#192: Jim Weisman (Ingine) — Revolutionizing Knowledge Generation for a Healthier Tomorrow

Jim Weisman [00:00:00]:
What we're really trying to do is to bring better knowledge to produce better medicine, and it's really as simple as that. Now what is that better knowledge? Well, it's composed of really producing better actionable information. And how do we go about that? Well, we use a better math.

Jeffrey Stern [00:00:22]:
Let's discover what people are building in the Greater Cleveland community. We are telling the stories of Northeast Ohio's entrepreneurs, builders, and those supporting them. Welcome to the Lay of the Land podcast, where we are exploring what people are building in Cleveland and throughout Northeast Ohio. I am your host, Jeffrey Stern, and today, I had the real pleasure of speaking with Jim Weissman, CEO of InGen, exploring deep into the intersection of artificial intelligence and health care. In our world where health care costs are soaring and drug development timelines stretch into decades, InGen is revolutionizing how we can analyze complex medical and healthcare data to produce better knowledge to create better medicine. By leveraging quantum mechanic principles and advanced probabilistic mathematics, they're tackling some of healthcare's most pressing challenges from drug discovery to personalized medicine. Co founded by doctor Barry Robson, a former chief science officer at IBM's Global Life Sciences division, Ingen is on a mission to unlock the full potential of that life sciences data. Jim brings over 3 decades of experience in health care and management to engine building on an educational foundation from the Wharton School at the University of Pennsylvania and Case Western Reserve University's Weatherhead School of Management.

Jeffrey Stern [00:01:40]:
His extensive experience includes leadership roles at Bioenterprise and HealthSync, strategic consulting for major financial institutions, and roles at organizations like OrthoBrain, which we have featured here on Lay of the Land back on episode 56. In addition to his service as president of the Ohio Venture Association, where he led the effort to establish Venture Ohio, a statewide Venture Capital Association. This was an insightful and reflective conversation. We explore Jim's journey, the pioneering technology and mathematics driving Injin, transitioning from concept and academia to reality, the challenges of building a deep technology company in Cleveland, learnings from the market, and ultimately, Indran's vision for the future of personalized medicine. So with that, please enjoy my conversation with Jim Weissman after a brief message from our sponsor. Lay of the Land is brought to you by John Carroll University's Boulder College of Business, widely recognized as one of the top business schools in the region. As we've heard time and time again from entrepreneurs here on Lay of the Land, many of whom are proud alumni of John Carroll University, success in this ever changing world of business requires a dynamic and innovative mindset, deep understanding of emerging technologies and systems, strong ethics, leadership prowess, acute business acumen, all qualities nurtured through the Bowler College of Business. With 4 different MBA programs of study spanning professional, online, hybrid, and 1 year flexible, the Bowler College of Business provides flexible timelines and various class structures for each MBA track, including online, in person, hybrid, and asynchronous, all to offer the most effective options for you, including the ability to participate in an elective international study tour, providing unparalleled opportunities to expand your global business knowledge by networking with local companies overseas and experiencing a new culture.

Jeffrey Stern [00:03:35]:
The career impact of a bowler MBA is formative and will help prepare you for this future of business and get more out of your career. To learn more about John Carroll University's Buller MBA programs, please go to business.jcu.edu. The Buller College of Business is fully accredited by AACSB International, the highest accreditation a college of business can have. Well, Jim, thank you for, joining us here on the podcast today.

Jim Weisman [00:04:04]:
You're welcome, Jeffrey.

Jeffrey Stern [00:04:05]:
So you have been in and around Cleveland and Ohio entrepreneurship for, you know, your your career from, you know, supporting institutions to venture capital to startups to running engine today. I wanted to start with, you know, how you thought about what the thread is that ties your career together and what really kinda drew you to entrepreneurship originally.

Jim Weisman [00:04:29]:
I think if you had to pick one thing that sort of serves as a thread, it would be sort of this discovery gene in me, which really, I think, relates to just sort of being in awe of the things that other people have done and the things that are out there that we haven't figured out yet or or are still discovering. So those new things, those novel things always excited me and always attracted me. And because of that, I think that's that's drawn me towards the entrepreneurial side of business. And even though I had my got my feet wet in large corporations and in the big city, I really enjoy kind of working in the smaller environments of startups and working with entrepreneurs who have have great vision.

Jeffrey Stern [00:05:10]:
How would you outline your path to engine? You know, how is it that the you know, it converged at at this opportunity?

Jim Weisman [00:05:18]:
Yeah. I I would have to say it's probably the most convoluted path you're gonna find. So, you know, coming out of an undergraduate education where my focus was on finance and then working in, as I mentioned, sort of large corporate environments. I started as a with a small consulting firm, but moved on to PepsiCo and Macy's and and more operational roles and and, you know, strategy roles. And then even from there, switched gears, moved into a family business, and didn't find my way into the entrepreneurial scene for, you know, well over a decade. So it probably isn't something that you could trace and say, ah, that makes sense. But at the end of the day, the experience that I gained working in all those different environments with all those different individuals and even learning from some of the leaders of these large corporations who had an entrepreneurial bent sort of directed me towards what eventually became involvement with a start up. And then from there, it's just sort of stayed, somewhat steady state into that environment.

Jeffrey Stern [00:06:16]:
So one specific, you know, chapter of that journey that I wanted to ask about was your time with the, you know, Ohio Venture Association. And, you know, really being on the flip side of the coin of a lot of entrepreneurship from the investing perspective. What kind of lessons you observed or picked up on from your time there and what you're carrying with you as and in application to the work you're doing as an entrepreneur now?

Jim Weisman [00:06:43]:
My involvement on the board of the Ohio Venture Association really came about because both the desire to learn more about venture and also just my involvement with startups. So I think initially, the membership there kind of paralleled when I joined HealthSync, which was the first startup that I was involved with. And it was just sort of a way of of, you know, enhancing my network, learning about venture and startups, and, you know, it blossomed from there. And that involvement with the OVA paralleled sort of my involvement then with startups, with venture capital, with bioenterprise, which was more of an advisory role to startups, and then back into a startup itself taking on more of a leadership position.

Jeffrey Stern [00:07:31]:
So in a sentence, there's a way to describe Injin as you've relayed it, you know, as this novel platform using adapted direct algebra and notation, information theory, and Bayesian inference for faster, less expensive, more accurate predictive analytics for life sciences and health and human health. And I I wanted to frame, you know, what we're going to discuss today in a bit of a Richard Feynman framing of things, you know, given the complexity of what is in that sentence and kind of the esoteric fields involved. So Feynman was kind of famous for many things as a theoretical physicist, but I always think about him as the master of explaining, you know, complex topics in a simple way with simple language. And so I wanted to kind of start with that in mind. You know, reading back that sentence of what engine does is there's a lot of complexity there. So help us set the stage for for what engine is all about and and your path to it and, you know, ultimately, what what you're working on.

Jim Weisman [00:08:37]:
Yeah. I mean, that that's a really good question. And, actually, I'm glad you pointed out the complexity of what that statement was because that actually was one of the first things I wrestled with, and I still wrestle with on this. And part of it is it's not an easy thing to just break down. And you're coming from some of the the highest levels of complexity that man has ever discovered, and that's, you know, quantum mechanics and and the the nature of that and the work that brilliant minds did over a 100 years ago, Paul Dirac and and and the like. And so the best way, I think, to maybe simplify that, maybe take a Feynman approach is to say, okay. What we're really trying to do is to bring better knowledge to produce better medicine. And it's really as simple as that.

Jim Weisman [00:09:22]:
Now what is that better knowledge? Well, it's composed of really producing better actionable information. And how do we go about that? Well, we use a better math. And, you know, a lot of people argue about what better math might be. But we go back to, again, a 100 years ago and the brilliant minds who had to tackle the thing known as quantum mechanics and understand how to work with it. And and up until Paul Dirac and his Dirac notation in algebra, it was a struggle. And Dirac somehow was able to tame that field and give us the tools that allowed us to do all the great work that was done in quantum mechanics from then through today. And Dirac himself said, I believe that this mathematics and notations, algebra and notation, is actually usable for all human endeavors beyond just what he's using it for. He further said he believes that a really good sign of a of a truly good notation and algebra is that it forces you to include the right information and doesn't allow you to include the wrong information.

Jim Weisman [00:10:43]:
And so, fortunately, I was able to team up with an individual who understood that because I really probably wouldn't have understood what he meant. But an individual who understood it and tried to take that and take what Dirac had said and take what Dirac had done and figure out how do you apply that to things outside of quantum mechanics. How do you apply that to things like the field of medicine? And so, again, you can say that long convoluted statement about what we're doing with direct notation and algebra and quantum mechanics and and all the complexity there. But at the end of the day, we're taking the data that exists. We're turning it into actionable information, which is really the basis of knowledge. So we're producing better knowledge. And at the end of the day, with better knowledge, you can generate better medicine. Now what can you do in the sense of where what is better medicine? Well, it's better applications in the clinical environment.

Jim Weisman [00:11:38]:
How do you make decisions better? How do you understand health care better? It goes into areas like insurance. How do you produce the most value for what you're paying for? It goes into discovery, drug discovery, drug development, you know, the life sciences side of the equation. How do you tackle all the complexity that comes with trying to figure out cures for cancer or diagnostics for cancer or rare diseases or just, you know, major diseases that we still haven't been able to really tackle. And that's really what we're trying to accomplish as we're saying, look. We're not gonna understand everything here, but we're gonna give you guys a tool that will let you produce better knowledge. And because of that, you will produce better medicine.

Jeffrey Stern [00:12:26]:
That is a a much more simple way of of thinking about it.

Jim Weisman [00:12:30]:
Okay.

Jeffrey Stern [00:12:31]:
You know, it's it's a broad framing, you know, for InGen, better knowledge for for better medicine. Where where did that come from?

Jim Weisman [00:12:41]:
Yeah. Look. That came from a lot of back and forth between myself, the founders, and other individuals who've been either advising us or hearing us out over the years. You know, our original story was a complex one, and we talked very much about, you know, all this direct notation and and quantum universal exchange language and all these other components that we had and the types of things it could do. And, you know, we understood and we heard from them that, look, you know, it's just too complicated. I can't understand what's going on. And and over time, we were able to boil that down. Now it took way too long in my mind.

Jim Weisman [00:13:16]:
I wish I had a better marketing mind that I could maybe, come to that a little sooner. But we finally really said, okay. This is what resonates. This is what we can explain things to people in in a simpler manner. And, hopefully, that will give them the clarity to really kind of understand. The other thing is we got lost along the way on our path in the AI revolution that sort of swept up around us. And, you know, sometimes you get you get lost in the woods, and you don't see the trees. And so we kind of got swept up a little bit.

Jim Weisman [00:13:49]:
We wanted to make sure that people understood, you know, where we stood vis a vis other AI technologies. And so we tried to communicate in the language of AI. And, really, I think that maybe confused our story for too long. So now we're really trying to reclaim our story, reclaim our our identity, and that's, you know, where we've been able to finally, come out with with the simplification that we think people can can understand.

Jeffrey Stern [00:14:14]:
Right. So so you had mentioned this analogy of, you know, kind of the forest through the trees and getting lost in the the AI, you know, zeitgeist we've gone through in the last few years. You know, one thing that I know many founders struggle with is technology for technology's sake or grounding it in, you know, what is the problem we are trying to solve? And so it sounds like, you know, there was both a recognition of the impact and significance of a certain mathematical and technological, you know, development on one side of it. I'm curious how you tried to ground what you saw as the potential of this technology in a specific set of problems that you're ultimately trying to solve.

Jim Weisman [00:14:58]:
Yeah. So that's actually a really interesting question because it brings in sort of the the concept of, you know, things change, and we're not in a static environment. So Yep. When the 2 cofounders when when Barry Robson, who's the the originator of the technology, started thinking about this, it was the kind of early 2000. And you just had a a, you know, a call from the president's council to to try to create interoperability in health care and better clinical decision support, evidence based medicine. And so he had been playing around with this technology, and he said, look. I can build out a better system to specifically address all of those problems, all of that those needs. And so that was his initial focus, was really how do we respond to interoperability.

Jim Weisman [00:15:48]:
How do we create evidence based medicine that people can use? And for many years, he was finishing up the technology, building that out, really focusing on that. And then he found his his cofounder, Sriniti Bora, who had a very similar, you know, insight into what was needed in health care and and where we could where they could go with the technology. And so that's what they launched as. They really launched as a solution to interoperability and a solution to delivering evidence based medicine, clinical decision support backed by good information, good knowledge. Unfortunately, when they came out, there was a lot of sort of pushback on new technology for interoperability. The existing standards wanted to maintain their position. And then when you started looking at the clinical decision support side of this, they hit basically the, you know, the year 2000 COVID challenge, which basically threw all hospitals into a downward spiral financially and then also kinda change the way that they were looking at bringing in new ideas and concepts and technologies. So they went from an organ an entity we went from an entity that was prepared to address evidence based medicine, help with clinical decision support, help with interoperability of health care records and information around the world to an environment that had been receptive to that a decade before, but now was not receptive.

Jim Weisman [00:17:17]:
So this this temporal change really kind of threw them from where they were gonna be able to take the the technology. And it took us a couple of years to sort of change gears, learn from the market what was going on, and shift where we were focusing. Now we had to make sure we could shift. So we had to do some of our own internal reviews and assessments. We had actually some proof of concept projects that we we undertook, which we had done prior proof of concept projects, but they were focused in that interoperability and clinical decision support area. So we were able to finally make that transition and come out of it. And for the last, probably now, 12 months, we've been able to really focus more on how we bring this to the life sciences as a better knowledge for better medicine solution. At the same time, you know, Barry was sort of leading edge on AI.

Jim Weisman [00:18:10]:
You know, he had been involved with AI since the seventies, and he was ahead of the game until he wasn't ahead of the game. And that's more of not so much the technology side, but a marketing side. So when they started to see some of these breakthroughs in AI and neural nets and deep learning machine learning and then also on the on the GPT side, those stories started clouding the market and clouding people's understanding of what what was good math and good technology versus what was promised and promoted. And so we ended up hitting that little tsunami and the combination of the the tsunami of AI, which is starting to filter out and and come back to to the ground. And the the changes in what was going on in health care, you sorta had your own miniature superstorm Sandy, which, you know, hit hit this region in 2012. Well, that's kinda what hit us. And so we've spent some time trying to recover from that. We had to sort of think about, you know, how do you deal with, you know, tech for tech's sake where we thought our tech was the greatest thing to deal with the problem, but now the problem has changed.

Jim Weisman [00:19:19]:
So now do you still have the best tech? And how do you how do you transform your your technology to be appropriately addressing the the problem that you you feel you really can address and is still needed to be addressed? So, you know, we've kind of had to deal with that. We've learned along along the way, and we've learned a lot about that. And, again, it's one of these things where you kinda wish you knew beforehand what was gonna happen because you you probably would have not stepped in the muck, you know, just known known to avoid it.

Jeffrey Stern [00:19:47]:
Yeah. So AI is used somewhat loosely as a term today in its application of, you know, basically in the way every company is a technology company, it seems like every company is an AI company. But there there is a very explicit and specific way in which, you know, you have thought about and an application are using it. And so I'd love to hear, you know, what is the actual AI that, you know, you are employing within Enjin and how is it different from other, you know, traditional AI models, and and what has, you know, happened in more recent times? And what specifically does it allow for you to do as an organization, and and why is it critical in the way that you are using it?

Jim Weisman [00:20:33]:
Yeah. So let me let me kind of tell you my definition of what people consider AI just so I I can make sure I'm on the same page as as you and and others. If you go back to the 19 fifties, you have the first development of the perceptron. And that sort of is the the genesis of the initial AI revolution or AI, and it's had its ups and downs over the years. But but, basically, you're looking at a concept of an attempt to mimic a neuron and mimic the brain. And if we add millions and millions of these neurons together and, you know, do math on them, we can recreate the capabilities of the brain, which means we'll we'll recreate the ability of a human being, and that'll be artificial intelligence. And so that has kind of exploded over the years, some, you know, advancements that people made. Back propagation was probably the biggest advancement that really changed the nature of that.

Jim Weisman [00:21:27]:
And then the the ability to scale. So now all of a sudden, the limitations of memory and of throughput no longer were the constraints that were holding back these analytic capabilities that people were building. That to me is what people think about or or know of as AI. And even when you break when you think about neural nets and you compare them to a GPT, a GPT is really built on neural nets. It just breaks apart the the information differently and then reconstructs it differently. But at the end of the day, you you're you're basically doing regression analysis. I hate to say it. It's just very complex regression analysis, nonlinear, and, you know, a lot a lot a lot of calculations.

Jim Weisman [00:22:10]:
Barry looked at that and basically said, that's interesting. Now you have to remember, he was at IBM at the time that they were developing Watson. Barry was in the chief science officer role for their entire global health care life sciences and pharma division. He himself had come out of their what the, the Watson Research Center and, you know, again, had a background in developing some of the, you know, early advances in information technology prior to his days at IBM. But he looked at all the what was going on and said, you know what, guys? This really isn't going to get you far. There are limitations on what is being done here. You need to go back and and look at this from an information theoretic perspective. You have some brilliant people over the last, you know, 60, 70, 80 years who have come up with some very important discoveries in information theory, including Berry himself.

Jim Weisman [00:23:03]:
And you have to couple that, as we stated earlier, with a better math. And you're dealing with complex systems, and complex systems are not deterministic. Or you're rarely gonna find a complex system that's deterministic, maybe a Rube Goldberg machine. You really have to think about this from a probabilistic perspective. So you have to find a probabilistic math that you can work with, and you have to find information theoretic concepts that you can build into that to allow you to do better math and produce better knowledge. And so Barry really stepped back and built his AI, and we still use the term AI because, you know, we are doing similar things in terms of prediction and classification and the like. Yep. But we built it on a completely different foundation and a completely different set of principles.

Jim Weisman [00:23:56]:
One of the the key principles is the belief that we have to have this as a glass box. We cannot have these with hidden layers and black box calculations and and random weights that no one really understands how they're derived or where they came from or or what they do. We had to have this built in a manner that's completely open, accessible, reviewable, understandable. And that's what, you know, Barry again set out to do. Now, fortunately, the use of the direct notation and algebra allows us to do that. Fortunately, Barry's concepts of information theory that are built in are easily understood. And so we can produce that glass box AI that others can't. Now the challenge then is how do you communicate that to everyone, and how do you get them to understand? And there there are very few people in the world who can grasp these all of these concepts, and, you know, we see that even with AI.

Jim Weisman [00:24:50]:
There's a few people who people look to as the gurus of AI, and they'll believe whatever they say. You know, Jeff Hinton and Juan LeCun. Well, you know, who's gonna argue for the other side of the equation? Who's gonna argue for the other information theoretic man capabilities and and measures that are brought into our AI? And that's where we have some of the challenge of communication.

Jeffrey Stern [00:25:13]:
Well, you know, I I always think about it, and very few people understand how the microwave works. But, you know, we can put the frozen dinner in there, and 30 seconds later, it comes out cooked. And, ultimately, people don't really care about the technology as much as they care that it solves, you know, a certain problem

Jim Weisman [00:25:31]:
for them. But, Jeff, you bring up an interesting point there. So, you know, the first microwaves didn't come around in the eighties nineties with you and I growing up or or 2000. The first microwaves came around in the fifties. And people were scared to death of them and would not have them in their house for anything because they feared, well, that's nuclear or we're gonna, you know, we're gonna come out with this, you know, some sort of mutations after using it. So the commercial applications were really the first uses of some of that technology, and it took a long time for people to understand the technology, get comfortable with it, and finally bring it into their home environment in, you know, in a manner that they could understand and use. So it's interesting looking back, the perspective is one of, well, you know, hey. People use complex things like microwave, and maybe they don't fully understand it, but it didn't happen overnight.

Jim Weisman [00:26:22]:
And that's that's part of our challenge is how do we speed up that process? How do we get into the market and convince people and and educate them enough so that we don't have to take 5 decades to become the standard or or household, you know, usable technology?

Jeffrey Stern [00:26:40]:
Right. And and at the highest level, I can understand without fully understanding how it works. Better math could lead to better knowledge, could lead to better medicine. Lay of the Land is brought to you by Impact Architects and by 90. As we share the stories of entrepreneurs building incredible organizations in Cleveland and throughout Northeast Ohio, Impact Architects has helped 100 of those leaders, many of whom we have heard from as guests on this very podcast, realize their own visions and build these great organizations. I believe in Impact Architects and the people behind it so much that I have actually joined them personally in their mission to help leaders gain focus, align together, and thrive by doing what they love. If you 2 are trying to build great, Impact Architects is offering to sit down with you for a free consultation or provide a free trial through 90, the software platform that helps teams build great companies. If you're interested in learning more about partnering with Impact Architects or by leveraging 90 to power your own business, please go to ia.layoftheland.fm.

Jeffrey Stern [00:27:47]:
The link will also be in our show notes. You mentioned, you know, more recently, it's begun to resonate. How do you describe as what the the big break was, you know, and and where, you know, engine is today and kinda grounded in a bit of, you know, practically, what what does this look like in the real world?

Jim Weisman [00:28:10]:
Yeah. And I'll be honest. Yeah. I'll be honest. We're still looking for that big break. We're we're talking with some organizations right now, and we think these are the organizations who have the ability to understand what we're doing and help us bring this technology forward in the in the right manner. So, you know, we're all working diligently that within the very short period of time, we will get that kind of big break and get get get this moving forward. But it has been a challenge.

Jim Weisman [00:28:37]:
Yep. And, you know, I don't wanna make it sound like it just happens overnight easily and and the, you know, the phrase, you know, better knowledge equals better medicine is is, you know, something that just all all of a sudden flips the switch. We still have to get in there and and and somewhat educate them. We've been very fortunate that a couple of high level individuals are helping us in an advisory role, and one of the one of the individuals really understands what we're doing. And so he's been able to help us get the the right connections and really start to, you know, move this forward a little bit in the way we need to.

Jeffrey Stern [00:29:13]:
What are you working on right now? And what what is the the broader vision and goal for Enjin today?

Jim Weisman [00:29:21]:
So we've really, as I said, about a year ago, really started rethinking and and focusing in the life sciences and understanding where our applications could be in the life sciences. And and we did some some POC work where we showed there's some real basic market segmentation or patient segmentation capabilities. We can support the real world evidence, real world, you know, data derived information. We can pull out of out of, large datasets that can help pharma companies with existing assets. But we've also started understanding that we can apply our technology to the the drug discovery process from its earlier stages. We can we can analyze chemical composition and help, you know, entities narrow down the number of compounds that they wanna move forward to their computational chemist and and from there. Barry has even done some work in protein folding and the understanding of what we could add to the insights gain, you know, for protein folding and what that could mean. Recently, we actually started looking at some genomic information and the types of things that we can do that would help understand cancers better and understand the breakdowns of of which genes are are, you know, interacting and how they're how they're probabilistically leading to a certain type of cancer or another type of cancer.

Jim Weisman [00:30:38]:
So we're kind of a little bit in a discovery mode, but at the same time, we've already shown some capabilities in this space. And we'd like to advance those capabilities, but also work with others to explore where else this foundational platform capability can be expanded.

Jeffrey Stern [00:30:54]:
You know, provide additional contrast for, you know, the the actual problem. You know, what what does this look like today? What what is the status quo of how, you know, this kind of work is done? And I think that can also, you know, further help illustrate where the opportunity is for engine.

Jim Weisman [00:31:13]:
Today, you have you have a lot of very brilliant individuals who are attacking this problem from a lot of different angles. And, you know, you're just getting quantities of genomic information at scale that can be analyzed. You're just able to really bring in even the the clinical information that maybe would be benefiting or adding value again at scale. But you've got a lot of people, and maybe I'll use this analogy again. They're out in the woods, and they're each wandering around or or there's a lot of wandering around doing and using different capabilities. So some are exploring, hey. You know, these powerful neural nets that we've seen so much about, let's let's apply them. Let's, you know, let's take that deep learning and fig find out what we can.

Jim Weisman [00:31:56]:
Lately, you know, people are thinking, well, why can't we use these these GPTs to analyze this? And and we're gonna build GPTs specific to life sciences and genomics, and we're gonna we're gonna let them tell us where to to, you know, develop solutions. So there's a lot of kind of random discovery going on or people are are using you know, there's a lot of old tools that people still rely on and, you know, to some success. What we're trying to do is say, there's another way that we can bring all of this together, and that is using this advanced, you know, math that others maybe haven't really fully applied to the sector. And we need to prove the capabilities, and that's what we've started to do, some of the proof of concepts we've we've started to undertake. But we also, at the same time, want to bring in some some voices to work with us. We wanna bring in some folks who have that big picture and are trying to address these challenges at a very high level. We wanna bring them in so that we can work with them and start to gain that respect and that gravitas that others rely on to break through.

Jeffrey Stern [00:33:07]:
As you demonstrate the what you could call maybe the efficacy of your approach in this, how do you layer on, you know, engine as a business and how you've thought about, you know, the the organization and and, you know, what does the company look like?

Jim Weisman [00:33:23]:
Yeah. So right now, we're we're a small shop. You know, it's it's the the core 3 of us. We've had some outside programmers who have worked with us over the course of time, and we have a series of advisers who we work with as well. We're always looking, you know, maybe just more advisers we wanna bring in. We initially thought about this as a direct to market. We're we're gonna bring the solution to the hospital. We're gonna charge them, you know, as a software, as a service.

Jim Weisman [00:33:48]:
You know, we'll figure out some some, you know, per model, per whatever. We kind of rethought a little bit of that. We wanna work with channel partners. So they're you know, the industry has a lot of big players, and they have a lot of bandwidth. And we'd like to add value to what they're doing. We don't wanna recreate everything and try to recreate the wheel ourselves. We'd like to work with some channel partners. At the same time, there are things that we can solve directly.

Jim Weisman [00:34:15]:
And so we will still explore and look at those opportunities where we could go direct and, again, as a software as a service. But more likely than not, we're gonna work with some channel partners and have them incorporate the technology into their offerings, and then that will help spread this. You know, at at the at the core of this, you know, Barry really wants to see this technology made available because he believes it will help solve some of of medicine's biggest problems. And so he wants this to be out there. And so, you know, my, you know, my goal and responsibility is to make sure that that happens one way or another.

Jeffrey Stern [00:34:53]:
What are you most excited about looking forward?

Jim Weisman [00:34:56]:
Well, it's it's interesting. This is you know, it's always a little bit of a roller coaster. So, you know, I I get excited when I get up on the top of a hill because I finally see the capabilities and and the vision of where this can go. And then sometimes, you know, you you you ride back down and you get in the trough because things start clouding you, and you you start hearing about others maybe, you know, launching a new product that maybe they say is better than what you're doing. And and, you know, it it can become a little disillusional. But I think really that it excites me when I when I get back up to the top and and Barry helps, you know, and helped me get back up there. And you really do understand this really is something that's unique and novel. And to date, any of the stumbling blocks put in front of us, we've really been able to conquer and show that they're not really stumbling blocks.

Jim Weisman [00:35:46]:
They're peep you know, they're things that people wanna present to us or tell us you guys can't do or or this is something else is better. And, really, every time we've we've hit that bump, we've been able to get a you know, to get over it and show not really you know, that's not the case. So it's exciting in that regard. It's exciting to learn at the feet of these 2 gentlemen who really have, you know, just great technical skills and and a great understanding of the challenges that are really at the core of some of the problems we have in health care and life sciences and in medicine. And so it excites me to be able to really try to figure out a way to then translate that excitement that they have, that knowledge that they have, and help them move it forward. And I and I hope I'm able to do that. I'm always looking for other partners who can who can fill in the gaps with me. And, you know, we we think we again, we've got some good advisers who were helping us with that.

Jim Weisman [00:36:45]:
But I know that it's gonna take more because this is this is a very challenging field. You know, the the technical challenges that others are throwing in front of us, you know, are are hard to deal with. I think we're starting to see more light in the sense that existing AI technologies people are finally really taking a deeper dive into them and understanding the limitations and what they really are capable of doing. So no longer are people starting to you know, they're still doing it, but they're not running around worried that the AI robots are gonna take over the world. You know, they're realizing that, look. I still can't get a self driving car that really works, and it's been couple of decades and lots of 1,000,000,000 of dollars thrown at that. So, you know, maybe we maybe we should not take it, you know, at face value what people are telling us about those technologies, and let's dig deeper, and let's find some that maybe do work better. And I I think that's where we are.

Jeffrey Stern [00:37:41]:
I do feel it is lost a bit that there are all these alternative approaches at the moment that that have, you know, merits of their own as, you know, the blanket AI has, you know, kinda demonstrated or at least captured the attention of of pretty much everyone.

Jim Weisman [00:37:57]:
Oh, I look. They're they're really very cool. You know, they're they're great at they're great. I mean, you know, it's fun playing with with chat GPT, and it's fun to, you know, to ask you questions and be amazed at how it answers them, or it's fun to think about the ability of some of these neural nets and and the types of, you know, classification they can do. You know, they can identify a face out of a crowd of 2 you know, 20,000. They can they can do that. But you really have to be careful about understanding what that what that really is doing and how that really gets applied to some of the more complex problems that are still out there and what limitations you get. So a recent analysis was looking at, you know, how good are these GPTs, these transformers in doing math.

Jim Weisman [00:38:44]:
And they said, hey. You give it any 4 digits times 4 digits, it'll solve it. And people are like, wow. That's amazing. You know, that's I can't do that with but then again, a handheld calculator does that. Now you can take a handheld calculator and do 12 digits by 12 digits. Well, you know what? Chat GPT and other they can't. They have limitations, and we just haven't really explored them and understood them or been willing to understand what those limitations are.

Jim Weisman [00:39:10]:
So I think we really need to, you know, step back and take a hard look. Now it's not easy to convince people that they have to do that, and that's unfortunately where maybe we are to try to get them to understand what our capabilities are and how we are different and how we do deliver more value. But that's that's, you know, that's what our challenge is, and that's what we're gonna we're gonna really try to do.

Jeffrey Stern [00:39:30]:
What does it meant to be on this roller coaster as you described it in Cleveland? What what has been the role and, you know, importance of Cleveland in this journey?

Jim Weisman [00:39:41]:
So I spent a a good part of my career, 14 years, working at Bioenterprise, working with startups, working with our entrepreneurial community and our venture community. And I will say Cleveland itself is sort of a little bit of a a microcosm of a roller coaster. You know, I think we deep down, we understand what we need to do. And at times, we do kind of peak and and hit a a top of a hill, but then we always seem to fall back into a trough. And I think it's it's a challenge for us to get out of that trough. And I love Cleveland. I I moved back here from New York City because I wanted to to live here in Cleveland. I think we have incredible promise and incredible upside.

Jim Weisman [00:40:22]:
I think we just haven't really gotten together as a community properly to really undertake what's necessary to do to get there. You know, I will continue to fight for Cleveland. Our goal is to grow the company here in Cleveland. Mhmm. That may be a challenge. We may not be able to to do that, but that's the goal. That's that's why I agreed to step in and work with these two gentlemen, and I said to them, look. One of the caveats is we will try to grow this year in Cleveland.

Jim Weisman [00:40:50]:
And you have to understand, my 2 partners are in India and England. So, you know, they kinda have their own brothers. But, you know, Cleveland is a is has got tremendous potential. I just don't want it to always have tremendous potential. I want it to show that it can be successful as well. And so, I'd like to, you know, use this as a stepping stone to help do that. You know? But I think we're gonna need a lot more outside of just one success to really, you know, change the tide here.

Jeffrey Stern [00:41:20]:
What does success look like and mean to you?

Jim Weisman [00:41:24]:
Again, getting back to kind of wanting to make sure that Barry's, you know, Barry's findings and and his his brilliance and and what he's built, making sure that sees the light of day and gets into the market and starts making a difference. It's not growing a $1,000,000,000 company. I mean, that hopefully, that would be the outcome of of being successful for Barry, but it may be something where, you know, we don't really have a company. We just make sure that that the technology gets out there and sees the light of day and and makes a difference in the world. Because, really, you know, at the end of the day, you know, we've got 7, 8,000,000,000 people living on this planet. We've got a lot of problems to deal with. And the success of one company, it's great, and it would be very impactful, but it's not gonna be the same thing as if you can really change the trajectory of medicine and make an impact for large numbers of people. And that's what Barry wants to be able to to do and bring with his technology.

Jim Weisman [00:42:23]:
And so that's part of what drives me in terms of my focus and efforts here.

Jeffrey Stern [00:42:29]:
This is kind of a a broader, you know, question than specifically to the work you're doing and kind of the the research and progress Barry has made. But how many good ideas do you feel are out there that don't have the translator? You know, in my mind, you're kind of like a a translator of sorts of this academic world and, you know, the real world implications of that work being done. How have you thought about that kind of space?

Jim Weisman [00:42:59]:
You know, I I think if early on, I was probably naive to that. And I thought, well, every great technology automatically moves forward and and and, you know, is brought out. I mean, why wouldn't it be? I mean, it just makes sense. And I think as as I experienced more and learn more and saw more things, and and and in different fields, not necessarily in the life sciences or health care, but in other fields, you realize, you know, it's kind of a standard story out there that there's advances in greater, better technology that don't see the light of day or don't succeed or are purposely, you know, held back. And it kinda makes you wonder why. But it's it's the reality of the world that we live in. Now maybe it's the result of, you know, a a system that needs to move forward by not necessarily, you know, advancing the best technology. Maybe it's the result of just, you know, people not being fully educated or or having that translator.

Jim Weisman [00:43:56]:
I don't know. But I I've seen it elsewhere. And, you know, it's interesting to see it. It really is enlightening to try to learn from it. And, you know, I think that is probably the one key difference between maybe a 20 year old entrepreneur and an older entrepreneur is that maybe you've seen more and maybe you can actually understand more of what's going on. Now sometimes that's a negative. That 20 year old who doesn't know that something might not work, they're gonna plow forward. And and, you know, we've seen a lot of great things happen because of that.

Jim Weisman [00:44:28]:
But at the same time, I think, you know, the the education you get over the years can be beneficial because it can bring to light some of these things that you just were too naive to understand early on.

Jeffrey Stern [00:44:38]:
What would you say have been some of your other biggest learnings in this journey?

Jim Weisman [00:44:43]:
You know, I I think the other learnings you know, part of it is just understanding the challenge of bringing change or or having change into inside of an organization. You know, I mentioned I I worked at large organizations early on, one of which was keenly focused on and the executive leadership was keenly focused on the importance of change. The CEO used to say, if it ain't broke, fix it. And that organization today, even even, you know, 35 years later, is a very successful organization. They've had their battles, but they're a very successful organization still. The other one has done nothing but sort of pair itself back and shrink over time because they were too, you know, they bought in too much to their existing business model and their greatness, and they did not change. So, you know, I think that's part of it is that, you know, kind of that ability to understand change and the necessity and and how you how you attack it. You know, I that's probably the biggest one, I I would I would say.

Jeffrey Stern [00:45:47]:
Yeah. What do you feel is is kind of unsaid in this, you know, story of, you know, both your own professional career and, of of the work you're doing with Ingen that we haven't talked about?

Jim Weisman [00:46:01]:
Well, I'll look and say for the region, what's unsaid is that if we really want to succeed better as a region, we need to start doing more. We were doing that for a while, and then we started kind of falling back. And I think if we wanna succeed as a region, we really need to understand and start to undertake some of those things that will propel the region forward more directly and and more, impactfully. You know, for for myself with engine, you know, I I think it's taken us a long time to try to clarify the story. We're still we're still wrestling with it. We still have documents that are too convoluted and too confusing. It's still hard to go beyond, you know, better knowledge equals better medicine because people then wanna say, well, what does that mean? And then you start getting into the details. So, you know, for me, it's it's probably doing a better job with some of that.

Jim Weisman [00:46:53]:
And then, you know, lastly, I don't know. It's it's just, you know, these personal journeys are hard, and you you really have to maybe take a step back every now and then and reposition yourself or or rebalance, rethink what you're doing, and make sure that you're comfortable with with what's going on and and where it's going. And and, you know, we've had a couple of those moments within the organization, and we may have one of those in a couple of months if if some of these things that we're working on right now don't, you know, move forward. And so you have to constantly be doing that. Mhmm.

Jeffrey Stern [00:47:29]:
Yeah. It it is certainly a a journey. Well and, you know, the the the forest and trees analogy just, I think, kinda rings true through, through all of it. You wanna constantly make sure, you know, are are we going in the in the right direction?

Jim Weisman [00:47:45]:
Yeah. And, you know, every time you you get to a clearing and you think you see where you're heading, you know, maybe you don't realize you're in another forest or you're in an, you know, you're in a a deeper forest. And it's hard. It's hard until you until you've gotten past that point to maybe understand what's going on. That's why it's it's important to maybe have some good advisers along the way. You You know, it's important to have a diversity of opinion on a team. You can't all, you know, believe that everything is perfect and what you're all doing is is exactly right because there's a chance it's not. And if someone's not asking those hard questions, you could get too far in before you realize that you're on the wrong path.

Jeffrey Stern [00:48:24]:
Well, I appreciate, Jim, you're, you know, in the spirit of Feynman, you know, taking us through what what is, you know, complicated math and and and application to, the company you're building and that you're building it here in Cleveland. So thank you.

Jim Weisman [00:48:40]:
You're welcome. You're welcome. But you'll you'll notice I really didn't get into the math too directly. And that was You know, like it's on purpose.

Jeffrey Stern [00:48:48]:
Like like the microwave. You know? Right. Well, I'll close it out with our traditional closing question then, which is for a hidden gem in Cleveland for something that other folks may not know about, but perhaps they should.

Jim Weisman [00:49:03]:
So up until about a month ago, my my favorite hidden gem in Cleveland was the parking lot under the I ninety bridge just outside of Progressive Field. And it was the best place to park your car. You could always find a spot. It was a short walk into the park, cheapest parking rate around until last week when I tried to get in there, and I found several thousand other people had found that hidden gem, and it was no longer accessible, even an hour before game game time. So my my new hidden gem is actually not a single location. There's a bike ride that some people here on the east side have developed called the post industrial ride. And it starts here in Shaker, but you can start anywhere on, you know, on on the east side, really. It takes you through the metro parks.

Jim Weisman [00:49:52]:
You go through the valley. You come up through the industrial section of Cleveland. You you you you bike past the steel mill. You get to Tremont. You get into the downtown area. You you then go along the waterfront. You come back up through MLK and up up through University Circle, and then you're back out in the suburbs. And it's it's amazing that in a in a single bike ride, you can see that diversity, and you can experience all aspects of Cleveland.

Jim Weisman [00:50:21]:
And they're all incredible. And if you've done the ride enough over the years, you can see the changes and the, you know, the the the growth in Tremont. You can see how an industrial section has been converted to kind of an open, you know, area that was nothing there. And now it's it's a ballpark for kids. You know, you go along the waterfront and you see the potential and the possibility. And then when you come back up through MLK, you see the beauty that probably a lot of people in town don't realize exists right in the heart, you know, of University Circle and in Cleveland. So to me, that's a that's a hidden gem, and I wish more people had a chance to really experience that. Now, unfortunately, it's kinda hard to do it unless you're on a bicycle.

Jim Weisman [00:51:04]:
But it's a tremendous route, and, I would really recommend anyone who's got, you know, the ability to to do that, take that route once and really keep your eyes open, and you will see the beauty and the incredible opportunity and promise that Cleveland holds.

Jeffrey Stern [00:51:19]:
Well said. Well, Jim, I just wanna thank you again. Really appreciate you coming on today.

Jim Weisman [00:51:24]:
Jeffrey, my pleasure. My pleasure. Thank you.

Jeffrey Stern [00:51:27]:
If people had anything they wanted to follow-up with you about, what would be the best way for them to do so?

Jim Weisman [00:51:34]:
You know, you can find me at, j, the initial j, and then weisman, w e I s m a n, at engine.com, ingine.com. Reach out to me anytime. You know, the link should be on our website, engine.com. You can find me if you if you wanna grab my Gmail. I don't know if this will be, you know, put into the, to the podcast at anywhere, but for a link, you know, just reach out to me.

Jeffrey Stern [00:52:00]:
Yep. We'll we'll make it happen. Cool. Okay. Well, thank you again.

Jim Weisman [00:52:05]:
Thanks, Jeffrey.

Jeffrey Stern [00:52:08]:
That's all for this week. Thank you for listening. We'd love to hear your thoughts on today's show. So if you have any feedback, please send over an email to jeffrey at lay of the land dot f m, or find us on Twitter at podlayoftheland or @sternjefe, j e f e. If you or someone you know would make a good guest for our show, please reach out as well and let us know. And if you enjoy the podcast, please subscribe and leave a review on iTunes or on your preferred podcast player. Your support goes a long way to help us spread the word and continue to bring the Cleveland founders and builders we love having on the show. We'll be back here next week at the same time to map more of the land.