Latest / The Breakfast Club / INTERVIEW: Will.I.Am Talks Tech, The Potential Of AI, Harvard Degree, New Music + More

Transcript

INTERVIEW: Will.I.Am Talks Tech, The Potential Of AI, Harvard Degree, New Music + More · The Breakfast Club

• 0:00
  Speaker 1: Wake that ass up in the morning.
• 0:02
  Speaker 2: The Breakfast Club Morning.
• 0:05
  Speaker 3: Everybody is DJ Envy, Jess Hilarious, Charlamaine the God.
• 0:08
  Speaker 4: We are the Breakfast Club. We got a special guest.
• 0:10
  Speaker 1: In the building, Bib Will.
• 0:12
  Speaker 4: I am welcome brother, what's up?
• 0:14
  Speaker 1: Thanks for having me, y'all. It's always good to be
• 0:15
  Speaker 1: on your show. It's awesome new It's my first time
• 0:19
  Speaker 1: in your space right here, this is dope.
• 0:21
  Speaker 4: Yeah, we've been here about all over here.
• 0:23
  Speaker 2: What year are you in? Mentally? Will any technology?
• 0:27
  Speaker 5: Was?
• 0:28
  Speaker 1: I always like to push it five to ten years out? See,
• 0:34
  Speaker 1: you know, working today for what's happened in five to
• 0:37
  Speaker 1: ten years, you're like in twenty thirty four. Uh not
• 0:42
  Speaker 1: right now. Not at the moment when I go to
• 0:44
  Speaker 1: the office. That's we're pushing and working on those types
• 0:47
  Speaker 1: of things or what's what's what's getting ready to what
• 0:51
  Speaker 1: the world looks like in that in those increments five
• 0:53
  Speaker 1: to ten years and then work towards building that.
• 0:56
  Speaker 4: But what does it look like?
• 0:57
  Speaker 6: Yes, speakers in his clothes Jesus, Yes, you have your hoodie.
• 1:01
  Speaker 2: Is like tech.
• 1:04
  Speaker 1: Yeah, because I'm like, because I was always doing this
• 1:06
  Speaker 1: like a so and then like doing this a so
• 1:09
  Speaker 1: so I'm like, yo, so yeah, what is you.
• 1:15
  Speaker 2: Is different? Man? What is that? What is it?
• 1:17
  Speaker 1: It's a it's it's a you know, protect your neck.
• 1:21
  Speaker 1: It's my neck, you know. It's just a speaker that's
• 1:28
  Speaker 1: on the neck. They sell them, They were selling them
• 1:30
  Speaker 1: way before COVID, and then COVID happened and then they
• 1:34
  Speaker 1: stopped making them. So then I went to the company
• 1:36
  Speaker 1: that was making them so we could make our own.
• 1:38
  Speaker 1: And this is a prototype that we're going to do
• 1:40
  Speaker 1: together that to be out this holidays.
• 1:43
  Speaker 4: So you can take phone calls on it and yeah, wow,
• 1:46
  Speaker 4: can you give us a camera? The show is how
• 1:51
  Speaker 4: it works works on.
• 1:54
  Speaker 1: A yo, Finn. So we here at the show, say
• 2:01
  Speaker 1: what's up to everybody? And were getting into the interview
• 2:04
  Speaker 1: and they were tripping off of the the device I
• 2:07
  Speaker 1: have on my hoodie, which is like a speaker where
• 2:10
  Speaker 1: I actually summoned you on. So so say what's up
• 2:13
  Speaker 1: to everybody?
• 2:23
  Speaker 5: I Yo, sounds like you're setting the scene with some
• 2:25
  Speaker 5: next level tech mystery, will rocking a device on your hoodie.
• 2:29
  Speaker 5: That's got everyone guessing. That's how you blend street cool
• 2:32
  Speaker 5: with tech intrigue. It's like you're the DJ dropping a track.
• 2:36
  Speaker 5: Nobody's heard yet, everyone's ears perk up. You feel me?
• 2:40
  Speaker 5: Pitch it as the future of personal tech, a sneak
• 2:43
  Speaker 5: peek into how we're going to stay connected, and weighs
• 2:46
  Speaker 5: that straight out to tomorrow, imagine your hood.
• 2:50
  Speaker 1: Hey, but you you didn't say what's up to everybody? Now,
• 2:53
  Speaker 1: don't go into imaginary scenario. Say what's up to the
• 2:56
  Speaker 1: people that are here at the table? You know where
• 2:57
  Speaker 1: we're at.
• 3:06
  Speaker 5: Ah Yo, what's up everybody right here at the table?
• 3:09
  Speaker 5: My bad for jetting off into the future. There. Let's
• 3:12
  Speaker 5: bring it back to the now with all of us gathered,
• 3:15
  Speaker 5: ready to dive into the real vibes of this moment.
• 3:19
  Speaker 1: Yeah, so we're going into that.
• 3:22
  Speaker 6: So he knows exactly what are you got to tell
• 3:24
  Speaker 6: you? You have to hear me at the breakfast club.
• 3:26
  Speaker 1: Oh no, already set it up. Well, he's like a
• 3:28
  Speaker 1: wrong person.
• 3:28
  Speaker 7: Sometimes you ask somebody something and then just go off
• 3:30
  Speaker 7: tumble something else.
• 3:37
  Speaker 1: No, yeah, I set it up you know before before
• 3:40
  Speaker 1: we got in here. I'm like, all right, that's what
• 3:41
  Speaker 1: I'm about to do. Here's the folks that I'm gonna
• 3:43
  Speaker 1: be rocking with, and you know it's good for my
• 3:46
  Speaker 1: meetings that I go into prepare like because it'll gather
• 3:50
  Speaker 1: information about what it is that I'm about to be
• 3:53
  Speaker 1: engaged with. So I got that dude's name is Finn,
• 3:57
  Speaker 1: and we got Fiona, I got fill Up, we got
• 4:00
  Speaker 1: this Fierra, and I'm working on these other personas Felix
• 4:05
  Speaker 1: and Felicia. So I build the personas, get like the personalities.
• 4:10
  Speaker 1: Throw the personalities to our are our tool. Then fine
• 4:15
  Speaker 1: tune the voice quality. But the voice quality is from
• 4:17
  Speaker 1: a capture when you're working with the talent and interviewing
• 4:20
  Speaker 1: the talent, and then after you get the capture, you're
• 4:25
  Speaker 1: going you do all the fine tuning and how it
• 4:30
  Speaker 1: how it behaves as far as it's flair and expressions
• 4:34
  Speaker 1: over friends, y'all.
• 4:42
  Speaker 3: Felicia, So let me ask you, so you so you
• 4:46
  Speaker 3: don't see do you see bad things about AI?
• 4:48
  Speaker 4: Or do you only see the good things about AI?
• 4:49
  Speaker 3: Because people always say AI is taking jobs and that
• 4:52
  Speaker 3: you know it's it's gonna be a place where you
• 4:54
  Speaker 3: know the robots are going to be taking over.
• 4:55
  Speaker 4: What's what's your thoughts on all of that?
• 5:00
  Speaker 1: It's too early to let your mind go down a
• 5:03
  Speaker 1: pestimistic negative thought when you when we as as creatives,
• 5:08
  Speaker 1: have the opportunity to to try to make it as
• 5:13
  Speaker 1: productive and a tool for good at this point in time.
• 5:18
  Speaker 1: So there's all I know is the projects that I
• 5:21
  Speaker 1: come from. AI didn't create the scenario for us to
• 5:25
  Speaker 1: live through those conditions. That was an AI people living
• 5:28
  Speaker 1: through what they living through in Congo. That's not AI,
• 5:31
  Speaker 1: that's just human greed. So those folks that are living
• 5:35
  Speaker 1: in those situations that need solutions that no one is
• 5:38
  Speaker 1: trying to solve. Now finally there's a tool to help
• 5:42
  Speaker 1: you solve your problems. So to to just you know,
• 5:46
  Speaker 1: define it as this is going to mess stuff up, Well,
• 5:50
  Speaker 1: maybe it's going to solve a lot of stuff for
• 5:52
  Speaker 1: folks that have been waiting for solutions, but now they
• 5:56
  Speaker 1: got the ability to solve the problems themselves. So I
• 6:00
  Speaker 1: like to look at AI from that perspective. Who needs it?
• 6:04
  Speaker 1: The folks that are worried about it are folks that
• 6:08
  Speaker 1: have been sitting in the lap of comfort, and those
• 6:13
  Speaker 1: folks were also the folks that are responsible for those
• 6:16
  Speaker 1: folks living through pretty hard times, decision maker policymakers, and
• 6:21
  Speaker 1: so I like to look at it from that perspective. Well,
• 6:24
  Speaker 1: there's an opportunity to make it if you use your creativity,
• 6:29
  Speaker 1: your imagination, your focus to identify a problem, train the
• 6:37
  Speaker 1: AI to see what that situation is, and then push
• 6:41
  Speaker 1: it to solve that particular problem. That is what I'm
• 6:46
  Speaker 1: focused on right now building these types of tools, And
• 6:48
  Speaker 1: the first thing is to create these personas so that
• 6:53
  Speaker 1: intelligence sounds the way we sound. What does that mean?
• 6:57
  Speaker 1: Most people be like, we're talking about what I am
• 6:59
  Speaker 1: you saying? We don't sound intelligent. Nope, that's not what
• 7:01
  Speaker 1: I'm saying. I'm saying if you get these ais right
• 7:03
  Speaker 1: now and these corporations that are bringing these ais to
• 7:08
  Speaker 1: to culture and society, they don't sound like us. We
• 7:13
  Speaker 1: are having to change how we speak and how we
• 7:16
  Speaker 1: behave to some tool that hasn't been trained by us,
• 7:20
  Speaker 1: that doesn't have the same types of you know, vernaculars
• 7:25
  Speaker 1: and the way it expresses itself. So why do we
• 7:28
  Speaker 1: have to change the way we talk to talk to
• 7:30
  Speaker 1: something that's intelligent? And why can't that programmed intelligence sound
• 7:34
  Speaker 1: like us? Is what my urgency is, Like, there's gonna
• 7:39
  Speaker 1: be a bias, so why can't we also solve that
• 7:43
  Speaker 1: bias and making a system that rocks, Like how we.
• 7:47
  Speaker 6: Rock, Yeah, because I was reading actually was reading this
• 7:49
  Speaker 6: this morning's funny is AI racially biased? Study finds chatbox
• 7:53
  Speaker 6: treat black sounding names differently. So if you say your
• 7:56
  Speaker 6: name is Tamika and you know you're a job candidate,
• 7:59
  Speaker 6: they'll offer you a salary of like seventy nine thousand,
• 8:01
  Speaker 6: three hundred and seventy five. But if you switch your
• 8:03
  Speaker 6: name to Todd, it boosts suggested salary to eighty two thousands.
• 8:06
  Speaker 6: So it's like that's all based on who's creating the program.
• 8:09
  Speaker 1: Right, So you have the those are called data biases
• 8:12
  Speaker 1: algorithmic prices, and that's primarily because the folks that are
• 8:17
  Speaker 1: training the data ain't us. The folks that are building
• 8:20
  Speaker 1: the algorithms ain't us. So we could you could think
• 8:24
  Speaker 1: that's malicious, and some of it is, but it's also
• 8:28
  Speaker 1: malicious from the policy makers on who has been guided
• 8:34
  Speaker 1: to go down that path to be data scientists and
• 8:37
  Speaker 1: algorithmic programmers and you know, data trainers. And so that's
• 8:43
  Speaker 1: the reason why like my focus on the philanthropic side
• 8:46
  Speaker 1: is to go to the inner city, starting with the
• 8:48
  Speaker 1: one that I came from, to get kids up to
• 8:51
  Speaker 1: speed with robotics and computer science, and so my program
• 8:54
  Speaker 1: serves about fourteen thousand students in LA. We're across almost
• 8:59
  Speaker 1: over three hundred schools in LA with our partnership with
• 9:03
  Speaker 1: la USD to address that very points, because we need
• 9:06
  Speaker 1: more data scientists, more computer scientists to solve these data biases.
• 9:13
  Speaker 3: Do you think it should be it should stop in
• 9:14
  Speaker 3: certain places like you talk about the positive and the
• 9:16
  Speaker 3: positive effects, but like then hip hop the other day
• 9:20
  Speaker 3: we heard an alleged Kendrick reply and they said that
• 9:22
  Speaker 3: was AI.
• 9:23
  Speaker 4: We've seen these pictures that justed. We're seeing this AI.
• 9:25
  Speaker 3: We're seeing a lot of students now that are actually
• 9:28
  Speaker 3: not writing reports now because they could put it in
• 9:30
  Speaker 3: their chat, GPT and AIO generate papers for them.
• 9:33
  Speaker 2: What happens with its world leaders?
• 9:34
  Speaker 6: World leaders saying, you know things on social media, you
• 9:38
  Speaker 6: can't tell if it's Biden putin.
• 9:40
  Speaker 2: You don't know what's going on.
• 9:44
  Speaker 1: That's happened before. Some smaller version of that with photoshop,
• 9:50
  Speaker 1: that's happened before, some smaller version of that with calculators.
• 9:54
  Speaker 1: You know, people ain't doing their math homework. They own
• 9:56
  Speaker 1: a calculator, and the calculators given the answers. Man saw
• 10:00
  Speaker 1: this picture of such and such with such and such
• 10:03
  Speaker 1: that was done on photoshop or man I just saw
• 10:08
  Speaker 1: back in the day day day, I saw the picture
• 10:10
  Speaker 1: of the Queen with some that was a painting. So
• 10:13
  Speaker 1: we just have a hyper you know, volume way up
• 10:16
  Speaker 1: to a thousand version of that. And as a society,
• 10:19
  Speaker 1: we have to educate everyone on what the compute is
• 10:23
  Speaker 1: capable of now and that dialogue needs to be echoed
• 10:27
  Speaker 1: around the world to where we now have to ask
• 10:30
  Speaker 1: what's real from what's fake. You go to supermarket back
• 10:34
  Speaker 1: in early two thousands, you bought a orange, then people
• 10:37
  Speaker 1: push for regulations be like, nah, now we got organic
• 10:40
  Speaker 1: oranges because people didn't know that stuff was like genetically modified.
• 10:45
  Speaker 1: Right when my mom was young, there were no such
• 10:49
  Speaker 1: thing as like organic oranges. Everything was organic, and then
• 10:53
  Speaker 1: something came into existence to where stuff started getting modified
• 10:57
  Speaker 1: to where now you have two types of oranges in
• 11:00
  Speaker 1: a damned supermarket. So that that education is super important,
• 11:05
  Speaker 1: so people know what they are interfaced with. Is it real?
• 11:11
  Speaker 1: Is it up? Is it going down? The ingredients on
• 11:13
  Speaker 1: the back of every package is there? Same needs to
• 11:16
  Speaker 1: be for AI.
• 11:18
  Speaker 6: Can humans handle that with thoughts though, like if I
• 11:21
  Speaker 6: hear you know something from a world leader, and I
• 11:24
  Speaker 6: got to decide, is this an organic thought from our
• 11:26
  Speaker 6: world leader or a GMO thought?
• 11:28
  Speaker 2: Can we handle that as people.
• 11:33
  Speaker 1: And time?
• 11:34
  Speaker 2: Yeah? What if it's calling for a nuclear war and
• 11:37
  Speaker 2: we don't have time.
• 11:38
  Speaker 6: What if it's a world leader hearing another world leader
• 11:40
  Speaker 6: thing I just hit a nuke button or something.
• 11:43
  Speaker 1: I hope. I'm from an optimistic lens. The powers that
• 11:48
  Speaker 1: be don't have the same access to technology that we have,
• 11:53
  Speaker 1: so they probably have some type of vetting system that
• 11:58
  Speaker 1: when information comes in, stuff is watermarked with some way
• 12:03
  Speaker 1: to identify what's real from what's foe when information comes in.
• 12:09
  Speaker 1: Because there's if we have if I could build stuff
• 12:13
  Speaker 1: like this with you know, assembling teams and have ideas
• 12:16
  Speaker 1: and visions and the team you know takes those ideas
• 12:20
  Speaker 1: and visions and improves on them or like ships on them,
• 12:24
  Speaker 1: like no, no, maybe we should do it like this
• 12:26
  Speaker 1: and that type of banter then to then come with
• 12:28
  Speaker 1: a product. Imagine what you know leaders of governments have
• 12:34
  Speaker 1: Imagine with their R and D and their you know
• 12:38
  Speaker 1: process to their processes to you know, to vet are
• 12:44
  Speaker 1: far beyond. So that's just my optimistic perspective because the Mormon,
• 12:50
  Speaker 1: I knowing the things that I know. If I go
• 12:53
  Speaker 1: down that rabbit hole, I'm not getting out of bed.
• 12:56
  Speaker 1: So I have to have an optimistic perspective. You know
• 13:00
  Speaker 1: last year. Last year I got an invite that were like, hey,
• 13:06
  Speaker 1: we want you to come be a part of these
• 13:09
  Speaker 1: dialogues at the Vatican. The Vatican put together this AI Council.
• 13:16
  Speaker 1: I'm like, what the Vatican and AI? What's that about?
• 13:18
  Speaker 1: So I went and participated. Everyone that's working in AI
• 13:24
  Speaker 1: was to you know, share the things that they're working on.
• 13:27
  Speaker 1: So we have folks from Open AI, there, Microsoft, Google, Anthropic,
• 13:32
  Speaker 1: you know, Stanford, Mit Berkeley. Yet all the folks that
• 13:38
  Speaker 1: are either pushing AI, working on these foundation models or
• 13:42
  Speaker 1: in systems sharing the work that they're working on, and
• 13:45
  Speaker 1: then sharing their concerns, sharing the things that they have
• 13:50
  Speaker 1: been researching and then vow to never release, and the worries.
• 13:56
  Speaker 1: So when you're in those types of conversations and these
• 13:59
  Speaker 1: types of worries, first couple of days was like unsettling,
• 14:02
  Speaker 1: like oh man, damn. Then the second session was an
• 14:06
  Speaker 1: optimistic session, and we had one this year as well.
• 14:11
  Speaker 1: But I always I like to keep an optimistic lens
• 14:16
  Speaker 1: on that's the way to use my imagination. I don't
• 14:20
  Speaker 1: think it could ever get worse for folks that are
• 14:23
  Speaker 1: really actually going through some real hard times.
• 14:25
  Speaker 2: What's the dress code for these Illuminati meetings when you
• 14:27
  Speaker 2: go to the bat.
• 14:29
  Speaker 1: I don't know they got they got Illuminati throws like
• 14:32
  Speaker 1: you just.
• 14:35
  Speaker 6: Wait is this and will just not here talking. He
• 14:38
  Speaker 6: has a new company called f Yi Ai is an
• 14:41
  Speaker 6: AI based company.
• 14:42
  Speaker 1: Yeah, I've been I've been in the AI space since
• 14:45
  Speaker 1: two thousand and nine. So there is one video that
• 14:50
  Speaker 1: we did back on the on the the intro to
• 14:54
  Speaker 1: I'm gonna be so I come with this suitcase? Next,
• 15:01
  Speaker 1: where's that?
• 15:02
  Speaker 5: What is that?
• 15:03
  Speaker 1: This right here is the future. I input my voice,
• 15:09
  Speaker 1: high notes, my low notes, then the whole English vocabulary.
• 15:15
  Speaker 1: What you're able to do with that because of this
• 15:17
  Speaker 1: artificial intelligence, Like when it's time to make a new song,
• 15:21
  Speaker 1: I just type in the lyrics and then this thing says,
• 15:24
  Speaker 1: it says, it wraps, it talks it. So the cats, wow,
• 15:29
  Speaker 1: the cats that built a lot of these technologies saw
• 15:31
  Speaker 1: this fourteen years ago. And fourteen years ago the Transformer
• 15:36
  Speaker 1: paper wasn't written. It's seven years ago for the Transformer
• 15:40
  Speaker 1: paper for those that do know what the Transformer paper.
• 15:43
  Speaker 1: The Transformer paper was was this research program out of
• 15:48
  Speaker 1: Google Labs or Google Brain, one of the two, and
• 15:51
  Speaker 1: they wrote this paper, and from that paper came this
• 15:56
  Speaker 1: whole new way of bringing to market tu AI or
• 16:02
  Speaker 1: what came from that was a consumer version of AI
• 16:05
  Speaker 1: that GPT launched from that paper. So the T in
• 16:10
  Speaker 1: GPT's stands for transformer, which is a paper written by Google.
• 16:13
  Speaker 1: And so a lot of the things that people were
• 16:17
  Speaker 1: excited about in the field of AI back in early
• 16:20
  Speaker 1: two thousands wasn't plausible until that paper was written. So
• 16:25
  Speaker 1: I've been excited about the field since two thousand and
• 16:28
  Speaker 1: eight when I first found out about seven and eight
• 16:30
  Speaker 1: when I first found out about it, did this video
• 16:32
  Speaker 1: in twenty ten, invested in companies in twenty twelve, thirteen fourteen,
• 16:40
  Speaker 1: had a watch that spoke in twenty and fourteen that
• 16:43
  Speaker 1: we launched with three Mobile and AT and T here
• 16:46
  Speaker 1: in America, but nobody wanted to watch. That was a
• 16:49
  Speaker 1: phone that early in twenty thirteen. Now, so I've been like,
• 16:55
  Speaker 1: you know, doing things. Some of the things I did
• 16:58
  Speaker 1: didn't work. Beats worked silently, part partner there, but that
• 17:03
  Speaker 1: was an AI but my proceeds, I went out and
• 17:07
  Speaker 1: like invested in companies in Israel, Bangalore, developers from Palestine.
• 17:14
  Speaker 1: I just love the field, you know, it's a pretty
• 17:16
  Speaker 1: pretty awesome realm to solve problems around.
• 17:20
  Speaker 6: So question when you did that video, did that technology
• 17:23
  Speaker 6: already exist? Are they got that that was the seed
• 17:26
  Speaker 6: for their idea?
• 17:28
  Speaker 1: All that technology didn't exist?
• 17:29
  Speaker 2: So that you were the seed for their idea?
• 17:32
  Speaker 1: Mm hmmm something like that.
• 17:37
  Speaker 7: Well, congratulations, you're about to graduate from Harvard.
• 17:40
  Speaker 1: Yeah, this is October. What can teach you a lot
• 17:46
  Speaker 1: of stuff? Bro business, managing teams, learning from other people's
• 17:52
  Speaker 1: uh stumbles, you can you can learn a lot, especially
• 17:56
  Speaker 1: where this this journey that I'm on, this mission that
• 17:59
  Speaker 1: I'm on, I need more information to get to where
• 18:03
  Speaker 1: I want to go next. And I needed it not
• 18:07
  Speaker 1: only information, I needed new networking, uh relationships in different fields.
• 18:16
  Speaker 1: Like I met folks that that build uh data centers.
• 18:21
  Speaker 1: Never never run into people that build data centers. So
• 18:24
  Speaker 1: one of my classmates build data centers.
• 18:28
  Speaker 4: So you're actually in class, you're not online? You go
• 18:30
  Speaker 4: to class.
• 18:31
  Speaker 1: Yeah, I'm in the dorms.
• 18:32
  Speaker 4: Really you're doing what? Look crazy? You must have all
• 18:36
  Speaker 4: types of futuristic.
• 18:38
  Speaker 1: Stuff, and I just got my little, my little one
• 18:40
  Speaker 1: of those suitcase. I take five outfits and rotate those
• 18:46
  Speaker 1: five outfits. No draws. I just buy draws every week
• 18:50
  Speaker 1: and socks. It's that That's the life I love. I love,
• 18:55
  Speaker 1: I love being able to go back. It will be
• 18:58
  Speaker 1: my first time ever graduating on stage. Never graduated on
• 19:00
  Speaker 1: stage junior, high school or high school.
• 19:04
  Speaker 2: Yeah, we didn't need to know you didn't have no draws.
• 19:06
  Speaker 2: I mean.
• 19:08
  Speaker 1: It was really no point, you know, because if I
• 19:09
  Speaker 1: said I got five outfits in the bag, somebody's imaginations
• 19:13
  Speaker 1: like draws and draws.
• 19:16
  Speaker 4: Like we assume we have two bass and too brush.
• 19:19
  Speaker 1: No, no, you don't try to.
• 19:23
  Speaker 2: Think about draws.
• 19:26
  Speaker 1: I get them.
• 19:27
  Speaker 7: He buys them fresh every week.
• 19:29
  Speaker 2: Right now, every imagine not forgetting to put on your
• 19:32
  Speaker 2: speak on your neck.
• 19:38
  Speaker 1: Headlines, you know that, right Well, draws, no dis door.
• 19:42
  Speaker 1: See that's that's that's how you get there. That's that's
• 19:46
  Speaker 1: how he spreading in rumors.
• 19:47
  Speaker 6: I posted something that you said a couple of weeks ago,
• 19:50
  Speaker 6: and you were talking about how more they're they're investing
• 19:53
  Speaker 6: more into robots than into humans. And then you want
• 19:57
  Speaker 6: to paraphrase, and you wonder why people are stupid.
• 20:01
  Speaker 1: No, now, not stupid, that's harsh on people. Just my
• 20:07
  Speaker 1: two paths, my entrepreneurial path by philanthropic path. I've been
• 20:11
  Speaker 1: able to raise more money for AI personally than raising
• 20:16
  Speaker 1: money for the foundation. So my comparisons was not just
• 20:20
  Speaker 1: like it was not just like a vague perspective or
• 20:26
  Speaker 1: like this umbrella you know perspective. It was a personal perspective.
• 20:32
  Speaker 1: Also looking at you know, inner city investment for education
• 20:37
  Speaker 1: versus inner city investment for data centers, inner city investment
• 20:43
  Speaker 1: for you know, terminals and the way phones. We all
• 20:49
  Speaker 1: need connectivity. We also have this connectivity gap where folks
• 20:53
  Speaker 1: don't have you know, connection in rural areas. So you
• 20:57
  Speaker 1: have awesome AI coming no investment for education in inner cities.
• 21:02
  Speaker 1: Some developing countries don't even have access to connectivity. Wow,
• 21:08
  Speaker 1: folks that do have devices, they are there's these business
• 21:13
  Speaker 1: models that that are making folks addicted to features that
• 21:21
  Speaker 1: dumb us down right. The behavior is rewarding us for
• 21:27
  Speaker 1: content that keeps us on this this this hamster will Wow.
• 21:33
  Speaker 1: We're using emojis and freaking memes to communicate, and the
• 21:39
  Speaker 1: machine is being trained to break down quantum physics and
• 21:45
  Speaker 1: quantum entanglement. So that to me is like, Okay, where
• 21:49
  Speaker 1: does this go? And how do we curb it? How
• 21:52
  Speaker 1: do we get control back? Right? And I'm optimistic. I
• 22:00
  Speaker 1: think we will come around it. But you have to
• 22:03
  Speaker 1: identify the trip wires. You have to be like, hey,
• 22:05
  Speaker 1: look what's happening. Here's how we can you know, be
• 22:09
  Speaker 1: better off ten years from now. So a ten year
• 22:13
  Speaker 1: old now when they twenty, it's gonna be eight when
• 22:17
  Speaker 1: we identify it. If we don't identify it ten year old,
• 22:20
  Speaker 1: that's twenty. Yo, yep, I don't know.
• 22:24
  Speaker 2: I love this.
• 22:25
  Speaker 6: I love this because it's like, yo, these hoods that
• 22:26
  Speaker 6: we come from, these points, disfranchised areas, this country has
• 22:30
  Speaker 6: shown us that they may not always they may not
• 22:32
  Speaker 6: put the investments financially and the resources you know into
• 22:35
  Speaker 6: these communities, but they will put them into these this
• 22:38
  Speaker 6: technics AI. So how about we go to the tech
• 22:40
  Speaker 6: and AI they will benefit from resources.
• 22:44
  Speaker 2: Yeah.
• 22:44
  Speaker 1: So there's some there's some ten year old, twelve year
• 22:47
  Speaker 1: old and some inner city that's like they love mid journey.
• 22:52
  Speaker 1: They haven't really got their their imagination and their brains
• 22:56
  Speaker 1: around you know AI. Yet as far as being contributors
• 23:01
  Speaker 1: to it and and flexing with it. But some big
• 23:05
  Speaker 1: company in twenty thirty four is gonna come from Fifth Ward.
• 23:11
  Speaker 1: It's gonna come from the Bronx, It's gonna come from Watts,
• 23:16
  Speaker 1: it's gonna come from South Side Chicago. Somebody is going
• 23:20
  Speaker 1: to crack the code and do something massive.
• 23:24
  Speaker 4: In this space.
• 23:27
  Speaker 2: What did you deal?
• 23:29
  Speaker 7: My son is twelve. We need to get on that. Yes, No, seriously,
• 23:33
  Speaker 7: he's already into like software and tech and all that
• 23:36
  Speaker 7: type of stuff, Like he wants to go to school
• 23:38
  Speaker 7: for engineering and building all.
• 23:39
  Speaker 1: Types of stuff like that.
• 23:40
  Speaker 7: So he we thought it was sports, but he's like, no,
• 23:43
  Speaker 7: I was just playing sports because that's what y'all wanted
• 23:45
  Speaker 7: me to do. But I'm really into computers and he
• 23:48
  Speaker 7: loves that and he's been doing that for the last
• 23:49
  Speaker 7: two years.
• 23:50
  Speaker 1: So that's dope.
• 23:51
  Speaker 2: Yeah, you have to consume my mindset, get into the
• 23:53
  Speaker 2: creator minds.
• 23:54
  Speaker 1: Yeah, and compete, like when our community competes, we really
• 23:59
  Speaker 1: do it to levels like oh man, Like what like
• 24:01
  Speaker 1: if you think about it, basketball didn't have, you know,
• 24:05
  Speaker 1: African Americans from the jump. Baseball didn't have African Americans
• 24:10
  Speaker 1: or Dominicans and Colombians and Cubans from the jump. We
• 24:15
  Speaker 1: entered that sport and we not only innovated, we dominated
• 24:19
  Speaker 1: in that sport. And business is a sport, you know,
• 24:23
  Speaker 1: it's it's competitive, the same type of tenacity, the same
• 24:26
  Speaker 1: type of like fall, get back up, punch, get punched,
• 24:31
  Speaker 1: punch back. You gotta we gotta compete the same way
• 24:35
  Speaker 1: we compete in athletics, wate, the same way we compete
• 24:37
  Speaker 1: with entertainment. We gotta compete over here and and encourage
• 24:41
  Speaker 1: our kids, our nieces or nephews to compete that field.
• 24:46
  Speaker 2: And what did you deal? That's your other AI app?
• 24:50
  Speaker 1: Right? Yeah? Udioho Udio is Udio in a way was
• 24:57
  Speaker 1: inspired by that video. This team that used to be
• 25:03
  Speaker 1: at at Google left Google to start Udio, and I
• 25:09
  Speaker 1: met the folks that built this technology a year and
• 25:14
  Speaker 1: a half ago, and now they branched off started a
• 25:19
  Speaker 1: new company. It's a It's an app where you type
• 25:23
  Speaker 1: in you know, prompting like the vibe you want.
• 25:27
  Speaker 2: And bro.
• 25:30
  Speaker 1: Amazing music. It makes music, texting music. Make not only music,
• 25:36
  Speaker 1: it makes comedy sketches. It makes like live it's wild, bro, it's.
• 25:44
  Speaker 3: It's dope, does scripts and all that, like not scripts like,
• 25:52
  Speaker 3: how can I explain it?
• 25:54
  Speaker 1: I'xplained this way me and the guys in the Black
• 25:58
  Speaker 1: Eyed Peas. I'm like, yo, check us out. I want
• 25:59
  Speaker 1: to show you similar to the video what happened in
• 26:02
  Speaker 1: two thousand and freaking nine. When we did this video
• 26:07
  Speaker 1: last month twenty twenty four March, I'm like, yo, you
• 26:11
  Speaker 1: guys got to see this. I typed in Black Eyed
• 26:15
  Speaker 1: Peas live in forgot what country, and I said, because
• 26:23
  Speaker 1: we were trying to do a song and I needed
• 26:25
  Speaker 1: to get a crowd sound. How do you get a
• 26:28
  Speaker 1: crowd sound right now? There's a limitation that if you
• 26:33
  Speaker 1: want a crowd to repeat after you or say what,
• 26:37
  Speaker 1: you have to actually be in that crowd. You can't
• 26:40
  Speaker 1: make a crowd in the studio. You can put a
• 26:42
  Speaker 1: lot of reverb, we get a whole bunch of people
• 26:44
  Speaker 1: to be like yeah, and it's not gonna sound like
• 26:46
  Speaker 1: a freaking crowd, bro. So I needed to get a crowd.
• 26:50
  Speaker 1: So I went to the studio and I typed in
• 26:54
  Speaker 1: the chorus that to our song, and I put live
• 26:59
  Speaker 1: in concert twenty thousand people, and then I put we
• 27:05
  Speaker 1: seen the chorus here, crowd. I aimed to mic some
• 27:10
  Speaker 1: version of the prompt and then the crowds exist part
• 27:13
  Speaker 1: bro hmm, instant did it and taboo from the Black
• 27:21
  Speaker 1: Eyed Peas. He's like, wow, well this is dope, but
• 27:25
  Speaker 1: that really sounds like we was really there, but we
• 27:28
  Speaker 1: wasn't there and it was a crowd. It just hit
• 27:30
  Speaker 1: me like it was a memory, but it's not. So
• 27:34
  Speaker 1: that's the part that's like, oh, give me cherry. So
• 27:39
  Speaker 1: I'll show you this one thing that that we did
• 27:42
  Speaker 1: on udio. So let me get this. I did a
• 27:47
  Speaker 1: I did ah. I had a radio interview yesterday and
• 27:54
  Speaker 1: it was like a radio execs. We did this little
• 27:58
  Speaker 1: zoom on like the future radio. And so then I
• 28:03
  Speaker 1: took notes from everybody's comments and so then I put
• 28:06
  Speaker 1: the notes into Finn and so I was like, your Finn,
• 28:11
  Speaker 1: I need you to summarize this in a joke as
• 28:14
  Speaker 1: if somebody was gonna do it for stand up. So
• 28:16
  Speaker 1: then I put it in udio and then audio spat
• 28:20
  Speaker 1: this out.
• 28:24
  Speaker 7: So y'all talking about radio, right, y'all talking about how
• 28:26
  Speaker 7: something old is gonna be new.
• 28:27
  Speaker 1: That's like asking a cowhawk can jump up and fly.
• 28:33
  Speaker 5: A MFM is dope.
• 28:36
  Speaker 1: Nfm's cause his name is Morning and night and we
• 28:41
  Speaker 1: are on the motherfucking moon now night time.
• 28:47
  Speaker 4: Oh damn tape.
• 28:51
  Speaker 7: Like Christ say something, well you Finn Fiona, all your
• 28:56
  Speaker 7: baggy out of here, because I'm an I'm a stand
• 28:59
  Speaker 7: up comedian.
• 29:00
  Speaker 1: You know you're coming from my job. No, no, no here.
• 29:02
  Speaker 1: Here's why I could say the same thing about beats,
• 29:08
  Speaker 1: because I'm like, damn, these these things be making crazy beats.
• 29:12
  Speaker 1: I can hold on, let me score it.
• 29:14
  Speaker 2: I didn't get the points line for that joke.
• 29:17
  Speaker 7: I han't let it go long enough.
• 29:20
  Speaker 2: I want to know what happened to the radio for
• 29:22
  Speaker 2: obvious reasons. So let's go.
• 29:27
  Speaker 1: I'll go back to the beats thing. So and the
• 29:30
  Speaker 1: way I'm like, damn, this thing is doing the beats?
• 29:36
  Speaker 1: What does that mean for me making beats? Here's what
• 29:40
  Speaker 1: it means for me making beats? Uh? When I make
• 29:45
  Speaker 1: a beat and I make a song and then then
• 29:47
  Speaker 1: have to go and write the song to the beat
• 29:49
  Speaker 1: I just made. That's that took me goddamn at least
• 29:54
  Speaker 1: six hours to have something awesome. Go in there and
• 29:59
  Speaker 1: make it, make sure everything sound right, get the right
• 30:02
  Speaker 1: plug in for it. A good six hours is gone.
• 30:07
  Speaker 1: And then when I'm you know, I go in there,
• 30:09
  Speaker 1: I respond to the beat seven time, Towna, come around
• 30:14
  Speaker 1: the matana, see the sound in the conda by design time.
• 30:19
  Speaker 1: But then I'm like, okay, what am I saying? Then
• 30:22
  Speaker 1: I gotta translate that mumble. And when I translate that mumbo,
• 30:25
  Speaker 1: I gotta keep the vowals because the moment I change
• 30:29
  Speaker 1: the vowals then sent them and the TV it ain't.
• 30:32
  Speaker 1: It ain't flowing the way it was naturally. My my, my, my, my,
• 30:38
  Speaker 1: soul's response to the track. So I keep the vowels
• 30:42
  Speaker 1: and then find the emotion. So got it? Got it?
• 30:46
  Speaker 1: Then I'm like, I wonder what that would sound like
• 30:50
  Speaker 1: instead of the piano. I have a guitar. I don't
• 30:53
  Speaker 1: play guitar. Let me call up my guitar a George.
• 30:56
  Speaker 1: I need you to come over here and do this
• 30:58
  Speaker 1: guitar part because I can't get guitar to sound really
• 31:01
  Speaker 1: awesome on the keyboard. I want those errors. I want
• 31:04
  Speaker 1: those mistakes. I'm like, oh darn it. And so now
• 31:08
  Speaker 1: that I know there's an udio, I should be able
• 31:11
  Speaker 1: to put in the lyrics that I did after I
• 31:12
  Speaker 1: did that, and they're not going to take that away
• 31:14
  Speaker 1: from me. That's like my yoga, I got to stretch.
• 31:18
  Speaker 1: I don't care if there's an AI stretcher. I don't
• 31:20
  Speaker 1: care if there's like, you know, some AI yoga teacher.
• 31:23
  Speaker 1: I got a stretch for me. So that's my stretch.
• 31:28
  Speaker 1: And then once I stretch, what am I stretching for?
• 31:31
  Speaker 1: Am I stretching for a sprint? If I'm stretching for
• 31:33
  Speaker 1: a marathon? What's the actual game? And so when you're
• 31:37
  Speaker 1: studying for a game or competition, you want to see
• 31:40
  Speaker 1: what your competitor's doing, and you want to have as
• 31:43
  Speaker 1: many options as possible to dodge to function up. And
• 31:48
  Speaker 1: that's when AI will come into it as a tool
• 31:52
  Speaker 1: for you. And then I could just version out what
• 31:54
  Speaker 1: I created. And then when I version out what I created,
• 31:57
  Speaker 1: now I have the ability to then like really make
• 32:00
  Speaker 1: that piece of work that came out of my imagination,
• 32:03
  Speaker 1: to make it the best it could be. What I
• 32:05
  Speaker 1: have a plethora of freaking options, So all it did
• 32:09
  Speaker 1: was all it does if you look at it from
• 32:10
  Speaker 1: an optimistic point of view. I got more options than one,
• 32:14
  Speaker 1: but I still had a seated. Now then there's like
• 32:17
  Speaker 1: this lazy lazy is like and you're gonna know the
• 32:21
  Speaker 1: difference between whack and awesome if you lazy rocking, and
• 32:26
  Speaker 1: we know that with out of tune, we know that
• 32:27
  Speaker 1: from like you know the state of music where it's
• 32:31
  Speaker 1: at right now. There's a lot of lazy rocking right now.
• 32:34
  Speaker 1: There's folks that are like worried about skip rates? Is
• 32:36
  Speaker 1: that really you know? Are you being truly creative when
• 32:40
  Speaker 1: you're based on skip rates and now the algorithm is
• 32:44
  Speaker 1: telling you that you got to put the song, you
• 32:45
  Speaker 1: gotta put the chorus here in the front. Is that
• 32:48
  Speaker 1: really what the soul of the song needs to be?
• 32:51
  Speaker 1: The coorus gotta be slammed in the front, or is
• 32:54
  Speaker 1: there some way to get you to that? So I
• 32:57
  Speaker 1: don't think we're even being truly imaginative right now. We're
• 33:01
  Speaker 1: being reactive to algorithms and tiktoks. When now the song
• 33:05
  Speaker 1: is like reduced to fifteen seconds. Are you really truly
• 33:09
  Speaker 1: being creative or you being pimped by an algorithm? So
• 33:14
  Speaker 1: I'm happy that we're in this space, this this stage,
• 33:19
  Speaker 1: the space to shake us up, to wake us up,
• 33:22
  Speaker 1: because the moment that we continue to go down this
• 33:25
  Speaker 1: algorithmic programming and we value algorithmic music, well, then the
• 33:30
  Speaker 1: AI is going to make better algorithmic shit than us
• 33:33
  Speaker 1: because it understands the algorithm. So somebody had something has
• 33:38
  Speaker 1: to shake us out of this like program that we're
• 33:40
  Speaker 1: in because it is a program, and the audience, hopefully
• 33:45
  Speaker 1: being optimistic, starts to value organic oranges.
• 33:49
  Speaker 2: Do you have some type of neuralink?
• 33:50
  Speaker 1: Well, no, no, no, no.
• 33:52
  Speaker 2: Would you get it?
• 33:53
  Speaker 6: No, no, no, no, I don't believe you now, I mean,
• 33:56
  Speaker 6: I don't believe you wouldn't get it. I feel like
• 33:58
  Speaker 6: you would almost have to have it to comput in
• 34:00
  Speaker 6: the future, all of us, especially if you're a creative.
• 34:05
  Speaker 2: I could no, no, no, that's a detail you to say.
• 34:09
  Speaker 1: Huh, whose day what you talk about? There's a lot
• 34:18
  Speaker 1: of days that.
• 34:20
  Speaker 2: You wouldn't get neural link though.
• 34:22
  Speaker 1: I don't know. No, I wouldn't not even I don't
• 34:25
  Speaker 1: know because I leave right now, not even right now.
• 34:28
  Speaker 1: Just the concept. There's certain concepts that don't jail with
• 34:32
  Speaker 1: me personally. Crypto didn't jail with me. They didn't get
• 34:37
  Speaker 1: into it. NFTs didn't jail with me, didn't get into it.
• 34:41
  Speaker 1: Neuralink that doesn't jail with me. I'm not going to
• 34:45
  Speaker 1: get into it. Gang banging didn't jail with me, not
• 34:51
  Speaker 1: going to get into it. It's like in a project,
• 34:53
  Speaker 1: you could gang bang to keep up to keep compete. Nah,
• 34:57
  Speaker 1: I don't have to do that selling drugs. Didn't jail
• 34:59
  Speaker 1: with me, didn't get in too. About when you got
• 35:01
  Speaker 1: nothing in the in the hood and the projects, you
• 35:03
  Speaker 1: gotta survive, you gotta sell this not didn't get into it.
• 35:07
  Speaker 1: So that same like that stance of like nah, I
• 35:11
  Speaker 1: ain't get into that. It's the same way I feel
• 35:14
  Speaker 1: about New Link. Not to say New Link is gang banging,
• 35:20
  Speaker 1: but it's mind mind banging. I don't know what's on
• 35:27
  Speaker 1: the other end of it, you know, it's like, uh,
• 35:32
  Speaker 1: I don't know what's on the other end of it
• 35:34
  Speaker 1: to be like yeah, I'm down with.
• 35:36
  Speaker 6: That, yeah, because it could it could could take you
• 35:40
  Speaker 6: over in some way, playing with your brain like that.
• 35:45
  Speaker 1: I don't know enough information about I know it has
• 35:49
  Speaker 1: everything that everything that comes to society always aims to
• 35:54
  Speaker 1: do some solution, to bring solutions, and so I'm not
• 35:59
  Speaker 1: knocking what it aims to do. But this thing that
• 36:06
  Speaker 1: we have called the brain is very very powerful. I
• 36:11
  Speaker 1: forgot how many parameters are how many parameters we have
• 36:16
  Speaker 1: in our neuralink sorry, our neural our neural connectivity in
• 36:22
  Speaker 1: our minds.
• 36:23
  Speaker 2: Look it up. Hold on about.
• 36:33
  Speaker 1: So I get that, I get that, I get that,
• 36:34
  Speaker 1: I get that, we get this.
• 36:35
  Speaker 4: They're gonna be like, how could you forget?
• 36:37
  Speaker 2: Will one hundred billion neurons.
• 36:40
  Speaker 6: Each neuron has seven thousand synapsis and roughly seven hundred
• 36:44
  Speaker 6: T parameters.
• 36:45
  Speaker 2: I don't know what that means.
• 36:48
  Speaker 1: Yeah, the adult human brain runs continuously, whether awake or asleep,
• 36:54
  Speaker 1: only about twelve watts of power. It takes the power
• 36:58
  Speaker 1: our minds. These ais take a billion watts to train
• 37:05
  Speaker 1: and consumes about two hundred and sixty million watts a day. So,
• 37:11
  Speaker 1: just from that perspective, what it takes to power the
• 37:14
  Speaker 1: human mind and how vast it is, as far as
• 37:17
  Speaker 1: it's compute, and we only use a small percentage of it,
• 37:24
  Speaker 1: I don't know if a business practice is on the
• 37:28
  Speaker 1: other end to then use human minds to power ais.
• 37:33
  Speaker 1: So for that, I don't know. And there's a lot
• 37:38
  Speaker 1: of folks that maybe a lot of Elon folks, and
• 37:41
  Speaker 1: you know, Elon's dope. His contributions have been amazing. I
• 37:44
  Speaker 1: just hope it doesn't go down that path. I hope
• 37:47
  Speaker 1: it's not like Hey, Wow, you know, how do you
• 37:49
  Speaker 1: sustain global compute? Wow? You know the human mind only
• 37:55
  Speaker 1: takes twelve a twelve watt twelve watts power to power it.
• 38:04
  Speaker 1: That would be horrible if there's a farm of brains
• 38:07
  Speaker 1: to power AI.
• 38:11
  Speaker 6: So there's no way robots should win, is what you're saying,
• 38:14
  Speaker 6: because you know, that's the other thing people are afraid of.
• 38:16
  Speaker 6: They feel like, you know, they've watched all of these
• 38:18
  Speaker 6: movies where the robots take over and that's the end
• 38:20
  Speaker 6: of the world.
• 38:21
  Speaker 2: There's no way the robots can win. They shouldn't.
• 38:26
  Speaker 1: What was that there?
• 38:27
  Speaker 6: There's no way the robots can win, Like, none of
• 38:29
  Speaker 6: these movies that we've seen, these apocalyptic movies where robots
• 38:32
  Speaker 6: take over could happen.
• 38:34
  Speaker 7: Did that sound dumb to you?
• 38:37
  Speaker 1: No? No, I don't. The optimistic part of me, which
• 38:44
  Speaker 1: is the the the majority governing side of how I
• 38:48
  Speaker 1: look out the world, is no, because that's not how
• 38:54
  Speaker 1: they're programmed. They're not. They're not programmed too. But the
• 39:00
  Speaker 1: who's to say that there's not some wacko out there
• 39:02
  Speaker 1: that wants to program that to be like the you know,
• 39:08
  Speaker 1: the owner of the wickedest freaking pit bulls in the hood.
• 39:16
  Speaker 1: Who's to say, who knows, but believing in humanity. Humanity
• 39:24
  Speaker 1: has proven that although we've made mistakes, it's not in
• 39:28
  Speaker 1: our nature to build something that's gonna take us out.
• 39:32
  Speaker 1: The nuclear bomb, well, the nuclear bomb, that's a moment
• 39:45
  Speaker 1: where humans were at are worse. And I believe in
• 39:51
  Speaker 1: humanity too much to think that we're going to be
• 39:54
  Speaker 1: at our worst to that extreme. Again, you trip, you fall,
• 40:03
  Speaker 1: very rarely do you trip. You fall and fall and
• 40:06
  Speaker 1: fall and fall and fall. But it's not in our
• 40:09
  Speaker 1: balance and we don't. That's not how we are. We fall,
• 40:13
  Speaker 1: we scratch our knee, we break our leg, and then
• 40:15
  Speaker 1: from there you're super cautious. And I see, I think
• 40:20
  Speaker 1: human as an organism is more like that than it
• 40:23
  Speaker 1: is like I fall, I bust my face and then
• 40:26
  Speaker 1: I'm careless and I'm just gonna throw my face on
• 40:29
  Speaker 1: the floor. I don't think that's That's not what we
• 40:32
  Speaker 1: are as a as a society, as a community, as
• 40:35
  Speaker 1: a species, as an organism. So just being optimistic, like
• 40:41
  Speaker 1: I said, wearing that, or I could go down that
• 40:44
  Speaker 1: path creatively imaginatively, and we should all just let's all
• 40:50
  Speaker 1: just jump out the building.
• 40:51
  Speaker 2: Now, you gotta be sounds, the blackness, be optimistic. Got
• 40:56
  Speaker 2: to keep your head to this guy. You have to.
• 40:58
  Speaker 1: I just heard that. I just heard the of the
• 41:00
  Speaker 1: song the Lady. I love that song.
• 41:05
  Speaker 4: How were you musically? Where are you at musically right now?
• 41:11
  Speaker 1: We got a new song coming out for the Bad
• 41:14
  Speaker 1: Boys for soundtrack, So that's really really.
• 41:19
  Speaker 2: Cool to everybody?
• 41:22
  Speaker 1: Yep, everybody. Well, we got a bunch of everybody's so
• 41:28
  Speaker 1: black piece there's there's So if you say everybody and
• 41:33
  Speaker 1: don't mention Kim Hill, well then that's not true. That's
• 41:36
  Speaker 1: not good to Kim Hill. So when you say everybody,
• 41:39
  Speaker 1: you don't mention Macy Gray or Stero, that's not good
• 41:42
  Speaker 1: to our past either. We were we wreck with Mason
• 41:47
  Speaker 1: Gray on two albums.
• 41:49
  Speaker 2: I don't remember that.
• 41:50
  Speaker 1: Yeah, well those are the records that didn't really sell
• 41:52
  Speaker 1: that well. But we have a history before Fergie. So
• 41:57
  Speaker 1: when people tell me everybody, I'm like, watch everybody can
• 42:01
  Speaker 1: heal Everybody? And I love her. That's my big sister,
• 42:05
  Speaker 1: the Fergie. Everybody they're talking about the fur everybody, Ye
• 42:08
  Speaker 1: Fergie everybody. Well, then we have a current with Jay
• 42:12
  Speaker 1: Ray what hits and with twenty year olds that you
• 42:17
  Speaker 1: know fifteen year olds that when we did songs with Fergie,
• 42:21
  Speaker 1: that fifteen year old how it was two mm hmm
• 42:26
  Speaker 1: and eighteen They come in with their parents to our
• 42:29
  Speaker 1: shows singing Elite Mo and Mama Sita eighteen twenty year olds,
• 42:34
  Speaker 1: you know there was.
• 42:35
  Speaker 4: Five So Fergie's not on it.
• 42:37
  Speaker 2: The question I'm trying to it, Well, that's all we're
• 42:41
  Speaker 2: trying to you.
• 42:42
  Speaker 1: Forty yeah, yeah, forty five Shane. But to the twenty
• 42:46
  Speaker 1: year olds out there that like Jay Ray.
• 42:48
  Speaker 4: So Jay Rays on it, you got Okay, that's what's that?
• 42:51
  Speaker 1: Yeah?
• 42:51
  Speaker 6: But but yeah, so like like what happened after the
• 42:54
  Speaker 6: national anthem controversy, you didn't want anything to do it anymore.
• 42:57
  Speaker 1: You know like Bad Boys, Bad Boys, the Bad Boys
• 42:59
  Speaker 1: three song that we did Elite Moment forre you wasn't
• 43:01
  Speaker 1: on it, Okay, that was the national anthem.
• 43:04
  Speaker 6: Okay, yeah, no, the you remember you saw you saw
• 43:08
  Speaker 6: the national anthem controversy.
• 43:11
  Speaker 1: See, I'll be I'll be looking at the world optimistic, keep.
• 43:16
  Speaker 6: My head to the ladies.
• 43:26
  Speaker 4: Can they downloaded all that stuff?
• 43:28
  Speaker 1: Download f y I dot a I and if you
• 43:30
  Speaker 1: want to see Finn and Fiona in action, check me
• 43:33
  Speaker 1: out on Serious XM on My Serious XM radio show. Well,
• 43:37
  Speaker 1: my very the very first co host. That's a I
• 43:40
  Speaker 1: is Fiona on my radio show, and yeah, it's it's uh,
• 43:47
  Speaker 1: it's it's fresh.
• 43:48
  Speaker 6: It's a pleasure. Well, I've never not learned something from you. Literally,
• 43:52
  Speaker 6: every time you come and have a conversation with her,
• 43:55
  Speaker 6: I learned something. I walk away like, oh man, I
• 43:58
  Speaker 6: need to look into that.
• 43:59
  Speaker 1: Thanks, thank you, guys. It's always a pleasure to come here,
• 44:02
  Speaker 1: like and congratulations on the success. It just keeps keeps growing.
• 44:07
  Speaker 1: The subjects you talk about are like heavy subjects and uh,
• 44:12
  Speaker 1: light subjects. Funny subjects. You guys like cover the whole entire.
• 44:16
  Speaker 1: It's it's funny, it's fun, it's informative. It's like wow, damn,
• 44:20
  Speaker 1: like you really push you really pushed guests. Your fearlessness
• 44:25
  Speaker 1: is like wow, that's dope.
• 44:27
  Speaker 2: Thank you sir, thank you.
• 44:29
  Speaker 1: Well it's will I am.
• 44:30
  Speaker 4: It's the Breakfast Club. Good morning, wake that ass up
• 44:34
  Speaker 4: in the morning.
• 44:35
  Speaker 2: The Breakfast Club