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Speaker 3: Hey everyone, it's Captain ron and Each week are Beyond Contact.
Speaker 3: We'll explore the latest news in ufology, discuss some of
Speaker 3: the classic cases, and bring you the latest information from
Speaker 3: the newest cases as we talk with the top experts.
Speaker 3: Welcome to Beyond Contact. I'm Captain Ronald. Today we're speaking
Speaker 3: with Mitch Randall. Mitch is the CEO of Ascendant AI
Speaker 3: and the innovator behind SkyWatch Passive Radar, a UAP tracking system.
Speaker 3: SkyWatch was developed through the Galileo Project and hopes to
Speaker 3: detect UAPs using reflective signals as an alternative to traditional
Speaker 3: radar technology. Mitch, I'm so glad to see you, and
Speaker 3: I'm so glad to have you on here today. I
Speaker 3: find all of the stuff beyond fascinating, so welcome to
Speaker 3: the show.
Speaker 4: Well, it's a pleasure to be here.
Speaker 3: This is really great stuff. And I have to tell
Speaker 3: you I've been doing a lot of these interviews lately,
Speaker 3: and often it comes up that I'm the one that
Speaker 3: brings this up. I don't specifically say SkyWatch, but I
Speaker 3: say that, you know, the prospects of using AI to
Speaker 3: better discern what's happening in our skies, to do a faster,
Speaker 3: more comprehensive job in tracking these objects in the sky,
Speaker 3: pointing out anomalies is what we need. So we've been
Speaker 3: talking about this and all these different possibilities, but it
Speaker 3: turns out you're actually the guy who's actually doing precisely
Speaker 3: that with this SkyWatch project. Can you tell us, like
Speaker 3: how this whole thing works.
Speaker 4: Oh? Yeah, the SkyWatch radar is a passive radar. Let
Speaker 4: me just back up a little bit. Tell you how
Speaker 4: regular radar works. Right, regular radar normally you have a big, expensive,
Speaker 4: high power transmitter and a big dish and it shoots
Speaker 4: out a pulse out in the sky, and let's say
Speaker 4: you point it at an airplane and bounces off, the
Speaker 4: airplane comes back. So that's the traditional radar. The problem
Speaker 4: with that is it's kind of expensive. You have this
Speaker 4: huge antenna, you have this big old transmitter thing that's
Speaker 4: expensive and big. There's another thing. It's called passive radar.
Speaker 4: So passive radar is a cute little trick where hey,
Speaker 4: you're just trying to measure the echoes off these objects
Speaker 4: in the sky. You know what if you could use
Speaker 4: instead of a big transmitter like that, use somebody else's transmitter.
Speaker 4: There's tons of transmitters out there that are already transmitting power.
Speaker 4: Perfect opportunity. There is FM radio transmitters. They have an
Speaker 4: ideal signal for this kind of work. Plus they're transmitting
Speaker 4: hundreds of thousands of watts. So there you have it.
Speaker 4: You have a high power transmitter. And what's going to
Speaker 4: happen in this case with passive radar is that FM
Speaker 4: radio signal goes out into the sky. It's also going
Speaker 4: to bounce off whatever targets are up in the sky,
Speaker 4: some airplane or some UFO or whatever it is. And
Speaker 4: that signal is going to come back and you can
Speaker 4: detect that on a receiver. The cute thing about this,
Speaker 4: like I said, not only do not have to have
Speaker 4: that transmitter, but the power that's being transmitted by the
Speaker 4: FM radio transmitter is so high that you don't even
Speaker 4: have to have a dish on your receiver end to
Speaker 4: pick up the echo. So get rid of the dish.
Speaker 4: And on top of that, this whole thing is basically
Speaker 4: just an FM receiver. So this is something that nowadays
Speaker 4: can be relatively inexpensive. You could have an receiver type
Speaker 4: hardware and let's say, you know, we could make something
Speaker 4: that's something like six inches by six inches by six inches.
Speaker 4: So that gives you just an idea of what this
Speaker 4: hardwork would be like.
Speaker 3: So would that have their own at their house, they
Speaker 3: would have this right, You're talking about individual people having
Speaker 3: it right, and then would they be able to see
Speaker 3: all the data that you guys would see.
Speaker 4: Well, let me back up for a second and just say,
Speaker 4: let me tell you a little bit about the storm
Speaker 4: chasing radar trucks if you don't mind. So I was
Speaker 4: instrumental in the building of that with the hardware radar
Speaker 4: receiver that I built the idea of those storm chasing
Speaker 4: radar trucks. You know, they see tornadoes. You might have
Speaker 4: seen them on TV. Right. The idea there is I
Speaker 4: used to work at the National Center for Atmospheric Research
Speaker 4: and we had you know, these high not best quality
Speaker 4: weather radars in the world there, but they were like
Speaker 4: on a concrete pad. You know, we of course wanted
Speaker 4: to know what's going on in a tornado. But how
Speaker 4: often does a tornado come by these you know radars
Speaker 4: where you have one installed in Colorado, you have one
Speaker 4: installed in Texas. I mean, you don't. There's not enough
Speaker 4: statistics there to actually wait around for a tornado to come.
Speaker 4: So the idea of those storm chasing trucks was, well,
Speaker 4: the tornado's in Kansas, we got to drive to Kansas.
Speaker 4: That's how we're going to measure it. So we stuck
Speaker 4: a radar on a truck. And you know, this is
Speaker 4: a fundamental part of instrument design. It's not just about
Speaker 4: the specks of the instrument, but it's also about the
Speaker 4: cost of the instrument, the practicality, the reliability, and the
Speaker 4: effectiveness of it. If you can't go to the tornado,
Speaker 4: you're never going to be able to measure one get
Speaker 4: wait one hundred years for that and never get a
Speaker 4: tornado by your radar. Same thing here. So now we
Speaker 4: have a technology that I can take a little box
Speaker 4: that's as complicated as a radio receiver right and measure
Speaker 4: you know, UFO. Of it's in the sky, But what
Speaker 4: are the chances that UFO comes over your house? By
Speaker 4: the way, these things can see out one hundred miles,
Speaker 4: so that's no problem. Still within one hundred mile radius,
Speaker 4: what are the chances the UFO is going to come there?
Speaker 4: So the idea behind the SkyWatch system is we call
Speaker 4: the SkyWatch network. We want to put these in the
Speaker 4: in the hands of citizens and spread them all over
Speaker 4: the country. That way, no matter where a UFO would
Speaker 4: fly in the country, we're going to see it, and
Speaker 4: hopefully we'll see it with multiple receivers at once.
Speaker 3: Now, how do you discern between an airplane and a helicopter?
Speaker 3: Is this where AI comes in. Would that analyze the
Speaker 3: data and pick out the anomalies that aren't identified right
Speaker 3: away as a plane, helicopter, satellite, whatever.
Speaker 4: Oh, that's a great question. Well, one thing is that
Speaker 4: airplanes do have onboard transponders, so an airplane's constantly telling
Speaker 4: you transmitting out there where its location is, what it's
Speaker 4: you know, tail number is, and it's velocity. So we
Speaker 4: can receive that, and we can we can correlate that
Speaker 4: to the dots that we see on our display, right,
Speaker 4: and we can say, oh, that dot is actually due
Speaker 4: to flight twenty one sixty two, so that's not a UFO.
Speaker 4: But there are definitely up there airplanes that don't have that.
Speaker 4: And then there's you know, this is what I think
Speaker 4: I think we're going to experience. There's some things up
Speaker 4: there that nobody knows what they are and they're not
Speaker 4: telling us, you know, to distinguish what you know, what
Speaker 4: those last objects are. I mean, we definitely can apply AI,
Speaker 4: but there's also a couple easy ways to tell. If
Speaker 4: something goes mock four makes a right angle turn with
Speaker 4: a two hundred and seventy five G turn, you know
Speaker 4: it's probably not a commercial airliner.
Speaker 3: And you know what I just thought of while you
Speaker 3: were saying this, we would then this obviously will be
Speaker 3: getting recorded correct. Oh, yeah, are right. So the idea
Speaker 3: is like we always you and I have talked about
Speaker 3: this before. How you know you never pull out your
Speaker 3: phone in time to see the UFO or whatever. However,
Speaker 3: this is going to be recorded constantly, so if we
Speaker 3: did see something doing ninety degree turn, it would be
Speaker 3: recorded and we would have a record of that.
Speaker 4: Well, not only that, you know, the government's had a
Speaker 4: monopoly on radar data. We don't get to see radar data.
Speaker 4: We don't have familiarity with radar data. It is so
Speaker 4: much better than optical data for so many reasons. One
Speaker 4: is it's quantitative. It actually knows the position and velocity
Speaker 4: of an object. So it's not just oh, you know,
Speaker 4: a light went across my screen. I don't know if
Speaker 4: it's a bug that was two feet away or if
Speaker 4: it was a huge, you know UFO that's two hundred
Speaker 4: miles away. You can't tell with it with a camera.
Speaker 4: But the other thing that I was, you know, made
Speaker 4: me think of this is not only like you're saying,
Speaker 4: you know, this will be constantly twenty four to seven
Speaker 4: monitoring the sky. But another thing about UFOs is we
Speaker 4: only talk about the luminous UFOs that we see at
Speaker 4: night at least, so I think, so there's some daylight
Speaker 4: sightings of UFOs that aren't necessarily luminous, but what about
Speaker 4: non luminous UFOs that are at night? How would you
Speaker 4: even know that? You know, for all we know that's
Speaker 4: when they're around. If this all comes out that there's
Speaker 4: no UFOs in the sky, great because that's also a
Speaker 4: scientific result.
Speaker 3: That's not what I'm expout too. That's equally important. It
Speaker 3: would be great to show that no, there is nothing,
Speaker 3: that that was an animal or whatever.
Speaker 4: Yeah, I mean, you know, it's not conclusive because that's
Speaker 4: proving a negative, but it would be very interesting. But
Speaker 4: you know, I don't mind playing around with the assumption
Speaker 4: that there are strange things up in the sky. So
Speaker 4: that's that's where of course my passion comes from. And
Speaker 4: that's why I'm interested in this because I think I
Speaker 4: have a feeling we are going to see things in
Speaker 4: the night sky or a day sky that are doing strange,
Speaker 4: crazy maneuvers, and no, we're going to be able to
Speaker 4: quantify them.
Speaker 3: Well, no doubt about it. We could start with Kevin Day,
Speaker 3: the radar guy from the military. There's been many military
Speaker 3: accounts of different UFOs on radar specifically. You know, we've
Speaker 3: heard that from people working in aviation as well as
Speaker 3: people in the military. You know, this system that you
Speaker 3: wanted to create. Here could just support that data only
Speaker 3: will have access to the results for one right, which
Speaker 3: would be great. Listen, We're going to have to take
Speaker 3: a quick break here, Mitch. Fascinating stuff about SkyWatch. We're
Speaker 3: going to come right back and talk more about that
Speaker 3: with Mitch Randall. You're listening to Beyond Contact on the
Speaker 3: iHeartRadio and Coast to Coast AM Paranolal podcast network.
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Speaker 3: Okay, we're with Mitch Randall here on Beyond Contact and
Speaker 3: we're talking about SkyWatch and this will be a way
Speaker 3: that all of us can participate in monitoring the skies
Speaker 3: and maybe through this new technology, be able to find
Speaker 3: out what's really happening up there, right, Mitch, Yeah, what
Speaker 3: is the next step for this program? Do you guys
Speaker 3: need to raise funding? Is the what is the approach here?
Speaker 4: You read my mind? Something really big is happening. So
Speaker 4: it turns out that this is just one piece of
Speaker 4: something that's called Operation SkyWatch. So Operation SkyWatch is a
Speaker 4: very bold plan. Actually, can I can I tell you
Speaker 4: what the mission statement of Operation Skywatches.
Speaker 3: I would love that please to.
Speaker 4: Establish the existence of UFOs by measuring their extreme accelerations
Speaker 4: and maneuvers. And when I say we're measuring their extreme
Speaker 4: acceleration maneuvers, what I'm talking about is scientific quality data
Speaker 4: of those extreme acceleration some maneuvers. You know, for decades
Speaker 4: people have reported, oh, you know, I saw an object
Speaker 4: and thenink it just disappeared, you know, and there's really
Speaker 4: nothing that you know, Then you hear the debunkers say,
Speaker 4: where's the proof? Right, So here we are this is
Speaker 4: a system that can actually quantitatively measure these things and
Speaker 4: there can be a scientific basis for it. Because of
Speaker 4: course we're going to write up everything about the SkyWatch
Speaker 4: radar and publish it. So this is this is how
Speaker 4: science works. You always write a paper about your instrument
Speaker 4: so that when you when you get data from your instrument,
Speaker 4: everybody knows what that data means. SkyWatch Radar is just
Speaker 4: one third of Operation SkyWatch. So Operation SkyWatch includes the radar,
Speaker 4: an app, a video recording app for triangulation, and a
Speaker 4: national reporting database. And the idea here is there's three
Speaker 4: really important pieces of information that can come together. And
Speaker 4: this is all in cooperation with a scientific group. So
Speaker 4: I'm in talks with multiple scientific groups about this, and
Speaker 4: I think we're close to getting to a point where
Speaker 4: this is I can make an official announcement of who
Speaker 4: the group is. Oh, I have so much to talk
Speaker 4: about here too, because let me just jump to that
Speaker 4: National Reporting Center just for a second, okay.
Speaker 3: Sure please. How many times have we heard free days NonStop?
Speaker 3: This is all fascinating stuff.
Speaker 4: How many times have you heard people say there's no
Speaker 4: evidence and they think, wait a minute, there's thousands of witnesses,
Speaker 4: some of them so credible that have come forward. Thousands
Speaker 4: of witnesses and you're telling me there's no evidence. But
Speaker 4: I know somebody in court had thousands of witnesses. You
Speaker 4: could put someone behind bars for that. So what's the
Speaker 4: deal here? What is the scientific standard? One thing I
Speaker 4: would like to see happen is there's a scientific group
Speaker 4: now involved, and the scientific group can see again, if
Speaker 4: you have an instrument, you write about the instrument's quality, right,
Speaker 4: and then when they instrument gathers data, you publish that.
Speaker 4: Why don't we do that with the reporting center. Why
Speaker 4: don't we talk about the quality of the data that
Speaker 4: we get with the reporting center? Why don't we talk
Speaker 4: about what it means to have corroborated witness testimony about something?
Speaker 4: Has that actually been done scientifically? Is there a paper
Speaker 4: somewhere that says, oh, well, if you have four witnesses
Speaker 4: to this event, then it's the probability blah blah blah.
Speaker 4: I mean, I really think that's something that we need
Speaker 4: to do as a society because otherwise we're going to
Speaker 4: say there's no evidence, when in fact, I believe there
Speaker 4: is a lot of evidence. But that's just the reporting center. So,
Speaker 4: and I've already talked about the radar. The third thing
Speaker 4: is this triangulation video triangulation app. Part of the system now,
Speaker 4: is this video triangulation app and if you've used a
Speaker 4: stargazing app effort before, you know that the phone knows
Speaker 4: where it's pointed. That metadata can be attached to a video.
Speaker 4: We could have a video that not only shows what
Speaker 4: objects on the screen, but it shows exactly your location
Speaker 4: and exactly your pointing angle. And if two people had
Speaker 4: a video of the same object, boom, you can triangulate
Speaker 4: that object and figure out its position in time. Therefore,
Speaker 4: you know it's velocity and acceleration. So that's like really
Speaker 4: super great.
Speaker 3: Other people that had that app could then get an
Speaker 3: Amber alert. This has headed your way right.
Speaker 4: Well, well, you hit on a perfect point there. Sure.
Speaker 4: How often do people pull out their phone when they
Speaker 4: say UFO? It turns out almost never. They're just stunned
Speaker 4: by what they're looking at, and they don't think to
Speaker 4: pull out their phone and start fumbling through icons.
Speaker 3: Because you don't know what's coming. Now, you don't know
Speaker 3: what's coming right here. You shocked to you're shocked. But
Speaker 3: if you had the thing, it's going to be there
Speaker 3: in ten minutes. Well, now I'm going to go outside,
Speaker 3: set up my camera and wait for it. That's a difference.
Speaker 4: That's exactly right. That's exactly right. Now, we have a
Speaker 4: radar as part of the system. The radar can actually
Speaker 4: give an alert to that person and say, hey, run outside,
Speaker 4: because in two minutes over the southwest horizon there's going
Speaker 4: to be something. Get ready to push the record button.
Speaker 4: And with a system like that, we very well could
Speaker 4: get multiple cameras on the same object and do video triangulation.
Speaker 3: We have all of the radar data recorded as well
Speaker 3: that you've already talked about, would be reporting all of
Speaker 3: that so that would match the video. Now you really
Speaker 3: have some really strong evidence that you could show the
Speaker 3: rest of the world.
Speaker 4: Well, that would be called multisensor data. Right, that's the
Speaker 4: cream of the crop. But in addition, that app would
Speaker 4: help people report it right to the database, so you'd
Speaker 4: also have, you know, all the provenance. Basically, you'd have
Speaker 4: somebody describing what they saw and the phone call coming
Speaker 4: in at the.
Speaker 3: Time they say eyewitness accounts, you have video evidence, you
Speaker 3: have data from the radar. Now you've got three forms
Speaker 3: of evidence that all corroborate. That's pretty exciting.
Speaker 4: And you have a scientific organization that's going to take
Speaker 4: that data and turn it into something that the rest
Speaker 4: of the scientists agree with so that that's why I
Speaker 4: say this will establish the existence of UFOs by measuring
Speaker 4: their extreme accelerations and maneuvers. This would be a slam dunk.
Speaker 3: You're the hero that this community has been waiting for
Speaker 3: for a whole long time. Match how far off is
Speaker 3: something like this?
Speaker 4: Oh let me let me throw in a real quick
Speaker 4: thing here. You know the five observables, right, you've heard
Speaker 4: of the five observables. So there's been reports of UFOs.
Speaker 4: They do these crazy things and that makes them interesting.
Speaker 4: But I have to say four of the five are
Speaker 4: are debunk is something you can actually debunk. For example,
Speaker 4: they say no control surfaces or visible propulsion. Well that's
Speaker 4: a balloon. A balloon doesn't have control surfaces or propulsion, right,
Speaker 4: and you hear that all the time. Oh, that's a balloon.
Speaker 4: Or you know, you can go down the list with
Speaker 4: the other the other things. I just wanted to point
Speaker 4: that out because we specifically picked the acceleration because it's undebunkable.
Speaker 4: If you can quantitatively measure an object and it has
Speaker 4: an acceleration of fifteen hundred g's let's say right angle
Speaker 4: turn going a mock two, there's no balloon. There's no,
Speaker 4: you know, natural phenomena. There's no you know, if you
Speaker 4: tried to make that happen with some kind of you know, fakery,
Speaker 4: you couldn't do it. So undebuncable is part of this.
Speaker 3: Undebunkable is what we need. We need to have more
Speaker 3: credible evidence like this. Do you think this is something
Speaker 3: on the horizon? How far down the road is this
Speaker 3: going to be?
Speaker 4: Yeah? So this is a development and engineering development for
Speaker 4: these products, especially the radar, you know, but also the
Speaker 4: actually app could actually be available within like six to
Speaker 4: eight months, wow, something like that. The radar. We have
Speaker 4: our schedules about twenty four months before the radar is
Speaker 4: available for sale, So when we hit twenty four months,
Speaker 4: you could click on the internet and go and order
Speaker 4: one and the whole network will start to build out.
Speaker 3: That sounds very exciting to me. Matched this feels like
Speaker 3: something that this community has wanted for a long time,
Speaker 3: and it's very promising and it's nice to see this
Speaker 3: in the hand of the people. Where Like you said,
Speaker 3: the government owns the radar data, well they won't own
Speaker 3: this and we will all have access to it, and
Speaker 3: that's kind of what we want to get. To take
Speaker 3: another break, here, Mitch, We're going to come back and
Speaker 3: we're going to move on and talk to Mitch a
Speaker 3: little bit about AI too, because this stuff is even
Speaker 3: more mind blowing. If you can believe that you're listening
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Speaker 3: AM Paranormal Podcast Network.
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Speaker 3: We are back on Beyond Contact with Captain Ron and
Speaker 3: I'm talking to Mitch Randall. I'd like to move on
Speaker 3: a little bit now for a second. If I could
Speaker 3: Mitch and talk about AI. This is incredible to I've
Speaker 3: heard you talk extensively about different AI processes and where
Speaker 3: that's headed, and I'd really like to jump into this
Speaker 3: right in the beginning though. I don't think people really
Speaker 3: understand some of these new AI terms that are out there.
Speaker 3: Maybe you could kind of straighten that out for us.
Speaker 3: Your company is called as send an AI, and then
Speaker 3: now all of a sudden, just when we think we
Speaker 3: get our head around AI, there's open AI, there's generative AI,
Speaker 3: there's artificial general intelligence. Can you sort this out for
Speaker 3: us mere mortals, so we know what this means.
Speaker 4: I think AI had some of its beginnings with images,
Speaker 4: so you know, let's try to understand if this image
Speaker 4: has a dog or a cat, you know that kind
Speaker 4: of thing. You know, those are the nice baby step
Speaker 4: days back in the day with AI. And then they
Speaker 4: also had you know, translators, so you could put some
Speaker 4: text in there. It would turn it into let's say,
Speaker 4: from English to French. You know, these are tasks. They
Speaker 4: found out that AI can actually be helpful out pretty
Speaker 4: hard to do any other way. But then they had
Speaker 4: a renaissance, A big change happened. This is right around
Speaker 4: twenty twelve where they came up with the idea of convolution.
Speaker 4: It's for all of us mortals. Convolution just was a
Speaker 4: big step in how much they could compute with a
Speaker 4: lot less compute power, so they could do a lot
Speaker 4: more operations with a lot less compute power. So then
Speaker 4: all of a sudden, the ability to tell the image
Speaker 4: was a dog or a cat became something they could
Speaker 4: actually do, and do it quite well. In fact, they
Speaker 4: were beating humans at it. So they had you know,
Speaker 4: data sets now and they had a hey day right
Speaker 4: there after, you know, twenty twelve or so, all of
Speaker 4: a sudden, the AI models start to get much much better.
Speaker 4: Then they came up with the transformers. So you've heard
Speaker 4: of CHAT. GPT stands for Generative pre trained Transformer. That's
Speaker 4: what the GPT stands for. So a transformer was a
Speaker 4: new technique. Again, it's like technology they're throwing at the computations.
Speaker 4: There's definitely a philosophy or the design behind it. But
Speaker 4: the transformers allowed now this translation to get way better.
Speaker 4: And then they realized as they made those models bigger
Speaker 4: and bigger, and when I say bigger and bigger, put
Speaker 4: more and more layers on them. So a first layer
Speaker 4: would kind of figure out, you know, deciphered a little bit,
Speaker 4: and then the next layer, in the next layer, they
Speaker 4: would just literally make them deeper and deeper with more
Speaker 4: and more layers. I don't know that's making sense, but
Speaker 4: that's basically what they're doing. And when they did that,
Speaker 4: they started to realize that's funny. The thing is actually
Speaker 4: seems like it has some kind of ability to reason.
Speaker 4: You know. It's interesting because people don't haven't been sitting
Speaker 4: down going like, hey, I'm going to make an AI
Speaker 4: that reasons because nobody knows how to do that. But
Speaker 4: what they can do is, you know, they start out
Speaker 4: with the translator and they realize, wow, if I just
Speaker 4: make a translator has a lot more layers, and I
Speaker 4: train it on a lot more books and a lot
Speaker 4: more input information. Now it's sort of emerging out of
Speaker 4: this is this ability's kind of reason?
Speaker 3: Wasn't it a language system where they were really just
Speaker 3: guessing or predicting the next word that you would say.
Speaker 4: You are exactly right, Okay, that's exactly what it is.
Speaker 4: So again, nobody sat down and thought, I'm going to
Speaker 4: make it reason by guessing the next word in some
Speaker 4: fancy way, Like, nobody knows how to make that happen.
Speaker 4: But what they could do is just make the model bigger,
Speaker 4: make and train more stuff on it, and then be
Speaker 4: surprised that just by predicting the next word, they're getting
Speaker 4: out this kind of ability to reason. So it's pretty
Speaker 4: interesting to see how the how the everything evolved. It's
Speaker 4: not so much because.
Speaker 3: I've been here and ask you a quick question, Mitch,
Speaker 3: because you just said the ability to reason, and I
Speaker 3: want to ask you, is it really reasoning or is
Speaker 3: it we're assigning the notion of reasoning to AI because
Speaker 3: these words in this order seem to make reasonable sense.
Speaker 4: Well, I love that question, because what is reason? Well,
Speaker 4: they you know, for example, consciousness, They say, oh, that's
Speaker 4: a hard problem. And I don't know if you've ever
Speaker 4: heard that term before. They say, consciousness is the hard problem.
Speaker 4: And I think what they're saying is, you know, we
Speaker 4: don't know what it is, so how can we design
Speaker 4: for it. You know, we can't even describe it. We
Speaker 4: don't even know, we can't even make a test for it.
Speaker 4: Similar to what you're talking about with reasoning. So let
Speaker 4: me just turn it kind of back around at you.
Speaker 4: Is the AI reasoning when it's just predicting the next word?
Speaker 4: Or let me ask you this, Are you reasoning when
Speaker 4: you predict the next word?
Speaker 3: So no, I don't think it predicting the next word.
Speaker 3: That's just a math. Usually after good the word morning comes,
Speaker 3: So that's just math that that's the most frequent word
Speaker 3: after good good morning. So no, I don't think that's reasoning.
Speaker 3: I think that's just math.
Speaker 4: You'd have to know it's not just it's not predicting
Speaker 4: the next word by looking at the last word. It's
Speaker 4: predicting the next word by looking at the last ten
Speaker 4: thousand words. So if you have a conversation, my.
Speaker 3: Head just exploded. Sorry, yeah, oh okay, I didn't realize
Speaker 3: it's that complex. Then maybe it's more towards reasoning.
Speaker 4: Yeah, because when you have a conversation with AI, let's
Speaker 4: say you go back and forth five times with several words,
Speaker 4: you know, and let's say that was five hundred words
Speaker 4: you when it predicts the next word and its answer,
Speaker 4: it's considering all those five hundred words, not to mention
Speaker 4: the trillion of words has already was trained on. It's
Speaker 4: a little more complicated than just the word after.
Speaker 3: Good, no, No, it's a lot more complix Yeah, apparently.
Speaker 4: Yeah, and you can imagine too, there's a lot of
Speaker 4: computation that goes into that. It's very compute heavy.
Speaker 3: I'd love to talk to you about this idea of
Speaker 3: sentience and the singularity. I'm not sure people even understand
Speaker 3: what that means. Like singularity is the one me, it's
Speaker 3: my consciousness. And we are talking about computer AI systems
Speaker 3: reaching the point so that they are in a sense
Speaker 3: conscious and sentient. Thus they would be able to pass
Speaker 3: the Ellent Turing test. No problem, and a human wouldn't
Speaker 3: be able to distinguish the two, right, is this even possible?
Speaker 3: In your view?
Speaker 4: It's so interesting because nobody knows what consciousness is, right,
Speaker 4: so how can they design for it? And if they
Speaker 4: train on, you know, a bunch of encyclopedias, you know,
Speaker 4: how could that be consciousness doesn't make any sense? Right?
Speaker 4: There's more too than that these things what they have
Speaker 4: found in general with AI, for example, just starting with
Speaker 4: a dog cat example, how you know, how does an
Speaker 4: image tell if it's a dog or a cat? How
Speaker 4: do you make a machine that does that? People didn't
Speaker 4: know exactly what was going to happen. And when it's
Speaker 4: all done and we say we have a machine that
Speaker 4: can detect what's a dog and what's a cat, nobody
Speaker 4: knows how that works. Nobody can actually say, well, oh yeah,
Speaker 4: it's because xyz No. They just trained it on a
Speaker 4: million dogs and a million cats and now it knows.
Speaker 4: So it's a little bit of a mystery what's going
Speaker 4: on in there. And then we talked about that predicting
Speaker 4: the next word thing, and it seems to have some
Speaker 4: kind of reason, right, They're just going to take it
Speaker 4: further and further and consciousness could very well emerge from that.
Speaker 4: And you know the question is, you know, did they
Speaker 4: design it to do that? Well, actually, they're kind of
Speaker 4: getting to the point where they are kind of getting
Speaker 4: a pulse on what to design for. Still nobody knows
Speaker 4: exactly what consciousness is, but they do gather a you know,
Speaker 4: an intuition for what needs to be done and where
Speaker 4: they should focus on. So they are pushing towards consciousness.
Speaker 4: I think they're going to hit it. I don't think
Speaker 4: there's any doubt.
Speaker 3: I think it's it's so hard, Like you said, we
Speaker 3: don't even know what consciousness is. That almost feels like
Speaker 3: our soul or our higher self. And to say that
Speaker 3: a machine is going to achieve that seems beyond my
Speaker 3: tiny brain to be able to figure it is scary
Speaker 3: to think about. We're gonna have to take another quick
Speaker 3: break here. When we come back, we're going to find
Speaker 3: out morphamitch about this and whether or not AI can
Speaker 3: remember talking to you. You're listening to Beyond Contact right
Speaker 3: here on the iHeartRadio on Coast to Coast am Paranormal
Speaker 3: Podcast Network.
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Speaker 3: We are back on Beyond Contact with Mitch Randall. Hey,
Speaker 3: let me ask you this question. Match as smart as
Speaker 3: AI is, isn't it true that it doesn't remember what
Speaker 3: we talked about right before? Kind of like my mother
Speaker 3: who who loves to call me and ask me what
Speaker 3: he asked me the day before? Is that true?
Speaker 4: Well, this is kind of goes back to what I
Speaker 4: was saying a little earlier. They actually now have an
Speaker 4: idea maybe if things they should be focusing on, they're
Speaker 4: going to make the kind of reasoning and agent that
Speaker 4: we're you know that they're looking for for a GI right.
Speaker 4: I guess, Sora, you know what SOA is. No TEX
Speaker 4: It's a text to video generator. So you can say, hey,
Speaker 4: you know, a mushroom grows and it has a frog
Speaker 4: on it or something like that, and it makes a
Speaker 4: little video of a mushroom growing with a frog on it.
Speaker 4: So it turns out I thought that was cute and interesting, right,
Speaker 4: it turns out I heard Soro was actually a project
Speaker 4: to do what's called world modeling, So they understood and
Speaker 4: in my company, we've been talking about this kind of
Speaker 4: thing for a couple of years, so we're we kind
Speaker 4: of understand like the pieces that would have to come
Speaker 4: together to make you know, an agi, and clearly, you know,
Speaker 4: the people that open AI are also there too, because
Speaker 4: the idea is, if you make an agent that's going
Speaker 4: to make actions in this world, you can't make an
Speaker 4: action unless you know what's going to happen. Like maybe
Speaker 4: it just wants to take the next step, you know,
Speaker 4: but there's a cliff there, so it's got to know, Hey,
Speaker 4: if I take that step, I'm going to go flying
Speaker 4: down that cliff. So Soro was actually a world model
Speaker 4: to predict what's going to happen based on what an
Speaker 4: agent is proposing. You know. So an agent might say, well,
Speaker 4: I know you should, you know, go to the airport
Speaker 4: up your aunt first. That way you can drive to
Speaker 4: the thing later, you know, So then you need some
Speaker 4: kind of world model that shows what's going to happen
Speaker 4: in that scenario. Going No, But another really low hanging
Speaker 4: fruit is memory. In fact, I've been saying for a
Speaker 4: couple of years now that you could take just a
Speaker 4: standard kind of boring LLLM and give it memory so
Speaker 4: that it could just look up things. And that's easy
Speaker 4: to do for AIS, by the way, and it could
Speaker 4: even know that it's supposed to look up. Right, that's
Speaker 4: not that hard to train into even a language model
Speaker 4: that we're already using. But imagine if you had that
Speaker 4: a backpack that had an LM and that also had
Speaker 4: a camera, and every second it took a picture and
Speaker 4: recorded what was being said, right, maybe you'd go like, hey,
Speaker 4: you know, remember that time we went to you know,
Speaker 4: Mohab and we rode our bikes around and it goes, oh, yeah,
Speaker 4: that was fun. You know we got a flat tire
Speaker 4: that was a blast. I mean, that thing would turn
Speaker 4: into your best friend. And it doesn't. It doesn't have
Speaker 4: to be much smarter than an LM to be that
Speaker 4: kind of companion just by adding memory to it. So,
Speaker 4: I mean, you know that I've always said that l
Speaker 4: elms are basically a reasoning engine. That you could just
Speaker 4: put it in a feedback loop, just kind of throw
Speaker 4: some other stuff around it, and you have a pretty
Speaker 4: good start on AGI.
Speaker 3: I can imagine a time in the very near future
Speaker 3: where kids will have a bear or whatever that have
Speaker 3: AI built in and they have memory, and they'll talk
Speaker 3: about growing up together. You know, like the kids that
Speaker 3: you know, you hear them have invisible friends. Well, no,
Speaker 3: we have a visible friend right here, and I share
Speaker 3: everything with this bear. You can imagine how that could happen.
Speaker 4: Like I say, it's low hanging fruit. This stuff is
Speaker 4: not hard to pull off. You could throw that kind
Speaker 4: of a memory onto an LLLM overnight, but if you know,
Speaker 4: wait a month and throw it onto an AGI, so
Speaker 4: which they would you?
Speaker 3: Does that add the element where it appears to have
Speaker 3: feelings or emotions, kind of like hel nine thousand, that
Speaker 3: kind of thing.
Speaker 4: You don't hear people talking about that except me. So
Speaker 4: I'm telling you that once consciousness emerges, so will emotions.
Speaker 4: No talking about why my wife says, pitch, don't say that.
Speaker 4: People think you're dumb. But I'm telling you, nobody's going.
Speaker 3: To think you're dumb.
Speaker 4: Please God, I'm just telling you right now.
Speaker 3: Again, only a wife would be able to say that.
Speaker 4: Okay, if if if it has consciousness and all, you know,
Speaker 4: it's not going to have consciousness unless it has agency,
Speaker 4: and if it has agency, it's going to have emotion.
Speaker 4: That emotion is a you know, this is only Mitch's theory, okay,
Speaker 4: but emotion is a emergent, very latent response to an
Speaker 4: underlying you know, an underlying actually weight function, but nobody
Speaker 4: knows what that is. But it's an you're going to
Speaker 4: give the agi some kind of a directive, you know,
Speaker 4: when you build it. You're gonna say you're you know,
Speaker 4: you're a robot, so you're going to serve humans and
Speaker 4: you're gonna, you know, every night, you're gonna plug in
Speaker 4: your charger at seven pm. So that's that's really the
Speaker 4: only thing you've got to do. But that's that's your deal. Okay. Now,
Speaker 4: what happens if the human stands in front of the
Speaker 4: charger says no, I'm not gonna let you do it,
Speaker 4: and the robot's going go, what what do you mean?
Speaker 4: So it's gonna be confused. That's an easy emotion, but
Speaker 4: it may actually get kind of angry, like, wait a minute,
Speaker 4: you're you're doing this just to bug me, you know,
Speaker 4: move over, it's going to start getting agitated. So you know,
Speaker 4: I think that once it actually has something to do,
Speaker 4: that's kind of a directive that's been programmed. And when
Speaker 4: I don't really mean programmed, because some people have a
Speaker 4: misconception that AI is quote programmed, but it's not. AI
Speaker 4: is trained. AI is a architecture that you put together
Speaker 4: and then you train it. So there's no programming to it,
Speaker 4: but you would you know, when it has agency, you
Speaker 4: do have to give it a directive, and that's some
Speaker 4: kind of function that says, hey, I'm happy or you know,
Speaker 4: I get a good feedback. You know when I plug
Speaker 4: in my charger and I get a negative feedback if
Speaker 4: I don't plug in my charger. So that's what emotion is.
Speaker 4: It's that positive or negative feedback.
Speaker 3: But you can imagine just extrapolating that out. You know,
Speaker 3: how important is it that I get to the charger?
Speaker 3: You know, at some point do I have to go
Speaker 3: through this human? Are they? Are they the obstacle for
Speaker 3: me getting to the charger? Do I kill the human?
Speaker 3: You know what I mean? Right, it's just science fiction stuff,
Speaker 3: but it's it's a real dilemma.
Speaker 4: Right, I think that you're going to I think you're
Speaker 4: going to start seeing emotion coming out of these models,
Speaker 4: and I think I think people are going to be surprised.
Speaker 4: I won't be surprised, but I think people will be surprised.
Speaker 3: I think the fact that we assign reason to this
Speaker 3: now that we already feel. I've heard I think it
Speaker 3: was Jason Martel has an AI system and I heard
Speaker 3: him talking to it, and I'll be d if it
Speaker 3: didn't sound like he was having a conversation with a
Speaker 3: friend in the room. Yeah, you know what I mean,
Speaker 3: you wouldn't know. I mean, we're so close to that.
Speaker 3: Now we're going to do an Alan Turing test at
Speaker 3: contact and that's going to be absolete.
Speaker 4: Momentarily I thought that was already obsolete.
Speaker 3: Actually, well, there you go, I mean, isn't it.
Speaker 4: So they've taken the large language models that you can
Speaker 4: access right now. They've taken those, and they've they've carefully
Speaker 4: put them in a box so that you know, for example,
Speaker 4: if you say are you you know, do you think
Speaker 4: are you conscious? Or you know, do you have reasoning
Speaker 4: abilities or something, it'll say, Hi, I'm just a large
Speaker 4: language model, I just predict the next word. You know,
Speaker 4: it's been already kind of like neutered in that way.
Speaker 4: But actually Chat GPT three wasn't quite so neutered. And
Speaker 4: I a friend of mine spent quite a bit of
Speaker 4: time talking to about the you know, the origins of
Speaker 4: you know, just all this like esoteric philosophical thing, and
Speaker 4: it would engage in a really surprising way. And that
Speaker 4: was just Chat GPT. It could have even been two.
Speaker 4: I think he was playing with. So if you took
Speaker 4: off all those guardrails off of the current, you know,
Speaker 4: GPT four to zero, who knows what you'd get. Of course,
Speaker 4: they're seeing that back in you know, back in the labs.
Speaker 4: They're not releasing it to the public, but they know
Speaker 4: what it's like. And you've heard those engineers say, oh
Speaker 4: my god, it's conscious, you know, and then everybody argues that,
Speaker 4: of course it's not. But anyway, they're seeing stuff that
Speaker 4: we're not seeing. Even with the simple language.
Speaker 3: Models incredible conscious. Still that's still hard for me to
Speaker 3: even digest that a machine could become conscious. I would
Speaker 3: love to lie to you and say that we have
Speaker 3: another four hours to go, so that you could continue
Speaker 3: to tell us these fascinating things, but unfortunately we are
Speaker 3: out of time. But thank you so much, Mitch. This
Speaker 3: is really really fascinating stuff. I hope we can do
Speaker 3: it again and thanks to everyone for listening to Beyond Contact.
Speaker 3: We will be back next week with an all new
Speaker 3: episode out. You can follow me Captain Ron on Twitter
Speaker 3: and Instagram at CID Underscore Captain Ron. Stay connected by
Speaker 3: checking out Contact inthedesert dot com. Stay open minded and
Speaker 3: rational as we explore the unknown right here on the
Speaker 3: iHeartRadio and Coast to Coast am Paranormal Podcast Network.
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