Transkrip penuh untuk AI Readiness Workshop: Demystifying AI with Avishkar Misra of Berrijam AI

results. 00:05:03.600 --> 00:05:05.223 So that's very important. 00:05:05.223 --> 00:05:16.862 We help organizations figure out what's the right strategy for them, not just hey, I came across this use case in some website or I saw this YouTube video and now I can just copy paste it for my company or my team. 00:05:16.862 --> 00:05:24.882 That's great for inspiration, but often means a disconnect between what I need versus what a vendor is trying to sell me. 00:05:24.882 --> 00:05:27.524 So we really focus first on the strategy piece. 00:05:27.524 --> 00:05:32.427 The second piece we work on is we do a lot of custom development work. 00:05:32.427 --> 00:05:50.810 So we're working right now with a neurology clinic, helping the workflow of doctors and streamline and potentially save them two to three hours per day, freeing up their time to see more patients, but also improve the quality of the care they deliver to their patients Again, very, very customized, specialized for them. 00:05:51.521 --> 00:05:56.706 And the third pillar of our service is our own AI system, which is focused on analytical AI. 00:05:56.706 --> 00:06:05.004 We call it BerryJam AI, so I'm sure people are familiar with ChatGPT and other large language models where you can ask questions and we can do stuff for you. 00:06:05.004 --> 00:06:08.935 They're really great for text-based engagement. 00:06:08.935 --> 00:06:13.286 They're good for synthesizing articles and understanding them and dissecting them. 00:06:13.286 --> 00:06:21.737 But when it comes to analyzing data structured data like tables it's a very risky proposition to go down using LLMs. 00:06:21.737 --> 00:06:31.052 So we built our own AI system which is grounded in much more statistically sound systems and algorithms that are defensible. 00:06:31.052 --> 00:06:52.084 So you don't want to bet the company's decisions and strategy on what an LLM might have hallucinated, but what you want to be able to do is ground that into statistics and machine learning methods that have actually been proven to be successful over the last 20, 30 years, especially at companies large companies like Amazon's and Microsoft's and other places. 00:06:52.084 --> 00:07:03.702 This software is very, very specialized, focused on helping people, going from data to actionable insights and doing it, you know, factor order of magnitude faster than conventional methods. 00:07:04.343 --> 00:07:04.764 I love it. 00:07:04.764 --> 00:07:11.887 So there's a few key things there that you said that I really resonate with, and with the work that we do at IPX as well is one. 00:07:12.067 --> 00:07:22.904 it's all about making sure your data is meaningful, your data is actionable, you have the insights to actually make robust decisions and do what you need to do with your data. 00:07:22.904 --> 00:07:24.430 This is key. 00:07:24.430 --> 00:07:30.125 So many people, we have so much data these days and we're not using it properly or not even using it at all. 00:07:30.125 --> 00:07:40.009 So I think it's really key to make sure that we're thinking about it from that perspective as well, as we know that organizations and leaders everywhere they want AI. 00:07:40.009 --> 00:07:41.766 They wanna stay on top of the curve. 00:07:41.766 --> 00:07:43.721 This is what people say. 00:07:43.721 --> 00:07:44.524 We need to have this. 00:07:44.524 --> 00:07:52.201 But it's not just about implementing something that they think it just exists out here in cyberspace. 00:07:52.201 --> 00:08:03.588 It's really about thinking about what pain points you have, what problems are you trying to solve, and think about it from a business process perspective of how do we make what we do more efficient, more effective, more accurate. 00:08:03.588 --> 00:08:13.812 So I love when you think about it like that, because it's really good project management principles, right of the proper planning on the front side, so you have the right expected outcomes on the backside. 00:08:14.639 --> 00:08:14.880 Indeed. 00:08:14.880 --> 00:08:22.233 And look, AI is not a, it's not an answer for every problem, and also, AI is not just one AI. 00:08:22.233 --> 00:08:25.971 You know this last 75 years of research in the field of AI. 00:08:25.971 --> 00:08:37.308 So it's very, very complex, very diverse, and what you need to do is, you know, we've got to get past the initial knee-jerk reaction of, oh, everyone's doing AI, let's bring it in and let's just copy paste and let's do that. 00:08:37.440 --> 00:08:40.028 No, you've got to start with what does your team need? 00:08:40.028 --> 00:08:41.745 What does your organization need? 00:08:41.745 --> 00:08:46.543 Be very, very specific about the specific capabilities. 00:08:46.543 --> 00:08:58.105 And that's one of the places where we focus a lot of effort on helping organizations, you know, start from where their pain point is and then work backwards towards figuring out oh, these are the capabilities of AI that I need. 00:08:58.105 --> 00:09:06.013 And then you have the conversation around okay, what is the right type of tool, framework, technology, partner that is relevant? 00:09:06.013 --> 00:09:08.427 So it's very important to start with a strategy. 00:09:08.427 --> 00:09:17.768 If you don't have a good strategy, you're likely to be one of the statistics of 80 to 90% of projects that are going to fail in AI are never going to see the results that we expect. 00:09:18.480 --> 00:09:20.729 Yep, it's requirements, lead tools follow. 00:09:21.279 --> 00:09:29.890 Indeed and having that sort of understanding of what that organization's cultural framework needs to be in place, right, how do you need to think about the space? 00:09:29.890 --> 00:09:31.746 It's very, very dynamic, very evolving. 00:09:31.746 --> 00:09:37.691 I know it's a lot of attention in the news, but the fundamentals of running a good business have not shifted. 00:09:37.691 --> 00:09:40.601 The fundamentals of teamwork and

collaboration have not shifted. 00:09:40.601 --> 00:09:47.044 The fundamentals of the creative processes and engineering rigor that goes into developing things have not changed. 00:09:47.044 --> 00:09:54.826 So we've always liked to ground it into good, sound foundations and then use these as tools to go further. 00:09:54.826 --> 00:09:57.591 That's why we work with medical researchers. 00:09:57.591 --> 00:10:03.111 We've done some work with Michigan Medicine where we've looked at analyzing the clinical trial data. 00:10:03.111 --> 00:10:16.908 So Dr Tapua had some data that he'd run through some clinical trials and we were able to use Berry Jam AI to do that 6,200 times faster because we designed that specifically for that particular analytics. 00:10:16.908 --> 00:10:20.826 You can't just use ChatGPT to go and say, oh, analyze this Excel file for me. 00:10:20.826 --> 00:10:23.649 So there's different types of it has different uses. 00:10:24.559 --> 00:10:26.361 Yeah, so you know to your point. 00:10:26.361 --> 00:10:30.129 Let's demystify AI just a little bit for a minute for our listeners. 00:10:30.129 --> 00:10:40.364 And so, Dr Avi, so you have a PhD in artificial intelligence and you've built some AI products, but many of us are not in AI, right. 00:10:40.364 --> 00:10:44.120 We're in engineering or we're in supporting functions or manufacturing, et cetera. 00:10:44.120 --> 00:10:47.809 So I guess I would like you, for Frida, to break things down a little bit for us. 00:10:47.809 --> 00:10:50.221 So, from your perspective, what is AI? 00:10:52.244 --> 00:11:10.870 So the way we like to frame AI is AI is a set of tools, techniques and frameworks or algorithms whatever it is that allows for intelligent decision-making and now that intelligent decision-making can be very simple or it could be very complex. 00:11:10.870 --> 00:11:23.907 So, for example, you have a sensor in the garden that's monitoring the moisture content of the soil and they're making a decision based on weather forecast to decide whether to turn on the sprinkles or not. 00:11:23.907 --> 00:11:34.144 If you think about it, it's a very narrow, narrow, narrow, specific domain of that particular system, but it is an artificially intelligent decision-making system. 00:11:34.144 --> 00:11:35.990 It's not a human involved in it. 00:11:35.990 --> 00:11:37.662 So that's an example. 00:11:37.662 --> 00:11:50.493 Meanwhile, you have more complex systems, right, figuring out, well, what's the next movie we should watch, right, that might look at signals of all your previous watches, movies that you've liked, rated, what's in the market, what's everyone watching. 00:11:50.493 --> 00:11:55.032 All of that combined could be considering lots of different signals and it's very narrowly target. 00:11:55.032 --> 00:12:01.533 So the key element is intelligent decision-making and the value that AI can bring in. 00:12:01.533 --> 00:12:05.409 It can superscale that beyond human capabilities. 00:12:05.409 --> 00:12:18.589 So AI can enable human-like intelligence and behaviors or decision making for very narrow, specific problem sets, but do it at immense scale and immense speed. 00:12:18.589 --> 00:12:30.135 So you can imagine considering millions of decisions and data points and personalizing the experience for every single customer who visits your website or every customer who walks into the door of your store. 00:12:30.135 --> 00:12:35.341 Right, those are very concrete examples of where you can't assign a human to do that kind of work. 00:12:35.341 --> 00:12:36.976 So it's very much about set of tools. 00:12:36.976 --> 00:12:40.921 It's not simple program that you can download and run. 00:12:40.921 --> 00:12:43.097 It's not one single algorithm. 00:12:43.097 --> 00:12:48.921 It's not the latest model that Google or Meta or OpenAI put out. 00:12:48.921 --> 00:13:03.964 It's a collection of tools, and so these collections of tools can be used in different areas, from robotics to automating manufacturing, to doing quality assurance tests, and you can think about doing recommendations and also personalization. 00:13:03.964 --> 00:13:13.500 But there's so many different fields that come up, it's very easy to get lost in there, and that's why we've actually built this framework called the Berrygem AI5C framework. 00:13:13.860 --> 00:13:23.105 We focus on bringing that complexity down into the core five capabilities that AI can help us with. 00:13:23.105 --> 00:13:26.572 We do very detailed workshops on this, but very quickly. 00:13:26.572 --> 00:13:30.395 The first capability is find finding things for us. 00:13:30.395 --> 00:13:35.855 So if you do Google search, it's an example of using the AI's find capabilities, document search capabilities. 00:13:35.855 --> 00:13:37.629 Then there's the make capability. 00:13:37.629 --> 00:13:44.039 If you tell Midjourney to create an artwork of a particular style, that's an example of making. 00:13:44.039 --> 00:13:53.158 You can use it to synthesize documents or write emails or prepare or even create new variations of a mechanical design that you want. 00:13:53.830 --> 00:13:55.638 Then there's also Connect. 00:13:55.638 --> 00:14:11.316 Connect allows you to find the connections between different measurements and outcomes, and so if you can understand the relationships within the data, then you can make prediction, which is the fourth kind of capability of AI to predict about an unknown future or unknown state. 00:14:11.316 --> 00:14:14.363 And then, of course, there is the fifth one, which is to optimize. 00:14:14.363 --> 00:14:25.136 How do you consider all those signals and pieces of information and figure out if not the optimal, a good enough solution for that complex, constrained satisfaction sort of space? 00:14:25.136 --> 00:14:40.921 So if you think about AI in those five terms, like broadly, that's a really good foundational place to begin when you want to go down the pathway of AI,

because otherwise you're going to get lost in the sea of 75 years of research. 00:14:40.980 --> 00:14:46.529 Academic of 75 years of research, academic and commercial work. 00:14:46.529 --> 00:14:48.517 So maybe a bit of a philosophical question for you. 00:14:48.517 --> 00:14:56.442 So can AI truly exhibit creativity, or is it simply mimicking human ideas at a mass scale? 00:14:57.311 --> 00:15:04.759 Very good question, right, by the way, I'm not an expert who'll be able to answer it definitively, because no one actually knows the answer to this right now. 00:15:04.759 --> 00:15:08.561 Because the question is are we humans doing the same thing? 00:15:08.561 --> 00:15:10.857 We are products of our experiences. 00:15:10.857 --> 00:15:19.019 Imagine certain artists who go to certain schools and they embody a certain style of creativity in the art and the music they create. 00:15:19.019 --> 00:15:23.429 It's because it is what was taught through their experiences with the teacher. 00:15:23.429 --> 00:15:31.038 Of course, there is innovation and creativity that comes afterwards, right, that they're embellishing and creating something new. 00:15:31.038 --> 00:15:39.970 So in some ways, if you look at it purely from an output perspective, they're mimicking the kind of creativity humans do, but at a humongous scale, right? 00:15:39.970 --> 00:15:47.216 So I know that would scare a lot of us, because the question then becomes more philosophical what is it unique to be human Right? 00:15:47.216 --> 00:15:47.960 What is it about us? 00:15:47.960 --> 00:15:53.812 The answer is we don't know yet and we have to find out, and this is the process. 00:15:53.851 --> 00:16:07.399 We are in a cusp of a massive economic, social and cultural evolution, thanks to AI leaps in the recent years, and it's opened some very, very important questions about how should societies operate. 00:16:07.399 --> 00:16:09.597 What does it mean to be human? 00:16:09.597 --> 00:16:11.355 What does it mean to create? 00:16:11.355 --> 00:16:13.057 What does it mean to engage? 00:16:13.057 --> 00:16:15.138 How does that reconfigure things? 00:16:15.138 --> 00:16:18.335 Now, don't panic, that's not required. 00:16:18.335 --> 00:16:29.254 Tomorrow, you know, we're going to have a little bit of a journey that we're going to go through, but collectively, it's important for us to be very open and transparent and not close our eyes to those possibilities and questions. 00:16:30.056 --> 00:16:33.461 Yes, agreed, dive in, Don't be left behind. 00:16:33.461 --> 00:16:36.345 We need to understand All right. 00:16:36.345 --> 00:16:43.543 You know, I know most of our listeners are aware or have used of ChatGPT and you mentioned that already. 00:16:43.543 --> 00:16:45.225 Where does ChatGPT fit, and you mentioned that already. 00:16:45.225 --> 00:16:47.615 Where does ChatGPT fit within Barry Jam's framework? 00:16:48.636 --> 00:16:49.339 Ah, good question. 00:16:49.339 --> 00:16:56.782 So ChatGPT, just

like any other AI tool or product, exhibits multiple capabilities. 00:16:56.782 --> 00:17:05.625 So remember the Barry Jam AI 5C framework is about capabilities that an AI can demonstrate or give us that we can leverage. 00:17:05.625 --> 00:17:08.659 So chat GPD has two broad capabilities. 00:17:08.659 --> 00:17:10.517 One is the find one. 00:17:10.517 --> 00:17:23.582 You know, if you give it a document and say, hey, go through, scan through this document, find me places in that document where this part is mentioned or this phrase is mentioned or this description is explained, that's what it can do. 00:17:23.582 --> 00:17:24.573 It can go and find that. 00:17:24.593 --> 00:17:41.854 So like imagine, and a practical example is you know you've got documents and documents everywhere and you want to track down where a particular part number is mentioned in those documents and then be able to go and understand the context in which it's described in, and it's not just a simple string matching, it's to understand the context around it. 00:17:41.854 --> 00:17:54.582 That's an example of the fine capabilities and that's what, like large language models like ChatGPT, gemini from Google, lama from Meta, claude from Anthropic they all do these kinds of things the fine capability. 00:17:54.582 --> 00:17:58.137 But they also do the make capability, which makes them even more powerful. 00:17:58.137 --> 00:18:03.496 Right, because what they are able to do is synthesize new ideas in a particular frame. 00:18:03.496 --> 00:18:11.326 You can ask ChatGPT to explain the complex world of astrophysics as a little poem or a haiku. 00:18:12.191 --> 00:18:17.029 That's an example of generating, but it's not just text that you can generate. 00:18:17.029 --> 00:18:25.083 A lot of companies, by the way, they're using this to generate documents, proposals, pitches, presentations. 00:18:25.083 --> 00:18:28.454 Now the challenge is going to be how do you get that right? 00:18:28.454 --> 00:18:28.796 Right? 00:18:28.796 --> 00:18:38.171 So, how do you make sure that it's generating the right content, it's accurate, it's consistent, it's aligned with your speaking style, your writing style? 00:18:38.171 --> 00:18:41.196 So back to your question what is ChatGPT exhibiting? 00:18:41.196 --> 00:18:44.183 It's exhibiting the find and the make capabilities. 00:18:45.069 --> 00:18:46.071 Understood. 00:18:46.071 --> 00:18:51.303 So let's envision, let's talk a little bit about, maybe, what the future looks like. 00:18:51.303 --> 00:18:53.861 I think we're all thinking about this like what does this mean? 00:18:53.861 --> 00:18:58.855 And we know AI is transforming our jobs, businesses across the globe. 00:18:58.855 --> 00:19:05.613 So how do you expect that we might continue to see this impact or how it might show up in our individual lives? 00:19:05.613 --> 00:19:07.385 What do you expect that we might continue to see this impact or how it might show up in our individual lives? 00:19:07.385 --> 00:19:08.875 What do you anticipate that we will see? 00:19:10.412 --> 00:19:14.660 It's already happening, and it has been happening for the last 20 or 30 years. 00:19:14.660 --> 00:19:31.875 By the way, If you've been online and you've seen an ad for a product and so I was at Amazon I built their ad targeting platform where we would use the data on your browsing purchasing history to predict what are the kind of things you would be interested in so you can show them relevant ad. 00:19:31.875 --> 00:19:33.419 Google does it, Meta does it. 00:19:33.419 --> 00:19:35.202 That's AI driven. 00:19:35.202 --> 00:19:39.262 Connect the dots and make prediction capabilities right. 00:19:39.262 --> 00:19:41.791 You are doing that, so you're already living that. 00:19:41.791 --> 00:19:51.122 It's been very transparent for you In terms of what we're seeing more and more these days is a new kind of capabilities that are being unlocked with it. 00:19:51.210 --> 00:20:10.045 So, if you think about what ChatGPT is doing, it's transforming the way people are producing content, how they're generating videos, images, artworks, creating websites, creating documents, synthesizing multiple pieces of information into creating a sort of summarized text, and so forth. 00:20:10.045 --> 00:20:18.480 So there's all sorts of ways that people are using it and they're going to need to use more, and this is going to transform the way businesses operate. 00:20:18.480 --> 00:20:37.959 If you look at it, healthcare doctors we're working with a neurologist who is actually looking at ways to improve their patient outcome by leveraging AI and streamlining their workflow. 00:20:37.959 --> 00:20:39.881 It's a very, very sensitive, secure area. 00:20:39.881 --> 00:20:41.242 We've got to protect the patient data. 00:20:41.242 --> 00:20:43.506 It's a place where people feel most uncertain about can AI play a role? 00:20:43.506 --> 00:20:46.309 But that's where an AI is playing a role. 00:20:46.309 --> 00:20:50.373 It's playing a supportive role. 00:20:50.373 --> 00:20:51.195 It's playing an assistant role. 00:20:51.195 --> 00:20:52.239 It's not eliminating the doctor. 00:20:52.239 --> 00:20:54.728 It's making the doctor more capable, and that sort of general trend is going to apply everywhere. 00:20:54.728 --> 00:21:04.644 Everyone's going to find AI components and capabilities to enhance particular parts of their daily workflows that basically supercharges. 00:21:05.684 --> 00:21:13.480 Yeah, you know, and so I know some of us seem a little afraid right that it's going to replace our jobs or you know something of that nature. 00:21:13.480 --> 00:21:15.337 People have concern. 00:21:15.337 --> 00:21:19.805 It is reshaping the job market and in ways that you're discussing. 00:21:19.805 --> 00:21:22.435 Should people be worried or should they be excited? 00:21:23.211 --> 00:21:26.555 It is definitely going to change and have an impact on a society. 00:21:26.555 -->

00:21:34.921 There are going to be some jobs that are going to be impacted and people are going to lose them and some jobs are going to be created as a result of that. 00:21:34.921 --> 00:21:37.856 But I want to think a little bit about the way I look at it. 00:21:37.856 --> 00:21:45.102 This way is like we now have a capability to create movies, if you think about it, using AI. 00:21:45.102 --> 00:21:47.897 Right, and AI can help someone write a script. 00:21:47.897 --> 00:21:51.720 They can then create and generate videos out of it. 00:21:51.720 --> 00:21:57.242 So you know, one argument would be hey, you don't need artists anymore to make movies. 00:21:57.242 --> 00:22:19.896 But in reality, if you think about the way the current system of movie making is set up, 99.999% of great ideas never make it to the screen because the cost, the effort, the people, the budgets required to take an idea and make it, get it onto the screen is a barrier for most of these ideas. 00:22:19.896 --> 00:22:36.862 So there's a massive graveyard of ideas that never saw the light of day and now we might see an opportunity for, because we've lowered the barrier on the cost of getting those ideas to the screen, more of those ideas manifest. 00:22:36.862 --> 00:22:42.781 So in some ways, there's going to be a complimentary, sort of a result of this right. 00:22:42.821 --> 00:22:53.057 You're going to see ideas that would never have been imagined because we didn't have the time, we didn't have the budget, we didn't have the people make their way into society. 00:22:53.679 --> 00:23:03.855 You might see the work that we do in terms of research and development, Like we've been working with medical researchers doing genetic research and applying AI for that. 00:23:03.855 --> 00:23:18.013 There's a fantastic area there where they're using alpha falls from Google to actually imagine new protein structures and then test those more deliberately to figure out are they stable for clinical healthcare outcomes. 00:23:18.013 --> 00:23:21.403 Same thing could be done for manufacturing process designing. 00:23:21.403 --> 00:23:36.623 If you think about designing new frames and structures for particular vehicles that are much lighter but stronger at the same time, that would have taken years to do, or some ideas never get been enhanced, but now they can be. 00:23:36.623 --> 00:23:40.035 So every domain is going to see an opportunity. 00:23:40.035 --> 00:23:43.421 Of course, jobs are going to shift. 00:23:43.421 --> 00:23:55.259 If you're not going to be using AI in the next 10 years to enhance and deliver and make your own work more exciting and fun, then chances are you are going to be impacted with layoffs. 00:23:55.259 --> 00:23:59.511 It's not going to be life as usual, as it used to be. 00:23:59.511 --> 00:24:02.500 It's going to be life transformed with new capabilities. 00:24:03.431 --> 00:24:03.973 Yeah, I agree. 00:24:03.973 --> 00:24:17.200 So what I hear you saying is it's up to the individual to see this as an opportunity to make their own skill set, their creativity or their expertise more efficient, more effective. 00:24:17.200 --> 00:24:23.563 It opens up the realm of possibility to allow people to shine in their skill set. 00:24:23.563 --> 00:24:29.798 So it's just figuring out what are the right ways to utilize it for that end result. 00:24:30.901 --> 00:24:31.522 Absolutely. 00:24:31.522 --> 00:24:35.352 And look, people are inherently very creative. 00:24:35.352 --> 00:24:52.010 They have the skill set to synthesize different ideas, work with people across different groups, and they use their imagination to bring and conceive of something and then get it to over the line, because an AI can be a fantastic tool to help them do that right. 00:24:52.010 --> 00:24:55.058 It's not just individuals, it's organizations as well, by the way. 00:24:55.058 --> 00:25:00.351 So, right, you know you've got to think about if you're a leader in an organization, what kind of organization am I creating? 00:25:00.351 --> 00:25:02.154 Uh, am I cultivating? 00:25:02.695 --> 00:25:06.163 Right, because there is a lot of economic pressures in organization. 00:25:06.163 --> 00:25:25.410 From the look at the conditions that are happening in the world around, the pressure on capital and investment availability and so forth, you're being asked, as a leader, to do more with less, less. 00:25:25.410 --> 00:25:40.355 And if you want to get to that world where you have to compete, everyone's doing it, by the way, and there's a fantastic BCG study out there which showed that there are companies who are actually leveraging AI to do more with less and they're seeing 1.5 and 1.6x improvements in their bottom lines, revenues, satisfactions, et cetera, et cetera. 00:25:40.355 --> 00:25:48.741 So it's individuals, companies, organizations all have to think about how do they leverage this new capability. 00:25:49.769 --> 00:25:59.661 Yes, and to think about it from a quantitative perspective, like you're saying, is are there ways we can make measurable improvements, so that way we can be more and justify things more? 00:26:00.963 --> 00:26:05.522 And then change the quality of your experience right Rather than doing the mundane elements of it. 00:26:05.522 --> 00:26:06.625 How do you change it to? 00:26:06.625 --> 00:26:10.766 You're enjoying the kind of work you do more and more so. 00:26:10.766 --> 00:26:15.602 For example, at Berry Jam, we actually use a lot of AI internally to critique our own work. 00:26:15.602 --> 00:26:20.800 We use AI to give it feedback and say, okay, I've written this code, help me make it better. 00:26:20.800 --> 00:26:23.045 You know how do we enhance it it? 00:26:23.066 --> 00:26:47.324 so there's ways to not get caught out in the mundane and focus more on the creative engagements for ourselves improve the output of the work we're already doing, as well as do more of the value-added work that we were hired to do,

instead of minutiae or wasting more time or spending time on something that may not be as value-add. 00:26:48.488 --> 00:26:49.490 Indeed, indeed. 00:26:49.490 --> 00:26:53.884 I mean, if you look at just look at most engineering companies and manufacturing companies. 00:26:53.884 --> 00:27:03.347 We hire amazingly talented, experienced engineers and then most of the time they're chasing around updating documents. 00:27:03.347 --> 00:27:05.172 They're not creating stuff. 00:27:05.813 --> 00:27:06.233 Yes, right. 00:27:06.700 --> 00:27:08.307 They're going from one meeting to the other. 00:27:08.307 --> 00:27:09.987 They're figuring out which document has what. 00:27:09.987 --> 00:27:13.002 There was a company in Australia they were looking at. 00:27:13.002 --> 00:27:18.243 They're an electrical company and they get a lot of requests for connections and so they put out. 00:27:18.243 --> 00:27:32.122 You know, it was interesting to see how much of their engineers electrical engineers' time is spent just responding to these requests rather than creating and doing the high value stuff that they need done. 00:27:32.823 --> 00:27:36.049 And so those are examples every organization has. 00:27:36.049 --> 00:27:50.760 I'm sure listeners can think of at least three examples in their own organization where, oh man, I wish I didn't have to do this, but we have to do this and I wish, with NAI, could help me either do it faster or just circumvent this. 00:27:51.584 --> 00:27:55.559 Yes, we hear that a lot on the engineering side as well, with the organizations we work with. 00:27:55.559 --> 00:27:56.643 Where engineering's not. 00:27:56.643 --> 00:28:03.230 They're spending too much time on administrative activities and we need them to be focused more on design activities. 00:28:03.230 --> 00:28:05.682 So, yes, that is a common pain point. 00:28:05.682 --> 00:28:11.374 So you know we're talking a lot about the skill sets and thinking about the future. 00:28:11.374 --> 00:28:27.805 What advice do you have for individuals, you know, moving into an AI driven economy, or even more of an AI driven economy, what skills should we be trying to think ahead and acquire and achieve and implement in our own jobs to be better, to adapt? 00:28:28.666 --> 00:28:41.006 Good question, and there is so much that's happening and things are changing so much it's very, very difficult to be confident in picking one pathway and say, hey, this is a tool, this is the capability. 00:28:41.006 --> 00:28:48.971 And I think what we want to be very circumspect about is knowing what is the right pathway, what's the right strategy, even for individuals. 00:28:48.971 --> 00:28:53.534 And so step number one is understand what AI means. 00:28:53.534 --> 00:28:55.582 It doesn't mean you're going and writing code. 00:28:55.582 --> 00:28:59.796 It doesn't mean you're going and building your own AI model and stuff. 00:28:59.796 --> 00:29:20.121 I would encourage them to think a little bit about more

of the strategy side of it, like if you think about the frameworks that we're doing at Berry Jam with the AI5C framework, that's designed specifically to help people contextualize and ground AI in a deeper and clearer understanding, so they can be very more deliberate about where they want to focus their efforts on. 00:29:20.121 --> 00:29:21.304 That's number one. 00:29:21.786 --> 00:29:43.513 Number two, and a shameless plug here they should do the AI Readiness Workshop, because that's a place where they get to learn about the fundamentals of AI and the leadership challenges of how to bring in AI so that it's successfully adopted and, within the organization, resisted and an elephant in the room that never gets adopted and used. 00:29:43.513 --> 00:29:44.942 So that's very. 00:29:44.942 --> 00:29:54.416 And then the other part we do with the AI readiness workshop is actually help you with the tools to figure out how to build the right business value cases with AI. 00:29:54.416 --> 00:29:56.086 Right, how should you think about it? 00:29:56.086 --> 00:29:58.970 How do you design the organization cultural? 00:29:58.970 --> 00:30:00.144 How do you design the strategy? 00:30:00.144 --> 00:30:11.933 How do you understand what you need so that you can then be very deliberate about what to ask when you're engaging with others who might be AI experts in those fields? 00:30:11.933 --> 00:30:15.650 Right, so you want to be in a situation where you're asking the right questions. 00:30:15.650 --> 00:30:19.470 Problem is, most people don't even know what the right questions are to begin with. 00:30:19.470 --> 00:30:21.804 So those things are very, very important. 00:30:22.425 --> 00:30:30.329 So you mentioned the workshops that you do and, as you know, this is why we're here is AI is a big topic in the IPX community. 00:30:30.431 --> 00:30:41.589 So we at IPX we wanted to bring in Barry Jam as a trusted solution in this space and you, dr Avi, as our trusted expert in this space, to support our community. 00:30:41.589 --> 00:30:55.345 So we are excited to now be hosting a series of these Kickstart workshops with you so our community can begin to understand how they can use AI effectively within their businesses and their jobs. 00:30:55.345 --> 00:30:56.449 What are the next steps? 00:30:56.449 --> 00:30:58.186 How do I speak about this intelligently? 00:30:58.186 --> 00:31:12.106 How do I think about how to scope this properly and start to figure out what are the next steps and just educate people and make them feel comfortable about how to scope this properly and start to figure out what are the next steps and just educate people and make them feel comfortable being able to engage in these kind of conversations within their businesses and with their leadership. 00:31:12.106 --> 00:31:21.044 So I would love for you just to tell us a little bit more about the workshop and what can participants expect to do and learn. 00:31:22.224 --> 00:31:36.450 Absolutely and very, very excited to partner with IPX and bringing AI readiness to this community, because I think there's a lot of opportunity here and it's going to have a transformational effect in every audience. 00:31:36.450 --> 00:31:41.940 The key element of the workshops is well, firstly, it's over two days. 00:31:41.940 --> 00:31:47.798 It's about three and a half to four hours where we dive deep into understanding the fundamentals of it. 00:31:47.798 --> 00:31:49.830 Don't worry, no math is involved. 00:31:49.830 --> 00:31:51.214 No code was written. 00:31:51.214 --> 00:31:54.433 You will not be asked to write in Python or C. 00:31:54.433 --> 00:32:01.538 It's very much a leadership-centered, strategic thinker sort of a workshop. 00:32:01.538 --> 00:32:11.532 So if you are a leader, decision maker or you aspire to be one in the evolving world of AI space, making sure understanding that space is very important. 00:32:11.532 --> 00:32:12.275 So this is for you. 00:32:12.275 --> 00:32:15.371 We break down some of the myths around it. 00:32:15.371 --> 00:32:17.115 We go through some use cases. 00:32:17.115 --> 00:32:20.230 We talk about the different AI capabilities. 00:32:20.230 --> 00:32:22.560 We also give you a little bit of a homework. 00:32:22.560 --> 00:32:49.325 You get to do a little bit of planning on figuring out what are the problems that are suitable for this within your day-to-day or the organization, and then we help you build the right business case how to think about building a right business case for this and evaluate yourself whether this is a worthwhile pursuit of your time and energy or not, because there's no point going down a very complex AI adoption program with things that don't matter. 00:32:49.705 --> 00:32:56.974 We recently did these workshops with a large precision agricultural company out of New Zealand where we got their executives in the room. 00:32:56.974 --> 00:33:01.797 About 20 people were in the room and this is after the online training. 00:33:01.797 --> 00:33:20.788 We actually did a separate session where they went in and they worked out their company's pain points, opportunities, built the roadmap around it, and there were 30 items on that roadmap that they are now figuring out what to do with in terms of getting funding, spinning up teams, bringing partners to execute all of that. 00:33:20.788 --> 00:33:33.660 Now they can't do any of that stuff without going through these kind of deeper understanding and making sure everyone aligns on what is valuable and what's the value of doing A before C and B before C. 00:33:33.660 --> 00:33:53.566 So those are the elements that the AI workshop that we're offering with IPX and through the IPX community is around helping you understand AI, make sure you understand how to frame the capabilities you need and then start building the very, very important business case around for you to convince your leadership and your teams to execute. 00:33:55.068 --> 00:34:03.249 I'm super excited to be doing these workshops with you, avi, and so, like Avi said, these are offered once a month here. 00:34:03.249 --> 00:34:04.730 They're a two-day workshop. 00:34:04.730 --> 00:34:08.798 It's offered May through September of this year. 00:34:08.798 --> 00:34:12.992 At this point, the workshops are formatted in the two half-day sessions. 00:34:12.992 --> 00:34:16.512 They're scheduled to be as convenient as possible in the latter half of your day. 00:34:16.512 --> 00:34:24.393 We encourage you to reach out to us at IPX with any questions that you may have, or Avi directly as well. 00:34:24.393 --> 00:34:25.751 That would be absolutely okay. 00:34:25.751 --> 00:34:43.672 So just let us know if there's anything that we can do to give you the information you need to go back and discuss this topic and this training within your organizations, and we hope that we hear from you guys, and we really appreciate Dr Avishkar Misra for spending time with us today. 00:34:43.672 --> 00:34:47.226 Thank you so much for your time and take care. 00:34:47.226 --> 00:34:47.570 We appreciate it. 00:34:47.570 --> 00:34:48.539 Thank you so much for your time and take care. 00:34:48.539 --> 00:34:49.143 We appreciate it. 00:34:50.166 --> 00:34:51.893 Thank you very much for having me my pleasure. 00:34:53.809 --> 00:34:55.143 Thank you for tuning in today. 00:34:55.143 --> 00:35:03.237 Don't forget to subscribe and review the show and for more information on IPX visit IPXHQcom.