Independent Analysis of Generative AI
Transcript
foreign [Music] does one plus one plus one plus one I think that's four ones equal eight is when you get together a group of independent analysts to talk Ai and ml I'm going to hand this conversation over to our you know what that we were joking pre-roll about AI Whispering our CIO Whisperer Tim Crawford Tim take it away hey Keith thanks for uh for bringing us together um great to be joined by Maribel Lopez from Lopez research Larry Carvalho from robust cloud and of course Keith
Townsend from the CTO advisor you know recently we've seen a couple of announcements that have popped out um from the three big Cloud public Cloud providers uh Amazon being the most recent but of course Microsoft and open Ai and their investment in open Ai and then also Google with Bard and then Amazon with their three um Three core pieces to this story so I figured I'd just start off by saying you know there there are a number of opportunities that are coming from these different products
but I think it's important that we look at the opportunities from different perspectives but also understand maybe some of the risks that are coming down the pike and so it's great to have have the four of us together to be able to maybe touch on that um and uh Keith let me kind of toss it back to you to offer your perspective and maybe we can kind of run around the horn so my perspective specifically on what's going on in AI in ml or kind of
the news in the in in the space well I think that you know when you look at the announcements and Enterprises are already starting to test the waters with chat GPT and generative AI but they're also starting to see some concerns that are coming up too both from a nefarious standpoint not just nation state but also um you've got uh you've got folks that that have more Sinister objectives uh that are using these more advanced tools to to work against their their contemporaries but then if
you look at it on the positive standpoint you know there are some concerns around great I can use gender of AI in very meaningful ways but I have some concerns around where's the source of the data where's the Providence of the data how do I know that the source is legitimate that it's not misinformation or disinformation and if I'm using that to make business decisions how can I trust it and so one of the things I'm seeing is that enterprises are maybe constraining some of that
by using just internal data rather than public domain data um as a way to kind of create a percent of a percent of a percent of risk rather than just opening up to the World Wide Web but the other piece that I'll say to that is we're also going to uh step into some legal Frameworks too here pretty quickly with intellectual property and I know you know code generation is is a great example of that so I'm going to put on my official thinking cap and
Tackle this uh because it's a big question and so I'm going to look at it from the lens of the Enterprise architect because that's my primary focus in audience so I'm going to look at it from three different angles the first angle let's talk about the security part of it it is scary from a lot of reasons I I was thinking about how AI improves my content workflow how I can X chat GTP a question like you know what who are the top five providers for
storage uh hybrid cloud storage and it gives me an idea it may not be right and I I go from there but it's extremely authoritative it's real well well written and I thought to myself what happens when a uh Intruder or uh someone tipping attempting phishing attempts gets their hands on this and now those easy errors that we spot in fishing attempts they kind of go away right uh the content becomes a lot more finished a lot more authoritative as these chat GDP data sources become
more accurate they can Target uh known folks Executives in the field so there's kind of the it makes everyone smarter not just the good guys but the bad folks as well uh they become better at their craft and what they do and then there's from the kind of I.T limiting risk outside of security you know we talk about in the past being putting guard rails around you know data curies Etc where uh preventing stuff like redlining uh using data to do things that uh our data
analysts are trained not to do so what happens when we give chat GTP to just delay person and they're coming up with all these questions that they're not trained to uh doing a way that keeps your organization station out of legal peril and then there's the feeling that I can't help but feel we're early Cloud days of how I.T kind of uh gave the clout the forearm and said no no no stay away and then we discovered too late that we needed to partner with the
business versus stopping the business from enabling it so from an Enterprise I.T perspective I'm thinking yeah this is something we have to as Enterprise Architects cios ctOS we have to get in front of this and how our business is using this one so that we can secure our data to so that we can ensure compliance and three so that we can actually enable the business to uh leverage this technology yeah I got a couple thoughts right so one I think what's really interesting about this is
we've obviously had AI for a while we've even had generative models for a while with Gant uh what's been so interesting is the Breakthrough to have this be more natural language and to be more accessible to organizations so I spend a lot of time talking to organizations about what that means for them what that could mean for them uh there are definitely some concerns about the confidence level of what things like chat GPT give you they make it sound very confident in the answer uh in
some cases we've seen models that have actually you know made up sources so they're they're definitely concerns with it but I I think it's like any other technology there's always the let's you know let's test the limits of what's possible but let's really be sure that we understand what it's good at so you know some of the things that's really good at Tech summarization surfacing a bunch of your documents so I think there there's a lot that you can do with um Foundation models the one
one of the things I thought was interesting about like the Amazon announcement specifically was the other announcements were very Cloud specific and it's not that Amazon's was not a cloud-specific announcement but I think it really did a good job of talking about hey there are a lot of great AI companies with a lot of great Foundation models and right now I see a lot of cios CMOS senior I.T leaders like all wrapped up in chat GPT and gpt3 like that's the only thing out there and
there's a lot of really interesting Foundation models that are kind of some which are very actually industry specific so if you want to do a protein analysis and generation Healthcare you know there is progen right so there's definitely different models for different things and that that I thought was some of the enthusiasm to talk about how we could start to create marketplaces around this with secure API connections start to wrap data governance around this those are some of the unanswered questions for a lot of organizations
they're like oh hey you know you're working with something like open AI or you're working with you know AI model uh du jour how do I know what happens to my data how do I I know if those models are being trained on my data how can I do security and governance but there's no doubt that we now have availability and accessible technology that we didn't have before an AI and I think everybody's so excited because now it's the first time that I'm talking to senior
IIT leaders or CMOS and they feel like they actually might be able to tap into the power of AI without 800 data scientists back in a month right that's a big win for the industry and for the world not without its challenges which you both just mentioned and we can dig more into those but I guess I just wanted to throw in the well here's why we're talking about this you know here's the upside of this no I think that that's a great start Larry your
initial thoughts yeah hello hello guys glad to be with this very exciting group of diverse views hello from Amsterdam where I'm here at the cloud native Computing Foundation conference my view of this whole area is for the moment it's very good in productivity you know productivity for end users you know hey give it a uh article and say Give me a summary of it or give it an email and say improve it or you know this this is these are things that you can do very
well I have you know one of my friends is a paralegal who lost her job and I said hey probably you know go really look at this because you may not have a job in about five years if you don't really look at it and guess what she used it for she used it to say hey how am I going to answer questions for the kind of recruiting I might talk to so what kind of questions and she said that she was well prepared and you
know out of four people that they interviewed they told her she was the top one so I mean look at the use of this which would have totally been unheard of in the past the other part of productivity where you know other than these kinds of things is is developers I focus on developers and and it's really good to take um your views of what you want in an application and create code for you so now it becomes a question of you know you have all
these low code local platforms coming up can you just ask chat GPT or this generative AI models to do something and the difference will come when you actually make it take action right now it's still to the point that it'll give you some ideas you know how do you want to use it I tell people if you are just going to cut and paste today it's the wrong thing you shouldn't be doing that you should be really using it for guidance for generating new ideas so
I see it in a lot of verticals artists are using it definitely legal is a great place for medical it's a it's a great place when especially when it starts becoming better than a radiologist that is guaranteed to be better than a radiologist when I say that that you know it will be better than a human reading a an x-ray or or something like that um early days uh you know everybody needs to look at how to leverage it without having all the issues that Keith
and Marybell brought up that that definitely needs to be taken to taken into consideration but every new technology comes with and it's it's important that you start taking some steps and understanding those risks and then implementing it regardless of the risks I remember like Keith was talking about in the early days of cloud everybody said oh my data in the cloud is going to be less secure and then after a few years people said my data in the cloud is going to be more secure you
know what that's right I mean things things changed on that so I think in the same way you're going to have uh the the whole generative AI Take A New Path and we can talk about you know specifically what individual companies have done in the later past but I have some opinions on what what each companies have done in in this space I think I think this is a great start uh for the conversation and you know when I speak with other cios and I.T leaders
and even those outside of it one of the concerns especially when you look at the broader Solutions and I want to distinguish between the broader Solutions like what Microsoft and and um Google have come out versus Amazon's approach which is when you look at the broader Solutions there's a lot of potential you know it's a blank sheet of paper but it's a blank sheet of paper and so that has both an upside and a downside um and so there's some concern around how do we ensure
that we're using it for positive purposes and that we can trust the data that's an incredible point for companies that might be using this more for making business decisions or using it for outcomes in some way versus creating boilerplate language like a privacy policy or you know the basics of a legal framework and then you get into Larry's you know some of the things you were talking about around code creation I think that also gets to be really interesting but also really concerning until we can
figure out intellectual property rights and so for the broader Solutions these problems are coming up in conversations amongst leaders within the Enterprise but Maribel to the point you made about Amazon I think Amazon's taking a really interesting approach as compared to the other two in the sense that they're focusing on very specific data sets and very specific types of projects checks which actually could be a better approach and maybe even a I might be overstating this but a safer approach as we're starting to kick the
tires and figure out how we do this but I think it's also important for us to be thinking about who do we work with you know as a company or as a developer or as a leader you know who do we focus our efforts on because we can't do it all we can't work with all three of them and let's face it it's not going to be just the three of them there will be more coming in short order I guess I want to throw on
this because I think this is a super important point so like any other technology you know one of the biggest issues people have with technology is they decide that they're going to roll in something but they don't know what they're trying to do with it right I'm just going to roll in some generative Ai and this is going to work I'm just going to roll in some iot and this is going to work right so this this problem is not a new problem for any uh
senior I.T Executives what I think is to to your point that's super important um I actually question whether most organizations actually are going to go to one of the three big cloud providers to do this because they don't have that expertise so I'm wondering if the route to market for them to get to generative AI is actually working with their other technology vendors so you've got a contact center provider you've got an Erp provider you've got a CRM provider they're the person that's going to be
figuring out how to build in the generative AI how to make that work for you right if you are some really super duper top 15 percent of the companies in the world yeah you're going to these guys and you're trying to create strategic differentiation even if you look at Microsoft's announcements it's interesting because one of the one of the things that I think was well underplayed Microsoft actually talked a lot about how it was embedding you know generative AI models into his existing business applications to
give you business value on your data with compliance and security but but somehow I think everybody just picked up on like the open AI investment and other things right so the route to market for generative AI I really think does come first embedded in applications that you know and love and and want to use and for those companies that were already at the top of their game and very strategic and probably already had data scientists and all these other things they're probably digging in a little
bit deeper and talking to you know Amazon and Google and Microsoft about how to take it to the next level but I don't want to I don't want to let that point go because I think it means that everybody can make a whack at doing something with generated AI without needing to have all that skill set in-house yeah no we we absolutely saw that too right in the early days of AI we saw it play out for example with Einstein in Salesforce and I think you're
absolutely right you know you look at the big enterprise software providers having them entrusting them to embed the technology in an appropriate way using appropriate data is a really smart way to go as opposed to looking at this as just another building block right Keith what's your kind of take on this because you look at things a little differently from that architecture standpoint so I love the point that Maribel made to your blank page perspective if you think about the Enterprise and the challenges you know
I've been coming up to speed on AI I still have to remember what's inferencing versus training and I follow this stuff and I say stuff I mean the whole Enterprise I.T landscape pretty closely and I know for a fact the industry at large doesn't have enough data scientists to take advantage of blank paper stuff we need uh kickstarts and I love kind of your example of AWS focusing on verticals I think uh and Larry mentioned the kind of AWS has had ml products in the past
and I looked at my previous series AWS every day and it triggered the thought about Health Link this ml solution that AWS already offered specifically for health care so focusing being kind of safe focusing on Health Care data if your Healthcare data is already in AWS in the health Lake how much easier would it be for you to consume generative AI against that labeled data how much more accurate can your queries be so when Larry talks about getting to reading MRIs more accurately this is how
we move the needle from a organization that isn't staffed with a hundred data scientists that really understands all the guard rails all the uh indications of bias that that you have to filter for uh you're not uh starting from unlabeled data Etc this is going to be baby steps I am going to look towards the microsofts of the worlds the saps the five nines uh people who are experts in these areas and I'm just building on top of that before I just dump out a box
of AI Lego on my desktop and build a system yeah the you know I think the that's a really interesting point Keith and and as I think more about this and think about past examples I mean Cloud's another example we've seen this through through the decades too right going to distributed computing then the internet then cloud and now ai we just don't have the expertise within the Enterprise to truly give it justice and so this is and at the same time you've been seeing over the
last decade or two where it organizations are moving further up the stack right further up the Osa model and this is really kind of given us legs for things like low code no code and getting away from this deep deep development and analytical mindsets to get more sophisticated and and do more meaningful things and and that's why I kind of separate between the more broad general purpose types of applications of generative AI versus very specific ones you're right I think we will see the saps I
mean case in point you see a company like move Works who is using this kind of technology to augment um what companies are doing with tools like servicenow um you know there's some really interesting examples I didn't have to create that within my organization I didn't have to build that expertise in my organization even if I could get those data scientists which I can't I can't get the people and I can't afford the people so how do we start to capitalize on this but also tap
into that expertise and I think if one thing that we'll have to watch is for these big three players is where they put their efforts with regards to creating an ecosystem where their technology gets embedded in these core larger higher functioning applications whether it's a workday an sap Oracle Etc how they're leveraging these tools as opposed to how an Enterprise says okay let me just put generative AI to use yeah let me say something about this whole uh you know uh Amazon versus Microsoft versus Google
I think with Microsoft and Google being in the application space they have a lot more area uh you know to to improve those products by putting generative area AI into those products however with Amazon not being so much into SAS they become more of a arms dealer and say hey use my platform one thing that at least Amazon came out and I had written a quick post on LinkedIn saying that Nvidia during their conference had missed out on putting Amazon when they had Microsoft Google and
Oracle on their charts but not Amazon with Amazon now coming up with both the trainium and inferentia chips they are having control of end to end in what they can do to improve the arms if you would that others can use whether those folks become you know more end users is it going to be a software developers they can win and like you're saying the vertical products are going to improve and if they bring the right pieces in place for more companies to build on their
platform at a low cost that's what you know they're saying it's very low it's it's the cost costs have become a big deal on this right you know we you've seen around Cloud this whole phenopsis movement you know how are we going to measure spend this brings spend to a whole different level and I feel you know if Amazon stays in its Lane of being the Arms Dealer they may they may be doing a good thing but when I was with IBM and IBM chose not
to go into the application space a lot of other people came and ran away with the application space was that a area that Amazon that IBM missed on in those days and sap and others took it over or does Amazon also want to do work higher up the stack and in conjunction do other work to get both the benefits I guess so it just remains to be seen where it goes but I think the movements from the top three Cloud providers are interestingly different in in
some ways and I I do see that the chips can perhaps make a difference in the future when when things start getting more and more clear um so I love your point Larry but we can't forget data oh yes yeah the you know I don't care how fast my inference and my training engines are if I'm gated by an interconnect between my on-premises systems my cloud systems and my data that's existing in my primary Cloud no third party can get to my data faster than the
primary cloud provider so I can have the chips I can have the horses but if I can't if there's no Road for them to run on then it doesn't really matter how fast they are the and I think uh Amazon is going to play the the game that Amazon plays well which is to to to receive your data and makes it and make it extremely difficult or expensive for you to use that data elsewhere so I think this is why they're keying in on Industries they're
already they already have a big foot footprint in as a health care provider if I if all of my data is sitting in Amazon I'm not going to go to a training engine in one of the other Cloud providers or even on-prem because it's the that that much data ingesting that much data into these engines is just cost prohibitive so this is where you know we're going really wake up as Enterprises and start to I think repatriate and maybe repatriate is the wrong word but have
a better level of control of our data maybe it will be in a cloud provider but it'll it may be in the cloud provider that gives me the best networking options and the least friction and cost for accessing that data wherever my engines or wherever my training engines may exist yeah I do I do think the you know the point of of chipsets is is going to be important and some might be listening to this going wait a second chips generative AI like are is that
kind of a bridge too far and I don't think that's the case especially as you think about the edge to Cloud Continuum of getting data getting access to data making decisions about data learning about that data and then feeding that back out and you can't assume that that happens in a batch process you also can't assume that that happens all the way back to public cloud and so there's going to be something all along the Continuum there and then you start layering layering in things like
security into the mix let's be honest nobody really does a good job of edge to Cloud today when it comes to infrastructure everybody has a very specific uh unique solution whether it's the the large Cloud providers public Cloud providers whether it's the infrastructure providers like Dylan and hpe but nobody really has has kind of captured that and when you look at the the semiconductor space you know you've got the intels and the nvidias that are specialized in different ways Amazon has their own uh portfolio of
silicon but let's not rule out companies like Qualcomm in the mix too of systems on a chip in imagining being able to do some of this analytical modeling in a very specific form factor so I think the chip piece I would definitely would not exclude from the conversation but Keith I think your point about data is very astute and very well spoken which is how do you manage this data effectively and to maribel's earlier point if I have that data or have a function maybe it's
specific to an industry or specific to an outcome maybe it's cancer research for example and that sits within one cloud provider or within one type of infrastructure I think it's very realistic that we could see some cross-functional pieces happening however we also cannot ignore the downsides to this too and I think quite often people are talking about oh the potential and the upside and where we could go with this and that's great but let's be careful that we're not running with Scissors here and so the
downside is you know and we're already starting to see this right Regulators are starting to get involved and sniff around um what they do with generative AI we're seeing countries and and um companies looking at blocking access to these tools I I think it's kind of a Fool's errand quite frankly but I do think we have to find a way to bring these stakeholders together and I don't think that's a legislative process but I do think that we have to think about how to put some
guard rails on these different tools and those are some of the things I'm going to be looking for from these companies that they aren't just bringing the arms out is kind of the Arms Dealer but they're putting some training in place to say Hey you know this is this is a weapon this this could be used in a very good way and it could be used in a very bad way and so you have to be mindful of that as you get on this path and
so I would say you know it's a proceed with caution but I'm really excited about the opportunity and the potential that each of these are bringing to the table all right so we've come up with uh chips matter data matters um to the Arms Dealer point I think marketplaces matter yes and then the other thing that matters is knowing what you're trying to do and working with a small subset of strategic providers to help bring you into the new world most companies just don't have enough
expertise to roll this one on their own they're really going to need to lean in heavily with some of their anchor application providers anchor infrastructure providers but it's a huge opportunity from what I see Keith yeah I think it's a huge opportunity one of the things that you said that is you know we need help or companies will need help my first thought too was from who like the you know it's kind of like the same problem we have in Cloud we don't have enough Cloud
experts to do all the cloud migrations needed we don't have enough data scientists to help us do all of the analysis needed but not just data scientists we don't have enough infrastructure folks versed in this we don't have enough security folks versed in the concerns we don't have enough compliance people so as we're talking about the thing that we want to do which is extract value from our technology who do we call that has the entire Spectrum expertise in the Cycles to actually help us in
the reasons reasonable amount of time I think a lot of the focus as we talk about you know we're not using AI to say hey create a module that does this that we need to think from a higher level a higher abstraction I think that is the same thing we have to walk back what is it that we want to achieve where's the gaps and how do I Cobble together a best of breed set of Partners to help me get there yeah Larry let me say
one last thing Tim before I let you wrap up but the uh the thing about replacing jobs is a big deal and I think White Collar jobs especially companies who have a large number of employees doing White Collar work it could be Accenture it could be cognizant it could be Infosys and those guys are going to be one some of the people who start taking advantage of these at and at the same time they could also turn into the advisory services that companies are going to
be need needing to say Hey how do you manage the data security all these other things that that we all brought up in this call uh some systems integrators have a good opportunity to start coming up and saying hey help you be two percent more productive five percent more productive this is where we can gain by while taking into account the risks it's it's going to be uh an interesting uh Battle of who takes that first step and says to companies large Enterprises that we can
actually help you harvest benefits out of these quickly yeah no these are these are all great points and I think you know just to sum it up um it's really apparent that when we're talking about generative AI it's going to play a role whether it's at the lowest levels from chipsets uh and processors all the way up through data elements infrastructure and analytics and then going vertically not just vertically but horizontally into jobs and organizations and culture and then of course Regulatory and cyber is going
to play a role there too so a lot of impact that this is going to have on a very broad both horizontally and vertically and then the other piece to this is looking at how the three big cloud providers Microsoft Google and Amazon and how they're approaching it I think is also very telling so thank you everyone for kind of sharing your perspectives on this um so for those of you watching it's a group of analysts getting together other group of independent analysts you might even
call us a council but we're really happy that you spent the time with us and we'd love to hear from you as well so thank you for your time thanks everybody thanks and look forward to the next one
