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Apple Hit with EU Fine · Bloomberg Intelligence

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Speaker 3: Looking about Apple here, stocks down two point seven percent.
0:28
Speaker 3: The European Union, who has always been tough on US
0:32
Speaker 3: technology I think twenty twenty five years ago with Microsoft
0:34
Speaker 3: in this scene called Windows, and they're still beating up
0:38
Speaker 3: on US tech companies. So another fine come along. Let's
0:42
Speaker 3: check in with anarag Rana. He covers all things technology
0:45
Speaker 3: for Bloomberg Intelligence. So put into context, what's what happened
0:50
Speaker 3: to Apple here from a regulatory standpoint and what it
0:52
Speaker 3: means for them.
0:53
Speaker 4: Just explain kind of what the fine is for.
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Speaker 5: Yeah, this has been, you know, going on for a while,
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Speaker 5: and tbxtions were that they were going to be fined
1:01
Speaker 5: about five hundred million euros. Now the more important thing
1:05
Speaker 5: is on March seven, the Digital Market Act goes live,
1:09
Speaker 5: and Apple's already made concessions in terms of all the
1:12
Speaker 5: things the European Union wants to do. I think the
1:15
Speaker 5: surprise to our analyst Hamblin, who is based in Europe,
1:18
Speaker 5: was the size of it. It's not you know, when
1:20
Speaker 5: we rumored was five hundred when it came to around
1:22
Speaker 5: one point eight billion. One thing is for clear at
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Speaker 5: this point that Apple is not going to just you know,
1:27
Speaker 5: pay this fine and walk away with the DMA or
1:31
Speaker 5: the Digital Markets Act. It's possible that the app store,
1:34
Speaker 5: you know, policies are going to be under more scrutiny,
1:37
Speaker 5: and I think that's what's driving the stock down. I mean,
1:40
Speaker 5: two billion dollars is not that big of a deal
1:42
Speaker 5: for Apple, but I think the twenty one billion dollars
1:46
Speaker 5: of revenue that the app store generates, I think that's
1:49
Speaker 5: that's a bigger issue.
1:51
Speaker 6: So here's my thing, though, is that the stock is
1:54
Speaker 6: down over ten percent since it's January high for this year.
1:57
Speaker 6: So clearly something else was going on, just not this fine,
2:00
Speaker 6: and that's just adding fuel to the fire here. What
2:03
Speaker 6: is happening to Apple?
2:05
Speaker 5: Yeah, I mean if you think about it, all the
2:07
Speaker 5: bad things that can happen to Apple are happening all
2:09
Speaker 5: at once. I mean, frankly speaking, it right now when
2:12
Speaker 5: you look at their iPhone sales that are slowing down,
2:15
Speaker 5: China competition, a lot of supply chain dependency on China.
2:21
Speaker 5: On top of that app store stuff. They are not
2:23
Speaker 5: a player in Jenei right now. So a lot of
2:26
Speaker 5: good things happening for Apple, and it's going to be
2:30
Speaker 5: a slow year for them. It's probably going to be
2:31
Speaker 5: another slow year next year. So Apple has to come
2:34
Speaker 5: out but in June when they do their developer conference
2:38
Speaker 5: and put some fresh I would say air or life
2:41
Speaker 5: into the company by saying that, Okay, we do have
2:44
Speaker 5: a generative AI strategy and this is how we are
2:47
Speaker 5: thinking of achieving it. I think other than that, it's
2:49
Speaker 5: going to be a be a difficult year for them.
2:54
Speaker 3: I think this June conference that you reference on Oki
2:56
Speaker 3: it's usually the conference maybe where they introduce a new
2:58
Speaker 3: cool product or something like that. But I feels like
3:01
Speaker 3: the pressure on the company this year is as great
3:04
Speaker 3: as I can ever remember it because they're either going
3:06
Speaker 3: to come out with something really cool as it relates
3:08
Speaker 3: to AI, or they're just going to come out and
3:10
Speaker 3: say no, we're not rushing into it, in which case
3:12
Speaker 3: the stock is going to presumably, you know, be challenging.
3:16
Speaker 4: What do you think is going to happen?
3:18
Speaker 5: Yeah, the June conference is going to be a lot
3:19
Speaker 5: of the software updates the Worldwide Developer Conference, and you
3:23
Speaker 5: do give you know, they are bound to give a
3:25
Speaker 5: lot more detail about what are the five, six, seven
3:28
Speaker 5: things that they're going to be updating on their software
3:30
Speaker 5: that has more Generator VII capabilities. I think it's not
3:34
Speaker 5: going to be that big just because these things take time.
3:37
Speaker 5: We just heard from Mark them and you know, not
3:39
Speaker 5: that long ago that they're shutting down the car and
3:42
Speaker 5: allocating that headcount to generative AI. But you know, it's
3:47
Speaker 5: what are you going to do in three months? I
3:48
Speaker 5: think it's going to be a little longer than that.
3:50
Speaker 5: They're going to make some promises, but frankly speaking, I mean,
3:53
Speaker 5: they do have a distribution network, so you know, maybe
3:58
Speaker 5: maybe we should give them some some points at this
4:01
Speaker 5: point to see if they can pull this off. It's
4:03
Speaker 5: going to lead to an iPhone refash cycle that we
4:05
Speaker 5: haven't seen before, but I think, but you know, I'm
4:08
Speaker 5: not betting on anything big at this point.
4:12
Speaker 6: So go to the AI strategy. If you had to
4:14
Speaker 6: articulate what Apple's AI strategy is, what would you say.
4:18
Speaker 5: Yeah, so let's think about all the different things that
4:21
Speaker 5: we do on our phone. So for example, City by itself,
4:24
Speaker 5: you know, could be far more sophisticated than what it
4:27
Speaker 5: is today. Imagine what all different things you can do
4:30
Speaker 5: with open AI or check GPT. Imagine if you could
4:33
Speaker 5: do that just on the phone itself without having to
4:37
Speaker 5: go to a third party and do it now. Apple
4:40
Speaker 5: has the whereworth in terms of dollars to do it,
4:43
Speaker 5: whether they actually have the capacity from a technology point
4:46
Speaker 5: of view, If they can embed a lot of those
4:48
Speaker 5: features like you asking a question to your phone and
4:50
Speaker 5: you getting the answer right there, they have the distribution
4:54
Speaker 5: network of over one billion connected devices, then what happens
4:57
Speaker 5: is you don't really need to go into an app
4:59
Speaker 5: to do a lot of that stuff. We also heard
5:01
Speaker 5: that they're going to develop a software that can write
5:03
Speaker 5: itself a little quicker. That's not going to help a
5:06
Speaker 5: consumer as much that they're going to help more developers.
5:08
Speaker 5: But you know, the thing that we want to see
5:10
Speaker 5: is what can be done on the phone itself that
5:13
Speaker 5: would force me not to tap into an app and
5:16
Speaker 5: I can get those answers immediately, And.
5:20
Speaker 3: That to me seems like something that Apple can absolutely do.
5:24
Speaker 3: If I were a long term investor, should I be
5:27
Speaker 3: buying the stock here. I mean, Ai, I'm going to
5:32
Speaker 3: get a monster bumped.
5:33
Speaker 4: In the multiple.
5:35
Speaker 5: I am absolutely on the same camp with you, you
5:39
Speaker 5: know at this point, Paul. But frankly, they have to
5:41
Speaker 5: prove it. Right now, things haven't worked for them in
5:44
Speaker 5: terms of, you know, the being on the edge of
5:47
Speaker 5: any technology at this point, so I think they have
5:50
Speaker 5: to prove it in order to get it. Remember, they
5:52
Speaker 5: have over eight hundred million phones that people have in
5:55
Speaker 5: their hand in terms of the cellular connection, so at
6:00
Speaker 5: any given year they sell roughly about two hundred and
6:02
Speaker 5: twenty million phones. If this thing really takes off, there
6:06
Speaker 5: are over forty percent of phones that are iPhone twelve
6:09
Speaker 5: and below or that is very low processing power, low memory,
6:13
Speaker 5: a lot that gets upgraded and that leads to a
6:16
Speaker 5: next big you know, cycle and iPhone. I think that
6:19
Speaker 5: can be done, but I think they'll have to prove it. Remember,
6:22
Speaker 5: for a company that grows that two to five percent,
6:25
Speaker 5: they're still trading at like twenty five times earning, So
6:27
Speaker 5: it's not it's not cheap from even you know, that
6:30
Speaker 5: point of view.
6:32
Speaker 6: But Anuraga, I thought that Apple wanted to eventually become
6:35
Speaker 6: primarily services revenue, Like, they didn't want to be dependent
6:38
Speaker 6: on hardware. They wanted to have people pay them stuff
6:41
Speaker 6: on like a monthly or yearly basis. How would that
6:43
Speaker 6: AI integration do that?
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Speaker 5: So the services revenue is an important piece, but it's
6:50
Speaker 5: nothing without the hardware. You have to have the hardware
6:53
Speaker 5: to get there.
6:53
Speaker 6: Right, But don't they want it to be Don't they
6:55
Speaker 6: want it to be about that and not the hardware.
6:57
Speaker 5: Yeah? Yeah, But at the same time, you know, if
7:00
Speaker 5: you are able to drive these higher value services, your
7:03
Speaker 5: ecosystem becomes stronger. People who have these older iPhones, let's
7:06
Speaker 5: say four years old, five years old, six years old,
7:09
Speaker 5: you're not going to be able to run those operations
7:11
Speaker 5: on it because it's going to be either too slow
7:13
Speaker 5: or your battery is going to die around very quickly.
7:15
Speaker 5: So you need to upgrade the hardware. Now I just
7:17
Speaker 5: talked about iPhones, but you can extend that to iPads,
7:20
Speaker 5: to watches, to any other product that they have. It
7:24
Speaker 5: is the critical portion of it. Anybody who controls a
7:27
Speaker 5: large portion of these large language models will have an
7:31
Speaker 5: edge down the road. So they really want to be
7:32
Speaker 5: a player in that market. You don't want to be
7:35
Speaker 5: left behind.
7:38
Speaker 3: So let's just go back to where we started here,
7:40
Speaker 3: which is some of the regulatory issues. Is this like
7:43
Speaker 3: twenty five years ago when the EU came after Microsoft
7:47
Speaker 3: for Windows and bundling all that stuff and they just
7:49
Speaker 3: kind of write some checks and you go away. Or
7:51
Speaker 3: is this something more of a challenge, more of a
7:54
Speaker 3: headwind for Apple do you think?
7:57
Speaker 5: I think it's more of a headline risk for the
7:59
Speaker 5: next several years. A lot of these things is not
8:01
Speaker 5: going to be just you know, they can't go out
8:03
Speaker 5: and tell them to reduce these fees. But having said that,
8:06
Speaker 5: there is going to be an overhang of the stock.
8:08
Speaker 5: But we think that they can do so much. They
8:10
Speaker 5: can really boost the advertising revenue that they really haven't
8:13
Speaker 5: played that lever. They can take iCloud pricing up, they
8:16
Speaker 5: can do Apple Care. I mean, Apple has a lot
8:18
Speaker 5: of levers to pull, and it's going to be interesting
8:22
Speaker 5: to see how different governments around the world approach this
8:25
Speaker 5: issue because Apple, I mean, they wrote a phenomenal piece
8:28
Speaker 5: today about fighting this particular you know, they're fine. I
8:33
Speaker 5: think if you read that the entire piece as it
8:36
Speaker 5: relates to Spotify, I think they make a very good
8:38
Speaker 5: case of why their ecosystem is important for developers and
8:42
Speaker 5: why they should have you know, at least some you know,
8:45
Speaker 5: you know, skin in the game in terms of what
8:47
Speaker 5: they bring to the party.
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Speaker 6: All Right, Honor Rock, thanks so much. We super appreciate it.
8:53
Speaker 6: The best person to go to when it comes to Apple.
8:54
Speaker 6: I should I have an iPhone fourteen?
8:56
Speaker 3: I forgot?
8:57
Speaker 4: Really?
8:57
Speaker 6: Yeah? Yeah, yeah, I eleven switched my switch from Verizon
9:01
Speaker 6: to T Mobile. I got a free phone.
9:02
Speaker 4: And how's upen, Oh, it's okay.
9:05
Speaker 6: The Wi Fi is a lot better, really, yes, but
9:08
Speaker 6: sometimes about the Wi Fi, it's a little Spottierka, It's interesting.
9:12
Speaker 6: It's interesting little trade off there. On Magrana joining us
9:14
Speaker 6: Bloomberg Intelligence a senior technology analyst.
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Speaker 3: It's Alex Steels pulsewhen you We're live here at the
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Speaker 3: New Jersey Institute of Technology here in Newark, New Jersey,
9:42
Speaker 3: and we're also on that YouTube things ahead over to
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Speaker 3: Bloomberg Podcast of all things.
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Speaker 4: I'll tell you know you've made it.
9:51
Speaker 3: When Donna Russo was in the house bringing it here,
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Speaker 3: she's kind of running everything over here. So we appreciate
9:57
Speaker 3: don having us over here. Let's talk AI. Michael Johnson
10:00
Speaker 3: joint Is. He's the president of New Jersey Innovation Institute. Michael,
10:05
Speaker 3: thanks so much for joining us here. What is the
10:07
Speaker 3: New Jersey Innovation Institute.
10:09
Speaker 7: It's a great question. So in the US, we have
10:12
Speaker 7: lots of research universities and there's lots of smart people,
10:15
Speaker 7: lots of great resources, but there's this fundamental problem in academia,
10:19
Speaker 7: which is it's tough for the outside world actually leverage
10:21
Speaker 7: those resources. So for governmental organizations, for industry, they want
10:25
Speaker 7: access to the cutting edge of AI, for example, but
10:27
Speaker 7: it's tough for them to actually make those connections and
10:29
Speaker 7: interact with faculty. So NJI is an organization. It's a
10:33
Speaker 7: five oh one C three wholly owned by NNGT, and
10:36
Speaker 7: the idea is that we are a standalone corporation that's
10:38
Speaker 7: a conduit between the outside world and NGT. So we
10:41
Speaker 7: make those facilitations, we create unique business models to work
10:45
Speaker 7: with faculty, and we're a quick moving organization, unlike academia,
10:49
Speaker 7: which is you know, tends to be slower and more
10:50
Speaker 7: difficult to work with. So we're that conduit between them
10:53
Speaker 7: the outside world and roughly have about one hundred and
10:55
Speaker 7: twenty folks out of organization and we're focused on that can.
10:58
Speaker 6: Just say it's really cool his three year old son,
10:59
Speaker 6: is he? I mean, what three year old is going
11:02
Speaker 6: to come and talk about AI? I feel like that
11:04
Speaker 6: just says it all at the end of the day, right,
11:06
Speaker 6: that is the future. So am I a company that
11:09
Speaker 6: goes to you and then you pair me up with
11:11
Speaker 6: something or is it sort of the technology that you're evolving,
11:13
Speaker 6: and then you go pitch it to companies. How does
11:15
Speaker 6: that work?
11:15
Speaker 7: It's a bit of inside out and outside in. So
11:17
Speaker 7: we can go to corporations and try and find out
11:19
Speaker 7: what their problems are, what their pain points are, and
11:21
Speaker 7: then go and find faculty you can help out. Or
11:23
Speaker 7: we might have a few faculty that have a very
11:25
Speaker 7: specific problem. They need access to software, they need to
11:27
Speaker 7: access the resources, and we go externally and find a
11:29
Speaker 7: way to work with corporations on that, but it's pairing
11:31
Speaker 7: the two with each other. And faculty are really smart,
11:34
Speaker 7: they're really focused on their research, but they don't always
11:36
Speaker 7: have the mind to go out and actually execute on
11:38
Speaker 7: consultant type projects for industry. So we help form that
11:41
Speaker 7: framework and along the way, we're trying to help with
11:43
Speaker 7: tech transfers. So getting technology out of the university into
11:46
Speaker 7: products and services was always a pain point, and also
11:49
Speaker 7: just generally accelerating innovation and also helping upskilled workers.
11:53
Speaker 3: You know, over the last several quarters, Bloomberg does this analysis.
11:56
Speaker 3: It shows you know, what are companies talking about on
11:59
Speaker 3: their totally conference calls, And for the last several quarters,
12:02
Speaker 3: every single company in Y S and P five hundred
12:04
Speaker 3: has talked about AI, with the exception of Apple last
12:07
Speaker 3: quarter and I mentioned AI, which is interesting.
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Speaker 6: What a company that's not doing? Now?
12:11
Speaker 3: Yeah, what are companies most commonly asking you for help with?
12:17
Speaker 1: Oh?
12:17
Speaker 7: Man, that goes all over the place. It depends in
12:19
Speaker 7: the companies. We have some small mom and pop businesses
12:21
Speaker 7: that just want help with trying to move towards technology.
12:24
Speaker 4: Towards computers.
12:25
Speaker 7: We have other companies, for example, that want to be
12:27
Speaker 7: the bleeding edge of some sword and fields. So for example,
12:30
Speaker 7: it might be life sciences, it might be AI for example,
12:32
Speaker 7: and they're asking us to help improve something that they're
12:35
Speaker 7: already doing, or it's a very specific project they're pushing
12:37
Speaker 7: us to find faculty to help out with. So it
12:39
Speaker 7: kind of depends. We have other folks. For example, Picatinny
12:42
Speaker 7: Arsenal and Department of Defense are looking for just workers,
12:45
Speaker 7: so helping us upscale workers for advanced manufacturing and all
12:48
Speaker 7: sorts of different programs they need help finding talent for
12:50
Speaker 7: so we're trying to help that with that as well.
12:52
Speaker 6: So JPM, we're going to know if you saw this,
12:54
Speaker 6: they had a great piece out that said that some
12:56
Speaker 6: of its corporate customers are slashing manual work by almost
12:59
Speaker 6: ninety percent with its cash flow management tool that runs
13:03
Speaker 6: on AI. And that's the fear, right that we're going
13:06
Speaker 6: to use AI and replace all the workers and those
13:08
Speaker 6: workers don't have any jobs. Is there any truth to that?
13:11
Speaker 7: It's a great question. So whenever you have technologies, they
13:14
Speaker 7: are disruptive, there are going to be jobs certainly that
13:16
Speaker 7: are going to go away. But if you look back
13:18
Speaker 7: to when Excel first came out, or when computers first
13:20
Speaker 7: came out. You look at accounting as a great use case.
13:23
Speaker 7: Accountants didn't go away because we were going from a
13:25
Speaker 7: ledger that was literally on paper to a computer based system.
13:27
Speaker 7: We found new questions to answer, new ways that we
13:29
Speaker 7: could look at our accounting and finances.
13:31
Speaker 4: So I think the.
13:32
Speaker 7: Jobs are going to change, But the overall number of
13:34
Speaker 7: jobs in that net, I don't know if it will
13:36
Speaker 7: actually reduce. It might increase in some cases. But we're
13:39
Speaker 7: going to answer different questions. We're going to do things
13:41
Speaker 7: much more quickly than we did in the past, for sure.
13:43
Speaker 3: You know, I guess my I guess my lack of
13:46
Speaker 3: knowledge of full appreciation of AIS is. I'm just not
13:49
Speaker 3: sure if it's something completely new or is it just
13:51
Speaker 3: the next next iteration of what the smart people NJIT
13:55
Speaker 3: typically do. Is I'm just not sure what's new about
13:59
Speaker 3: it other than man, everybody's talking about it. And it
14:02
Speaker 3: was a theme for Why the Stock One of the
14:05
Speaker 3: themes that drove the stock market in twenty twenty three
14:07
Speaker 3: was a concept of AI and the average trader across
14:10
Speaker 3: the river in New York has no idea what AI is,
14:13
Speaker 3: but he's buying stocks because he thinks they're an AI play.
14:15
Speaker 7: It's been around for decades, right, but we have a
14:18
Speaker 7: couple of technology that came out in the last two
14:19
Speaker 7: years that have really transformed the way we see AI.
14:21
Speaker 7: And while we're talking about we were talking about it
14:23
Speaker 7: last night at my family's Sunday dinner, and the reason
14:26
Speaker 7: is because now it's accessible. So, for example, two years ago,
14:29
Speaker 7: if I go into Google and I type how do
14:30
Speaker 7: I make chicken Palm? I got all these ads, I
14:33
Speaker 7: get all these things that tell me about chicken parm.
14:35
Speaker 7: I go in to chat ept and I typed that
14:36
Speaker 7: for example, and I got a perfect recipe on how
14:38
Speaker 7: to actually make that, so it becomes very accessible to anyone.
14:41
Speaker 7: And I think that go to market strategy. The open
14:43
Speaker 7: I had of making accessible is what really changed the game.
14:46
Speaker 7: And also the same time computing power is exponentially increasing,
14:49
Speaker 7: it's more accessible. We're now able to use it everywhere
14:51
Speaker 7: from making chicken palm to try and do research.
14:54
Speaker 6: So what kind of cool stuff are you guys working
14:56
Speaker 6: on right now? Like, what were you most excited about?
14:58
Speaker 2: For us?
14:59
Speaker 7: As ANGI, what we're very focused on is trying to
15:01
Speaker 7: get things out of the university into the real world.
15:03
Speaker 7: And one specific project that we're working on is actually
15:06
Speaker 7: on law enforcement and body cams. So bodycams is there
15:09
Speaker 7: a sensor that generates a huge amount of data, and
15:12
Speaker 7: from those those data sets, we're usually looking at them
15:15
Speaker 7: after the fact, so after something bad happens, we're trying
15:17
Speaker 7: to review that situation. What we're trying to do is
15:20
Speaker 7: can we look at that data and predict something bad
15:22
Speaker 7: is going to happen before it happens. So if we
15:24
Speaker 7: see a pattern between some behaviors, running back time for a.
15:28
Speaker 6: Second, so you have a buye So you're tracking behavior
15:30
Speaker 6: to then model behavior later.
15:33
Speaker 7: Yes, So for example, let's say we see an officer
15:35
Speaker 7: is running more frequently, they're yelling more frequently. That is
15:38
Speaker 7: probably correlated to some behavior outcomes such as excessive use
15:41
Speaker 7: of force. So for example, we might identify this officer
15:44
Speaker 7: as at a much higher likelihood of excessive use of
15:46
Speaker 7: force in the future. Let's intervene and get them training
15:48
Speaker 7: before something bad happens. So we're trying to build that
15:50
Speaker 7: a software we can actually put onto the hardware and
15:53
Speaker 7: help with law enforcement and help with de escalating situations.
15:56
Speaker 6: Wow, that's really cool. What other stuff like, what are
16:00
Speaker 6: the thing you excited about Oh man, there's so many
16:03
Speaker 6: we'll think your second best.
16:04
Speaker 7: My second best would definitely be in the drone space.
16:07
Speaker 7: So drones are another sensor. We're collecting huge amounts of
16:09
Speaker 7: imagery data, and today a lot of that work is
16:11
Speaker 7: actually a person looking at videos, scrolling through video like
16:14
Speaker 7: you would from a VHS tape, and we're using computer
16:16
Speaker 7: vision and AI to actually analyze that data and try
16:19
Speaker 7: to predict what's happening, try to classify certain imagery and
16:22
Speaker 7: answer very specific questions like is a power line going
16:24
Speaker 7: to fail based upon a single picture from a simple drone?
16:27
Speaker 6: Oh, now that could be really helpful depually all the
16:29
Speaker 6: wildfires and stuff that we've had. And then as all
16:31
Speaker 6: the utilities are kind of grappling with like old infrastructure
16:34
Speaker 6: that is not easy to replace, kind of how you
16:35
Speaker 6: manage that? Is it expensive for these companies to use this?
16:41
Speaker 7: Usually the bottleneck today is data generation and data annotation
16:44
Speaker 7: because there's lots of data, but we have to annotate
16:46
Speaker 7: the data to be actually able to use it. So,
16:48
Speaker 7: for example, with the body cams, we have to know
16:50
Speaker 7: what those events are that we're trying to predict and
16:52
Speaker 7: actually classifying them ahead of time. So that's the real
16:54
Speaker 7: the bottleneck for it in a lot of cases.
16:57
Speaker 6: All right, Michael, thanks so much. It was really great.
16:59
Speaker 6: This is really fun. Get your perspective. Is your son
17:01
Speaker 6: gonna stay.
17:01
Speaker 7: Or is he gonna he's don't listen all day?
17:03
Speaker 6: Well, awesome, we like that future generation. Michael Johnson, president
17:07
Speaker 6: of New Jersey Innovation Institute, and Ji, I thanks very much.
17:11
Speaker 6: It was really great to get that perspective. That's really interesting.
17:14
Speaker 6: I think the bodycam situation too, like it's not a
17:19
Speaker 6: profiling and profiling thing. It's like you're gonna get the
17:21
Speaker 6: help that you need down the road, which I think
17:23
Speaker 6: is really cool. And it's good to hear these actual
17:25
Speaker 6: use cases because it's easy to just say AI is cool,
17:27
Speaker 6: is going to do stuff, But to get an actual
17:29
Speaker 6: use case that you can do is quite interesting.
17:32
Speaker 4: Yeah. Absolutely.
17:35
Speaker 2: You're listening to the Bloomberg Intelligence Podcast. Catch us live
17:39
Speaker 2: weekdays at ten am Eastern on Apple card Playing and
17:42
Speaker 2: broid Otto with the Bloomberg Business app. Listen on demand
17:45
Speaker 2: wherever you get your podcasts, or watch us live on YouTube.
17:50
Speaker 6: Paul and I are here broadcasting live from the campus
17:52
Speaker 6: of the New Jersey Institute of Technology n J. It
17:55
Speaker 6: where we're talking about all things AI and sort of
17:57
Speaker 6: how you create the thing and then move it outside
18:00
Speaker 6: and bring it to companies or businesses that need it
18:02
Speaker 6: and bridging that gap between those two and one person
18:05
Speaker 6: in part very much responsible for that here in New
18:07
Speaker 6: Jersey is Beth Simone Novak. She is Chief AI Strategist
18:12
Speaker 6: of the State of New Jersey. What a cool title, Beth,
18:16
Speaker 6: What does that mean?
18:17
Speaker 8: First in the country?
18:19
Speaker 4: What does it mean?
18:19
Speaker 3: So?
18:19
Speaker 6: I mean, are you like, hey, business, you should use that,
18:22
Speaker 6: or hey, government, let's use this?
18:23
Speaker 8: All of the above?
18:24
Speaker 6: Okay.
18:24
Speaker 8: So Governor Murphy has said very loud and clear, we
18:27
Speaker 8: have to do better when it comes to technology in
18:29
Speaker 8: terms of embracing the use of technology to grow the economy,
18:33
Speaker 8: to grow jobs in the state, but also to improve
18:35
Speaker 8: how government works. So my job is to work on
18:39
Speaker 8: all of the above and to see what we can
18:40
Speaker 8: do as government to make that easier, to make that better,
18:43
Speaker 8: and to embrace the responsible and ethical use of AI
18:46
Speaker 8: to ensure that we're doing right by our residents.
18:48
Speaker 3: So what are some of the applications that you know,
18:52
Speaker 3: the governor and the Governor's office thinks AI can do
18:55
Speaker 3: over the next several years.
18:56
Speaker 4: Where will the residents of your New Jersey see it?
18:59
Speaker 4: Do you think?
18:59
Speaker 8: So this is not a several years from now. The
19:02
Speaker 8: future is already here. And we've been using AI for
19:04
Speaker 8: quite some time, and Generative AI since the very beginning,
19:07
Speaker 8: so in many ways that you don't even see or
19:09
Speaker 8: know about. So, for example, if you're getting a letter
19:11
Speaker 8: from the State of New Jersey about let's say your
19:13
Speaker 8: unemployment benefits, you're getting a letter that has been simplified,
19:17
Speaker 8: that has been written in plain English, that's been written,
19:20
Speaker 8: we hope, more clearly than it would have been before
19:22
Speaker 8: because Generative AI can help us to do a first draft.
19:25
Speaker 8: If you're calling up about your anchor tax relief that
19:28
Speaker 8: the State of New Jersey is giving out to residents,
19:31
Speaker 8: you are hopefully getting your call resolved faster because you
19:34
Speaker 8: get a menu option that's we've written with the help
19:37
Speaker 8: of AI. Because voice to text, our call center operators
19:40
Speaker 8: know people are calling in asking the following kinds of questions,
19:43
Speaker 8: we should write these menu options and these instructions and
19:46
Speaker 8: answers so people can get that information faster. When you're
19:50
Speaker 8: going out, for example, and typing in on a website
19:53
Speaker 8: and telling us comments of how we can do something
19:55
Speaker 8: better on a website like business dot NJ dot gov,
19:58
Speaker 8: where you can go to start and run grow your
20:00
Speaker 8: business everything you need from one place. We're taking the
20:03
Speaker 8: comments we're getting from citizens about what they need, about
20:05
Speaker 8: what they want, using AI to help us summarize those
20:08
Speaker 8: those comments, synthesize them, and turn that into the information
20:12
Speaker 8: that people want and need front and center. So the
20:15
Speaker 8: goal is government that's more responsive, more informative, and providing
20:20
Speaker 8: services twenty four to seven that are responsive to what
20:22
Speaker 8: people actually want and need.
20:23
Speaker 6: That's a pretty good pitch. You were also the chief
20:26
Speaker 6: of innovation, right, h Jersey.
20:28
Speaker 8: I was for many years the chief innovation officer. Yes.
20:31
Speaker 6: Did the chief innovation officer become the AI strategist or
20:34
Speaker 6: is there also an innovation officer? And I guess I'm
20:36
Speaker 6: trying to understand, like is the innovation thing now AI
20:39
Speaker 6: or can there be other stuff?
20:41
Speaker 8: There is still other stuff. We have a wonderful new
20:43
Speaker 8: Chief Innovation Officer, Dave Cole, has taken over that title
20:49
Speaker 8: and is leading our efforts to use technology to improve
20:51
Speaker 8: how we bring services to residents. So projects like business
20:55
Speaker 8: dot J dot gov to take the business one for example,
20:58
Speaker 8: or other digitization of residence services so that instead of
21:01
Speaker 8: having to go to a government office, you know, between
21:04
Speaker 8: nine and five, you can come to a website, you
21:06
Speaker 8: can use your mobile phone.
21:07
Speaker 6: Oh my gosh, that'd be amazing.
21:08
Speaker 8: Forransact with government twenty four to seven. That's work that's
21:13
Speaker 8: been underway for a long time, and that doesn't just
21:15
Speaker 8: depend on AI. That is about again, clearer instructions, planer
21:18
Speaker 8: English things available online, giving you the information front and
21:22
Speaker 8: center that you want and need in the way that
21:24
Speaker 8: people have become accustomed to from the best businesses. We
21:27
Speaker 8: think that government should serve citizens in much the same way,
21:30
Speaker 8: except in the public interest.
21:31
Speaker 3: Well, New Jersey's had a long history of technological innovation.
21:35
Speaker 3: I think of telecommunications with Bellcore and Bell Labs supporting
21:38
Speaker 3: eighteen teen Verizon.
21:39
Speaker 4: I think about some of.
21:40
Speaker 3: The biotech and you know, pharmaceutical companies like Johnson and
21:43
Speaker 3: Johnson based here in New Jersey. I'm wondering, is there
21:46
Speaker 3: support for the young NJ grads that are in a
21:51
Speaker 3: garage somewhere in Jersey City coming up with the next
21:53
Speaker 3: AI type thing.
21:54
Speaker 4: How do we support those people?
21:56
Speaker 8: Absolutely so, there are a whole number and rain of
22:00
Speaker 8: investments that are out there to support people starting new businesses.
22:04
Speaker 8: That's what my colleagues at EDA work on in particular,
22:07
Speaker 8: is ensuring that we're providing those kinds of incentives for
22:11
Speaker 8: people who want to start their business in New Jersey
22:13
Speaker 8: and grow their business in New Jersey. That's particularly why
22:15
Speaker 8: the government is here to help support those businesses going
22:19
Speaker 8: out and in particular now to look at how we
22:21
Speaker 8: can support new AI businesses or existing businesses who are
22:25
Speaker 8: asking how we can turn around and use AI to
22:28
Speaker 8: improve what we do. It's a question we've been answering
22:31
Speaker 8: for a long time. Before we called it AI, we
22:33
Speaker 8: called it big data, right, So the more the people
22:36
Speaker 8: we're using a lot of businesses have asked themselves, how
22:39
Speaker 8: can I go out and start using data to measure
22:42
Speaker 8: what's working, to measure what customers want, and again to
22:44
Speaker 8: deliver new kinds of services across a range of industries.
22:48
Speaker 8: It's why we've been starting new partnerships, such as with
22:52
Speaker 8: Princeton around this new AI hub that's been set up
22:55
Speaker 8: so that we can connect some of that tremendous innovation
22:58
Speaker 8: that's coming out of universities like NGIT, like Rutgers, like Princeton.
23:03
Speaker 8: We're of course known in this state for having the
23:04
Speaker 8: best universities and the best education system in the country,
23:07
Speaker 8: and we want to connect that back to how we're
23:10
Speaker 8: growing the economy and growing jobs here in the state.
23:12
Speaker 6: What's the hardest part of your job.
23:15
Speaker 8: There's only twenty four hours in the day, and there's
23:17
Speaker 8: a very, very lot to do, both on the public
23:19
Speaker 8: sector side and on the private sector side.
23:21
Speaker 6: Do you feel like it's awareness, is it implementation? Is
23:25
Speaker 6: it finding the cool technology? Is it having too many
23:28
Speaker 6: problems to solve?
23:30
Speaker 8: Well, the cool technology is very much there, and I
23:34
Speaker 8: think what we're trying to do now is to ensure that,
23:37
Speaker 8: especially in government, we are building not just awareness but
23:41
Speaker 8: actually use of these tools to improve how we serve
23:44
Speaker 8: residents across a whole range of domains and across agencies.
23:48
Speaker 3: So we know that Governor Murphy feels that AI is
23:51
Speaker 3: important and administration feels that AI is important, as the
23:54
Speaker 3: rest of the government within the state share that as well.
23:57
Speaker 3: Or is it require kind of a promotional pitch for
24:00
Speaker 3: the office.
24:01
Speaker 8: Well, you know, the governor is the salesman in chief
24:05
Speaker 8: for the State of New Jersey, and of course, setting
24:07
Speaker 8: this message about the importance of AI, the ways we
24:10
Speaker 8: should be embracing these tools going out early. We're one
24:13
Speaker 8: of the first states to actually put out a policy
24:15
Speaker 8: that says we should responsibly and ethically use AI to
24:20
Speaker 8: better serve residents. And one of the things we're doing
24:23
Speaker 8: is promoting upskilling and learning across the whole of public sector.
24:26
Speaker 8: It's not enough to have just the governor supporting AI,
24:29
Speaker 8: to have a chief AI strategist. We need every public
24:32
Speaker 8: servant out there to be asking themselves, how can I
24:34
Speaker 8: use these powerful new tools, again ethically and responsibly safeguarding
24:39
Speaker 8: privacy and security and people's data. But how can I
24:42
Speaker 8: go out and use these tools to write that better
24:44
Speaker 8: first draft of the email, to write that clearer website,
24:47
Speaker 8: to be able to write a better policy. This is
24:49
Speaker 8: the next generation, if you will, of word processor. To
24:53
Speaker 8: put it very simply, but that we should be using
24:56
Speaker 8: to be able to serve residence better and we need
24:58
Speaker 8: everybody to know how to do that.
25:00
Speaker 6: Bet, thanks so much. We really appreciate your time. We
25:02
Speaker 6: know you're quite busy. Beth Simona and Novak, chief AI
25:04
Speaker 6: strategist from the State of New Jersey. That was actually
25:07
Speaker 6: really helpful. Okay, so this is like the next version
25:09
Speaker 6: of the stuff that we do normally. Like that really
25:11
Speaker 6: helps me because I think for people like you and me,
25:13
Speaker 6: it's hard to understand the practical applications. It just becomes
25:16
Speaker 6: like AI. Yep, whatever that winds up meaning exactly.
25:21
Speaker 2: You're listening to the Bloomberg Intelligence Podcast. Catch us live
25:25
Speaker 2: weekdays at ten am Eastern on applecar Play and Android
25:29
Speaker 2: Auto with the Bloomberg Business. You can also listen live
25:32
Speaker 2: on Amazon Alexa from our flagship New York station Just
25:35
Speaker 2: say Alexa play Bloomberg eleven thirty.
25:39
Speaker 6: But I am learning a lot of cool stuff about AI,
25:41
Speaker 6: particularly the implementation. It's not just this thing that we
25:44
Speaker 6: talk about, like it can actually be used for certain
25:46
Speaker 6: areas and apparently it can also be used in sports.
25:50
Speaker 6: AI in the role of sports. So here to help
25:53
Speaker 6: us break that down on what that all means is
25:55
Speaker 6: Evana Scherich. She is Zealous Analytics senior product iientist also
26:01
Speaker 6: former basketball player. Right basketball player, You also know all
26:05
Speaker 6: the things about technology. How do you use AI in sports?
26:09
Speaker 1: Yeah, So this field had expanded in last maybe ten
26:14
Speaker 1: years of a lot in other sports. Even before that,
26:18
Speaker 1: it was in baseball that was one of the first sports.
26:20
Speaker 1: If you've seen Moneyball.
26:21
Speaker 6: That's really yes, yeah, okay, I like me the moneyball, okay.
26:25
Speaker 6: And so it's basically like how to position, like what
26:28
Speaker 6: players to put where combinations? Is it that kind of stuff?
26:30
Speaker 1: Correct? Correct? So so player evaluation in game decision strategy,
26:35
Speaker 1: that's sort of sort of things.
26:36
Speaker 8: Yeah.
26:37
Speaker 3: So again, played for your starter for NJIT's basketball. You
26:42
Speaker 3: also represented your native Croatia and youth basketball. So you're
26:46
Speaker 3: great at basketball, but you're also a math nerd to
26:48
Speaker 3: the nth degree. She got a BS and a pH
26:52
Speaker 3: degree and applied mathematics from nj T, focusing on computational
26:56
Speaker 3: fluid dynamics.
26:57
Speaker 6: I don't know what that means.
26:58
Speaker 4: I don't know what that means. That's but okay, I
27:00
Speaker 4: don't know.
27:01
Speaker 3: So a great mathematician, great bad basketball player. Let's put
27:04
Speaker 3: it all together. What what are some of the leagues,
27:08
Speaker 3: What are some of the really good applications for some
27:10
Speaker 3: of that technology? Because we've seen you mentioned moneyball for
27:14
Speaker 3: you know that we've seen it in baseball. What other
27:16
Speaker 3: applications are out there that you think? It seems like
27:18
Speaker 3: we're in the very early innings of that.
27:20
Speaker 1: Yeah. Yeah, So early on it started with just using basic, basic, data,
27:26
Speaker 1: so box scores, play by play, and then a lot
27:28
Speaker 1: of sports in recent years have what's called player tracking data,
27:32
Speaker 1: meaning we have locations of the players on the court
27:35
Speaker 1: or on a pitch, on a field, whichever sport we're
27:38
Speaker 1: talking about, in at a high resolution. So so from
27:42
Speaker 1: that data we can extract not only things that are
27:44
Speaker 1: counted in a box score, but also other things that
27:47
Speaker 1: happened during the game that you wouldn't see u counted
27:51
Speaker 1: in like a basic box score for example.
27:54
Speaker 6: What are some of the common questions that like coaches
27:57
Speaker 6: or owners come to you.
27:58
Speaker 1: With the biggest question is how do we value players?
28:04
Speaker 1: How do we how do we find which which players,
28:06
Speaker 1: which teams should sign, which how how long of a contract,
28:10
Speaker 1: how how much money should be on a contract. So
28:13
Speaker 1: that's that's one side. So so that's the player evaluation side,
28:16
Speaker 1: and then the other side is coaching and in game
28:19
Speaker 1: decision making. So which situations are producing the most value
28:23
Speaker 1: for for the teams, Which situations are creating creating better
28:29
Speaker 1: opportunities to score.
28:31
Speaker 3: I know, like in baseball, major league baseball and in
28:34
Speaker 3: minor league baseball.
28:34
Speaker 4: Now it's coming into all the other parts of baseball.
28:36
Speaker 3: The analytics people the data people versus maybe some of
28:40
Speaker 3: the more traditionalists and they're kind of they kind of
28:44
Speaker 3: butt heads on occasion.
28:45
Speaker 4: And how much analytics do you use? Imagine knows what
28:48
Speaker 4: I'm talking about.
28:49
Speaker 3: So how much analytics do you use versus just my
28:52
Speaker 3: gut I think this player will do well?
28:54
Speaker 4: Or how do you kind of bridge that topic? Yeah?
28:57
Speaker 1: Yeah, So that's a that's a big important thing because
28:59
Speaker 1: you can just have data without the domain expertise. And
29:03
Speaker 1: I think that's something that we a Zellas have a
29:05
Speaker 1: really good strength is that we have the experts in
29:08
Speaker 1: in data and statistics, in AI, in machine learning, but
29:12
Speaker 1: we also have a lot of people who worked in
29:14
Speaker 1: sports teams and have that sort of experience and know
29:17
Speaker 1: which questions the teams want to answer, what's useful for
29:20
Speaker 1: them and uh and how can we help them best?
29:23
Speaker 6: So yeah, because when you were saying what AI could
29:26
Speaker 6: help you do, it feels like that's not what a
29:28
Speaker 6: coach is supposed to do. But you're saying that you
29:30
Speaker 6: need someone to interpret how to manage that and stuff.
29:33
Speaker 1: Right, right, So you need like a bridge between the
29:36
Speaker 1: data and what's what's happening on the court.
29:39
Speaker 3: All right, If I'm an agent representing a player. Now,
29:41
Speaker 3: this is I got to learn this stuff because the
29:44
Speaker 3: team's gonna come.
29:44
Speaker 6: At me and say, this is what the program tells
29:47
Speaker 6: me that, yeah, your.
29:47
Speaker 3: Client's worth blank because his or her ops is this
29:51
Speaker 3: and blah blah blah blah blah blah. And you got
29:53
Speaker 3: to come back and say, no, I think he's better
29:55
Speaker 3: than that.
29:55
Speaker 4: And I think he's really more. So did they do
29:58
Speaker 4: you have?
29:58
Speaker 3: Do you work with the agents and players and selves
30:00
Speaker 3: as well, because they better be smart.
30:02
Speaker 4: On this stuff?
30:03
Speaker 1: Yeah, yeah, that's it's a great area where whereas elles
30:05
Speaker 1: is growing as well in some of our sports. But
30:08
Speaker 1: but yeah, it's it's not you know, an agent cannot
30:12
Speaker 1: learn all of this on their own, so so having
30:16
Speaker 1: a company or a contractor who can.
30:18
Speaker 3: So do you guys work with agents and players and directly.
30:22
Speaker 1: In in certain sports?
30:24
Speaker 6: Yes, yeah, but not all across the board. So you also,
30:26
Speaker 6: as Paul was mentioned earlier, you got your BS and
30:29
Speaker 6: your PhD in applied mathematics and nj I T because
30:35
Speaker 6: we're here and we're talking about nj I T kind
30:37
Speaker 6: of bridges the gap between learning stuff and then putting
30:39
Speaker 6: it out into the world. How did this help you
30:42
Speaker 6: evolve your career and leave you where you are today.
30:45
Speaker 1: Yeah, even though I studied competitional fluidnamics, it's not exactly
30:50
Speaker 1: data science, but I've learned a lot of skills that.
30:52
Speaker 6: Were by the way, so you can pretend it is.
30:56
Speaker 1: There's a little skills that transfer from from one field
30:58
Speaker 1: to the other and coding, analyzing large data sets, of
31:03
Speaker 1: creating visualizations and communicating scientific results to to regular audience,
31:08
Speaker 1: to anybody else who can understand to understand it.
31:10
Speaker 3: Are there some sports that are embracing AI or just
31:14
Speaker 3: technology analytics more than others?
31:17
Speaker 1: I think that's that's historically in baseball, particularly because they
31:21
Speaker 1: had the more advanced data for the longest time. But
31:25
Speaker 1: other sports now also have the player tracking data and
31:30
Speaker 1: are starting to get more more on that side.
31:32
Speaker 6: How did you wind up in this? Because if you
31:35
Speaker 6: played basketball, right, because you're originally from Croatia, right, So
31:37
Speaker 6: you played basketball and then you somehow wound up and
31:40
Speaker 6: deep into analytics. How did how did you do that?
31:43
Speaker 4: Well?
31:44
Speaker 1: I always loved math and I always loved basketball, and
31:46
Speaker 1: this was a perfect combination of the two.
31:50
Speaker 3: So in I'm kind of wondering where are we do
31:54
Speaker 3: you think in terms of the evolution of applying data
31:57
Speaker 3: and AI to sports, because it just this satistics. I've
32:00
Speaker 3: been following sports my entire life, and I'm listening to
32:03
Speaker 3: a broadcast and they're saying stuff.
32:05
Speaker 4: I have no idea what they're talking about, Like now batting.
32:07
Speaker 3: Average is an important anymore to baseball, and now it's
32:10
Speaker 3: on bass plus slugging.
32:13
Speaker 4: I don't know.
32:14
Speaker 3: I mean, it seems like we need a tutorial on a
32:16
Speaker 3: lot a lot of these broadcasts.
32:17
Speaker 4: I mean, how do you where? Where can this go?
32:20
Speaker 4: Do you think? Yeah?
32:21
Speaker 1: I wouldn't know about baseball because I don't really understand
32:24
Speaker 1: the rules coming from Croatia. Uh but but in basketball,
32:27
Speaker 1: we you know, for now we have the player location data.
32:31
Speaker 1: But but it's also growing towards player kinematics data, which
32:36
Speaker 1: which NBA has available for this season. Kinematics and kinematics,
32:41
Speaker 1: So the locations of players waist, elbow, shoulder, all of
32:46
Speaker 1: the joints, the more detailed data of like player movements
32:50
Speaker 1: and and uh yeah, so so how how players are shooting?
32:54
Speaker 1: And you can you can extract all this more more
32:57
Speaker 1: detailed information and that's the next up in basketball.
33:01
Speaker 6: Wow, part of me thinks that's cool. And also creepy
33:04
Speaker 6: like all at the same time. Any sports where this
33:07
Speaker 6: like doesn't at all work for I mean this feels
33:10
Speaker 6: like this makes sense in like team sports. What about
33:12
Speaker 6: like more individual sports like gymnastic skiing, Like how does
33:16
Speaker 6: it work in those kind of things?
33:17
Speaker 1: Yeah, so it's those sextual works in golf, which is
33:20
Speaker 1: obviously an individual sport, and there we work directly with
33:24
Speaker 1: the players.
33:25
Speaker 3: And what kind of data do you look at there
33:27
Speaker 3: for the golfer? Mean to me, it's just can I
33:29
Speaker 3: keep it on the fairway?
33:30
Speaker 6: Can you line it up and shoot it in?
33:32
Speaker 4: Yeah?
33:32
Speaker 6: What can you tell me about my non game golf?
33:37
Speaker 1: I actually, you know, don't play golf and don't completely
33:40
Speaker 1: understand that fair.
33:41
Speaker 6: But the same idea that they can storm, like how
33:44
Speaker 6: you stand like that kind of thing, Like what kind
33:46
Speaker 6: of tools are like where you hit it?
33:48
Speaker 1: Where the ball I guess it's called falls and.
33:53
Speaker 4: Clubhead speed, and I mean they're.
33:54
Speaker 6: Breaking it head speed, you have stuff.
33:56
Speaker 4: Yeah, they got it all now with the track man.
33:58
Speaker 4: Everybody's got a little computer sitting right.
34:00
Speaker 3: Behind them on the driving range and it measures basically everything.
34:03
Speaker 3: So now it's all about spin, ray, club head speed.
34:07
Speaker 3: All this kind of stuff. But for those of us
34:09
Speaker 3: are just trying to hit it on the grass and
34:11
Speaker 3: not the water or like the desert, because it's.
34:14
Speaker 4: Not very helpful.
34:15
Speaker 6: Yeah, just like go that way, all right, Avanna thanks
34:18
Speaker 6: a lot of enersherk zealous analytics really appreciate it. That's
34:21
Speaker 6: like an amazing story. I've never gotten into golf though.
34:24
Speaker 4: Why not?
34:26
Speaker 6: It's boring?
34:26
Speaker 4: Yeah, I mean is it?
34:28
Speaker 6: Is it boring? You play it? It's boring to watch it?
34:30
Speaker 3: No, it's people are passionate about it, and oh it's
34:33
Speaker 3: I had it.
34:33
Speaker 4: Like my kids were young. You put golf on.
34:35
Speaker 3: It's nice and serene, it's and it keeps them yet
34:39
Speaker 3: exactly keeps them the safe.
34:40
Speaker 4: That was my strategy with the four when when they
34:43
Speaker 4: were young.
34:43
Speaker 6: So brilliant. Yeah, why did not think of that?
34:45
Speaker 4: Yeah? So anyway, you got some good golf start.
34:47
Speaker 3: Yeah, Analytics in sports, Uh, it's everywhere. It's getting bigger
34:52
Speaker 3: and teams are investing more in it.
34:54
Speaker 4: So it's just the future.
35:00
Speaker 2: Listening to the Bloomberg Intelligence Podcast catch us live weekdays
35:04
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35:07
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35:10
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35:13
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35:17
Speaker 3: We're live here today from the New Jersey Institute of
35:20
Speaker 3: Technology NJ for the cool kids in Newark, New Jersey,
35:24
Speaker 3: talking about AI and boy, there's a lot of smart people.
35:27
Speaker 3: We came to the right place for that, including our
35:30
Speaker 3: next guest, Anita Givanni Global ahead of Honor of Innovation
35:33
Speaker 3: in Avanad. Avanad was founded by Microsoft and Xcenture. Anita,
35:38
Speaker 3: thanks so much for joining us here.
35:40
Speaker 4: Could you talk to.
35:40
Speaker 3: Us about how you guys at Avanon approach AI. Where
35:43
Speaker 3: do you try to help out in the equation?
35:45
Speaker 6: Yeah, So we.
35:46
Speaker 9: Are a global consultancy, as you mentioned, Microsoft Etcenter joint venture,
35:50
Speaker 9: sixty thousand employees around the world, and what we do
35:52
Speaker 9: is think about AI from a client perspective. How is
35:56
Speaker 9: it that we can support organizations across sectors be AI
36:00
Speaker 9: first and at the same time we're all going through
36:03
Speaker 9: this journey together. So thinking about ourselves as an organization,
36:07
Speaker 9: how can we be AI first in our own business
36:09
Speaker 9: processes and for our own people.
36:11
Speaker 6: So I'm a company, Can I come to you? What
36:13
Speaker 6: do you do for me?
36:14
Speaker 9: We think about a lot of things. Are you guys
36:17
Speaker 9: prepared from a people perspective, an organizational perspective, and a
36:20
Speaker 9: process perspective. For example, a lot of people that we
36:24
Speaker 9: interviewed in an AI readiness report said they were enthusiastic
36:27
Speaker 9: and optimistic about AI.
36:29
Speaker 8: That's great.
36:29
Speaker 9: However, half of the leaders said they weren't ready and
36:33
Speaker 9: only a third of CEOs believe that their top leadership
36:37
Speaker 9: is AI fluent. So there is a dissonance between the
36:39
Speaker 9: excitement and enthusiasm and the reality of the preparedness of organizations.
36:44
Speaker 9: And what we do is make sure that organizations have
36:47
Speaker 9: the coaching and support they need to get there.
36:49
Speaker 3: I would think one of the challenges, just speaking for
36:51
Speaker 3: myself is I learned a whole lot today speaking to
36:53
Speaker 3: again and the smart people from NJIT kind what AI is.
36:57
Speaker 3: I'm one of those people that says if you can't
36:59
Speaker 3: explain it more and sends you don't understand it, And
37:01
Speaker 3: I don't think I understand it. How do you what's
37:04
Speaker 3: the basic framework that you try to get across your
37:06
Speaker 3: clients about what AI is and what it can mean
37:08
Speaker 3: for them.
37:09
Speaker 9: Yeah, think about AI and one of the biggest, one
37:12
Speaker 9: of the biggest generative AI tools right now through Microsoft
37:15
Speaker 9: is copilots. Think of it as a co pilot, not
37:18
Speaker 9: necessarily a replacement. Pilot that can allow you to articulating, Yeah,
37:23
Speaker 9: allow you to do your job more effectively and more efficiently.
37:27
Speaker 9: And so instead of thinking about AI as a job replacement,
37:30
Speaker 9: think about it as a way to replace key tasks
37:33
Speaker 9: and allow you to spend your days in ways that
37:36
Speaker 9: you want to, talking to people, being more relationship focused
37:40
Speaker 9: rather than necessarily summarizing emails or going through data sets,
37:44
Speaker 9: et cetera.
37:44
Speaker 6: So's a partner. So basically I could have some AI,
37:48
Speaker 6: think go through my email and like correlate the important
37:51
Speaker 6: parts and give it out, for example, and take it
37:53
Speaker 6: and give it to me, so I don't have to
37:54
Speaker 6: spend my whole morning going through and reading reports. Yeah, exactly.
37:57
Speaker 6: That's really cool.
37:58
Speaker 4: Yeah, and that.
37:59
Speaker 6: Would how much time to go do other stuff?
38:01
Speaker 4: Yeah?
38:01
Speaker 9: I mean think about when you come back from vacation.
38:03
Speaker 9: You probably check your email when you're on vacation. I don't,
38:06
Speaker 9: but for that exact.
38:07
Speaker 6: Reason, because if I come back, I have like two
38:09
Speaker 6: thousand emails being gone for like a week, and I
38:12
Speaker 6: can't keep that. I can't do it.
38:13
Speaker 9: If you had the AI tool, what you could do
38:15
Speaker 9: after being away for two weeks. I don't check my
38:17
Speaker 9: email and probably get in trouble for that, but I don't.
38:19
Speaker 6: I can come back and say, what did I miss.
38:21
Speaker 9: Over the last two weeks, go through all my pings
38:23
Speaker 9: on teams, go through all my outlook, and can you
38:25
Speaker 9: prepare for me a summary so that now that I
38:27
Speaker 9: come back, I can actually be ready and can prioritize.
38:30
Speaker 9: That's where it really comes into.
38:33
Speaker 6: Wow, that's a handly cool.
38:34
Speaker 3: Yeah, So what when you sit down with your clients,
38:36
Speaker 3: I mean, what's some of the common requests you get
38:39
Speaker 3: from them? Or you know, what are some of the
38:42
Speaker 3: what do they ask for most of the help with
38:44
Speaker 3: I guess.
38:44
Speaker 8: Yeah.
38:45
Speaker 9: One of the things that's really top of mind for
38:47
Speaker 9: people is about skill set and training and capability building.
38:50
Speaker 9: So in our survey, we found that eight out of
38:54
Speaker 9: ten people said that twenty hours of their work week,
38:57
Speaker 9: almost fifty percent of their work week can be replaced
39:00
Speaker 9: with AI tools. The challenges they don't know how to
39:03
Speaker 9: use the tools in the most effective and efficient way,
39:06
Speaker 9: so the training around that is critical in the process.
39:09
Speaker 9: The other is a responsible AI A governance set, right, Yeah,
39:13
Speaker 9: what are the guard rails that we have to put
39:15
Speaker 9: into place so that people can play creatively in the space.
39:18
Speaker 6: Do you feel like people and CEOs or board levels,
39:22
Speaker 6: do they now know what they don't know? They are
39:25
Speaker 6: beginning to figure it out, or we're still in the
39:27
Speaker 6: beginning part of that.
39:28
Speaker 9: I believe we're in the infancy of it. I think
39:30
Speaker 9: there's an infancy of the learning curve, but also an
39:32
Speaker 9: infancy of having the right people in the room, having
39:35
Speaker 9: diverse perspectives. As we think about responsible AI.
39:38
Speaker 3: And we're hearing you mentioned the I guess the ethical
39:41
Speaker 3: use of AI.
39:43
Speaker 4: I don't know how that's going to evolve.
39:45
Speaker 3: Is that going to be some partnership between public, private,
39:48
Speaker 3: the individual.
39:49
Speaker 6: I'm not sure I actually know what that means.
39:51
Speaker 8: Boy, it just seems like ethical use of AI.
39:53
Speaker 3: Yeah, it just seems like the technology could get out
39:56
Speaker 3: of control.
39:57
Speaker 9: You will look as AI and generative AI becomes more
40:00
Speaker 9: are ubiquitous. With increased scale comes increased risk. That's just
40:03
Speaker 9: the reality of things. So how do you mitigate those risks?
40:06
Speaker 9: I think one of the most important ways to do
40:08
Speaker 9: that is to have the right people in the room. So,
40:11
Speaker 9: whether that's from a diversity perspective of gender whether that's
40:14
Speaker 9: having people of color in the room, people from diverse backgrounds.
40:18
Speaker 9: It's one of the reasons that we do the scholarship
40:20
Speaker 9: program for women in STEM at this very institute, because
40:23
Speaker 9: we want to make sure that they're not brought in
40:25
Speaker 9: as a second thought, but rather at.
40:27
Speaker 6: The very beginning of the conversation. So, what's like an
40:30
Speaker 6: unethical use of AI? Like, where does AI get bad?
40:34
Speaker 4: Yeah?
40:34
Speaker 9: Well, I mean, look, you can use you can use
40:37
Speaker 9: AI to create images that don't actually exist. You can
40:40
Speaker 9: put voices on people to say things through their own
40:43
Speaker 9: voice when they may maybe have not said that video.
40:46
Speaker 9: You can think about putting in questions into generative AI
40:49
Speaker 9: that perhaps share data with the broader public that you
40:51
Speaker 9: didn't want to share that's company specific data. So there's
40:54
Speaker 9: a security component, there's a falsification component, There's lots of
40:58
Speaker 9: different ways you kind of have to be proact and.
41:00
Speaker 3: On this front, once again, maybe at no fault of
41:03
Speaker 3: their own, the government is generations behind where the technology is.
41:08
Speaker 3: I don't know how this plays out, I really don't.
41:10
Speaker 3: I mean, is there a feeling that the industry for
41:13
Speaker 3: a while is going to have to police itself or
41:15
Speaker 3: is there going to be some again public private partnership
41:18
Speaker 3: in terms of regulating this, because this is not the
41:20
Speaker 3: FCC regulating the airwaves.
41:23
Speaker 4: This is really really difficult.
41:26
Speaker 8: Yeah, it gets complicated.
41:28
Speaker 9: Look, I think there's an individual level to it, an
41:31
Speaker 9: individual level of responsibility, But at the end of the day,
41:33
Speaker 9: it's going to fall on the leaders, the leaders of
41:36
Speaker 9: organizations across the board. If the senior leaders are not
41:39
Speaker 9: thinking about responsible AI, they're not thinking about the AI fluency,
41:44
Speaker 9: no one else is going to think about that. So
41:47
Speaker 9: the responsibility on the leaders is very high.
41:49
Speaker 6: How do they get fluent aside from talking to you?
41:52
Speaker 9: Yeah, well, there's a defining AI understanding and feeling comfortable
41:56
Speaker 9: with the language of AI. And then there's some very
41:58
Speaker 9: tactical things like prompt engineering. When you put in a
42:01
Speaker 9: question into jen AI and get a response, there are
42:04
Speaker 9: ways you can position that question in a more intelligent
42:07
Speaker 9: way to get a response that more aligns with your need.
42:09
Speaker 9: So there's very tactical things you can do through some
42:12
Speaker 9: more AI fluent.
42:13
Speaker 4: What's that? I wonder what the technology we do?
42:15
Speaker 3: We know what the technology investments can be required to
42:18
Speaker 3: be proficient in AI, because I feel like there's gonna
42:20
Speaker 3: be a lot of companies, a lot of people left behind.
42:24
Speaker 3: It's not just having the ability to have a laptop
42:26
Speaker 3: on your desk. It feels like it's a.
42:28
Speaker 4: Lot more than that.
42:29
Speaker 9: Yeah, I mean, we talked about this very briefly.
42:31
Speaker 6: But data is going to be critical.
42:33
Speaker 9: Data and AI are interlinked. So without strong data sources,
42:37
Speaker 9: the AI won't be as powerful as it has the
42:39
Speaker 9: potential to be. And so a lot of the technology
42:42
Speaker 9: investment right now, besides the people investment in training, is
42:44
Speaker 9: going to be on cleaning up and making sure that
42:47
Speaker 9: we have good, strong data to work off of.
42:49
Speaker 6: So interesting, Anita, thank you so much, really appreciate Anita Vann.
42:54
Speaker 6: Did I say that right? We'll get it eventually? Okay,
42:59
Speaker 6: all right, we're going to get it. That will be Paul,
43:01
Speaker 6: and our quest is to get that crap.
43:04
Speaker 3: You know.
43:04
Speaker 6: I have to say, I feel like I learned a lot.
43:06
Speaker 6: I have a little bit of an understanding as to like, okay,
43:08
Speaker 6: now this is how people like you and I can
43:10
Speaker 6: understand it a.
43:11
Speaker 3: Little bit, which is very cool, right, But who gets
43:13
Speaker 3: a PhD in like applied mathematics, not.
43:15
Speaker 6: Us or what was a fluid math of something like that?
43:19
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