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welcome to the next installment of the
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analytics at Wharton series focused on
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artificial intelligence I'm Eric bradow
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professor of marketing and statistics
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here at the Wharton School and I'm also
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Vice dean of analytics at Wharton we
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think one of the important applications
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and areas that we as a business school
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should focus on is what we're calling
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today's session AI in action and I can
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think of no two better people to speak
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to us about this than number one my
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colleague Bob Meyer who will be joining
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us Bob is the Frederick H eer bet life
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insurance Professor he's also the
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co-director of the Wharton impact of
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technology initiative so first Bob uh
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Welcome to our podcast welcome it's good
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good to be here and then second um is Mr
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Roger Goo uh Roger is the co-founder and
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president of Wai a prominent independent
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mobile based platform for comprehensive
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Wealth Management in China uh Rogers got
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a career spanning multiple decades both
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in the United States and in China he
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also serves and I'm very proud to have
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Roger both as a friend but as a valued
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member of the analytics at Wharton
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Advisory board so Roger Welcome to our
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podcast well thanks for having pleasure
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so Bob let me start with you um you do a
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lot of work on the impact of technology
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let's call it broadly defined on human
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behavior so could you all and of course
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employees are humans too so could you
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talk about in your perspective some
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interesting let's say uses of artificial
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intelligence today in companies either
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by employees themselves or by firms and
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kind of the impact that you think it's
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having uh well in many respects you know
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when when did it all start or how long
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have we've been using it and and I think
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you actually have to go back to through
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decades where basically different kinds
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of artificial intelligence have
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basically been an integral part of
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companies forever I I remember I I like
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to say I was on the ground floor uh when
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I first started my career I was at car
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melon University and I used to play
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poker with some people from the computer
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science department and one of their
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complaints was that they had to go walk
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all the way down the hallway to get find
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out what was in the Coke machine because
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they would go down there and find out it
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was empty so basically they they they
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programmed one of the very first uh uses
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of FID technology to to program uh their
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uh the Coke machine so they could sit
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their computers and find out where uh uh
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whether it was empty or not it was worth
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the trip and so of course today there's
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you know how many Internet connected
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devices there are is like you know three
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times the world's population so it's
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basically been integrated throughout
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every single function of a business
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particularly in manufacturing consumer
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use and so forth and so forth um and so
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I I think some of the things that are
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happening today is we're shifting from
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uh artificial intelligence as a way of
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looking up information processing data
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to actually generating new knowledge and
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um uh and that's sort of like for and so
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one of the challenges I think for a lot
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of employees if you're saying
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advertising it I no longer going to be
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needed to generate um advertising copy
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when I can just throw it into chat GPT
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and it will generate the advertising so
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yeah I think one of the big areas that
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we've talked about on this series is
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what I say and I agree ai's been around
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for a long time as I was a PhD student
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almost 30 years ago is that predictive
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Ai and the use of AI to is as a data
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source whether it's computer vision
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sound Etc that's been around as you
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pointed out it's the generative AI part
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that's got people really excited today
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so Roger you actually have a company an
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actual company with i that does work in
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this area so could you elaborate on some
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specific areas where your organiz or
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your organization is implementing AI
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today yeah sure yeah initially we also
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started with like you said in predictive
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AI because in our platform people par
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their financial accounts information
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like their bank accounts credit cards
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Insurance retirement plans so we have
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lot of C structured data so we would use
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the data to improve our you experience
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and also help help our partners to sell
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their products But as time moves out you
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know we got unstructured data like voice
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image as you mentioned and the last few
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years it is the generative AI that came
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along it's quite interesting we launched
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a business called online financial
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literacy program about three years ago
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when people were locked down during the
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coid period when this live streaming
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e-commerce went on like crazy you know
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so we launched this uh online financial
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literacy and so far we have about three
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million people paid for our uh wealth
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management courses and it's very
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interesting uh the large language model
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and the GPD 3 I think they emerged about
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two years ago and we took notice it's
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not until last November when this chat
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GPT GPT 3.5 came along it hit us at the
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right time because back then we were
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Hing the right bottleneck of high we
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cannot hide our Shing assistant fast
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enough and train them fast enough then
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we turn on to the models you know uh
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generally AI helped quite a bit one it
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is the uh kind of help enhance the
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interactions by doing more C
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more uh kind of uh deeper and far
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reaching with automatic content
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generation so we develop an Robo ta and
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what it does is uh this traditional kind
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of
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instruction exercise quiz cycle it take
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aot with each individual student and
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when the test is done it will not tell
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the people that you know what you did
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wrong and help them to review the
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content the content is tor made
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individually on each knowledge point
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that they have missed so this PowerPoint
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is generated on the spot and the people
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can do it interactively until they click
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I understand button so it been very
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helpful and also we had daily financial
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news kind of analysis program and today
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I think more than
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90% of the content is generated by this
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uh uh large length model generative AI
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our research analyst only to do a very
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brief review and then click on the
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publish button so it does help us quite
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a lot I think over 80% of interactions
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are performed by AI today rather than
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human being so Bob could you talk uh
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given you know the center you you run
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now well you're one of the co-directors
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of AI at Wharton um but it was also the
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Wharton impact of technology initiative
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um how do humans whether it's Learners
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as in the case Roger was talking about
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employees respondents in studies how do
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they tend to respond when they know
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something maybe they know we'll get to
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ro back to Roger in a second whether
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they know it's generated by uh an AI
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engine how do consumers tend to respond
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to the difference between the two yeah
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that's very interesting there's been uh
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increasing amount of work naturally on
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that I think one of the challenges of
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course right now there was a time when
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uh you you could tell whether or not
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something was you were interacting for
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for example in a a text interaction with
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a service person where you knew you were
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dealing with a robot and people didn't
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like that okay whereas now it's very
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very difficult to tell and sort of one
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of the issues in uh in like online
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advertising is deep fakes where
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basically you cannot tell the difference
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between it and so that sort of
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represents sort of an ethical issue and
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certainly as you might expect people
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don't like it when they think that
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they're being fooled and so that there's
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some evidence of that another area that
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we're looking into I have a colleague
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that's looking into is one of the things
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that the generative AI is doing is that
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it's synthesizing information and
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offering summaries and advice in task
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domains where you used to do it manually
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so for example um if you needed to know
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something about a topic you would go to
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Google and you would go through Bunches
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of sites and you were left to do the
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synthesis yourself and form your own
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conclusion now you could just go to
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whether it's Bing or chat GPT ask a
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question you know how do you do this
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what's your best advice for this and
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it'll take it will eventually do all
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that work for you synthesize it and give
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you an answer and what we're finding and
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it's it's sort of early in the process
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but basically people don't necessarily
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like that all that much in some sense
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it's a it's a thing where you feel that
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you have less ownership over it and so
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right now one of the biggest outstanding
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question is how much intelligence do
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people want okay and the the reality is
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is I guess just the same way that you
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don't necessarily necessarily um you
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know want to order all your meals out
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sometimes you want to cook them yourself
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in a lot of cases it could be there are
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going to be some domain where people are
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going to be more trusting and feel more
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ownership if they're Gathering the
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information themselves rather than
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having a computer do it even if it's the
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case with the computer uh advice is
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actually maybe a little bit better so
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Roger Bob's response is a perfect segue
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to my next question to you um as the
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president of a large company that's
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impacting millions of not only learners
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but also so investors people doing their
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private wealth management how do you
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decide what to kind of assign to the an
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AI engine whether it's an AI chatbot or
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an AI automatic grader or an AI person
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that gives or an AI engine that gives
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feedback and what do you leave to humans
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is it purely one of scale is it one of
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Finance H how do you think about what to
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assign to
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whom oh I think it's a in practice it's
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an emerging process process it's a gray
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scale kind of segmentation and we do
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have a lot of content uh generated by Ai
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and the people would know it's AI for
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example it's a Q&A session when they ask
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general question or AI ask question they
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answer they know they're dealing with AI
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that's kind of a level level one but
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level two we
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have people people would think they are
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interacting with a live person
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but actually this life person is largely
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assisted by the AI uh for the for
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example a piece of news analysis uh
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actually our research ANS are not that
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powerful of gathering so much
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information on time you know they are
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really empowered by the AI and and the
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highest level and we have people kind of
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upgrade to the ultimate level they
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become a a member they pay the
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membership fee for our advisory services
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and they would appreciate uh not only
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the assistant with AI so uh it
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definitely helps a lot but I don't think
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at this stage AI can completely replace
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human being even for know I the uh I
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have three followers and my company a
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digital version of R and didn't do that
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well I don't like it yet so I still try
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to publish my V
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uh in person so I think it's a it's a
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process emerging ongoing but you know
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looking back in less than year it has
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made tremendous
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progress yeah so Bob um building on
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Roger point and the point you made
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earlier how do you see you know we
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always try to say it's not humans or AI
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it's humans and AI how do you see that
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partnership evolving and also do you
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know you could imagine if I was an
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employee and I was being strategic if in
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some sense I prove to the company that
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AI can replace me that may not be great
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either so how do you see those two
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interacting well I think Roger spot on
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and saying the real challenge is is how
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do you figure what's their optimal blend
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like what are the things and uh we
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lastly in the um um a couple months ago
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we ran a uh generative AI conference out
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in San Francisco and uh some of one of
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the big topics there was trying to
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figure out uh such how good is BET The
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generative AI in generating creative Sol
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solutions to problems and um and it
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seems to be the case that the the in
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virging consensus is that what it does
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do is um is if people if you let uh say
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chat GPT work on a creative solution to
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a problem what it does is it it's much
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better at bringing up the low end of of
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human ability so basically if people who
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are not creative not good problem
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solvers you definitely want the machine
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stepping in because they do a much
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better job on the other hand what it
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does do do is it tends to um make
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Solutions seem very similar that it
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comes up with and basically there's the
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highend tale of people who there are
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people who are particularly skilled of
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problem solving and uh and in that case
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that what happen is you'll compare the
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the best of the human judgments and
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those tend to be better as judged by
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outside people uh than than Chachi PT
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solution so so the task is is that that
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you I think consistent with what Roger
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was talking about um you want to
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identify people within the organization
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who really do have these very special
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creativity skills and so forth and you
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want to let them free you know and maybe
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work work you know with tools but
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basically you don't want to replace that
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it's actually an interesting Theory
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which I'm sure people will test over
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time which is does this if you'd like AI
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engine actually inhibit creativity on
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the part of the humans it's why I always
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say you know the last thing I always
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tell this to PhD students and then Roger
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I have another question for you the last
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thing I do when I try to come up with a
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creative idea is when to read someone
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else's paper or synopsis paper because
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it does not help me come up with a
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creative idea at all um so Roger let me
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ask you um Robo advisory Services seems
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maybe to me a much higher Stakes type of
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decision than someone trying to learn
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financial literacy so how do you think
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I'll just use the language that we use
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in Academia all the time there's a very
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different loss function between an AI
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engine making some portfolio allocation
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or recommendation to me and then I go
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bankrupt as a result or you know I take
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some sort of literacy test and the AI
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engine kind of gets it wrong and I'm
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like all right well that's not great but
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it's not the end of the world how do you
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think about Bob was talking about let's
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called employee heterogenity how do you
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think about task importance heterogenity
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and the role of AI given high stakes
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versus low stakes types of decisions
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well this is a very important question
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you know uh I take the Global advisory
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is that high stake uh it's interesting
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this uh this uh this new phenomenon
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called generative AI tend to be
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sometimes very creative especially GPT
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you know sometimes it's imaginary uh
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well llama to tends to be more specific
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so in this uh Robo advisory has two
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sides oftion uh one side is the customer
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profiling custom the other side are the
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asset understanding asset Char Char
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istics so on the right hand side asset
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characteristics we want to be very
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careful with in terms of AI it's still
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classical AI you know classical uh
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statistics uh partial differential
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equation uh some sort of uh recursive
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neural network just understand uh
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understand the
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sigas the betas uh the gamas you know
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make sure that part differ frer are
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still there but the left hand side the
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customer needs uh the the life you know
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uh objectives can be very can be very
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suggestive because in the past the
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classical way uh doing this advisory
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Services is used to ask people about you
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know your personal asset liability
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income how many kids you have when are
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they going to school when they want to
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retire then come up with tailor Med
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Solutions but now with generative AI
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this can go a lot deeper uh you can
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having conversations about life
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objectives one is of course retirement
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you also have example your wedding
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anniversary but then in the The Advisory
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who are wedding anniversary the risk
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profile appetite could be very different
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from a kind of 20-year retirement plan
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so for wedding anniversary if you take
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on risk like play with derivatives or on
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Leverage trading if it works well you
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know you go on skiing in Switzerland if
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it does not work you always have you
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know Disneyland in Florida so I think
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with the help of AI this can go a lot
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deeper a lot more creative inactive and
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also gim finish me meanwhile still holds
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the seriousness of financial advisory so
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I think it definitely add value you need
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to understand where to use them kind of
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uh correctly in the in the Prudence
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fashion yeah Roger I have a question for
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you uh do you ever worry a little bit
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that as um particularly in the context
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of financial advising that the uh the
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tool becomes so good that P maybe put
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too much trust in it um so for example
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if you're dealing with a human advisor
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you know you're dealing with a human and
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you know that humans are are fallible
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and so if the human advisor basically
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tells you this is what you should be
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doing with your money this is what you
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should retirement you'll follow that
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advice but basically with u um which you
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know it's potentially somewhat fallible
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but on the other hand if if you have a u
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a very Advanced um computer tool which
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is uh and you you sell it as being you
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know optimized based on whatever uh is
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there a worry that people would become
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then go the other extreme of being too
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trusting of it well but I'm not worried
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this part actually people believe what
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they believe they can believe a real
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person they can believe into an
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algorithm which is also humanized have
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content generation if the results is
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good and they believe into it so the
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beauty here is you don't have one key
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opinion leader you have dozens you know
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because even different AI have different
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right characteristics people have my
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value based I'm my gross based so and
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it's fun what I do worry is actually on
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the regular regulatory pieces because
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you know generally like I said before
00:19:02
tend to be more creative so but there
00:19:05
are rules and regulations what you can
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say what you cannot say for example uh
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with certain uh kind of uh licenses
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you're not allowed to recommend
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individual stocks or maybe you are not
00:19:18
allowed to recommend something to people
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not at this risk rate so this part you
00:19:23
got to be very careful if you ever cross
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a line it is actually the companies the
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people behind those this AI are holding
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held responsible so I want to be very
00:19:35
careful about that but unfortunately uh
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the lines are not very clear given from
00:19:42
The Regulators because it's a new thing
00:19:43
for them as well so Bob you and I are
00:19:46
both obviously marketing professors um
00:19:49
you had mentioned about let's call it ad
00:19:51
creation as one area what do you see and
00:19:54
then I'm going to ask Roger about his
00:19:55
area in fintech but what do you see as
00:19:58
the major application areas that you see
00:20:01
not just us as academic studying but
00:20:03
that you see AI is going to be used in
00:20:05
our home field of marketing well I think
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you had mentioned the case of usually
00:20:09
when people say how there's two
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different cases of it okay certainly s
00:20:13
of predictive AI that's been around
00:20:14
forever and product design so forth um
00:20:17
uh and and certainly in the case of
00:20:19
every time you go to Amazon you're
00:20:21
seeing AI okay you're basically seeing
00:20:24
products recommended for you and that's
00:20:26
been around for for for for a while so
00:20:28
presumably they'll see improvements on
00:20:30
that you're going to be seeing um uh
00:20:32
better customizations when you go there
00:20:34
you go wow how did it know that's
00:20:36
exactly what I wanted and some people
00:20:38
might find that scary but other people
00:20:40
might find that's exactly sort of the
00:20:42
product I want uh the other side in
00:20:44
terms of the creativity part that's sort
00:20:47
of a little bit we don't quite know yet
00:20:49
um in terms of whether or not if we
00:20:52
Fally U fully turn over all of um the
00:20:56
creative process and advertising design
00:20:58
creative process and strategy
00:21:00
formulation uh po the potential is
00:21:02
certainly there for example with u large
00:21:05
language models and general generative
00:21:06
AI at whole to basically generate all of
00:21:09
this now whether or not that's end up
00:21:10
going to be the type of thing which is
00:21:12
going to generate uh that outof the-box
00:21:15
type of AD Campaign which really makes
00:21:17
the difference for a company as opposed
00:21:20
to whether or not it makes it generates
00:21:22
a whole bunch of advertising which is
00:21:24
all the same okay so effectively it's
00:21:26
just sort of you know govering the Mass
00:21:28
rather than the tail and usually we like
00:21:30
to focus more on that that breakthrough
00:21:33
creative idea and I'm not so sure
00:21:35
they've conver very much out whether or
00:21:37
not um generative AI can produce that
00:21:39
kind of a breakthrough and Roger how
00:21:42
about in fintech what do you see as the
00:21:45
biggest uses of AI today well we just
00:21:49
follow on what you guys were discussing
00:21:50
about in marketing I think uh I'm
00:21:53
actually very optimistic just like help
00:21:56
to discover new you know DNA patterns
00:21:59
new drugs and also in ALA go you know AI
00:22:02
discovered new ways of playing go and in
00:22:05
this marketing especially in the fintech
00:22:08
area every months we spend the tens of
00:22:10
millions uh into those the we call you
00:22:14
know uh information on Tik Tok or
00:22:19
equivalent to kind of Google app kind of
00:22:22
on on those sides so every week or every
00:22:26
point of time usually we have hands if
00:22:31
we call marketing plan don't and which
00:22:33
one's are going to stick you don't know
00:22:36
but with generative AI you can create
00:22:38
those
00:22:39
creatives uh very easily it's like it
00:22:42
disperse different patterns you the way
00:22:46
figure out the dnas from those marketing
00:22:49
plans maybe it is you know the way you
00:22:52
bring up a a pet versus a young boy you
00:22:57
know maybe maybe it is the feeling of a
00:23:00
retirement age something like that but
00:23:03
the underlying I call DNA of marketing
00:23:07
materials now can be S of tried and Ed
00:23:10
and discovered very wild and in this
00:23:14
whole Space of digital marketing I think
00:23:16
it takes to the next level so I think
00:23:18
that's on the one hand because every
00:23:21
company needs toire customers and manage
00:23:24
customer experience I think AI could
00:23:27
help lot uh I think what furthermore
00:23:30
because this chat gbt uh this Genera AI
00:23:34
a large language model usually it's not
00:23:36
as good as sort of doing new numerical
00:23:40
calculations but now with this plugins
00:23:43
you can actually combine you know those
00:23:45
tools with the traditional Ai and
00:23:48
actually even on the asset management
00:23:51
side I can see new kind of AI powered
00:23:56
kind of trading algorith is very
00:23:58
different from the traditional ones and
00:24:01
competing on part with the uh uh the
00:24:05
kind of traditional hedge funds uh
00:24:08
programs so I think uh it's a big thing
00:24:12
it's coming and only we right now only
00:24:15
scratching the surface so we only have
00:24:17
about a minute left so Bob maybe in 30
00:24:19
seconds or so um if we're sitting here
00:24:21
10 years from now what have we been
00:24:23
talking about what are we going to be
00:24:25
talking about that's happened over the
00:24:26
last 10 years that's an awesome question
00:24:29
I have no idea whatsoever and and I
00:24:31
think that in it's it's from my
00:24:33
perspective as a researcher this is like
00:24:35
uh the most exciting time to be alive
00:24:37
because basically what we're in the
00:24:39
precipice of is just really very
00:24:41
fundamental Transformations as to uh how
00:24:44
people get information how people
00:24:46
generate information and we're just
00:24:48
beginning to understand how this is
00:24:50
affecting society and so to me there's
00:24:53
just so much to that we have to learn as
00:24:55
researchers going forward so it's an
00:24:57
awesome time to be here and Roger from
00:24:59
your point of view um how do you think
00:25:01
about the business World investing what
00:25:03
do you think are going to be the big
00:25:04
breakthrough issues in AI in the next
00:25:06
few years well I think companies have to
00:25:08
figure out their position this big AI
00:25:10
game you know it's like a big tree right
00:25:13
you have open AI Google those guys are
00:25:16
the roots right and you have maybe uh F
00:25:20
Tech players like us there are this
00:25:23
industry specific applications they are
00:25:25
like a Trunks and there are many many Le
00:25:28
applications so I think uh either you
00:25:30
are a young entrepreneur right into the
00:25:33
game or whether you are established
00:25:35
company I think it's very important
00:25:37
understand that Financial uh the
00:25:38
technology Trends and figuring out your
00:25:41
position in your field I think know
00:25:44
keeping an open mind would be very very
00:25:47
helpful and uh things change and we got
00:25:50
adap well this is Eric bradow professor
00:25:53
of marketing and statistics here at the
00:25:54
Wharton School and also Vice Jean of
00:25:56
analytics I'd like to thank my two
00:25:57
guests my colleague and friend Bob Meyer
00:25:59
the Frederick H Ecker Met Life Insurance
00:26:01
professor and also one of our
00:26:03
co-directors of our Center on artificial
00:26:04
intelligence and and I'd also like
00:26:07
to thank Roger goo who's the co-founder
00:26:08
and president of aai a prominent
00:26:10
independent mobile based platform for
00:26:12
personal Wealth Management in China and
00:26:14
as I also mentioned one of our valued
00:26:16
board members at analytics at Wharton so
00:26:18
Bob and Roger thank you for joining us
00:26:20
today thank you very
00:26:25
much
