens, then you're going to be faced with all
Speaker 3: these issues of emotions and perhaps self awareness and consciousness
Speaker 3: and all these kinds of things. But there doesn't seem
Speaker 3: to be any strong likelihood of those sorts of things
Speaker 3: coming into play given the way these large language models
Speaker 3: are built. I'm thinking more in terms of some completely
Speaker 3: new kind of technology that we can't really visualize now.
Speaker 3: But we couldn't have visualized a lot of technology we
Speaker 3: have now one hundred years ago, a thousand years ago,
Speaker 3: of course, So it would be foolish to say that
Speaker 3: there couldn't be some kind of intelligence technology in the
Speaker 3: future that would be much more human like. But we're
Speaker 3: not there yet.
Speaker 2: Lauren, what do you think of these technologies where you
Speaker 2: can take twenty seconds of bodio of an individual and
Speaker 2: turn it into a full speech. Yeah, and it sounds
Speaker 2: just like him.
Speaker 3: Certainly it's bad for voiceover artists, isn't it. I mean,
Speaker 3: there goes, there goes a whole, a whole profession downmitrain.
Speaker 3: I guess in a lot of cases, you know, the issue,
Speaker 3: of course, is so many of these technologies, right, they're
Speaker 3: they're tools, and the question is what are you using
Speaker 3: them for? If they're being used in a way that's
Speaker 3: you know, fraudulent, right, if you're trying, you know, if
Speaker 3: it's a deep think, if you're trying to make it
Speaker 3: sound or look like a politician is saying or doing
Speaker 3: something that they really didn't do, or anybody else for
Speaker 3: that matter. I mean, that's a real hell, that's a
Speaker 3: real problem, and governments are attempting to find ways to
Speaker 3: deal with that through the legal system. If it's being
Speaker 3: used in ways to uh, you know, help in terms
Speaker 3: of education and positive applications, uh, that that's something else again.
Speaker 3: But there's another aspect to and this may just be
Speaker 3: my you know, my my personal preference. I generally don't
Speaker 3: like AI voices. I won't claim that I can detect
Speaker 3: them one hundred percent of the time, because they're getting
Speaker 3: pretty good, but I detect them a lot of the time.
Speaker 3: Often there's you know, even when the voice itself is
Speaker 3: pretty good, there's off a little pacing problems or bizarre
Speaker 3: pronunciation problems, and you know, pauses and things like that,
Speaker 3: and I just I don't find it appealing at all.
Speaker 3: And actually, one of the fastest ways to get me
Speaker 3: to a boarding YouTube video is what I realize. I'm
Speaker 3: listening to a to a to an AI voice. And
Speaker 3: Google actually is in the process of making it even
Speaker 3: easier for YouTube creators to to do this, to create
Speaker 3: AI generated videos and AI generated voices, you know, to
Speaker 3: which I kind of say to myself, you know, what
Speaker 3: to do? This is not something that I'm interested in.
Speaker 3: YouTube is a marvelous is a marvelous thing. It's a
Speaker 3: wonder of the world in many ways, but it's already
Speaker 3: saturated with a lot of often low quality AI generated
Speaker 3: videos and more of them. Is not something that's going
Speaker 3: to be appealing to me. I'm probably at fact, I'm
Speaker 3: sure I'm not in YouTube's preferred demographic, but that's how
Speaker 3: I feel about it. Anyway.
Speaker 2: When we were talking in news with you about over
Speaker 2: the year television and television sets, let's talk a little
Speaker 2: bit about streaming. Who had Blockbuster dropped the ball to
Speaker 2: let Netflix come roaring in when Blockbuster had hundreds and
Speaker 2: hundreds of video stores all around the country.
Speaker 3: Yeah, it's worth remembering, of course, that Netflix was until
Speaker 3: relatively recently. They started off as a DVD distribution service.
Speaker 2: That they mailed you back and forth, all right.
Speaker 3: They'd mail you stvds and you'd mail them back, and
Speaker 3: that was it. I mean, it was basically a mail
Speaker 3: order version of Blockbuster or whatever. And it was only
Speaker 3: over a period of time as more people had, you know,
Speaker 3: if not high speed internet, at least mediocre speeds enough
Speaker 3: to get video, that they started moving more and more
Speaker 3: towards streaming. And then of course that kind of opened
Speaker 3: the you know, open the floodgates, and that we have
Speaker 3: lots and lots of streaming services, only some of which
Speaker 3: will survive, some of which have already died, some of
Speaker 3: the more interesting ones there are not necessarily ones that
Speaker 3: that most people probably even know about. But you know
Speaker 3: that that changed the whole the whole focus and and Blockbuster,
Speaker 3: you know, and I guess other if you think about
Speaker 3: you know, there were other in person video there were
Speaker 3: you know, automated systems for buying DVDs and things like that,
Speaker 3: you know, while vending machines and things.
Speaker 2: I wonder if there was a young guy who went
Speaker 2: to his boss at Blockbuster and said, Hey, there's this
Speaker 2: new thing where people are going to be able to
Speaker 2: watch movies without any DVDs. They just have a TV
Speaker 2: set and turn it on.
Speaker 3: Yeah, this is this is the old joke, right, with
Speaker 3: a response like people are always going to want DVDs.
Speaker 3: They wanted to hold it in their hand, right. So,
Speaker 3: you know, often these kinds of technologies kind of kind
Speaker 3: of sneak up on traditional you know, traditional media and
Speaker 3: traditional business business models. I mean, I can remember a
Speaker 3: point where I was having a technical discussion with someone
Speaker 3: and we were trying to work out the numbers about
Speaker 3: whether it was really going to be practical to send
Speaker 3: audio over the Internet in any significant way. You know,
Speaker 3: the thought of doing video over it was was completely
Speaker 3: you know, just fantastic, But there was a lot of
Speaker 3: technology that had to be developed before it became possible.
Speaker 3: There had to be hardware, codex and a lot of
Speaker 3: advances in the hardware and the speed of these systems
Speaker 3: to make it to make it possible. It's important to
Speaker 3: remember that when we started on the arpinet, the backbone
Speaker 3: of the internet, the high speed lines that connected fights
Speaker 3: on the arpinet where fifty six kilobits per second, that's
Speaker 3: thousand bits per second, which you know would have been
Speaker 3: a low speed modem not all that many years ago
Speaker 3: that people would have of having their homes. So the
Speaker 3: kinds of speeds that are common now when people can
Speaker 3: get them, and a lot of people can't get them,
Speaker 3: a lot of people still can't really get decent internet
Speaker 3: at all. If that's part of our shame in this
Speaker 3: country that we can't get a good Internet to anybody,
Speaker 3: you know.
Speaker 2: Where do you see the future over the next few years.
Speaker 3: Lord, Well, I think we can expect that prices to
Speaker 3: go up. I think we can be pretty sure that
Speaker 3: things are going to be getting more expensive for everything
Speaker 3: related to the to the Internet, in terms of Internet access,
Speaker 3: the streaming services. Already we've seen lots and lots of
Speaker 3: price increases in streaming services. Plus they're trying to worry
Speaker 3: that one time the whole point of getting a streaming
Speaker 3: service was you could get rid of commercials. Now most
Speaker 3: of the streaming services, at least at certain tiers, have
Speaker 3: commercials and more and more of them, and of course
Speaker 3: that would drove people away from cable packages in the
Speaker 3: first place. And now we go full circle and now
Speaker 3: it's you know, the sort of the same stuff, but
Speaker 3: now it's delivered over the internet, maybe over the same
Speaker 3: cable line that you got your your your cable TV
Speaker 3: proper and also but it's it's another one of those
Speaker 3: you know, the more things change, the more they stay
Speaker 3: the same. But you know, prices, prices will will be
Speaker 3: going up. I really don't see. I don't think we're
Speaker 3: in a period now where we can expect to see
Speaker 3: major improvements in Internet service because there just aren't pressures
Speaker 3: to do that. The big Internet companies are kind of
Speaker 3: sitting pretty right now, and to the extent that they
Speaker 3: don't have to do buildouts at and T has already
Speaker 3: said they're not going to do a lot of the
Speaker 3: fiber buildouts that they originally had said they wanted to do,
Speaker 3: so they have to said they're not going to bother
Speaker 3: So I think we may be entering kind of a
Speaker 3: period of stagnation in terms of that aspect of it.
Speaker 3: But the prices will stagnate, they'll they'll be going up.
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