Beyond Efficiency: AI's creative Potential
Welcome, dear AI community from diverse domains and forward-thinking pioneers from every corner of our interconnected globe. I hope you all had an energizing summer. We're happy to be back and engage with all of you. It is my great pleasure to welcome you to Beyond Efficiency, AI's Creative Potential. We spam time zones, industries,
disciplines, and cultures. Yet we are united by a singular curiosity to uncover the expensive horizons of AI as it catapults from emerge tools of efficiency to a catalyst of boundless creativity. For many of us, the journey of AI began as a technology that increases efficiency and quality and takes on repetitive tasks. Today we stand at a groundbreaking moment where it's not just about accelerating processes, but it is also about unlocking doors to a wonder world where only our imaginations is the limit. AI has seamlessly transitioned from an operator to a co-pilot, steering us through life's complexities, inspiring fresh perspectives, and redefining the realms of possibility. Our conference today brings together 20 of the world's leading AI minds.
Leaders whose insights and explorations have already begun reshaping industries from novel strategists, ignited by generative AI to renewed operations and the transformative potential of the workforce. We are on the cusp of an era where AI doesn't just compliment human effort, but elevates it, but admits this development. It's pivotal to remember and recognize our human qualities. It is a human being that brings technology to its next level.
And on the other side, it is technology that brings us humankind to its next level. Over the next three hours, our visionaries will share hands-on insights into the world of creativity, innovation, and transformative processes, all fueled by the smart combination of human and ai. Let's get the chat going, engage and converse and connect on LinkedIn to exchange knowledge and experiences and strengthen the AI community over the whole globe. My dear AI Enthusia, the dawn of the new AI area waits.
Let's start our journey of exploration and discovering together share for success. Now I'm honored to welcome our first speaker, Igor, on our virtual stage who will guide us through the nuance of generative ai. While this technology promises and present its breakthrough, especially natural language processing, it's not without its challenges with Igor, we will look at both sides of the coin and also leave you with a positive outlook packed with solutions that foster sustainable advancements. Dear audience, as we embark on this exploration,
let me approach AI with enthusiasm, tempered by caution. Lets voyage of understanding comments. Igor, a warm welcome on our virtual stage. Thanks for your joining us today from the Netherlands.
As we have hundreds of people out here, let me introduce you to all of them. Igor, you are a cybersecurity mavin. With over 15 years in IT and out OT security, both dynamic blend of tech savvy and entrepreneurial talent and alumnus of Singularity University, you are at the forefront of disruptive technology, not just a techie, your Excel, a scouting startup and concealing top tier boards on innovation and cyber resilience with a knack for emerging tech insights with business strategies. You are an InDemand speaker inventor.
We are looking forward to diving into today's topic on generative AI with your unprepared experience in the ever-changing tech realm. Igor, it's truly a privilege to have you with us. The virtual stage is yours. Alright, thanks for having me here. My name is Iko Hamer. I am the lead cybersecurity and AI at its people in the Netherlands, and it's really a pleasure to be here and to connect to all those fellow AI generated people. I'm now looking for my clicker. I'm really sorry. Alright. It should work now. Lemme check again.
Screen is awake. Okay. Okay. This is my presentation. I see it very small, so it's good. I have my glasses on, so I'll give you a short overview about the landscape of generative ai. Then I show you also how it let's say evolves in the coming years.
And last but not least, I will also show you how I created generative AI business cases in the Netherlands and in Europe. So let me check again. So I don't know if you know it, but actually when you look at the landscape of generative ai, it changes all the time. You have the feeling one day you understand what generative AI is all about, and the next day you really realize you need to learn what you need to learn. Again, that's an issue for me. I love learning, don't get me wrong,
but the pace of development is so fast that you almost need a full-time job to understand how it actually works. So as the c e O of Google mentioned, artificial intelligence is more relevant for humans than actually the invention of fire. And I totally can relate to that. Why? Because generative AI and AI will basically change the way how we work, live, entertain for the next decade. And I'm really, let's say, intrigued by the amount of speed in the generative AI landscape. And I will show you also how I look at it. So the generative AI ecosystem is really exploding.
So every time there is new technology in the field of fission, in the field of audio or in the field of data, and it's just a matter of let's say time, that you realize all those AI systems are basically connecting. So you see that actually all those a AI becomes multi-model. So image processing, audio processing, cybersecurity, but also co-generation video data and enterprise software. And basically I think the future of generative AI is generative as a whole. So everything becomes literally generative. And I personally think that the trade of a programmer will be obsolete in the next five years or so. So top generative AI use cases are specifically in content generation in access enterprise data and basically optimizing the quality of data, but also understanding what's in the data.
So what insights hindsight and foresights are available, data analysts and also of course large but not least co-generation. If you see how fast the amount of, let's say artificial intelligent generated data is produced on several repos, it's really mind boggling. Of course there's a good site and a bad site because you will see there is content which will have basically more quality. You will have new technological features like say you have improved anomaly detection, but you can also do something about proactive defense related to cyber. That's of course the good side.
The bad side is that you can use generative AI to manipulate people and to undermine our information society by creating content which is more or less dangerous to produce. So every company is basically now looking at generative AI as a way to create value. It's not about you don't do generative ai. Everybody should have a generative AI roadmap where you basically spec out two things or basically three things. First, you determine what is the business value in generative ai. Then you look at, let's say, how can you plot it into certain, let's say initiatives, and last, what needs you prioritize? What is the most relevant pilot in your organization? So it's very irrelevant that you start with the foundational responsible AI guide principles. So looking at privacy, looking at fairness and bias detection, but also looking at explainability, transparency, safety and security, validity and reliability, accountability. And last but not least,
your own company principles. So it's a part of a transformation process. What you see right now that everybody is going in this curve of so-called the IA maturity model. So everybody is now exploring the new technologies, looking at ways how to incorporate that to let's say, create a proliferation in your organization, optimize the code, and last but not least, use generative AI to build a transformation in your organization. So that's basically how I look at it from a al point of view.
I created also a platform which is called Cyber Resilience Pro, which is basically solving the issue of 2.4 million vacancies, which are not available in let's say in the Netherlands. And with this generative ai, I'm able to mimic the role of the cso, the eso, the operational technology specialist. But that's that role. It's not only,
let's say cyber, let's say as one of the use cases, which you're also looking at healthcare, you're looking at legal and many, many rules to come. Basically, I believe generative AI is here to stay and it will transform our information society. And with this technology and working together, we can really create new ways how humanity will interact with information technology and basically create a better world. So I'm really delighted that I was able to give this small talk to you. It's limited on 12 minutes. So yeah, I will keep my mouth shut now.
So thank you very much and over to the studio. Thanks a lot. It was great and short and crisp. Let me see what kind of questions we do have. Dear audience,
please share your questions in the chat. Let me just check the chat quickly. So one question is like where do you see, can you hear me? Hello? Yes. Okay, where do you see.
Yes, I can hear. You. Great, thanks. Where do you see the biggest chances, or actually, which industries would you say for now, do you see the biggest chances with generative AI to have a very fast development. A good development? So positive? Yeah, I would say so positive. Okay, clear. Just to make sure that I understand correctly where we can actually.
Discuss the other side then too. Okay. Okay. So entertainment, legal, healthcare, education, and of course engineering as a whole, because with generative ai, you are a imagineer. So it's all about imagination. And by using generative ai, you don't have to be, let's say a hardware engineer or a software engineer, you are able to blend several technologies together and build the application required for your idea.
Let's remain there. And this is something that myself, I'm thinking a lot about that. I mean, until now we need a lot of engineers. But with generative ai, what is your opinion for people who are not in tech but who understand the power of technology and see the business models and now with generative AI can actually develop their own algorithms and solutions? Do we still need as many engineers in the future? To be really honest, engineers are always required. Why?
Because you need those hyper geeks who understand really what's going on. But the power of generative AI is in my, let's say, humble opinion, the way to scale the technology. So you will see a lot of, let's say no-code platforms which will interact and will create and foster new creative ways for people who don't have a typical engineering background. So let me elaborate this with a small example.
Suppose a kid from India gets access to this generative AI and he looks at a way to produce electricity in an innovative form. He doesn't have to have a degree in, let's say electronics first to build up this prototype. No, just talk like visual voice to an AI which understands your voice and then generates several prototypes and based on the simulations of the prototypes, ranks the likelihoods of a successful product development.
So the next ideas will not be from typical, let's say engineering guys, so or techie. I think the mixture of people and multi, let's say let's not the typical engineer, but let's say people with auto creative fashion, let's say an artist. An artist has now really has superpower to build something which is sustainable for the planet. Exactly. That's fantastic. That's the true revolution of generative ai.
But this is exactly what I meant before when I'm talking about, okay, now we have people like artists like Philosophists, like nutritions, doesn't matter which industries, they know their business best, they know their challenges best, and if they understand the power of the technology, in my opinion, they will be able to actually develop solutions based on generative AI tools that obviously are being developed further and further and further. And there we need the engineers, but in my opinion, I see a huge advantage for people who are not in technology now, who are not developers, who are not coding to be able to provide solution to global challenges based on generative ai. But let me just have, there's another question came from the audience. How do you see the coming EU AI act in his shown AI guiding principles? So what is your view on the EU AI Act? Yeah. I think you need to have a kind of, let's say, co compliancy framework for ai. That's no question about it,
but you have to also be very careful and balanced because if you over-regulate the chance that you build, let's say new technology and new economic relevant services in the EU region could be in danger. So overregulation leads to nothing. So it's all about balance. So by providing the right argument and looking at the way, how can we as a community, as the EU Union work together to create a competitive edge and build generative AI use cases which scale, so engineered in the eu, skilled to the planet. So what you actually said, there are global grand challenges on let's say healthcare, on let's say longevity, let's say on the environmental issues. So we need all the brainpower we can get work in a way that works, but don't over-regulate because that will basically end the innovation era, which we are in.
And I think it's a fantastic time to be alive and use those tech generative technologies. Thanks again. Great, great. Thanks. Actually, that was such a nice sentence. You finalized it. I would like to remain with that one in the year of our audiences. So thank you Igor for your compelling keynote and also for the insights now with answering those questions. It was a great privilege to have you on stage, keep in touch and dear audience, get in touch with ego via LinkedIn and I'm sure you will be able to get much more information by following him.
Thanks a lot and see you soon. Thanks a lot. Thanks for having me. Cheers. Bye-bye. Bye. Now to keep up to the Swiss timing, I'm just about to lead you to our next panel and actually our first panel of this conference. This one will be packed with five incredible minds with well over a hundred years of combined experience in AI and other cognitive technologies in diverse sectors around the world for a deep dive into operations, redefining by generative ai. And honestly, just by listening to myself, I think having over a hundred years of experience as a human being, it was something, but now with ai, we can just bring it together, condense in such a few minutes. So anyway, as we stand at the crossroad of innovation, this discussion aims to shed light on AI's profound impact on operations globally. I invite Monique, Jeff, Jarret, Simon,
and Geor to our virtual stage to uncover the nuance and collaborative potential of this transformative technology. A warm welcome to you all. Now, before I hand it over to you, let me introduce you to the panel, to our global audience and start with Monique. As Monique, you are a leading this great panel. I would like to introduce you first.
Monique, my dear friend, with over 25 years at technology's forefront, your expertise spans cybersecurity, blockchain, and ethical tech design. From Cisco to Heera hash graphs board, sorry, you shape the industry's trajectory, founder of the humanized internet, a nonprofit championing digital identity you awarded, including Forbes top 50 women in tech and Europe's top hundred women in cybersecurity, a multi-award winner. You are a true tech leader reshaping our digital future.
It's a great honor to have you with us, Monique cheering, chairing our panel. And now with you on the panel, we do have Jeff, another familiar face. As always, it is our great pleasure to have you with us here. Jeff,
there were many changes in the last month. We are eager to hear your views and experiences. Jeff, you are a digital transformation now in the manufacturing, real wielding industries, 4.0 tools at Hitachi Solutions.
You are not just bridging tech and business gaps, but also igniting conversations about the future of the fourth industrial revolution. Besides t strategizing, you mentor innovative and passionately shared stories on LinkedIn. In a nutshell, Jeff, you are a tech savvy storyteller and industry 4.0
enthusiasts. Jeff, welcome back to our stages. It's great to have you here again. Thanks. I'm excited to be here.
Thank you. Now to you, Jared, great to see you again too. You are the AI meister at a D m, an $85 billion global nutrition juggernaut. You're not just about crunching numbers, you're revolutionizing how the perceiving the global supply chain.
The dance between human microbiome means in food and transportation's vast expense. It's your innovative spirit that's helping transform our dietary landscape from farms to forks, roads to oceans. In essence, you're bridging tech and nutrition foraging a future where people eat smarter, live healthier, and navigate a rapidly changing world with ease. Jared, a warm welcome and thanks for being back. Great, thank you very much.
With you on the panel. Now we have Simon, another very dear friend of ours. I'm thrilled to see you here, my dear friend coming in from Singapore. Simon, you have one foot in the industry and another in academia. Not only have you been helping Nvidia to shape its technology agenda, but you have also been working with the university to develop and train new talents.
You also have more than 200 peer review publications and book under your name. You have been with Nvidia for almost five years and you're their global head of AI Technology Center and AI Nation. Simon for us, you are one of the leading people in AI and we're so happy to have you with us today on the panel. Together with you, we have George. Thank you very much. Welcome George. Also warm, welcome to you. You are an AI founder,
building the future of price optimization, raised $9 million in funding and worked with Fortune 500 customers worldwide, top hundred thought leader in AI by Thinkers 360, having designed and launched AI application and created predictive models that proved accurate over the years. Part of work. Sorry, part of your work has been published in renewed international journals and book publishers like Elsevier and Foresight, your latest endeavor in your over 25 years, careers in Future Up combining this extensive experience in pricing and AI technologies to help enterprises optimize pricing and achieve their sales and profitability goals. What an incredible panel where I just realized that actually three of them are Swiss cognitive global AI ambassadors. What an exciting 30 minutes. I would like to hand over to you, Monique, the virtual stage is yours. Hey, thank you. Thank you so much, Dalith, my dear friend. What a wonderful,
wonderful panel and what I'm just so thrilled at what the W cognitive does and listen, we have a great conversation here that we're going to on stage. One of the first questions I'm going to ask our panelists is it's a warmup question really. It's really about how should business leaders be thinking about generative ai? And I'm going to start with you, Jeff, and move to Jared, George and Simon. So Jeff,
what are your thoughts on this? Well, if you look at AI in general, you need to understand kind of what it's trying to do and what it's trying to solve. And artificial intelligence conceptually is a way of replicating human intelligence, which means it can be used to help augment or automate decisions. And those are the two big areas that you need to think about this. And if you think about it that way, it'll help you apply it to the right areas. Where are places that we can have a technology to make better decisions for us? And then where can we apply to help us make better decisions ourselves? So I think if you categorize it that way, you'll start to see the big implication of where this can be applied, which is to nearly every industry, nearly every function from a frontline worker all the way up to the c e o.
And that's what makes this technology so ubiquitous in its application and applicable everywhere. So it doesn't have to be dystopian as we all what's out there in the industry as a fearful thing. And I love the thought of this conversation, let's keep it going with Jared. How do you feel about this from a business perspective? I think what Jeff, how he clearly stated it is exactly how leaders should be looking at it. Another aspect of it is human aspect of it is that everything that we can do with AI is fundamentally about speed.
So expect all your processes, how you're communicating via empowerment of the workforce, the speed is going to increase dramatically. Things will be done much faster, much more accurately, much clearer. There's going to be a lot of disruption in this space.
Expect your business models to be disrupted. So look at it as an opportunity to continuously improve how you plan on doing things and see if there's new ways of using these kinds of technologies to grow your business. And really, I mean the sky's the limit. So acceptances, were going to be very key. It's not going to go away. So it's really key for the business aspect here, but continuing the discussion. George, how about you? What are your thoughts on how business leaders should be thinking about this, about generative AI specifically? I've talked to many business leaders about this and some of them are hesitant, actually. Some embrace it, but some are hesitant. And when I see that,
I always say, okay, think about your competitors and what they can do with ai, generative ai, or any other form of ai. And then you will invest in it because you'll understand the data of not doing nothing. And I mean, it's a big revolution. It's all around us. AI is here to stay 100%. It's going to change our lives and it's going to change every company's the way of operations of doing things, regardless if these companies within the AI market or the technology market or somewhere else. So I will say to all people,
don't be afraid or be feel intimidated by new technology. Learn more about it, understand the potential risks obviously, but also the great benefits and try to embrace it and leverage all the great things that are happening all around us. And it's great because this great AI revolution really is happening right now because now it's the first time that AI hit work, the main street market.
Everybody's talking about it, not just some people from the academia or some people like us who are the technology enthusiast. That's the early market. Now we are talking about the main street market, which is practically the whole pa, everyone. So detecting a lot of interesting themes here about operational efficiency and acceptance and impact to the business it seems is positive. How about you, Simon, from your perspective, because you balance between academia but also business, somebody, what are your perspectives here? Yes, so what Jared, Jeff has spoken about, AI is going to touch on everything, automated automations, faster decision makings speeds and so forth. And one of the very important thing, in fact, we should be thinking about what if we do not adopt ai? AI is going to be extremely disruptive to many aspects of our work. And if we must think and business guys must think of that, if we do not adopt it, what will happen to our business and what will the competitors be doing and what will it do to cannibalize our business? So this is something that the business guy should be thinking about very, very carefully.
Awesome, Simon, because I see that as an opportunity cost, you avoid it, there could be that negative impact to your business. And so that's a great provocative thought, Jeff. I would like to kind of continue this conversation as we start to double click down the advantages here and benefits to the business overall, what makes generative AI so unique and compared to other types of ai, even other technologies. Now remember we're talking about generative AI because people can get confused.
Ai, you're talking deep machine learning. What about generative ai? What makes it so special? Well, when you think about it, chat, G P T was launched on November 30th, 2022, so about nine-ish months ago. It took five days to reach 1 million users and a hundred million in around two months, and now they're hitting almost 2 billion page views per month.
And about four months after chat G P T was released, the G PT four model was released with more parameters, higher performance, higher accuracy, and the world had to figure out how to respond. I mean, if you look at just students alone, they started using it for homework. Schools scrambled to figure out how to respond to that. Italy even banned chat G P T for a short amount of time. And remember nine months ago, most of the world had never heard of any of the G P T models, generative AI or open to ai. And there's two big reasons why I would say this particular technology is different. First is the speed of adoption chat.
G P T is one of the easiest technology tools that you've ever really used. You don't need to be trained on how to use it. It's unbelievably simple, a single prompt for you to type in any question you want. Can you think of any other technology that so many people, so many different backgrounds and educational levels can adopt so quickly? I would say there are other technologies out there such as blockchain and other forms of AI that are arguably just as disruptive, but they require a substantially higher set of resources, time, knowledge, and money to utilize as a company. And assuming you have good data, you can start using chat G P T models in hours. The second one that makes this technology specifically generative ai, so disruptive is Microsoft's investment in OpenAI. In January,
Microsoft announced a plan to invest 10 billion into open AI just a few months after chat p t release. And that's a serious level of investment that if you look at chat G P T, they're already starting to integrate the concept of generative AI in most of their products that they have. And since Microsoft is such a big company and most people use a lot of their general everyday tools from teams to word to PowerPoint, whether you like it or not as a professional, you're going to start to see this integrated into nearly everything you do on a daily basis. The question is are you going to be taking advantage of it? That's a great question. Are we taking a advantage overall? Because you have check G P T hitting the consumers as you just well pointed out, as well as the business. And it's a tool. I mean we have to accept it. And I think Jared,
moving to you as we continue this really interesting and this provocative discussion to some extent, how is generative AI transforming traditional business workflows, particularly in sectors like manufacturing, design and content creation? And can you provide some notable examples of case studies making it concrete for our audience of our operations that have seen the significant change so the audience from the business perspective can understand the. I'd say let's start with why it took off in November and it was really because like to use the term, it was a delightful experience and it was really the first time where most people got to interact with an actual artificial intelligence computational source. I'm talking about Siri or what you see on Netflix or what you have on your phone because AI's been around for a while, but an actual interaction where you as an individual got to see something being generated, it results specific to your prompting to how you were asking a question. And this is just a very early stage of this kind of technology where you're going to have a dialogue with an artificial entity.
Now, from an operational perspective, what we've seen very quickly is how we can use this in operations. One specific use case is how very complex systems of manufacturing, for example, there's a lot of training that needs to occur there. There may be many manuals or documents that have to be digested by an individual for the proficient under job, but we know with the modern workforce, things are moving very quickly.
So now we've got a tool through a dialogue interface as an example, that consume vast amounts of information and provide information back specifically to an individual, let's say within a manufacturing facility. If they ask a prompt or a question, not like an F a Q list or some other tool, but rather a generative tool that can provide results of information to that individual, how they can operate the machines, as an example. Another thing that they've seen very rapidly is that in terms of customer interactions, groups like when you have to call any call a help desk or something, they're finding out these tools are very applicable in that regard because they're providing much more accurate, much more refined information specific to that individual. We can bring in all sorts of different languages that could be translated on the fly and businesses are seeing this like, wow, this is really effective here.
Now something that I think is interesting, it's just in the nine months, but Jeff pointed out is how transformative people are seeing these kinds of tools can be applied and how disruptive they are to many of these processes. Mustafa Solomon, I believe he's one of the founders of DeepMind publishing a book, and he just said In the next 18 months, expect a thousand fold increase in the capabilities of these kinds of language tools and these models. The power of which we're accelerating here in terms of how these things are going to be disruptive are, I don't even think people can really comprehend. We're really. The last nine months.
We're really at the tip of the iceberg here. And you used the term transformative. Which leads me to you, George. Basically the question I have is how do you experience digital transformation being impacted by AI and particularly generative ai? And what are the current AI applications that you see? We've talked about the impact of business and business efficiencies and over time how quickly it's moving. From your perspective, what are your thoughts here from a digital transformation angle? Well, digital transformation is already happening.
Obviously it's a work in progress for many companies, but I think AI, as in many other fields, is going to act as a catalyst. It's going to accelerate things in the pace of digital transformation. IT is going to do that for a couple of reasons.
There will be a category of companies that are going to just be reactive, let's say, and they will be dragged into investing money and effort and everything into AI and therefore into their digital transformation journey to respond to competitors' actions, to competitors' investments. That's nice, but let's a me too strategy. It's not the best thing to do. Usually you need to be proactive. And that brings us to the next category of companies, and I think these companies are going to drive the AI revolution, companies that are willingly going to or investing into ai, and therefore the digital transformation journey as well. And they do that
because they have identified something great related to AI and certain initiatives. It could be something that accelerates revenue or increase profitability or productivity or anything. There are many, many different areas that you can improve with ai. And let us not forget that AI is not a single thing. There are many, many different applications, and that's the important one of the many challenges that companies are going to have when they invest into AI plus digital transformation. They accelerate the digital transformation is to pick up the right strategy in the right area of AI to invest in, because again, it's a big, big topic.
It's a big, big market. It's not just one thing, many, many different things. So everybody would say, okay, it's an obvious what we should do. We should invest in generative ai. Yeah, that's great. But right now, if you see all the reports out there that have all the figures behind the investments in different areas, generative AI has all the hype. Yes, definitely everybody's talking about that. It's the top of the town, but right now it has less than 5% of the overall AI investment.
Obviously this is going to change in the future, but right now there are other areas that have more funds, more investments. Frankly speaking, applied ai. Predictive AI is in the top of the investment right now with most of the projects or the biggest projects being channeled there. Again, I think this is going to change in the future because now a new market let's rate of AI market is being, let's say, is emerging and things are going to change in the future.
But right now this market is the biggest within the AI market, and I think that should be also a great candidate to invest money. Let us not forget that predictive ai, for instance, is something that goes directly not just to the bottom line, but also top line profitability, pricing, all these things. So it's a great area to invest against some competitive advantage. And of course, depending on the specialization of your company, of course there are other more specialized, let's say, area or also specialized AI areas like process automation for instance. That's an obvious thing, but it would be something more specialized like computer vision or energy or something else, depending again on the nature of your product. So we see a lot of investment opportunities here.
And of course predictive AI is a great example. And I think to you, Jeff, can you give maybe a simple example and more and something that's more complex example, if you will, of how companies can adopt generative ai. I mean, we've been not only looking at investments, but what are simple versus complex here for adoption? Sure. And since generative AI technology is fairly new,
there aren't as many full production case studies as we would like, but there's a lot of companies with successful pilots that are transitioning into full scale. Now, from a simple way to look at it is really any company that has a lot of PDFs. It could be anything like HR policies, contracts, engineering documents, or my personal favorite is equipment user manuals. PDFs are easy to load into a cognitive form recognizer that makes the data available so that users can access that information very quickly. Imagine the time savings of an operator, kind of similar to what Jared talked about, of typing into a prompt, Hey, what does the blinking red light on my machine mean? At a basic level, it can tell you that answer quickly. At a more advanced level,
you can integrate it into your C M M S or your maintenance system and simultaneously pull from your maintenance logs to combine them with the user manual. So not only will it tell you what the blinking red light means and how to fix it, but can tell you what others did the last time. Now that being said, CarMax is one of the earlier adopters before chat G P T came out and they have a great case study. So CarMax, the used car retailer, harnessed the power of generative AI to completely change its customer experience and content generation process.
They use generative AI to streamline the process for all their car research pages, which boosted their search engine optimization and their customer experience. They initially summarized customer reviews for 5,000 car pages that initially they were going to anticipate would take 11 years, and they achieved this with 80% editorial approval rate in just a couple months. And their Q 4 20 22 revenues saw nearly 50% rise to almost 8 billion year over year, and they didn't need to hire an army of content writers because they were able to complete the tasks in a fraction of their time.
So that's a pretty complex way of doing it, and it's a pretty powerful example that you can look up. It's all publicly available. I love that simple to complex and I can relate to the PDFs manuals. So those are really great examples. But for Simon, we are talking about also the applications, but we also have to look at the basics. What types of infrastructure from a company perspective, what types of infrastructure is required for generative AI business? So this is a very important question, so we must take a look from, first of all, we must define the problems.
There is in generative, in ai, there is a training phase and there is an inference phase. The training phase usually take out most of the computations, and there is a lot of data that is needed for it. So in the inference phase, usually you need heavy computational machine things like the GPUs for large language models, like the foundation models used by G B T, usually there consists of hundreds if not thousands of GPUs, not only the G P itself, because the huge amount of data, you need to have a interconnect that connects to the G G P U in such a way they can fit the accelerator or the G P U in a very fast way. So that is a training phase, but again, like I say, again, bent upon the size of the types and the type of generative AI that you want to build. The second thing is that the inference, now the inference will be pretty much difference because the inference, what you want from the inference usually is about latency, how fast you want to get the result. When you ask check G B T,
you want to get the result very, very quick. And that is mostly about latency and usually you do not need a lot of data. There's no training though you do not need a lot of datas, and what you need is that it's able to retrieve the information very quickly. So there'll be much smaller GPUs, but what you need to do is to have infrastructure that's able to provide the information very, very quickly. So again, depends upon the type of problems and the skills of the problems people like check G P T require.
Because one of the things is that they do require to support millions of users at one goal. It may be a millions of users accessing at the same time, and they are generative AI where you use it in the desktops, and those are the applications where you only have one user yourself or a couple of users. So it depends upon the user and that the influence engine could be just a simple C P U all the way up to a very powerful gpu, depends against that, depends upon the skills that you want to get and the type of applications. So Simon, thank you so much for breaking that down in terms of infrastructure because you're spot on in terms of the problem and that we're trying to solve or the problem that the business is trying to solve, which is really, really key here. But Jeff, continue on Simon's thoughts.
Where should companies start? Especially when we're talking about infrastructure, going back on the premise here, it depends, right? If you have multiple users, you're talking about learning and inference, you're talking about also latency. What would you be your point of view in terms of where businesses should start? Because not everybody's going to have that kind of level of infrastructure, right? So what is your perspective? So I'm actually going to talk about it at a broader level because regardless of your industry or your digital maturity, every company needs to come up with a policy and strategy for AI and specifically generative AI within that. When I say a policy, I mean any established rules, guidelines, and standards that govern behavior within a company, facilitating consistency on how it's used. And when I say strategy, I mean an approach to achieve long-term goals and competitive advantage, providing direction and defining key actions for success. So there's policies and strategies in three areas I recommend people and companies form, and one is on the public use of chat, G P T. That one is so powerful that you need to have a dedicated policy and strategy around it. Do you have rules around your employees using it?
What information they're allowed, not allowed to load into it? I guarantee most employees are using it and they probably don't know that the moment they load something into chat G P T, it becomes part of the corpus and accessible to everyone. Plus you need to know when your employees can rely on the information. For example, summarizing an article is low risk, creating a calculation can be high risk. The second is generative AI is being included into most companies products out there, as I just mentioned earlier, Microsoft publicly announced they're going to be including it into everything.
Does your company have a policy and strategy around when that's used and how it's used in all functions of the company? That's a pretty powerful thing to not have a grasp on. And third is when you start to leverage AI internally at your company, whether you're leveraging it to help your employees be more productive or whether you're helping it change the way that you provide value to your customers, you need to have a policy and strategy around both. So policy and strategy, very, very key. I'm hearing that as a common theme, which is important especially around chat G P T and how it's used. In fact, I've heard that some organizations will actually bar you if you've written a speech and chat G P T, which is kind of interesting. But aside from that, I'm going to go to you, Jared.
Where do you think companies should start here? It's interesting. You can hear all the different aspects that have to be looked at here, especially just from an AI perspective, the tooling, the hardware, the software, but also companies need to look at what's happening with, it's not just an isolated piece of work here. Everything is being disrupted. So all your downstream systems,
if you do something much faster, more optimized, and you're getting better results, well, how does that change your downstream systems? Do you need new kinds of tools? Do you need a new kind of process? Do you even need those kinds of people to do the kind of work that they're currently doing or there's a whole system need to be reevaluated. This is where business models are being transformed by this technology. So the folks who are really looking at what's happening in the future with this stuff, they need to look, they need to remind themselves to look at a much broader context of what's happening here. This is disruptive, this is transformative. Not just in an isolated, oh, this is a really cool nice AI widget, but rather your whole business model could change. Somebody might come up with something much more powerful, much more efficient, that just disrupts everything that you're doing.
So it's something the folks that have an understanding of this need to look at the entire system. You can't think of things as just an isolated event or as isolated kind of technology that might improve certain aspects of your business. You got to look at everything in business. Totally agree, and I totally agree.
I think it goes going back, as I said before, to strategy overall, it's multimodal. Indeed. And George, from your perspective, we're hearing that it's used everywhere technologies, it's the cat is out of the box, right? So from an operation sort of perspective, do you see any issues with it? Does it work in practice from premier experience? Okay, that's the trillion dollar dollars question if it, Because let's face it, it's a magnificent technology, both generative AI and all the other forms of ai. If it does work, it can do wonders for us and our way living, but if it doesn't, it can be extremely dangerous. I would say that so far we have a great first supporting evidence that it's working great in many, many, many different cases, although there is no universal answer for all use cases and for all tools, again, because of the diversity of the AI space. But generally speaking,
we do have good feedback, good evidence. Think of the obvious example that we've been talking about it for so many months, Chad, G P t, and all the other tools that launched and initiated the generative AI era before a year or so. That's very recent. If you compare all these tools to the previous generation of chatbots using banks for instance, you can immediately spot the difference. You can tell that we're talking about highly sophisticated tools that give intelligent responses.
You can have an intelligent discussion. You cannot have that with the chatbot in the bank or whatever. You are getting furious and you want to get rid of these tools.
I mean, even passing the curing test, which used to be a major issue, a big thing, it's not a big thing anymore because the bar is higher and we're expecting even more from this tool. So something definitely has changed and that's evidence that this tool are working. This is why they're so high. They're highly accepted by almost everyone around the world. If you see other areas, again, generative AI like image video processing, you can see all the things there are with mid journey stability, diffusion, all these things. But on the negative side, you do have some negative publicity, raising concerns with fake news, fake videos, false responses from chat, G P T and so on and so forth. Overall, I would say what we need, we need human and computer AI interaction.
We need people who understand the domain under which the AI operates in order to crosscheck everything and get the most out of the system. Collaboration. We have about 20 seconds left, and I'm going to have to summarize because this is really important, the discussion fundamentally, we didn't get to the ethics component of it, but I would say that the human in the loop is going to be very important. And I think also ethics must be part of the strategy, and that's an area that I am working on within the i e E. Without further ado, thank you so very much my panelist. Thank you very much Olivia for helping set this up and back to you, our host Dallet.
A huge thanks also from our side, such a dynamic panel, Monique and the brilliant panelist, diving into operations, redefining by generative AI with all of you was extremely revealing. Monique, Jeff, Jared, Simon, and George. I hope to see you online soon and if possible, even in person. So take care and good evening, good night, good day, wherever you are. Thank you. Thank. You. Thank you so much. Now, dear audience, keep that chat buzzing, engage with our esteemed speakers and come out with AI enthusiast worldwide together. We're not just spectators,
we're influencers shaping AI's evolution for businesses and society. So let's unite our voices, share insights, and jointly drive the future of ai. Your contribution today will echo into more's innovations. Let's collaborate and inspire. We are approaching the next session of this conference shaping the future, AI innovation, impact and wealth creation with Robert.
Robert. A warm welcome to our virtual stage that is reaching over a hundred countries worldwide. It is great to have you with us here today and focusing on the incredible potentials that AI offers across industries. Let me introduce you, Robert. To our dear audience,
you are the c e O of Alpha 10 x an AI startup purpose for the emerging class of impact unicorns startup that are valued at over $1 billion and proof the life of at least 1 billion people. A former Silicon Valley vc, investment banker, public company, c e o, director of the Microsoft m and a team and serial entrepreneur. You are inspired by big ideas, dreams, and dreamers, the ones who see things differently, who are crazy enough to think they can change the world and do something in real.
So this is definitely the stage where we want to hear about you, Robert, shaping the future AI innovation impact and wealth creation. The virtual stage is yours. Thank you so much. Thank you so much for inviting me and for this opportunity to share some thoughts that would hopefully be useful to the participants, spark some imagination, perhaps a little bit of crazy would be useful too.
I think we certainly live in a time of exponential innovation and the large language model evolution and G P T of the last six months has proven that this is unprecedented in the history of any form of innovation that we see so much movement so quickly. So a really extraordinary moment in time, a time of great opportunity and a time of great risk. If you look at the traditional hype cycle that Gartner has formulated and proven over many decades now, we're entering an extraordinary wave of hype where you'll see a significant amount of money going into ai and many of these startups, many of the companies that attract capital over the next three, four years will probably not survive for very long. The reality is that 90% of Silicon Valley based VC backed startups fail in three years. The failure rate is extremely high, high, and what my company is focused on, what I'm passionate about is about finding the intersection between innovation, investment, and impact.
I think most of us would agree that we live in a world that is fraught with difficulty. I'm particularly interested in climate change, in sustainability, in the evolution of the entire global infrastructure towards clean and sustainable technologies. I think this represents an opportunity that is perhaps the greatest commercial opportunity in the history of the planet. If you look at the kind of money that is going into that evolution towards sustainability, it's almost beyond imagination. $194 trillion will be needed by 2050 to achieve net zero. The numbers go on and on and on. Many of the large Silicon Valley players are now delivering a message around tech for good, clean tech, the evolution towards impact. If you look at many of the large investment banks,
bankers, large funds, the focus has moved very much towards impact E S G sustainability. So I see a tremendous opportunity for investors in this intersection between impact sustainability and ai. I've been involved in AI since the late nineties originally through healthcare and expert systems. It's a domain I understand really well. I'm shocked by the impact of G P T over the last several months. The emergence and the mainstreaming of AI in this short period of time has proven a great boon to us, to my company, to what I do and hopefully a boon to the world in many different ways. But I believe it's not just about making money.
It's not just about greater productivity and improving business productivity in general. It has to be about more than that. There's definitely an intersection between making money and impact. So it's not about philanthropy per se, it's about finding this intersection between the power of AI and how AI can predict the future, see the future, understand the future measure, quantify the value of various different technologies and companies and the teams that build them in order to allow investors invest in technologies that really will change the world in a very significant way. We have this aspiration of impacting, positively impacting the AA population of over a billion people. It is possible in the world of AI to do that as it's been proven very quickly by open AI in a very short period of time. The particular passion that I have around impact and sustainability intersects with that capability with that exponentiality that we see now around ai.
So we see a massive movement of capital towards ai. We see a massive movement of capital towards E S G, sustainability impact and the evolution of the global economy towards Cleantech. And in that intersection, the opportunity is absolutely extraordinary. We have the data. There is an enormous amount of data available in the world of innovation and investment. Large companies tend not to be very efficient at innovating.
They look increasingly towards smaller companies, startups to accelerate their innovation, to acquire talent in order to push themselves forward more efficiently. And we're very much about that intersection between innovation, investment startups and how if you are able to identify the most valuable startups earlier enough in this cycle prior to getting to the innovation trigger that garter describes where you really have product market fit. The opportunity in terms of alpha, in terms of generating returns, r o i is beyond imagination. So in this world of thousands of startups generating extraordinary technologies that can not only deal with covid, that can not only deal with climate change, can deal with cancer, can eliminate so much suffering in the world, there is great opportunity. And if we converge the ability of these technologies to accelerate innovation, we can also alleviate many of the major challenges that the world faces today. So I see an opportunity in the application of ai,
not just towards business productivity, but towards impact, towards saving the world. I think we live in a time of existential challenge. If you look at what has been happening in climate just over the last couple of years, just this last summer, burning man, you name it, the rapid movement towards the negative impact of climate change is almost beyond imagination.
So many of us have children, I have three. I would certainly like them to live a good life. I would like them to have good air to breathe 25 years from now. If we don't affect that pivot, we face an existential challenge that is almost beyond imagination, and I think the world is gradually opening up to that reality.
So analyzing sufficient data. If you have access to sufficient data, it is possible to analyze these data, extract knowledge, insights, and highly accurate predictions for the future in order to drive capital towards the technologies that will generate most impact in the world. This is not to say that there's nothing bad about productivity and improving productivity in large organizations or small organizations. That's clearly a part of our global economy and very important. But my message here and my passion is around how the power of ai, not just generative ai, but these deeper technologies, the extreme technologies, extreme deep tech can be applied to climate change impact and ultimately the development of a more sustainable world, a more hopefully peaceful world and a more sane world in the future.
So this is the core message I bring. It is around impact sustainability and the convergence, essentially the idea of Capitalism 2.0 where there is a direct convergence between the immersive nature of technology, the way we experience it today, the prevalence of technology and the correct application or the positive application, these technologies towards dealing with our deepest, most prolific challenges in the world, from climate change to all manner of diseases, and how we can reach a point where we can cure and deal with these issues. That's the fundamental message that I have to bring. I hope it is resonant with some of you in the audience, and I hope to see you on the revolution because it really is a revolution that goes way beyond simply business as usual. It goes towards saving the planet.
Definitely be kudos to you, Robert, for your retrieving keynote on shaping the future AI innovation impact and wealth creation. I think your insights illuminated the path ahead, sparking inspiration and deep reflection. I would really like to check with, we have already two questions from the audience I would like to share with you, so please remain with us. The first question is, the AI ecosystem often relies on collaboration between academia, industry, and startups. Can you share examples of successful interdisciplinary partnerships or initiatives that have accelerated AI and ML innovation? That's an excellent question. That's not an easy question to answer.
There is a clear path that starts with scientific research that moves into scientific papers, that moves into conferences, that moves into patents. It's typically a 10 year cycle from that really early scientific research, fundamental research, all the way through to hitting the innovation trigger where we see a clustering of innovation, a clustering of investment going towards that innovation. And at that point, we typically see an explosion of companies as we're seeing right now around large language models. I can't say I can think, I'm working with a number of top universities myself.
My board includes a number of top academics. We're working very closely with academia. I can't say I can off the top of my head think of the precise evolution of academia towards an applicable technology. But for example,
if you look at Siri, that may be one useful example. Siri evolved out of Stanford research in Silicon Valley originally named open age and architecture is the foundation of Siri. And so there you see deep fundamental research that was focused on natural language process that evolved into a life transforming technology that we all those of us with iPhones enjoy today.
Great, thank you. We'll go ahead because questions coming in more and more. How do you see the role of explainable AI evolving in critical sectors like healthcare and finance where trust and transparency are paramount? Are there any recent development or best practice you'd like to share? This is also not an easy question. No, this is a good, it's a great question. This one is easier than the previous question. So we're particularly focused on the knowledge graph and if you think about a knowledge graph, for those of you who don't know what it is, it is essentially a visual database where we understand relations between different entities and entity may be a person, a company, a patent to scientific paper.
We understand adjacencies between these entities and the edges that connect them and through this understanding we get a contextual understanding of any given topic. This is very much the way the brain operates, this is the way we think. We think contextually, we'