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Target Earnings, AI in Focus · Bloomberg Intelligence

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Speaker 3: Today, we'll look at why Apple says it's iPhone sales
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Speaker 3: in China are falling.
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Speaker 4: Plus we're going to dive into the world of art
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Speaker 4: intelligence and how it's going to impact job organizations going forward.
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Speaker 3: But first we'll begin in the retail sector. Earlier in
1:06
Speaker 3: the week, Target reported fourth quarter profit that beat analyst expectations.
1:10
Speaker 4: So this comes as the company reduced its stockpile of
1:12
Speaker 4: merchandise by about twelve percent during the quarter. So Target
1:15
Speaker 4: also confirmed it's going to launch a paid membership program,
1:17
Speaker 4: going up against rivals like Amazon and Walmart.
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Speaker 3: For more, co host Bailey Lipschutz and I were joined
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Speaker 3: by Jennifer bartashis Bloomberg's senior retail analysts. Were first asked
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Speaker 3: Jennifer why investors are excited about Target.
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Speaker 5: They've really revealed their plan for growth over the next
1:31
Speaker 5: several years and that is resonating well with investors, and
1:35
Speaker 5: it's all about recapturing top plane growth, traffic, market share,
1:39
Speaker 5: and they're a little bit more upbeat on where they
1:41
Speaker 5: see the consumer right now.
1:43
Speaker 6: And when we look at inventory being an issue that
1:45
Speaker 6: was such a big problem for Target during the pandemic,
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Speaker 6: it does seem like that's being worked through. Kind of
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Speaker 6: what's your expectation with how Target is handling their inventory
1:54
Speaker 6: ahead of schedule or not a head schedule.
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Speaker 5: Yeah, they've done a major pivot with inventory, and obviously
1:59
Speaker 5: they had huge issues a couple of years ago with markdowns,
2:03
Speaker 5: and they've really been able to write size inventory that's
2:05
Speaker 5: shown up where inventory right now is actually lower than
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Speaker 5: it was last year, and yet in stocks are better
2:11
Speaker 5: than they've been. That focus on those retail fundamentals of
2:14
Speaker 5: making sure that you have things in the stores and
2:16
Speaker 5: in stock has really been playing through and driving some
2:20
Speaker 5: of the results that they've seen, and a lot of
2:22
Speaker 5: that stems from inventory. And so right now they're very
2:25
Speaker 5: well positioned going into twenty twenty four and that should
2:28
Speaker 5: hopefully be a tailwind for them for the rest of
2:30
Speaker 5: this fiscal year.
2:31
Speaker 3: An Affinity Card, can you explain what's going on there?
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Speaker 5: They're kind of reimagining their loyalty program. So the baseline
2:37
Speaker 5: was Target Circle, which was free to join, and they
2:40
Speaker 5: have one hundred million users that have joined it, and
2:43
Speaker 5: that's where you can go in and save different offers
2:46
Speaker 5: and you get an extra ten percent off your paper
2:49
Speaker 5: towels you know that week and that brand. What they're
2:51
Speaker 5: doing is they're making it so you don't have to
2:53
Speaker 5: search and save. You get the things automatically when you
2:56
Speaker 5: check out, and if you're a Red Card holder, for
2:59
Speaker 5: forty nine dollars, you can get unlimited free delivery, same
3:03
Speaker 5: day free delivery to your house and they're calling this
3:06
Speaker 5: Target three sixty. You can also subscribe to just that
3:09
Speaker 5: if you're not a Red Card holder. Also an introductory
3:12
Speaker 5: price of forty nine, but it will go up over time,
3:14
Speaker 5: although they didn't say to what. So it's a move
3:17
Speaker 5: where they're trying to get, you know, a little bit
3:19
Speaker 5: deeper into consumers' lives, you know, doing more home delivery.
3:22
Speaker 5: There's some additional perks in there where you can access
3:26
Speaker 5: all of what Ship does, which is their same day
3:28
Speaker 5: delivery service, and that includes being able to shop at
3:30
Speaker 5: other retailers. So that's kind of the differentiator here with
3:34
Speaker 5: regards to the membership program that once you have it,
3:36
Speaker 5: you could also have your Target run include something from
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Speaker 5: Costco for example. But it's early days and we'll see
3:44
Speaker 5: how much appetite consumers have for yet another membership that
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Speaker 5: they have to pay for.
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Speaker 6: Well, what are your expectations on that? Because I feel
3:51
Speaker 6: like everyone pays for Amazon Prime and Walmart Plus I
3:54
Speaker 6: know has had a strong roll out and has partnerships
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Speaker 6: with different credit cards. Like, what can Target really do
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Speaker 6: to get people to pay another annual fee when it
4:02
Speaker 6: seems like, at least looking at the streaming platforms they're
4:05
Speaker 6: struggling to maintain users.
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Speaker 5: It's a great question, and I think that's one of
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Speaker 5: the big questions that we're going to need to see
4:11
Speaker 5: as they really talk about what differentiates this program, because
4:15
Speaker 5: you know, forty nine dollars is cheap, but it is
4:17
Speaker 5: an introductory price, and there are a lot of people
4:20
Speaker 5: out there that do delivery, and so you know, when
4:23
Speaker 5: I look at it, I think in order for it
4:25
Speaker 5: to be a successful program, they're going to have to
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Speaker 5: really do something extraordinary that appeals to people to prompt
4:30
Speaker 5: them to pay extra And historically, you know, Target's formula
4:34
Speaker 5: for success has never been mimicking others. It's been about
4:37
Speaker 5: kind of forging their own path. And I think this
4:39
Speaker 5: is an example where it's going to be very very
4:41
Speaker 5: important for them to do that. But it's not clear
4:44
Speaker 5: yet how that's going to be realized.
4:46
Speaker 6: And how does Target play into a recession. Are they
4:50
Speaker 6: one of the companies that can weather the storm because
4:52
Speaker 6: of their loyalty or how does that play out just
4:54
Speaker 6: given their typical demographics.
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Speaker 5: If there is a recession. With their typical demographics, Target's
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Speaker 5: customer base is extremely loyal, but Target's product mix does
5:03
Speaker 5: skew to more discretionary items, and so whenever there's a
5:07
Speaker 5: pullback and spending, it tends to have an outsized impact
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Speaker 5: on Target versus other big box retailers. Now, Target has
5:13
Speaker 5: been really focused in the last eighteen months, especially on value.
5:17
Speaker 5: They've launched some new private label brands that are meant
5:20
Speaker 5: to be the cheapest option in every category in the store.
5:23
Speaker 5: So they're trying to respond to that. So they have
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Speaker 5: the breadth of category and they have the loyalty of
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Speaker 5: consumers that they can weather a recession, but it will
5:32
Speaker 5: make it harder for them to get back to that
5:34
Speaker 5: top line growth and that expansion of margin that they're
5:37
Speaker 5: talking about if we find ourselves in the midst of
5:39
Speaker 5: a recession.
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Speaker 3: Hey, Jen, how do I differentiate how much shopper between
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Speaker 3: Target and Walmart?
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Speaker 5: It's all about perception, right When you think about Walmart,
5:48
Speaker 5: it is usually about lowest price. Walmart is a EDLP.
5:52
Speaker 5: Every day you get the lowest possible price. They've got
5:55
Speaker 5: a very big assortment and they have some perks with
5:58
Speaker 5: regards to their Walmart Plus program. When people think of Target,
6:02
Speaker 5: they think a little bit more of discovery, They think
6:05
Speaker 5: a little bit more about inspiration than functionality, and that's
6:08
Speaker 5: where you really see the divergence between those two retailers.
6:11
Speaker 6: I will say, when I go to Target, I always
6:13
Speaker 6: end up buying more than I want, where if I
6:15
Speaker 6: go to Walmart, I'm kind of in and out. Jen
6:17
Speaker 6: question off of that comparison, I always talk up the
6:20
Speaker 6: fact that Walmart owned Sam's Club. When you look at
6:22
Speaker 6: the company makeup, how much does that differentiate Walmart versus
6:26
Speaker 6: some of the other peers, just given as you mentioned
6:29
Speaker 6: Target potentially partnering with Costco if you're using their membership card.
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Speaker 5: Yeah, so obviously there's a great benefit to Walmart of
6:36
Speaker 5: having both it's US Walmart stores as well as the
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Speaker 5: Sam's Club stores, because they really can capture spending by
6:44
Speaker 5: different groups of people and for different shopping reasons. Now,
6:47
Speaker 5: when I talked about shipped, it's not so much that
6:49
Speaker 5: you get the benefits of a Costco membership, for example,
6:52
Speaker 5: with Target. It's just more that you can have your
6:55
Speaker 5: delivery person pick up purchases from other retailers, which is
6:59
Speaker 5: something that isn't in vetted in a Walmart program, for example.
7:02
Speaker 5: But that Target membership fee is going to be competing
7:06
Speaker 5: with your Costco membership fee. It'll be you know, competing
7:09
Speaker 5: with a bjays membership fee along with all of your
7:11
Speaker 5: streaming services and everything else out there. So you know,
7:14
Speaker 5: Walmart definitely benefits from the scale of having both the
7:17
Speaker 5: course stores and Sam's Club, but it is a little
7:20
Speaker 5: bit of a different business model than what we're talking
7:22
Speaker 5: about with Target.
7:23
Speaker 7: Oh.
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Speaker 3: Thanks to Jen Bartashis Bloomberg Intelligence senior retail analysts.
7:26
Speaker 4: All right, we turned out a big tech So this
7:28
Speaker 4: week we learned that Apple iPhone sales in China fell
7:31
Speaker 4: by a surprising twenty four percent. Now this was over
7:33
Speaker 4: the first six weeks of twenty twenty four. Those figures
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Speaker 4: came from the Counterpoint Research firm.
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Speaker 3: For more on this, co host Bailey Lipshaltz and I,
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Speaker 3: we're joined by anaag Rana, Bloomberg Intelligence Senior technology analyst.
7:43
Speaker 3: We first asked Anurag what's causing this to decline in
7:46
Speaker 3: the early part of twenty twenty four.
7:48
Speaker 8: This is the same story that's been going on for
7:50
Speaker 8: the last six to nine months, ever since Wallwei released
7:53
Speaker 8: their higher end phone last year. So Apple's been losing
7:56
Speaker 8: market share there that has been you know, you could
7:59
Speaker 8: say opened or coupled with consumer weakness in China. And
8:03
Speaker 8: it's all that's you know, driving these problems right now.
8:06
Speaker 6: When I look at the PGEL function on the terminal,
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Speaker 6: iPhone accounts for more than half of Apple's revenue China
8:14
Speaker 6: call it a fifth of their sales anak. When you
8:17
Speaker 6: look at those numbers, what does this actually mean for
8:20
Speaker 6: Apple if they're going to see that competition eating into
8:23
Speaker 6: their sales.
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Speaker 8: Yeah, I think it's going to be a tough time
8:25
Speaker 8: for Apple in China for that at least this year
8:29
Speaker 8: and maybe into next year before they can, let's say,
8:31
Speaker 8: make some inroads in India and other markets. Emerging markets
8:34
Speaker 8: is the real growth driver for Apple, there is no
8:37
Speaker 8: two ways about it. And and you know iPhone is
8:39
Speaker 8: the big growth driver. So if phones are not selling
8:42
Speaker 8: in China, that's a problem for Apple. It means numbers
8:45
Speaker 8: need to come down even more for Apple this year.
8:48
Speaker 8: You know, we saw about double digit sales drop in
8:51
Speaker 8: China last quarter. I looked up on MDL consensus is
8:56
Speaker 8: about seven percent drop in China for this quarter. I
8:59
Speaker 8: think that number needs to creep up to someone in
9:01
Speaker 8: the low double digit decline going forward. It's a painful
9:05
Speaker 8: situation for Apple, and frankly, there aren't at many rosy
9:09
Speaker 8: things looking forward, at least for twenty twenty four.
9:11
Speaker 3: So I guess the biggest concern for Apple obviously is competition,
9:16
Speaker 3: because they haven't really had that robust of a competitor
9:19
Speaker 3: and they're part of the market. Are there any responses
9:23
Speaker 3: Apple can make here from a competitive landscape other than
9:26
Speaker 3: lowering the price point?
9:28
Speaker 8: Yeah, Paul, I think there is a little more hype
9:30
Speaker 8: in that competition news than reality only because Huahwei didn't
9:34
Speaker 8: release a phone for many years, So that you can
9:36
Speaker 8: think about it. If you have an install base of
9:38
Speaker 8: WAWE phones. I mean, let's say you know that's X
9:41
Speaker 8: and that hasn't been updated for four or five six years,
9:44
Speaker 8: and you just certainly get a brand new phone. All
9:46
Speaker 8: of those people are going to go and refresh that,
9:49
Speaker 8: So I think you should take that as a bigger factor,
9:52
Speaker 8: plus the subsidies they are getting in China from the
9:55
Speaker 8: local providers. So I think Apple will do okay in
9:58
Speaker 8: China over the long term. But I think that's not
10:01
Speaker 8: going to be a twenty twenty fourth story.
10:03
Speaker 6: And you mentioned emerging markets as the next driver. What countries,
10:06
Speaker 6: what regions are going to be able to pay up
10:08
Speaker 6: for what I would say, is quite an expensive phone.
10:11
Speaker 8: Yeah, I think that's the most important question, and I
10:13
Speaker 8: think you know, just by the sheer size of it,
10:16
Speaker 8: India is the next one. But frankly speaking, right now,
10:19
Speaker 8: Apple doesn't even operate in you know, five percent of
10:22
Speaker 8: the entire market because of the price point of the phone.
10:25
Speaker 8: I think the strategy India is going to be a
10:27
Speaker 8: mix of the lower phone the se as well as
10:30
Speaker 8: the refurbished phone where the price point is even lower.
10:33
Speaker 8: But having said that, I think India is a developing country.
10:36
Speaker 8: The middle class is getting more richer, so I think
10:38
Speaker 8: that's going to be the next big growth catalyst. But
10:40
Speaker 8: this is it's not going to play out in twenty
10:42
Speaker 8: four to twenty five. That's more of a I would
10:45
Speaker 8: submit to long term story.
10:46
Speaker 3: We've talked about this before that Apple might introduce a
10:49
Speaker 3: lower price phone into India for just that reason. Is
10:52
Speaker 3: that something they're still considering or will they just wait
10:56
Speaker 3: for the market to kind of move up to where
10:58
Speaker 3: the Apple phone price point is.
10:59
Speaker 8: I think it's going to be the latter. I've done
11:01
Speaker 8: a lot of analysis of how much share they can
11:04
Speaker 8: gain if they drop the S price by fifty dollars
11:06
Speaker 8: hundred dollars, and you know, when I publish that stuff,
11:09
Speaker 8: I think they actually raise the price by fifty bucks.
11:11
Speaker 8: So they don't believe in dropping prices. They are more
11:14
Speaker 8: on a margin story. So think of Apple more on
11:17
Speaker 8: the long term basis right now, not on the short term.
11:20
Speaker 8: I don't think they're going to you know, I would
11:23
Speaker 8: say swap margins for market share. They've never done that
11:27
Speaker 8: in their history, whether it was on the mac side
11:30
Speaker 8: or on the phone side.
11:31
Speaker 6: And quickly anorak, I look at the news, there's no
11:35
Speaker 6: more Apple car. There's tepid reception to the vision pro
11:39
Speaker 6: What actually drives the stock, what drives sales in the
11:42
Speaker 6: next twelve to eighteen months.
11:43
Speaker 8: Yeah, I think that's the most important question for Apple investors.
11:46
Speaker 8: And I think there's going to be an event in June,
11:48
Speaker 8: the World Wide Developers Conference, where they have pledged that
11:51
Speaker 8: they're going to show a lot of AI enhancements to
11:54
Speaker 8: the operating system. I think that really is the one
11:57
Speaker 8: wildcard that can completely change the sentiment of the company,
12:00
Speaker 8: both in terms of sales and the gloomy investor sentiment
12:04
Speaker 8: only because remember, Apple has a distribution network that stands,
12:07
Speaker 8: you know, next to nobody out there in terms of
12:10
Speaker 8: you know, affluent people using their phones. More than one
12:13
Speaker 8: billion devices connected just on the smartphone. I think that
12:16
Speaker 8: really is the big driver. One of the things I
12:18
Speaker 8: was thinking about was if you go back, you know,
12:21
Speaker 8: five years, seven years, there were apps such as trip
12:24
Speaker 8: Advisors an Yelp where people used to go for their
12:27
Speaker 8: you know, specialized functions for restaurant advices and tourism. But
12:31
Speaker 8: when you look at somebody like Google, a lot of
12:34
Speaker 8: that traffic has moved on to them because they control
12:36
Speaker 8: the distribution. I think Apple has the same ability, but
12:40
Speaker 8: they have to show up with some AI products otherwise
12:43
Speaker 8: that's not going to.
12:44
Speaker 3: Flow our Thanks to Ana rog Rana Bloomberg Intelligence Senior
12:47
Speaker 3: Technology Channels, I com.
12:48
Speaker 4: Going to bring a break down the role of AI
12:50
Speaker 4: in sports analytics.
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13:22
Speaker 4: Earlier in the week, Bloomberg Intelligence broadcasted live from the
13:25
Speaker 4: campus of the New Jersey Institute of Technology. Paul was
13:28
Speaker 4: pumped and the main topic of conversation was AI.
13:31
Speaker 3: We took a look at zealous Analytics and Austin based
13:34
Speaker 3: sports analytics company that evaluates, predicts, and improves player and
13:38
Speaker 3: team performance in sports.
13:39
Speaker 4: So one of our guests was Evana Serk, senior product
13:42
Speaker 4: scientist at Zealis Analytics, and we asked Havana how Zealos
13:46
Speaker 4: uses AI in sports.
13:47
Speaker 1: This field that has expanded in last maybe ten years
13:51
Speaker 1: of a lot in other sports. Even before that, it
13:54
Speaker 1: was in baseball that was one of the first sports.
13:56
Speaker 1: If you've seen Moneyball, that's that's really yes.
13:58
Speaker 4: Yeah, okay, the moneyball Okay. And so it's basically like
14:02
Speaker 4: how to position, like what players to put where combinations?
14:05
Speaker 4: Is it that kind of stuff?
14:06
Speaker 1: Correct? Correct? So, so player evaluation in game decision strategy,
14:11
Speaker 1: that's sort of sort of things. Yeah.
14:12
Speaker 3: So again, played for your starter for nja's basketball. You
14:17
Speaker 3: also represented your native Croatia and youth basketball. So you're
14:21
Speaker 3: great at basketball, but you're also a math nerd to
14:23
Speaker 3: the nth degree. She got a BS and a pH
14:26
Speaker 3: degree and applied mathematics from njiit focusing on computational fluid dynamics.
14:32
Speaker 4: I don't know what that means.
14:32
Speaker 3: I don't know what that means. That's art, but okay,
14:34
Speaker 3: I don't know. So a great mathematician, great basketball player.
14:38
Speaker 3: Let's put it all together. What are some of the
14:40
Speaker 3: really good applications for some of that technology we've seen?
14:43
Speaker 3: You mentioned moneyball for you know that we've seen it
14:45
Speaker 3: in baseball. What other applications are out there that you think?
14:48
Speaker 3: It seems like we're in the very early innings of that.
14:50
Speaker 1: Early on, it started with just using basic data, so
14:54
Speaker 1: box scores, play by play, and then a lot of
14:56
Speaker 1: sports in recent years have a what's called player tracking data,
15:01
Speaker 1: meaning we have locations of the players on the court
15:03
Speaker 1: or on a pitch, on a field, whichever sport we're
15:06
Speaker 1: talking about, at a high resolution. So from that data
15:09
Speaker 1: we can extract not only things that are counted in
15:11
Speaker 1: a box score, but also other things that happened during
15:14
Speaker 1: the game that you wouldn't see counted in like a
15:17
Speaker 1: basic box score for example.
15:19
Speaker 4: What are some of the common questions that like coaches
15:22
Speaker 4: or owners come to you with the.
15:24
Speaker 1: Biggest question is how do we value players? How do
15:26
Speaker 1: we find which players teams should sign, how long of
15:29
Speaker 1: a contract, how much money should be on a contract.
15:32
Speaker 1: That's one side, So that's the player evaluation side, and
15:35
Speaker 1: then the other side is coaching and in game decision making.
15:38
Speaker 1: So which situations are producing the most value for the teams?
15:42
Speaker 1: Which situations are creating better opportunities to score?
15:47
Speaker 3: I know, like in baseball, major league baseball and in
15:49
Speaker 3: minor league baseball. Now it's coming into all the other
15:51
Speaker 3: parts of baseball. The analytics people, the data people versus
15:55
Speaker 3: maybe some of the more traditionalists. They kind of butt
15:58
Speaker 3: heads on occasion. So how much chanalytics do you use
16:01
Speaker 3: versus just my gut I think this player will do well.
16:04
Speaker 3: How do you kind of bridge that topic?
16:06
Speaker 1: Yeah? Yeah, So that's that's a big important thing, because
16:09
Speaker 1: you can just have data without the domain expertise. And
16:13
Speaker 1: I think that's something that we Adzealas have a really
16:16
Speaker 1: good strength, is that we have the experts in data
16:19
Speaker 1: and statistics in AI in machine learning. But we also
16:22
Speaker 1: have a lot of people who worked in sports teams
16:24
Speaker 1: and have that sort of experience and know which questions
16:27
Speaker 1: the teams want to answer, what's useful for them, and
16:30
Speaker 1: how can we help them best.
16:32
Speaker 4: So yeah, because when you were saying what AI could
16:34
Speaker 4: help you do, it feels like that's not what a
16:36
Speaker 4: coach is supposed to do. But you're saying that you
16:38
Speaker 4: need someone to interpret how to manage that and stuff.
16:41
Speaker 1: Right, right, So you need like a bridge between the
16:44
Speaker 1: data and what's what's happening on the court.
16:46
Speaker 3: All right, If I'm an agent representing a player, now
16:49
Speaker 3: this is I got to learn this stuff because the
16:51
Speaker 3: team's gonna come at.
16:52
Speaker 4: Me and say, this is what the program tells me.
16:54
Speaker 3: That, Yeah, your client's worth blank because his or her
16:58
Speaker 3: OPS is this and blah blah blah blah blah, And
17:01
Speaker 3: you got to come back and say, no, I think
17:02
Speaker 3: he's better than that, and I think he's really more.
17:04
Speaker 3: So do you work with the agents and players themselves
17:06
Speaker 3: as well, because they better be smart on this stuff.
17:09
Speaker 1: Yeah, Yeah, that's it's a great area where where ZELS
17:11
Speaker 1: is growing as well in some of our sports. But
17:14
Speaker 1: but Yeah, an agent cannot learn all of this on
17:16
Speaker 1: their own, so having a company or a contractor who can.
17:21
Speaker 3: So do you guys work with agents and players directly.
17:24
Speaker 1: In certain sports?
17:25
Speaker 4: Yes, yeah, but not all across the board. So you also,
17:28
Speaker 4: as Paul was mentioned earlier, you got your BS and
17:31
Speaker 4: your PhD in applied mathematics and nj T. Because we're
17:36
Speaker 4: here and we're talking about NJIT kind of bridges the
17:39
Speaker 4: gap between learning stuff and then putting it out into
17:41
Speaker 4: the world. How did this help you evolve your career
17:44
Speaker 4: and leave you where you are today?
17:46
Speaker 3: Yeah?
17:46
Speaker 1: Even though I studied competitional fluidnamics, it's not exactly data science,
17:50
Speaker 1: but I've learned a lot of skills.
17:51
Speaker 4: There were transitions, by the way, so you can pretend
17:54
Speaker 4: it is.
17:55
Speaker 1: There's a little skills that transfer from from one field
17:58
Speaker 1: to the other, for example, coding, analyzing large data sets,
18:01
Speaker 1: creating visualizations, and communicating scientific results to regular audience.
18:06
Speaker 3: Are there some sports that are embracing AI or just
18:09
Speaker 3: technology analytics more than others?
18:12
Speaker 1: That's historically in baseball, particularly because they had more advanced
18:15
Speaker 1: data for the longest time, But other sports now also
18:19
Speaker 1: have the player tracking data and are starting to get
18:23
Speaker 1: more on that side.
18:24
Speaker 4: How did you wind up in this? Because if you
18:26
Speaker 4: played basketball, right, because you're originally from Croatia, right, So
18:29
Speaker 4: you played basketball and then you somehow wound up and
18:32
Speaker 4: deep into analytics. How did you do that?
18:34
Speaker 7: Well?
18:34
Speaker 1: I always loved math and I always loved basketball, and
18:37
Speaker 1: this was a perfect combination of the two.
18:39
Speaker 3: Where are we do you think in terms of the
18:41
Speaker 3: evolution of applying data and AI to sports? Because it
18:45
Speaker 3: just the statistics. I've been following sports my entire life,
18:48
Speaker 3: and I'm listening to a broadcast and they're saying stuff.
18:50
Speaker 3: I have no idea what they're talking about, Like now
18:52
Speaker 3: batting average is an important anymore to baseball and now
18:55
Speaker 3: it's on bass plus slugging. I don't know. I mean,
18:59
Speaker 3: it seems like we need a tutorial a lot of
19:01
Speaker 3: these broadcasts. Where can this go? Do you think?
19:03
Speaker 1: Yeah? I wouldn't know about baseball because I don't really
19:06
Speaker 1: understand the rules coming from Croatia. But in basketball, we
19:09
Speaker 1: you know, from now we have a player location data,
19:12
Speaker 1: but it's also growing towards player kinematics data, which NBA
19:17
Speaker 1: has available for Saturday's season Kinematics kinematics, so the locations
19:22
Speaker 1: of players waist, elbow, shoulder, all of the joints, so
19:27
Speaker 1: more detailed data of like player movements and yeah, so
19:31
Speaker 1: how how players are shooting? And you can extract all
19:33
Speaker 1: this more detailed information.
19:35
Speaker 4: Are thanks now to Havana Serk, senior product scientists at
19:39
Speaker 4: Zalis Analytics.
19:40
Speaker 3: Let's stick with our conversations on artificial intelligence. At the
19:43
Speaker 3: New Jersey Institute of Technology, we looked at the company Avonaut,
19:46
Speaker 3: a leading provider of cloud and advisory services. Avonaut was
19:49
Speaker 3: founded as a joint venture between Microsoft and Accenture.
19:52
Speaker 4: We were joined by Anita Giovanni, global head of Innovation
19:55
Speaker 4: at Avonat, and we asked her how the company approaches
19:58
Speaker 4: AI and how AI will impact organizations going forward.
20:01
Speaker 9: Yeah, so, we are a global consultancy, as you mentioned
20:04
Speaker 9: MICROSOFTIC Center, joint venture, sixty thousand employees around the world,
20:08
Speaker 9: and what we do is think about AI from a
20:10
Speaker 9: client perspective. How is it that we can support organizations
20:13
Speaker 9: across sectors be AI first and at the same time,
20:17
Speaker 9: we're all going through this journey together. So thinking about
20:19
Speaker 9: ourselves as an organization, how can we be AI first
20:23
Speaker 9: in our own business processes and for our own people
20:25
Speaker 9: so I'm a company and I come to you, what
20:28
Speaker 9: do you do for me? We think about a lot
20:30
Speaker 9: of things. Are you guys prepared from a people perspective,
20:33
Speaker 9: an organizational perspective, and a process perspective. For example, a
20:37
Speaker 9: lot of people that we interviewed in an AI readiness
20:40
Speaker 9: report said they were enthusiastic and optimistic about AI. That's great. However,
20:45
Speaker 9: half of the leaders said they weren't ready, and only
20:47
Speaker 9: a third of CEOs believe that their top leadership is
20:51
Speaker 9: AI fluent. So there is a dissonance between the excitement
20:54
Speaker 9: and enthusiasm and the reality of the preparedness of organizations.
20:58
Speaker 9: And what we do is make sure that organizations have
21:01
Speaker 9: the coaching and support they need to get there.
21:03
Speaker 3: I would think one of the challenges, just speaking for
21:05
Speaker 3: myself is I learned a whole lot speaking to again
21:07
Speaker 3: and the smart people from NJIT what AI is. I'm
21:11
Speaker 3: one of those people that says, if you can't explain
21:13
Speaker 3: it in one sense, you don't understand it. And I
21:15
Speaker 3: don't think I understand it. What's the basic framework that
21:17
Speaker 3: you try to get across your clients about what AI
21:19
Speaker 3: is and what it can mean for them?
21:21
Speaker 9: Yeah, think about AI and one of the biggest generative
21:24
Speaker 9: AI tools right now through Microsoft is copilots. Think of
21:27
Speaker 9: it as a co pilot, not necessarily a replacement pilot that.
21:31
Speaker 4: Can allow you to articulating.
21:34
Speaker 9: Yeah, allow you to do your job more effectively and
21:37
Speaker 9: more efficiently. And so instead of thinking about AI as
21:40
Speaker 9: a job replacement, think about it as a way to
21:42
Speaker 9: replace key tasks and allow you to spend your days
21:46
Speaker 9: in ways that you want to, talking to people, being
21:49
Speaker 9: more relationship focused rather than necessarily summarizing emails or going
21:54
Speaker 9: through data sets, et cetera.
21:55
Speaker 3: So it's a partner.
21:57
Speaker 4: So basically I could have some AI. Think go throw
22:00
Speaker 4: my email and like correlate the important parts and give
22:02
Speaker 4: it out, for example, and take it and give it
22:04
Speaker 4: to me, so I don't have to spend my home
22:06
Speaker 4: morning going through and reading reports.
22:08
Speaker 3: Yeah exactly.
22:09
Speaker 4: That's really cool. Yeah, and that would make me so
22:11
Speaker 4: much time to go do other stuff.
22:12
Speaker 3: Yeah.
22:13
Speaker 9: I mean, think about when you come back from vacation.
22:14
Speaker 9: You probably check your email when you're on vacation. I don't,
22:17
Speaker 9: but for an exact.
22:18
Speaker 4: Reason, because if I come back, I have like two
22:21
Speaker 4: thousand emails being gone for like a week, and I can't.
22:23
Speaker 9: Keep that I can't do it if you had the
22:25
Speaker 9: AA tool, what you could do after being away for
22:27
Speaker 9: two weeks. I don't check my email and probably get
22:29
Speaker 9: in trouble for that.
22:30
Speaker 5: But I don't.
22:30
Speaker 9: I can come back and say, what did I miss
22:32
Speaker 9: over the last two weeks, go through all my pings
22:34
Speaker 9: on teams, go through all my outlook, and can you
22:36
Speaker 9: prepare for me a summary so that now that I
22:39
Speaker 9: come back, I can actually be ready and can prioritize.
22:41
Speaker 9: That's where it really comes into.
22:44
Speaker 4: Wow, that's really cool.
22:45
Speaker 3: Yeah, So what when you sit down with your clients,
22:48
Speaker 3: I mean, what's some of the common requests you get
22:50
Speaker 3: from them or what do they ask for most of
22:52
Speaker 3: the help with I guess.
22:53
Speaker 8: Yeah.
22:53
Speaker 9: One of the things that's really top of mind for
22:55
Speaker 9: people is about skill set and training and capability building.
22:59
Speaker 9: So in our survey, we found that eight out of
23:02
Speaker 9: ten people said that twenty hours of their work week,
23:06
Speaker 9: almost fifty percent of their work week can be replaced
23:09
Speaker 9: with AI tools. The challenges they don't know how to
23:11
Speaker 9: use the tools in the most effective and efficient way,
23:14
Speaker 9: so the training around that is critical in the process.
23:17
Speaker 9: The other is a responsible AI a governance set. Right, Yeah,
23:21
Speaker 9: what are the guard rails that we have to put
23:23
Speaker 9: into place so that people can play creatively in the space.
23:26
Speaker 4: Do you feel like people and CEOs or board levels
23:30
Speaker 4: are do they now know what they don't know, they
23:32
Speaker 4: are beginning to figure it out, or we're still in
23:34
Speaker 4: the beginning part of that.
23:35
Speaker 9: I believe we're in the infancy of it. I think
23:37
Speaker 9: there is an infancy of the learning curve, but also
23:39
Speaker 9: an infancy of having the right people in the room,
23:42
Speaker 9: having diverse perspectives. As we think about responsible AI.
23:45
Speaker 3: And we're hearing you mentioned the I guess the ethical
23:48
Speaker 3: use of AI. I don't know how that's going to evolve.
23:52
Speaker 3: Is that going to be some partnership between public, private,
23:54
Speaker 3: the individual.
23:55
Speaker 4: I'm not sure I actually know what that means. Well,
23:58
Speaker 4: it just seems like ethical use of AI.
24:00
Speaker 3: Yeah, it just seems like the technology could get out
24:02
Speaker 3: of control.
24:03
Speaker 9: Look, as AI and generative AI becomes more ubiquitous, with
24:07
Speaker 9: increased scale comes increased risk. That's just the reality of things.
24:10
Speaker 9: So how do you mitigate those risks? I think one
24:13
Speaker 9: of the most important ways to do that is to
24:15
Speaker 9: have the right people in the room. So, whether that's
24:18
Speaker 9: from a diversity perspective of gender, whether that's having people
24:21
Speaker 9: of color in the room, people from diverse backgrounds. It's
24:24
Speaker 9: one of the reasons that we do the scholarship program
24:26
Speaker 9: for women in STEM at this very institute, because we
24:29
Speaker 9: want to make sure that they're not brought in as
24:31
Speaker 9: a second thought, but rather at the very beginning of
24:34
Speaker 9: the conversation.
24:35
Speaker 4: So, what's like an unethical use of AI? Like, where
24:37
Speaker 4: does AI get bad?
24:39
Speaker 5: Yeah?
24:39
Speaker 9: Well, I mean, look, you can use you can use
24:41
Speaker 9: AI to create images that don't actually exist. You can
24:44
Speaker 9: put voices on people to say things through their own
24:47
Speaker 9: voice when they may maybe have not said that video.
24:50
Speaker 9: You can think about putting in questions into generative AI
24:53
Speaker 9: that perhaps share data with the broader public that you
24:56
Speaker 9: didn't want to share that's company specific data. So there's
24:58
Speaker 9: a security component, there's a falsification component, There's lots of
25:02
Speaker 9: different ways you kind of have to be proactive about.
25:04
Speaker 3: And on this front, once again, maybe at no fault
25:07
Speaker 3: of their own, the government is generations behind where the
25:11
Speaker 3: technology is. I don't know how this plays out, I
25:14
Speaker 3: really don't. I mean, is there a feeling that the
25:17
Speaker 3: industry for a while is going to have to police
25:18
Speaker 3: itself or is there going to be some again public
25:21
Speaker 3: private partnership in terms of regulating this, because this is
25:24
Speaker 3: not the FCC regulating the airwaves. This is really really difficult.
25:30
Speaker 9: Yeah, it gets complicated. Look, I think there's an individual
25:33
Speaker 9: level to it, an individual level of responsibility, But at
25:36
Speaker 9: the end of the day, it's going to fall on
25:38
Speaker 9: the leaders, the leaders of organizations across the board. If
25:41
Speaker 9: the senior leaders are not thinking about responsible AI, they're
25:44
Speaker 9: not thinking about the AI fluency, no one else is
25:48
Speaker 9: going to think about that. So the responsibility on the
25:50
Speaker 9: leaders is very high.
25:51
Speaker 3: Our thanks to Anita Givanni, Global head of Innovation at ABANAT.
25:54
Speaker 4: Coming up on the program, our conversation on AI continues,
25:57
Speaker 4: we speak with Michael Johnson, President of the New Jersey
25:59
Speaker 4: Innovation Institute.
26:00
Speaker 3: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in
26:03
Speaker 3: depth research and data on two thousand companies and one
26:05
Speaker 3: hundred and thirty industries. You can access Bloomberg Intelligence via Bigone,
26:09
Speaker 3: the Terminal on Paul Sweeney, m Alex Steele, and.
26:11
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26:17
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26:21
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26:25
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26:28
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26:31
Speaker 2: Just say Alexa play Bloomberg eleven thirty.
26:36
Speaker 4: Earlier in the week, Bloomberg Intelligence broadcasted live from the
26:39
Speaker 4: campus of the New Jersey Institute of Technology, and the
26:42
Speaker 4: main topic of conversation was AI.
26:44
Speaker 3: We spoke with Michael Johnson, president of the New Jersey
26:47
Speaker 3: Innovation Institute or NJII. It's a standalone corporation owned by NJIT.
26:53
Speaker 3: We first asked Michael what NJII does.
26:56
Speaker 10: In the US. We have lots of research universities and
26:59
Speaker 10: there's lots of smart people, lots of great resources, but
27:02
Speaker 10: there's this fundamental problem in academia, which is it's tough
27:05
Speaker 10: for the outside world actually leverage those resources. So for
27:08
Speaker 10: governmental organizations for industry, they want access to the cutting
27:11
Speaker 10: edge of AI, for example, but it's tough for them
27:13
Speaker 10: to actually make those connections and interact with faculty. So
27:16
Speaker 10: NGI is an organization. It's a five oh one C
27:19
Speaker 10: three wholly owned by NGT, and the idea is that
27:22
Speaker 10: we are a standalone corporation that's a conduit between the
27:24
Speaker 10: outside world and NGT, So we make those facilitations, we
27:29
Speaker 10: create unique business models to work with faculty, and we're
27:31
Speaker 10: a quick moving organization, unlike academia, which is you know,
27:35
Speaker 10: tends to be slower and more difficult to work with.
27:37
Speaker 10: So we're that conduit between them and the outside world
27:39
Speaker 10: and roughly have about one hundred and twenty folks out
27:41
Speaker 10: of organization we're focused on that can.
27:43
Speaker 4: Just say it's really cool his three year old son
27:45
Speaker 4: is here. I mean, what three year old is going
27:48
Speaker 4: to come and talk about AI. I feel like that
27:50
Speaker 4: just says it all at the end of the day, right,
27:52
Speaker 4: that is the future? So am I a company that
27:54
Speaker 4: goes to you and then you pair me up with
27:56
Speaker 4: something or is it sort of the technology that you're evolving,
27:59
Speaker 4: and then you go pitch it to companies.
28:00
Speaker 5: How does that work?
28:01
Speaker 10: It's a bit of inside out and outside in. So
28:03
Speaker 10: we can go to corporations and try and find out
28:04
Speaker 10: what their problems are, what their pain points are, and
28:06
Speaker 10: then go and find faculty you can help out. Or
28:08
Speaker 10: we might have a few faculty that have a very
28:10
Speaker 10: specific problem. They need access to software, they need to
28:12
Speaker 10: access the resources, and we go externally to find a
28:15
Speaker 10: way to work with corporations on that, but it's pairing
28:17
Speaker 10: the two with each other. And faculty are really smart,
28:20
Speaker 10: they're really focused on their research, but they don't always
28:22
Speaker 10: have the mind to go out and actually execute on
28:23
Speaker 10: consultant type projects for industry. So we help form that
28:26
Speaker 10: framework and along the way we're trying to help with
28:29
Speaker 10: tech transfers, so getting technology out of the university into
28:31
Speaker 10: products and services is always a pain point, and also
28:34
Speaker 10: just generally accelerating innovation and also helping upskille workers.
28:39
Speaker 3: You know, over the last several quarters, Bloomberg does this analysis.
28:41
Speaker 3: It shows what are companies talking about on their quarterly
28:44
Speaker 3: conference calls, And for the last several quarters, every single
28:47
Speaker 3: company in Y S and P five hundred has talked
28:49
Speaker 3: about AI, with the exception of Apple. Last quarter have
28:52
Speaker 3: I mentioned AI? Which is interesting? What are companies most
28:55
Speaker 3: commonly asking you for help with?
28:58
Speaker 4: Oh?
28:58
Speaker 2: Man?
28:58
Speaker 10: That goes all over the place. It depends in the companies.
29:01
Speaker 10: We have some small mom and pop businesses that just
29:03
Speaker 10: want help, but trying to move towards technology, towards computers.
29:06
Speaker 10: We have other companies, for example, that want to be
29:08
Speaker 10: the bleeding edge of some sort and field. So for example,
29:11
Speaker 10: it might be life sciences, it might be AI for example,
29:13
Speaker 10: and they're asking us to help improve something that they're
29:15
Speaker 10: already doing, or it's a very specific project they're pushing
29:18
Speaker 10: us to find faculty to help out with. So it
29:20
Speaker 10: kind of depends. We have other folks. For example, Picatinny
29:23
Speaker 10: Arsenal and Department of Defense are looking for just workers,
29:26
Speaker 10: so helping us upscale workers for advanced manufacturing and all
29:29
Speaker 10: sorts of different programs they need help finding talent for
29:31
Speaker 10: so we're trying to help that with that as well.
29:33
Speaker 4: So JPM we're going to interfew saw this. They had
29:35
Speaker 4: a great piece out that said that some of its
29:37
Speaker 4: corporate customers are slashing manual work by almost ninety percent
29:41
Speaker 4: with its cash flow management tool that runs on AI.
29:46
Speaker 4: And that's the fear, right that we're going to use
29:47
Speaker 4: AI and replace all the workers and those workers don't
29:50
Speaker 4: have any jobs. Is there any truth to that?
29:52
Speaker 10: It's a great question. So whenever you have technologies they
29:55
Speaker 10: are disruptive, there are going to be jobs certainly that
29:57
Speaker 10: are going to go away. But if you look back
29:59
Speaker 10: to when accel first came out, or when computers first
30:01
Speaker 10: came out, you look at accounting as a great use case.
30:04
Speaker 10: Accountants didn't go away because we were going from a
30:06
Speaker 10: ledger that was literally on paper to a computer based system.
30:08
Speaker 10: We found new questions to answer, new ways that we
30:10
Speaker 10: could look at our accounting and finances. So I think
30:12
Speaker 10: the jobs are going to change, But the overall number
30:15
Speaker 10: of jobs in that net, I don't know if it
30:17
Speaker 10: will actually reduce. It might increase in some cases. But
30:19
Speaker 10: we're going to answer different questions. We're going to do
30:21
Speaker 10: things much more quickly than we did in the past,
30:23
Speaker 10: for sure.
30:24
Speaker 3: I guess my lack of knowledge of full appreciation of
30:26
Speaker 3: AIS is I'm just not sure if it's something completely
30:29
Speaker 3: new or is it just the next iteration of what
30:31
Speaker 3: the smart people at NJIT typically do. Is it I'm
30:35
Speaker 3: just not sure what's new about it other than Man,
30:38
Speaker 3: everybody's talking about it, and it was a theme. One
30:41
Speaker 3: of the themes that drove the stock market in twenty
30:43
Speaker 3: twenty three was a concept of AI and the average
30:45
Speaker 3: trader has no idea what AI is, but he's buying
30:48
Speaker 3: stocks because he thinks they're an AI play.
30:49
Speaker 10: It's been around for decades, right, but we have a
30:52
Speaker 10: couple of technologies that came out in the last two
30:53
Speaker 10: years that have really transformed the way we see AI
30:55
Speaker 10: and while we're talking about it, and the reason is
30:57
Speaker 10: because now it's accessible. So for example, to years ago,
31:00
Speaker 10: if I go into Google and I tell you how
31:01
Speaker 10: do I make chicken palm? I got all these ads,
31:04
Speaker 10: I get all these things that tell me about chicken parm.
31:06
Speaker 10: I go in a chat GPT and I type that
31:07
Speaker 10: for example, and I get a perfect recipe on how
31:09
Speaker 10: to actually make that, So it becomes very accessible to anyone.
31:12
Speaker 10: And I think that go to market strategy. The open
31:14
Speaker 10: I had of making accessible is what really changed the game.
31:17
Speaker 10: And also the same time computing power is exponentially increasing,
31:20
Speaker 10: it's more accessible. We're now able to use it everywhere
31:23
Speaker 10: from making chicken palm to try and do research.
31:25
Speaker 4: So what kind of cool stuff are you guys working
31:27
Speaker 4: on right now? Like what were you most excited about?
31:29
Speaker 10: For us as NGI, what we're very focused on is
31:31
Speaker 10: trying to get things out at the university into the
31:33
Speaker 10: real world. And one specific project that we're working on
31:36
Speaker 10: is actually on law enforcement and body cams. So bodycams
31:39
Speaker 10: is there a sensor that generates a huge amount of
31:41
Speaker 10: data and from those data sets, we're usually looking at
31:44
Speaker 10: them after the fact, so after something bad happens, we're
31:47
Speaker 10: trying to review that situation. What we're trying to do
31:50
Speaker 10: is can we look at that data and predict something
31:52
Speaker 10: bad is going to happen before it happens. So if
31:54
Speaker 10: we see a pattern between some behaviors, running back time for.
31:57
Speaker 4: A second, so you have a BTE so you're tracking
31:59
Speaker 4: the behavior to then model behavior later.
32:02
Speaker 10: Yes. So for example, let's say we see an officer
32:04
Speaker 10: is running more frequently, they're yelling more frequently. That has
32:07
Speaker 10: probably correlated to some behavior outcome, such as excessive use
32:10
Speaker 10: of force. So for example, we might identify this officer
32:13
Speaker 10: as at a much higher likelihood of excessive use in
32:15
Speaker 10: force in the future. Let's intervene and get them training
32:17
Speaker 10: before something bad happens. So we're trying to build that
32:19
Speaker 10: a software we can actually put onto the hardware and
32:22
Speaker 10: help the law enforcement and help with de escalating situations.
32:25
Speaker 4: Wow, that's really cool.
32:27
Speaker 3: What other stuff like?
32:28
Speaker 4: What are the thing are you excited about?
32:29
Speaker 10: Oh man, there's so many Take your second best. My
32:32
Speaker 10: second best would definitely be in the drone space. So
32:34
Speaker 10: drones are another sensor. We're collecting huge amounts of imagery data,
32:37
Speaker 10: and today a lot of that work is actually a
32:39
Speaker 10: person looking at videos, scrolling through video like you would
32:41
Speaker 10: from a VHS tape, and we're using computer vision and
32:44
Speaker 10: AI to actually analyze that data and try to predict
32:47
Speaker 10: what's happening and try to classify certain imagery and answer
32:49
Speaker 10: very specific questions like is a power line going to
32:52
Speaker 10: fail based upon a single picture from a simple drone?
32:54
Speaker 4: Oh, now that could be really helpful, dependually all the
32:56
Speaker 4: wildfires and stuff that we've had. And then as all
32:58
Speaker 4: the utilities are kind of grappling with like old infrastructure
33:01
Speaker 4: that is not easy to replace, kind of how you
33:03
Speaker 4: manage that? Is it expensive for these companies to use this?
33:07
Speaker 10: Usually the bottleneck today is data generation and data annotation
33:10
Speaker 10: because there's lots of data, but we have to annotate
33:12
Speaker 10: the data to be actually able to use it. So,
33:14
Speaker 10: for example, if the body cams, we have to know
33:16
Speaker 10: what those events are that we're trying to predict and
33:18
Speaker 10: how she classifying them ahead of time. So that's the
33:20
Speaker 10: real the bottleneck for it in a lot of cases.
33:22
Speaker 4: All right, thanks to Michael Johnson, president of the New
33:24
Speaker 4: Jersey Innovation.
33:25
Speaker 3: Institute, let's stick with our conversations on artificial intelligence at
33:28
Speaker 3: the New Jersey Institute of Technology. New Jersey Governor Phil
33:31
Speaker 3: Murphy recently laid out further details of a so called
33:34
Speaker 3: AI moonshot plan. The proposed plan would include seven million
33:38
Speaker 3: dollars to advance AI utilization and opportunities in the state.
33:41
Speaker 4: We were joined by AI expert Beth Simon Novic, who
33:44
Speaker 4: was recently appointed as New Jersey's first ever Chief AI Strategist,
33:48
Speaker 4: and we asked Beth how she helps implement Governor Phil
33:51
Speaker 4: Murphy's vision of having New Jersey lead the nation in
33:54
Speaker 4: the advancement of AI.
33:55
Speaker 7: Governor Murphy has said very loud and clear, we have
33:58
Speaker 7: to do better when it comes to technology in terms
34:00
Speaker 7: of embracing the use of technology to grow the economy,
34:04
Speaker 7: to grow jobs in the state, but also to improve
34:06
Speaker 7: how government works. So my job is to work on
34:09
Speaker 7: all of the above and to see what we can
34:11
Speaker 7: do as government to make that easier, to make that better,
34:14
Speaker 7: and to embrace the responsible and ethical use of AI
34:16
Speaker 7: to ensure that we're doing right by our residents.
34:19
Speaker 3: So what are some of the applications that the governor
34:23
Speaker 3: and the Governor's office thinks AI can do Over the
34:26
Speaker 3: next several years, Where will the residents of New Jersey
34:29
Speaker 3: see it? Do you think?
34:29
Speaker 7: So this is not a several years from now. The
34:32
Speaker 7: future is already here. And we've been using AI for
34:35
Speaker 7: quite some time, and generative AI since the very beginning,
34:37
Speaker 7: so in many ways that you don't even see or
34:39
Speaker 7: know about. So, for example, if you're getting a letter
34:41
Speaker 7: from the State of New Jersey about let's say your
34:43
Speaker 7: unemployment benefits, you're getting a letter that has been simplified,
34:47
Speaker 7: that has been written in plain English, that's been written,
34:50
Speaker 7: we hope, more clearly than it would have been before
34:53
Speaker 7: because generative AI can help us to do a first draft.
34:56
Speaker 7: If you're calling up about your anchor tax relief that
34:59
Speaker 7: the State of New Jerse is giving out to residents,
35:01
Speaker 7: you are hopefully getting your call resolved faster because you
35:05
Speaker 7: get a menu option that's we've written with the help
35:07
Speaker 7: of AI. Because voice to text our call center operators
35:10
Speaker 7: know people are calling in asking the following kinds of questions,
35:13
Speaker 7: we should write these menu options and these instructions and
35:16
Speaker 7: answers so people can get that information faster. When you're
35:20
Speaker 7: going out, for example, and typing in on a website
35:23
Speaker 7: and telling us comments of how we can do something
35:25
Speaker 7: better on a website like business dot J dot gov,
35:28
Speaker 7: where you can go to start and run and grow
35:30
Speaker 7: your business everything you need from one place. We're taking
35:33
Speaker 7: the comments we're getting from citizens about what they need
35:35
Speaker 7: about what they want, using AI to help us summarize
35:38
Speaker 7: those comments, synthesize them, and turn that into the information
35:42
Speaker 7: that people want and need front and center. So the
35:44
Speaker 7: goal is government that's more responsive, more informative, and providing
35:49
Speaker 7: services twenty four to seven that are responsive to what
35:52
Speaker 7: people actually want and need.
35:53
Speaker 4: That's a pretty good pitch. You were also the chief
35:56
Speaker 4: of innovation, right, h Jersey.
35:58
Speaker 7: I was for many years the chief Innovation Office.
36:00
Speaker 4: So did the Chief Innovation Officer become the AI strategist
36:04
Speaker 4: or is there also an innovation officer? And I guess
36:06
Speaker 4: I'm trying to understand, like is the innovation thing now
36:08
Speaker 4: AI or can there be other stuff?
36:11
Speaker 7: There is still other stuff. We have a wonderful new
36:13
Speaker 7: Chief Innovation Officer, Dave Cole, has taken over that title
36:18
Speaker 7: and is leading our efforts to use technology to improve
36:20
Speaker 7: how we bring services to residents. So projects like business
36:24
Speaker 7: dot J dot gov to take the business one, for example,
36:27
Speaker 7: or other digitization of residence services, so that instead of
36:30
Speaker 7: having to go to a government office, you know, between
36:33
Speaker 7: nine and five, you can come to a website, you
36:35
Speaker 7: can use your mobile phone.
36:36
Speaker 4: Oh my gosh, that'd be.
36:36
Speaker 7: Amazing transact with government twenty four to seven. That's work
36:41
Speaker 7: that's been underway for a long time, and that doesn't
36:43
Speaker 7: just depend on AI. That is about again, clearer instructions,
36:46
Speaker 7: planer English, things available online, giving you the information front
36:50
Speaker 7: and center that you want and need in the way
36:52
Speaker 7: that people have become accustomed to from the best businesses.
36:55
Speaker 7: We think that government should serve citizens in much the
36:58
Speaker 7: same way, except in the public interest.
37:00
Speaker 3: Well, New Jersey's had a long history of technological innovation.
37:03
Speaker 3: I think of telecommunications with Bellcore and Bell Labs supporting
37:07
Speaker 3: eighteen teen Verizon. I think about some of the biotech
37:09
Speaker 3: and pharmaceutical companies like Johnson and Johnson based here in
37:12
Speaker 3: New Jersey. I'm wondering, is there support for the young
37:17
Speaker 3: NJ grads that are in a garage somewhere in Jersey
37:20
Speaker 3: City coming up with the next AI type thing. How
37:22
Speaker 3: do we support those people?
37:24
Speaker 7: Absolutely so, there are a whole number and range of
37:27
Speaker 7: investments that are out there to support people starting new businesses.
37:32
Speaker 7: That's what my colleagues at EDA work on in particular,
37:34
Speaker 7: is ensuring that we're providing those kinds of incentives for
37:37
Speaker 7: people who want to start their business in New Jersey
37:40
Speaker 7: and grow their business in New Jersey. That's particularly why
37:42
Speaker 7: the government is here to help support those businesses going
37:45
Speaker 7: out and in particular now to look at how we
37:47
Speaker 7: can support new AI businesses or existing businesses who are
37:51
Speaker 7: asking how we can turn around and use AI to
37:54
Speaker 7: improve what we do. It's a question we've been answering
37:56
Speaker 7: for a long time. Before we called it AI. We
37:58
Speaker 7: called it big data.
38:00
Speaker 3: Yep, right.
38:01
Speaker 7: So the more the people we're using a lot of
38:03
Speaker 7: businesses have asked themselves, how could I go out and
38:05
Speaker 7: start using data to measure what's working, to measure what
38:08
Speaker 7: customers want, and again to deliver new kinds of services
38:11
Speaker 7: across a range of industries. It's why we've been starting
38:15
Speaker 7: new partnerships, such as with Princeton, around this new AI
38:18
Speaker 7: hub that's been set up so that we can connect
38:20
Speaker 7: some of that tremendous innovation that's coming out of universities
38:23
Speaker 7: like NJIT, like Rutgers, like Princeton. We're of course, known
38:27
Speaker 7: in this state for having the best universities and the
38:29
Speaker 7: best education system in the country, and we want to
38:31
Speaker 7: connect that back to how we're growing the economy and
38:34
Speaker 7: growing jobs. Hearing the state all right?
38:36
Speaker 4: Thanks to Bethsimono Noovic, Chief AI Strategist of the State
38:39
Speaker 4: of New Jersey.
38:40
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