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