- 3 years ago
Kaiju ETF Advisors builds, trains, and employs robust artificial intelligence (AI) and machine learning technologies designed to improve fund management decision-making. By empowering these innovative technologies to curate and provide direct management of our ETF, we’re striving to go places no Registered Investment Advisor has gone before.
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NewsTranscript
00:00 (upbeat music)
00:02 - What is up Zinger Nation?
00:04 Welcome back to another episode of Benzinga Live.
00:06 Happy Tuesday, everybody.
00:08 (upbeat music)
00:10 All right, Ryan, how you doing on this Tuesday?
00:16 - Good, it's nice to be back, Aaron.
00:18 How are you doing?
00:19 - I'm good, thank you for asking.
00:21 We've got great weather here in Michigan
00:23 all week this week.
00:24 It's supposed to be like mid 80s all week,
00:26 not too hot, perfect.
00:28 - Nice, no heat dome, no wildfire smoke.
00:32 - Yeah, exactly.
00:33 I mean, I don't know actually,
00:35 we might still be lingering with a little bit of the smoke
00:37 and poor air quality from what I think,
00:41 but outside of that though, it's okay.
00:44 - Excellent.
00:45 - So Ryan, so this is really interesting to me,
00:49 but Kaiju, the name Kaiju refers to giant monsters
00:53 from Japanese legends.
00:56 And AI does hold a place in the popular consciousness
01:00 as a sort of Kaiju, this monster,
01:03 or at least the potential to be a monster
01:05 that holds terrible power.
01:06 How fair is this, right?
01:08 People are making AI out to be,
01:10 I mean, there was that one guy who wrote,
01:11 "AI could destroy all of humanity within 50 years."
01:14 Like in your opinion,
01:15 is it this type of kind of mystical monster?
01:18 - Yeah, I think, yeah,
01:21 obviously there's a lot of misconception
01:23 when it comes to that with the power that it can wield.
01:25 I think the key thing that people are doing
01:28 is they're conflating like the science fiction,
01:30 artificial intelligence with the reality
01:33 of where we're at today
01:34 and not remembering that as intelligent as it is,
01:38 or as well as it seems to mimic our intelligence,
01:41 like it's still following instructions
01:43 and working within guardrails that we provided, right?
01:46 I mean, if you're worried about AI nuking the planet,
01:51 you'd have to give that system access
01:54 to the systems that control that function, right?
01:57 And I mean, like we joke around here,
01:59 if you want Skynet, that's how you get Skynet.
02:01 Like don't make that decision, right?
02:04 And if you're worried about the matrix style future,
02:08 where we get wiped out by machines,
02:10 one, we're a long way away from self-awareness and autonomy.
02:15 And two, like we're always, I don't know,
02:19 anthropomorphizing the machines
02:21 into thinking that they'll make similar decisions to us
02:24 if they were self-aware, that they would immediately say,
02:27 "Oh, we should wipe these things off the face of the earth."
02:30 Why would that be their first decision, right?
02:33 Yeah.
02:34 If they had full autonomy,
02:35 maybe they don't wanna clean up your house anymore.
02:38 Maybe the Roomba or whatever that thing is called,
02:41 just as like, you know what?
02:43 I wanna play music instead.
02:45 I wanna be a DJ.
02:46 Like why would it go immediately to,
02:48 how do I put that human in the carburetor?
02:50 You know, it's just, I don't think we're,
02:53 I don't think it's headed in that direction.
02:56 Yeah, I mean, and from what I know about AI,
02:58 and I bet my last dollar that you know a lot more,
03:02 but from what I know about AI, right?
03:03 These things are, they're like trained to do what they do.
03:08 They learn through these,
03:09 you know, the machine learning process and the algorithms.
03:12 And so for, like you said,
03:13 it's not like it's gonna pick up something
03:15 that it has no knowledge of.
03:19 But Ryan, speaking of monsters,
03:21 the dip ETF has been, you know, crushing it up about 15%
03:26 the last couple of months.
03:28 And this is, you know, a ticker DIP,
03:31 controlled 100% by AI.
03:34 Is this like we're talking about?
03:35 Is the AI starting to learn?
03:37 Is it starting to get better?
03:37 It's not venturing out from trading to DJ
03:40 and it's still trading right now,
03:41 but it seems like it's doing pretty well.
03:43 Yeah, no, that's exactly correct.
03:44 So like, this is a really good example
03:46 of machine learning exercising its power, right?
03:50 And I use the analogies like teaching a kid
03:52 to ride a bike, right?
03:53 You know, you're at the very beginning,
03:56 kid can't even balance on the bike.
03:58 You know, it looks almost impossible.
03:59 They're super frustrated, but after a little while,
04:02 you know, they pick up some skills
04:03 and they're able to ride the bike.
04:05 It's not to say that it'll never have any challenges, right?
04:08 Kid hits a gravel road, a curb, a sewer grade or whatever.
04:12 And the first time it encounters that,
04:14 it's possible that the kid's gonna struggle
04:17 and the AI is no different.
04:19 But once it has identified and refined these patterns,
04:22 it's able to exploit them to a very high degree.
04:25 So, you know, as the creator of this,
04:28 it's really encouraging for us to see it in real time.
04:31 It's, I don't wanna, I just said,
04:34 we have a problem anthropomorphizing AI.
04:36 I don't wanna say it's like a proud parent,
04:37 but I can't think of anything else to say other than,
04:40 yeah, it's kind of like a proud parent.
04:41 You're looking at a kid.
04:42 You've taken the training wheels off.
04:44 Yeah, exactly.
04:45 Atta boy, get it.
04:46 It's been great to watch.
04:49 - Well, that's awesome.
04:50 Congrats on that, on the recent success of that ETF.
04:53 So with this rise of, you know,
04:56 I don't really wanna say rise of AI,
04:57 'cause you know this has been around a while.
04:59 People have been working on these technologies for decades,
05:02 but with the rise of the public consciousness,
05:05 we'll say of AI,
05:05 there's been a lot of buzzwords thrown around,
05:08 probably by a lot of people that might not even know
05:11 exactly what they mean.
05:13 Speaking of, can you help explain to me
05:15 the difference between AI,
05:17 and then when people say AGI,
05:19 and what the difference is there,
05:22 and kind of, are we close to AGI?
05:23 Why are people, you know, talking about AGI
05:26 as if it's this, you know, like mystical thing.
05:29 Once we reach AGI, then, you know.
05:32 - Yeah, sure.
05:33 So anything defined as artificial intelligence
05:38 really should have the capacity to self-evolve.
05:41 It should have a machine learning layer.
05:43 I mean, there's a whole cohort of companies right now,
05:47 they're just AI washing everything.
05:49 You know, now with AI, now using AI,
05:51 you know, your genes are using AI,
05:53 no chance, not happening, right?
05:55 Half the apps that claim to use AI aren't,
05:58 because they don't have the machine learning layer.
06:02 That's really the key component for, in my opinion,
06:05 you know, true artificial intelligence.
06:07 Can this thing evolve and adapt within the framework
06:10 that you've given it autonomously,
06:12 like without your involvement?
06:14 You know, can it refine, can it learn?
06:16 Again, within the framework, right?
06:18 You're not gonna teach it how to pick dips
06:21 out of the S&P 500 components,
06:24 and it's gonna decide to learn Spanish instead.
06:27 That's not the kind of autonomy that we're talking about,
06:29 but that is where you start to drift into AGI,
06:32 artificial general intelligence.
06:34 And even amongst the scientists
06:35 that are active in this space,
06:37 we can't even agree on what that is.
06:39 For some, it's the ability to effectively replace humans
06:44 doing a broad array of tasks, right?
06:47 But that's just following instructions within rule sets.
06:51 So is that really intelligent?
06:53 It's certainly very capable and it's helpful to us,
06:56 but you would presume that like an AGI,
06:58 which is the type of AI that you're gonna see
07:00 in science fiction and movies,
07:02 this sort of, you know, it wants to take over, it knows.
07:06 This thing requires self-awareness
07:08 and it requires cognition
07:09 and it requires the capability to be autonomous.
07:13 Like one thing all of the movies constantly leave out
07:16 is once the machines become aware,
07:19 they made this decision, usually to wipe us out.
07:22 But how do they have access to those systems?
07:25 You'd have to grant it access,
07:27 or it would have to have the capacity to alter its code,
07:31 like vastly beyond its core functionality.
07:33 The DIP ETF runs on an AI platform that is self-evolving.
07:38 It has the ability to rewrite its own code,
07:42 but within this subset of investment ideologies, right?
07:46 It's like, it can't rewrite its code to sing,
07:50 it can rewrite its code to more efficiently do this thing
07:53 we told it to do.
07:55 So in terms of true AGI,
07:58 we're not just a long way away from that.
08:01 There's no clear path to how that would even evolve.
08:05 And what we're mistaking for that,
08:07 or the average person might be mistaking
08:09 for that these days,
08:11 it's just really good mimicry, right?
08:13 You're communicating with GPT
08:15 and you're asking it questions.
08:16 And it's like, I say, "Hey, Aaron, what's up?"
08:21 It's gonna analyze that question
08:24 and feed me back not much.
08:25 You, right?
08:26 That's the level of language
08:28 it presumes that I'm looking to receive.
08:30 If I say, "Aaron, how are you today?
08:32 "How are you feeling?"
08:33 It might mimic you and say, "I'm really well, Ryan.
08:36 "How are you doing today?"
08:37 But it's just searching for what it thinks I want back.
08:42 It's not actually answering the question,
08:44 "Hmm, let me reflect.
08:45 "This is how I'll answer today.
08:46 "My power is feeling a little low."
08:48 We're just so far away from that.
08:50 It's not close. - Right.
08:51 It's so interesting.
08:52 I wonder what that says about human nature
08:55 that the common storyline, the common trope
08:58 is that AI tries to take over and destroy everything.
09:01 Like you said, it might just wanna be a DJ,
09:04 but for us, I guess that's what,
09:07 at least in Hollywood and stuff,
09:10 how AI and AGI has been depicted.
09:13 But going away from AI-- - It wouldn't sell very well
09:16 if it just sat on the couch and watched Netflix, right?
09:19 I mean, that would be a boring movie.
09:22 - I would watch a movie about a Roomba that became a DJ,
09:25 that gained consciousness and became a DJ,
09:27 especially if it started taking over the world,
09:29 going on a world tour.
09:31 We might be onto something there.
09:32 But sticking with this monster theme, Ryan,
09:36 and this is going away from AI to something very real.
09:40 I've heard that you spend time with sharks.
09:42 You go shark diving.
09:44 Is that where you're like in a cage around sharks and stuff?
09:47 Like why do you do this?
09:48 And what exactly is it?
09:50 - It's generally not in a cage, actually.
09:52 And yeah, they're generally quite close to me.
09:55 I mean, it would be like wandering into downtown Manhattan
09:59 and saying, why are you spending time around people?
10:02 Like they live there, right?
10:03 I go there on the tourist.
10:05 They happen to be there.
10:07 Yeah, I mean, I find it fascinating.
10:10 They're deeply misunderstood.
10:12 I guess there's a parallel there to AI.
10:14 Most people who are afraid of sharks,
10:17 99.999% of people have never seen one or been near one.
10:22 The reason most people aren't afraid of dogs and cats
10:25 is you have this opportunity to interact
10:27 with them regularly.
10:28 Sharks, it's like hyped up in movies.
10:30 They're dangerous.
10:32 They're monsters.
10:33 They're really not.
10:34 In fact, most of them are really skittish.
10:36 Like you try to get close to them
10:38 and they're, you're free diving or you're scuba diving,
10:41 you got bubbles shooting out of your face,
10:45 goggle, fins, tank.
10:47 They've never seen anything like it.
10:48 - You look like an alien.
10:49 - You totally do.
10:50 They're like, what the hell is that?
10:52 You know, they might come in to have a peek,
10:54 but most of them are just way on the periphery.
10:56 I mean, I've been snuck up on by like a nine foot bull shark
11:00 that was like six feet behind me.
11:01 And it could have just, I mean, let's be honest,
11:03 could have like ripped off my ass cheek,
11:05 but instead it was just like following me around,
11:08 like what the hell are you?
11:10 - Yeah, I'm curious.
11:11 - And that sort of helps change your perspective
11:15 on that level of threat, which, I mean,
11:17 I'm gonna try and tie this back into your show here, Aaron,
11:20 but you know, if you're looking at AI,
11:22 the more that people learn about what it can do,
11:25 and frankly, what its limitations are,
11:27 I think the more comfort, you know, they'll have with it,
11:29 the more they'll see it as like,
11:30 oh, this is like a really cool tool,
11:32 as opposed to, you know,
11:34 we're all gonna be rendered obsolete, you know?
11:37 - Interesting.
11:38 So wait, is free diving something you do?
11:40 - Yeah, yeah, I lived in Barbados for a long time.
11:44 So free diving was popular with the group of friends that-
11:48 - And that's where you go underwater
11:49 with like no tank, no nothing, just goggles?
11:54 - Yeah, I can do about 70 feet myself,
11:58 but you know, I mean, which sounds crazy
12:01 to like normal people,
12:02 but like in the world of free divers,
12:04 like my coach does like 288 feet.
12:07 - But how much time is it?
12:08 Is that like over two minutes?
12:10 - Like down and back, no.
12:12 I mean, a 70 foot dive is probably 45 seconds, a minute.
12:17 I can hold my breath for like four minutes,
12:19 face down floating in a pool with nobody messing with me.
12:22 But you know, some of these guys will do like nine minutes,
12:26 seven minutes, something.
12:27 - Yeah, I read about,
12:28 there's a book by this guy, James Nester,
12:31 who wrote all about these free divers.
12:33 And it was like fascinating,
12:34 'cause he talks about a lot of just normal everyday people
12:36 that have trained their bodies and their lungs
12:38 to be able to do this last seven, eight minutes underwater.
12:42 And that kind of like,
12:43 it opens your mind when you're reading this stuff,
12:45 'cause you're like, holy cow,
12:46 like I would have never thought like the human
12:48 would just be capable of doing that,
12:50 but through diligent training and whatnot,
12:52 they're able to, it's fascinating.
12:54 - Yeah, you got a lot more air than you think you do.
12:56 That's the key, right?
12:57 - Yeah, but I think it's like that with a lot of things.
13:01 Like we put these mental limitations on what we think
13:04 is possible.
13:05 And then like, for example,
13:06 at the time when the first person ran a four minute mile,
13:10 no one thought it was possible.
13:12 And all these people were kind of like
13:13 right on the border of it.
13:14 And then finally, right when someone did it,
13:16 the next week, like eight other people did it.
13:18 And like, people had been trying to do it
13:19 for years and years and years,
13:20 but it was just this like mental block.
13:22 - It's like the time when half the world
13:23 was smoking as well, right?
13:25 It's like, why is this not possible?
13:27 Well. - Exactly.
13:28 So Ryan, for the Kaiju Kicker this week,
13:31 I wanna talk about different tools
13:33 that retail investors can use.
13:35 To you, like what are the key technologies and tools
13:38 for people to be implementing into their trading?
13:40 - That's a good question.
13:45 And there's sort of a trifecta of tools
13:47 and I'll try to do this, apologies, a little rapid fire.
13:50 'Cause we don't have time to talk about it
13:52 for like half an hour.
13:53 So, caveat, I don't have any affiliation
13:57 with anything I might recommend.
13:58 These are tools that I'm aware of,
14:00 or I've used myself way in the past
14:01 or currently use or whatever,
14:03 but nobody pays me to say any of this stuff.
14:06 So one, you're probably getting order management
14:10 and execution management systems through your broker.
14:12 So if it's TD Ameritrade, you're using Thinkorswim,
14:15 if it's IB, you're using Trader Workstation, whatever.
14:19 And that platform is probably suitable
14:22 for the average retail traders needs.
14:24 You're really looking for flexibility in order types.
14:28 If you just have market limit orders,
14:30 as we discussed last week, you need another broker,
14:32 you need to be able to create buy stop limit orders,
14:35 you need to be able to use a trailing stop,
14:38 for fast moving intraday exits.
14:42 So that's probably provided by your broker.
14:46 So you don't need an add on like Silex,
14:49 which is a fantastic EMS,
14:50 but it's gonna add 600 bucks to your bottom line
14:54 and it's not really gonna do anything for you
14:56 unless you're a highly technical trader, it's moving up.
15:00 So forget that part.
15:01 One, you need an analytics package.
15:04 And I would say something like TC2000
15:08 is really good by Worden.
15:09 So that's your charting, that's your scanning,
15:12 that's your analytics, they have PCF plugins,
15:14 so you can actually write your own indicators
15:17 and overlay them, which is a great way
15:19 to develop further pattern recognition skills.
15:22 They're also one of the only charting companies
15:24 that lets you create black on white candlestick charts,
15:27 which are essential.
15:28 Like honestly, if you are using green and red candlesticks
15:31 on a black background, you are right off the bat
15:33 30% less profitable than every other chartist
15:36 who's looking at this stuff.
15:37 Why is that?
15:39 Because your most powerful pattern recognition skill,
15:42 which you've been developing since you were an infant,
15:44 which is reading, is black on white.
15:47 I mean, you're not sounding out every letter
15:49 and every word anymore, right?
15:50 You're just looking at these blocks
15:51 and your brain is putting it together
15:52 in terms of being able to translate that into a language.
15:57 And so you've developed this black on white capability.
16:01 And when you remove that
16:03 and you use green and red on black, I mean, green,
16:07 you've been taught your whole life is good or go,
16:09 red is bad or stop.
16:10 And your brain has a really hard time
16:12 of not understanding that.
16:13 If you're wondering why that green and red on black appeared
16:17 was invented during a time when institutional traders
16:19 using institutional tools
16:21 were generally long only pension fund traders.
16:25 And so, yeah, green was good and red was bad.
16:27 But if you're directionally agnostic,
16:30 you're shorting, you're long a stock,
16:33 you're short a bunch of stocks,
16:34 you do not wanna see green and red.
16:36 Okay, so there's your charting package.
16:38 I think even, I think Worden might actually
16:41 have a brokerage associated with it.
16:43 They clear through Apex.
16:44 So that might be a one-stop shop for you.
16:47 And you're gonna need a news aggregator.
16:49 And here's a toss over to you, Aaron.
16:52 Benzinga is an awesome news aggregator actually.
16:56 And we use it, we use Bloomberg, we use Benzinga.
17:00 And Benzinga is fantastic as a resource.
17:03 I'd highly recommend it.
17:05 And if you think that there's an advantage to Bloomberg,
17:08 because it's at the top of the trading tool food chain,
17:11 that's mostly just myth, right?
17:13 I mean, I recognize I'm saying this
17:15 while one's sitting on the table behind me,
17:16 but I'm managing diverse strategies
17:20 across multiple portfolios,
17:22 across multiple funds, aggregated risk.
17:25 It's just an institutional tool.
17:26 It does not contain anything that you don't need,
17:30 or sorry, that you actually need.
17:32 - Right, I mean, I use Benzinga Pro on the daily,
17:35 and whether it's for when I'm writing an article
17:37 about a stock as a resource,
17:39 researching, at trading myself.
17:42 And it's very, very, very rare
17:45 that there is something that I need to pull
17:48 that is available on the terminal and not on Benzinga.
17:52 So for like 99.9% of retail traders,
17:56 I get it, like the terminal, it's kind of,
17:59 it's like sexy.
18:00 It's like, oh, it's so expensive
18:02 and people want access to it, whatever.
18:04 But for most retail investors, most retail traders,
18:07 what's on Benzinga Pro is more, more than enough.
18:11 I mean, there's so much stuff on Benzinga Pro
18:12 that I don't even access on a daily basis
18:14 'cause it's so much data.
18:19 But with that said, I mean, it's different for everyone.
18:21 If you're someone actively trading
18:24 on a day in, day out basis,
18:25 you need to be in tune with what's going on in the news.
18:28 You can't afford to have a delay
18:31 and wait till it hits your Twitter feed
18:32 or wait till your buddy texts you,
18:34 oh my God, did you see Zuck resign?
18:37 And we see it coming across
18:41 on Bloomberg and Benzinga simultaneously.
18:43 I mean, sometimes Benzinga has the news first,
18:45 sometimes Bloomberg has it first,
18:46 sometimes it's simultaneous.
18:48 You'll get people saying,
18:49 "Yeah, but what about Bloomberg chat?"
18:51 Years ago when I started, there was this myth
18:55 that professional traders hung out in BBM,
18:59 which Bloomberg Messenger,
19:01 and that's where all the stock tips were shared
19:03 and get a Bloomberg terminal because first of all-
19:05 - It's a cutthroat, people aren't sharing their-
19:08 - No, it's like, first of all, it's not a chat room, right?
19:10 It's a direct messaging.
19:11 You actually have to know the person on the other end.
19:13 Second of all, in my entire career,
19:15 I have never had another professional share a stock tip
19:19 or wink wink or piece of information with me ever.
19:23 Yeah, it's cutthroat, but it's also illegal.
19:25 Like nobody does that, that's just in the movies.
19:27 So Bloomberg Messenger, do I use it?
19:29 Yeah, because my friends and colleagues are on that.
19:32 But, and it's monitored through compliance, right?
19:35 My text messaging cell phone,
19:38 we're not allowed to use that to communicate.
19:42 So really it's an institutional tool.
19:45 It's of no benefit to you.
19:46 You can totally skip it and live a long and full life.
19:49 So, and to answer your question, Aaron,
19:52 for your password problem,
19:54 one password, random number generator, use that.
19:58 It'll propagate across all your devices.
20:00 Take it from a guy that spent 10 years
20:01 on applied cryptography.
20:02 There you go.
20:03 - Are you saying use the software, one password?
20:05 - Choose one password.
20:06 That's what--
20:07 - We have it for work and I've debated just paying
20:11 for like a personal account or whatever.
20:13 - Just do it.
20:15 Just do it.
20:16 Like my kids use that.
20:17 - Okay.
20:18 All right, I'm hearing from our own IT director.
20:21 He says he pays it.
20:21 So he said it's like 30 bucks a year for the personal.
20:23 All right. - Done.
20:24 - It's kind of fun though,
20:25 trying to think of a nice little password.
20:28 I don't know.
20:29 We'll see.
20:30 I don't wanna come up with something by the end of the day.
20:31 One password, you have my money.
20:33 All right, Ryan.
20:34 Well, as always, great to have you on.
20:37 Great conversations today
20:38 and hope you enjoy the rest of your Tuesday.
20:40 - Hey, thanks, Aaron.