Good afternoon everybody. Welcome to the Open Group Singapore Conference. This is AI Workshop Transforming Processes with Artificial Intelligence. I am Andrash Sakal. We are both with IM. This is Michael Flores. We are with our Public Sector CTO office. We're here to talk to you about how to make AI relevant to your business. So a lot of information, you know, being thrown at you in the media, maybe even by vendors, maybe even by my company occasionally. That's kind of hard to decipher how you actually make AI relevant to your business. If you're here today to learn about neural networks of all different types, or TensorFlow or Caffe or anything, I'm sorry you're going to be disappointed because we are intentionally not going down to how you actually do that at that level. And we'll talk about why. Primarily because that's not really at a level that businesses, the average organization is going to implement AI. Now, of course you're going to have Netflix or some other company like Google implement AI, you know, and they're going to go soup to nuts, and they're going to use some of these other AI frameworks to do that. And we have quite a few offerings that are based on TensorFlow, Caffe and so on and so forth. But that's not really the kind of AI that we're going to talk about today because we want to make it relevant to the business. So Michael, you want to talk about the agenda? Certainly. So as Andras mentioned, today's discussion is going to be focused on giving you enough insight and context around AI so that when you come across a business problem or have one of your favorite C-level clients say, tell me about AI, I want to do AI, you'll have tools and insight that allow you to have an effective conversation, that allow you to work with them to build a prototype, a POC that will be successful, and that will allow you to use architecture the entire time that you do it. So we're going to go from the basics of AI, try to establish some real-world definitions. We'll share insight around, you know, how do you get started with AI in business if you've never done either. Then we'll talk about some of the best practices that we've come up with, based on repeated customer experiences. We'll then take a minute to showcase some actual new AI applications that we've built over the past few months, showcasing AI being applied to the business of the open group, such as Togath and the Open Career Framework. And finally, we'll take a minute to share some great lessons, learn the great costs, insights gained from some of our most complex but still successful implementations. So Michael and I, especially Michael, has been working with our customers to implement AI-based applications and has been through the trenches and the war with what that means. So let's talk about the basics, and we'll start from the beginning with an introduction to AI. First off, AI is not machine learning. Machine learning is really all of the algorithms that are used as a broad landscape and framework to implement artificial intelligence. This is a great history. I love the shows because actually, Carrie and I went to college at graduate school at the same time, and he mentioned that the fact that he had a concentration in AI and I did too, and both of us were kind of talking about the fact that we went to school, got our degrees and came out and crickets with respect to AI afterwards. No talk about it up until the last five, six, seven years, maybe even when Watson won Jeopardy. So this is a great chart that shows kind of the beginning with Turing, talking about the Turing test and complete lists and NP complete processes and the creation of the original Minsky neural net, which is actually still quite valid today. And then this group of folks thought, well, in 56, we're just going to go off and work for a few weeks. In fact, they allocated themselves two weeks to start this group to come up with an artificial intelligence framework. I thought that was kind of interesting that they gave themselves only two weeks to mimic human intelligence. So they found it was a little bit more difficult. You started to see algorithms play checkers, then you came across semantic networks. You had the first chatbot, and we'll talk a lot about assistants or chatbots here because we actually implemented one for the open group as part of our demonstration in Eliza. And then we went into this AI winter where not much happened. Then you came out of it. You started hearing a lot about expert systems, predictability. That was really more about analytics, quite frankly, prescriptive analytics than it was AI in my mind. And then the second winter occurred up until the point where you and I went to school and even then afterwards, not a whole lot. But then IBM used learning algorithms, machine learning algorithms to beat Kasparov with heat flu. A lot of interest and vitality went back into artificial intelligence there. Then still, from where I was standing after that, there was a lull in interest in AI. And then the DARPA Grand Challenge, around the year 2000, actually, we started working on Watson and thought about what the Grand Challenge might be. They were sitting in a bar and Jeopardy was on TV. And the scientist from IBM Research said, well, why don't we build an artificial intelligence, a system that can win at Jeopardy because that is a very complex game. I'll give you an example. One of the winning categories was British television. And the question, or rather the answer, if you know what Jeopardy is, you're given categories and then answers and you're supposed to figure out what the question is, an answer in the form of a question. The answer was this time machine appeared on BBC television one day after Kennedy's assassination. Anybody know the answer to that? It's hard as that. Doctor Who, you gotta be a Whovian, right? I love Doctor Who. I like the new series too. So anyway, you had to build a machine that understood the semantics of what it meant to answer in the form of a question. You had to build a machine that understood natural language processing and the context and the sentiment in order to actually win that game. And it took them quite a few years between the time they were at the bar and they said, hey, let's build a system that actually does this to the time when they actually went on television and won against two of the top world players. And by their way, there were a lot of simulated, they built a whole jeopardy simulation in IBM and had many opportunities to play the game against just average researchers. You could sign up to play the game. And part of artificial intelligence and machine learning is actually getting that data, getting the reference data that allows you to actually train your models and learn over a period of time. Well then, you know, fast forward here. Not so long ago, Google starts playing Go, which is one of the most complicated board games, which has billions of different permutations. And really to IBMers, we were like, that's not all that interesting. Why? Because it was an extension of what we did with the game that played chess and the Kasparov Deep Blue. The only thing that they did was really interesting was they really came up with back propagation and kind of the deep learning networks that learn from learning. So then they turned the machine around after it learned the basic skills of Go and they had it play itself or play another version of it until it actually learned all sorts of new patterns that nobody had figured out before. So that was the innovation there. And then Alpha Zero, which was, again, remind me, what was the difference between Alpha Go and Alpha Zero? Well, I think a lot of that is representative of the different iterations of the learning pattern used. In this case, the system, as you mentioned, would play against itself and they'd have a modern version of it, say version nine playing against version eight or seven. It's a very interesting and cool thing. As we'll talk about many of the systems that exist today, you have to sort of hand-feed it or create your own way to feed it and update it. So this idea of having a system that could simply learn from itself without human intervention is still one that's noteworthy and I imagine we'll see more of it in the future. I suppose we will. I think that's actually the really cool part about artificial intelligence. So machine learning is really about the algorithms and the frameworks. It's about trying to mimic human intelligence and from IBM's point of view, it's more about the human in the loop or the aiding the human versus replacing the human. For a lot of different reasons, we don't believe that the singularity, you hear this idea that the singularity is coming, that it's an autonomous sentient personality. We don't believe that's going to happen anytime soon. For a lot of different reasons. But this particular AI timeline is really about taking all these machine learning algorithms and so on and so forth and then turning it into a fit for purpose solution that started with deep blue. You see Kismet up there. I have a Roomba.  I liked it. It was fun, but my wife's just like, come your robot isn't cleaning over there. Siri is the front end of the chatbot really is what Siri is. And back end learning algorithms. You had Watson, you got Eugene, the use of Alexa, which by the way is a series of frameworks. Some of them are AI, some of them are not. Recently, I think Tay was in the news, right? She's making Southeast Asia tour right now. You may be thinking of Sophia, the robot. Sophia, they look the same to me. And they're the same guy that developed them? I don't mind me. Tay is an interesting example though. Tay showcases what happens if you are optimistic about a system being able to learn from everything it sees. For those who don't know about Tay, Tay was an artificial intelligence chat system that people could interact with online. I think it was on Twitter. It was in a completely naive, completely impressionable mode. Basically, it learned from every single interaction. So humans being humans and humans behind keyboards being somewhat less than that, you ended up with a system that had learned a lot of really, really, shall we say, rude things. Though Tay is a fun example over social media and vulgarity, the lesson from Tay is still relevant to today. Any system you create that's based on AI learns from the data you give it. So in more complex examples, that quality of that data is going to be one of the most important things with your AI system, just as much if not more so than your algorithm itself. Right. So here's your take-home definition for AI. It's the theory and development of computer systems that mimic or perform tasks that are normally required human intelligence. We like to think of it as the human in the middle or the human in the loop most of these AI solutions are really fit for purpose. So some of the AI solutions that you see out there that are relevant to today, speech and vision, natural language processing, natural language translation, image processing recognition, categorization, machine learning. So being able to actually learn, you know, create a framework that actually learns particular patterns. One of the things that we have been doing with working with NASA to use drone images on rockets before they're launched to determine if there are failure patterns. Normally it would take engineers to walk around the tower and then as you know, they have to eventually remove the walk tower from the rocket because you're going to take off. So drone can be there until the very last minute and pick out anomalies at pretty much machine speed if it gets enough patterns of failure categorized. Expert systems and robotics. My lab has three or four different robots and, you know, Michael and I just really love these things.  Oh yeah, I mean robots are one of those, I think they're the media darlings right now when it comes to the AI space. If your robot is impressive enough, people will think your AI system is impeccable. But if you run out with a cardboard robot, and I know because I have one, IBM open source one, people are not so impressed. But you know, whatever the form factor is, robot, website, mobile application, that still exists separate from the AI system itself. In the case of a robot, it answers multiple inputs similar to the case of smart cities that we heard earlier. Right, a robot might see, a robot might hear, but it's an AI system on the back end that's taking that vision and categorizing, oh that's Andros, oh that's Michael. Taking the audio and recognizing, oh they're speaking English, and oh he's saying hello to me. And then taking some other algorithm to say, oh I should probably say hi back. But in the end, separate the robot and the inputs from the AI model itself. Most people confuse it too. There's a lot of form factors that are, you know, that they can relate to. A little bit of latex and fake hair goes a long way. But it's not, you know, that's really not the solution that you have to think about. And many of these robots, by the way, are not as easy to maintain as we thought they were. They overheat, the gears break down, and so on and so forth. So we've had plenty of experience with that. I went into Incheon Airport on my way over here and they have one of the iRobot form factors providing information about gates and time of flights and so on and so forth. And as I walked up to it, it decided that it needed to dock because it had run out of power so I didn't get the opportunity to play with it. I'm sorry, I have to find my docking station. So what we have is essentially this perception of what AI is between machine learning of the scientific and academic space through to this concept of artificial intelligence like HAL. And it's all really relevant somewhere here in the middle. Watson is real, but Watson for playing Jeopardy was a fit for purpose system that cost billions of dollars to make and build a room like this of computers. So is it practicable? I hardly think so. Was it useful for IBM to build frameworks to learn how to actually provide business solutions? Yeah, absolutely. So why are we really talking about AI today? You know, Carrie and I went to school and we learned back propagation and neural networking and annealing and all sorts of crazy algorithms and those algorithms are really the same algorithms that we're using today with minor differences and tweaks. Well, why is it that AI has come here and into the fold today? Well, one is the ubiquity of computing power, storage, massive amounts of storage, compute, and networking. So cloud computing. The other is the vast amount of data that we're generating because of these great little things, mobile devices that we all have. And those mobile devices are generating information that is used to train the algorithms. So lots of training data and lots of training data that's provided in real time. The other is the miniaturization of devices in general, specifically devices like accelerometers and temperature devices. And this device, I think my particular phone is an iPhone XR, not so long ago, and it has 300 different measuring devices. One of them is barometric pressure and if you're using an IBM weather app, because we own the weather company, we're actually using you as a little weather station as part of the agreement for you when you download the app. Your phone is taking barometric pressure and we're pumping that data back into IBM and learning about what the local weather is like in your area. But I think it's important to recognize that we're not unique in doing that. There are other apps that likely everyone here uses about traffic of some kind and when you use that traffic app it tells you when you're going to get there you find out mid-route it lies to you, right? And it shows you're going to get there 20 minutes later. That insight, that real-time data insight whether it's in Google, they use green, yellow and red as a way to distinguish how much traffic there is. That's all user generated and it's not obvious at first if you just use something like Google Maps. So if you use another app like Waze, Waze is all about user input.  Do you agree? Yes or no? And in these small kinds of interactions enabled by these portable computers we're creating data that can feed AI models, that can feed AI systems whether it's something that just says here's the weather according to the phones in this room or whether it's something that says I'm fairly certain there's bad traffic here in Singapore because of all these cars and all the phones inside the cars. So the diagram in the back of me shows essentially the difference between cognitive, machine learning and AI. AI is a convergence set of technology. Cognitive computing is prescriptive analytics essentially and machine learning are those algorithms like neural nets that actually allow you to identify patterns, visualizations for example. This robot up here is a robot the NASA folks created, right? wasn't it NASA? I think Oh, sorry, it's BBC I knew it was one of those big organizations so anyway, BBC created this robot, some of the engineers and really again this kind of speaks back to why AI is becoming so interesting it's the miniaturization of all these devices that create you know, relevant real-time data streams that can be used to make decisions around these decision frameworks I love Bill, Michael So I'm going to let Michael say a lot about this because he knows quite a bit but generally speaking there's two types and I talked a little bit about this two categories of AI that's generalized and then they're specialized and it's not intuitive as to which is what so when you use these new modern-day frameworks for business they're specialized frameworks a lot of the work has been done for you, they're essentially neuro-learning algorithms that have been built for you to use for your business whereas the specialized is a lot of pieces parts that you would have to put a significant development effort behind fund it and then develop some solution from those parts and we offer those parts like any other company we have a partnership with Google we have a machine called Power AI which has all of this stuff built into it and we're using that quite a bit as well but quite frankly for you you're not going to spend $60 million for a relatively minor project in your business and then have to sustain that maybe Netflix, maybe Google maybe Amazon, maybe IBM these innovators of innovators they're going to do this but not you, you're going to use democratized APIs and fit-for-purpose solutions, what do you think? I really think it's what you see in front of you here that is really responsible for the AI explosion that we see today on the right side my right, your left your purpose AI frameworks are very powerful because they are a tool set so if you have some problem that you want to apply AI to and you have the time, the skills the funding or the desire to learn any of that, go crazy and with those sets of frameworks you could build a model to abstract any kind of problem connect it, point it to your data teach it how to understand your data make your data good, curated well and off you go that effort, the noble and worthwhile effort for certain kinds of business problems has a lot of work involved with it the majority of AI projects do not pursue something like this that's what we're generally seeing instead to try to make AI more accessible and Andros used the word IBM likes democratized you have this idea of specialized AI frameworks you can think of these as AI prepackaged for a use case that is as easy as connecting to Twitter with any of these capabilities all one has to do as a developer is get some API key read some documentation and throw data at an end point and you get back some kind of AI brilliance that you then embed into your business logic that makes your team look like a bunch of professionals this is what has made AI explode over even hackathons right, I've had opportunities to work with first or second semester computer science students, people with low coding backgrounds again give them documentation and a web service and even they're able to start running with AI the caveat with these specialized AI frameworks that IBM and other vendors provide is that you have to understand how are these different AI use cases spelled out what use cases do these different services address and will they address my use case this is where vendors like IBM and our system integrator partners and the system integrators of the world are really showing expertise that clients come to them and say I want to build a question and answer system and then based on details and requirements you end up with a recommendation IBM's got a capability maybe Amazon, maybe Google, maybe Microsoft maybe some other company not shown here but it's this ease of access that is really fueling the AI explosion and the innovation and the fact that everyone everywhere is doing AI something again there's still goodness in the general purpose world but the prerequisite of deep expertise such as PhDs, machine learning, maybe even AI background makes that not as common. I mean if I'm to be candid I don't have formalized machine learning AI training but I've done a number of implementations, the number of clients thanks to many of the democratized capabilities of specialized AI so this is where we see much of that action today and I do but I would say Michael is even ahead of me because he's spent a lot of time with these frameworks and it's really in these frameworks that we're beginning to see the application of AI in general so you have the innovators innovator using these really deep learning frameworks in PyTorch, in cafe and so on and so forth instead of actually hand coding them from research papers which you could buy 60 or 70 really high powered PhDs to go do now you actually have this wrapped up in a package that you could basically spend half that then you're going to try to apply it to your own business. It's still a long mountain to climb a high mountain to climb but it's come down quite a bit so you're seeing a lot of innovation coming out of the general purpose space. So the specialized space has traditionally been all cloud based but now we're seeing second generation AI for the enterprise and you're seeing many of these AI frameworks actually be available for your enterprise on print. Up until recently you only had the API implementation. Now there's still API based in the development approach but the solution is ending up on things like OpenShift and IBM ICP, IBM Cloud Private that is being installed in the enterprise so as a result you're seeing some of the heavier duty still largely expensive solutions like IBM as an example IBM machine learning the API based which is relatively new. So Michael, tell me all about these lovely cats and dogs. Certainly and we'll buzz through this to keep on schedule here with about a minute left. So when it comes to understanding machine learning again in this session we're not going to give you a very deep explanation. There's plenty of great resources. We have two folks who have asked themselves as having formalized education on it but at a high level you can think of learning as either supervised or unsupervised. In the case of supervised learning you provide data to a system and you label it.   Optionally we might have another set that's called negative and that's neither dogs nor cats some lesser animal it's not fit to be a pet. As you give the system picture after picture and you say this is a dog, this is a cat this is a dog, this is a cat the system has a memory of what makes a dog and what makes a cat. Now for this to be good you want to use different types of dogs and different types of cats that way when you then submit to a system a new picture it looks at it and says do I think this is the thing you showed me that you called a dog do I think it's something that called a cat or do I think it's none of the above? This type of system takes advantage of some of the machine learning methods such as regression and classification but all you really care about from a business standpoint is is this a dog or is this a cat? I know amongst many of your clients in your own businesses you likely can think of a few problems where you want to know what type of a thing is it? Very simple basic problem and so supervised learning can be used to do some of that. On the other side we have unsupervised learning and this is based on methods like clustering. In this case you just throw all your pictures at the system and you do not give labels to your data so you don't use the word dog or cat or not dog or not cat you just throw the data and the system looks at the data so what are the ways that this data can be related to itself and then creates clusters using established patterns or it might infer them on its own based on the type of learning and at the end of the day you end up with oh these two cutie pipe pops look very similar I don't know why they have fur, they have cute little button noses I've got ideas and by the way these somewhat cute meat looking cats they're all one other group I know because they've got the pointy ears they've got a very stern look in this way you know we can teach the system the same thing but by telling it explicitly where we know the language we want and the outcome we want or letting the system come to its own conclusion when we don't really know what are the patterns to be identified I hear cats are better judgement characters there's machine learning and then there's deep learning and from where I see them Michael deep learning is really all about things like back propagation and the ability to actually learn from other AI learning frameworks what is it for you deep learning when I think of deep learning I think of pulling humans further and further out of the problem because when I think of classical machine learning I think of the person in that chair spending time trying to figure out what are the meaningful features is the word we use in our data in the case of our cat and dog example you might have fur vs. hair you might have nose you might have eyes you might have the prevalence of whiskers a human can do that in general machine learning but in deep learning we tend to leave that up to the system so what you're saying is that in machine learning I need to basically describe that a dog has a body and it might have four legs and an image and it has a square face and a cat but a cat has pointed ears and then try and see what your outcome is when you run it through the neural network and then you go back and change the scoring right? so in deep learning you're using things like IBM's recent neural chip what's the name of the neural chip? oh you caught me I don't know that one come on so we have actually deep learning chip that is used to actually program deep learning algorithms deep learning algorithms usually take multiple layers of machine learning and use back propagation and automate feature extraction from already set patterns so it's a lot more complicated and it's kind of closer to the metal but you can get better results if you're looking for something very very specific so any questions up to this point? we need to grab the mic I guess I'm going to play the mirror well I think they want you on the recording the mic's right here there it is gotta get you for the people in the chip seats go ahead so two questions actually great presentation by the way I like the dual presenter format a particular thought leader that we all know well has expressed concern over AI and has labeled it an existential threat question one would be your thoughts on that and then secondly closely related is thoughts on national or international regulatory bodies that should be looking at AI, R&D so again great two questions so let's talk about Elon Musk a little bit I think setting aside the fact that he likes to be controversial and stir up dialogue which is good because I think this entire space is going to move very quickly but beyond that he probably benefits more from AI than any person in this city like self-driving vehicles or AI systems so he gets to see this stuff in real time and I think that he said it himself that he's kind of amazed at how quickly this is all evolved and he's concerned with the fact that you might have machines that are thinking at speeds faster than humans I absolutely think you're going to find a machine that is better at a particular set of data of finding the answer using natural language processing than a human is going to and that's true today too but the question is will machines ever be able to reason and is being alive being an organism that is a living animal give you a leg up in many ways and this is a very existential question some people are asking and it goes all the way down to quantum mechanics but for example one of the things that make you very unique is that you don't live forever and you have survival in the real world that you're faced with so there may be some experiences that you will always have in its current form I believe personally that we will in the further future probably not be replaced by machines but integrate devices and technology into the human experience in fact a lot of folks say that this right here already is integrated with you I don't know how many of you had this experience where you put your phone down leave it and then somebody says well when is that ball game and you immediately think I need to enter that information into my phone and your brain is already integrated this technology into the fiber of your being so technology is actually getting more part of the human so we are probably heading more towards being a cyborg and being something different than having a machine replace humans that's what I think now if you want to read something entertaining that along the same line go check out the most recent book by the guy who did DaVinci's Code Origin it's really relevant to the conversation we're having today I won't spoil it for you so the other question was what was it again oh, regular question so we have heard people in the federal government space in multiple countries in the EU and the UK talk about what is the euphemism they use it's called Algorithmic Transparency so there is an Algorithmic Transparency today so why they believe there is going to be just simply because we're using learning algorithms I don't really understand that but AI and learning algorithms are still dependent on the business process being defined so the business process the step one through A what it is that we want these systems to do is still very much part of a solution that isn't going to go away it doesn't just magically decide how it's going to serve your customer you define how you're going to use these machine learning algorithms in the context of the overall business process and in so much as that the government would like to have transparency for its citizens or the other way around I agree that everybody should understand how they're being treated in the systems that are supposed to reflect the law we're constantly updating government systems to reflect changes in the law but how do you know they're right well you have some oversight right I guess so there is a reason to be concerned about whether or not the IT systems that we have today reflect the outcomes we expect with respect to the law and how we treat our citizens and I'll add one thing to that answer that Androff just provided in my work at least I find most of the bias from AI systems is intentionally provided to match cultural expectations AI systems may show you something that you may not like about your business process there's a tendency to just rig the system a little to the left so it tells you what you want to hear this is not an apartment and we're likely to see a lot of it probably for the next maybe decade maybe less and so laws coming and bringing any kind of regulation are not going to fix that and that's going to be an evolution of business I think later on by the way we'll talk about bias and we'll use some really good examples that have come out recently around bias your comment about the law changing the system to match the law my understanding of those kinds of things requires a lawyer and or many lawyers and or a judge and or a supreme court to get involved so interpreting the law is in and of itself perhaps a requirement for machine learning and artificial intelligence yeah my point was simply that we have IT systems that are out there today that are reflections of our legal and social framework and you have to ask yourself are they really reflective of what the goal was ultimately you don't know as a citizen most people just take it as for granted let's do one more question and then I think we need to move on I'm pretty sure I turned it on go ahead thank you the question here is regarding the ROI the common business because what everybody is talking about is the AI if you want to implement and the cost is big because that's one of the scariest stuff most of the society are the business for the SME they are not ready to take a call but what is a call what will be the transition is going to be happening like now is very much specific to some industries as you mentioned some of the industries still they are scared about that because the data of the volume data what you are going to analyze that is one of the counting it's going to take bigger cost and they are not ready to spend it but again it is going towards the digital transformations and this is one of the AI's only the way we can able to materialize it this is some caps and the technology influence factors where is going to match the other party to have it won the first question another one is on the same questions regulations as an individual like Alexa and Google Assistant suddenly what is happening it's activated by the name but if that is a case always is recording all the information that means is hearing us what is the security is there what kind of private privacy is there with the home when you are in Alexa Google Assistant all your message will be recorded someone is using it for some other purpose what kind of that is one of the things maybe the regulator may be asking we want to see the engine how it is working first because are you taking only by command and then it will be activating or in general everything is going to be captured and it is going to be taking for your own purpose so let me answer the second question is actually quite interesting there are companies that are offering free online social services free services in general that they are using to analyze your behavior and resell the data there are companies currently that are providing AI solutions that actually use the information that you provide at a lower cost and they reserve the right to learn about your use of the AI and the data that you store in their cloud that is not my company's approach in fact my company's approach is to explicitly not compete with the entities that use our products but there are companies that are competing with those companies that are using their AI frameworks so you have to be cognizant about the implications of that and ultimately what happens what do you think Michael? the saying has always been true if the service is provided to you for free then you're not the customer, you're the product and we know this in the case of our favorite email application and perhaps a few others and for those of us who just want free email sweet for email right even better, less slash fewer ads and as an individual interacting with a big tech company right something that rhymes with Google you say that's not a big deal right I'm not in the email business that doesn't really matter but what we see very often is where a business is partnering with another business and that other business also happens to do the same thing in this context client does and all the great analytics and insight whatever they're provided by their technology partner is now insight that that same technology partner can use to enable their business to be more effective who for instance is in direct competition with this customer in this context so there's a lot of weirdness in the world of AI and now there's been weirdness around genetics I don't know how many of you all have done any genetic testing my background is in bioinformatics technology, genetics back in the 90's this has been done on a national scale in some countries where they paid a whole company to DNA sequence everyone that company went under and as part of trying to come out from under the debt sold off all that data I don't know that I'll do it unless someone forces me to but people are happily spitting into these cups to find out your X percent whatever background but once you've done that you've given away that data now I guess the average person isn't going to get into the business of DNA sequencing but what if one of your DNA sequences is like a wonder drug well it's like narcing on your future self too if you get yourself in trouble right so I think it's a gray area where regulations still hasn't caught up that if you're providing AI services capabilities analytics there is some level of transparency to say hey this is how we're using this data let me explain it to you in human readable language and you can choose whether you still want to work with us or not in some cases you're okay if they read your email and you don't mind because you're not competing in other cases though maybe a concern if your business is relying on it for enterprise function you don't want that to be used against you either by that company or another one of your competitors so you have to be cognizant of who you're using and whether they have parasitic kinds of business practices because we have seen these companies actually launch using this data their own brands against the folks that are paying to use their platform so well should we continue on or what do we do I'd say we should continue on we're a little bit behind but I think we'll make it out okay now incorporating AI in your business we're going to talk a little bit about the human in the middle how much work should AI do I mean how should you apply it in your IT systems to make it properly work for you what projects should you start with and maintaining and evolving your AI models and then after this section we'll take a break I believe is that a break after this no it's a break after the one out of 330 and ultimately we are going to get to show you the real deal real stuff real stuff real stuff and some of the staff members are coming in later because they are really interested in what this looks like because we pulled in things like Togep into some of the AI and created a chat bot for the open group so what we're saying here in this chart generally speaking is that if you are a business or organization and you don't already have a data governance strategy and you don't have an analytics strategy and you don't have any data scientists or any kind of that competency in your organization whatsoever then using AI is going to be a heavy lift because AI is all about the data and your provenance over that data and its structure all influence the usefulness of the data and how much of that can be used by AI so there is a process of making data ready for your AI there is a process of using analytics to understand which is the most important data the other thing that's really important for folks to understand is that there's a lot of reference data sets you're using in your business or company right now organization is probably owned by somebody else you just don't know it and when you get into AI and you start using training models or reference models you're going to end up paying for that data one of the things that we did was we figured out early on because of Watson we needed access to all of this encyclopedic amounts of data so we were already pulling in the entire internet four times a day just the good parts Wikipedia primarily we excluded Urban Dictionary for those who are wondering but we also realized that if you were going to be a Lance or Jeopardy questions you're probably going to have to know everything about anatomy well who owns all of the information about anatomy folks maybe who publish grades in anatomy well are they going to give you all of their content in machine readable format for free guess what the answer is no and it goes on and on and on and on so somebody owns these data sets by the way we have actually acquired the rights to many of them and we're allowing our customers to use them either for free or for a very low cost but if you go outside of our ecosystem you're still going to have to use some of these reference sets and when you use them it's going to be ready for royalty eventually well if you're a consultant and you're looking to figure out how to make millions and billions of dollars with AI this is it you can leave after this slide because this is really the journey that every organization will have to go on the rest of our session here is great I promise but if you're a consultant this is it and what you'll find with many organizations is they don't have a way of thinking about these problems and the panelists say this thing is going to change the world they hire a guy or pay someone to talk about how great this thing is maybe spend some money into a project and then move on in the case of AI this has to be something that you prepare for and something that you build foundations to so Andros talked about knowing your data we'll go into detail what that looks like later understanding what's your relevant data again you can have all the data in the world you can't really design an effective system around it and we see this going all the way up to establishing trust in systems most people that have AI right now they trust that the answers are good enough but they don't really have a way to explain or justify it earlier today we heard someone assert that it's okay if a system does something and humans don't get it I strongly disagree with that and there are companies like IBM and others that have designed capabilities to help you understand what are the biases in your data because bias isn't inherently wrong but you have to understand what the bias is to make the assertion that your AI is not an ethical we're going to talk a little bit more about this later on too but an AI system has a different maintenance life cycle than your IT system so you're going to have to have the skills to be able to manage that AI system that is somebody watching the training and constantly watching over the learning process and you're going to have to have that means there's going to be somebody who's representing the business who understands where the business is going to make sure that the outputs from the artificial intelligence are going in strategically in the direction you want to with your organization you're going to have somebody understanding the implications of training making sure the data formats don't change I mean overnight data formats can change and then all of a sudden your data might be training your AI system to believe in something else and we'll talk about what the implications of that is as we go forward here so an AI can be augmented intelligence not just artificial what do you mean by that Michael? well you know AI as an artificial intelligence was the original name of much of this field I believe it was also a movie about a cute little boy robot going home but what IBM has found after actually applying AI it makes more sense as augmented you know there are some people who are brilliant and who wonder and worry that AI computers will replace humans at some point but we still believe in the value of having a human individual partially because it eases the complexity of ethical dilemmas like if the AI told me I should punch him and then I opted to follow through and punch him that's still on me, that's not the AI systems fault but if we had a robot here and the AI said to punch him and the robot punched him then we have some complex moral dilemma this accounts for this helps account for errors in your AI the AI's analysis and say I shouldn't punch this guy, he's a cool guy and he knows a lot and we work together that's a really dumb idea I can then correct the training data so we see humans being a part of these processes as something critical because as a human being I can see that data that comes out, I can see the recommendation I can understand why and then make an assertion of is this surprising to me is there something about him I didn't know that says oh man he really needs one this is where I think many businesses are going to have real eureka moments also too Michael, don't forget that in many of the AI systems that we're building we're putting the end user in the place of helping train the system the end user if they notice that the chatbot is off can actually give some really good feedback we'll show you how that works that tells us that the chatbot or the assistant and the neural network is not trained properly so how many of you guys know Brady Booch Brady Booch, you got a few so Brady is actually an IBM research now he certainly has passion around software archaeology these days but he's really working on Watson he's doing a lot of work with NASA and AI and what he tackled was the think tank around is it possible to have a singularity anytime in the near future and when would that be so the think tank that he led came to the conclusion that basically it was probably a couple hundred years away and then even then you know it's more difficult as it turns out for an intelligence to escape a form factor like a computing system then it is for us to integrate it into our daily lives or our existence as almost a cyborg like character that evolves into a more machine kind of human experience so we're really thinking about human in the loop here not you know AI by itself right? that's essential as mentioned because the AI system may make a suggestion that is a horrible, horrible idea and the AI system may make a suggestion that feels wrong but when a human takes a look they realize it's the right idea and again at times the AI may make a suggestion that is fueled by the data but that may need to be adjusted for cultural expectation whether that's within the society or within the business alone human in the loop makes all of that much much easier and we see that as probably the way of the future for quite some time right? so we have seen some customers who come to us especially on the fintech side and they say what we're looking for is a predictive model that tells us when there's going to be a next economic downturn or something just crazy like this and then they give us their data and then there's a lot of you know craziness in this like for example all of these financial institutions have analysts all the analysts write basically reports in different formats in unstructured formats usually that ends up in a pdf and each one has their own way of characterizing the information that they're providing so trying to synthesize information and even tear apart the data in these reports is very difficult so that's the first problem the second problem is doing predictive analytics is not AI so much as it is analytical science which gives you a range of potential predictive options and so folks again have made some misunderstanding about what AI is versus analytics and statistical analysis and the implications of the data itself yeah I mean this is where good marketing can go too far people see AI and they see the way it's portrayed and IBM has the best and worst commercial simultaneously who we position AI as this wonder can in many ways AI can be a silver bullet because it addresses a domain of problems that prior have been really hard to address but when it comes to building a system to do something novel that's game changing for your enterprise it doesn't all have to be AI I mean Andras's examples come from a client that we're still happily partnering with and their initial understanding was just give this data in a random format to AI and tell it economics that we get into where folks want to use AI quite a bit is cybersecurity they say that you know you've got all this log data and all this information you are generating a lot of it especially if you've got a sim that's running on a lot of your devices but how do you find the needle in the haystack and how do you predict the next attack and this is something that my team actually worked on quite significantly with IBM research turns out it's a really, really gnarly problem you can't really predict something that has no basis for understanding because every single cyber attack is kind of a one off and so you can predict you can baseline what is normal performance in your organization and identify a serious anomaly but guess what by the time you actually do that you're probably already been hacked so we did actually do a few interesting really interesting AI based machine learning cyber solutions and my team actually was the original developer of the IBM cybersecurity solution IBM Q-radar with Watson Q-radar with Watson and what it does is it helps you actually take all this information about cyber threats and curates it and gives it to you in a way that correlates it to the type of attack that you might be experiencing so you can do that that's not a problem and that is AI but it's not actually trying to find the unknown attack right which isn't really possible and I think it's worth emphasizing here that for a company like IBM that we sell IT this meets sense for us to pursue because we have a security product we have AI capability we have clever people and teams led by guys like this who say we'll take on that problem and we'll address it but if you're an enterprise that doesn't have that budget but you still want to do AI and security you're back to our earlier slide do I want to try to do this by myself do I believe I have a skill set and a need to build a custom approach you might I can't speak for every business out there is it feasible and reasonable for you to buy a vendor provided product that might have that kind of capability integrated within I mean this is where we go back to the notion of democratized AI and so when we talk about picking the right projects my biggest thing is always figure out what's been done before if there's models and data and a vendor like IBM or others who have a thing you can use that does most of what you need and all you have to do is just curate your data and send it in real time that's a good project but if you do something that no one's ever done before that's a hard project and if you're a company that wants to sell AI solutions great if you're not you know be prepared for a I won't say life-changing experience but a lot of good lessons learned so when you go into a project Michael I mean what do you look for I look for you know I look the data first like do they own the data is it structured in any way how much effort am I going to actually expend to cleanse it put it into some format and is it learnable right AI wants to learn from the data so if you've got data that's you know spewing out of a giant pipe you know in real time that's good data in a lot of ways if you're talking about a very small you know set of data that doesn't really change very often then it's not really very interesting what do you think no you're exactly right I mean the hardest problems in AI right now where the algorithms are known are hard because the data is not there and you know there's all kinds of really cool approaches to making up I won't say falsifying making data from existing data we call it data synthesis it's a better word than line it's where you take whatever you have and modify it to better represent reality because the the reality of the world is him and I can work together over several months and create a great chatbot that says great things but we don't know what any of you are going to ask we can come up with a hundred ways to say what is the open group and I bet someone in this room or someone in this conference is likely to come up with a hundred and one way that we didn't think of that is nothing like our other hundred ways so the AI still misses it and that's just the reality so when you have a lot of data that gives you some greater semblance of having a better system early on probably the core of your business probably right so tell us about this chart I mean there's lots of different roles that now get established in your enterprise that you have to consider that you probably haven't considered before that interact in ways that never interacted before right yeah I mean that's exactly right when you think AI I know the tendency is to think of IT alone and we put on our propeller hats and say we'll go fix it right we get a wrench and start banging on something but the reality is this problem this domain is very unique in terms of the groups that have to come together I mean at the core you have I would say at the core you know I like this chart mostly but at the core is some business process owner that's my take on it you have someone that has a need they have something that they're trying to do and there's an opportunity to do it better right maybe they have to write read a hundred documents every month and that takes a lot of their time maybe they have to write minutes every week and that takes a lot of their time and they have this great idea that if only they could have a system that could do some of that work they could do greater things they could focus on the harder problems you connect that person who knows the problem space who knows what the business is trying to do and you start connecting them with these other pieces you you might elevate that up to the CIO or CTO and say hey we want to look at new ways to address this we might then go to our data science folks and say hey what data do we have that is if you have data science folks so you have to have some data science folks eventually eventually that's correct and you got to have a CTO organization who's kind of thinking about things outside of the box right that definitely helps that's for sure yeah so and then you need the developers to understand what it means to actually develop these applications because these are very different than the normal structured programming of Java and C and C++ yep anything else well you've got your ops for AI piece right so we talk about data we talk about you know your data scientists can say here's the data we have here's a way we think we can build a model around it Andros mentioned the app developers who build the pretty front end that does the thing that connects you to the AI but then separate from that you have this ops for AI piece and this is perhaps one of the most critical pieces of the whole picture and these are your doctors, your nurses your caretakers of your AI system I mean I don't like to call it children but think of it like a pet you have to care and feed it you have to occasionally ask it to sit, to stand to roll over and you have to validate that it's doing the thing you expected to do in the way that you expected to do it because those developers are off on a new project right these are guys that are watching things operationally right oh yeah and the beauty of it I mean one of them these systems exist in a loose coupling so if your AI folks decide that they need to pivot because the business process realizes you know the business is going in a new direction that can all be done and your wonderful new front end that looks pretty and renders on phones nicely that remains relevant you might need a few tweaks here and there if things go change significantly but generally it remains stable and we see that as important because this AI model you might have a nice little mobile website you might have a big enterprise application you might have a reporting system and all of those different applications may use the same underlying AI model for different business processes yeah so separate the model creation from the application development I think you kind of talked a little bit about that so the ideation the design thinking phase the data scientists who's trying to figure out whether this is actually going to work or not they're really different from the app developers who are being given you know kind of the template for how you want to integrate this into your overall IT environment yeah that's exactly right I mean this to me is critical this is the slide I carry into every customer moving forward because when they think of an AI system being created they want to call it software they say yep you'll write it you'll have your Java guy sitting next to your AI guy or gal and they'll do it and they'll be done but the reality is the AI work frankly kind of never ends and it's not a bad thing at the same time you likely want your AI model your AI part of the system to be ready a little earlier than your application because in the process of building AI you always learn something and you might realize that you characterize your business problem in a way that doesn't really make sense for the AI system you're trying to build and what you don't want to do is have a finished beautiful front end application that then is built on an understanding that's dated of your AI model because that might include some rework so Michael, since you're millennialish I'm technically a millennial you can call me a millennial well we got Z's out there my daughter is in design thinking industrial design so she's a Z so you're getting on soon they'll be talking about great hair already but tell me you're programming in a whole different set of skills and programming languages than in the last even five years what, you know, give me an example of some of the programming skills that are relevant to actually doing this work I would say the biggest one is and I want to connect it to design thinking a little bit, right the thing in the AI programming world whatever language you use it's ultimately irrelevant, right you could likely integrate AI with COBOL if you wanted to but you could where the new set of skills that's required here and customer by customer will have a different opinion about who owns these skills it's going to be around understanding how to look at a business problem and determine what kind of AI system you want to create for that and understanding how to tweak and shape the AI model to fit because even if you're on the far left again my left, you're right of specialized systems you know, insight back there's still expertise required to say how do I go from natural language understanding to a meaningful application like a chatbot how do I do that abstraction down to something the AI model can understand so that I find goes hand in hand with design thinking goes hand in hand with understanding how to shape your problem in terms of AI can understand and operationalize that was a very millennial non-answer to the question I had but I'll answer it for you so when I started working with Michael and I started working with some of the AI frameworks I realized that Python would use heavily JavaScript is a foundational requirement but JSON on top of that Node-RED and general scripting and big data databases like Couch, DB and several others like Cloudin if you don't have those skills I'm telling you every single one of these frameworks is using those programming language to manipulate data it was an interesting experience for me that I had to catch up to because I was a Java C++ C guy and so I had to learn all these interpreted languages well to be fair you can still use Java we even support it as an SDK I know but that's not what you've seen the most of that's fair so we're back to another Q&A period how much time we got we got 28 minutes before the break so we're behind by quite a bit alright well we'll take a question so who's got the question can I see the hand again there we go let me bring you the mic thank you you covered a couple of items in terms of data and also structured data and unstructured data as well which brings a couple of questions to my mind we're talking about using AI for business processes so it wouldn't be an incorrect assumption that you could use AI to point out or bring about a business process that needs to be resolved by just using unstructured data sometimes data could be clean data could not be clean as well so could you then apply artificial intelligence on either unstructured data which is not clean to help improve the business process sure absolutely I mean that's part of data cleansing we do that all the time in fact we have annotators now that take information and scrub it from PDF files and then we're going to go through a whole painstaking effort that we took TOGEP and we pulled it into discovery Watson Discovery and what we found out was that there wasn't even though you look at TOGEP and you look at the book and it looks like there's a standard set of structured chapters and sub chapters and so on so forth you had to actually use exponential data wrangling cleansing skills to figure out where a chapter began and that section header and different sub types so that you could actually categorize those and feed it into Watson because otherwise you just get a giant blob of text and it becomes non-context sensitive I guess that's the right I mean human readable is not really machine readable and we've known this for quite some time but the reality is some of the great sources of insight to feed AI systems and it's a point where companies like IBM have created products around this take advantage of data and forms that we never really built for anything other than people so they just needed to look nice so we'll show in one of our examples how even some of the great open standards that are out there were built for human consumption and so putting them into a computer system is a little tricky because they represent combined human expertise there's no single database of TOGAP because TOGAP is a complex thing a lot of great insight ideas and guidance so we'll show a little bit about the process and show you the end result of the kind of systems you have and the kind of value they can provide we're going to take a break no one more we do three and then the break is three-third we've got 15 minutes until the break so it starts with data we talk a little bit maybe we can we can probably zip through this evaluate continuum of problems handling easy AI and addressing harder one so this is my favorite slide his least favorite slide I've just heard the story a few times so my son we live in a neighborhood where there is a grocery store chain called Harris teeter and when he was very young three or so years old you know we said we were going to Harris teeter and he goes oh we're going to Harris teeter and we were like yeah that's a great name for that grocery store we'll just call that Harris teeter totter from now on out and we and my wife would go hey I'm going over to Harris teeter totter yeah okay I'll see you later and 10 years went by and we were out in the parking lot going to my parents house and we dropped by Harris teeter because we need to pick something up and we were in the parking lot talking about what it is that we need to get and we're having this heated discussion between my wife and I and no I don't really think you know you have to bring wine and all of a sudden my son goes hey it's not Harris teeter totter it's Harris teeter well why is that important or relevant here because you can teach in AI the wrong thing and essentially my son kind of learned it wasn't intentionally mean we all thought it was an open joke but he never realized it never really read Harris teeter he just in his mind saw Harris teeter totter your AI can do very similar it can learn the wrong thing and give you the wrong answer and so you have to test your AI against reference data to make sure that it's at least on the right path and then make an incremental step as you go on to train it for more instances Michael do you have anything to say a critical assumption in what you've just shared is reference data this is why in that nice ladder right the consultancy cheat sheet you're trying your data if you don't really have a reference data set of whatever you're trying to characterize this becomes super hard if none of the Harris teeters had any kind of signage his son would still be calling it Harris teeter totter it would be the world's longest lived open joke so without reference data without having a sense of what's happening in your business and what's the data that says yes this just happened AI becomes a much harder problem because you don't know if you're right or not with the data without the data there can really be no AI that's central to your business beyond simple solve problems like visual recognition of a car or you know natural language processing based on maybe an existing model like the one in IBM Watson or other vendors so if you want to create something super customized to your enterprise you'll need to have that data available otherwise it's I don't want to say impossible but it's probably close to that right yeah all right so you got to know your data too that means you have to actually have some you know map or data governance to understand what data you have and that's really back to EA right without the data continuum in EA then you don't really have an idea of what data your enterprise has and what structure it's in so that's going to be kind of a starting point you know who's the data scientist or the DBA or the data team that owns your data repository right and if you again another consultancy pro tip if you want to really seem like a sweet expert and your client says you want to do AI just ask what data they have and once you get a sense of the data they have many of the AI ideas aren't connected to the availability of data but if you look at the existing data set and say here's what we have what we think it will tell us then you have a basis to start figuring out you know what might be a reasonable project like if I want to understand how people feel about me based on the text they send after we go out on lunch I have no way of solving that because I'm not a government agency with text from everyone's phones if however it was in a group chat and I wanted to see you know what the group chat was talking about and how they felt if I'm in that group chat I can download all those texts and I can do analysis on sentiment of the group around you know but it's all about the data you have so before you come up with some crazy idea figure out do I have data that supports it or can I buy data that supports it can I get rights to it as on-drawer and is it annotated alright so if it's not annotated nobody really understands the meaning of your data you're in big trouble so there is a pipeline of data that feeds into AI right and you know the data warehouse of the past you know really kind of informs the use of our AI engine because ETLM and transformation is now a thing right so you still have to do that that work so what do you think well I mean in the case of AI systems what I like and this is a perhaps it's just novel to me because this is the first time I've encountered it but the reality that you can feed some kind of big data AI analysis system starting from something super unstructured like a pdf or a word document this seems like magic for those folks who haven't seen it before but it is now looking to create insight and systems that you can query from documents that are literally published for human consumption and every time somebody spits out a pdf you can pull it in automatically you can pull it in automatically but the gory details of making that work successfully will come later because there's some nuance to it so we've got to consider a range of problems yep and this comes down to understanding what data do you have you don't just say we do this AI problem and we bet the business on because that's probably not a good idea but you say here are our business processes that are central to our business if we did this 5% more we'd make 20% more revenue figure out what are the business processes that are impactful and lay them out on the table based on impact lay them out based on perceived risk availability of data whether internally or externally the more you're going to have to develop from scratch you use some of those generalized frameworks the more resources you're going to have to apply but it's a decision like any other decision in the enterprise to adopt technology and even though it's just AI and it's a great big umbrella you still have to be very intentional about how you pursue it almost like the way EA is developed you have to decide what's the right component so some low hanging fruit might be? we've got some examples in the next slide so it goes down to picking the right type of AI based on your problem and this is where we figure out what are our low hanging fruits so my usual guidance is if an AI system exists that can reasonably solve your problem then you should probably use that existing implementation so what are good fun and easy examples on this document analysis is an easy one that's doable everywhere you can do it on prem you can do it through open source libraries you can buy products and license APIs visual recognition is another great one well I mean it's really relevant today with twitter so you're getting a lot of feedback from customers from twitter or from social media doing sentiment analysis on your own documents is probably pretty dull but doing sentiment analysis on people will think of perceived your enterprise well that's in a whole different ball game and when you use all that data that's coming down to you in real time you can actually graph out your sentiment over a period of time with AI systems where they are today even on the super specialized out of the box stuff they can even give you a finer level of granularity they won't just say that he was really upset about something they'll say he was really upset and he mentioned this AI session two guys from IBM must have not been a very good session then you have insight let's not repeat that session it's that level of insight that's really valuable because it's not enough to just know that someone hated your product or hated the experience you want to know what they didn't like about it and likely you want to know that has that person have a pattern of criticizing things right that the person who bombed on this session also bombed on literally every other session they talked about if that's the case then maybe you might not wait everything they say as much versus if someone else who's been loving Open Group for years had an open critique after being an avid supporter that might be a data point that you might consider a little more closely you know the world's leaders they speak on an almost ongoing everyday basis and you can take sentiment analysis from you know what they say and infer you know what might happen and this is something that governments are actually using AI for to tell whether or not you're about to get into a scrum with another company our country sorry you know because of the tone of the leader and how it escalates over a period of time subtleties that you Michael probably wouldn't understand well there's another fun example one of our other colleagues at IBM another distinguished engineer in one of my first mentors at the company she wrote an application that would do a tone check for all of her emails but she had gotten the feedback multiple times that she came off a bit critical and so now she's got a system that will analyze the tone of her outgoing emails so she can determine is this appropriately toned for my intent and again that wasn't a very huge lift or a hard application to create but she understood the need, she recognized that she wanted to align her tone to her intent and to her audience and she was able to do that you know another low hanging fruit is translating from one language to the next you know I know that we do a lot of that here but you know some of these systems have gotten so good at doing that that they can do it in real time and be very close to absolutely correct but we talked a little bit about some of the low hanging fruit what you do it the low hanging fruit isn't really where you want to go well you know you've got two choices here that you need to evaluate the first choice is the choice where you say I know a bunch of these existing specialized AI capabilities I know they're easy to use maybe I have experience using them on a prior project let me look at these different capabilities from a single vendor across vendors and figure out can I combine them in some way to create the new novel capability that I want is there a way for me to do some kind of analysis that says hey you know I can create a data processing pipeline where I get a tweet I run it through some analysis to do sentiment and then maybe I run it through a separate analysis to do inference and then I end up with a set of data and an inference engine that can tell me given the topic how do I think people will react that inference engine on its own recommendation engine is a relatively difficult thing but if you combine some of the existing specialized AI you can create it IBM's Jeopardy Watson is an example of a recommendation system where you were asking very explicit questions and it took all this data into account and made a recommendation of this is the thing I think it is there wasn't ever just one answer there were multiple but we always went with the best answer at the same time there's a possibility that nothing in the specialized AI world seems to do it or it's not sufficient and in that case you might use a custom AI implementation using some of the lower level frameworks you can still pull in something like IBM Watson Microsoft or Google or others but you might have to go whole hog still on that base of a custom AI implementation so we haven't said this yet but a lot of folks think that somehow RPA is AI is RPA AI I'd say it depends on who you ask in the end write RPA is about automating a process and you have your inputs and outputs but it doesn't really go much beyond that but you might have RPA vendors who do some AI magic for you that maybe looks at data that makes an inference that says well I bet this should go in this business process that might be AI but at the core RPA doesn't require AI to happen yep, no, I'd say it's not in both cases most vendors that are providing and are just providing and I'm not saying it's not interesting because I've seen some really interesting RPA implementations that save a lot of money but I don't really think of it as AI so deep learning systems that might be hefty duty that's a maybe a space military aerospace application kind of opportunity there might be other applications as well but to get to that point you have to have a problem where you really understand the different factors that you want to pick up on because again when you think of any of these systems just in your mind picture who has one job and teaching them how to look at the data and make some decision or recommendation again, R vote you put a human between that system and the decision but think of that as the system so even in a deep learning system even with that approach you still have to have data that's useful and you still have to know how to translate it to a business problem that you want to solve we've made it back to the questions and then we're going to take it we've got a solid minute for questions minute question there we go again thank you very much pretty much so many things are happening here just my question here is very straight when we are talking about AI the applications there one other point is the way is not mature yet if I'm not wrong still it's not mature yet because we are also translating from human walk to the is coming down to the floor and therefore now anything we are going to train it won't be applicable that's why now what I'm thinking is my understanding anything you are going to train the data it won't be relevant to the next digital revolution right how is going to be applied that means if you are going to make it a surprise unsupervised machine learning or those things is coming to the picture AI is coming and unsupervised machine learning if I'm not wrong and someone has to monitor it what are the things is happening and any anomaly detections what we are going to observe it we have to retrain it what is your view on that that means AI is not pretty much robust as it's now so I think that's part of the issue is that AI is about I don't ever think that the training systems will get better but I don't see that you cannot simply just let an AI off the leash by itself it doesn't mean that it's immature it just means that it is what it is it's a learning system and you are going to have to watch it from a bias point of view and a training point of view and an output point of view what do you think Mike? and also provide a window into our own souls it gives you as a business an opportunity to understand what might you be missing today and where I say there's immaturity and my client engagements is the ability to accept that criticism or I've had systems that I've designed and led development now that provide an answer that the organization says is not correct and I've stood in front like a PhD making the case for the AI that this is very much the correct answer but the business said not the way we think and so we put the blinders on and the system now says the right answer based on their expectation to me that's the greatest immaturity is that many organizations that say they're data driven are only data driven insofar as they understand the data and they may not be ready as far as their enterprise culture goes


So hello everyone, my name is Rakesh Sharma and I am founder and CEO of Crest Labs. Today, I'd like to talk about how we used capabilities of OSDU to build and maintain consumption-ready data using our data quality management solution on top of the OSDU data platform. So, AlloidEQM is the platform having built-in data and business rules derived from emerging OSDU standards and PPDM that controls the ingestion of data. So, AlloidEQM is based on mic services and it's a cloud-data application orchestrated by Kubernetes. It's a highly scalable application available for conventional and new energy streams. So, now basically, we know that to push data in OSDU platform, we can anticipate data migration jobs, ETL pipelines, and CLI tools and various other data ingestion approaches to finally populate data in platform. Now, as data is, as OSDU is data-centric and a schema being its blueprint, quality of data should be centrally defined and managed to build fit-for-purpose data, regardless of ingestion source and approach. So, we built AlloidEQM that is data quality as service on top of the OSDU data platform to build and maintain reliable and fit-for-purpose data in OSDU. And we believe that data of known quality will certainly exhalate and promote adoption of OSDU. So, now I'll talk about a little bit of the benefits of AlloidEQM, which it brings to OSDU. So, it certainly elevates trust in data with full data lifecycle APIs and built-in data and business rules derived from OSDU standards and PPDM. It helps data governance team to monitor and oversee data cross-organization and they can intervene when required. And this will eventually help to have consumption-ready data in your data supply chain. It reduces significant time wastage and possible risk as QC, then necessarily done by individuals, may not be qualified to do so. And for instance, if a new entity or custom schema is added to the system, associated data quality rules will provide a gateway to various publishers and data of known quality can be guaranteed to the consumers of the data ecosystem. Again, data center and schema being the blueprint, relevant business and data rules can be incorporated easily with our plug-and-play framework to produce reliable data for other streams like Wend, Solar, CCUS, etc. Now, I like to highlight major advantages of OSDU data platform that helped in our AlloidEQM journey. Trust call services are provided by a platform. It reduced major efforts to develop solutions. This eventually saved a lot of time and reduced time to the market. And being a unified platform for conventional new energy stream, you were able to develop data quality management system for any stream supported by OSDU. Now, with regards to interoperability, quality attributes can be consumed in other applications, not limited to UI, like various analytics data and domain apps. We strongly believe that AlloidEQM on top of data platform will have great advantages for consumers. So AlloidEQM will act as a quality gateway to your data and eventually reliable data can be pushed and consumed in data supply chain. Now, data-centric quality rules can be defined and relevant changes will be rolled out data ecosystem wide and thus reduces efforts and certainly reduces time to market to produce fit-for-purpose data. Majorly cost being function of efforts and time, substantial cost can be saved to build and maintain stable data foundation. So as far as usage is concerned, AlloidEQM can be used in pre-ingest, post-ingest and enrichment stages. To give you idea about pre-ingest, so certainly with a rich set of APIs, UI, data of known quality can be built and maintained in OSDU. So for instance, we know that with OSDU design principles, data should be inserted in platform with very less friction. But however, we know that this obviously enters harvest data into the system. So in those cases, pre-ingest checks can be performed and various enrichment stages can be awarded to have a fit-for-purpose data. And in post-ingest stages, governance team can have corporate storage by data health checkup. And we also got capability to tap the notifications from OSDU data platforms. So with every insert or update system can perform quality scan to potentially find issues. And eventually it can trigger various manual or automated workflows for enrichment or auto-fix potential issues. Now let's proceed for a short demo. So I have two instance up and running. So this one is pointing to Google pre-shipping instance. And the second one is pointing to the instance provided by photo-sample lining. So for the sake of differentiation, I turned on the dark mode on the other instance. So to start with dashboard, so this dashboard gives a data storage while data health check check highlights. So with the total number of records and number of consumption ready, and this gives the number of records being scanned by the system and the potential quality issues. This is the scorecard of complete storage. And this is the quality over time. So this will simply help governance team to check if the quality of complete data store is above a particular threshold level. We also got quality coverage highlights. So this will also help governance team to see if a particular entity was kept in pre-ingest or post-ingest stage. And then a development scan can be initiated to have fit-for-purpose data in the system. We also got a very good feature. We call it rule impact. So this will give a preview of major errors in the system. So in this system, there are lots of broken references and other errors also. And then this will help obviously data owners and governance team to come up with a solution to fix those errors. And then they can plan strategies to avoid those errors in future. This is another error impact area where the system presents if an error is repeated in multiple records. So for instance here, this value is missing in 26K lockup type. So once we fix, obviously once this broken reference is fixed, then most of the errors in lockup type can be resolved. So the users can also filter dashboard by entity. Yeah, there you go. And we can see here that for well-kind, we got some substantial errors. And that users can go to these particular errors to find the relevant details. So let's try to pick this one. Okay, so that errors is affecting 31 worlds. So let's drill down. And obviously, this screen presents various dimensions calculated by system. As you can see here that the UWI and well name is missing for this well record. And apart from that, this section presents a scorecard for this individual dataset data. And the number of errors and these are the timeline of business rules checked by system. Also, we can also see here system performed quality checks based on schema. And this is a very, very good feature where an system performed checks based on schema out of the box. So we don't need any configuration or mapping for that. So system checked all of the references based on the schema metadata and the patterns and if the required properties exist or not. So let's clear this. Now let's go to quality run screen. So this screen gives you the quality runs performed on entire dataset. And then users can obviously filter quality runs or the records where these business rules were failed. So let's pick a very, very, very minor one. So leading and trailing by space and that's that's a minor error, but eventually it affects a whole data supply chain because we know that practically in various scripts or in various system spaces are skipped, right? So eventually it affects data quality. Let's check this. Right. So this data.name got a space, right? So this can be fixed if we know that error. Now let's go to rules library that I was describing. So we got a built-in rule library and how that library used to derive all these results. So to stop it, I'll pick schema-based check. So this is a library which performs the quality scan based on the schema metadata, right? And we don't need any configuration for that. So this is a default rule set applied to all schemas. Well, let's pick another one of course. So it contains the rules required to do the various velity accuracy and completeness can available. And furthermore, more and more rules can be added to our built-in library because of their plug-and-play nature. Now apart from built-in library, we understand that to produce good quality record, there are corporate specific requirement to perform specific checks. To cover that thing, we also got a section where custom rules can be created inside the application. So this one is a custom rule that I was filtering record. So let's pick this one. So users can define custom rules based on simple expressions, right? And they can select relevant dimension. And those can be attached to any schema. And obviously these are the details so that we can filter out those business rules while applying to schema. So you can also specify energy streams. So once more and more energy streams are supported on OSU data platform, so we would be adding various built-in and various other data rules to the system. Now let's go to schema. So we got a very good feature where the system scans the schema from OSU instance, right? Apart from scanning the metadata, it also scans all of the attributes and the required, like if that property is required or not. This is a section where users can assign business rule to the schema. So considering schema as a blueprint of data, so the data quality rules should be assigned as close as possible to the data itself and then the schema, right? Rather than scattering to various other applications or utilities. So users can pick business rules from our library or the custom rules they added in the system. Now let's go to a data set and let's try to perform a quality scan. And for information, the instance is fetching data live from OSU instance. So we are not caching any data. So this is the button to initiate a scan. And sometimes it takes time because obviously it needs to check all of the references. And once the system is deployed along the OSU cluster, then obviously that time can be reduced drastically. Let's drill down to the detail. And obviously this section lists out all of the quality runs performed on this data set. And this highlights the quality of the time for this particular data, selected data set. And as you can see here, system checked all of the references as defined by the schema metadata. That's a check. Plus system also checked some of the other data based on built-in library assigned to the schema. Now let's try to run a complete scan on a particular data set. And that's important because once we have data in the system, so we may need to add more rules that needs to be checked for the particular data set. And for those cases, we also got capability to bulk check the data set. So let's initiate a scan. Let's go back to quality runs page. And you can see here, a quality scan is initiated for 426 records. Let's refresh that. So that's how a bulk scan can be initiated. And apart from all these system, as I mentioned earlier, system also got a feature to tap notification for OSU. So for instance, if a record is being updated or a new record is added to the system, Alloy DQM will perform the relevance scan automatically. Rakesh, quick two minute reminder. Okay. Right. So now, okay. So to re-emphasize, so Alloy DQM can be used in full complete data life cycle to have consumption ready and fit for purpose data on the top of OSU. And this concludes my presentation and demo. And I'm happy to answer questions if you have any.


If you've been following my newsletter, you've heard me talk about prebiotics before. Simply put, they're the food that feeds probiotics, the bugs in your gut. And if you want a healthy, well-balanced gut, you need prebiotics. Think of them like fertilizer for the garden in your gut. Maybe you saw my video explaining leaky gut. It's a great chance to see prebiotics in action. And if you haven't watched it yet, I'll put a link in the description of this video. Along with leaky gut, prebiotics are connected to improved nutrient absorption, more comfortable, smoother digestion, improved triglyceride levels and cholesterol levels. And they've even been linked to more efficient weight loss. But how can you make sure you're getting enough prebiotics? And where the heck can you find them? Well, one of my favorite ways is to drink my prebiotics. In fact, that's why I created Prebiothrive, my powdered prebiotic formula that dissolves in any drink. I suggest trying it with vital reds. But it's also a good idea to incorporate plenty of prebiotic rich foods into your diet. So today I want to share a few of my favorites with you. First, flax seed. Not only is it a great source of fiber, but it's rich in healthy omega-3 fats. And you can bake with it, add it to stir fries, or coconut yogurt, or even blend it into a smoothie. Now it's important to realize that flax seeds have no lectins, and they can't be digested whole. So don't waste your time buying the flax seed cereal or the flax seed crackers and seeing all those whole flax seeds. They're worthless. But buy it whole and grind it yourself in a coffee or a spice grinder. But then keep it in the fridge or the freezer to keep it fresh longer. Next up, artichokes. You can prepare them whole and eat them. But if you want to make your life easier, I suggest buying frozen artichoke hearts. It takes the prep work down to nothing. And they basically look like this. And if you want a delicious and lectin-free way to enjoy those artichoke hearts, keep an eye on my channel in the coming weeks. I'll be sharing one of my favorite recipes from the Plant Paradox with you right here. And third, we've got leeks. Now, they're a cousin of onions.  They're easy to cook with and delicious. So just make sure to slice them in half the long way and then rinse them thoroughly in cold water before using. Or they may taste a little gritty. After that, use them in cooked food, anywhere you'd use onions. Next up, okra. Now, this is one of those foods that people either love or hate. And most of the time, it comes down to one thing. The texture. The sliminess. I'll be the first to say it. Okra can be a little slimy, especially if you don't know how to prepare it. But it's a great source of prebiotic fiber and it absorbs lectins. It's even an ingredient in my lectin shield. So how do you prepare it correctly? My suggestion, saute it over very high heat or roast it until it's crispy.  And the good news, if you can't find it fresh, no worries. Frozen okra is available in most grocery stores and it's just as good. Just make sure to thaw it out and pat it dry before you use it to cut down on the slime factor. And last but not least, jicama. Now, this one may be brand new to you. Or you may have tried it before. It's a delicious crispy vegetable that tastes like a cross between an apple and a potato. And it's incredibly high in prebiotic fiber. Now, you can cook with jicama. It actually stays nice and crisp in a stir fry or saute. But in my opinion, jicama tastes best when it's raw. It's good shredded into a slaw, chopped with cilantro and onion and salsa. Or my favorite, cut into matchsticks or chips and used to scoop up guacamole.  Whatever you do, however you eat it or drink it, don't skip your prebiotic fiber. Your gut will thank you, your body will thank you. Because I'm Dr. Gundry and I'm always looking out for you. Thanks for watching. Click the circular channel icon to subscribe and make sure you don't miss a single video. Click on the left to watch another great video and don't forget to visit my website for more of my best tips. Because I'm Dr. Gundry and I'm always looking out for you.