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Jean: I will give you one or two, I have to write 20 and I am not there yet, but one the side of data, I really want to send a message that if you don’t have good data, you won’t get good AI, it just really key, and unfortunately a lot of our clients or all clients in general not just ours, they are number one problem is data. And that’s just because of the immersion the whole the year the system of record that are different legacy essentially that are creating massive amount they’re recitals so there is an approach to managing data which is the strategy no longer a tech which is very important , which is data mesh, and data mesh is a way you can break down info silo that you have into your enterprise, in a way that does not remove ownership of the orders of the silos because they are business owner, so they understand the application, but make that data available to whoever needs it within their enterprises itself, data mesh strategy again we cant say it enough to our customers is not a technology it’s a reporting instrument, data mesh strategy supported with thing called data product and metadata stores and governance is going to catch on fire in 2023, 24 25.Transcript of today's podcast by Sean with Jean.
Disclosure: At times I found it hard to understand a few words by Jean, so there will be some errors in the transcript, but I have made every effort to stay aligned with the message and context.
I have omitted a few filling words, to make it easy when I was typing.
Some parts of introduction and conclusion are omitted, because I couldn't care less
Hopefully I haven't missed much in between, if you find any grave errors, feel free to speak up.
Anyways, here's Sean and Jean.
Sean: Why don’t you start by taking a moment to position Accenture for the viewers who may not be familiar with their firm and a little bit about yourself?
Jean: Sure, so as you mentioned we are the largest TSM integrator out there, we are now over 700,000 people strong which is the size of a small city and we focus on helping our customers through their digital transformation journey. Of course, they are not all at the same stage, some are beginning, some are in the middle of it some are tuning it, but what we do we really help people harness the best the technology can give in order to transform their business and to make them more efficient.
We like to say and it is very true that we love to find net new value in our customer’s business. So, it is all about what else can I do to keep the customer I have happy and capture new customers but all of that not by selling tech for tech but really selling tech for value and AI is a good example of the type of thing that we do. As far as AI is concerned, we are a group of about 25k people now an extending network is much bigger than that probably around 60 to 80k people that we can draw upon because not everything in AI is about an algorithms that’s all the change management associated with AI of course, but that about the size of the our group, and we do listing in AI we do the plastic I would say Advanced genetic type of work, the more modern type of algorithmic approach that AI has brought to light, and we do a lot of information also, so all of that is under that umbrella and the blook of AI is data so of course we do a lot of work around data and we in fact have a sister group that all they are focusing is dealing with data in order to make sure AI is going to do the right thing. Last thing I want is from a customer point of view, we serve the global 2000. This is really where we are focused, and we do that with a very strong industry angle, meaning that we they are very proud of the fat that we serve about 20 large industry and we have groups that are really understand the business of our customers and that is how you do good business is when you understand new customers business and you can really emphasis with that challenge and helps them get you where you want to go.
Sean: That’s great, with that kind of customer base we know that the requirements and demands are very high and very unrelenting so we know the quality work that Accenture does to meet that customer base. Having said that share a little bit about your current thinking and your view about AI in general in the state of AI.
Jean: Well AI is finally out of the not sweet dreadful winters. I would say that started many moons ago, I think when I got out of school you know AI was existing but it was just very hard to do but now it has come out to play for real and with in business starting about five years ago really where we were able to find application of AI that were what I am calling pragmatic AI this is what was not about the sexy or plus side of AI or I can you know find gaps on YouTube and all that kind of stuff but more about how can I use the various techniques that AI has in its tool box we need to transform the business we think that are very tangible right now I sometimes use the example of chat bot. Chat bots have been around for long time, in all common days at HP I think we met a few of those, but they tend to be very done to certain extent, you would try to find something behind the scene the system will try to find the few words that were in the final document and give whatever it would call an answer, but really not understanding what was your intent in your question and what was the right thing to answer. But today, chat bots are I am not going to say they are not human like yet, but they are getting there and really it is a pleasant experience as opposed to something you know you were not going to get an answer anyway so just were going through motion. So that what I call pragmatic AI and there other e.g., hyper personalisation for e.g., which is very important into market, so we are in an era of pragmatic ai were we see every day significant improvement that can really be used by client in those digital transformation that I spoke about.
Sean: How about some comments about state of edge AI where BRN is focused on what is going on from your point of view on edge ai?
Jean: I think you are in the right place, because edge ai is now sort of emerging from darkness, starting to be used in some very specific domain, it is not yet everywhere, but it certainly is getting some action today and its where probably in the long term most of the AI will sit in fact, a lot of people are thinking AI demands massive infrastructure behind the scene, which is not untrue there are still some aspect of AI that are very demanding in terms of computing power and we all require the customers thought their partner like cloud providers or others who have significant infrastructure to address the problem. But thanks to the progress that are being made on the silicon side or other semiconductor you can use but not just generic terms, the progress is now allowing AI to move to the edge, they are still constraint on the edge, such as power in general is always a challenge and space in terms real estate you have available however I think a company like yours are really taking that in account because they’re too hard and now bringing the power of AI with a full understanding of those complaints if you put something in satellite for e.g. you got to make sure you know every people be using because it is limited so Edge AI certainly very exciting domain and 2023-2024 is going to be really the time where we are going to see a lot more of it. I mean they are of course and you are already successful others are successful but I think that we’re going to see the next situation like deployment of this technology on the edge.
Sean: That’s great and its certainly consistent with our interactions with our customers 23 and 24 the interest level is very high right now but its also what you comment were a great set for next question I was going to ask you we talked about power elements on edge, what’s your thoughts on neuromorphic which of course that the basis of our tech how do you see neuromorphic what do you see in the future, do you see the direction for a lot of ai being neuromorphic?
Jean: Yes, the reason is the approach of neuromorphic AI is not to try to bend the business of ai to what the plastic techniques are doing, I never believe you should try to mould your customer run the technology you are but try to have technology that can embrace the needs of customers. In this case, you know when you talk about AI we are talking about some form of emulation or aspiring to emulate how the human brain is functioning and a lot of these exercises going to build digital human brain I’ve been down for at least ten year there were all about trying to see how they can make a mirror copy of it, which is kind of an engineering approach right, I am going to break down how the brain function I’m going to try to rebuild the pcs that makes the brain, the neuron of course but these techniques have proven to be unsuccessful and not scalable which is really important, and that to me was trying to make sure you could make literally a computer version of your brain, but I don’t think that what neuromorphic is doing, the effort that I see in the neuromorphic world is understand the brain function so take it a with a cognitive view of the world and then mould the silicon and techniques you are going to be using to but try to achieve the same goal that brain is using when they make a decision forces, so I am a big believer that neuromorphic tech in general is going to be a significant part of the future of AI.
Sean: I couldn’t agree with you more on that because that’s exactly how we view it, we certainly not trying to copy the brain, and usually when I run into people don’t understand the tech well what we do is obviously we take the best part of brain which is a very efficient computation machine, and use that so that’s all we are simply doing inspired by the brain, taking those principles to get a lot more done break through the Von Newman bottleneck and just get things done with less power its just that simple and allow you to apply that for the use cases you are doing not mimic the brain, but use the principles of the brain so agree 100 percent with that. Well let’s shift gears little bit lets talk about what’s going on with models I know you think a lot about that what’s going on with the latest trends on models what’s new with transformers gives us couple of comments about that kind of stuff.
Jean: yes, it’s a very interesting domain we are in now for full disclosure I am a big fan of Chris Re at Stanford which to me is really one of the best vision of the evolution of AI today, and elimination of what we call model ideas, so in the world of AI its very tempting for data scientists to create model for everything every problem they have they want to create a model right, and there’s also a lot of duplication because there’s not enough sharing happening in this domain, but that’s another subject, but there’s this model like we create way many models, in industry and these models they all highly tuned to very specific use cases scenario but this is dramatically changing there has been an emergence of new technique and model that are called transformers of course we call them supermodel right we are engineers so we are going to have some form of humour and the supermodel they are just better and you know, doing the work than there’s a bunch of little ones that are bespoke but they are very function defined better say they are very focused on type of information you are processing, so we see a lot of transformer activity in the tech space, because the first barrier to break down has been natural language processing and that’s where transformer have made literally significant progress I think if quote Dr Re, google translate by itself was 500,000 line of code five years ago, today its 500 lines of code by using transformers. So these transformer born in tech space, you will hear names like gpt3, which is very popular around here, what this supermodel do they have capability to understand pretty much everything in their domain and they just need to be tuned when there is specificity, so say it would be they speak English, but you can tune them for may an industry that is specific dialect and that’s you have to do, once you tune in dialect it will understand it is about you know interpreting text in industry, we see that happening in video and in voice. So future is less model, that are bespoke, more transformer, and then a shift in the personality of the data scientist that is going to be using AI instead of being people you picture as a white lab code and blackboard writing mathematical formula, data scientist will wear a hard hat, if thy are gas industry, meaning they will be very industry savvy and they’ll be able to tune these transformer and select the right data in order to solve the problem in their industry and we going to see the across all industry and there will be a shift in profile of data scientist and also we are seeing it today because data is the fuel for AI, we will see an emergence a massive number of data engineer s people that can prep data, right in order to behave it ready to fed to the transformers to then solve the problem of the client, it s a fascinating domain and in full evolution and I think we are still at the beginning, so more innovation is going to happen as days are going by.
Sean: its great we might have to come back and talk about that at a later date, yeah but let’s get ready for close, but I want to ask you I know you usually put out a prediction for the year but I would love to hear your thoughts, have you got a prediction one or two?
Jean: I will give you one or two, I have to write 20 and I am not there yet, but one the side of data, I really want to send a message that if you don’t have good data, you won’t get good AI, it just really key, and unfortunately a lot of our clients or all clients in general not just ours, they are number one problem is data. And that’s just because of the immersion the whole the year the system of record that are different legacy essentially that are creating massive amount they’re recitals so there is an approach to managing data which is the strategy no longer a tech which is very important , which is data mesh, and data mesh is a way you can break down info silo that you have into your enterprise, in a way that does not remove ownership of the orders of the silos because they are business owner, so they understand the application, but make that data available to whoever needs it within their enterprises itself, data mesh strategy again we cant say it enough to our customers is not a technology it’s a reporting instrument, data mesh strategy supported with thing called data product and metadata stores and governance is going to catch on fire in 2023, 24 25.
Because we have to breakdown the silo, we really enable AI in human knowledge in something that is understandable by computers, knowledge is absolutely necessary in every single type of business application in today’s world, and 5 years ago every application we have not just database and UI but database, knowledge graph and UI, is proven true. I can see that being deployed very well, so that’s my prediction, its going to keep on going. I promise I will find some more, 18 are missing.
Data mesh - Wikipedia
Data mesh | Thoughtworks
Pioneered by Thoughtworks, data mesh enables independent teams to build reusable data products that accelerate the delivery of business insight.
