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Speaker 1: Bloomberg Audio Studios, podcasts, radio news.

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Speaker 2: This is Bloomberg Intelligence with Alex Steinel and Paul Sweeny.

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Speaker 3: The real app performance has been in US corporate high yield.

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Speaker 4: Are the companies lean enough? Have they trimmed all the fats?

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Speaker 3: The semiconductor business is a really cyclical business.

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Speaker 2: Breaking market headlines and corporate news from across the globe.

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Speaker 4: Do investors like the M and A that we've seen?

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Speaker 3: These are two big time blue chip companies.

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Speaker 4: The window between the peak and cut changing super fast.

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Speaker 2: Bloomberg Intelligence with Alex Steinel and Paul'sweeny on Bloomberg Radio.

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Speaker 3: On Today's Bloomberg Intelligence Show, we dig inside the big

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Speaker 3: business stories impacting Wall Street and the global markets.

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Speaker 4: Each and every week, are going to provide in depth

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Speaker 4: research and data on some of the two thousand companies

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Speaker 4: and one hundred and thirty industries our analysts cover worldwide.

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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

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Speaker 3: the week, Target reported fourth quarter profit that beat analyst expectations.

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Speaker 4: So this comes as the company reduced its stockpile of

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Speaker 4: merchandise by about twelve percent during the quarter. So Target

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Speaker 4: also confirmed it's going to launch a paid membership program,

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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

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Speaker 5: several years and that is resonating well with investors, and

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Speaker 5: it's all about recapturing top plane growth, traffic, market share,

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Speaker 5: and they're a little bit more upbeat on where they

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Speaker 5: see the consumer right now.

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Speaker 6: And when we look at inventory being an issue that

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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

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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

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Speaker 5: they had huge issues a couple of years ago with markdowns,

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Speaker 5: and they've really been able to write size inventory that's

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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

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Speaker 5: than they've been. That focus on those retail fundamentals of

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Speaker 5: making sure that you have things in the stores and

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Speaker 5: in stock has really been playing through and driving some

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Speaker 5: of the results that they've seen, and a lot of

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Speaker 5: that stems from inventory. And so right now they're very

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Speaker 5: well positioned going into twenty twenty four and that should

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Speaker 5: hopefully be a tailwind for them for the rest of

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Speaker 5: this fiscal year.

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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

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Speaker 5: was Target Circle, which was free to join, and they

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Speaker 5: have one hundred million users that have joined it, and

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Speaker 5: that's where you can go in and save different offers

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Speaker 5: and you get an extra ten percent off your paper

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Speaker 5: towels you know that week and that brand. What they're

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Speaker 5: doing is they're making it so you don't have to

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Speaker 5: search and save. You get the things automatically when you

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Speaker 5: check out, and if you're a Red Card holder, for

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Speaker 5: forty nine dollars, you can get unlimited free delivery, same

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Speaker 5: day free delivery to your house and they're calling this

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Speaker 5: Target three sixty. You can also subscribe to just that

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Speaker 5: if you're not a Red Card holder. Also an introductory

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Speaker 5: price of forty nine, but it will go up over time,

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Speaker 5: although they didn't say to what. So it's a move

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Speaker 5: where they're trying to get, you know, a little bit

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Speaker 5: deeper into consumers' lives, you know, doing more home delivery.

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Speaker 5: There's some additional perks in there where you can access

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Speaker 5: all of what Ship does, which is their same day

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Speaker 5: delivery service, and that includes being able to shop at

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Speaker 5: other retailers. So that's kind of the differentiator here with

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Speaker 5: regards to the membership program that once you have it,

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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

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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

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Speaker 6: like everyone pays for Amazon Prime and Walmart Plus I

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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

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Speaker 6: seems like, at least looking at the streaming platforms they're

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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

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Speaker 5: as they really talk about what differentiates this program, because

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Speaker 5: you know, forty nine dollars is cheap, but it is

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Speaker 5: an introductory price, and there are a lot of people

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Speaker 5: out there that do delivery, and so you know, when

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Speaker 5: I look at it, I think in order for it

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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

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Speaker 5: them to pay extra And historically, you know, Target's formula

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Speaker 5: for success has never been mimicking others. It's been about

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Speaker 5: kind of forging their own path. And I think this

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Speaker 5: is an example where it's going to be very very

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Speaker 5: important for them to do that. But it's not clear

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Speaker 5: yet how that's going to be realized.

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Speaker 6: And how does Target play into a recession. Are they

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Speaker 6: one of the companies that can weather the storm because

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Speaker 6: of their loyalty or how does that play out just

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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

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Speaker 5: skew to more discretionary items, and so whenever there's a

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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

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Speaker 5: been really focused in the last eighteen months, especially on value.

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Speaker 5: They've launched some new private label brands that are meant

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Speaker 5: to be the cheapest option in every category in the store.

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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

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Speaker 5: make it harder for them to get back to that

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Speaker 5: top line growth and that expansion of margin that they're

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Speaker 5: talking about if we find ourselves in the midst of

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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,

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Speaker 5: it is usually about lowest price. Walmart is a EDLP.

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Speaker 5: Every day you get the lowest possible price. They've got

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Speaker 5: a very big assortment and they have some perks with

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Speaker 5: regards to their Walmart Plus program. When people think of Target,

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Speaker 5: they think a little bit more of discovery, They think

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Speaker 5: a little bit more about inspiration than functionality, and that's

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Speaker 5: where you really see the divergence between those two retailers.

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Speaker 6: I will say, when I go to Target, I always

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Speaker 6: end up buying more than I want, where if I

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Speaker 6: go to Walmart, I'm kind of in and out. Jen

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Speaker 6: question off of that comparison, I always talk up the

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Speaker 6: fact that Walmart owned Sam's Club. When you look at

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Speaker 6: the company makeup, how much does that differentiate Walmart versus

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Speaker 6: some of the other peers, just given as you mentioned

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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

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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

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Speaker 5: different groups of people and for different shopping reasons. Now,

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Speaker 5: when I talked about shipped, it's not so much that

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Speaker 5: you get the benefits of a Costco membership, for example,

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Speaker 5: with Target. It's just more that you can have your

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Speaker 5: delivery person pick up purchases from other retailers, which is

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Speaker 5: something that isn't in vetted in a Walmart program, for example.

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Speaker 5: But that Target membership fee is going to be competing

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Speaker 5: with your Costco membership fee. It'll be you know, competing

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Speaker 5: with a bjays membership fee along with all of your

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Speaker 5: streaming services and everything else out there. So you know,

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Speaker 5: Walmart definitely benefits from the scale of having both the

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Speaker 5: course stores and Sam's Club, but it is a little

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Speaker 5: bit of a different business model than what we're talking

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Speaker 5: about with Target.

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Speaker 7: Oh.

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Speaker 3: Thanks to Jen Bartashis Bloomberg Intelligence senior retail analysts.

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Speaker 4: All right, we turned out a big tech So this

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Speaker 4: week we learned that Apple iPhone sales in China fell

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Speaker 4: by a surprising twenty four percent. Now this was over

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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.

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Speaker 3: We first asked Anurag what's causing this to decline in

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Speaker 3: the early part of twenty twenty four.

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Speaker 8: This is the same story that's been going on for

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Speaker 8: the last six to nine months, ever since Wallwei released

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Speaker 8: their higher end phone last year. So Apple's been losing

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Speaker 8: market share there that has been you know, you could

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Speaker 8: say opened or coupled with consumer weakness in China. And

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Speaker 8: it's all that's you know, driving these problems right now.

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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

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Speaker 6: call it a fifth of their sales anak. When you

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Speaker 6: look at those numbers, what does this actually mean for

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Speaker 6: Apple if they're going to see that competition eating into

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Speaker 6: their sales.

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Speaker 8: Yeah, I think it's going to be a tough time

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Speaker 8: for Apple in China for that at least this year

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Speaker 8: and maybe into next year before they can, let's say,

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Speaker 8: make some inroads in India and other markets. Emerging markets

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Speaker 8: is the real growth driver for Apple, there is no

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Speaker 8: two ways about it. And and you know iPhone is

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Speaker 8: the big growth driver. So if phones are not selling

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Speaker 8: in China, that's a problem for Apple. It means numbers

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Speaker 8: need to come down even more for Apple this year.

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Speaker 8: You know, we saw about double digit sales drop in

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Speaker 8: China last quarter. I looked up on MDL consensus is

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Speaker 8: about seven percent drop in China for this quarter. I

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Speaker 8: think that number needs to creep up to someone in

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Speaker 8: the low double digit decline going forward. It's a painful

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Speaker 8: situation for Apple, and frankly, there aren't at many rosy

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Speaker 8: things looking forward, at least for twenty twenty four.

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Speaker 3: So I guess the biggest concern for Apple obviously is competition,

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Speaker 3: because they haven't really had that robust of a competitor

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Speaker 3: and they're part of the market. Are there any responses

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Speaker 3: Apple can make here from a competitive landscape other than

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Speaker 3: lowering the price point?

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Speaker 8: Yeah, Paul, I think there is a little more hype

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Speaker 8: in that competition news than reality only because Huahwei didn't

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Speaker 8: release a phone for many years, So that you can

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Speaker 8: think about it. If you have an install base of

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Speaker 8: WAWE phones. I mean, let's say you know that's X

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Speaker 8: and that hasn't been updated for four or five six years,

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Speaker 8: and you just certainly get a brand new phone. All

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Speaker 8: of those people are going to go and refresh that,

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Speaker 8: So I think you should take that as a bigger factor,

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Speaker 8: plus the subsidies they are getting in China from the

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Speaker 8: local providers. So I think Apple will do okay in

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Speaker 8: China over the long term. But I think that's not

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Speaker 8: going to be a twenty twenty fourth story.

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Speaker 6: And you mentioned emerging markets as the next driver. What countries,

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Speaker 6: what regions are going to be able to pay up

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Speaker 6: for what I would say, is quite an expensive phone.

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Speaker 8: Yeah, I think that's the most important question, and I

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Speaker 8: think you know, just by the sheer size of it,

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Speaker 8: India is the next one. But frankly speaking, right now,

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Speaker 8: Apple doesn't even operate in you know, five percent of

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Speaker 8: the entire market because of the price point of the phone.

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Speaker 8: I think the strategy India is going to be a

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Speaker 8: mix of the lower phone the se as well as

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Speaker 8: the refurbished phone where the price point is even lower.

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Speaker 8: But having said that, I think India is a developing country.

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Speaker 8: The middle class is getting more richer, so I think

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Speaker 8: that's going to be the next big growth catalyst. But

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Speaker 8: this is it's not going to play out in twenty

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Speaker 8: four to twenty five. That's more of a I would

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Speaker 8: submit to long term story.

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Speaker 3: We've talked about this before that Apple might introduce a

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Speaker 3: lower price phone into India for just that reason. Is

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Speaker 3: that something they're still considering or will they just wait

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Speaker 3: for the market to kind of move up to where

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Speaker 3: the Apple phone price point is.

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Speaker 8: I think it's going to be the latter. I've done

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Speaker 8: a lot of analysis of how much share they can

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Speaker 8: gain if they drop the S price by fifty dollars

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Speaker 8: hundred dollars, and you know, when I publish that stuff,

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Speaker 8: I think they actually raise the price by fifty bucks.

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Speaker 8: So they don't believe in dropping prices. They are more

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Speaker 8: on a margin story. So think of Apple more on

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Speaker 8: the long term basis right now, not on the short term.

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Speaker 8: I don't think they're going to you know, I would

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Speaker 8: say swap margins for market share. They've never done that

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Speaker 8: in their history, whether it was on the mac side

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Speaker 8: or on the phone side.

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Speaker 6: And quickly anorak, I look at the news, there's no

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Speaker 6: more Apple car. There's tepid reception to the vision pro

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Speaker 6: What actually drives the stock, what drives sales in the

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Speaker 6: next twelve to eighteen months.

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Speaker 8: Yeah, I think that's the most important question for Apple investors.

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Speaker 8: And I think there's going to be an event in June,

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Speaker 8: the World Wide Developers Conference, where they have pledged that

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Speaker 8: they're going to show a lot of AI enhancements to

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Speaker 8: the operating system. I think that really is the one

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Speaker 8: wildcard that can completely change the sentiment of the company,

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Speaker 8: both in terms of sales and the gloomy investor sentiment

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Speaker 8: only because remember, Apple has a distribution network that stands,

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Speaker 8: you know, next to nobody out there in terms of

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Speaker 8: you know, affluent people using their phones. More than one

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Speaker 8: billion devices connected just on the smartphone. I think that

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Speaker 8: really is the big driver. One of the things I

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Speaker 8: was thinking about was if you go back, you know,

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Speaker 8: five years, seven years, there were apps such as trip

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Speaker 8: Advisors an Yelp where people used to go for their

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Speaker 8: you know, specialized functions for restaurant advices and tourism. But

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Speaker 8: when you look at somebody like Google, a lot of

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Speaker 8: that traffic has moved on to them because they control

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Speaker 8: the distribution. I think Apple has the same ability, but

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Speaker 8: they have to show up with some AI products otherwise

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Speaker 8: that's not going to.

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Speaker 3: Flow our Thanks to Ana rog Rana Bloomberg Intelligence Senior

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Speaker 3: Technology Channels, I com.

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Speaker 4: Going to bring a break down the role of AI

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Speaker 4: in sports analytics.

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Speaker 3: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in

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Speaker 3: depth research and data on two thousand companies and one

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Speaker 3: hundred and thirty industries. You can access Bloomberg Intelligence v

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Speaker 3: A BI. I go in the terminal, I'm Paul Swimming and.

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Speaker 4: Am Alex Steel and this is Bloomberg.

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Speaker 2: You're listening to the Bloomberg Intelligence podcast. Catch us live

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Speaker 2: weekdays at ten am Eastern on fo car Play and

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Speaker 2: wherever you get your podcasts, or watch us live on YouTube.

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Speaker 4: Earlier in the week, Bloomberg Intelligence broadcasted live from the

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Speaker 4: campus of the New Jersey Institute of Technology. Paul was

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Speaker 4: pumped and the main topic of conversation was AI.

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Speaker 3: We took a look at zealous Analytics and Austin based

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Speaker 3: sports analytics company that evaluates, predicts, and improves player and

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Speaker 3: team performance in sports.

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Speaker 4: So one of our guests was Evana Serk, senior product

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Speaker 4: scientist at Zealis Analytics, and we asked Havana how Zealos

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Speaker 4: uses AI in sports.

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Speaker 1: This field that has expanded in last maybe ten years

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Speaker 1: of a lot in other sports. Even before that, it

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Speaker 1: was in baseball that was one of the first sports.

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Speaker 1: If you've seen Moneyball, that's that's really yes.

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Speaker 4: Yeah, okay, the moneyball Okay. And so it's basically like

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Speaker 4: how to position, like what players to put where combinations?

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Speaker 4: Is it that kind of stuff?

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Speaker 1: Correct? Correct? So, so player evaluation in game decision strategy,

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Speaker 1: that's sort of sort of things. Yeah.

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Speaker 3: So again, played for your starter for nja's basketball. You

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Speaker 3: also represented your native Croatia and youth basketball. So you're

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Speaker 3: great at basketball, but you're also a math nerd to

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Speaker 3: the nth degree. She got a BS and a pH

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Speaker 3: degree and applied mathematics from njiit focusing on computational fluid dynamics.

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Speaker 4: I don't know what that means.

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Speaker 3: I don't know what that means. That's art, but okay,

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Speaker 3: I don't know. So a great mathematician, great basketball player.

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Speaker 3: Let's put it all together. What are some of the

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Speaker 3: really good applications for some of that technology we've seen?

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Speaker 3: You mentioned moneyball for you know that we've seen it

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Speaker 3: in baseball. What other applications are out there that you think?

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Speaker 3: It seems like we're in the very early innings of that.

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Speaker 1: Early on, it started with just using basic data, so

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Speaker 1: box scores, play by play, and then a lot of

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Speaker 1: sports in recent years have a what's called player tracking data,

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Speaker 1: meaning we have locations of the players on the court

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Speaker 1: or on a pitch, on a field, whichever sport we're

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Speaker 1: talking about, at a high resolution. So from that data

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Speaker 1: we can extract not only things that are counted in

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Speaker 1: a box score, but also other things that happened during

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Speaker 1: the game that you wouldn't see counted in like a

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Speaker 1: basic box score for example.

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Speaker 4: What are some of the common questions that like coaches

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Speaker 4: or owners come to you with the.

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Speaker 1: Biggest question is how do we value players? How do

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Speaker 1: we find which players teams should sign, how long of

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Speaker 1: a contract, how much money should be on a contract.

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Speaker 1: That's one side, So that's the player evaluation side, and

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Speaker 1: then the other side is coaching and in game decision making.

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Speaker 1: So which situations are producing the most value for the teams?

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Speaker 1: Which situations are creating better opportunities to score?

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Speaker 3: I know, like in baseball, major league baseball and in

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Speaker 3: minor league baseball. Now it's coming into all the other

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Speaker 3: parts of baseball. The analytics people, the data people versus

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Speaker 3: maybe some of the more traditionalists. They kind of butt

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Speaker 3: heads on occasion. So how much chanalytics do you use

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Speaker 3: versus just my gut I think this player will do well.

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Speaker 3: How do you kind of bridge that topic?

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Speaker 1: Yeah? Yeah, So that's that's a big important thing, because

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Speaker 1: you can just have data without the domain expertise. And

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Speaker 1: I think that's something that we Adzealas have a really

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Speaker 1: good strength, is that we have the experts in data

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Speaker 1: and statistics in AI in machine learning. But we also

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Speaker 1: have a lot of people who worked in sports teams

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Speaker 1: and have that sort of experience and know which questions

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Speaker 1: the teams want to answer, what's useful for them, and

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Speaker 1: how can we help them best.

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Speaker 4: So yeah, because when you were saying what AI could

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Speaker 4: help you do, it feels like that's not what a

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Speaker 4: coach is supposed to do. But you're saying that you

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Speaker 4: need someone to interpret how to manage that and stuff.

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Speaker 1: Right, right, So you need like a bridge between the

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Speaker 1: data and what's what's happening on the court.

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Speaker 3: All right, If I'm an agent representing a player, now

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Speaker 3: this is I got to learn this stuff because the

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Speaker 3: team's gonna come at.

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Speaker 4: Me and say, this is what the program tells me.

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Speaker 3: That, Yeah, your client's worth blank because his or her

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Speaker 3: OPS is this and blah blah blah blah blah, And

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Speaker 3: you got to come back and say, no, I think

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Speaker 3: he's better than that, and I think he's really more.

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Speaker 3: So do you work with the agents and players themselves

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Speaker 3: as well, because they better be smart on this stuff.

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Speaker 1: Yeah, Yeah, that's it's a great area where where ZELS

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Speaker 1: is growing as well in some of our sports. But

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Speaker 1: but Yeah, an agent cannot learn all of this on

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Speaker 1: their own, so having a company or a contractor who can.

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Speaker 3: So do you guys work with agents and players directly.

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Speaker 1: In certain sports?

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Speaker 4: Yes, yeah, but not all across the board. So you also,

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Speaker 4: as Paul was mentioned earlier, you got your BS and

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Speaker 4: your PhD in applied mathematics and nj T. Because we're

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Speaker 4: here and we're talking about NJIT kind of bridges the

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Speaker 4: gap between learning stuff and then putting it out into

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Speaker 4: the world. How did this help you evolve your career

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Speaker 4: and leave you where you are today?

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Speaker 3: Yeah?

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Speaker 1: Even though I studied competitional fluidnamics, it's not exactly data science,

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Speaker 1: but I've learned a lot of skills.

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Speaker 4: There were transitions, by the way, so you can pretend

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Speaker 4: it is.

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Speaker 1: There's a little skills that transfer from from one field

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Speaker 1: to the other, for example, coding, analyzing large data sets,

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Speaker 1: creating visualizations, and communicating scientific results to regular audience.

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Speaker 3: Are there some sports that are embracing AI or just

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Speaker 3: technology analytics more than others?

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Speaker 1: That's historically in baseball, particularly because they had more advanced

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Speaker 1: data for the longest time, But other sports now also

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Speaker 1: have the player tracking data and are starting to get

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Speaker 1: more on that side.

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Speaker 4: How did you wind up in this? Because if you

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Speaker 4: played basketball, right, because you're originally from Croatia, right, So

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Speaker 4: you played basketball and then you somehow wound up and

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Speaker 4: deep into analytics. How did you do that?

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Speaker 7: Well?

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Speaker 1: I always loved math and I always loved basketball, and

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Speaker 1: this was a perfect combination of the two.

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Speaker 3: Where are we do you think in terms of the

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Speaker 3: evolution of applying data and AI to sports? Because it

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Speaker 3: just the statistics. I've been following sports my entire life,

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Speaker 3: and I'm listening to a broadcast and they're saying stuff.

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Speaker 3: I have no idea what they're talking about, Like now

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Speaker 3: batting average is an important anymore to baseball and now

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Speaker 3: it's on bass plus slugging. I don't know. I mean,

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Speaker 3: it seems like we need a tutorial a lot of

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Speaker 3: these broadcasts. Where can this go? Do you think?

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Speaker 1: Yeah? I wouldn't know about baseball because I don't really

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Speaker 1: understand the rules coming from Croatia. But in basketball, we

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Speaker 1: you know, from now we have a player location data,

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Speaker 1: but it's also growing towards player kinematics data, which NBA

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Speaker 1: has available for Saturday's season Kinematics kinematics, so the locations

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Speaker 1: of players waist, elbow, shoulder, all of the joints, so

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Speaker 1: more detailed data of like player movements and yeah, so

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Speaker 1: how how players are shooting? And you can extract all

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Speaker 1: this more detailed information.

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Speaker 4: Are thanks now to Havana Serk, senior product scientists at

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Speaker 4: Zalis Analytics.

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Speaker 3: Let's stick with our conversations on artificial intelligence. At the

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Speaker 3: New Jersey Institute of Technology, we looked at the company Avonaut,

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Speaker 3: a leading provider of cloud and advisory services. Avonaut was

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Speaker 3: founded as a joint venture between Microsoft and Accenture.

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Speaker 4: We were joined by Anita Giovanni, global head of Innovation

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Speaker 4: at Avonat, and we asked her how the company approaches

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Speaker 4: AI and how AI will impact organizations going forward.

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Speaker 9: Yeah, so, we are a global consultancy, as you mentioned

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Speaker 9: MICROSOFTIC Center, joint venture, sixty thousand employees around the world,

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Speaker 9: and what we do is think about AI from a

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Speaker 9: client perspective. How is it that we can support organizations

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Speaker 9: across sectors be AI first and at the same time,

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Speaker 9: we're all going through this journey together. So thinking about

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Speaker 9: ourselves as an organization, how can we be AI first

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Speaker 9: in our own business processes and for our own people

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Speaker 9: so I'm a company and I come to you, what

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Speaker 9: do you do for me? We think about a lot

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Speaker 9: of things. Are you guys prepared from a people perspective,

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Speaker 9: an organizational perspective, and a process perspective. For example, a

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Speaker 9: lot of people that we interviewed in an AI readiness

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00:20:40,000 --> 00:20:44,640
Speaker 9: report said they were enthusiastic and optimistic about AI. That's great. However,

441
00:20:45,280 --> 00:20:47,640
Speaker 9: half of the leaders said they weren't ready, and only

442
00:20:47,720 --> 00:20:51,480
Speaker 9: a third of CEOs believe that their top leadership is

443
00:20:51,520 --> 00:20:54,680
Speaker 9: AI fluent. So there is a dissonance between the excitement

444
00:20:54,720 --> 00:20:58,800
Speaker 9: and enthusiasm and the reality of the preparedness of organizations.

445
00:20:58,800 --> 00:21:01,399
Speaker 9: And what we do is make sure that organizations have

446
00:21:01,440 --> 00:21:03,440
Speaker 9: the coaching and support they need to get there.

447
00:21:03,560 --> 00:21:05,399
Speaker 3: I would think one of the challenges, just speaking for

448
00:21:05,480 --> 00:21:07,720
Speaker 3: myself is I learned a whole lot speaking to again

449
00:21:07,720 --> 00:21:11,360
Speaker 3: and the smart people from NJIT what AI is. I'm

450
00:21:11,359 --> 00:21:13,040
Speaker 3: one of those people that says, if you can't explain

451
00:21:13,080 --> 00:21:15,040
Speaker 3: it in one sense, you don't understand it. And I

452
00:21:15,080 --> 00:21:17,560
Speaker 3: don't think I understand it. What's the basic framework that

453
00:21:17,600 --> 00:21:19,600
Speaker 3: you try to get across your clients about what AI

454
00:21:19,840 --> 00:21:21,320
Speaker 3: is and what it can mean for them?

455
00:21:21,720 --> 00:21:24,680
Speaker 9: Yeah, think about AI and one of the biggest generative

456
00:21:24,680 --> 00:21:27,760
Speaker 9: AI tools right now through Microsoft is copilots. Think of

457
00:21:27,800 --> 00:21:31,920
Speaker 9: it as a co pilot, not necessarily a replacement pilot that.

458
00:21:31,920 --> 00:21:33,920
Speaker 4: Can allow you to articulating.

459
00:21:34,000 --> 00:21:37,359
Speaker 9: Yeah, allow you to do your job more effectively and

460
00:21:37,400 --> 00:21:40,320
Speaker 9: more efficiently. And so instead of thinking about AI as

461
00:21:40,359 --> 00:21:42,879
Speaker 9: a job replacement, think about it as a way to

462
00:21:42,960 --> 00:21:46,480
Speaker 9: replace key tasks and allow you to spend your days

463
00:21:46,800 --> 00:21:49,680
Speaker 9: in ways that you want to, talking to people, being

464
00:21:49,680 --> 00:21:54,240
Speaker 9: more relationship focused rather than necessarily summarizing emails or going

465
00:21:54,280 --> 00:21:55,880
Speaker 9: through data sets, et cetera.

466
00:21:55,960 --> 00:21:57,159
Speaker 3: So it's a partner.

467
00:21:57,400 --> 00:22:00,240
Speaker 4: So basically I could have some AI. Think go throw

468
00:22:00,320 --> 00:22:02,920
Speaker 4: my email and like correlate the important parts and give

469
00:22:02,920 --> 00:22:04,959
Speaker 4: it out, for example, and take it and give it

470
00:22:04,960 --> 00:22:06,440
Speaker 4: to me, so I don't have to spend my home

471
00:22:06,480 --> 00:22:08,000
Speaker 4: morning going through and reading reports.

472
00:22:08,200 --> 00:22:08,919
Speaker 3: Yeah exactly.

473
00:22:09,000 --> 00:22:11,159
Speaker 4: That's really cool. Yeah, and that would make me so

474
00:22:11,240 --> 00:22:12,640
Speaker 4: much time to go do other stuff.

475
00:22:12,720 --> 00:22:12,960
Speaker 3: Yeah.

476
00:22:13,000 --> 00:22:14,600
Speaker 9: I mean, think about when you come back from vacation.

477
00:22:14,720 --> 00:22:17,400
Speaker 9: You probably check your email when you're on vacation. I don't,

478
00:22:17,440 --> 00:22:18,840
Speaker 9: but for an exact.

479
00:22:18,600 --> 00:22:21,000
Speaker 4: Reason, because if I come back, I have like two

480
00:22:21,080 --> 00:22:23,720
Speaker 4: thousand emails being gone for like a week, and I can't.

481
00:22:23,560 --> 00:22:25,159
Speaker 9: Keep that I can't do it if you had the

482
00:22:25,200 --> 00:22:27,560
Speaker 9: AA tool, what you could do after being away for

483
00:22:27,600 --> 00:22:29,640
Speaker 9: two weeks. I don't check my email and probably get

484
00:22:29,640 --> 00:22:30,240
Speaker 9: in trouble for that.

485
00:22:30,160 --> 00:22:30,560
Speaker 5: But I don't.

486
00:22:30,560 --> 00:22:32,359
Speaker 9: I can come back and say, what did I miss

487
00:22:32,359 --> 00:22:34,679
Speaker 9: over the last two weeks, go through all my pings

488
00:22:34,680 --> 00:22:36,760
Speaker 9: on teams, go through all my outlook, and can you

489
00:22:36,800 --> 00:22:38,919
Speaker 9: prepare for me a summary so that now that I

490
00:22:39,000 --> 00:22:41,879
Speaker 9: come back, I can actually be ready and can prioritize.

491
00:22:41,880 --> 00:22:43,760
Speaker 9: That's where it really comes into.

492
00:22:44,280 --> 00:22:45,280
Speaker 4: Wow, that's really cool.

493
00:22:45,400 --> 00:22:47,680
Speaker 3: Yeah, So what when you sit down with your clients,

494
00:22:48,040 --> 00:22:50,560
Speaker 3: I mean, what's some of the common requests you get

495
00:22:50,560 --> 00:22:52,359
Speaker 3: from them or what do they ask for most of

496
00:22:52,400 --> 00:22:53,399
Speaker 3: the help with I guess.

497
00:22:53,680 --> 00:22:53,920
Speaker 8: Yeah.

498
00:22:53,960 --> 00:22:55,840
Speaker 9: One of the things that's really top of mind for

499
00:22:55,880 --> 00:22:59,240
Speaker 9: people is about skill set and training and capability building.

500
00:22:59,359 --> 00:23:02,480
Speaker 9: So in our survey, we found that eight out of

501
00:23:02,640 --> 00:23:06,000
Speaker 9: ten people said that twenty hours of their work week,

502
00:23:06,040 --> 00:23:09,000
Speaker 9: almost fifty percent of their work week can be replaced

503
00:23:09,040 --> 00:23:11,639
Speaker 9: with AI tools. The challenges they don't know how to

504
00:23:11,720 --> 00:23:14,160
Speaker 9: use the tools in the most effective and efficient way,

505
00:23:14,400 --> 00:23:17,440
Speaker 9: so the training around that is critical in the process.

506
00:23:17,480 --> 00:23:21,480
Speaker 9: The other is a responsible AI a governance set. Right, Yeah,

507
00:23:21,480 --> 00:23:23,520
Speaker 9: what are the guard rails that we have to put

508
00:23:23,560 --> 00:23:26,760
Speaker 9: into place so that people can play creatively in the space.

509
00:23:26,920 --> 00:23:30,280
Speaker 4: Do you feel like people and CEOs or board levels

510
00:23:30,320 --> 00:23:32,480
Speaker 4: are do they now know what they don't know, they

511
00:23:32,480 --> 00:23:34,680
Speaker 4: are beginning to figure it out, or we're still in

512
00:23:34,720 --> 00:23:35,560
Speaker 4: the beginning part of that.

513
00:23:35,600 --> 00:23:37,800
Speaker 9: I believe we're in the infancy of it. I think

514
00:23:37,840 --> 00:23:39,840
Speaker 9: there is an infancy of the learning curve, but also

515
00:23:39,920 --> 00:23:42,600
Speaker 9: an infancy of having the right people in the room,

516
00:23:42,680 --> 00:23:45,760
Speaker 9: having diverse perspectives. As we think about responsible AI.

517
00:23:45,680 --> 00:23:48,200
Speaker 3: And we're hearing you mentioned the I guess the ethical

518
00:23:48,480 --> 00:23:51,480
Speaker 3: use of AI. I don't know how that's going to evolve.

519
00:23:52,040 --> 00:23:54,520
Speaker 3: Is that going to be some partnership between public, private,

520
00:23:54,800 --> 00:23:55,640
Speaker 3: the individual.

521
00:23:55,960 --> 00:23:58,080
Speaker 4: I'm not sure I actually know what that means. Well,

522
00:23:58,080 --> 00:23:59,760
Speaker 4: it just seems like ethical use of AI.

523
00:24:00,160 --> 00:24:02,520
Speaker 3: Yeah, it just seems like the technology could get out

524
00:24:02,520 --> 00:24:03,200
Speaker 3: of control.

525
00:24:03,480 --> 00:24:07,080
Speaker 9: Look, as AI and generative AI becomes more ubiquitous, with

526
00:24:07,280 --> 00:24:10,720
Speaker 9: increased scale comes increased risk. That's just the reality of things.

527
00:24:10,720 --> 00:24:13,240
Speaker 9: So how do you mitigate those risks? I think one

528
00:24:13,240 --> 00:24:15,080
Speaker 9: of the most important ways to do that is to

529
00:24:15,080 --> 00:24:18,000
Speaker 9: have the right people in the room. So, whether that's

530
00:24:18,040 --> 00:24:21,119
Speaker 9: from a diversity perspective of gender, whether that's having people

531
00:24:21,160 --> 00:24:24,160
Speaker 9: of color in the room, people from diverse backgrounds. It's

532
00:24:24,160 --> 00:24:26,879
Speaker 9: one of the reasons that we do the scholarship program

533
00:24:26,920 --> 00:24:29,560
Speaker 9: for women in STEM at this very institute, because we

534
00:24:29,600 --> 00:24:31,720
Speaker 9: want to make sure that they're not brought in as

535
00:24:31,760 --> 00:24:34,520
Speaker 9: a second thought, but rather at the very beginning of

536
00:24:34,520 --> 00:24:35,080
Speaker 9: the conversation.

537
00:24:35,200 --> 00:24:37,840
Speaker 4: So, what's like an unethical use of AI? Like, where

538
00:24:37,880 --> 00:24:39,000
Speaker 4: does AI get bad?

539
00:24:39,200 --> 00:24:39,360
Speaker 5: Yeah?

540
00:24:39,440 --> 00:24:41,600
Speaker 9: Well, I mean, look, you can use you can use

541
00:24:41,640 --> 00:24:44,400
Speaker 9: AI to create images that don't actually exist. You can

542
00:24:44,400 --> 00:24:47,240
Speaker 9: put voices on people to say things through their own

543
00:24:47,320 --> 00:24:49,720
Speaker 9: voice when they may maybe have not said that video.

544
00:24:50,240 --> 00:24:53,400
Speaker 9: You can think about putting in questions into generative AI

545
00:24:53,480 --> 00:24:56,159
Speaker 9: that perhaps share data with the broader public that you

546
00:24:56,200 --> 00:24:58,760
Speaker 9: didn't want to share that's company specific data. So there's

547
00:24:58,760 --> 00:25:02,600
Speaker 9: a security component, there's a falsification component, There's lots of

548
00:25:02,600 --> 00:25:04,720
Speaker 9: different ways you kind of have to be proactive about.

549
00:25:04,840 --> 00:25:07,520
Speaker 3: And on this front, once again, maybe at no fault

550
00:25:07,560 --> 00:25:11,040
Speaker 3: of their own, the government is generations behind where the

551
00:25:11,080 --> 00:25:14,240
Speaker 3: technology is. I don't know how this plays out, I

552
00:25:14,320 --> 00:25:17,080
Speaker 3: really don't. I mean, is there a feeling that the

553
00:25:17,119 --> 00:25:18,879
Speaker 3: industry for a while is going to have to police

554
00:25:18,920 --> 00:25:21,520
Speaker 3: itself or is there going to be some again public

555
00:25:21,600 --> 00:25:24,280
Speaker 3: private partnership in terms of regulating this, because this is

556
00:25:24,320 --> 00:25:29,760
Speaker 3: not the FCC regulating the airwaves. This is really really difficult.

557
00:25:30,119 --> 00:25:33,760
Speaker 9: Yeah, it gets complicated. Look, I think there's an individual

558
00:25:33,840 --> 00:25:36,399
Speaker 9: level to it, an individual level of responsibility, But at

559
00:25:36,400 --> 00:25:37,919
Speaker 9: the end of the day, it's going to fall on

560
00:25:38,000 --> 00:25:41,920
Speaker 9: the leaders, the leaders of organizations across the board. If

561
00:25:41,920 --> 00:25:44,640
Speaker 9: the senior leaders are not thinking about responsible AI, they're

562
00:25:44,640 --> 00:25:48,359
Speaker 9: not thinking about the AI fluency, no one else is

563
00:25:48,400 --> 00:25:50,280
Speaker 9: going to think about that. So the responsibility on the

564
00:25:50,359 --> 00:25:51,280
Speaker 9: leaders is very high.

565
00:25:51,320 --> 00:25:54,399
Speaker 3: Our thanks to Anita Givanni, Global head of Innovation at ABANAT.

566
00:25:54,440 --> 00:25:56,960
Speaker 4: Coming up on the program, our conversation on AI continues,

567
00:25:57,040 --> 00:25:59,359
Speaker 4: we speak with Michael Johnson, President of the New Jersey

568
00:25:59,440 --> 00:26:00,640
Speaker 4: Innovation Institute.

569
00:26:00,800 --> 00:26:03,600
Speaker 3: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in

570
00:26:03,640 --> 00:26:05,800
Speaker 3: depth research and data on two thousand companies and one

571
00:26:05,880 --> 00:26:09,320
Speaker 3: hundred and thirty industries. You can access Bloomberg Intelligence via Bigone,

572
00:26:09,320 --> 00:26:11,800
Speaker 3: the Terminal on Paul Sweeney, m Alex Steele, and.

573
00:26:11,800 --> 00:26:12,840
Speaker 4: This is Bloomberg.

574
00:26:17,880 --> 00:26:21,760
Speaker 2: You're listening to the Bloomberg Intelligence podcast. Catch us live

575
00:26:21,840 --> 00:26:25,360
Speaker 2: weekdays at ten am Eastern on applecar Play and Android

576
00:26:25,400 --> 00:26:28,160
Speaker 2: Auto with the Bloomberg Business app. You can also listen

577
00:26:28,280 --> 00:26:31,399
Speaker 2: live on Amazon Alexa from our flagship New York station.

578
00:26:31,760 --> 00:26:35,520
Speaker 2: Just say Alexa play Bloomberg eleven thirty.

579
00:26:36,440 --> 00:26:39,280
Speaker 4: Earlier in the week, Bloomberg Intelligence broadcasted live from the

580
00:26:39,280 --> 00:26:42,520
Speaker 4: campus of the New Jersey Institute of Technology, and the

581
00:26:42,560 --> 00:26:44,480
Speaker 4: main topic of conversation was AI.

582
00:26:44,680 --> 00:26:47,359
Speaker 3: We spoke with Michael Johnson, president of the New Jersey

583
00:26:47,480 --> 00:26:53,040
Speaker 3: Innovation Institute or NJII. It's a standalone corporation owned by NJIT.

584
00:26:53,600 --> 00:26:56,520
Speaker 3: We first asked Michael what NJII does.

585
00:26:56,720 --> 00:26:59,199
Speaker 10: In the US. We have lots of research universities and

586
00:26:59,240 --> 00:27:02,359
Speaker 10: there's lots of smart people, lots of great resources, but

587
00:27:02,400 --> 00:27:05,440
Speaker 10: there's this fundamental problem in academia, which is it's tough

588
00:27:05,480 --> 00:27:08,320
Speaker 10: for the outside world actually leverage those resources. So for

589
00:27:08,440 --> 00:27:11,520
Speaker 10: governmental organizations for industry, they want access to the cutting

590
00:27:11,600 --> 00:27:13,920
Speaker 10: edge of AI, for example, but it's tough for them

591
00:27:13,920 --> 00:27:16,520
Speaker 10: to actually make those connections and interact with faculty. So

592
00:27:16,760 --> 00:27:19,359
Speaker 10: NGI is an organization. It's a five oh one C

593
00:27:19,480 --> 00:27:22,359
Speaker 10: three wholly owned by NGT, and the idea is that

594
00:27:22,400 --> 00:27:24,840
Speaker 10: we are a standalone corporation that's a conduit between the

595
00:27:24,880 --> 00:27:28,919
Speaker 10: outside world and NGT, So we make those facilitations, we

596
00:27:29,000 --> 00:27:31,920
Speaker 10: create unique business models to work with faculty, and we're

597
00:27:31,920 --> 00:27:35,159
Speaker 10: a quick moving organization, unlike academia, which is you know,

598
00:27:35,280 --> 00:27:37,280
Speaker 10: tends to be slower and more difficult to work with.

599
00:27:37,560 --> 00:27:39,440
Speaker 10: So we're that conduit between them and the outside world

600
00:27:39,520 --> 00:27:41,680
Speaker 10: and roughly have about one hundred and twenty folks out

601
00:27:41,680 --> 00:27:43,720
Speaker 10: of organization we're focused on that can.

602
00:27:43,640 --> 00:27:45,240
Speaker 4: Just say it's really cool his three year old son

603
00:27:45,320 --> 00:27:48,159
Speaker 4: is here. I mean, what three year old is going

604
00:27:48,240 --> 00:27:50,080
Speaker 4: to come and talk about AI. I feel like that

605
00:27:50,200 --> 00:27:52,160
Speaker 4: just says it all at the end of the day, right,

606
00:27:52,280 --> 00:27:54,719
Speaker 4: that is the future? So am I a company that

607
00:27:54,760 --> 00:27:56,480
Speaker 4: goes to you and then you pair me up with

608
00:27:56,520 --> 00:27:59,119
Speaker 4: something or is it sort of the technology that you're evolving,

609
00:27:59,119 --> 00:28:00,399
Speaker 4: and then you go pitch it to companies.

610
00:28:00,440 --> 00:28:01,120
Speaker 5: How does that work?

611
00:28:01,200 --> 00:28:03,000
Speaker 10: It's a bit of inside out and outside in. So

612
00:28:03,040 --> 00:28:04,680
Speaker 10: we can go to corporations and try and find out

613
00:28:04,680 --> 00:28:06,639
Speaker 10: what their problems are, what their pain points are, and

614
00:28:06,640 --> 00:28:08,760
Speaker 10: then go and find faculty you can help out. Or

615
00:28:08,800 --> 00:28:10,440
Speaker 10: we might have a few faculty that have a very

616
00:28:10,440 --> 00:28:12,600
Speaker 10: specific problem. They need access to software, they need to

617
00:28:12,600 --> 00:28:15,040
Speaker 10: access the resources, and we go externally to find a

618
00:28:15,040 --> 00:28:17,200
Speaker 10: way to work with corporations on that, but it's pairing

619
00:28:17,200 --> 00:28:20,040
Speaker 10: the two with each other. And faculty are really smart,

620
00:28:20,080 --> 00:28:22,080
Speaker 10: they're really focused on their research, but they don't always

621
00:28:22,119 --> 00:28:23,800
Speaker 10: have the mind to go out and actually execute on

622
00:28:23,840 --> 00:28:26,679
Speaker 10: consultant type projects for industry. So we help form that

623
00:28:26,720 --> 00:28:29,000
Speaker 10: framework and along the way we're trying to help with

624
00:28:29,040 --> 00:28:31,880
Speaker 10: tech transfers, so getting technology out of the university into

625
00:28:31,920 --> 00:28:34,440
Speaker 10: products and services is always a pain point, and also

626
00:28:34,560 --> 00:28:38,440
Speaker 10: just generally accelerating innovation and also helping upskille workers.

627
00:28:39,040 --> 00:28:41,800
Speaker 3: You know, over the last several quarters, Bloomberg does this analysis.

628
00:28:41,800 --> 00:28:44,760
Speaker 3: It shows what are companies talking about on their quarterly

629
00:28:44,800 --> 00:28:47,760
Speaker 3: conference calls, And for the last several quarters, every single

630
00:28:47,800 --> 00:28:49,800
Speaker 3: company in Y S and P five hundred has talked

631
00:28:49,800 --> 00:28:52,160
Speaker 3: about AI, with the exception of Apple. Last quarter have

632
00:28:52,240 --> 00:28:55,240
Speaker 3: I mentioned AI? Which is interesting? What are companies most

633
00:28:55,440 --> 00:28:57,320
Speaker 3: commonly asking you for help with?

634
00:28:58,280 --> 00:28:58,400
Speaker 4: Oh?

635
00:28:58,520 --> 00:28:58,720
Speaker 2: Man?

636
00:28:58,800 --> 00:29:01,240
Speaker 10: That goes all over the place. It depends in the companies.

637
00:29:01,240 --> 00:29:03,080
Speaker 10: We have some small mom and pop businesses that just

638
00:29:03,120 --> 00:29:06,400
Speaker 10: want help, but trying to move towards technology, towards computers.

639
00:29:06,640 --> 00:29:08,200
Speaker 10: We have other companies, for example, that want to be

640
00:29:08,240 --> 00:29:11,120
Speaker 10: the bleeding edge of some sort and field. So for example,

641
00:29:11,120 --> 00:29:13,360
Speaker 10: it might be life sciences, it might be AI for example,

642
00:29:13,600 --> 00:29:15,920
Speaker 10: and they're asking us to help improve something that they're

643
00:29:15,920 --> 00:29:18,240
Speaker 10: already doing, or it's a very specific project they're pushing

644
00:29:18,280 --> 00:29:20,280
Speaker 10: us to find faculty to help out with. So it

645
00:29:20,360 --> 00:29:23,400
Speaker 10: kind of depends. We have other folks. For example, Picatinny

646
00:29:23,480 --> 00:29:25,920
Speaker 10: Arsenal and Department of Defense are looking for just workers,

647
00:29:26,120 --> 00:29:29,120
Speaker 10: so helping us upscale workers for advanced manufacturing and all

648
00:29:29,160 --> 00:29:31,440
Speaker 10: sorts of different programs they need help finding talent for

649
00:29:31,600 --> 00:29:33,000
Speaker 10: so we're trying to help that with that as well.

650
00:29:33,200 --> 00:29:35,320
Speaker 4: So JPM we're going to interfew saw this. They had

651
00:29:35,320 --> 00:29:37,280
Speaker 4: a great piece out that said that some of its

652
00:29:37,280 --> 00:29:41,240
Speaker 4: corporate customers are slashing manual work by almost ninety percent

653
00:29:41,840 --> 00:29:45,640
Speaker 4: with its cash flow management tool that runs on AI.

654
00:29:46,160 --> 00:29:47,920
Speaker 4: And that's the fear, right that we're going to use

655
00:29:47,960 --> 00:29:50,080
Speaker 4: AI and replace all the workers and those workers don't

656
00:29:50,080 --> 00:29:51,760
Speaker 4: have any jobs. Is there any truth to that?

657
00:29:52,600 --> 00:29:55,080
Speaker 10: It's a great question. So whenever you have technologies they

658
00:29:55,120 --> 00:29:57,400
Speaker 10: are disruptive, there are going to be jobs certainly that

659
00:29:57,440 --> 00:29:59,040
Speaker 10: are going to go away. But if you look back

660
00:29:59,080 --> 00:30:01,640
Speaker 10: to when accel first came out, or when computers first

661
00:30:01,640 --> 00:30:03,680
Speaker 10: came out, you look at accounting as a great use case.

662
00:30:04,000 --> 00:30:06,480
Speaker 10: Accountants didn't go away because we were going from a

663
00:30:06,520 --> 00:30:08,640
Speaker 10: ledger that was literally on paper to a computer based system.

664
00:30:08,680 --> 00:30:10,440
Speaker 10: We found new questions to answer, new ways that we

665
00:30:10,440 --> 00:30:12,880
Speaker 10: could look at our accounting and finances. So I think

666
00:30:12,960 --> 00:30:15,320
Speaker 10: the jobs are going to change, But the overall number

667
00:30:15,360 --> 00:30:17,440
Speaker 10: of jobs in that net, I don't know if it

668
00:30:17,440 --> 00:30:19,880
Speaker 10: will actually reduce. It might increase in some cases. But

669
00:30:19,880 --> 00:30:21,880
Speaker 10: we're going to answer different questions. We're going to do

670
00:30:21,920 --> 00:30:23,560
Speaker 10: things much more quickly than we did in the past,

671
00:30:23,600 --> 00:30:23,960
Speaker 10: for sure.

672
00:30:24,520 --> 00:30:26,840
Speaker 3: I guess my lack of knowledge of full appreciation of

673
00:30:26,840 --> 00:30:29,120
Speaker 3: AIS is I'm just not sure if it's something completely

674
00:30:29,160 --> 00:30:31,479
Speaker 3: new or is it just the next iteration of what

675
00:30:31,520 --> 00:30:35,240
Speaker 3: the smart people at NJIT typically do. Is it I'm

676
00:30:35,280 --> 00:30:38,520
Speaker 3: just not sure what's new about it other than Man,

677
00:30:38,560 --> 00:30:41,560
Speaker 3: everybody's talking about it, and it was a theme. One

678
00:30:41,600 --> 00:30:43,440
Speaker 3: of the themes that drove the stock market in twenty

679
00:30:43,480 --> 00:30:45,600
Speaker 3: twenty three was a concept of AI and the average

680
00:30:45,640 --> 00:30:48,000
Speaker 3: trader has no idea what AI is, but he's buying

681
00:30:48,040 --> 00:30:49,720
Speaker 3: stocks because he thinks they're an AI play.

682
00:30:49,880 --> 00:30:52,160
Speaker 10: It's been around for decades, right, but we have a

683
00:30:52,160 --> 00:30:53,760
Speaker 10: couple of technologies that came out in the last two

684
00:30:53,840 --> 00:30:55,840
Speaker 10: years that have really transformed the way we see AI

685
00:30:55,840 --> 00:30:57,440
Speaker 10: and while we're talking about it, and the reason is

686
00:30:57,480 --> 00:31:00,440
Speaker 10: because now it's accessible. So for example, to years ago,

687
00:31:00,440 --> 00:31:01,800
Speaker 10: if I go into Google and I tell you how

688
00:31:01,800 --> 00:31:04,360
Speaker 10: do I make chicken palm? I got all these ads,

689
00:31:04,400 --> 00:31:06,400
Speaker 10: I get all these things that tell me about chicken parm.

690
00:31:06,440 --> 00:31:07,880
Speaker 10: I go in a chat GPT and I type that

691
00:31:07,920 --> 00:31:09,640
Speaker 10: for example, and I get a perfect recipe on how

692
00:31:09,680 --> 00:31:12,560
Speaker 10: to actually make that, So it becomes very accessible to anyone.

693
00:31:12,600 --> 00:31:14,400
Speaker 10: And I think that go to market strategy. The open

694
00:31:14,440 --> 00:31:17,280
Speaker 10: I had of making accessible is what really changed the game.

695
00:31:17,640 --> 00:31:20,520
Speaker 10: And also the same time computing power is exponentially increasing,

696
00:31:20,560 --> 00:31:23,200
Speaker 10: it's more accessible. We're now able to use it everywhere

697
00:31:23,240 --> 00:31:25,320
Speaker 10: from making chicken palm to try and do research.

698
00:31:25,560 --> 00:31:27,080
Speaker 4: So what kind of cool stuff are you guys working

699
00:31:27,120 --> 00:31:28,960
Speaker 4: on right now? Like what were you most excited about?

700
00:31:29,320 --> 00:31:31,680
Speaker 10: For us as NGI, what we're very focused on is

701
00:31:31,720 --> 00:31:33,560
Speaker 10: trying to get things out at the university into the

702
00:31:33,560 --> 00:31:36,040
Speaker 10: real world. And one specific project that we're working on

703
00:31:36,400 --> 00:31:39,360
Speaker 10: is actually on law enforcement and body cams. So bodycams

704
00:31:39,480 --> 00:31:41,760
Speaker 10: is there a sensor that generates a huge amount of

705
00:31:41,840 --> 00:31:44,800
Speaker 10: data and from those data sets, we're usually looking at

706
00:31:44,840 --> 00:31:47,400
Speaker 10: them after the fact, so after something bad happens, we're

707
00:31:47,400 --> 00:31:49,959
Speaker 10: trying to review that situation. What we're trying to do

708
00:31:50,080 --> 00:31:52,320
Speaker 10: is can we look at that data and predict something

709
00:31:52,360 --> 00:31:54,360
Speaker 10: bad is going to happen before it happens. So if

710
00:31:54,400 --> 00:31:57,960
Speaker 10: we see a pattern between some behaviors, running back time for.

711
00:31:57,920 --> 00:31:59,760
Speaker 4: A second, so you have a BTE so you're tracking

712
00:31:59,840 --> 00:32:02,160
Speaker 4: the behavior to then model behavior later.

713
00:32:02,520 --> 00:32:04,600
Speaker 10: Yes. So for example, let's say we see an officer

714
00:32:04,640 --> 00:32:07,560
Speaker 10: is running more frequently, they're yelling more frequently. That has

715
00:32:07,600 --> 00:32:10,600
Speaker 10: probably correlated to some behavior outcome, such as excessive use

716
00:32:10,600 --> 00:32:13,240
Speaker 10: of force. So for example, we might identify this officer

717
00:32:13,280 --> 00:32:15,200
Speaker 10: as at a much higher likelihood of excessive use in

718
00:32:15,240 --> 00:32:17,520
Speaker 10: force in the future. Let's intervene and get them training

719
00:32:17,560 --> 00:32:19,880
Speaker 10: before something bad happens. So we're trying to build that

720
00:32:19,880 --> 00:32:22,800
Speaker 10: a software we can actually put onto the hardware and

721
00:32:22,880 --> 00:32:25,640
Speaker 10: help the law enforcement and help with de escalating situations.

722
00:32:25,720 --> 00:32:27,000
Speaker 4: Wow, that's really cool.

723
00:32:27,440 --> 00:32:28,120
Speaker 3: What other stuff like?

724
00:32:28,120 --> 00:32:29,720
Speaker 4: What are the thing are you excited about?

725
00:32:29,920 --> 00:32:32,160
Speaker 10: Oh man, there's so many Take your second best. My

726
00:32:32,240 --> 00:32:34,360
Speaker 10: second best would definitely be in the drone space. So

727
00:32:34,480 --> 00:32:37,680
Speaker 10: drones are another sensor. We're collecting huge amounts of imagery data,

728
00:32:37,840 --> 00:32:39,240
Speaker 10: and today a lot of that work is actually a

729
00:32:39,280 --> 00:32:41,760
Speaker 10: person looking at videos, scrolling through video like you would

730
00:32:41,760 --> 00:32:44,280
Speaker 10: from a VHS tape, and we're using computer vision and

731
00:32:44,320 --> 00:32:47,120
Speaker 10: AI to actually analyze that data and try to predict

732
00:32:47,120 --> 00:32:49,480
Speaker 10: what's happening and try to classify certain imagery and answer

733
00:32:49,560 --> 00:32:52,040
Speaker 10: very specific questions like is a power line going to

734
00:32:52,080 --> 00:32:54,440
Speaker 10: fail based upon a single picture from a simple drone?

735
00:32:54,480 --> 00:32:56,160
Speaker 4: Oh, now that could be really helpful, dependually all the

736
00:32:56,200 --> 00:32:58,560
Speaker 4: wildfires and stuff that we've had. And then as all

737
00:32:58,560 --> 00:33:01,200
Speaker 4: the utilities are kind of grappling with like old infrastructure

738
00:33:01,280 --> 00:33:03,000
Speaker 4: that is not easy to replace, kind of how you

739
00:33:03,040 --> 00:33:06,240
Speaker 4: manage that? Is it expensive for these companies to use this?

740
00:33:07,040 --> 00:33:10,000
Speaker 10: Usually the bottleneck today is data generation and data annotation

741
00:33:10,120 --> 00:33:12,000
Speaker 10: because there's lots of data, but we have to annotate

742
00:33:12,000 --> 00:33:14,160
Speaker 10: the data to be actually able to use it. So,

743
00:33:14,200 --> 00:33:16,120
Speaker 10: for example, if the body cams, we have to know

744
00:33:16,160 --> 00:33:18,400
Speaker 10: what those events are that we're trying to predict and

745
00:33:18,440 --> 00:33:20,360
Speaker 10: how she classifying them ahead of time. So that's the

746
00:33:20,440 --> 00:33:22,280
Speaker 10: real the bottleneck for it in a lot of cases.

747
00:33:22,480 --> 00:33:24,600
Speaker 4: All right, thanks to Michael Johnson, president of the New

748
00:33:24,680 --> 00:33:25,800
Speaker 4: Jersey Innovation.

749
00:33:25,480 --> 00:33:28,920
Speaker 3: Institute, let's stick with our conversations on artificial intelligence at

750
00:33:28,960 --> 00:33:31,840
Speaker 3: the New Jersey Institute of Technology. New Jersey Governor Phil

751
00:33:31,920 --> 00:33:34,720
Speaker 3: Murphy recently laid out further details of a so called

752
00:33:34,840 --> 00:33:38,640
Speaker 3: AI moonshot plan. The proposed plan would include seven million

753
00:33:38,680 --> 00:33:41,800
Speaker 3: dollars to advance AI utilization and opportunities in the state.

754
00:33:41,920 --> 00:33:44,640
Speaker 4: We were joined by AI expert Beth Simon Novic, who

755
00:33:44,880 --> 00:33:48,760
Speaker 4: was recently appointed as New Jersey's first ever Chief AI Strategist,

756
00:33:48,960 --> 00:33:51,480
Speaker 4: and we asked Beth how she helps implement Governor Phil

757
00:33:51,560 --> 00:33:54,000
Speaker 4: Murphy's vision of having New Jersey lead the nation in

758
00:33:54,040 --> 00:33:55,000
Speaker 4: the advancement of AI.

759
00:33:55,440 --> 00:33:58,320
Speaker 7: Governor Murphy has said very loud and clear, we have

760
00:33:58,440 --> 00:34:00,680
Speaker 7: to do better when it comes to technology in terms

761
00:34:00,680 --> 00:34:04,200
Speaker 7: of embracing the use of technology to grow the economy,

762
00:34:04,240 --> 00:34:06,240
Speaker 7: to grow jobs in the state, but also to improve

763
00:34:06,280 --> 00:34:09,759
Speaker 7: how government works. So my job is to work on

764
00:34:09,840 --> 00:34:11,319
Speaker 7: all of the above and to see what we can

765
00:34:11,360 --> 00:34:13,720
Speaker 7: do as government to make that easier, to make that better,

766
00:34:14,120 --> 00:34:16,640
Speaker 7: and to embrace the responsible and ethical use of AI

767
00:34:16,800 --> 00:34:19,000
Speaker 7: to ensure that we're doing right by our residents.

768
00:34:19,400 --> 00:34:23,360
Speaker 3: So what are some of the applications that the governor

769
00:34:23,400 --> 00:34:26,040
Speaker 3: and the Governor's office thinks AI can do Over the

770
00:34:26,120 --> 00:34:29,000
Speaker 3: next several years, Where will the residents of New Jersey

771
00:34:29,000 --> 00:34:29,799
Speaker 3: see it? Do you think?

772
00:34:29,920 --> 00:34:32,360
Speaker 7: So this is not a several years from now. The

773
00:34:32,400 --> 00:34:35,040
Speaker 7: future is already here. And we've been using AI for

774
00:34:35,160 --> 00:34:37,640
Speaker 7: quite some time, and generative AI since the very beginning,

775
00:34:37,880 --> 00:34:39,880
Speaker 7: so in many ways that you don't even see or

776
00:34:39,920 --> 00:34:41,879
Speaker 7: know about. So, for example, if you're getting a letter

777
00:34:41,920 --> 00:34:43,680
Speaker 7: from the State of New Jersey about let's say your

778
00:34:43,760 --> 00:34:47,279
Speaker 7: unemployment benefits, you're getting a letter that has been simplified,

779
00:34:47,440 --> 00:34:50,800
Speaker 7: that has been written in plain English, that's been written,

780
00:34:50,800 --> 00:34:52,960
Speaker 7: we hope, more clearly than it would have been before

781
00:34:53,280 --> 00:34:55,840
Speaker 7: because generative AI can help us to do a first draft.

782
00:34:56,239 --> 00:34:59,160
Speaker 7: If you're calling up about your anchor tax relief that

783
00:34:59,239 --> 00:35:01,440
Speaker 7: the State of New Jerse is giving out to residents,

784
00:35:01,719 --> 00:35:05,040
Speaker 7: you are hopefully getting your call resolved faster because you

785
00:35:05,120 --> 00:35:07,719
Speaker 7: get a menu option that's we've written with the help

786
00:35:07,719 --> 00:35:10,400
Speaker 7: of AI. Because voice to text our call center operators

787
00:35:10,440 --> 00:35:12,960
Speaker 7: know people are calling in asking the following kinds of questions,

788
00:35:13,360 --> 00:35:16,600
Speaker 7: we should write these menu options and these instructions and

789
00:35:16,640 --> 00:35:20,719
Speaker 7: answers so people can get that information faster. When you're

790
00:35:20,760 --> 00:35:23,360
Speaker 7: going out, for example, and typing in on a website

791
00:35:23,360 --> 00:35:25,480
Speaker 7: and telling us comments of how we can do something

792
00:35:25,520 --> 00:35:28,360
Speaker 7: better on a website like business dot J dot gov,

793
00:35:28,600 --> 00:35:30,560
Speaker 7: where you can go to start and run and grow

794
00:35:30,600 --> 00:35:33,440
Speaker 7: your business everything you need from one place. We're taking

795
00:35:33,480 --> 00:35:35,799
Speaker 7: the comments we're getting from citizens about what they need

796
00:35:35,840 --> 00:35:38,680
Speaker 7: about what they want, using AI to help us summarize

797
00:35:38,719 --> 00:35:42,640
Speaker 7: those comments, synthesize them, and turn that into the information

798
00:35:42,719 --> 00:35:44,719
Speaker 7: that people want and need front and center. So the

799
00:35:44,760 --> 00:35:49,800
Speaker 7: goal is government that's more responsive, more informative, and providing

800
00:35:49,840 --> 00:35:52,400
Speaker 7: services twenty four to seven that are responsive to what

801
00:35:52,440 --> 00:35:53,640
Speaker 7: people actually want and need.

802
00:35:53,719 --> 00:35:56,200
Speaker 4: That's a pretty good pitch. You were also the chief

803
00:35:56,239 --> 00:35:57,960
Speaker 4: of innovation, right, h Jersey.

804
00:35:58,080 --> 00:36:00,000
Speaker 7: I was for many years the chief Innovation Office.

805
00:36:00,520 --> 00:36:04,040
Speaker 4: So did the Chief Innovation Officer become the AI strategist

806
00:36:04,160 --> 00:36:06,040
Speaker 4: or is there also an innovation officer? And I guess

807
00:36:06,040 --> 00:36:08,839
Speaker 4: I'm trying to understand, like is the innovation thing now

808
00:36:08,920 --> 00:36:11,040
Speaker 4: AI or can there be other stuff?

809
00:36:11,320 --> 00:36:13,560
Speaker 7: There is still other stuff. We have a wonderful new

810
00:36:13,640 --> 00:36:17,800
Speaker 7: Chief Innovation Officer, Dave Cole, has taken over that title

811
00:36:18,160 --> 00:36:20,880
Speaker 7: and is leading our efforts to use technology to improve

812
00:36:20,920 --> 00:36:24,360
Speaker 7: how we bring services to residents. So projects like business

813
00:36:24,400 --> 00:36:27,200
Speaker 7: dot J dot gov to take the business one, for example,

814
00:36:27,320 --> 00:36:30,600
Speaker 7: or other digitization of residence services, so that instead of

815
00:36:30,640 --> 00:36:33,040
Speaker 7: having to go to a government office, you know, between

816
00:36:33,120 --> 00:36:35,000
Speaker 7: nine and five, you can come to a website, you

817
00:36:35,040 --> 00:36:36,080
Speaker 7: can use your mobile phone.

818
00:36:36,160 --> 00:36:36,960
Speaker 4: Oh my gosh, that'd be.

819
00:36:36,920 --> 00:36:41,319
Speaker 7: Amazing transact with government twenty four to seven. That's work

820
00:36:41,360 --> 00:36:43,280
Speaker 7: that's been underway for a long time, and that doesn't

821
00:36:43,360 --> 00:36:46,799
Speaker 7: just depend on AI. That is about again, clearer instructions,

822
00:36:46,880 --> 00:36:50,520
Speaker 7: planer English, things available online, giving you the information front

823
00:36:50,520 --> 00:36:52,680
Speaker 7: and center that you want and need in the way

824
00:36:52,680 --> 00:36:55,400
Speaker 7: that people have become accustomed to from the best businesses.

825
00:36:55,800 --> 00:36:58,040
Speaker 7: We think that government should serve citizens in much the

826
00:36:58,080 --> 00:36:59,959
Speaker 7: same way, except in the public interest.

827
00:37:00,360 --> 00:37:03,760
Speaker 3: Well, New Jersey's had a long history of technological innovation.

828
00:37:03,800 --> 00:37:07,360
Speaker 3: I think of telecommunications with Bellcore and Bell Labs supporting

829
00:37:07,360 --> 00:37:09,600
Speaker 3: eighteen teen Verizon. I think about some of the biotech

830
00:37:09,640 --> 00:37:12,839
Speaker 3: and pharmaceutical companies like Johnson and Johnson based here in

831
00:37:12,840 --> 00:37:17,200
Speaker 3: New Jersey. I'm wondering, is there support for the young

832
00:37:17,320 --> 00:37:20,120
Speaker 3: NJ grads that are in a garage somewhere in Jersey

833
00:37:20,160 --> 00:37:22,799
Speaker 3: City coming up with the next AI type thing. How

834
00:37:22,800 --> 00:37:24,040
Speaker 3: do we support those people?

835
00:37:24,239 --> 00:37:27,920
Speaker 7: Absolutely so, there are a whole number and range of

836
00:37:27,960 --> 00:37:31,759
Speaker 7: investments that are out there to support people starting new businesses.

837
00:37:32,080 --> 00:37:34,600
Speaker 7: That's what my colleagues at EDA work on in particular,

838
00:37:34,800 --> 00:37:37,799
Speaker 7: is ensuring that we're providing those kinds of incentives for

839
00:37:37,840 --> 00:37:40,120
Speaker 7: people who want to start their business in New Jersey

840
00:37:40,120 --> 00:37:42,480
Speaker 7: and grow their business in New Jersey. That's particularly why

841
00:37:42,520 --> 00:37:45,560
Speaker 7: the government is here to help support those businesses going

842
00:37:45,560 --> 00:37:47,440
Speaker 7: out and in particular now to look at how we

843
00:37:47,440 --> 00:37:51,000
Speaker 7: can support new AI businesses or existing businesses who are

844
00:37:51,040 --> 00:37:54,120
Speaker 7: asking how we can turn around and use AI to

845
00:37:54,280 --> 00:37:56,840
Speaker 7: improve what we do. It's a question we've been answering

846
00:37:56,840 --> 00:37:58,880
Speaker 7: for a long time. Before we called it AI. We

847
00:37:58,960 --> 00:38:00,400
Speaker 7: called it big data.

848
00:38:00,040 --> 00:38:01,200
Speaker 3: Yep, right.

849
00:38:01,239 --> 00:38:03,360
Speaker 7: So the more the people we're using a lot of

850
00:38:03,400 --> 00:38:05,319
Speaker 7: businesses have asked themselves, how could I go out and

851
00:38:05,320 --> 00:38:08,480
Speaker 7: start using data to measure what's working, to measure what

852
00:38:08,560 --> 00:38:11,080
Speaker 7: customers want, and again to deliver new kinds of services

853
00:38:11,080 --> 00:38:15,400
Speaker 7: across a range of industries. It's why we've been starting

854
00:38:15,400 --> 00:38:18,080
Speaker 7: new partnerships, such as with Princeton, around this new AI

855
00:38:18,200 --> 00:38:20,760
Speaker 7: hub that's been set up so that we can connect

856
00:38:20,760 --> 00:38:23,680
Speaker 7: some of that tremendous innovation that's coming out of universities

857
00:38:23,920 --> 00:38:27,359
Speaker 7: like NJIT, like Rutgers, like Princeton. We're of course, known

858
00:38:27,400 --> 00:38:29,440
Speaker 7: in this state for having the best universities and the

859
00:38:29,440 --> 00:38:31,800
Speaker 7: best education system in the country, and we want to

860
00:38:31,840 --> 00:38:34,520
Speaker 7: connect that back to how we're growing the economy and

861
00:38:34,560 --> 00:38:36,080
Speaker 7: growing jobs. Hearing the state all right?

862
00:38:36,080 --> 00:38:39,280
Speaker 4: Thanks to Bethsimono Noovic, Chief AI Strategist of the State

863
00:38:39,360 --> 00:38:40,040
Speaker 4: of New Jersey.

864
00:38:40,760 --> 00:38:45,279
Speaker 2: This is the Bloomberg Intelligence Podcast, available on Apples, Spotify,

865
00:38:45,480 --> 00:38:48,400
Speaker 2: and anywhere else you will get your podcasts. Listen live

866
00:38:48,480 --> 00:38:52,080
Speaker 2: each weekday ten am to noon Eastern on Bloomberg dot Com,

867
00:38:52,200 --> 00:38:55,600
Speaker 2: the iHeartRadio app, tune In, and the Bloomberg Business app.

868
00:38:55,719 --> 00:38:58,840
Speaker 2: You can also watch us live every weekday on YouTube

869
00:38:58,920 --> 00:39:00,600
Speaker 2: and always on the Blue Umbergton