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Speaker 1: The public has had a long held fascination with detectives.

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Speaker 1: Detective see aside of life the average person is never

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Speaker 1: exposed to. I spent thirty four years as a cop.

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Speaker 1: For twenty five of those years I was catching killers.

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Speaker 1: That's what I did for a living. I was a

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Speaker 1: homicide detective. I'm no longer just interviewing bad guys. Instead,

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Speaker 1: I'm taking the public into the world in which I operated.

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Speaker 1: The guests I talk to each week have amazing stories

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Speaker 1: from all sides of the law. The interviews are raw

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Speaker 1: and honest, just like the people I talk to. Some

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Speaker 1: of the content and language might be confronting. That's because

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Speaker 1: no one who comes into contact with crime is left unchanged.

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Speaker 1: Join me now as I take you into this world.

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Speaker 1: In part two of my chat, we've retired Detective Graham Simfendorfer.

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Speaker 1: We dive further into the world of AI and how

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Speaker 1: it's going to change the world of crime. It's quite

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Speaker 1: mind blowing where we're headed. My sense is, if pleasing

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Speaker 1: in investigations, the life of a criminal is going to

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Speaker 1: get a lot tougher. But maybe I'm looking at things simplistically,

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Speaker 1: because no doubt the crooks are also going to embrace

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Speaker 1: AI as well. That may counterbalance the advantages law enforcement have.

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Speaker 1: We also had a close look at how AI may

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Speaker 1: help solve cold cases and how it could be used

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Speaker 1: in the court system. It's a really interesting chat that

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Speaker 1: I learned a lot from Graham. Welcome back to part

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Speaker 1: two of Eye Catch Killers. I'm scared AI is taking

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Speaker 1: over the world. Yeah, I want to jump straight into

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Speaker 1: cold cases because homicide was my passion in my career,

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Speaker 1: and I know you worked homicide and people are just

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Speaker 1: fascinated with cold cases and the thing with homicide until

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Speaker 1: they're solved. It doesn't matter if they're fifty years old.

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Speaker 1: People want results when someone's life has been taken. How

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Speaker 1: can we use AI in the cold case space?

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Speaker 2: Yeah, great question, and some significant pieces can play a

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Speaker 2: really important part in this. So there's the aspect of

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Speaker 2: reviewing all the old information, laying a lens across what

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Speaker 2: we know, open source around that information as well. So

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Speaker 2: it's large data sets of information that, like you said,

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Speaker 2: to get your head around a cold case, you literally

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Speaker 2: sit with a box for a month.

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Speaker 3: Or so trying to understand it all.

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Speaker 2: If you can put all that data into AI, it'll

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Speaker 2: pull out for you what the key issues are where

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Speaker 2: the gaps are perhaps some avenues of inquiry. Even so, again,

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Speaker 2: like we said before, you don't want to lose the

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Speaker 2: skills as an investigator, but it can process that large

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Speaker 2: set of information really quickly and accurately with what's been

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Speaker 2: given in those statements and may even be able to say, hey,

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Speaker 2: look we need to go back further because we've seen

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Speaker 2: with cold case is the way statements were taken ten

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Speaker 2: years ago, twenty years ago, very almost almost leading, if

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Speaker 2: you will, because you're trying to cover your points of

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Speaker 2: proof and not that whole story that you get. So

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Speaker 2: one of those aspects is going back if you can

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Speaker 2: to speak to some of those witnesses and the AI can,

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Speaker 2: So let's go back and explore this part a bit more,

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Speaker 2: or the person that witnessed that vehicle drive pilet's go

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Speaker 2: back and see that. There's that aspect of understanding the

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Speaker 2: investigation and I guess the investigation plan. But then there's

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Speaker 2: also review of either CCTV or face images, and that's

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Speaker 2: where the facial recognition technology could come in and play

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Speaker 2: a part.

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Speaker 1: Right, So the cold cases, okay, and when you said

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Speaker 1: you know, the AI might say, have you thought of

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Speaker 1: this or line of inquiry, not taking that personal as

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Speaker 1: an investigator, because what you're dealing with AI, they're drawing

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Speaker 1: on the experience of all the in theory at its best,

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Speaker 1: all the world's greatest investigators looking at this case, all

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Speaker 1: the information that they've learned from past things. Because this

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Speaker 1: am I describing it correctly. Basically, they're drawing on all

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Speaker 1: the information.

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Speaker 3: That's right.

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Speaker 2: You're taking every into account and thinking outside the box

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Speaker 2: if it needs to, and that's the skills of great

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Speaker 2: investigator processing that, and you can teach it to think

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Speaker 2: about the laws of evidence and making sure that whatever

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Speaker 2: strategy or tactic that it's thinking of is complying with

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Speaker 2: the law and complying with the rules of evidence. So

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Speaker 2: it'll do all that for you. But yeah, you're right,

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Speaker 2: it's taking the best of everything and applying it to

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Speaker 2: your case. So for cold cases, it's worth a review.

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Speaker 2: It's worth looking at a ability to either you know,

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Speaker 2: fix fix some footage or make the CCTV less grainy,

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Speaker 2: or looking at a face image that was previously no

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Speaker 2: one knew who this was or couldn't be solved. And

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Speaker 2: that's where this journey for me kicked off. Actually was

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Speaker 2: looking at. A friend of mine showed me a documentary

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Speaker 2: series that I think was on Netflix about the there

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Speaker 2: was nineteen seventy nine Lunar Park fire.

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Speaker 3: Oh yeah, I remember that.

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Speaker 1: There seven people died in the on the Gates train.

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Speaker 3: Is that one?

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Speaker 2: And I watched it. Got a degree in fire investigation,

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Speaker 2: So the scientific brain of me was very intrigued by

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Speaker 2: by that aspect. But then as the documentary went on,

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Speaker 2: it was Caro Meldrem Hannah that did that documentary, an

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Speaker 2: amazing documentary by the way, And towards the end, there's

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Speaker 2: this face image of who they believed lit the fire,

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Speaker 2: and there was allegations of being linked to by keys

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Speaker 2: all the like, but there was this face image, and

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Speaker 2: I just could not understand how in this day and

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Speaker 2: age we don't have technology that could turn that face

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Speaker 2: image from just the sketch image that we're used to

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Speaker 2: seeing into a actual person and then use the data

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Speaker 2: sets and AI two search for that person through whether

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Speaker 2: criminal networks or you know, there's some ethical considerations around

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Speaker 2: searching against open source, but how can we not have that?

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Speaker 2: And my rest that sent me on the journey of

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Speaker 2: researching who surely someone could be doing this and my

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Speaker 2: investigations globally is no one.

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Speaker 3: No one is.

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Speaker 2: So we started that journey with program AI around turning

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Speaker 2: a cold case composite sketch image of the old hand

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Speaker 2: drawn ones, using AI to turn that into a three

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Speaker 2: D verse of a person, and then you've got a

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Speaker 2: much better identity of who that person could be searching

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Speaker 2: against either open source or criminal databases for an identity,

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Speaker 2: and you need to potentially age that person. Obviously, if

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Speaker 2: you're talking about a cold case now we've all seen

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Speaker 2: it on your Instagram reels, is what are you going

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Speaker 2: to look like when you're seventy The AI will aid you,

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Speaker 2: and it can aid you, but again it's only as

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Speaker 2: good as what's put into it. So you're relying on

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Speaker 2: the witnesses version of what they remembered, and we all

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Speaker 2: know we would see you know, this is a seventy

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Speaker 2: percent likeness or this is an eighty percent likeness. There

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Speaker 2: I can turn that seventy percent likeness into something and

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Speaker 2: use it as a tool to search the sources, but

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Speaker 2: it's still the investigator's job to actually is that the

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Speaker 2: person or not?

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Speaker 1: Okay, And so there's a responsibility of the type of

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Speaker 1: information you put in And you've said with creating that

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Speaker 1: or the image and whether you put it into open

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Speaker 1: source when you're talking open source, you put in out

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Speaker 1: there on social media everywhere.

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Speaker 3: Yeah, that's right.

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Speaker 2: Yeah, so open sources, open source intelligences is the web. Yeah, Instagram, Facebook.

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Speaker 1: So Google put put out there anyone.

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Speaker 2: Yeah, And there symmetical considerations because you know, when you

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Speaker 2: upload your photo to Facebook, you're not really giving permission

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Speaker 2: for that to be used or scraped is the term.

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Speaker 2: You're not you're not giving anyone authorized to scrape all that.

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Speaker 2: So symmetical considerations that we looked at. But that's the

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Speaker 2: stuff we need.

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Speaker 3: To work through.

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Speaker 2: If we're looking for a murderer or a rapist, are

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Speaker 2: the laws going to be sufficient to help support law

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Speaker 2: enforcement to identify that person?

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Speaker 1: Well, it should be like we've had to as technology

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Speaker 1: evolves and the powers need you need the authority and

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Speaker 1: it might be for listening devices from the Supreme Court,

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Speaker 1: that type of thing to get permissioned on the ghost

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Speaker 1: train one, did that evolve into anything that what you're

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Speaker 1: looking at.

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Speaker 2: Still a work in progress. So we're still using that

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Speaker 2: as a test case too. It's a great test case actually,

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Speaker 2: to turn that image into a person, but also age

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Speaker 2: that person and what they might look like years ago.

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Speaker 2: Because seventy nine there was no Facebook. You need to

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Speaker 2: aid that person to see if that person is actually

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Speaker 2: out there, either on a license photo or a passport photo.

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Speaker 2: So law enforcement has access to all that already. So

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Speaker 2: that's where we're at, and we're just trying to train

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Speaker 2: that so to train the AI to do it. I've

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Speaker 2: had some friends sketch myself and then running across open source,

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Speaker 2: and it's showing some really positive results.

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Speaker 1: We've got positive in terms of the way you're aging.

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Speaker 2: That's right, not like a fine wine though positive In yeah,

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Speaker 2: it's around ninety six to nine. Anything over ninety six

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Speaker 2: ninety seven percent is showing as accurate okays speak.

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Speaker 1: If we break it down in percentages, that's a good

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Speaker 1: percentage to work with, good.

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Speaker 3: Tool, isn't it.

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Speaker 2: So to either rule that person in or rout, you

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Speaker 2: might get a hit on this likeness is like this person,

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Speaker 2: but then your investigation says, actually that can't be that person.

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Speaker 2: That person left the country two weeks before the offense.

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Speaker 3: Okay.

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Speaker 1: Another thing that comes in so with AI, like we

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Speaker 1: used to get when I first started place. Are the

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Speaker 1: big penries. You know, someone during the sketch, this is

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Speaker 1: what Graham looks like on that, And there was some

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Speaker 1: good and there was Subjectivity came into it when people

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Speaker 1: are doing the sketches. But AI, when it's doing, they're

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Speaker 1: dragging all that information, isn't it. Where there's been failings,

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Speaker 1: where there's been successes. That's the type of access to

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Speaker 1: the material we're having.

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Speaker 2: And that's how you want it to learn. So you

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Speaker 2: don't want it with a bias towards a certain culture

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Speaker 2: or a certain a certain inference. I guess you want

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Speaker 2: it to be accurate to what it is you're looking for.

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Speaker 2: So your sketch that's only seventy percent accurate of the

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Speaker 2: person is only going to get seventy percent accuracy on

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Speaker 2: what you're searching for using the AI. So you've still

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Speaker 2: it to use investigative brain to think about who it is,

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Speaker 2: et cetera. But it'll do it so quickly, and I'll

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Speaker 2: search your license or your your prison photos that are

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Speaker 2: taken time and time again, I'll do it in seconds.

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Speaker 1: This is hypothetical, and it's hypothetical, and it's talking about

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Speaker 1: actual investigations, but it's just got my mind ticking over

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Speaker 1: the barrable. I call it cyril killing of three Aboriginal children,

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Speaker 1: Colin Walker, Evelyn green Up and Clinton Speedy back in

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Speaker 1: nineteen ninety one, over a five month period, they disappeared

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Speaker 1: and their bodies were found. That case has been worked

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Speaker 1: on for over thirty years. I became involved in the investigation,

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Speaker 1: I think five or six years after the children disappeared,

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Speaker 1: for a reinvestigation, and we revisited like you do on

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Speaker 1: a cold case, revisited, and the big argument initially was

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Speaker 1: were these matters linked? And we went through tendency and

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Speaker 1: coincidence evidence and strongly show that the likelihood they have

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Speaker 1: been linked. I'm just thinking of what you're talking about.

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Speaker 1: With all that and my memory, there's probably about the

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Speaker 1: lever arch folders sometimes I presented to the courts, because

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Speaker 1: it's been through courts time and time again. It might

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Speaker 1: be fifteen twenty lever arch folders information. And there was

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Speaker 1: an analyst that worked on the case, Bianchor, and myself

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Speaker 1: and another detective Jerry Bowden and Jason Evers, and we

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Speaker 1: were walking. We were the walking talking knowledge of that

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Speaker 1: investigation only because we had to do an old school

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Speaker 1: and read the documents and everything, so we retain and

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Speaker 1: we summarize the information, We analyze the information, and Bianca

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Speaker 1: did some extraordinary work as the analyst. But with Ai

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Speaker 1: Fai looked at that there's a very real possibility they

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Speaker 1: could pick up something despite our best efforts that we

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Speaker 1: might have missed. Would that be fair the.

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Speaker 3: Same, Yeah, definitely fair.

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Speaker 2: It may pick up an avenue of inquiry that or

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Speaker 2: a link that you didn't quite put together, or it

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Speaker 2: might question a link.

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Speaker 1: Yeah.

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Speaker 2: It could work in the opposite too, can't it. So

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Speaker 2: it's a search for the truth. We all know that

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Speaker 2: around investigations. It could actually do both, could find a

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Speaker 2: new avenue of acquiry or so actually you went down

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Speaker 2: the wrong path on that one. Let's go back to

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Speaker 2: that intersection where you went down there and provide you

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Speaker 2: with some insight. But it'll process all of those lever

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Speaker 2: arch folders rapidly and so thoroughly that it could map

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Speaker 2: it out.

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Speaker 1: And there were legal issues and legislation was changed for

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Speaker 1: that case, and there was high court decisions and all

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Speaker 1: those things could be broken down into it. And like

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Speaker 1: we had some of the four and agains, but some

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Speaker 1: of the best legal minds in the country working on it.

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Speaker 1: But AI might even take it to the next level.

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Speaker 3: It can if it looked at it.

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Speaker 2: That's right, and it can, and I think there's a

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Speaker 2: there's a place for it. Again, coming back to it's

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Speaker 2: got to be the appetite I guess of the agencies

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Speaker 2: around the country to want to lean into that and

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Speaker 2: explore it, because without their assistance, without their access to

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Speaker 2: these files or these data sets, you can't train the model.

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Speaker 2: But again, the first point's got to be safe, secure

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Speaker 2: and for the right purpose. And that's why you know,

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Speaker 2: we're looking at partnering with the right investors for this country.

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Speaker 2: Not people just want to make profit. It is about

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Speaker 2: solving crime and just just even if it solves one

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Speaker 2: cold case, how good is that for the.

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Speaker 1: Fairt Well, you know that you've been there, you've seen

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Speaker 1: that the pain that if anyone's case hasn't been solved,

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Speaker 1: or someone's missing and there's no answers. I also think

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Speaker 1: and I talk William Tyrell case, and that's the case.

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Speaker 1: It's very close to me and I'm heavily involved in it,

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Speaker 1: and I think of all the information, we were just

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Speaker 1: overwhelmed with information because it was a high profile and

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Speaker 1: when I was running it for the four years that

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Speaker 1: there was so much information that could have gone through

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Speaker 1: and I could see something like I'd like AI to

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Speaker 1: look through it and has anything been missed? And it's

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Speaker 1: been running now for six years, and I think I'm

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Speaker 1: as confused as the rest of the public what's happening

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Speaker 1: on the investigation. But it'd be interesting for.

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Speaker 2: AI and it could in another positive there in that case,

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Speaker 2: In that scenario, and without knowing it to be anywhere

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Speaker 2: near the little that you have lived, it could take

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Speaker 2: some of the conscious or unconscious bias, yes, out of

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Speaker 2: the situation and deal with the facts and case law

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Speaker 2: and evidence processes, procedures and just apply.

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Speaker 1: That's interesting that you say, like the biases, because that's

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Speaker 1: been part of the conflict in that investigation people, so

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Speaker 1: it didn't get on board with what I was looking

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Speaker 1: at and vice versa. That would be good. I would

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Speaker 1: love an objective opinion on it. I'm calling for a

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Speaker 1: public inquiry on the teral investigation or the judicial inquiry,

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Speaker 1: some form of independent review of what's been done on

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Speaker 1: that case. Yeah, maybe we call for an AI.

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Speaker 2: Inquiry, maybe the cysts and another tool to assist and

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Speaker 2: that investigation again removing any of those biases and going

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Speaker 2: right back to the facts and the information at the

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Speaker 2: time that was given, and it can unpack it all

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Speaker 2: as incredible.

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Speaker 1: Is AI capable of removing the biases? If how here's

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Speaker 1: the brief of evidence, give an objective opinion. I'm thinking,

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Speaker 1: what would I be typing in the GPT if I

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Speaker 1: had access to all the evidence that was gathered from

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Speaker 1: the wim terial investigation. Could you please review this investigation

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Speaker 1: and give an unbiased opinion or an objective opinion on the.

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Speaker 2: Me in accordance with the rules of evidence, facts of law,

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Speaker 2: and you could even look at it from that criminal

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Speaker 2: point of view. But you then put that lens of

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Speaker 2: as an investigator, where are the avenues of inquiry that

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Speaker 2: we may have missed or where are some choke points

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Speaker 2: here that we can go back and revisit, et cetera.

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Speaker 2: So yeah, and it's about training it. But you've got

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Speaker 2: to have access to some of these older, probably less intense,

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Speaker 2: cold cases to train your model to think as an investigator.

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Speaker 1: I would like to do it. And I'm mainly referring

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Speaker 1: to those two investigations because I'm very familiar with them.

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Speaker 1: But I'd like to put all the information from both

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Speaker 1: those investigations, put it into AI and identify. The question

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Speaker 1: would be identify the person most likely to have committed

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Speaker 1: these offenses. That'd be interesting.

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Speaker 2: What would exercise? Yeah, yeah, and one hundred percent it

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Speaker 2: would be And it can do it. It just got

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Speaker 2: to be able to you know, as I said at

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Speaker 2: the start, it does cost a lot of money to

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Speaker 2: build them things. But the money is there in how

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Speaker 2: has it cost to solve a murder?

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Speaker 1: Well, I look at there's always it's always a contentious

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Speaker 1: environment the cold case units in any state, and I

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Speaker 1: see it in all states because you've got family members

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Speaker 1: that are pushing for we want everything done to have

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Speaker 1: this case solved, and you've got police saying, well we've

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Speaker 1: looked at this, and yeah, there's pushback. There's always you're

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Speaker 1: not going to satisfy everyone. Like if one case is unsolved,

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Speaker 1: there's going to be a family that's unhappy. That's the

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Speaker 1: nature of it. But yeah, it just gets me thinking.

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Speaker 1: Quite often the cold case units are restructured, renamed, and

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Speaker 1: we're going to do this and going to do that.

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Speaker 1: But wouldn't it be interesting if they said, Okay, we're

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Speaker 1: going to get put all these cold cases through AI

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Speaker 1: and see if anything's been missed, because invariably they bring

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Speaker 1: a fresh team of investigators and you know the old saying,

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Speaker 1: the fresh set of eyes. Sometimes it works, but we're

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Speaker 1: all detectives, we're all probably trained in the same line.

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Speaker 1: The thinking it would be good having that pendance of

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Speaker 1: AI looking through the briefs.

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Speaker 2: Couldn't a more I think that's having the appetite and

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Speaker 2: the courage to go down that path because it obviously

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Speaker 2: potentially will create more work for the police.

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Speaker 3: But you start to trade.

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Speaker 2: Off the benefits of using AI and the administrative aspect

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Speaker 2: we discussed earlier. You're freeing up some more time to

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Speaker 2: potentially revisit here, but it's a case by case. Traditionally

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Speaker 2: the cold cases are brought up because there's a new

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Speaker 2: piece of information. Well, this is a new tool that

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Speaker 2: may be able to assist, and I'm all for it,

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Speaker 2: and there are you know you mentioned chat CHAIRBT, but

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Speaker 2: I'll give them a plug the main code.

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Speaker 3: Guys.

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Speaker 2: They've built Matilda, the Australian version that's going to compete

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Speaker 2: with chat CHAIRBT because it's going to be built on

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Speaker 2: our bias. Australian culture, Australian knowledge and using the data

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Speaker 2: sets to build that that are from here, okay, and

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Speaker 2: we have access to and everyone has ability to input

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Speaker 2: to that, not using a UK or an American or

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Speaker 2: an agent.

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Speaker 1: So it's based on our environment or relable to it.

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Speaker 1: So it's what's that.

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Speaker 2: Called Matilda Matilda the guys main Code Dave and his

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Speaker 2: team there have built Matilda. They launched it recently. So

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Speaker 2: it's exciting Fustralia and we could be global leaders in

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Speaker 2: this space of AI. We don't need to rely on

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Speaker 2: what's being done elsewhere.

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Speaker 1: Well, it's something that I think we need to embrace.

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Speaker 1: And as I said, I'm not the first to adopt technology,

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Speaker 1: but I'm starting to look and think you're going to

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Speaker 1: get left behind if we don't look at this. But

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Speaker 1: breaking down what we just talked about, my mind's spinning

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Speaker 1: in terms of what could be done with cold cases,

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Speaker 1: and quite often it's just that small piece, that missing

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Speaker 1: piece of information that with all the good intent of

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Speaker 1: all the investigators working on it tirelessly, that is missed.

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Speaker 2: Yeah, exactly that. What's that old game was mind Sweeper? Yeah,

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Speaker 2: we just click on that one little square that opens

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Speaker 2: up open all up. So I think that's part of

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Speaker 2: As long as we're adhering to and working with the

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Speaker 2: partners in the real moral, ethical sense, adhering to the

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Speaker 2: principles that they want, it can be built. But we're

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Speaker 2: just going to get the right investors with the right

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Speaker 2: purpose and integrity that we think we share to come

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Speaker 2: with us on that journey to build it for the

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Speaker 2: investigators and so will solve the heat.

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Speaker 1: And we keep coming back to that the integrity and

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Speaker 1: the ethics of it that's crucial to it, isn't it

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Speaker 1: because it can could go off track very quickly if

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Speaker 1: it's not used properly.

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Speaker 2: Yeah, And as you said, you know, you get one

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Speaker 2: wrong because for whatever reason it may be, well, the

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Speaker 2: whole thing comes crashing down.

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Speaker 1: AI in the courts that we had a magistrate on

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Speaker 1: a few weeks ago on by catch killers and we're

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Speaker 1: talking about AI in the court system, and he made

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Speaker 1: the comment that say sentencing of drink driving charges pc

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Speaker 1: I prescribe concentration of alcohol for sentencing, you could access

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Speaker 1: what were the sentences on everyone that's been sentenced for

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Speaker 1: that defense and then make your determination on what sentence

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Speaker 1: you should give, whether it's a suspension, of license and

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Speaker 1: that type of thing. I think it's going to have

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Speaker 1: to change the face of courts.

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Speaker 2: Yeah, I agree, I think it's I have to. You know,

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Speaker 2: if they're not thinking about it, they should be, but

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Speaker 2: I'm sure they are, and you know, very smart minds

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Speaker 2: in there. And what a way to be able to

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Speaker 2: draw all that information from such a massive amount of

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Speaker 2: history or data for the judges to have at their

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Speaker 2: fingertips or barristers to have to suggest to judges around

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Speaker 2: sentencing parameters. Makes everything more efficient, more effective and actually comparable.

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Speaker 2: And that's what the whole point is, if we could

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Speaker 2: have that with an URL judiciary or the.

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Speaker 1: I suppose the argument is that you've got to hang

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Speaker 1: on to the human side of it, the human part

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Speaker 1: of it. But in my limited experience playing with AI,

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Speaker 1: it comes back with some very emotional awareness that quite

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Speaker 1: surprises me. Like if it's things I'm thinking, I didn't

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Speaker 1: expect a computer to spit that back at me. So, yeah,

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Speaker 1: you feel all that into court. I don't know what

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Speaker 1: the role of a solicitor. I know, if I was charged,

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Speaker 1: and I hope not to be charged again, I'm just

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Speaker 1: put that record, But if I charged again, like I

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Speaker 1: worked on my defense and worked with my great legal

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Speaker 1: team for recording a conversation on the telephone that almost

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Speaker 1: seems like I did embrace technology leader on it. I

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Speaker 1: would put it in AI and go, where is the

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Speaker 1: strength for the case against me? Wants a brief of

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Speaker 1: evidence being served? What is the strength of the case

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Speaker 1: against me? And this is my case, and then present

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Speaker 1: the cagun argument.

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Speaker 3: For sure yep.

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Speaker 1: So is there a role for solicits anymore?

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Speaker 3: Well, do we need less solicitors? We won't go if

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Speaker 3: we put it.

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Speaker 2: Look like we were saying before on my lens through

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Speaker 2: policing was a certain bias, and that's so changed now

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Speaker 2: that I've come out on the other side and really

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Speaker 2: respect and admire the work that I've done by defense

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Speaker 2: teams because they've got to go through so much information

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Speaker 2: on these serious cases, and you know, is there an

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Speaker 2: avenue there to explore or not? Is this the right

440
00:21:55,720 --> 00:21:58,680
Speaker 2: person or not? That's our judicial system. But there's definitely

441
00:21:58,680 --> 00:22:02,080
Speaker 2: a place for AI. And imagine the hours that are

442
00:22:02,080 --> 00:22:06,720
Speaker 2: spent on that. But you know, the research solicitors, the well.

443
00:22:06,920 --> 00:22:11,200
Speaker 1: The poweralegals, legals, so find this case law and all that.

444
00:22:11,800 --> 00:22:15,040
Speaker 2: Yeah, sorry, guys, it might be, it might be changing

445
00:22:15,080 --> 00:22:18,080
Speaker 2: for you, but you know it'll shift to another focus.

446
00:22:18,280 --> 00:22:20,680
Speaker 2: We saw this with computers and everyone thought, oh, it's

447
00:22:20,680 --> 00:22:21,800
Speaker 2: going to be the end of it all. Even's going

448
00:22:21,840 --> 00:22:23,399
Speaker 2: to be out of jobs. But we've seen it now

449
00:22:23,400 --> 00:22:26,879
Speaker 2: advanced manufacturing. It's just shifted and I dare also use

450
00:22:26,880 --> 00:22:28,480
Speaker 2: that word that I hate from COVID pivot.

451
00:22:31,280 --> 00:22:33,280
Speaker 1: You're clearly from down Victoria.

452
00:22:33,280 --> 00:22:33,600
Speaker 2: That's right.

453
00:22:33,680 --> 00:22:36,840
Speaker 1: We heard it every stingy Daniel Andrews every day.

454
00:22:38,400 --> 00:22:41,800
Speaker 2: But yeah, we've seen the advancements and new technology does

455
00:22:41,880 --> 00:22:44,879
Speaker 2: bring new challenges and brings new things, but it takes

456
00:22:44,920 --> 00:22:47,120
Speaker 2: that to the next step and next generation. Who knows

457
00:22:47,160 --> 00:22:49,400
Speaker 2: what we're talking about in twenty years, like we look

458
00:22:49,440 --> 00:22:51,960
Speaker 2: now at you know, you look at some images and

459
00:22:52,040 --> 00:22:55,080
Speaker 2: videos now and you go, that's a I think in three,

460
00:22:55,160 --> 00:22:56,760
Speaker 2: four or five years, you're not about.

461
00:22:56,600 --> 00:22:59,680
Speaker 1: To tell we went well, the young child down in

462
00:22:59,720 --> 00:23:04,000
Speaker 1: South Australia that disappeared gus that they were getting a

463
00:23:04,040 --> 00:23:07,800
Speaker 1: lot of AI and a lot of media. Different organizations

464
00:23:07,840 --> 00:23:09,720
Speaker 1: reached out to me to comment on it. Just based

465
00:23:09,760 --> 00:23:13,840
Speaker 1: on my experiences with the Willim Turreal matter and what

466
00:23:14,000 --> 00:23:16,280
Speaker 1: I acknowledged with the Willim Tural matter, it was very

467
00:23:16,280 --> 00:23:18,840
Speaker 1: difficult because people there were a lot of sightings of

468
00:23:19,240 --> 00:23:21,919
Speaker 1: William Tyrell and yeah, you had to sift through it,

469
00:23:21,960 --> 00:23:24,520
Speaker 1: you had to follow it up. And we ended up

470
00:23:24,720 --> 00:23:27,480
Speaker 1: working out the triarche system on what weight we'd carry

471
00:23:27,800 --> 00:23:31,520
Speaker 1: a siding of wim. But then another layer came in

472
00:23:31,520 --> 00:23:34,240
Speaker 1: with the gust thing because there was AI generated images

473
00:23:34,320 --> 00:23:39,000
Speaker 1: of and yeah, you could work it out relatively easily

474
00:23:39,160 --> 00:23:42,160
Speaker 1: that it was AI generated, But as you said, it's

475
00:23:42,200 --> 00:23:44,000
Speaker 1: going to get better and that's going to be very

476
00:23:44,000 --> 00:23:47,960
Speaker 1: hard for investigators to sift through what's real and what's not.

477
00:23:48,080 --> 00:23:52,040
Speaker 2: Yeah, it's pretty scary, not just the images, but then voice,

478
00:23:52,480 --> 00:23:55,280
Speaker 2: the use of people's voice. And you know, we did

479
00:23:55,280 --> 00:23:57,200
Speaker 2: do a bit of this on the show Hunted where

480
00:23:57,240 --> 00:24:01,200
Speaker 2: we cloned the person and we're able to write the

481
00:24:01,240 --> 00:24:04,879
Speaker 2: script and rang their mother as if we were the

482
00:24:04,960 --> 00:24:09,560
Speaker 2: fugitive sneak. Yeah, but you know there's a criminal element

483
00:24:09,600 --> 00:24:11,600
Speaker 2: that comes in on this too, doesn't it. So you know,

484
00:24:11,680 --> 00:24:14,800
Speaker 2: we see a lot of frauds that that happen. Organized

485
00:24:14,840 --> 00:24:18,160
Speaker 2: crime internationally will use this technology to try and commit fraud,

486
00:24:18,640 --> 00:24:20,200
Speaker 2: which is why I have with my kids. The best

487
00:24:20,240 --> 00:24:21,479
Speaker 2: way to try and get around is you have your

488
00:24:21,480 --> 00:24:23,880
Speaker 2: code word. Right, So if my kids are overseas, Hey Dad,

489
00:24:24,000 --> 00:24:28,240
Speaker 2: I'm stuck on need some money, which is your classic case, son,

490
00:24:28,280 --> 00:24:31,040
Speaker 2: what's the code word for the day. Yeah, I won't

491
00:24:31,080 --> 00:24:33,000
Speaker 2: know that, so it might sound like my son might

492
00:24:33,040 --> 00:24:34,119
Speaker 2: be purporting to be here.

493
00:24:34,000 --> 00:24:38,920
Speaker 1: Because that's your protection. That's a very simple, practical.

494
00:24:38,760 --> 00:24:40,520
Speaker 3: Easy way. But then they've got to remember the code.

495
00:24:41,520 --> 00:24:43,160
Speaker 3: But no, that's that's our simple one.

496
00:24:43,200 --> 00:24:45,080
Speaker 2: And I know if I meet the talking to them

497
00:24:45,080 --> 00:24:47,320
Speaker 2: on the phone, or you know, they've sent me a

498
00:24:47,359 --> 00:24:49,960
Speaker 2: voice message, you know, then fact check what's the what's

499
00:24:50,000 --> 00:24:50,439
Speaker 2: the code?

500
00:24:50,680 --> 00:24:54,960
Speaker 1: So with AI and voice copying the voice, there's all

501
00:24:54,960 --> 00:24:58,000
Speaker 1: these hours and you're you're the same in the stuff

502
00:24:58,000 --> 00:25:00,280
Speaker 1: that you've done in the media, and you've got me

503
00:25:00,480 --> 00:25:04,560
Speaker 1: talking here on the podcast. They in effect could copy

504
00:25:04,600 --> 00:25:07,600
Speaker 1: that voice and at this point it could be leaving

505
00:25:07,600 --> 00:25:09,440
Speaker 1: the message. But I dare say it would get to

506
00:25:09,520 --> 00:25:11,440
Speaker 1: the point where they could have a conversation.

507
00:25:11,040 --> 00:25:12,240
Speaker 3: In my voice exactly.

508
00:25:13,840 --> 00:25:14,080
Speaker 1: Yeah.

509
00:25:14,080 --> 00:25:15,160
Speaker 3: Scary stuff, isn't it.

510
00:25:15,240 --> 00:25:16,560
Speaker 1: Especially if they fhm, my mother.

511
00:25:18,520 --> 00:25:22,560
Speaker 2: That's right, And that's that's the vulnerability. We always see

512
00:25:22,760 --> 00:25:25,320
Speaker 2: organized crime will keep shifting to where the gap is.

513
00:25:25,440 --> 00:25:27,840
Speaker 2: We've seen that in the tobacco industry. Yes, we saw

514
00:25:27,880 --> 00:25:29,600
Speaker 2: it through the armed robbery days. You know, when was

515
00:25:29,600 --> 00:25:31,520
Speaker 2: the last time we saw a bank robbery. Well that's

516
00:25:31,520 --> 00:25:33,960
Speaker 2: because we target harden, didn't We know now that the

517
00:25:34,000 --> 00:25:37,479
Speaker 2: real opportunity is online fraud and scams and targeting our

518
00:25:37,520 --> 00:25:39,600
Speaker 2: elderly that aren't up with the tech. And there's a

519
00:25:39,640 --> 00:25:41,600
Speaker 2: lot of work being done by the banks to educate,

520
00:25:42,280 --> 00:25:44,320
Speaker 2: you know, fact check, you know, if that email doesn't

521
00:25:44,320 --> 00:25:47,399
Speaker 2: look quite right, and don't do it. So there's a

522
00:25:47,400 --> 00:25:50,119
Speaker 2: lot of space. So that's why it's so critical to

523
00:25:50,600 --> 00:25:52,879
Speaker 2: have this developed. It's going to be developed. The crooks

524
00:25:52,920 --> 00:25:55,040
Speaker 2: are going to use it to law enforcement needs to

525
00:25:55,080 --> 00:25:57,760
Speaker 2: be right up there with them, not falling behind.

526
00:25:58,160 --> 00:26:01,440
Speaker 1: Well, we have to because I thing about the crookxy

527
00:26:01,600 --> 00:26:04,560
Speaker 1: creative in the way they can exploit the situation and

528
00:26:04,880 --> 00:26:07,680
Speaker 1: moving as you said with the tobacco wars or whatever,

529
00:26:07,960 --> 00:26:10,680
Speaker 1: if they see an area they can exploit. But it

530
00:26:11,480 --> 00:26:15,600
Speaker 1: makes me concerning that even before you'd go to court,

531
00:26:15,680 --> 00:26:18,119
Speaker 1: if you had the phato of someone there or something

532
00:26:18,240 --> 00:26:22,520
Speaker 1: and there's a hard evidence, we don't know. Sound like

533
00:26:22,560 --> 00:26:25,679
Speaker 1: Donald Trump, fake news, fake.

534
00:26:24,960 --> 00:26:26,240
Speaker 3: Image, that's right?

535
00:26:26,720 --> 00:26:27,800
Speaker 1: Where is it? Where's it going to?

536
00:26:27,960 --> 00:26:28,560
Speaker 3: Where does it end.

537
00:26:28,640 --> 00:26:31,160
Speaker 2: Yeah, Yeah, that's a scarity there are you know, there

538
00:26:31,200 --> 00:26:33,119
Speaker 2: are ways of trying to find if that is AI

539
00:26:33,200 --> 00:26:36,280
Speaker 2: generated or not. And we're getting into a complete area

540
00:26:36,280 --> 00:26:39,160
Speaker 2: where which is outside my wheelhouse of how that's done.

541
00:26:39,680 --> 00:26:43,080
Speaker 2: But that's part of the reason of building it, building

542
00:26:43,119 --> 00:26:44,919
Speaker 2: it with the ethics that it needs to be.

543
00:26:45,920 --> 00:26:49,639
Speaker 1: Getting back to two investigations, let's just put their heads

544
00:26:49,680 --> 00:26:53,760
Speaker 1: to how else it could help the fact sheet and

545
00:26:53,840 --> 00:26:57,160
Speaker 1: all these things seem relatively little, but in a high turnover,

546
00:26:57,480 --> 00:26:59,760
Speaker 1: the high volume crimes, the type of things that the

547
00:27:00,160 --> 00:27:02,720
Speaker 1: these police are out doing on a daily basis, and

548
00:27:02,760 --> 00:27:06,760
Speaker 1: it might be arrest arresting someone for a break and enter,

549
00:27:06,960 --> 00:27:11,040
Speaker 1: and that should in theory free police up if we

550
00:27:11,400 --> 00:27:14,119
Speaker 1: embrace what AI can do for us.

551
00:27:14,160 --> 00:27:16,320
Speaker 2: Yeah, I think that's a no brainer. Yeah, it should

552
00:27:16,359 --> 00:27:18,880
Speaker 2: free up countless hours. How do you We'll just give

553
00:27:18,920 --> 00:27:21,360
Speaker 2: one example of the one homicide detective, then a few

554
00:27:21,680 --> 00:27:24,800
Speaker 2: times at by the volume crime as well, which you know,

555
00:27:25,080 --> 00:27:28,760
Speaker 2: potentially the crime mapping, the predictive data of where the

556
00:27:28,760 --> 00:27:31,120
Speaker 2: hot spots are. We've seen a lot of great work

557
00:27:31,160 --> 00:27:33,480
Speaker 2: done in that space in policing over the years. But

558
00:27:33,520 --> 00:27:36,600
Speaker 2: there's another argument for another aspect of AI to look

559
00:27:36,640 --> 00:27:38,720
Speaker 2: at the crime mapping. You know, where where's all your

560
00:27:38,760 --> 00:27:40,840
Speaker 2: cars getting broken into? What do we used to say that,

561
00:27:40,920 --> 00:27:43,240
Speaker 2: You know, you'd see a crime spike of those volume crimes.

562
00:27:43,240 --> 00:27:45,640
Speaker 2: You go, well, there's a dealer around here somewhere, because

563
00:27:45,640 --> 00:27:47,399
Speaker 2: they're not going to hold onto the stolen gear for long.

564
00:27:47,440 --> 00:27:48,760
Speaker 2: They want to offload that really quick.

565
00:27:50,640 --> 00:27:53,560
Speaker 1: They'd be someone could be as simple as someone targeting

566
00:27:53,800 --> 00:27:56,359
Speaker 1: a shopping center car park. Car has been broken into,

567
00:27:56,800 --> 00:27:59,320
Speaker 1: and you know we'd put a car in there, settle

568
00:27:59,400 --> 00:28:01,879
Speaker 1: up with clear left unlocked and wait till the person

569
00:28:01,920 --> 00:28:07,880
Speaker 1: come to arrest them. But identifying patterns in crime would

570
00:28:07,920 --> 00:28:10,560
Speaker 1: be good. Linking crimes is another thing, and we've got

571
00:28:10,560 --> 00:28:12,680
Speaker 1: a lot better, I think in law enforcement because there's

572
00:28:12,720 --> 00:28:17,520
Speaker 1: more of an exchange of information between jurisdictions at state

573
00:28:17,560 --> 00:28:21,920
Speaker 1: police and that. But serial offenders the type of thing

574
00:28:22,000 --> 00:28:27,320
Speaker 1: that quite often went unnoticed because there was no link

575
00:28:27,520 --> 00:28:30,080
Speaker 1: to the crime that this person had committed in another location.

576
00:28:30,320 --> 00:28:31,120
Speaker 3: Yeah, exactly.

577
00:28:31,119 --> 00:28:32,800
Speaker 2: And I think you just speak to the core of

578
00:28:32,840 --> 00:28:35,879
Speaker 2: what I'm hopefully trying to push is that all law

579
00:28:35,960 --> 00:28:38,680
Speaker 2: enforcements need to get on board this and once the

580
00:28:38,680 --> 00:28:41,200
Speaker 2: border they don't care about the borders. We've seen that

581
00:28:41,240 --> 00:28:44,200
Speaker 2: and we've sadly learned the hard lessons of those serial

582
00:28:44,200 --> 00:28:47,640
Speaker 2: offenders that will jump jurisdictions because we weren't communicating. We're

583
00:28:47,640 --> 00:28:50,360
Speaker 2: so much better now, but we can still improve and

584
00:28:50,360 --> 00:28:52,320
Speaker 2: if we're going to do this, I think everyone needs

585
00:28:52,320 --> 00:28:55,120
Speaker 2: to get on board because you know, the I guess

586
00:28:55,160 --> 00:28:57,440
Speaker 2: the armed robbery or the sex offense that's committed over

587
00:28:57,440 --> 00:29:01,320
Speaker 2: in way, maybe the exactly the same or the way

588
00:29:01,320 --> 00:29:03,560
Speaker 2: of offending. Then all of a sudden it started up

589
00:29:03,560 --> 00:29:06,479
Speaker 2: here in Queensland and there's a real space there as

590
00:29:06,480 --> 00:29:09,720
Speaker 2: well to understand that and learn from that and market

591
00:29:09,760 --> 00:29:11,040
Speaker 2: as a flag and go, well that needs to be

592
00:29:11,040 --> 00:29:13,120
Speaker 2: looked at. Is that possible that that could be the

593
00:29:13,120 --> 00:29:13,720
Speaker 2: same offender?

594
00:29:13,880 --> 00:29:17,720
Speaker 1: Okay, interview So I keep coming back to the interview room.

595
00:29:17,960 --> 00:29:20,240
Speaker 1: You're playing with my head thinking I could sit here

596
00:29:20,280 --> 00:29:22,800
Speaker 1: with interview you for a couple of hours, and then

597
00:29:22,840 --> 00:29:25,480
Speaker 1: my phone tells me you've forgot to ask this or

598
00:29:25,560 --> 00:29:26,360
Speaker 1: forgot to ask that.

599
00:29:27,480 --> 00:29:28,000
Speaker 3: You could.

600
00:29:28,160 --> 00:29:32,080
Speaker 1: A lot of the preparation for the interview room was

601
00:29:32,120 --> 00:29:35,000
Speaker 1: preparing for the interview, and that quite often meant you

602
00:29:35,000 --> 00:29:36,760
Speaker 1: had to read through the whole broef and get all

603
00:29:36,760 --> 00:29:39,240
Speaker 1: the details so you had it in your head, I

604
00:29:39,240 --> 00:29:42,400
Speaker 1: would imagine AI could help with that.

605
00:29:42,680 --> 00:29:44,840
Speaker 2: Yeah, the same question as what we asked before, rather

606
00:29:44,880 --> 00:29:47,600
Speaker 2: than the question to the AI is put this together

607
00:29:48,120 --> 00:29:51,400
Speaker 2: for the district court or Supreme court? Is with all

608
00:29:51,400 --> 00:29:54,040
Speaker 2: the available evidence to me at this stage? Draft mean,

609
00:29:54,080 --> 00:29:56,360
Speaker 2: and if you plan based on these offenses and in

610
00:29:56,360 --> 00:29:58,920
Speaker 2: line with the points of proof to prove the charge,

611
00:29:59,040 --> 00:29:59,560
Speaker 2: you can't.

612
00:29:59,560 --> 00:30:01,760
Speaker 1: Now that I could see the benefits of it. But

613
00:30:01,960 --> 00:30:05,280
Speaker 1: I also think, and you said, you need that human oversite.

614
00:30:05,280 --> 00:30:08,600
Speaker 1: You need an experienced police officer there listening to how

615
00:30:08,640 --> 00:30:12,000
Speaker 1: you answered the questions, because I've seen mistakes people have made,

616
00:30:12,000 --> 00:30:14,360
Speaker 1: and I've probably made it myself that you work so

617
00:30:14,520 --> 00:30:17,960
Speaker 1: hard to Okay, I got to ask these questions. You've

618
00:30:17,960 --> 00:30:21,520
Speaker 1: got twenty questions written out. Good, I've asked that one,

619
00:30:21,600 --> 00:30:23,720
Speaker 1: I've asked that one. But you're not listening to the stances.

620
00:30:23,800 --> 00:30:24,000
Speaker 2: Yeah.

621
00:30:24,160 --> 00:30:27,760
Speaker 1: So yeah, it's a balance between the human oversite and

622
00:30:28,200 --> 00:30:29,640
Speaker 1: what AI can bring.

623
00:30:29,720 --> 00:30:32,000
Speaker 2: Yeah. I've been caught by that, I've got to say embarrassingly,

624
00:30:32,200 --> 00:30:34,840
Speaker 2: because you're so focused on your interview plan and where

625
00:30:34,840 --> 00:30:37,080
Speaker 2: you're going. But that's why I would have your corroborator

626
00:30:37,080 --> 00:30:39,040
Speaker 2: next to you to hopefully pick up on that. And

627
00:30:39,080 --> 00:30:41,840
Speaker 2: when I first started homicide, you know, I'm on my

628
00:30:42,080 --> 00:30:44,600
Speaker 2: introduction to the homicide world. I think it was day

629
00:30:44,600 --> 00:30:47,040
Speaker 2: two I was there. We'd actually had a fellow in

630
00:30:47,080 --> 00:30:49,800
Speaker 2: for interview, you know, and your teams watching through the

631
00:30:49,840 --> 00:30:52,560
Speaker 2: other window, listening, and they're all contributing because everyone has

632
00:30:52,560 --> 00:30:55,600
Speaker 2: different aspects of the brief, aren't they. So that was

633
00:30:55,640 --> 00:30:57,880
Speaker 2: how it was done and here's the next step to that.

634
00:30:58,360 --> 00:31:00,320
Speaker 2: So if you you know, you still have people listening,

635
00:31:00,320 --> 00:31:03,080
Speaker 2: you still have your boss listening or checking, but you've

636
00:31:03,120 --> 00:31:06,480
Speaker 2: got your AI assistant. They're making sure that you've covered

637
00:31:06,520 --> 00:31:10,120
Speaker 2: everything off and hopefully validate where you're at.

638
00:31:10,160 --> 00:31:12,560
Speaker 1: Well in what we're talking about. You would think that,

639
00:31:13,440 --> 00:31:16,000
Speaker 1: because I'm not sure if we spoke on camera or

640
00:31:16,000 --> 00:31:19,320
Speaker 1: off camera, we're certainly talking about that the struggles that

641
00:31:19,760 --> 00:31:23,920
Speaker 1: most police forces in Australia are getting recruiting people at

642
00:31:23,960 --> 00:31:26,479
Speaker 1: the moment. So the police that we've got, we've got

643
00:31:26,520 --> 00:31:31,479
Speaker 1: to use them smart, use them most efficiently. And if

644
00:31:31,560 --> 00:31:35,760
Speaker 1: law enforcement embrace this, one would think that it would

645
00:31:35,800 --> 00:31:37,920
Speaker 1: free up police from a lot of the work behind

646
00:31:37,920 --> 00:31:38,320
Speaker 1: the desk.

647
00:31:38,600 --> 00:31:41,280
Speaker 2: Yeah, I definitely feel that, and I feel that it's

648
00:31:41,320 --> 00:31:43,400
Speaker 2: about trying to identify what's going to be your big

649
00:31:43,400 --> 00:31:45,480
Speaker 2: spang for buck, and right now I think that's the

650
00:31:45,520 --> 00:31:49,440
Speaker 2: administration piece. My heart goes to helping solve crime. Obviously

651
00:31:49,480 --> 00:31:53,000
Speaker 2: that's what I want. But the biggest piece I would

652
00:31:53,080 --> 00:31:57,080
Speaker 2: think here, I guess for the senior police is getting

653
00:31:57,120 --> 00:31:59,480
Speaker 2: rid of the administrative tasks that are day to day.

654
00:31:59,720 --> 00:32:03,880
Speaker 2: If you LANs your brief preparation, your fact checking, your rosters,

655
00:32:04,760 --> 00:32:07,640
Speaker 2: all those administrative tasks that could be taken away and

656
00:32:07,680 --> 00:32:10,760
Speaker 2: all of a sudden, you know, your analysts hours a cutdown,

657
00:32:11,040 --> 00:32:13,640
Speaker 2: investigators hour as a cutdown. We're trying to work through

658
00:32:13,680 --> 00:32:16,320
Speaker 2: some data now on actually putting a monetary value on

659
00:32:16,360 --> 00:32:18,560
Speaker 2: how much that is to be able to sell. This

660
00:32:18,600 --> 00:32:20,120
Speaker 2: is why we need to invest in this, and this

661
00:32:20,160 --> 00:32:22,080
Speaker 2: is why we need you to come on this journey

662
00:32:22,080 --> 00:32:22,520
Speaker 2: with us.

663
00:32:22,760 --> 00:32:25,600
Speaker 1: That would be interesting. And I think with an analyst,

664
00:32:25,640 --> 00:32:28,560
Speaker 1: and I've worked with some great analysts over the years,

665
00:32:28,560 --> 00:32:33,360
Speaker 1: and I think how they would readily adapt to using AI,

666
00:32:33,640 --> 00:32:38,120
Speaker 1: like as in not making them obsolete by using it.

667
00:32:38,120 --> 00:32:40,600
Speaker 1: It just frightens me the amount of information they'd be

668
00:32:40,640 --> 00:32:42,400
Speaker 1: able to spit out so it doesn't have to be

669
00:32:42,480 --> 00:32:48,360
Speaker 1: the dumb lead investigator. The analysts and the analyst is

670
00:32:48,400 --> 00:32:49,040
Speaker 1: embracing it.

671
00:32:49,200 --> 00:32:52,640
Speaker 2: Yeah, another tool for them to use, and they're forward

672
00:32:52,640 --> 00:32:55,160
Speaker 2: facing to the investigations that matter and using their skills

673
00:32:55,440 --> 00:32:58,840
Speaker 2: that matter, not just a monthly reporting. It's an important

674
00:32:58,840 --> 00:33:02,520
Speaker 2: part of the piece for the big strategic vision. But

675
00:33:02,600 --> 00:33:04,880
Speaker 2: again that doesn't need to be done by a human

676
00:33:04,960 --> 00:33:07,000
Speaker 2: can be fact checked and looked at. But if you

677
00:33:07,080 --> 00:33:10,000
Speaker 2: train it appropriately and it's all there for everyone to

678
00:33:10,000 --> 00:33:13,520
Speaker 2: see live. We've seen all the advancements in body worn

679
00:33:13,560 --> 00:33:16,520
Speaker 2: cameras and well that it's all there. It just needs

680
00:33:16,520 --> 00:33:17,160
Speaker 2: to come together.

681
00:33:17,440 --> 00:33:21,680
Speaker 1: Well, people might think this is an exaggeration, but I

682
00:33:21,720 --> 00:33:24,760
Speaker 1: think you might be able to corroborate me here. I

683
00:33:24,880 --> 00:33:29,320
Speaker 1: retired as a detective chief inspector. I was an inspector

684
00:33:29,320 --> 00:33:32,120
Speaker 1: for the last fifteen years, or a detective inspector in

685
00:33:32,160 --> 00:33:35,400
Speaker 1: the police. I reckon thirty to forty percent of my

686
00:33:35,560 --> 00:33:41,440
Speaker 1: time spent when I'm leading investigations, leading all different murder investigations,

687
00:33:41,640 --> 00:33:44,880
Speaker 1: thirty to forty percent of my time with been administration

688
00:33:45,760 --> 00:33:50,960
Speaker 1: and menial administration. I would I'd cringe saying it, but

689
00:33:51,040 --> 00:33:54,120
Speaker 1: it was the reality that once a month I'd be

690
00:33:54,120 --> 00:33:57,320
Speaker 1: spending two or three hours adding up card diries making

691
00:33:57,360 --> 00:34:01,440
Speaker 1: sure the car diaries calculated. And if I missed doing

692
00:34:01,440 --> 00:34:04,160
Speaker 1: the car dory, that was probably worse than not solving

693
00:34:04,160 --> 00:34:07,440
Speaker 1: the crime. I should submit the car dories on time.

694
00:34:07,520 --> 00:34:10,120
Speaker 1: They had to check all the people under me, so

695
00:34:10,600 --> 00:34:13,840
Speaker 1: things like that that should just be by the wayside.

696
00:34:13,880 --> 00:34:14,840
Speaker 3: Yeah, again, no brainer.

697
00:34:14,960 --> 00:34:17,759
Speaker 2: Yeah, make it happen for everyone up and you know

698
00:34:18,000 --> 00:34:19,840
Speaker 2: there was a joke about it, but there is that

699
00:34:19,880 --> 00:34:23,239
Speaker 2: stress of the ADMIN that comes with it as the

700
00:34:23,280 --> 00:34:25,359
Speaker 2: team leader as well. The monthly times come around and

701
00:34:25,440 --> 00:34:27,279
Speaker 2: you know you need to make more time for your admin.

702
00:34:27,360 --> 00:34:29,040
Speaker 2: Need to sit down do this not Well, we're just

703
00:34:29,080 --> 00:34:31,799
Speaker 2: going from job to job to job and if we

704
00:34:31,840 --> 00:34:34,759
Speaker 2: need that now you need to make these red lights green.

705
00:34:35,920 --> 00:34:38,720
Speaker 1: I don't think I'm unique. I don't think you speak

706
00:34:38,760 --> 00:34:41,880
Speaker 1: to any operational police officers and the frustration I used to,

707
00:34:41,920 --> 00:34:46,719
Speaker 1: in fact joke sometimes say about one o'clock, okay, admin done,

708
00:34:46,800 --> 00:34:50,000
Speaker 1: let's start investigating this murder. But it was quite often

709
00:34:50,040 --> 00:34:52,480
Speaker 1: we're all office bound because we're catching up on record

710
00:34:52,600 --> 00:34:54,040
Speaker 1: keeping administrators.

711
00:34:54,120 --> 00:34:57,400
Speaker 2: Yeah, it's definitely got its place. Yeah, look, it's a

712
00:34:57,400 --> 00:35:00,279
Speaker 2: no brainer for me. But we're just we're really close

713
00:35:00,320 --> 00:35:03,600
Speaker 2: to I think getting on that right path it's got

714
00:35:03,640 --> 00:35:05,880
Speaker 2: to have some courage to do it. And there's been

715
00:35:05,880 --> 00:35:08,160
Speaker 2: a lot of changes in senior management and chief commissions

716
00:35:08,160 --> 00:35:11,160
Speaker 2: around the country, so you know there's time to come

717
00:35:11,160 --> 00:35:12,040
Speaker 2: in here and lean on.

718
00:35:13,000 --> 00:35:16,000
Speaker 1: I don't blame anyone that might have fallen behind or

719
00:35:16,040 --> 00:35:18,720
Speaker 1: not realized because it is changing so rapidly. We wouldn't

720
00:35:18,760 --> 00:35:21,400
Speaker 1: be having this conversation two years ago. It would be

721
00:35:21,480 --> 00:35:25,120
Speaker 1: completely different. We'd be talking like it's a sci fi movie,

722
00:35:25,560 --> 00:35:29,720
Speaker 1: but now it's reality. So we talk about saving time.

723
00:35:30,040 --> 00:35:33,480
Speaker 1: But the thing that excites me most about is how

724
00:35:33,480 --> 00:35:35,120
Speaker 1: it can enhance investigations.

725
00:35:35,280 --> 00:35:36,480
Speaker 3: Yeah, definitely.

726
00:35:36,680 --> 00:35:40,400
Speaker 1: It just seems like a no brain of the human factor,

727
00:35:40,520 --> 00:35:43,839
Speaker 1: the sharp minded analysts that you'd have on an investigation

728
00:35:44,160 --> 00:35:50,160
Speaker 1: and the detectives pouring over briefs that can potentially be done.

729
00:35:49,480 --> 00:35:53,760
Speaker 2: So much quicker, and imagine what we could do preventing

730
00:35:53,800 --> 00:35:54,840
Speaker 2: crime and the.

731
00:35:56,640 --> 00:35:57,920
Speaker 3: Unseeing cost of prevention.

732
00:35:58,239 --> 00:36:00,880
Speaker 2: You know that it was always that movement towards the

733
00:36:00,920 --> 00:36:02,360
Speaker 2: end of my career was let's try and stop the

734
00:36:02,400 --> 00:36:03,560
Speaker 2: crime before it even happens.

735
00:36:03,719 --> 00:36:07,480
Speaker 1: Well, that's an interesting point too, and I haven't covered

736
00:36:07,480 --> 00:36:10,080
Speaker 1: and that's something that I wanted to wanted to cover,

737
00:36:10,200 --> 00:36:15,279
Speaker 1: is that we've been focusing on solving crimes, but how

738
00:36:15,320 --> 00:36:18,759
Speaker 1: can AI be used to prevent crimes? Give us some

739
00:36:18,800 --> 00:36:19,640
Speaker 1: examples there.

740
00:36:19,680 --> 00:36:22,520
Speaker 2: Yeah, well, I think the clearest example is then predicting

741
00:36:22,560 --> 00:36:24,359
Speaker 2: where the hot spots are, And we see a lot

742
00:36:24,400 --> 00:36:27,640
Speaker 2: of work with that at the moment, but that's generally

743
00:36:27,800 --> 00:36:31,440
Speaker 2: data predicting where these hot spots are. The AI aspect

744
00:36:31,520 --> 00:36:35,200
Speaker 2: might come into that potentially, And where I see it

745
00:36:35,239 --> 00:36:37,839
Speaker 2: more so is you know you'd see two, three, four,

746
00:36:38,000 --> 00:36:42,759
Speaker 2: five street robberies or that's one example where if we

747
00:36:42,800 --> 00:36:45,120
Speaker 2: can identify this offender after the first one or the

748
00:36:45,120 --> 00:36:48,879
Speaker 2: second one, we know they'll generally keep going because either

749
00:36:48,920 --> 00:36:52,479
Speaker 2: their motivation is either greed or drug induced, camera or whatever.

750
00:36:52,480 --> 00:36:54,680
Speaker 2: If we can identify that person on day one or two,

751
00:36:55,320 --> 00:36:58,359
Speaker 2: how many offenses have we stopped by being committed by

752
00:36:58,440 --> 00:37:00,919
Speaker 2: getting that person here not down the road. That's one

753
00:37:00,920 --> 00:37:04,040
Speaker 2: aspect to it. There are some other discussions I'm having

754
00:37:04,040 --> 00:37:08,200
Speaker 2: with with other developers around the human behavior models. So

755
00:37:08,520 --> 00:37:12,120
Speaker 2: you know we've all seen you know, the kids or

756
00:37:12,160 --> 00:37:15,280
Speaker 2: anyone just before you know, you've watched the footage and go, okay,

757
00:37:15,320 --> 00:37:17,520
Speaker 2: I can see they're starting to get a thousand yards there.

758
00:37:17,560 --> 00:37:19,759
Speaker 2: They're doing this and it's about to be on. So

759
00:37:19,960 --> 00:37:23,920
Speaker 2: you can see the human behaviors through footage that something's

760
00:37:23,960 --> 00:37:24,600
Speaker 2: about to happen.

761
00:37:24,800 --> 00:37:25,000
Speaker 1: Right.

762
00:37:25,040 --> 00:37:28,600
Speaker 2: You know, when we've investigated either you know, your machety

763
00:37:28,640 --> 00:37:30,560
Speaker 2: attacks or the like that we see a shopping centers

764
00:37:30,560 --> 00:37:32,680
Speaker 2: these days, there's a lead up to that. It just

765
00:37:32,680 --> 00:37:35,120
Speaker 2: does not all of a sudden explode as a general leader.

766
00:37:35,440 --> 00:37:37,640
Speaker 2: So we can train AI to look at those human

767
00:37:37,680 --> 00:37:40,920
Speaker 2: behaviors and what are the signals here, what's happening here.

768
00:37:40,960 --> 00:37:41,759
Speaker 3: So we're getting into a.

769
00:37:41,719 --> 00:37:44,440
Speaker 2: Whole other area now of non policing. But you know,

770
00:37:44,480 --> 00:37:47,920
Speaker 2: I know private security is very interested in those aspects.

771
00:37:47,400 --> 00:37:50,520
Speaker 1: And that's interesting, and we'll use it just by way

772
00:37:50,520 --> 00:37:53,719
Speaker 1: of an example, the attacks in the shopping centers because

773
00:37:53,880 --> 00:37:56,279
Speaker 1: it's been in the media a bit. But yes, it

774
00:37:56,360 --> 00:37:57,280
Speaker 1: would be a gathering.

775
00:37:57,640 --> 00:37:59,880
Speaker 3: Yeah, I'm just featuring like a thing.

776
00:38:00,040 --> 00:38:02,400
Speaker 1: Spirits investigator might be able to pick that up, but

777
00:38:02,520 --> 00:38:04,840
Speaker 1: AI might be able to do it even better.

778
00:38:05,040 --> 00:38:07,480
Speaker 2: And we're trying to monitor I don't want to name

779
00:38:07,719 --> 00:38:10,080
Speaker 2: particular shopping centers, but if you're trying to monitor hundreds

780
00:38:10,080 --> 00:38:13,120
Speaker 2: and hundreds of cameras, yeah, or big public events, the

781
00:38:13,160 --> 00:38:15,480
Speaker 2: AI can do that, and it can alert the person

782
00:38:15,520 --> 00:38:17,759
Speaker 2: that's there in charge of looking at those cameras. We're

783
00:38:17,800 --> 00:38:20,080
Speaker 2: seeing something here on camera forty six and forty seven

784
00:38:20,120 --> 00:38:22,520
Speaker 2: of a gathering of some it could.

785
00:38:22,360 --> 00:38:24,520
Speaker 1: Be let's break it down. That could be put if

786
00:38:24,560 --> 00:38:28,520
Speaker 1: there's one hundred cameras around the shopping center and AI

787
00:38:28,760 --> 00:38:32,520
Speaker 1: had put in place that if you see people dressed

788
00:38:32,719 --> 00:38:37,120
Speaker 1: in this style of more than four gather at a location,

789
00:38:37,680 --> 00:38:42,640
Speaker 1: alert that saw draw correcting. We want to know and yeah.

790
00:38:42,680 --> 00:38:44,200
Speaker 2: That operator and then look at that and go oh,

791
00:38:44,239 --> 00:38:46,840
Speaker 2: that's nothing, or well, we've got a problem here and

792
00:38:46,880 --> 00:38:50,360
Speaker 2: you're onto it twenty thirty seconds a minute beforehand. Hopefully

793
00:38:50,360 --> 00:38:52,760
Speaker 2: you can get there and disperse them before anything even happens,

794
00:38:52,880 --> 00:38:55,719
Speaker 2: so you prevented the crime. That's always So there's an

795
00:38:55,719 --> 00:38:58,759
Speaker 2: aspect of human behavior. So you biomechanics that you need

796
00:38:58,800 --> 00:39:01,400
Speaker 2: to train with data sets, so you need access to

797
00:39:01,440 --> 00:39:03,400
Speaker 2: all these different ones that have occurred in the past,

798
00:39:04,000 --> 00:39:05,960
Speaker 2: and the data is there. We've got a partner with

799
00:39:05,960 --> 00:39:08,759
Speaker 2: the right people to build that data and train the

800
00:39:08,760 --> 00:39:12,799
Speaker 2: AI to Okay, that's all we're looking for, or weapons identification.

801
00:39:13,280 --> 00:39:16,080
Speaker 2: I know there's a great project that's been done through

802
00:39:16,120 --> 00:39:18,839
Speaker 2: one of the people I mentioned before. Around identifying firearms,

803
00:39:18,880 --> 00:39:21,239
Speaker 2: similar to what I said about vehicles. Yeah, so you

804
00:39:21,239 --> 00:39:23,680
Speaker 2: know what gun you're looking for or exactly which one

805
00:39:23,680 --> 00:39:24,239
Speaker 2: it is, and.

806
00:39:25,000 --> 00:39:28,640
Speaker 1: What you're talking here, Just so I understand and hopefully

807
00:39:28,680 --> 00:39:31,680
Speaker 1: other people understand, Like, identify a gun. If I get

808
00:39:31,719 --> 00:39:34,360
Speaker 1: out of the glock, I can tell it's a glock,

809
00:39:34,920 --> 00:39:37,920
Speaker 1: but it might have a scratch on the barrel or

810
00:39:38,040 --> 00:39:42,120
Speaker 1: scratch on the pistol grip. It might be something like that.

811
00:39:42,440 --> 00:39:45,280
Speaker 1: But AI would pick up on that and go, this weapon,

812
00:39:45,280 --> 00:39:48,800
Speaker 1: there's the same weapon that was used in this shooting.

813
00:39:48,880 --> 00:39:52,840
Speaker 2: Yeah, if it's all talking holistically to each other and

814
00:39:52,840 --> 00:39:55,240
Speaker 2: you've got good footage, so yes, you've got good footage.

815
00:39:55,239 --> 00:39:58,800
Speaker 2: But even post offense, if you've got your suspects arrested

816
00:39:58,800 --> 00:40:01,879
Speaker 2: with is you know, with twenty two or a glock

817
00:40:01,960 --> 00:40:04,879
Speaker 2: or whatever the case may be, that's that particular weapon.

818
00:40:04,920 --> 00:40:06,920
Speaker 2: All of a sudden, you're plugging into all these unsolved

819
00:40:07,000 --> 00:40:10,279
Speaker 2: ballistic reports of this actually was a clock that was

820
00:40:10,560 --> 00:40:13,480
Speaker 2: the weapon that killed this person in this murder, because

821
00:40:13,480 --> 00:40:16,000
Speaker 2: we've got the ejected casing, et cetera. All of a sudden,

822
00:40:16,040 --> 00:40:19,160
Speaker 2: you're linking all these cases again cross border stuff that

823
00:40:19,280 --> 00:40:22,200
Speaker 2: is volumes and volumes of forensic material that it can

824
00:40:22,239 --> 00:40:25,120
Speaker 2: do in seconds if you're focusing on the right problem.

825
00:40:25,200 --> 00:40:27,400
Speaker 2: What is the problem we're trying to solve here? So

826
00:40:27,719 --> 00:40:34,040
Speaker 2: weapons identification is one similar to vehicles, they're definitive. There

827
00:40:34,160 --> 00:40:36,160
Speaker 2: is an exhaustive list. There's only so many makes and

828
00:40:36,200 --> 00:40:39,839
Speaker 2: models of firearms on the planet, so you can teach

829
00:40:39,880 --> 00:40:42,759
Speaker 2: it too to come up with those options of this

830
00:40:42,800 --> 00:40:45,080
Speaker 2: is what we definitely think it is, or this is

831
00:40:45,080 --> 00:40:47,920
Speaker 2: potentially what the type of weapon is. And again then

832
00:40:47,960 --> 00:40:50,319
Speaker 2: you look at your execution of search rights. We know

833
00:40:50,360 --> 00:40:54,480
Speaker 2: we're looking for a particular maker, model firearm as opposed

834
00:40:54,520 --> 00:40:58,640
Speaker 2: to the guesswork. So there's a lot there. I'm sort

835
00:40:58,640 --> 00:41:02,279
Speaker 2: of overwhelming the conversation with a different aspects, but I

836
00:41:02,320 --> 00:41:05,080
Speaker 2: think it really comes back to that initial piece of

837
00:41:05,160 --> 00:41:07,120
Speaker 2: what is the issue that we're trying to use AIEN

838
00:41:07,200 --> 00:41:11,480
Speaker 2: to solve? Is it an administration? Is it prevention of crime?

839
00:41:12,080 --> 00:41:13,040
Speaker 2: Is it cold cases?

840
00:41:13,560 --> 00:41:16,840
Speaker 1: And there's all these different things that could enhance.

841
00:41:16,960 --> 00:41:18,360
Speaker 3: Or assist working with it.

842
00:41:19,520 --> 00:41:22,680
Speaker 1: Hey guys, it's Gary jubilin here. Want they get more

843
00:41:22,719 --> 00:41:25,200
Speaker 1: out of I Catch Killers? Then you should head over

844
00:41:25,239 --> 00:41:28,600
Speaker 1: to our new video feed on Spotify, where you can

845
00:41:28,640 --> 00:41:32,360
Speaker 1: watch every episode of I Catch Killers. Just search for

846
00:41:32,480 --> 00:41:36,280
Speaker 1: I Catch Killers video in your Spotify app and start watching.

847
00:41:36,280 --> 00:41:41,759
Speaker 1: Today you said about overwhelmed with information. It made me

848
00:41:41,800 --> 00:41:45,040
Speaker 1: think while you were talking about how we were overwhelmed

849
00:41:45,040 --> 00:41:49,240
Speaker 1: with the availability of information, that when I first started

850
00:41:49,440 --> 00:41:52,280
Speaker 1: as a homicide detective, there was a couple of people

851
00:41:52,320 --> 00:41:55,960
Speaker 1: had mobile phones. You could find out or listen to

852
00:41:55,960 --> 00:41:58,680
Speaker 1: their mobile phones, or they had landlines and you could

853
00:41:58,719 --> 00:42:01,640
Speaker 1: track them that way, and that was you know, that

854
00:42:01,640 --> 00:42:04,480
Speaker 1: that was the line of inquiry. A bank might have

855
00:42:04,520 --> 00:42:07,839
Speaker 1: a CCTV camera out the front, but only a bank

856
00:42:07,960 --> 00:42:12,040
Speaker 1: or a licensed premises. And I did actually see towards

857
00:42:12,080 --> 00:42:15,120
Speaker 1: the latter part of my career we were getting overwhelmed

858
00:42:15,200 --> 00:42:19,080
Speaker 1: with information, like there was so someone's murdered. You'd get

859
00:42:19,120 --> 00:42:22,960
Speaker 1: called out. And the instructions I was given in the

860
00:42:23,000 --> 00:42:25,839
Speaker 1: early days as a young homicide detective go out and

861
00:42:26,239 --> 00:42:30,319
Speaker 1: collect all the CCTV footage from businesses and you'd come back, Oh,

862
00:42:30,360 --> 00:42:32,319
Speaker 1: that service station had one, and you'd be all proud

863
00:42:32,360 --> 00:42:34,880
Speaker 1: of yourself, and yeah, there was one one at the bank.

864
00:42:35,080 --> 00:42:38,719
Speaker 1: Now you say that private homes cars have got you know.

865
00:42:38,920 --> 00:42:43,160
Speaker 1: And then as my career progressed, I'm seeing at the top,

866
00:42:43,160 --> 00:42:44,960
Speaker 1: and I'm looking at all the information we've got and

867
00:42:45,000 --> 00:42:47,279
Speaker 1: I'm going I'd be pulling my hair out if I

868
00:42:47,320 --> 00:42:52,640
Speaker 1: had had hair, just overwhelmed with the information. And AI

869
00:42:53,440 --> 00:42:56,560
Speaker 1: swings the pendulum back in favor of being able to

870
00:42:56,560 --> 00:42:58,880
Speaker 1: handle all that information, doesn't it, because we did get

871
00:42:58,920 --> 00:43:00,400
Speaker 1: to the point where we were able well.

872
00:43:00,480 --> 00:43:02,640
Speaker 3: Well, information intelligence is key, isn't it.

873
00:43:02,680 --> 00:43:02,879
Speaker 1: Yeah.

874
00:43:02,960 --> 00:43:05,360
Speaker 2: So the sooner you can get that in investigator's hands

875
00:43:05,600 --> 00:43:08,880
Speaker 2: and understand it, yeah, the better you position for your strategy.

876
00:43:09,040 --> 00:43:12,239
Speaker 2: So all that information that you're getting from CCTV, from

877
00:43:12,239 --> 00:43:14,160
Speaker 2: that murder, getting it all together, if you miss that

878
00:43:14,200 --> 00:43:16,560
Speaker 2: bit of footage that shows it turned left onto the freeway,

879
00:43:17,160 --> 00:43:19,480
Speaker 2: then you know that's a complete game change, isn't it.

880
00:43:19,520 --> 00:43:22,920
Speaker 2: So you won't miss that. And whether you're plugging in

881
00:43:22,920 --> 00:43:26,000
Speaker 2: one hundred cameras worth the footage into this AI, it'll

882
00:43:26,040 --> 00:43:27,560
Speaker 2: track that you tell it. I want you to track

883
00:43:27,640 --> 00:43:29,880
Speaker 2: that car. That's the one we want out of all

884
00:43:29,920 --> 00:43:32,440
Speaker 2: this information. We've got timeline and tell me where it's

885
00:43:32,440 --> 00:43:34,200
Speaker 2: been and what it's done and bang.

886
00:43:34,120 --> 00:43:38,000
Speaker 1: And what people didn't When I say people, the public

887
00:43:38,080 --> 00:43:40,600
Speaker 1: if you're not involved directly in it. When we've got

888
00:43:40,600 --> 00:43:44,800
Speaker 1: listening devices in houses, you put a listening device in here,

889
00:43:45,200 --> 00:43:48,080
Speaker 1: and you've got someone monitoring it. They can even monitor

890
00:43:48,120 --> 00:43:52,080
Speaker 1: it live and if something happens or invariably you've got

891
00:43:52,120 --> 00:43:54,480
Speaker 1: to come back and review it because it's not been

892
00:43:54,560 --> 00:43:57,919
Speaker 1: monitored live. So you've got twenty four hours of listening. Yes,

893
00:43:58,760 --> 00:44:02,359
Speaker 1: like people going, have you gone gone through that? Well,

894
00:44:02,600 --> 00:44:05,279
Speaker 1: it's basically in real time. And then when you hear

895
00:44:05,320 --> 00:44:07,719
Speaker 1: the conversation, then you've got to document the conversation.

896
00:44:08,239 --> 00:44:10,879
Speaker 2: It was so time, and then they're more blow our

897
00:44:10,880 --> 00:44:13,680
Speaker 2: minds with then your hat a layer of you know,

898
00:44:13,760 --> 00:44:18,160
Speaker 2: if you need a translator, Yeah, so yeah, I get

899
00:44:18,440 --> 00:44:21,200
Speaker 2: one hundred percent your point. And that's that's those complex

900
00:44:21,880 --> 00:44:24,360
Speaker 2: pieces of information and large amount of data that it

901
00:44:24,400 --> 00:44:26,759
Speaker 2: can deal with. And that definition of AI you said

902
00:44:26,840 --> 00:44:29,440
Speaker 2: right off the top, that's what it's doing. It's looking

903
00:44:29,480 --> 00:44:33,120
Speaker 2: at all of those conversations and if you're after key

904
00:44:33,160 --> 00:44:35,879
Speaker 2: words or if you're after a key you want them

905
00:44:35,880 --> 00:44:39,160
Speaker 2: talking about the victim of a planned murder. You know,

906
00:44:39,200 --> 00:44:41,799
Speaker 2: you've got a contract, kill job, whatever you want to

907
00:44:41,800 --> 00:44:42,920
Speaker 2: know whenever that pops up.

908
00:44:43,200 --> 00:44:47,239
Speaker 1: And we would get to the point where I've used yeah,

909
00:44:47,719 --> 00:44:51,439
Speaker 1: limited times when I have people look at the terminology

910
00:44:51,560 --> 00:44:54,760
Speaker 1: used by by an offender from a psychological or suspect

911
00:44:54,760 --> 00:44:57,600
Speaker 1: from a psychological point of view. You could even feed

912
00:44:57,640 --> 00:45:00,359
Speaker 1: that into yeah, couldn't you. It appears that and he's

913
00:45:00,400 --> 00:45:04,160
Speaker 1: talking about that Issue's demeanor or his language is changing.

914
00:45:04,280 --> 00:45:09,239
Speaker 1: Little things like that, and it's only incremental victories, but

915
00:45:09,280 --> 00:45:11,360
Speaker 1: they're the little things that make the difference.

916
00:45:11,520 --> 00:45:15,560
Speaker 2: And gives the advantage back to the police. Yeah, there's

917
00:45:15,560 --> 00:45:17,360
Speaker 2: one other piece that I will talk to and it

918
00:45:17,680 --> 00:45:19,200
Speaker 2: can be quite confronting, so I don't want to trigger

919
00:45:19,200 --> 00:45:22,560
Speaker 2: anyone on here, but a really I think crucial piece

920
00:45:22,600 --> 00:45:24,520
Speaker 2: of where AI can help as well as in the

921
00:45:24,560 --> 00:45:29,360
Speaker 2: child exportation material space, you know you would a basic example,

922
00:45:29,680 --> 00:45:31,480
Speaker 2: police do a search one on a laptop and you've

923
00:45:31,480 --> 00:45:33,840
Speaker 2: gond a laptop and you've got hundreds or thousands of

924
00:45:33,960 --> 00:45:37,920
Speaker 2: really horrific images of child exportation material. Some of that

925
00:45:38,239 --> 00:45:40,560
Speaker 2: you may be concerned is contact defending. So you're trying

926
00:45:40,560 --> 00:45:43,920
Speaker 2: to identify this kid that's clearly in the footage or

927
00:45:43,960 --> 00:45:46,799
Speaker 2: in the images with you're accused. You need to find

928
00:45:46,800 --> 00:45:48,880
Speaker 2: out who this kid is to stop the contact defending.

929
00:45:48,920 --> 00:45:51,799
Speaker 2: So then you're madly trying to figure out how how

930
00:45:51,880 --> 00:45:53,480
Speaker 2: is this possible? Where is that you're looking at the

931
00:45:53,480 --> 00:45:56,000
Speaker 2: metadata of the images, you're looking at all this sort

932
00:45:56,040 --> 00:45:58,680
Speaker 2: of stuff. The AI should be able to work out that,

933
00:45:59,120 --> 00:46:02,440
Speaker 2: and it may be to use facial recognition technology of

934
00:46:02,520 --> 00:46:04,640
Speaker 2: the child to try and identify who is this child.

935
00:46:05,480 --> 00:46:08,360
Speaker 2: So that's the piece. I feel that that's where the

936
00:46:08,440 --> 00:46:12,600
Speaker 2: trigger for scraping social media is important, because that kid's

937
00:46:12,600 --> 00:46:14,960
Speaker 2: photo might be in the background at someone's birthday or

938
00:46:15,000 --> 00:46:17,879
Speaker 2: at a any event, and all of a sudden, you've

939
00:46:17,880 --> 00:46:20,080
Speaker 2: got a match on this kid that's being offended against,

940
00:46:20,719 --> 00:46:23,319
Speaker 2: and you can immediately stop that offending through the use

941
00:46:23,360 --> 00:46:25,640
Speaker 2: of the AI to identify that kid. There is some

942
00:46:25,680 --> 00:46:28,200
Speaker 2: work being done in that space by the AFP and

943
00:46:28,239 --> 00:46:30,760
Speaker 2: monash Uni, and that's fantastic, but you know, it needs

944
00:46:30,760 --> 00:46:33,040
Speaker 2: more support, it needs more funding, and it needs more

945
00:46:33,080 --> 00:46:36,319
Speaker 2: attention to help protect these kids because it's crucial because

946
00:46:36,320 --> 00:46:39,040
Speaker 2: there's nothing worse than investigator. I can't find this kid

947
00:46:39,120 --> 00:46:40,959
Speaker 2: and we know they're being offended against, and you can't

948
00:46:40,960 --> 00:46:42,560
Speaker 2: release it to the media. You know that you've got

949
00:46:42,600 --> 00:46:44,440
Speaker 2: to protect the identity of these kids.

950
00:46:44,239 --> 00:46:48,239
Speaker 1: That seems like a no brainer that if I can

951
00:46:48,280 --> 00:46:50,760
Speaker 1: help in that space, and I understand what you're saying

952
00:46:50,800 --> 00:46:54,239
Speaker 1: the kid that's been sexually abused and you've got to

953
00:46:54,280 --> 00:46:57,799
Speaker 1: identify that child. I could help.

954
00:46:57,960 --> 00:46:59,520
Speaker 2: That's why I want to write. Partner with the right

955
00:46:59,560 --> 00:47:02,040
Speaker 2: investors that have the same morals and purpose that we

956
00:47:02,080 --> 00:47:04,600
Speaker 2: have at Perigrin to get this done because we can

957
00:47:05,080 --> 00:47:06,040
Speaker 2: protect kids.

958
00:47:06,040 --> 00:47:11,960
Speaker 1: We touched just briefly. Fiscally, this seems like a no

959
00:47:12,080 --> 00:47:17,320
Speaker 1: brainer for law enforcement. Yeah, freeing up, freeing up more success,

960
00:47:17,400 --> 00:47:20,160
Speaker 1: reducing crime. There seems to be benefits all over.

961
00:47:20,680 --> 00:47:21,239
Speaker 3: Yeah, I think so.

962
00:47:21,440 --> 00:47:23,480
Speaker 2: But it's also challenging times, isn't it. And I can

963
00:47:23,560 --> 00:47:26,160
Speaker 2: only talk in the Victorian context because that's what I

964
00:47:26,200 --> 00:47:29,440
Speaker 2: saw when they were seeking a new Chief commissioner. You

965
00:47:29,600 --> 00:47:32,239
Speaker 2: to have to save ordered by the government to save

966
00:47:32,280 --> 00:47:35,719
Speaker 2: five hundred million per year in your budget, so you know,

967
00:47:36,000 --> 00:47:40,000
Speaker 2: good luck, chief, but better you than me. But that's

968
00:47:40,080 --> 00:47:43,319
Speaker 2: the first step off point. So you're then coming along

969
00:47:43,400 --> 00:47:45,239
Speaker 2: or a company like Peramn's coming along, going we want

970
00:47:45,239 --> 00:47:46,959
Speaker 2: you to partner with us because we want to invest

971
00:47:47,000 --> 00:47:49,880
Speaker 2: in this. You really can't expect the government to be

972
00:47:49,920 --> 00:47:51,680
Speaker 2: stepping or the police to be stepping up in their

973
00:47:51,719 --> 00:47:55,640
Speaker 2: budget to assist. Hence what we need corporate support. But

974
00:47:56,520 --> 00:47:59,080
Speaker 2: it is a no brainer but eventually they'll get there

975
00:47:59,120 --> 00:48:01,920
Speaker 2: to see the benefits. We've saved so many hours in

976
00:48:01,920 --> 00:48:06,120
Speaker 2: investigators time and administrative tasks. But right now it'd be

977
00:48:06,200 --> 00:48:07,520
Speaker 2: very challenging and for them to commit.

978
00:48:07,920 --> 00:48:10,239
Speaker 1: I hope they see it that way because, as we said,

979
00:48:10,280 --> 00:48:15,279
Speaker 1: sometimes police, when technology changes, we drag our Yeah, I'm

980
00:48:15,320 --> 00:48:18,279
Speaker 1: sure there's not many detectives that have to g to

981
00:48:18,480 --> 00:48:22,040
Speaker 1: a private company to get something that the police. You

982
00:48:22,080 --> 00:48:24,840
Speaker 1: would assume police had capabilities of but didn't.

983
00:48:25,040 --> 00:48:27,680
Speaker 2: Well, like I said, I was at a conference eighteen

984
00:48:27,719 --> 00:48:29,719
Speaker 2: months two years ago and it was around the child

985
00:48:29,800 --> 00:48:34,239
Speaker 2: expectation and very senior police and I won't name the organization.

986
00:48:34,280 --> 00:48:36,960
Speaker 2: We're there going when is corporate? When is private sector

987
00:48:37,000 --> 00:48:38,839
Speaker 2: going to step up and help us? Went, oh, let's

988
00:48:38,880 --> 00:48:42,560
Speaker 2: have a conversation FORFE So well yeah, so yeah, it's close.

989
00:48:42,680 --> 00:48:45,839
Speaker 2: We just need to keep it going, keep the conversation going,

990
00:48:46,120 --> 00:48:48,960
Speaker 2: and keep developing it. But look, coming back to it,

991
00:48:49,440 --> 00:48:51,400
Speaker 2: I just thought of when were speaking before around the

992
00:48:51,520 --> 00:48:54,360
Speaker 2: use of AI and cameras and that now it's actually present.

993
00:48:54,400 --> 00:48:57,120
Speaker 2: Now I've got your cameras around my place and they're

994
00:48:56,960 --> 00:49:00,399
Speaker 2: they're giving a plug to the UFI system, but they've

995
00:49:00,440 --> 00:49:02,480
Speaker 2: got AI in there, So you can now load someone's

996
00:49:02,520 --> 00:49:05,360
Speaker 2: face onto your home CCTV system and it'll alert me

997
00:49:05,400 --> 00:49:08,120
Speaker 2: when that person is there, whether I want them there

998
00:49:08,200 --> 00:49:10,600
Speaker 2: or not. So the AI is already plugged into some

999
00:49:10,640 --> 00:49:13,440
Speaker 2: of these things that are there, so you know, as

1000
00:49:13,480 --> 00:49:14,640
Speaker 2: you said, it's coming, it's here.

1001
00:49:14,880 --> 00:49:17,160
Speaker 1: Well that that could you just made me think we'll

1002
00:49:17,200 --> 00:49:20,040
Speaker 1: go into an area, very real area that like domestic

1003
00:49:20,120 --> 00:49:23,439
Speaker 1: violence and then that type of thing. But you could

1004
00:49:23,520 --> 00:49:26,320
Speaker 1: forewarned if someone's someone's turning up.

1005
00:49:27,320 --> 00:49:30,480
Speaker 2: So that's that's that natural evolution of what used to

1006
00:49:30,480 --> 00:49:33,279
Speaker 2: just be, you know, an old school c CDV camera.

1007
00:49:33,280 --> 00:49:35,200
Speaker 2: They've got chunky ones on there that are now these

1008
00:49:35,640 --> 00:49:38,200
Speaker 2: small little ones that are either powered by solo or battery,

1009
00:49:38,280 --> 00:49:40,160
Speaker 2: and they've got AI into them that can alert you

1010
00:49:40,200 --> 00:49:42,440
Speaker 2: to know whether you do want them or don't want

1011
00:49:42,440 --> 00:49:44,160
Speaker 2: them there, can give you an alert to your phone,

1012
00:49:44,200 --> 00:49:45,600
Speaker 2: and you can be anywhere in the world. I think

1013
00:49:45,600 --> 00:49:47,319
Speaker 2: I was lucky enough to be overseas not too long ago,

1014
00:49:47,360 --> 00:49:49,359
Speaker 2: and I'm getting alerts from my camera back home. Oh

1015
00:49:49,600 --> 00:49:52,160
Speaker 2: that's thank you. That's the person turn up to feed

1016
00:49:52,200 --> 00:49:56,800
Speaker 2: my turtle. Thanks mate, Yeah, fair cool turtles.

1017
00:49:57,719 --> 00:49:59,319
Speaker 3: Turtles, turtle's got the weight.

1018
00:49:59,600 --> 00:50:03,319
Speaker 1: Well, look I really enjoyed the chat, and thanks for

1019
00:50:03,360 --> 00:50:05,319
Speaker 1: coming on. You're the right person, because I know that

1020
00:50:05,400 --> 00:50:08,720
Speaker 1: someone that brings me crashing into this brave, brave new world.

1021
00:50:09,120 --> 00:50:11,880
Speaker 1: But the way it's evolved, you know, I might have

1022
00:50:11,920 --> 00:50:13,480
Speaker 1: to have you back in six months time, and it

1023
00:50:13,560 --> 00:50:15,520
Speaker 1: might even be you. It might be a hologram or.

1024
00:50:15,440 --> 00:50:19,040
Speaker 3: Something I might remote in. Virtually, Yeah, yeah.

1025
00:50:19,120 --> 00:50:22,360
Speaker 1: I think if you've given you've given me an understanding.

1026
00:50:22,400 --> 00:50:25,359
Speaker 1: I hope the listener is an understanding of what's out there.

1027
00:50:25,520 --> 00:50:27,759
Speaker 2: I hope so, and I hope that point of just

1028
00:50:28,080 --> 00:50:30,480
Speaker 2: looking a bit further about what I can do. It's

1029
00:50:30,520 --> 00:50:34,640
Speaker 2: not just a one shop or one scenario fits all.

1030
00:50:34,920 --> 00:50:37,680
Speaker 2: It's what's the problem, what is it? And to have

1031
00:50:37,719 --> 00:50:39,680
Speaker 2: some courage to lean into it. But good luck trying

1032
00:50:39,680 --> 00:50:41,319
Speaker 2: to sleep tonight. Carry I've blown your mind.

1033
00:50:42,480 --> 00:50:45,520
Speaker 1: I'm down the rabbit hole. Now I'm gone. But we

1034
00:50:45,680 --> 00:50:48,520
Speaker 1: finish off with because I do think that's important, because

1035
00:50:48,520 --> 00:50:53,040
Speaker 1: we know how power can corrupt in police and that

1036
00:50:53,280 --> 00:50:56,840
Speaker 1: the ethics and the integrity that needs with this type

1037
00:50:56,840 --> 00:50:58,279
Speaker 1: of type of.

1038
00:50:58,239 --> 00:51:01,640
Speaker 2: Thing part of part of our lives absolutely critical, which

1039
00:51:01,680 --> 00:51:04,600
Speaker 2: is why again I've said countless times need to be

1040
00:51:04,640 --> 00:51:06,200
Speaker 2: built here for here.

1041
00:51:06,680 --> 00:51:08,160
Speaker 1: Just hold on for a second. I'm just going to

1042
00:51:08,160 --> 00:51:11,760
Speaker 1: put in chat GPT. How I say goodbye to Graham.

1043
00:51:12,520 --> 00:51:15,800
Speaker 1: I love it, New Graham for coming on I catch Killers.

1044
00:51:15,920 --> 00:51:20,040
Speaker 1: It's been a very entertaining discussion. Thanks Hasburt Chees