Transcript
Speaker: Welcome to HSBC Global Viewpoint, the podcast series that brings together business leaders and industry experts to explore the latest global insights, trends, and opportunities.
Speaker: Make sure you're subscribed to stay up to date with new episodes.
Speaker: Thanks for listening.
Speaker: And now onto today's show.
Speaker: Hello, and welcome to our GEMS 2023 panel on digital innovation, specifically focused today on generative AI in capital markets.
Speaker: Some of you might have been with us last year where we just talked about AI and machine learning.
Speaker: This is almost the next step, and we'll see a lot has happened in a year.
Speaker: My name is Jeff Wertheimer.
Speaker: I am the global co-head of electronic sales and the America's head of distribution platforms.
Speaker: We have three domain experts here today.
Speaker: I'm going to introduce them if that's okay.
Speaker: And then we're going to ask some questions.
Speaker: So in no particular order, we have Dr. Dara Sosulsky.
Speaker: Dara is the head of AI and model management for markets and security services.
Speaker: She's led a variety of teams specializing in data science, analytics, and modeling.
Speaker: So she's a good person to have with us today.
Speaker: She also has a neuroscience PhD from Columbia University, which I think is actually relevant as well.
Speaker: She then went on to the University College of London on a fellowship in the Royal Society.
Speaker: So, Dara, thank you for being with us.
Speaker: Thank you.
Speaker: Next, we have Mark McDonald.
Speaker: So, Mark is the head of data science and analytics for HSBC Global Research.
Speaker: Mark's the lead author of HSBC's flagship quant publication, Data Matters, and is responsible for our proprietary risk-on risk-off analysis, the prize indices, and the Little Mac FX valuations.
Speaker: Mark also advises investors on building systematic models and applying machine learning techniques to the financial markets.
Speaker: So again, another good expert here, someone who's out talking to clients, and will not just have an HSBC view, but will have a good sense of what's out there happening.
Speaker: with our client base.
Speaker: He has a applied mathematics degree from the University of Oxford, doctoral degree.
Speaker: And last but certainly not least is Tom Croft.
Speaker: So Tom is the head of EFX options distribution for markets and security services.
Speaker: He runs the distribution for electronic FX options.
Speaker: He has an MA in math from Edinburgh and an MSc in maritime engineering
Speaker: from South Hampton, which I think is the most interesting degree I've heard.
Speaker: I will find out if that is relevant or not, but what is very relevant is Tom is the lead on a new HSBC product called AI Markets that is employing some of the technologies we will talk about today.
Speaker: Without further ado, let's get started.
Speaker: I appreciate people have different levels of understanding, but let's just make sure we're all on the same page.
Speaker: So Mark, I'd love to start with you if that's okay.
Speaker: And ask, what is the difference between generative AI and just historical old artificial intelligence?
Speaker: That is a great question because obviously there have been many previous waves of hype and excitement about AI.
Speaker: And you could argue that this current wave of AI hype began back in 2012, but it's really been kicked into overdrive since sort of November last year when OpenAI released ChatGPT.
Speaker: And what's really changed with generative AI rather than normal AI is generative AI, these are machine learning models that can create new content.
Speaker: And until recently,
Speaker: This act of being creative, that's something that we thought of as like a uniquely human capability, but it turns out that machines are really good at this too.
Speaker: And as a result, people have been amazed by the ability of machines to do this.
Speaker: I think, you know, people who
Speaker: who are not experts in the space, were probably not expecting the capability to arrive so soon and so suddenly.
Speaker: And so it's led to a great deal of excitement.
Speaker: And of course, with excitement tends to come a lot of hype.
Speaker: And so one of the challenges for people is trying to work out what's hype and what's real in this space.
Speaker: Thanks, Mark.
Speaker: Sarah, maybe a question for you.
Speaker: So we've all seen the expansive growth.
Speaker: My child is talking about it.
Speaker: My mother is talking about it.
Speaker: What was that catalyst to go from
Speaker: I can use the term geek, right?
Speaker: I mean, certainly people have been looking at this for a while, but now it's gotten very mainstream.
Speaker: Do you have a sense of what really created that inflection point with the technology?
Speaker: Yeah, of course.
Speaker: I won't be offended by your use of the word geek either, Jeff, to describe us old guard who have been using AIML for many years now, basically.
Speaker: So I think the sea change really happened for two reasons with generative AI.
Speaker: First is that it's widely applicable.
Speaker: So like you said, the reason that your kids or my husband won't stop talking about it is because for the first time, I think that people realize that these tools can be used for use cases that aren't the traditional purview of kind of analytics and data.
Speaker: So these are things like Q&A, chatbots, summarizing documents, creating content, images, music, you know, what have you basically.
Speaker: And the other really key thing here is that for the first time ever, a lot of these tools are accessible to a wide variety of people.
Speaker: So I'm sure many of the people on this call have already experimented with chat GPT and maybe stable diffusion mid journey, some of the other offerings we have already in Gen AI.
Speaker: And that's because these tools are being presented to people.
Speaker: on the web via interfaces where pretty much anybody can pay or just have access to them straight off the bat and kind of get a sense of the art of the possible, really.
Speaker: Thank you very much.
Speaker: Not to dive in too deep, but I think now might be the appropriate time, Mark, to talk about the large language models.
Speaker: How are they able to generate
Speaker: the text, which just sounds so informed and intelligent.
Speaker: I'm also curious about just, you know, how is it and where is it pulling all this data from?
Speaker: Yeah, of course.
Speaker: So the way that these models are trained is you feed in lots of examples of text, of words in a sentence, and you ask it to predict missing words.
Speaker: So either words that are missing in the sentence or the ones that we use for generative models, trying to predict what words come next.
Speaker: And so these models are really trained to just
Speaker: produce plausible continuations of an input.
Speaker: And it might seem bizarre that a model like this can appear intelligent when trained at such a simple seeming task.
Speaker: But let me give you an example.
Speaker: Let's say I start a sentence and say, as a data scientist, I am good at.
Speaker: Now there are some words which are likely to follow that.
Speaker: So maybe coding or statistics or data science.
Speaker: Um, there are some words that are not likely to follow that.
Speaker: If I said, as a data scientist, I am good at cage fighting.
Speaker: Now that is a weird sentence, right?
Speaker: And anyone who's like half listening to this call while checking their emails would suddenly be like, hang on a minute.
Speaker: What did he just say?
Speaker: It's clearly absurd.
Speaker: And we all know it's absurd because we have the context of how the world normally works and what are typical things for people to say.
Speaker: For a AI model to be able to complete any sentence on any topic in a sensible manner, it's actually a very hard task and it needs to encode a lot of knowledge about how the world typically works in there.
Speaker: And this is how you train them.
Speaker: You give it pretty much all the text you can find on the internet.
Speaker: And this
Speaker: Once you have a big enough model that's powerful enough, it can encode a lot of information about how language is typically used.
Speaker: And as a result, it's seen the whole internet.
Speaker: So it's seen all the classic works of literature in every language.
Speaker: It's seen vast amounts of technical information on any topic.
Speaker: It's seen huge amounts of commentary on news and current affairs.
Speaker: And so it can seem intelligent and well-informed about all these things.
Speaker: The downside of course, is that it's seen all the things like Reddit and 4chan and perhaps less salubrious forms of commentary.
Speaker: And this is one of the reasons why one of the big challenges about these large language models is how do you constrain the information that they produce so that they don't end up encoding and exacerbating existing biases.
Speaker: And I have to say, Mark, that's probably the clearest description I have heard on generative AI so far.
Speaker: So I really do appreciate that.
Speaker: That's incredibly useful.
Speaker: Tom, maybe a tougher one for you, but when we did this session on AI and machine learning last year, one of the interesting points was sometimes when a model is running, and we were talking about more economic models or trading models, the best outcomes
Speaker: could come about through illegal means, right?
Speaker: Through ways of engaging the market that are considered unethical, perhaps, but if it's just a machine that's learning these things, you know, maybe it is possible.
Speaker: So just curious how we avoid outcomes that we have.
Speaker: you know, we certainly don't want to see.
Speaker: No, that's a good question, Jeff.
Speaker: I think that's, you know, that area of risk management is really the interesting one or one of the kind of core of a lot of conversations on this.
Speaker: I think the best way to look at it is that large language models are just that.
Speaker: They're a model.
Speaker: They're a way of deriving some output from a set of inputs.
Speaker: And, you know,
Speaker: the techniques that might have a large language model or generative AI at them from potentially, as you say, doing some trading or generating some text or even generating some code are really just the same kind of things that we do and have done for many, many years.
Speaker: So, you know, we have, you know, we have governance forums and we have, we have model review that's looking at if you've got a trading model or you've got a mathematical model or, you know, some sort of a system that's designed to do something else.
Speaker: taking some inputs and producing some modeled output.
Speaker: We have governance and we have framework around that.
Speaker: And this is no exception.
Speaker: So I think it's fashionable and it's current, but in many ways, the ethical side of it in terms of bias in one way or another is akin to bias or ethical review that's needed in trading models.
Speaker: All right.
Speaker: Thank you, Tom.
Speaker: I appreciate that.
Speaker: I was fortunate
Speaker: earlier this year in a training course to hear Peter Hinson speak.
Speaker: Peter is a well-known speaker and author who wrote The New Normal and The Day After Tomorrow, which were books that sort of examine these topics.
Speaker: He said the transformation we're going through right now with AI and Gen AI is not that uncommon.
Speaker: If you look back at major technology shift, whether it was the railroad, the phone,
Speaker: the internet.
Speaker: He said the difference is if you look, it used to take eight to 10 years for real change to come about, and now it's happening just at lightning speed.
Speaker: Dar, maybe you're the best position to answer this.
Speaker: I'm just curious, what are the dangers of moving this quickly?
Speaker: Is it as simple as the change just happens faster, or are we at risk for potentially frightening outcomes?
Speaker: Oh my gosh, it's such an interesting and multifaceted and difficult question to answer, right?
Speaker: I guess just to pick up on a few threads I think are worth drawing out.
Speaker: So great start of a 10 there in terms of risk management and our approach to balancing some of these challenges and the risks that are posed by these technologies, while still making sure we can adopt them in a really agile manner that best benefits the business, right?
Speaker: And so in most cases, I think sometimes people act like we've reached a singularity and the models, like the minute you open the box, they can just do whatever they want and they can all collude and distort the markets or, you know, whatever.
Speaker: In reality, it's a lot more.
Speaker: more rote.
Speaker: And we have really well trawled risk management frameworks and processes for model development and moving models into production and then monitoring models to make sure they're performing as expected and nothing is biased.
Speaker: Outputs aren't being produced and everybody's behaving properly.
Speaker: So that's the thing that I think it's worth just reminding people, because sometimes when we talk about generative AI, we think it's like we're unleashing some kind of beast.
Speaker: But in reality, at least at HSBC, it's certainly not the case.
Speaker: Thank you.
Speaker: Thank you.
Speaker: So now we've level set what gen AI is.
Speaker: I actually probably understand it a bit better than I did when I went to bed last night.
Speaker: I'd like to talk a little bit more about how it's being used today, and then we'll kind of work our way into what does that mean for the markets?
Speaker: Maybe it's a better question just to say, is it used today, right?
Speaker: So I know AI is used in capital markets.
Speaker: Is generative AI really used today?
Speaker: in markets today.
Speaker: And actually I'd like to hear from Mark and Tom.
Speaker: Why don't we start with Mark, but I'm curious what Tom thinks as well.
Speaker: Yeah, of course.
Speaker: So I think, as Dara said, people have this idea that something like ChatGPT can do anything now.
Speaker: And so there are definitely people who think the way generative AI will be used is it will come up with your trade ideas for you and it will pick the stocks you should be investing in and it will do the research for you.
Speaker: I think that is unlikely.
Speaker: As I mentioned earlier, these models are trained to produce plausible sounding continuations of text.
Speaker: And what that really means is that they're trying to come up with words that are the sort of things that everybody else would say.
Speaker: Now, as an investor, if you do the sorts of things that everybody else does, then you tend to get the same results as everybody else, in which case you may as well go for a passive investment product.
Speaker: It seems unlikely that a generative model will by itself come up with ideas and investment ideas that lead to outperformance.
Speaker: Having said that, generative AI is having a huge impact on financial markets today.
Speaker: And, you know, there are kind of several ways that that's happening.
Speaker: So one is obviously there are some sectors like tech and semiconductors where the prospect of potential future earning streams from generative AI are having a significant impact as those future earning streams are being priced into asset prices today.
Speaker: And then the sort of second round impact, I would say, is that
Speaker: Now, if you had to summarize what generative AI is likely to do in a sentence, it's that it will dramatically reduce the cost of producing content.
Speaker: So any industry or any sector or any section of a company which is primarily involved in producing content is likely to be severely disrupted.
Speaker: And so probably the winners and losers within those sectors of the economy are likely to be those that can use this technology more effectively in order to
Speaker: and make their existing workforce more effective in producing this content.
Speaker: Good content?
Speaker: Will it produce good content or just content?
Speaker: Right.
Speaker: I'm sort of curious based on what we're saying is it's really good at following patterns, right?
Speaker: I think if I think of what our HSBC research team does today, it's, you know, it certainly looks at patterns, but then we come up with our own ideas.
Speaker: Just curious if it can get there or there's a level it will never touch.
Speaker: I mean, never is a dangerous word given the pace of change in this area.
Speaker: I mean, if you look at the sorts of things that ChatGPT can do or models like that, people have this idea that, oh, it's just reproducing exactly the same stuff it's seen before, but it's not quite doing that.
Speaker: It is more creative than that.
Speaker: It shows elements of genuine creativity.
Speaker: But it needs somebody to provide the kernel of a great idea.
Speaker: So you need to tell it what you want and you can tell it to do something ridiculous, like please answer all these questions in the form of a haiku.
Speaker: And it will do that.
Speaker: It's clearly not reproducing haikus about this subject.
Speaker: It can be creative, but it didn't come up with the idea to do it all in the form of haiku itself.
Speaker: Basically, the way I think about these technologies is they're kind of like auto-complete on steroids.
Speaker: You could give it some bullet points by what you want, and it will produce coherent text.
Speaker: You could say, please reformat that existing text that I created in the form of a Twitter thread or, you know, like the script for a video.
Speaker: You can use the image generation models to produce like visual assets for slides in a PowerPoint deck.
Speaker: And I think for many investors, it's quite a rare investor that would have the luxury of being able to come up with an investment idea and then just immediately go and implement it.
Speaker: Generally, you've got somebody that you've got to go and convince, whether it's your boss, an investment committee, you've got to convince end investors to give you capital to invest.
Speaker: Somebody needs to be convinced.
Speaker: And so
Speaker: The process of coming up with investment strategies and then getting them implemented for many people will involve creating some kind of content.
Speaker: And I think generative AI can help a lot with that sort of area.
Speaker: I think actually a more urgent problem for investors at the moment is trying to sort out is what's priced in currently
Speaker: stocks that are strongly influenced by this technology, is it appropriate or is it hype?
Speaker: Because we've seen with previous waves of market excitements about disruptive technologies, the market tends to get excited too early.
Speaker: Now, in this case, I would say that it's not too early.
Speaker: This technology is, it's technology that
Speaker: is widely available.
Speaker: General consumers can get easy access to this technology.
Speaker: So it's not like some disruptive technologies where people get excited about it years before it's come to fruition.
Speaker: But the longer the excitement goes on, there seems to be a tendency for markets to either price in too big an impact or price in an impact too early or to price in an impact that will happen, but will actually be a winner take all kind of impact.
Speaker: They price it in broadly.
Speaker: So all the stocks in a sector see the benefit from it, even though in reality, one or two of them will end up dominating as a result of the new tech.
Speaker: So that's kind of how I see it influencing markets at the moment.
Speaker: But interested to hear what Tom thinks.
Speaker: I think it's really interesting to listen to Mark's view and obviously from a kind of equity and a firm and a stock perspective.
Speaker: I think the way I look at it is more about some of the diverse.
Speaker: Overall, you know, the question I think is, is it being used?
Speaker: Right.
Speaker: And I think broadly the answer to that is no, not really.
Speaker: At the moment, I think we're at the beginning of the journey.
Speaker: I think it will be a very interesting journey.
Speaker: I think when you, especially when you start to consider some of the areas that generative AI will lend to.
Speaker: So I think we've talked quite a lot about text generation and content generation.
Speaker: I think that's really interesting.
Speaker: I'm sure we'll see that feeding into all aspects, whether that's something you do as part of your day job, whether that is your job generating content as a research analyst, whatever it might be.
Speaker: There's lots of other areas that I think it will permeate into potentially more quickly where it's very, very good at some more specific things.
Speaker: Maybe that's helping with data science.
Speaker: Maybe you need a one-line operation to run to work out.
Speaker: Maybe it's looking at what your revenues were in a particular sector on a particular data set or what a client's doing or whatever it might be.
Speaker: You know, maybe you're looking at a chatbot example or natural language processing like we do within HSBC AI markets.
Speaker: And I think when you start coming down to some of those more niche areas, no, we're not live yet.
Speaker: But I think in many cases, the journey to using it as an engineering tool will be easier and quicker.
Speaker: And we'll start to see those coming out quite, quite sooner.
Speaker: In fact, maybe we won't.
Speaker: Maybe there'll be tools like other software development tools that will just kind of sit there and feed our products and services more quickly.
Speaker: Thank you.
Speaker: I'm still thinking about AI Markets, the product that you work on, uses natural language processing, NLP.
Speaker: I'm just curious, as I'm thinking about it, the ability to process language is a component of it.
Speaker: Yeah.
Speaker: How important is it?
Speaker: Are they in lockstep, the development of the two?
Speaker: Are they dependent on each other?
Speaker: Just curious how that plays out.
Speaker: Yeah, that's a good question.
Speaker: They're not dependent on each other.
Speaker: They're not in lockstep.
Speaker: But I think, you know, what we offer in AI markets is really a portal for accessing the best of HSBC.
Speaker: So, you know, whether that's drawing data from our global desks, whether it's accessing liquidity and accessing prices.
Speaker: And in many, many cases, those are specific things that one of our clients are looking to do or a member of our staff.
Speaker: And
Speaker: that kind of contextual awareness and that generative AI, you know, I understand the meaning and some of the context and the background is not applicable.
Speaker: And we have, you know, AI markets live now using many, many non-generative AI applications and it's very successful.
Speaker: I think,
Speaker: The interesting thing will be where that contextual awareness and understanding a little bit more about what you're trying to do can expand what we're trying to do in our markets.
Speaker: Give an example where we look at research and maybe some of the content that Mark's putting out.
Speaker: Mark's publishing.
Speaker: you know, a piece every day on, let's say it's on AI or on emerging markets or whatever it might be, you know, our clients can read that and can access that in a number of places, but maybe they want a four-line summary of the last one week's discussion on emerging market or property in China or on elections in Turkey.
Speaker: And generative AI can help distill both the question
Speaker: and understand that context and drive the search or help distill the content and give summarization.
Speaker: And we're looking at all of those things with AI market.
Speaker: So, you know, I think it will be, they're not in lockstep, but it will be a very, very interesting aspect to it.
Speaker: So how do companies who are employing generative AI give clients confidence by providing a level of transparency?
Speaker: And if that's not a hard enough question,
Speaker: you know, are the regulators up to speed?
Speaker: You know, is this something that the regulators will be able to get their hands around?
Speaker: But I am curious just what you think in terms of transparency.
Speaker: How do you give your clients comfort without giving away your intellectual property?
Speaker: Yeah, I'm going to answer that in two parts.
Speaker: If I think the transparency bit, number one, I'm going to hand back to Dara, who I know is the expert on the regulatory view.
Speaker: For me, I think
Speaker: When you're thinking about transparency, the first thing to think about is how much are you asking for of your generative AI?
Speaker: So I think if you're saying, tell me everything that I need to do and I need to know about the world in three sentences and you don't look at anything else, you're asking an awful lot of a model.
Speaker: And obviously, the accuracy threshold on that needs to be very high.
Speaker: So the first thing I'd say is where we're looking at it in practice, we're looking at it on the applied AI side, is to try and keep those use cases
Speaker: small and succinct.
Speaker: And, you know, we're asking for individual things where the measurement of transparency is easier, right?
Speaker: And that might be, you know, give me some news or give me some commentary on something.
Speaker: And there, rather than relying on, you know, on that three-line output, for example, we'll provide the references.
Speaker: And I think that kind of traceability, you know, yes, we've got a four-line summary of what's happening in the market.
Speaker: You know, here's the six links.
Speaker: I won't name names, but, you know, to some well-known news sites that's going to explain away where I got that content from.
Speaker: So you've got that summary, but it's not a black box, right?
Speaker: But I think having that transparency in those early client-facing and internal live-facing models is really, really important.
Speaker: And I think being able to dig into, you know, we've got some of these things that we're looking at in development, and I can ask for summaries on news, summaries on market events.
Speaker: But the first thing I'm doing is validating that.
Speaker: So if I see there's been something interesting happening in my two-line summary or eight bullet points that I've asked for to summarize a particular piece of market activity, the first thing you're doing is cross-checking that and you're understanding the detail behind it.
Speaker: And I don't think that's just for me.
Speaker: I think that would be for in all cases, you know, with this kind of thing, that that transparency from front to back is something that's going to be really key.
Speaker: And also, I think that's a place that we can
Speaker: You know, we can differentiate ourselves because if you can have an implementation that's not a black box, you're using some of that generative AI technology under the bonnet, but you can provide those references out to the world to allow people to a certain extent do their own independent research.
Speaker: You can provide that reliability and that confidence, you know, that your distillations are right.
Speaker: Dara, to the regulatory point, let me hand back to you.
Speaker: Sure.
Speaker: I actually think the regulators, particularly in the US, are being very clever about how they proceed with requirements for two things in particular, which everybody has probably heard about in the context of AI and ML.
Speaker: So that's transparency, as we just spoke about.
Speaker: So first, disclosing when AI is being used, first and foremost, which isn't always obvious, right?
Speaker: And two, enabling people to understand how an AI system is developed, trained, operates, and how it's deployed.
Speaker: And that's something that, like Tom says, is model risk management 101.
Speaker: So that's nothing new there.
Speaker: In terms of having to be a real IP or anything like that, actually, the regulators and regulators
Speaker: The consensus view is that's actually not required in most cases.
Speaker: And they appreciate the fact that in some contexts, it's not possible.
Speaker: In some, in other contexts or in many contexts, it's not actually that valuable for someone to just send a whole bunch of code or an enormous, you know, data set or something across and saying, well, here it all is.
Speaker: So they don't even bother making that a requirement to begin with.
Speaker: And a lot of the way regulators certainly in the US are proceeding as well is to look at that question of, okay, we know that a lot of risk management frameworks already exist, model risk management, data risk management, third party risk, all sorts, right?
Speaker: What are the gaps?
Speaker: Are there any AI specific gaps that need to be plugged here?
Speaker: If so, what are they before they start blanketing us with kind of new requirements, which I must say is very, very appreciated in my line of work, certainly.
Speaker: Mark, can, can Jenny, I tell you how it derived an answer.
Speaker: Can I, can I ask it how it derived an answer and does that have an impact on information security for our client?
Speaker: Meaning, you know, these, these models are going out and trying to collect patterns and collect information.
Speaker: it almost seems like we have to be more careful than ever to make sure that we're only exposing levels of information that we are comfortable doing so.
Speaker: So I guess two questions.
Speaker: Number one, can I go ask it to tell me how it derived an answer and is it as simple as seeing it?
Speaker: So you can ask a generative model how it derived the answer.
Speaker: If you just go direct to a generative model and say, answer this question, tell me where you got this information from,
Speaker: because it's just generating plausible continuations of text, it's likely to give you a confident sounding answer and then make up fake references.
Speaker: So it'll say, oh, I found it in this academic article by some academics with names that sound plausible and a journal name that sounds plausible but doesn't actually exist, or like some lawyer discovered in the US.
Speaker: that it will generate a legal case for you filled with precedents that don't actually exist.
Speaker: So generally the sorts of workarounds to this are actually constrain the way that you ask it the question.
Speaker: So for example, many of the sort of question answering questions that we're starting to see in heavily regulated industry, a user puts in a question.
Speaker: What actually gets sent to the generative model is the first step is
Speaker: that question is searched in a normal way, like standard technology that's been around for a long time, searched in a database of reference material, whether it's,
Speaker: let's say it was an HSBC specific thing and you were looking in all the HSBC documents that were internal.
Speaker: I'd say, go and look in this database and find records in this database that seem to be good search responses for this question.
Speaker: And then what actually gets sent to the generative model is please answer this question using this information.
Speaker: And then what the generative model can send back is hopefully the generative response will then take the little snippets of information that it's given and answer the question just using that rather than making things up.
Speaker: And as Tom was mentioning earlier, the system can be built in a way that will show the links and say, if you want to go and verify this information, this is the information which a standard search would say,
Speaker: should answer these questions.
Speaker: So you can actually go and if you want to interrogate this in more detail, you can like read the raw source material yourself.
Speaker: That seems to be a much safer way than just asking the model because the model, it's trained on everything that humans say, humans sometimes lie and make things up too.
Speaker: And that's, that is the downside.
Speaker: Mark, Dara, Tom, thank you so much for your comments.
Speaker: Generative AI, emerging technology, these are things that clients are looking to understand.
Speaker: I think we've started a conversation here.
Speaker: You guys have given me a really good sense, first of all, of exactly what is generative AI.
Speaker: But I think I have an understanding of where we are on the continuum.
Speaker: And it feels like there's value to be derived today, but it's nothing compared to the value that we're going to see play out.
Speaker: in the course of the coming days, months, and years.
Speaker: So I look forward to updating, you know, hopefully sooner, but if not anything else on the next GEMS panel in the following year.
Speaker: And I can't wait to see how far we've gone.
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