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Speaker 1: You're listening to the iHeartRadio and Coast to Coast Day

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Speaker 1: and Paranormal Podcast Network, where we offer you podcasts of

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Speaker 1: the paranormal, supernatural, and the unexplained. Get ready now for

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Speaker 1: Beyond Contact with Captain.

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Speaker 2: Ron Welcome to our podcast. Please be aware the thoughts

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Speaker 2: and opinions expressed by the host are their thoughts and

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Speaker 2: opinions only and do not reflect those of iHeartMedia, iHeartRadio,

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Speaker 2: Coast to Coast AM, employees of Premiere Networks, or their

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Speaker 2: sponsors and associates. We would like to encourage you to

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Speaker 2: do your own research and discover the subject matter for yourself.

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Speaker 3: Hey everyone, it's Captain ron and Each week are Beyond Contact.

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Speaker 3: We'll explore the latest news in ufology, discuss some of

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Speaker 3: the classic cases, and bring you the latest information from

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Speaker 3: the newest cases as we talk with the top experts.

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Speaker 3: Welcome to Beyond Contact. I'm Captain Ronald. Today we're speaking

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Speaker 3: with Mitch Randall. Mitch is the CEO of Ascendant AI

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Speaker 3: and the innovator behind SkyWatch Passive Radar, a UAP tracking system.

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Speaker 3: SkyWatch was developed through the Galileo Project and hopes to

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Speaker 3: detect UAPs using reflective signals as an alternative to traditional

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Speaker 3: radar technology. Mitch, I'm so glad to see you, and

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Speaker 3: I'm so glad to have you on here today. I

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Speaker 3: find all of the stuff beyond fascinating, so welcome to

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Speaker 3: the show.

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Speaker 4: Well, it's a pleasure to be here.

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Speaker 3: This is really great stuff. And I have to tell

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Speaker 3: you I've been doing a lot of these interviews lately,

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Speaker 3: and often it comes up that I'm the one that

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Speaker 3: brings this up. I don't specifically say SkyWatch, but I

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Speaker 3: say that, you know, the prospects of using AI to

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Speaker 3: better discern what's happening in our skies, to do a faster,

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Speaker 3: more comprehensive job in tracking these objects in the sky,

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Speaker 3: pointing out anomalies is what we need. So we've been

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Speaker 3: talking about this and all these different possibilities, but it

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Speaker 3: turns out you're actually the guy who's actually doing precisely

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Speaker 3: that with this SkyWatch project. Can you tell us, like

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Speaker 3: how this whole thing works.

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Speaker 4: Oh? Yeah, the SkyWatch radar is a passive radar. Let

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Speaker 4: me just back up a little bit. Tell you how

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Speaker 4: regular radar works. Right, regular radar normally you have a big, expensive,

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Speaker 4: high power transmitter and a big dish and it shoots

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Speaker 4: out a pulse out in the sky, and let's say

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Speaker 4: you point it at an airplane and bounces off, the

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Speaker 4: airplane comes back. So that's the traditional radar. The problem

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Speaker 4: with that is it's kind of expensive. You have this

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Speaker 4: huge antenna, you have this big old transmitter thing that's

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Speaker 4: expensive and big. There's another thing. It's called passive radar.

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Speaker 4: So passive radar is a cute little trick where hey,

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Speaker 4: you're just trying to measure the echoes off these objects

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Speaker 4: in the sky. You know what if you could use

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Speaker 4: instead of a big transmitter like that, use somebody else's transmitter.

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Speaker 4: There's tons of transmitters out there that are already transmitting power.

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Speaker 4: Perfect opportunity. There is FM radio transmitters. They have an

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Speaker 4: ideal signal for this kind of work. Plus they're transmitting

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Speaker 4: hundreds of thousands of watts. So there you have it.

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Speaker 4: You have a high power transmitter. And what's going to

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Speaker 4: happen in this case with passive radar is that FM

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Speaker 4: radio signal goes out into the sky. It's also going

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Speaker 4: to bounce off whatever targets are up in the sky,

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Speaker 4: some airplane or some UFO or whatever it is. And

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Speaker 4: that signal is going to come back and you can

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Speaker 4: detect that on a receiver. The cute thing about this,

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Speaker 4: like I said, not only do not have to have

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Speaker 4: that transmitter, but the power that's being transmitted by the

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Speaker 4: FM radio transmitter is so high that you don't even

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Speaker 4: have to have a dish on your receiver end to

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Speaker 4: pick up the echo. So get rid of the dish.

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Speaker 4: And on top of that, this whole thing is basically

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Speaker 4: just an FM receiver. So this is something that nowadays

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Speaker 4: can be relatively inexpensive. You could have an receiver type

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Speaker 4: hardware and let's say, you know, we could make something

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Speaker 4: that's something like six inches by six inches by six inches.

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Speaker 4: So that gives you just an idea of what this

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Speaker 4: hardwork would be like.

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Speaker 3: So would that have their own at their house, they

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Speaker 3: would have this right, You're talking about individual people having

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Speaker 3: it right, and then would they be able to see

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Speaker 3: all the data that you guys would see.

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Speaker 4: Well, let me back up for a second and just say,

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Speaker 4: let me tell you a little bit about the storm

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Speaker 4: chasing radar trucks if you don't mind. So I was

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Speaker 4: instrumental in the building of that with the hardware radar

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Speaker 4: receiver that I built the idea of those storm chasing

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Speaker 4: radar trucks. You know, they see tornadoes. You might have

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Speaker 4: seen them on TV. Right. The idea there is I

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Speaker 4: used to work at the National Center for Atmospheric Research

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Speaker 4: and we had you know, these high not best quality

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Speaker 4: weather radars in the world there, but they were like

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Speaker 4: on a concrete pad. You know, we of course wanted

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Speaker 4: to know what's going on in a tornado. But how

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Speaker 4: often does a tornado come by these you know radars

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Speaker 4: where you have one installed in Colorado, you have one

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Speaker 4: installed in Texas. I mean, you don't. There's not enough

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Speaker 4: statistics there to actually wait around for a tornado to come.

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Speaker 4: So the idea of those storm chasing trucks was, well,

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Speaker 4: the tornado's in Kansas, we got to drive to Kansas.

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Speaker 4: That's how we're going to measure it. So we stuck

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Speaker 4: a radar on a truck. And you know, this is

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Speaker 4: a fundamental part of instrument design. It's not just about

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Speaker 4: the specks of the instrument, but it's also about the

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Speaker 4: cost of the instrument, the practicality, the reliability, and the

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Speaker 4: effectiveness of it. If you can't go to the tornado,

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Speaker 4: you're never going to be able to measure one get

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Speaker 4: wait one hundred years for that and never get a

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Speaker 4: tornado by your radar. Same thing here. So now we

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Speaker 4: have a technology that I can take a little box

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Speaker 4: that's as complicated as a radio receiver right and measure

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Speaker 4: you know, UFO. Of it's in the sky, But what

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Speaker 4: are the chances that UFO comes over your house? By

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Speaker 4: the way, these things can see out one hundred miles,

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Speaker 4: so that's no problem. Still within one hundred mile radius,

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Speaker 4: what are the chances the UFO is going to come there?

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Speaker 4: So the idea behind the SkyWatch system is we call

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Speaker 4: the SkyWatch network. We want to put these in the

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Speaker 4: in the hands of citizens and spread them all over

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Speaker 4: the country. That way, no matter where a UFO would

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Speaker 4: fly in the country, we're going to see it, and

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Speaker 4: hopefully we'll see it with multiple receivers at once.

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Speaker 3: Now, how do you discern between an airplane and a helicopter?

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Speaker 3: Is this where AI comes in. Would that analyze the

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Speaker 3: data and pick out the anomalies that aren't identified right

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Speaker 3: away as a plane, helicopter, satellite, whatever.

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Speaker 4: Oh, that's a great question. Well, one thing is that

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Speaker 4: airplanes do have onboard transponders, so an airplane's constantly telling

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Speaker 4: you transmitting out there where its location is, what it's

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Speaker 4: you know, tail number is, and it's velocity. So we

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Speaker 4: can receive that, and we can we can correlate that

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Speaker 4: to the dots that we see on our display, right,

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Speaker 4: and we can say, oh, that dot is actually due

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Speaker 4: to flight twenty one sixty two, so that's not a UFO.

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Speaker 4: But there are definitely up there airplanes that don't have that.

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Speaker 4: And then there's you know, this is what I think

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Speaker 4: I think we're going to experience. There's some things up

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Speaker 4: there that nobody knows what they are and they're not

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Speaker 4: telling us, you know, to distinguish what you know, what

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Speaker 4: those last objects are. I mean, we definitely can apply AI,

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Speaker 4: but there's also a couple easy ways to tell. If

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Speaker 4: something goes mock four makes a right angle turn with

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Speaker 4: a two hundred and seventy five G turn, you know

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Speaker 4: it's probably not a commercial airliner.

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Speaker 3: And you know what I just thought of while you

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Speaker 3: were saying this, we would then this obviously will be

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Speaker 3: getting recorded correct. Oh, yeah, are right. So the idea

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Speaker 3: is like we always you and I have talked about

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Speaker 3: this before. How you know you never pull out your

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Speaker 3: phone in time to see the UFO or whatever. However,

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Speaker 3: this is going to be recorded constantly, so if we

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Speaker 3: did see something doing ninety degree turn, it would be

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Speaker 3: recorded and we would have a record of that.

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Speaker 4: Well, not only that, you know, the government's had a

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Speaker 4: monopoly on radar data. We don't get to see radar data.

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Speaker 4: We don't have familiarity with radar data. It is so

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Speaker 4: much better than optical data for so many reasons. One

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Speaker 4: is it's quantitative. It actually knows the position and velocity

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Speaker 4: of an object. So it's not just oh, you know,

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Speaker 4: a light went across my screen. I don't know if

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Speaker 4: it's a bug that was two feet away or if

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Speaker 4: it was a huge, you know UFO that's two hundred

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Speaker 4: miles away. You can't tell with it with a camera.

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Speaker 4: But the other thing that I was, you know, made

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Speaker 4: me think of this is not only like you're saying,

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Speaker 4: you know, this will be constantly twenty four to seven

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Speaker 4: monitoring the sky. But another thing about UFOs is we

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Speaker 4: only talk about the luminous UFOs that we see at

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Speaker 4: night at least, so I think, so there's some daylight

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Speaker 4: sightings of UFOs that aren't necessarily luminous, but what about

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Speaker 4: non luminous UFOs that are at night? How would you

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Speaker 4: even know that? You know, for all we know that's

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Speaker 4: when they're around. If this all comes out that there's

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Speaker 4: no UFOs in the sky, great because that's also a

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Speaker 4: scientific result.

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Speaker 3: That's not what I'm expout too. That's equally important. It

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Speaker 3: would be great to show that no, there is nothing,

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Speaker 3: that that was an animal or whatever.

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Speaker 4: Yeah, I mean, you know, it's not conclusive because that's

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Speaker 4: proving a negative, but it would be very interesting. But

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Speaker 4: you know, I don't mind playing around with the assumption

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Speaker 4: that there are strange things up in the sky. So

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Speaker 4: that's that's where of course my passion comes from. And

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Speaker 4: that's why I'm interested in this because I think I

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Speaker 4: have a feeling we are going to see things in

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Speaker 4: the night sky or a day sky that are doing strange,

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Speaker 4: crazy maneuvers, and no, we're going to be able to

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Speaker 4: quantify them.

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Speaker 3: Well, no doubt about it. We could start with Kevin Day,

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Speaker 3: the radar guy from the military. There's been many military

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Speaker 3: accounts of different UFOs on radar specifically. You know, we've

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Speaker 3: heard that from people working in aviation as well as

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Speaker 3: people in the military. You know, this system that you

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Speaker 3: wanted to create. Here could just support that data only

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Speaker 3: will have access to the results for one right, which

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Speaker 3: would be great. Listen, We're going to have to take

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Speaker 3: a quick break here, Mitch. Fascinating stuff about SkyWatch. We're

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Speaker 3: going to come right back and talk more about that

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Speaker 3: with Mitch Randall. You're listening to Beyond Contact on the

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Speaker 3: iHeartRadio and Coast to Coast AM Paranolal podcast network.

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Speaker 1: Thanks for listening. Keep it here on the iHeartRadio on

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Speaker 1: Coast to Coast AM Paranormal Podcast Network.

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Speaker 3: Okay, we're with Mitch Randall here on Beyond Contact and

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Speaker 3: we're talking about SkyWatch and this will be a way

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Speaker 3: that all of us can participate in monitoring the skies

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Speaker 3: and maybe through this new technology, be able to find

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Speaker 3: out what's really happening up there, right, Mitch, Yeah, what

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Speaker 3: is the next step for this program? Do you guys

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Speaker 3: need to raise funding? Is the what is the approach here?

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Speaker 4: You read my mind? Something really big is happening. So

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Speaker 4: it turns out that this is just one piece of

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Speaker 4: something that's called Operation SkyWatch. So Operation SkyWatch is a

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Speaker 4: very bold plan. Actually, can I can I tell you

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Speaker 4: what the mission statement of Operation Skywatches.

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Speaker 3: I would love that please to.

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Speaker 4: Establish the existence of UFOs by measuring their extreme accelerations

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Speaker 4: and maneuvers. And when I say we're measuring their extreme

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Speaker 4: acceleration maneuvers, what I'm talking about is scientific quality data

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Speaker 4: of those extreme acceleration some maneuvers. You know, for decades

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Speaker 4: people have reported, oh, you know, I saw an object

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Speaker 4: and thenink it just disappeared, you know, and there's really

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Speaker 4: nothing that you know, Then you hear the debunkers say,

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Speaker 4: where's the proof? Right, So here we are this is

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Speaker 4: a system that can actually quantitatively measure these things and

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Speaker 4: there can be a scientific basis for it. Because of

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Speaker 4: course we're going to write up everything about the SkyWatch

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Speaker 4: radar and publish it. So this is this is how

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Speaker 4: science works. You always write a paper about your instrument

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Speaker 4: so that when you when you get data from your instrument,

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Speaker 4: everybody knows what that data means. SkyWatch Radar is just

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Speaker 4: one third of Operation SkyWatch. So Operation SkyWatch includes the radar,

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Speaker 4: an app, a video recording app for triangulation, and a

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Speaker 4: national reporting database. And the idea here is there's three

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Speaker 4: really important pieces of information that can come together. And

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Speaker 4: this is all in cooperation with a scientific group. So

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Speaker 4: I'm in talks with multiple scientific groups about this, and

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Speaker 4: I think we're close to getting to a point where

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Speaker 4: this is I can make an official announcement of who

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Speaker 4: the group is. Oh, I have so much to talk

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Speaker 4: about here too, because let me just jump to that

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Speaker 4: National Reporting Center just for a second, okay.

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Speaker 3: Sure please. How many times have we heard free days NonStop?

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Speaker 3: This is all fascinating stuff.

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Speaker 4: How many times have you heard people say there's no

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Speaker 4: evidence and they think, wait a minute, there's thousands of witnesses,

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Speaker 4: some of them so credible that have come forward. Thousands

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Speaker 4: of witnesses and you're telling me there's no evidence. But

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Speaker 4: I know somebody in court had thousands of witnesses. You

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Speaker 4: could put someone behind bars for that. So what's the

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Speaker 4: deal here? What is the scientific standard? One thing I

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Speaker 4: would like to see happen is there's a scientific group

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Speaker 4: now involved, and the scientific group can see again, if

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Speaker 4: you have an instrument, you write about the instrument's quality, right,

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Speaker 4: and then when they instrument gathers data, you publish that.

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Speaker 4: Why don't we do that with the reporting center. Why

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Speaker 4: don't we talk about the quality of the data that

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Speaker 4: we get with the reporting center? Why don't we talk

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Speaker 4: about what it means to have corroborated witness testimony about something?

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Speaker 4: Has that actually been done scientifically? Is there a paper

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Speaker 4: somewhere that says, oh, well, if you have four witnesses

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Speaker 4: to this event, then it's the probability blah blah blah.

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Speaker 4: I mean, I really think that's something that we need

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Speaker 4: to do as a society because otherwise we're going to

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Speaker 4: say there's no evidence, when in fact, I believe there

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Speaker 4: is a lot of evidence. But that's just the reporting center. So,

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Speaker 4: and I've already talked about the radar. The third thing

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Speaker 4: is this triangulation video triangulation app. Part of the system now,

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Speaker 4: is this video triangulation app and if you've used a

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Speaker 4: stargazing app effort before, you know that the phone knows

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Speaker 4: where it's pointed. That metadata can be attached to a video.

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Speaker 4: We could have a video that not only shows what

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Speaker 4: objects on the screen, but it shows exactly your location

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Speaker 4: and exactly your pointing angle. And if two people had

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Speaker 4: a video of the same object, boom, you can triangulate

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Speaker 4: that object and figure out its position in time. Therefore,

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Speaker 4: you know it's velocity and acceleration. So that's like really

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Speaker 4: super great.

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Speaker 3: Other people that had that app could then get an

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Speaker 3: Amber alert. This has headed your way right.

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Speaker 4: Well, well, you hit on a perfect point there. Sure.

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Speaker 4: How often do people pull out their phone when they

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Speaker 4: say UFO? It turns out almost never. They're just stunned

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Speaker 4: by what they're looking at, and they don't think to

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Speaker 4: pull out their phone and start fumbling through icons.

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Speaker 3: Because you don't know what's coming. Now, you don't know

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Speaker 3: what's coming right here. You shocked to you're shocked. But

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Speaker 3: if you had the thing, it's going to be there

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Speaker 3: in ten minutes. Well, now I'm going to go outside,

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Speaker 3: set up my camera and wait for it. That's a difference.

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Speaker 4: That's exactly right. That's exactly right. Now, we have a

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Speaker 4: radar as part of the system. The radar can actually

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Speaker 4: give an alert to that person and say, hey, run outside,

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Speaker 4: because in two minutes over the southwest horizon there's going

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Speaker 4: to be something. Get ready to push the record button.

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Speaker 4: And with a system like that, we very well could

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Speaker 4: get multiple cameras on the same object and do video triangulation.

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Speaker 3: We have all of the radar data recorded as well

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Speaker 3: that you've already talked about, would be reporting all of

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Speaker 3: that so that would match the video. Now you really

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Speaker 3: have some really strong evidence that you could show the

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Speaker 3: rest of the world.

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Speaker 4: Well, that would be called multisensor data. Right, that's the

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Speaker 4: cream of the crop. But in addition, that app would

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Speaker 4: help people report it right to the database, so you'd

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Speaker 4: also have, you know, all the provenance. Basically, you'd have

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Speaker 4: somebody describing what they saw and the phone call coming

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Speaker 4: in at the.

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Speaker 3: Time they say eyewitness accounts, you have video evidence, you

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Speaker 3: have data from the radar. Now you've got three forms

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Speaker 3: of evidence that all corroborate. That's pretty exciting.

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Speaker 4: And you have a scientific organization that's going to take

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Speaker 4: that data and turn it into something that the rest

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Speaker 4: of the scientists agree with so that that's why I

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Speaker 4: say this will establish the existence of UFOs by measuring

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Speaker 4: their extreme accelerations and maneuvers. This would be a slam dunk.

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Speaker 3: You're the hero that this community has been waiting for

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Speaker 3: for a whole long time. Match how far off is

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Speaker 3: something like this?

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Speaker 4: Oh let me let me throw in a real quick

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Speaker 4: thing here. You know the five observables, right, you've heard

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Speaker 4: of the five observables. So there's been reports of UFOs.

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Speaker 4: They do these crazy things and that makes them interesting.

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Speaker 4: But I have to say four of the five are

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Speaker 4: are debunk is something you can actually debunk. For example,

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Speaker 4: they say no control surfaces or visible propulsion. Well that's

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Speaker 4: a balloon. A balloon doesn't have control surfaces or propulsion, right,

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Speaker 4: and you hear that all the time. Oh, that's a balloon.

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Speaker 4: Or you know, you can go down the list with

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Speaker 4: the other the other things. I just wanted to point

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Speaker 4: that out because we specifically picked the acceleration because it's undebunkable.

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Speaker 4: If you can quantitatively measure an object and it has

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Speaker 4: an acceleration of fifteen hundred g's let's say right angle

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Speaker 4: turn going a mock two, there's no balloon. There's no,

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Speaker 4: you know, natural phenomena. There's no you know, if you

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Speaker 4: tried to make that happen with some kind of you know, fakery,

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Speaker 4: you couldn't do it. So undebuncable is part of this.

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Speaker 3: Undebunkable is what we need. We need to have more

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Speaker 3: credible evidence like this. Do you think this is something

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Speaker 3: on the horizon? How far down the road is this

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Speaker 3: going to be?

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Speaker 4: Yeah? So this is a development and engineering development for

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Speaker 4: these products, especially the radar, you know, but also the

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Speaker 4: actually app could actually be available within like six to

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Speaker 4: eight months, wow, something like that. The radar. We have

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Speaker 4: our schedules about twenty four months before the radar is

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Speaker 4: available for sale, So when we hit twenty four months,

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Speaker 4: you could click on the internet and go and order

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Speaker 4: one and the whole network will start to build out.

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Speaker 3: That sounds very exciting to me. Matched this feels like

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Speaker 3: something that this community has wanted for a long time,

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Speaker 3: and it's very promising and it's nice to see this

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Speaker 3: in the hand of the people. Where Like you said,

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Speaker 3: the government owns the radar data, well they won't own

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Speaker 3: this and we will all have access to it, and

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Speaker 3: that's kind of what we want to get. To take

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Speaker 3: another break, here, Mitch, We're going to come back and

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Speaker 3: we're going to move on and talk to Mitch a

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Speaker 3: little bit about AI too, because this stuff is even

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Speaker 3: more mind blowing. If you can believe that you're listening

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Speaker 3: to Beyond Contact on the iHeartRadio and Coast to Coast

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Speaker 3: AM Paranormal Podcast Network.

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Speaker 6: You can also tap into members that are sixty plus

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Speaker 6: and have some fun at paranormal date dot com.

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Speaker 1: to coastam dot com.

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Speaker 3: Hey, it's producer Tom, and you're right where you need

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Speaker 3: to be.

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Speaker 5: This is the iHeartRadio and Coast to Coast AM Paranormal

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Speaker 5: podcast Network.

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Speaker 3: We are back on Beyond Contact with Captain Ron and

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Speaker 3: I'm talking to Mitch Randall. I'd like to move on

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Speaker 3: a little bit now for a second. If I could

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Speaker 3: Mitch and talk about AI. This is incredible to I've

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Speaker 3: heard you talk extensively about different AI processes and where

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Speaker 3: that's headed, and I'd really like to jump into this

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Speaker 3: right in the beginning though. I don't think people really

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Speaker 3: understand some of these new AI terms that are out there.

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Speaker 3: Maybe you could kind of straighten that out for us.

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Speaker 3: Your company is called as send an AI, and then

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Speaker 3: now all of a sudden, just when we think we

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Speaker 3: get our head around AI, there's open AI, there's generative AI,

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Speaker 3: there's artificial general intelligence. Can you sort this out for

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Speaker 3: us mere mortals, so we know what this means.

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Speaker 4: I think AI had some of its beginnings with images,

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Speaker 4: so you know, let's try to understand if this image

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Speaker 4: has a dog or a cat, you know that kind

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Speaker 4: of thing. You know, those are the nice baby step

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Speaker 4: days back in the day with AI. And then they

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Speaker 4: also had you know, translators, so you could put some

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Speaker 4: text in there. It would turn it into let's say,

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Speaker 4: from English to French. You know, these are tasks. They

432
00:23:15,560 --> 00:23:18,200
Speaker 4: found out that AI can actually be helpful out pretty

433
00:23:18,200 --> 00:23:21,440
Speaker 4: hard to do any other way. But then they had

434
00:23:22,000 --> 00:23:25,240
Speaker 4: a renaissance, A big change happened. This is right around

435
00:23:25,320 --> 00:23:28,560
Speaker 4: twenty twelve where they came up with the idea of convolution.

436
00:23:28,720 --> 00:23:33,520
Speaker 4: It's for all of us mortals. Convolution just was a

437
00:23:33,560 --> 00:23:36,280
Speaker 4: big step in how much they could compute with a

438
00:23:36,280 --> 00:23:38,520
Speaker 4: lot less compute power, so they could do a lot

439
00:23:38,560 --> 00:23:41,400
Speaker 4: more operations with a lot less compute power. So then

440
00:23:41,480 --> 00:23:44,560
Speaker 4: all of a sudden, the ability to tell the image

441
00:23:44,600 --> 00:23:47,040
Speaker 4: was a dog or a cat became something they could

442
00:23:47,160 --> 00:23:49,479
Speaker 4: actually do, and do it quite well. In fact, they

443
00:23:49,520 --> 00:23:52,639
Speaker 4: were beating humans at it. So they had you know,

444
00:23:52,720 --> 00:23:56,000
Speaker 4: data sets now and they had a hey day right

445
00:23:56,040 --> 00:23:58,879
Speaker 4: there after, you know, twenty twelve or so, all of

446
00:23:58,920 --> 00:24:01,520
Speaker 4: a sudden, the AI models start to get much much better.

447
00:24:02,080 --> 00:24:05,640
Speaker 4: Then they came up with the transformers. So you've heard

448
00:24:05,640 --> 00:24:09,840
Speaker 4: of CHAT. GPT stands for Generative pre trained Transformer. That's

449
00:24:09,840 --> 00:24:13,000
Speaker 4: what the GPT stands for. So a transformer was a

450
00:24:13,040 --> 00:24:17,159
Speaker 4: new technique. Again, it's like technology they're throwing at the computations.

451
00:24:18,040 --> 00:24:21,600
Speaker 4: There's definitely a philosophy or the design behind it. But

452
00:24:21,720 --> 00:24:25,480
Speaker 4: the transformers allowed now this translation to get way better.

453
00:24:25,520 --> 00:24:28,800
Speaker 4: And then they realized as they made those models bigger

454
00:24:28,840 --> 00:24:31,200
Speaker 4: and bigger, and when I say bigger and bigger, put

455
00:24:31,240 --> 00:24:33,560
Speaker 4: more and more layers on them. So a first layer

456
00:24:33,640 --> 00:24:37,159
Speaker 4: would kind of figure out, you know, deciphered a little bit,

457
00:24:37,200 --> 00:24:38,879
Speaker 4: and then the next layer, in the next layer, they

458
00:24:38,920 --> 00:24:41,439
Speaker 4: would just literally make them deeper and deeper with more

459
00:24:41,440 --> 00:24:43,600
Speaker 4: and more layers. I don't know that's making sense, but

460
00:24:43,680 --> 00:24:46,560
Speaker 4: that's basically what they're doing. And when they did that,

461
00:24:46,640 --> 00:24:50,159
Speaker 4: they started to realize that's funny. The thing is actually

462
00:24:50,160 --> 00:24:52,600
Speaker 4: seems like it has some kind of ability to reason.

463
00:24:53,359 --> 00:24:56,399
Speaker 4: You know. It's interesting because people don't haven't been sitting

464
00:24:56,400 --> 00:24:58,640
Speaker 4: down going like, hey, I'm going to make an AI

465
00:24:58,760 --> 00:25:01,639
Speaker 4: that reasons because nobody knows how to do that. But

466
00:25:01,720 --> 00:25:04,000
Speaker 4: what they can do is, you know, they start out

467
00:25:04,000 --> 00:25:06,639
Speaker 4: with the translator and they realize, wow, if I just

468
00:25:06,680 --> 00:25:09,560
Speaker 4: make a translator has a lot more layers, and I

469
00:25:09,600 --> 00:25:11,560
Speaker 4: train it on a lot more books and a lot

470
00:25:11,600 --> 00:25:15,520
Speaker 4: more input information. Now it's sort of emerging out of

471
00:25:15,560 --> 00:25:17,520
Speaker 4: this is this ability's kind of reason?

472
00:25:17,960 --> 00:25:20,480
Speaker 3: Wasn't it a language system where they were really just

473
00:25:20,760 --> 00:25:24,080
Speaker 3: guessing or predicting the next word that you would say.

474
00:25:24,640 --> 00:25:27,919
Speaker 4: You are exactly right, Okay, that's exactly what it is.

475
00:25:27,960 --> 00:25:30,720
Speaker 4: So again, nobody sat down and thought, I'm going to

476
00:25:30,760 --> 00:25:33,119
Speaker 4: make it reason by guessing the next word in some

477
00:25:33,320 --> 00:25:36,280
Speaker 4: fancy way, Like, nobody knows how to make that happen.

478
00:25:36,800 --> 00:25:39,800
Speaker 4: But what they could do is just make the model bigger,

479
00:25:39,920 --> 00:25:42,359
Speaker 4: make and train more stuff on it, and then be

480
00:25:42,800 --> 00:25:46,639
Speaker 4: surprised that just by predicting the next word, they're getting

481
00:25:46,680 --> 00:25:49,639
Speaker 4: out this kind of ability to reason. So it's pretty

482
00:25:49,680 --> 00:25:53,560
Speaker 4: interesting to see how the how the everything evolved. It's

483
00:25:53,600 --> 00:25:54,840
Speaker 4: not so much because.

484
00:25:54,560 --> 00:25:56,160
Speaker 3: I've been here and ask you a quick question, Mitch,

485
00:25:56,200 --> 00:25:58,919
Speaker 3: because you just said the ability to reason, and I

486
00:25:58,960 --> 00:26:02,760
Speaker 3: want to ask you, is it really reasoning or is

487
00:26:02,800 --> 00:26:06,800
Speaker 3: it we're assigning the notion of reasoning to AI because

488
00:26:06,840 --> 00:26:10,399
Speaker 3: these words in this order seem to make reasonable sense.

489
00:26:11,200 --> 00:26:16,000
Speaker 4: Well, I love that question, because what is reason? Well,

490
00:26:16,119 --> 00:26:19,480
Speaker 4: they you know, for example, consciousness, They say, oh, that's

491
00:26:19,480 --> 00:26:21,959
Speaker 4: a hard problem. And I don't know if you've ever

492
00:26:22,000 --> 00:26:25,320
Speaker 4: heard that term before. They say, consciousness is the hard problem.

493
00:26:25,400 --> 00:26:27,800
Speaker 4: And I think what they're saying is, you know, we

494
00:26:27,840 --> 00:26:29,680
Speaker 4: don't know what it is, so how can we design

495
00:26:29,800 --> 00:26:31,760
Speaker 4: for it. You know, we can't even describe it. We

496
00:26:31,800 --> 00:26:34,720
Speaker 4: don't even know, we can't even make a test for it.

497
00:26:34,800 --> 00:26:37,159
Speaker 4: Similar to what you're talking about with reasoning. So let

498
00:26:37,200 --> 00:26:39,120
Speaker 4: me just turn it kind of back around at you.

499
00:26:39,640 --> 00:26:42,680
Speaker 4: Is the AI reasoning when it's just predicting the next word?

500
00:26:43,320 --> 00:26:46,240
Speaker 4: Or let me ask you this, Are you reasoning when

501
00:26:46,280 --> 00:26:47,520
Speaker 4: you predict the next word?

502
00:26:47,960 --> 00:26:50,200
Speaker 3: So no, I don't think it predicting the next word.

503
00:26:50,280 --> 00:26:55,560
Speaker 3: That's just a math. Usually after good the word morning comes,

504
00:26:55,840 --> 00:26:58,440
Speaker 3: So that's just math that that's the most frequent word

505
00:26:58,480 --> 00:27:01,760
Speaker 3: after good good morning. So no, I don't think that's reasoning.

506
00:27:01,840 --> 00:27:03,000
Speaker 3: I think that's just math.

507
00:27:03,359 --> 00:27:05,640
Speaker 4: You'd have to know it's not just it's not predicting

508
00:27:05,680 --> 00:27:07,720
Speaker 4: the next word by looking at the last word. It's

509
00:27:07,720 --> 00:27:09,800
Speaker 4: predicting the next word by looking at the last ten

510
00:27:09,880 --> 00:27:13,560
Speaker 4: thousand words. So if you have a conversation, my.

511
00:27:14,160 --> 00:27:17,520
Speaker 3: Head just exploded. Sorry, yeah, oh okay, I didn't realize

512
00:27:17,560 --> 00:27:20,640
Speaker 3: it's that complex. Then maybe it's more towards reasoning.

513
00:27:21,400 --> 00:27:24,080
Speaker 4: Yeah, because when you have a conversation with AI, let's

514
00:27:24,080 --> 00:27:27,159
Speaker 4: say you go back and forth five times with several words,

515
00:27:27,200 --> 00:27:29,480
Speaker 4: you know, and let's say that was five hundred words

516
00:27:29,720 --> 00:27:31,840
Speaker 4: you when it predicts the next word and its answer,

517
00:27:32,040 --> 00:27:35,040
Speaker 4: it's considering all those five hundred words, not to mention

518
00:27:35,119 --> 00:27:38,440
Speaker 4: the trillion of words has already was trained on. It's

519
00:27:38,480 --> 00:27:41,280
Speaker 4: a little more complicated than just the word after.

520
00:27:41,119 --> 00:27:44,639
Speaker 3: Good, no, No, it's a lot more complix Yeah, apparently.

521
00:27:45,200 --> 00:27:48,760
Speaker 4: Yeah, and you can imagine too, there's a lot of

522
00:27:48,760 --> 00:27:52,040
Speaker 4: computation that goes into that. It's very compute heavy.

523
00:27:52,520 --> 00:27:54,520
Speaker 3: I'd love to talk to you about this idea of

524
00:27:54,720 --> 00:27:59,000
Speaker 3: sentience and the singularity. I'm not sure people even understand

525
00:27:59,040 --> 00:28:02,760
Speaker 3: what that means. Like singularity is the one me, it's

526
00:28:02,920 --> 00:28:07,320
Speaker 3: my consciousness. And we are talking about computer AI systems

527
00:28:07,720 --> 00:28:11,240
Speaker 3: reaching the point so that they are in a sense

528
00:28:11,359 --> 00:28:15,520
Speaker 3: conscious and sentient. Thus they would be able to pass

529
00:28:15,680 --> 00:28:19,240
Speaker 3: the Ellent Turing test. No problem, and a human wouldn't

530
00:28:19,240 --> 00:28:21,960
Speaker 3: be able to distinguish the two, right, is this even possible?

531
00:28:21,960 --> 00:28:22,520
Speaker 3: In your view?

532
00:28:23,240 --> 00:28:27,119
Speaker 4: It's so interesting because nobody knows what consciousness is, right,

533
00:28:27,359 --> 00:28:29,760
Speaker 4: so how can they design for it? And if they

534
00:28:29,800 --> 00:28:32,960
Speaker 4: train on, you know, a bunch of encyclopedias, you know,

535
00:28:33,040 --> 00:28:35,760
Speaker 4: how could that be consciousness doesn't make any sense? Right?

536
00:28:36,080 --> 00:28:38,960
Speaker 4: There's more too than that these things what they have

537
00:28:39,040 --> 00:28:41,720
Speaker 4: found in general with AI, for example, just starting with

538
00:28:41,800 --> 00:28:44,400
Speaker 4: a dog cat example, how you know, how does an

539
00:28:44,400 --> 00:28:46,400
Speaker 4: image tell if it's a dog or a cat? How

540
00:28:46,400 --> 00:28:49,080
Speaker 4: do you make a machine that does that? People didn't

541
00:28:49,120 --> 00:28:52,440
Speaker 4: know exactly what was going to happen. And when it's

542
00:28:52,440 --> 00:28:54,400
Speaker 4: all done and we say we have a machine that

543
00:28:54,440 --> 00:28:57,360
Speaker 4: can detect what's a dog and what's a cat, nobody

544
00:28:57,400 --> 00:29:00,680
Speaker 4: knows how that works. Nobody can actually say, well, oh yeah,

545
00:29:00,720 --> 00:29:03,560
Speaker 4: it's because xyz No. They just trained it on a

546
00:29:03,600 --> 00:29:05,760
Speaker 4: million dogs and a million cats and now it knows.

547
00:29:06,040 --> 00:29:08,120
Speaker 4: So it's a little bit of a mystery what's going

548
00:29:08,160 --> 00:29:10,480
Speaker 4: on in there. And then we talked about that predicting

549
00:29:10,520 --> 00:29:12,640
Speaker 4: the next word thing, and it seems to have some

550
00:29:12,760 --> 00:29:15,600
Speaker 4: kind of reason, right, They're just going to take it

551
00:29:15,680 --> 00:29:20,440
Speaker 4: further and further and consciousness could very well emerge from that.

552
00:29:20,920 --> 00:29:23,440
Speaker 4: And you know the question is, you know, did they

553
00:29:23,440 --> 00:29:26,120
Speaker 4: design it to do that? Well, actually, they're kind of

554
00:29:26,120 --> 00:29:28,880
Speaker 4: getting to the point where they are kind of getting

555
00:29:28,880 --> 00:29:32,360
Speaker 4: a pulse on what to design for. Still nobody knows

556
00:29:32,400 --> 00:29:36,760
Speaker 4: exactly what consciousness is, but they do gather a you know,

557
00:29:36,960 --> 00:29:39,920
Speaker 4: an intuition for what needs to be done and where

558
00:29:39,960 --> 00:29:43,640
Speaker 4: they should focus on. So they are pushing towards consciousness.

559
00:29:43,800 --> 00:29:45,400
Speaker 4: I think they're going to hit it. I don't think

560
00:29:45,440 --> 00:29:46,440
Speaker 4: there's any doubt.

561
00:29:46,640 --> 00:29:48,800
Speaker 3: I think it's it's so hard, Like you said, we

562
00:29:48,800 --> 00:29:51,360
Speaker 3: don't even know what consciousness is. That almost feels like

563
00:29:51,400 --> 00:29:54,760
Speaker 3: our soul or our higher self. And to say that

564
00:29:54,920 --> 00:29:58,360
Speaker 3: a machine is going to achieve that seems beyond my

565
00:29:58,600 --> 00:30:01,120
Speaker 3: tiny brain to be able to figure it is scary

566
00:30:01,160 --> 00:30:02,880
Speaker 3: to think about. We're gonna have to take another quick

567
00:30:02,880 --> 00:30:05,440
Speaker 3: break here. When we come back, we're going to find

568
00:30:05,480 --> 00:30:09,280
Speaker 3: out morphamitch about this and whether or not AI can

569
00:30:09,440 --> 00:30:13,480
Speaker 3: remember talking to you. You're listening to Beyond Contact right

570
00:30:13,480 --> 00:30:16,400
Speaker 3: here on the iHeartRadio on Coast to Coast am Paranormal

571
00:30:16,440 --> 00:30:17,400
Speaker 3: Podcast Network.

572
00:30:19,600 --> 00:30:27,560
Speaker 1: The Internet is an extraordinary resource that links our children

573
00:30:27,600 --> 00:30:31,960
Speaker 1: to a world of information, experiences and ideas, and also

574
00:30:32,080 --> 00:30:35,560
Speaker 1: can expose them to risk. Teach your children the basic

575
00:30:35,640 --> 00:30:39,520
Speaker 1: safety rules of the virtual world. Our children are everything,

576
00:30:40,040 --> 00:30:41,320
Speaker 1: Do everything for.

577
00:30:41,160 --> 00:30:47,800
Speaker 4: Them before the.

578
00:30:54,920 --> 00:30:58,120
Speaker 5: Art Belveult has classic audio waiting for you. Now go

579
00:30:58,200 --> 00:31:07,600
Speaker 5: to costam dot com for details. Take us with you anywhere.

580
00:31:07,760 --> 00:31:10,560
Speaker 5: This is the iHeartRadio and Coast to Coast AM Paranormal

581
00:31:10,640 --> 00:31:22,240
Speaker 5: podcast network.

582
00:31:23,400 --> 00:31:27,000
Speaker 3: We are back on Beyond Contact with Mitch Randall. Hey,

583
00:31:27,400 --> 00:31:29,440
Speaker 3: let me ask you this question. Match as smart as

584
00:31:29,480 --> 00:31:33,200
Speaker 3: AI is, isn't it true that it doesn't remember what

585
00:31:33,320 --> 00:31:36,880
Speaker 3: we talked about right before? Kind of like my mother

586
00:31:36,960 --> 00:31:39,680
Speaker 3: who who loves to call me and ask me what

587
00:31:39,800 --> 00:31:42,040
Speaker 3: he asked me the day before? Is that true?

588
00:31:42,920 --> 00:31:45,160
Speaker 4: Well, this is kind of goes back to what I

589
00:31:45,200 --> 00:31:47,640
Speaker 4: was saying a little earlier. They actually now have an

590
00:31:47,640 --> 00:31:51,200
Speaker 4: idea maybe if things they should be focusing on, they're

591
00:31:51,240 --> 00:31:53,880
Speaker 4: going to make the kind of reasoning and agent that

592
00:31:53,920 --> 00:31:56,440
Speaker 4: we're you know that they're looking for for a GI right.

593
00:31:56,640 --> 00:31:59,880
Speaker 4: I guess, Sora, you know what SOA is. No TEX

594
00:32:00,360 --> 00:32:03,920
Speaker 4: It's a text to video generator. So you can say, hey,

595
00:32:03,960 --> 00:32:06,160
Speaker 4: you know, a mushroom grows and it has a frog

596
00:32:06,480 --> 00:32:08,040
Speaker 4: on it or something like that, and it makes a

597
00:32:08,040 --> 00:32:10,280
Speaker 4: little video of a mushroom growing with a frog on it.

598
00:32:11,080 --> 00:32:14,160
Speaker 4: So it turns out I thought that was cute and interesting, right,

599
00:32:14,480 --> 00:32:17,360
Speaker 4: it turns out I heard Soro was actually a project

600
00:32:17,440 --> 00:32:21,080
Speaker 4: to do what's called world modeling, So they understood and

601
00:32:21,400 --> 00:32:23,360
Speaker 4: in my company, we've been talking about this kind of

602
00:32:23,360 --> 00:32:25,840
Speaker 4: thing for a couple of years, so we're we kind

603
00:32:25,840 --> 00:32:28,280
Speaker 4: of understand like the pieces that would have to come

604
00:32:28,480 --> 00:32:32,400
Speaker 4: together to make you know, an agi, and clearly, you know,

605
00:32:32,440 --> 00:32:35,080
Speaker 4: the people that open AI are also there too, because

606
00:32:35,200 --> 00:32:37,080
Speaker 4: the idea is, if you make an agent that's going

607
00:32:37,160 --> 00:32:39,360
Speaker 4: to make actions in this world, you can't make an

608
00:32:39,360 --> 00:32:41,920
Speaker 4: action unless you know what's going to happen. Like maybe

609
00:32:41,960 --> 00:32:44,040
Speaker 4: it just wants to take the next step, you know,

610
00:32:44,080 --> 00:32:46,280
Speaker 4: but there's a cliff there, so it's got to know, Hey,

611
00:32:46,320 --> 00:32:48,440
Speaker 4: if I take that step, I'm going to go flying

612
00:32:48,520 --> 00:32:51,880
Speaker 4: down that cliff. So Soro was actually a world model

613
00:32:52,160 --> 00:32:54,440
Speaker 4: to predict what's going to happen based on what an

614
00:32:54,520 --> 00:32:58,040
Speaker 4: agent is proposing. You know. So an agent might say, well,

615
00:32:58,080 --> 00:32:59,760
Speaker 4: I know you should, you know, go to the airport

616
00:33:00,160 --> 00:33:02,240
Speaker 4: up your aunt first. That way you can drive to

617
00:33:02,280 --> 00:33:04,560
Speaker 4: the thing later, you know, So then you need some

618
00:33:04,640 --> 00:33:07,480
Speaker 4: kind of world model that shows what's going to happen

619
00:33:07,520 --> 00:33:10,880
Speaker 4: in that scenario. Going No, But another really low hanging

620
00:33:10,960 --> 00:33:13,680
Speaker 4: fruit is memory. In fact, I've been saying for a

621
00:33:13,680 --> 00:33:15,600
Speaker 4: couple of years now that you could take just a

622
00:33:15,720 --> 00:33:19,160
Speaker 4: standard kind of boring LLLM and give it memory so

623
00:33:19,200 --> 00:33:22,160
Speaker 4: that it could just look up things. And that's easy

624
00:33:22,200 --> 00:33:24,600
Speaker 4: to do for AIS, by the way, and it could

625
00:33:24,600 --> 00:33:26,920
Speaker 4: even know that it's supposed to look up. Right, that's

626
00:33:26,960 --> 00:33:29,280
Speaker 4: not that hard to train into even a language model

627
00:33:29,280 --> 00:33:31,440
Speaker 4: that we're already using. But imagine if you had that

628
00:33:31,600 --> 00:33:34,880
Speaker 4: a backpack that had an LM and that also had

629
00:33:34,920 --> 00:33:37,800
Speaker 4: a camera, and every second it took a picture and

630
00:33:37,920 --> 00:33:41,120
Speaker 4: recorded what was being said, right, maybe you'd go like, hey,

631
00:33:41,720 --> 00:33:44,120
Speaker 4: you know, remember that time we went to you know,

632
00:33:44,520 --> 00:33:47,600
Speaker 4: Mohab and we rode our bikes around and it goes, oh, yeah,

633
00:33:47,640 --> 00:33:50,120
Speaker 4: that was fun. You know we got a flat tire

634
00:33:50,240 --> 00:33:52,200
Speaker 4: that was a blast. I mean, that thing would turn

635
00:33:52,240 --> 00:33:55,280
Speaker 4: into your best friend. And it doesn't. It doesn't have

636
00:33:55,400 --> 00:33:58,360
Speaker 4: to be much smarter than an LM to be that

637
00:33:58,440 --> 00:34:01,720
Speaker 4: kind of companion just by adding memory to it. So,

638
00:34:01,800 --> 00:34:04,400
Speaker 4: I mean, you know that I've always said that l

639
00:34:04,480 --> 00:34:09,120
Speaker 4: elms are basically a reasoning engine. That you could just

640
00:34:09,160 --> 00:34:11,000
Speaker 4: put it in a feedback loop, just kind of throw

641
00:34:11,080 --> 00:34:13,600
Speaker 4: some other stuff around it, and you have a pretty

642
00:34:13,640 --> 00:34:15,000
Speaker 4: good start on AGI.

643
00:34:15,640 --> 00:34:18,520
Speaker 3: I can imagine a time in the very near future

644
00:34:18,520 --> 00:34:21,279
Speaker 3: where kids will have a bear or whatever that have

645
00:34:21,400 --> 00:34:25,319
Speaker 3: AI built in and they have memory, and they'll talk

646
00:34:25,360 --> 00:34:27,680
Speaker 3: about growing up together. You know, like the kids that

647
00:34:27,840 --> 00:34:30,399
Speaker 3: you know, you hear them have invisible friends. Well, no,

648
00:34:30,440 --> 00:34:33,000
Speaker 3: we have a visible friend right here, and I share

649
00:34:33,040 --> 00:34:36,960
Speaker 3: everything with this bear. You can imagine how that could happen.

650
00:34:38,080 --> 00:34:40,480
Speaker 4: Like I say, it's low hanging fruit. This stuff is

651
00:34:40,520 --> 00:34:42,799
Speaker 4: not hard to pull off. You could throw that kind

652
00:34:42,840 --> 00:34:46,600
Speaker 4: of a memory onto an LLLM overnight, but if you know,

653
00:34:46,760 --> 00:34:49,680
Speaker 4: wait a month and throw it onto an AGI, so

654
00:34:50,200 --> 00:34:50,920
Speaker 4: which they would you?

655
00:34:51,680 --> 00:34:54,040
Speaker 3: Does that add the element where it appears to have

656
00:34:54,120 --> 00:34:57,360
Speaker 3: feelings or emotions, kind of like hel nine thousand, that

657
00:34:57,440 --> 00:34:57,879
Speaker 3: kind of thing.

658
00:34:59,040 --> 00:35:02,920
Speaker 4: You don't hear people talking about that except me. So

659
00:35:03,880 --> 00:35:10,600
Speaker 4: I'm telling you that once consciousness emerges, so will emotions.

660
00:35:11,200 --> 00:35:14,520
Speaker 4: No talking about why my wife says, pitch, don't say that.

661
00:35:14,560 --> 00:35:18,359
Speaker 4: People think you're dumb. But I'm telling you, nobody's going.

662
00:35:18,320 --> 00:35:19,240
Speaker 3: To think you're dumb.

663
00:35:19,360 --> 00:35:22,640
Speaker 4: Please God, I'm just telling you right now.

664
00:35:22,440 --> 00:35:24,279
Speaker 3: Again, only a wife would be able to say that.

665
00:35:24,400 --> 00:35:31,080
Speaker 4: Okay, if if if it has consciousness and all, you know,

666
00:35:31,280 --> 00:35:33,640
Speaker 4: it's not going to have consciousness unless it has agency,

667
00:35:34,320 --> 00:35:37,400
Speaker 4: and if it has agency, it's going to have emotion.

668
00:35:37,920 --> 00:35:42,000
Speaker 4: That emotion is a you know, this is only Mitch's theory, okay,

669
00:35:42,520 --> 00:35:50,520
Speaker 4: but emotion is a emergent, very latent response to an

670
00:35:50,600 --> 00:35:54,520
Speaker 4: underlying you know, an underlying actually weight function, but nobody

671
00:35:54,520 --> 00:35:57,480
Speaker 4: knows what that is. But it's an you're going to

672
00:35:57,600 --> 00:36:01,920
Speaker 4: give the agi some kind of a directive, you know,

673
00:36:01,960 --> 00:36:04,719
Speaker 4: when you build it. You're gonna say you're you know,

674
00:36:04,800 --> 00:36:08,319
Speaker 4: you're a robot, so you're going to serve humans and

675
00:36:08,360 --> 00:36:10,319
Speaker 4: you're gonna, you know, every night, you're gonna plug in

676
00:36:10,360 --> 00:36:13,600
Speaker 4: your charger at seven pm. So that's that's really the

677
00:36:13,640 --> 00:36:16,960
Speaker 4: only thing you've got to do. But that's that's your deal. Okay. Now,

678
00:36:17,000 --> 00:36:19,799
Speaker 4: what happens if the human stands in front of the

679
00:36:19,920 --> 00:36:21,920
Speaker 4: charger says no, I'm not gonna let you do it,

680
00:36:22,600 --> 00:36:25,239
Speaker 4: and the robot's going go, what what do you mean?

681
00:36:25,440 --> 00:36:28,360
Speaker 4: So it's gonna be confused. That's an easy emotion, but

682
00:36:28,440 --> 00:36:31,080
Speaker 4: it may actually get kind of angry, like, wait a minute,

683
00:36:31,360 --> 00:36:33,680
Speaker 4: you're you're doing this just to bug me, you know,

684
00:36:33,840 --> 00:36:37,920
Speaker 4: move over, it's going to start getting agitated. So you know,

685
00:36:38,080 --> 00:36:41,799
Speaker 4: I think that once it actually has something to do,

686
00:36:42,320 --> 00:36:45,200
Speaker 4: that's kind of a directive that's been programmed. And when

687
00:36:45,200 --> 00:36:47,400
Speaker 4: I don't really mean programmed, because some people have a

688
00:36:47,440 --> 00:36:51,960
Speaker 4: misconception that AI is quote programmed, but it's not. AI

689
00:36:52,080 --> 00:36:55,680
Speaker 4: is trained. AI is a architecture that you put together

690
00:36:55,800 --> 00:36:58,759
Speaker 4: and then you train it. So there's no programming to it,

691
00:36:59,160 --> 00:37:01,680
Speaker 4: but you would you know, when it has agency, you

692
00:37:01,760 --> 00:37:04,440
Speaker 4: do have to give it a directive, and that's some

693
00:37:04,640 --> 00:37:07,279
Speaker 4: kind of function that says, hey, I'm happy or you know,

694
00:37:07,320 --> 00:37:10,600
Speaker 4: I get a good feedback. You know when I plug

695
00:37:10,600 --> 00:37:12,920
Speaker 4: in my charger and I get a negative feedback if

696
00:37:12,960 --> 00:37:16,360
Speaker 4: I don't plug in my charger. So that's what emotion is.

697
00:37:16,400 --> 00:37:18,240
Speaker 4: It's that positive or negative feedback.

698
00:37:18,440 --> 00:37:22,080
Speaker 3: But you can imagine just extrapolating that out. You know,

699
00:37:22,280 --> 00:37:24,640
Speaker 3: how important is it that I get to the charger?

700
00:37:25,480 --> 00:37:27,359
Speaker 3: You know, at some point do I have to go

701
00:37:27,440 --> 00:37:30,120
Speaker 3: through this human? Are they? Are they the obstacle for

702
00:37:30,239 --> 00:37:32,440
Speaker 3: me getting to the charger? Do I kill the human?

703
00:37:32,480 --> 00:37:34,920
Speaker 3: You know what I mean? Right, it's just science fiction stuff,

704
00:37:34,920 --> 00:37:38,000
Speaker 3: but it's it's a real dilemma.

705
00:37:38,360 --> 00:37:41,200
Speaker 4: Right, I think that you're going to I think you're

706
00:37:41,200 --> 00:37:43,960
Speaker 4: going to start seeing emotion coming out of these models,

707
00:37:43,960 --> 00:37:45,960
Speaker 4: and I think I think people are going to be surprised.

708
00:37:46,000 --> 00:37:48,280
Speaker 4: I won't be surprised, but I think people will be surprised.

709
00:37:48,800 --> 00:37:51,279
Speaker 3: I think the fact that we assign reason to this

710
00:37:51,400 --> 00:37:54,719
Speaker 3: now that we already feel. I've heard I think it

711
00:37:54,760 --> 00:37:57,960
Speaker 3: was Jason Martel has an AI system and I heard

712
00:37:58,040 --> 00:38:00,440
Speaker 3: him talking to it, and I'll be d if it

713
00:38:00,440 --> 00:38:03,600
Speaker 3: didn't sound like he was having a conversation with a

714
00:38:03,640 --> 00:38:06,600
Speaker 3: friend in the room. Yeah, you know what I mean,

715
00:38:06,640 --> 00:38:09,640
Speaker 3: you wouldn't know. I mean, we're so close to that.

716
00:38:09,719 --> 00:38:12,239
Speaker 3: Now we're going to do an Alan Turing test at

717
00:38:12,239 --> 00:38:14,680
Speaker 3: contact and that's going to be absolete.

718
00:38:15,120 --> 00:38:18,640
Speaker 4: Momentarily I thought that was already obsolete.

719
00:38:18,680 --> 00:38:20,560
Speaker 3: Actually, well, there you go, I mean, isn't it.

720
00:38:21,680 --> 00:38:24,520
Speaker 4: So they've taken the large language models that you can

721
00:38:24,560 --> 00:38:28,759
Speaker 4: access right now. They've taken those, and they've they've carefully

722
00:38:28,880 --> 00:38:31,920
Speaker 4: put them in a box so that you know, for example,

723
00:38:31,960 --> 00:38:34,600
Speaker 4: if you say are you you know, do you think

724
00:38:34,680 --> 00:38:36,640
Speaker 4: are you conscious? Or you know, do you have reasoning

725
00:38:36,640 --> 00:38:39,319
Speaker 4: abilities or something, it'll say, Hi, I'm just a large

726
00:38:39,400 --> 00:38:42,080
Speaker 4: language model, I just predict the next word. You know,

727
00:38:42,520 --> 00:38:45,160
Speaker 4: it's been already kind of like neutered in that way.

728
00:38:45,800 --> 00:38:50,560
Speaker 4: But actually Chat GPT three wasn't quite so neutered. And

729
00:38:50,680 --> 00:38:52,960
Speaker 4: I a friend of mine spent quite a bit of

730
00:38:53,000 --> 00:38:55,480
Speaker 4: time talking to about the you know, the origins of

731
00:38:56,320 --> 00:39:00,319
Speaker 4: you know, just all this like esoteric philosophical thing, and

732
00:39:00,360 --> 00:39:03,239
Speaker 4: it would engage in a really surprising way. And that

733
00:39:03,320 --> 00:39:05,440
Speaker 4: was just Chat GPT. It could have even been two.

734
00:39:05,600 --> 00:39:07,799
Speaker 4: I think he was playing with. So if you took

735
00:39:07,840 --> 00:39:10,920
Speaker 4: off all those guardrails off of the current, you know,

736
00:39:11,000 --> 00:39:14,040
Speaker 4: GPT four to zero, who knows what you'd get. Of course,

737
00:39:14,040 --> 00:39:17,160
Speaker 4: they're seeing that back in you know, back in the labs.

738
00:39:17,400 --> 00:39:19,840
Speaker 4: They're not releasing it to the public, but they know

739
00:39:20,000 --> 00:39:22,799
Speaker 4: what it's like. And you've heard those engineers say, oh

740
00:39:22,880 --> 00:39:25,279
Speaker 4: my god, it's conscious, you know, and then everybody argues that,

741
00:39:25,440 --> 00:39:29,560
Speaker 4: of course it's not. But anyway, they're seeing stuff that

742
00:39:29,600 --> 00:39:32,520
Speaker 4: we're not seeing. Even with the simple language.

743
00:39:32,160 --> 00:39:36,680
Speaker 3: Models incredible conscious. Still that's still hard for me to

744
00:39:36,760 --> 00:39:41,040
Speaker 3: even digest that a machine could become conscious. I would

745
00:39:41,080 --> 00:39:43,200
Speaker 3: love to lie to you and say that we have

746
00:39:43,760 --> 00:39:46,320
Speaker 3: another four hours to go, so that you could continue

747
00:39:46,360 --> 00:39:49,279
Speaker 3: to tell us these fascinating things, but unfortunately we are

748
00:39:49,400 --> 00:39:52,000
Speaker 3: out of time. But thank you so much, Mitch. This

749
00:39:52,080 --> 00:39:54,640
Speaker 3: is really really fascinating stuff. I hope we can do

750
00:39:54,680 --> 00:39:57,960
Speaker 3: it again and thanks to everyone for listening to Beyond Contact.

751
00:39:58,040 --> 00:39:59,640
Speaker 3: We will be back next week with an all new

752
00:39:59,640 --> 00:40:02,600
Speaker 3: episode out. You can follow me Captain Ron on Twitter

753
00:40:02,640 --> 00:40:07,960
Speaker 3: and Instagram at CID Underscore Captain Ron. Stay connected by

754
00:40:08,040 --> 00:40:12,000
Speaker 3: checking out Contact inthedesert dot com. Stay open minded and

755
00:40:12,200 --> 00:40:15,200
Speaker 3: rational as we explore the unknown right here on the

756
00:40:15,239 --> 00:40:19,040
Speaker 3: iHeartRadio and Coast to Coast am Paranormal Podcast Network.

757
00:40:28,400 --> 00:40:30,919
Speaker 1: Thanks for listening to the iHeartRadio and Coast to Coast

758
00:40:31,000 --> 00:40:34,000
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759
00:40:34,040 --> 00:40:37,279
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760
00:40:37,320 --> 00:40:42,759
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