Ryan Chacon is joined by SymphonyAI CTO Vijay Raghavendra on this episode of the IoT For All Podcast to debate AI’s function within the IoT business. Vijay begins by introducing himself and the corporate earlier than speaking concerning the significance of AI in IoT. He then talks extra particularly about AI’s match into enterprise know-how and begin adopting it. Vijay and Ryan then go extra high-level with conversations round challenges to adoption and the way price impacts the business’s development earlier than ending the podcast with Vijay letting us know what to search for from SymphonyAI sooner or later.
About Vijay
Vijay Raghavendra is a know-how chief and entrepreneur with in depth expertise main know-how groups in corporations starting from startups to Fortune 1. Most lately, Vijay was the CTO at Acuity Manufacturers, an industrial know-how firm. At Acuity, he was accountable for all points of software program know-how technique and supply, together with edge computing and IoT. Earlier than that, Vijay served as SVP of service provider know-how at Walmart, the place he and his groups had been accountable for all platforms, functions, and algorithms that drove the expertise for Walmart’s retailers and suppliers and a major a part of the shopper expertise throughout shops and on-line.
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About SymphonyAI
SymphonyAI is constructing the main enterprise AI firm for digital transformation throughout crucial and resilient development industries, together with retail, client packaged items, monetary providers, manufacturing, media, and IT service administration. SymphonyAI companies have many main enterprises as shoppers in every of those industries. Since its founding in 2017, SymphonyAI has grown quickly, approaching 2,000 gifted leaders, information scientists, and different professionals. SymphonyAI is a SAIGroup firm backed by a $1 billion dedication from a profitable entrepreneur and philanthropist, Dr. Romesh Wadhwani.
Key Questions and Matters from this Episode:
(01:54) Introduction to Vijay and SymphonyAI
(06:23) Position of AI in IoT
(08:57) How AI matches into enterprise know-how
(14:00) Find out how to begin adopting AI
(18:27) Challenges to adoption
(22:38) How does price have an effect on adoption
(26:42) What to look out for from SymphonyAI?
Transcript:
– [Voice Over] You might be listening to the IoT For All Media Community.
– [Ryan] Good day, everybody, and welcome to a different episode of the, IoT For Al Podcast, the primary publication and useful resource for the Web of Issues. I’m your host, Ryan Chacon. I do ask in case you are watching this on YouTube to please give this video a like, and subscribe to our channel, in case you haven’t already completed so. And in case you’re listening to this on a podcast listing, please be happy to subscribe, to get the newest episode as quickly as they’re out. All proper, on right now’s episode, we now have a Vijay Raghavendra, the Chief Know-how Officer at SymphonyAI. They’re an organization that’s constructing a number one EnterpriseAI firm for digital transformation throughout crucial and resilient development industries. Together with retail, client package deal items, and plenty of others. Actually good dialog right here. We speak loads about form of AI, the function of AI in IoT. We speak concerning the applied sciences that they’ve constructed, the applied sciences that they work together with regularly, how folks can get began with adopting AI into their resolution. Why vertical particular experience is absolutely vital, with regards to that kind of integration and bringing AI into an answer, is especially an IoT resolution. And we additionally speak loads concerning the challenges that they see from their aspect of issues, because it pertains to bringing options to life. So, all in all nice dialog, plenty of worth right here. I feel you’ll take pleasure in it. However, earlier than we get into it Works With, by Silicon Labs has emerged because the go-to developer convention for constructing the talents wanted to create impactful linked gadgets. On September thirteenth by way of the fifteenth, Silicon Labs is bringing collectively influential, know-how manufacturers, ecosystem companions, and builders for 3 days of technical coaching and workshops, keynotes and professional panels. Works With is stay on-line and free. Register at workswith.silabs.com workswith.silabs.com And with out additional ado, please take pleasure in this episode of the, IoT For All Podcast. Welcome Vijay to the, IoT For All Podcast. Thanks for being right here this week.
– [Vijay] Thanks loads, Ryan. Nice to be right here.
– [Ryan] Completely. Very enthusiastic about this dialog. I wished to kick it off by having you give a fast introduction about your self, to our viewers, in case you wouldn’t thoughts.
– [Vijay] Nice. I’m Vijay Raghavendra, I’m the CTO at SymphonyAI. I got here to SymphonyAI about seven months in the past because the CTO and previous to Symphony, I used to be the CTO at an industrial know-how firm known as Acuity Manufacturers. Acuity, along with being one of many largest industrial tech participant with the lighting and lighting controls additionally has a linked gadgets play for constructing administration, for location administration methods as nicely. And I spent a while working with Acuity to construct these capabilities out. And previous to Acuity, I spent about seven years at Walmart, a number one numerous elements of engineering and product at Walmart. And I got here to Walmart as by way of an acquisition of an organization the place I used to be a co-founder and CTO that I bought my co-founders and I bought to Walmart.
– [Ryan] Implausible. Yeah. Very in depth background expertise. Seems like a fairly enjoyable journey to get to the place you at the moment are.
– [Vijay] It’s been, yeah.
– [Ryan] So let, let me ask you this. So let’s discuss SymphonyAI actual fast. Inform our viewers a bit bit concerning the firm, what the main focus is, the function you all play in IoT, that form of factor?
– [Vijay] Yeah, so SymphonyAI is an EnterpriseAI firm and our focus is to use AI and machine studying to unravel issues in numerous verticals that we play in. From retail to monetary crime, to industrial to media, IT providers and federal. So, the main focus for us is to allow our clients in every of those verticals to actually remodel what their companies and clear up actual issues by way of the appliance of AI and machine studying. The entire work that we do is grounded in our AI platform that we name Eureka. We not solely help the entire capabilities that you’d count on from any of the AI platforms, however we even have some distinctive capabilities round know-how that we’ve constructed particular algorithms, similar to topological information evaluation. And particularly for every of those verticals. Considered one of our essential methods during which we add worth, or we carry worth to our clients is thru the deep vertical experience and our pre-trained fashions and capabilities that we now have throughout these totally different verticals. And particularly coming to the appliance of IoT, relying upon the vertical take industrial, for example, we work with some very giant manufacturing companies and skim information from a variety of totally different sensors. As you may think, in plenty of manufacturing facility to then use the info, to determine, to foretell outcomes similar to when a compressor could also be going unhealthy. How do you then do preventive upkeep on these gear to do it, to stop a a lot greater downside from taking place downstream? And with that, we herald the entire capabilities, not simply with all kinds of various sensors and gadgets that we learn information from, however edge computing, digital twins, and deep studying fashions within the cloud.
– [Ryan] Completely. Yeah, incredible overview. Thanks a lot for form of giving us a bit little bit of context there. I do wanna ask you although, simply from a excessive degree standpoint, after we discuss AI and IoT, they oftentimes at the moment are going extra hand-in-hand than ever earlier than. Inform me concerning the function and the way you view it of an AI firm within the IoT area?
– [Vijay] Yeah, I feel that’s an ideal query. And I feel for the longest time, I actually imagine that folks have at all times believed within the worth of IoT and the potential of IoT. And you’ll see that within the client area. And I feel that possibility of IoT within the client area has, clearly, it’s change into mainstream now, however within the enterprise area, we’ve at all times been on the cusp of realizing the worth. And up till lately, I might argue that we hadn’t totally realized the worth, however with the entire adjustments which can be taking place and have occurred, with the price of {hardware} coming down with the density of the compute in edge gadgets. Getting to a degree the place it turns into actually attention-grabbing for us to do plenty of processing on the edge with the flexibility now to embed a TPU, for instance, in an edge machine. So we are able to run tiny ML fashions on the edge, mix it with working a deep studying fashions or extra subtle fashions within the cloud after which pushing the outcomes again. I actually assume we now have the entire underlying capabilities we have to actually carry the facility of AI mixed with IoT to unravel actually attention-grabbing issues, such because the one I discussed just a bit bit earlier. So, I actually imagine that AI turns into the conduit for unlocking the worth from IoT, as a result of with out the flexibility for us to do one thing attention-grabbing and helpful with the entire information that you just’re getting from these gadgets, it turns into clean. And with the entire adjustments within the {hardware} with cloud, with edge, with the development and AI, I actually assume it’s a good way for us to carry the facility of IoT and clear up actual issues.
– [Ryan] One hundred percent agree. So inform me a bit bit about after we discuss AI, you realize, there’s a lot of corporations on the market who do AI, say they do AI, there’s a lot of options on the market. And oftentimes for the people who wish to carry AI into their resolution, they don’t at all times know precisely what the distinction is between corporations that play within the area that the choices, what they need to be on the lookout for. So inform me, it’s principally, it’s two questions I’ve. One is to inform me how, what you all do form of differentiates from different gamers out there? And the opposite is how does the sort of know-how actually slot in to the way forward for enterprise know-how as an entire?
– [Vijay] Yeah, so, I feel you hit on a very essential level with the appliance of AI and enterprises. I feel there are a variety of research that present that 80 plus % of all AI tasks or AI tasks in enterprises which can be attempting to undertake AI fail. And so they fail for a variety of totally different causes. Beginning with actually a lack of know-how of those particular kind of issues there’s attempting to unravel for, for which AI and ML are a very good match, not having the fitting information or information tradition, and never having the fitting perhaps mindset or adjustments within the processes that they should do to actually benefit from this know-how. So, I can preserve going. However the reality is that plenty of enterprises wrestle right now with the appliance of this actually attention-grabbing half know-how to unravel issues. So, how can we, or the place can we play and the way can we assist our clients actually get previous this essential problem? As I discussed in my intro, one of many essential locations the place we’ve invested as we’ve constructed out our merchandise, and product providing within the totally different verticals is the deep vertical experience that we’ve constructed over time. So, we’re not a generic AI or ML platform with a number of fashions and we are able to throw it over the fence to our clients and say, “Nice, go have at it.” What we’re centered on due to our experience is with each single vertical, we’re tackling the particular set of issues the place AI and ML are an excellent match that. And the place we are able to go assist clear up these issues in a singular and differentiated method. So take one thing like determining which assortment you have to be carrying in a retail retailer and the way a lot of every assortment you have to be carrying and the place? That may be a very particular downside that each single retailer has to unravel, large, giant, or small. And what we’ve completed with our deep experience and experiences, we’ve constructed over time these pre-trained fashions that clear up this downside for retail and retailers and CPGs. And with the partnership that we now have with our clients, we then begin from our pre-train fashions that aren’t simply the fashions and the options, but additionally embed the data of what a service provider at a big retailer does. How do they give thought to what the fitting assortment is? What the combo is? We embed that intelligence to then work with our clients, to then optimize these fashions and options to unravel the issues. And we do the identical factor with monetary crime and anti-money laundering, for instance, and so forth. And as in, so doing, we carry some very distinctive IP. I discussed the topological information evaluation. Which is a very distinctive and attention-grabbing method to consider information and discover information in an unsupervised studying method. Which takes very giant dimensional information and permits us to assume, have a look at this information and discover relationships that won’t in any other case be apparent or that ordinary clustering algorithms might not offer you. So, all of those collectively allows us to actually differentiate ourselves from others and deal with fixing the issues for our clients.
– [Ryan] Yeah, it makes whole sense. One factor you talked about in there that I truly wished to observe up and ask you about is you had been speaking about form of that vertical, particular experience of the area experience that’s tremendous beneficial and form of the differentiator for you. I do know after we speak to, let’s say platform corporations within the IoT area, that’s a giant factor for them to assist separate themselves out is versus simply having this normal platform that may do all of it, or at the least that’s how they promote it. They discovered worth in buying clients with extra of a focused focus primarily based on area expertise that they’ve for fixing a specific downside or explicit use circumstances inside an business. So let me ask you when a listener to that is seeking to form of be taught extra about getting AI parts concerned of their resolution. They’re seeking to undertake AI know-how. How ought to they form of go about getting began down that course of and why is it so vital to discover a firm with the vertical particular experience that connects to them to assist simply improve the chance of success?
– – [Vijay] Yeah. Wonderful query, once more. My recommendation to corporations that wish to incorporate AI to unravel issues could be for them to be very clear and spend the time to actually perceive that the outcomes they’re attempting to have an effect on and the particular kind of issues they’re attempting to unravel and actually get educated both by way of partnerships or working with corporations, similar to ours to basically perceive the varieties of issues for which AI is an effective match, as a result of it isn’t a panacea for each downside that each firm’s gonna have. So, it’s actually vital for them to grasp that. The second is, and I can’t emphasize this sufficient, AI or any of those machine studying algorithms simply don’t work, or it means nothing in case you don’t have a tradition of excellent information and a tradition of interested by information as the important thing enabler for the appliance of AI. If it’s actually rubbish in and rubbish out. So in case you don’t have good clear information, and in case you are probably not being attentive to that as a core elementary a part of the way you construct your merchandise and your methods, frankly, nothing else is gonna work.
– [Ryan] Proper. So, I might say I might actually encourage corporations to actually basically deeply perceive each the issue, but additionally then deal with the info and guaranteeing that they’re not solely have the fitting information, however they’ve a tradition of guaranteeing good clear information as a result of that then turns into an enormous unlock. After which, clearly, specializing in not simply broad enabling platforms, which more and more have gotten a commodity, however working with and partnering with corporations that may actually herald that very particular area experience turns into a key solution to guaranteeing that you just’re fixing issues that matter as a result of, in the end, the constructing blocks for the way you do carry information from numerous sources, clear the info and remodel the info and the way you construct the fashions themselves, are more and more a commodities. The secret is gonna be, do you actually perceive the verticals and the domains? So you might be extracting the fitting options. You perceive when information or the fashions are drifting. All of these key issues are key issues, I feel are what’s going to make it profitable.
– [Ryan] Completely. Yeah, completely. Let me ask you this slight little pivot right here to a bit totally different space of focus, however if you form of have a look at the evolution of the know-how, not simply in IoT, but additionally in AI and form of how they work collectively, what do you assume have been a number of the largest challenges to getting the know-how the place it must be? To extend adoption, to form of, you realize, these expectations or these numbers that we’ve been promised for therefore a few years concerning the development and adoption of those applied sciences, what do you assume have been the most important perhaps roadblocks or pace bumps which have form of acquired in the way in which to that form of main as much as the place we at the moment are?
– [Vijay] Yep, I might say, in case you assume again to the final decade or so, as I discussed a short time again, I feel the promise whether or not it’s with IoT and even broadly with enterprises has at all times been there, however the adoption, and extra importantly, the success from the adoption of this know-how hasn’t fairly saved up with the expectations. And the explanations are, I feel, once more, a elementary, perhaps lack of know-how of how this know-how works. And as I discussed, the deal with information, and I do know I’m harping on this, like fairly a bit however I’m doing it as a result of it’s so vital. I feel corporations, generally, have underestimated the significance of getting an excellent information, however extra importantly, having a tradition of excellent information that’s inherent within the firm. And if you don’t have that, it turns into very tough to appreciate the worth actually at scale. So, I might say that’s perhaps one of many largest elementary challenges that I’ve seen. The second is I feel, as we’ve developed, particularly within the final 5 to seven years, the capabilities that we now have from whether or not it’s from cloud frameworks to different open supply frameworks, to a variety of Python libraries, to transformer fashions that at the moment are obtainable, or has basically modified the sport. To the purpose you don’t want a PhD in math and stats and laptop science to begin constructing and realizing worth. So, I feel the know-how has additionally developed actually quickly within the final a number of years, that makes it way more attention-grabbing, way more tractable downside to unravel. And let’s face it the final and the most important downside is at all times gonna be for us to search out good expertise at scale and information science and ML Expertise might be one of many hardest expertise for us to search out. And that’s the place the flexibility for us to have the ability to leverage plenty of these open supply fashions for a citizen information scientist, for instance, to have the ability to use plenty of these fashions and options to unravel enterprise issues while not having a staff of knowledge scientists. I feel all of those collectively will assist unlock the worth quicker.
– [Ryan] Yeah. Completely agree. I imply, there’s new applied sciences day by day, proper? You realize, BLE Wideband, Extremely-Wideband. Edge computing’s changing into extra highly effective, the cloud. All very large enablers of what we’re form of speaking about. How do you assume the price factor elements into the form of adoption? I imply, clearly price appear to be happening throughout the board for IoT elements tech. Whether or not connectivity, the {hardware}, or the software program, you title it. How do you assume that mixed with the evolution and development of the know-how facet is enjoying a task and influencing the longer term development of what we’re attempting to construct?
– [Vijay] Yeah. I feel price, particularly with IoT within the enterprise is gonna be a giant issue. Apparently, a number of the work that I did at my earlier firm, and that was simply having a dialog with one of many corporations that was utilizing Extremely-Wideband to do look monitoring, for instance. The size for, in case you take retail.
– [Vijay] The size at which at the least a big retailer who has a number of places, the size at which they function, it turns into the price of the {hardware} turns into a really, very materials price, particularly for corporations which can be working on very small margins to start with. And I feel that’s the place it needs to be a mix of price of the {hardware} has to maintain coming down and we now have to way more environment friendly concerning the density of the {hardware} or the beacons. And so forth that we’d like in very giant areas. The, as you talked about, edge computing and the density of the {hardware}, the facility of the {hardware} on the sting is changing into increasingly more amenable for us to do plenty of processing on the edge, which then helps us with the egress and ingress cross to the cloud. However, in the end, I feel we’re at some extent the place all of those are trending in the fitting method. The price is coming down. The know-how with Extremely-Wideband, BLE, VLC with the NextGen of what’s coming with 5G for the enterprise are all, I feel driving efficiencies and have gotten create enablers for functions that we are able to clear up. And in the end, if the functions that we are able to construct on all of those applied sciences, doesn’t ship sufficient worth to justify the worth then clearly it’s gonna fail because it ought to. However my agency perception is we’re at some extent the place we now have all of those constructing blocks. The price despite a number of the challenges, for instance, for a big retailer is at some extent the place it is vitally a lot within the ballpark of being very manageable for a retailer. And the worth that they will get from it, I feel, is justifiable and can solely get higher any further. After which you possibly can apply the identical analogy to numerous verticals as nicely.
– [Ryan] Completely agree. Completely agree. Yeah. It’s all superb factors. I imply, the expansion and the brand new applied sciences popping out is a large half. The price happening is a giant enabler. The simply developments throughout the board and all the things occurring. The extra use circumstances, the extra profitable deployments, the extra area experience that we talked about earlier. Like simply the extra obtainable that data and data is, and applied sciences is for folks, the extra choices they need to construct the fitting resolution for his or her explicit use case. So, as we wrap up right here, I wished to ask form of wanting ahead from the place we at the moment are popping out of SymphonyAI, and form of what you all have occurring. What’s the massive subsequent step? Like what ought to we be on lookout for being attentive to popping out of your man aspect of issues?
– [Vijay] Yeah, so one of many locations that we’re centered on is, as you concentrate on the vertical particular experience and fixing issues in several verticals, particularly with the AI and together with using IoT and different gadgets, the way in which we’re interested by the issues we’re fixing and what’s coming subsequent is absolutely the notion of an AI enabled digital employee who’s actually working in live performance in-hand-in hand with the people within the loop, if you’ll. And actually enabling the people who’re working, whether or not it’s a enterprise analyst, a enterprise professional, somebody on meeting line, or a retailer supervisor to do their jobs a lot, way more effectively than they will right now with what are perhaps primary analytics and even some primary AI enabled functions. In order that’s the place we imagine we’re going to see the actual mainstream, not simply adoption, however a step perform improve within the worth that this know-how can actually drive in with the appliance. Which brings collectively IoT and the entire ecosystem round it with edge and the cloud with AI and ML, with the vertical experience. Actually bringing all of these collectively in a method that actually augments and helps the people in a elementary method. Which makes it in a seamless elementary method, I feel is the unlock.
– [Ryan] Couldn’t agree extra. Yeah, it’s very thrilling stuff. What you all have occurring over on the firm may be very fascinating from the analysis that I’ve completed. I hope our viewers takes a while to kinda look into what you’ve gotten occurring. For our viewers on the market who does have questions, might wish to observe up, be taught a bit extra, what’s one of the best ways that they will try this?
– [Vijay] So, our web site symphonyai.com is a good useful resource. There are hyperlinks there. There’s plenty of actually nice content material for the issues we’re fixing in every of the verticals. And there are hyperlinks there for anybody who needs to ping us with any query as nicely. So that will be an ideal place for any of the listeners to ping us.
– [Ryan] Implausible. Nicely, thanks a lot to your time. Actually recognize it. We don’t have the chance to speak loads about AI currently. So I actually recognize you taking the time to do this. I feel our viewers is getting a ton of worth out of this dialog. So thanks once more, and we hopefully like to have you ever again, different members of your staff again to proceed this dialogue and speak extra about how AI and IoT actually work collectively to take issues ahead.
– [Vijay] Nice. I recognize the chance, Ryan. It was nice to speak to you. Thanks.
– [Ryan] Thanks. All proper, everybody. Thanks once more for watching that episode of, IoT For All Podcast. Should you loved the episode, please click on the thumbs up button. Subscribe to our channel and make sure to hit the bell notifications so that you get the newest episodes as quickly because it change into obtainable. Aside from that, thanks once more for watching and we’ll see you subsequent time.
