OM DENNE EPISODE
Explore how AI can accelerate research, uncover hidden connections across massive data sources, and help organizations protect intellectual property earlier in the innovation cycle. In this episode of AI Changes Everything see how increasing innovation velocity can help leaders move from ideas to commercial value faster at https://social.ora.cl/6008BDMPGC In this episode of AI Changes Everything, Mel Morris CBE, serial entrepreneur, investor, and CEO at Corpora.ai, discusses how AI is changing the way organizations approach research, innovation, and intellectual property protection. Drawing on more than a decade of work building technology designed to analyze vast amounts of unstructured information, Morris explains how researchers can move beyond traditional search methods to discover connections across disciplines that might otherwise remain hidden. The conversation explores how AI can help teams define problems more effectively, identify unexpected sources of insight, and accelerate the path from research to innovation. Morris shares how Corpora.ai enables researchers to search across a broader universe of knowledge, uncovering relevant information from domains they may never think to explore. He explains why innovation velocity is becoming a critical competitive advantage and how earlier problem definition and solution generation can help organizations protect intellectual property before competitors reach the same conclusions. The discussion also examines sovereign AI, the United Kingdom's opportunity to strengthen innovation through AI-enabled research, and the challenges organizations face when turning ideas into commercial outcomes. Morris offers lessons from building and scaling successful businesses, including his experience helping grow King during the Candy Crush era, and shares why leaders often underestimate the importance of scale when pursuing growth. The episode concludes with a practical prompting technique, perspectives on responsible AI adoption, and a thoughtful discussion about areas where AI requires careful oversight, including cybersecurity, healthcare, medical innovation, and warfare. For business leaders, researchers, innovators, and technology decision-makers, this conversation provides valuable insight into how AI can help accelerate discovery, protect innovation, and create new opportunities for growth.
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Episode Transcript:
00:00:00:00 - 00:00:15:04 Unknown The views expressed in this podcast are those of the individual speakers, and do not necessarily reflect the views or policies of their organizations or or its affiliates.
00:00:15:06 - 00:00:38:23 Unknown What if I could surface critical insights from sources you'd never think to explore? What if you could compress months of research into days? What if it could protect your IP from the outset? And what if it could unlock a diverse universe of knowledge for researchers? Imagine the impact that could have.
00:00:39:01 - 00:01:01:01 Unknown Hello everyone, and welcome to the AI Changes Everything video series. I'm joined today by Mel Morris, serial entrepreneur. Hope you don't mind me calling you back. Mel, investor and now CEO of corporate AI. Okay. Mel, if you don't mind, just to kick off. Just describe the journey that's led you to computer AI and what it is and what it stands for.
00:01:01:01 - 00:01:27:08 Unknown If you could please the journey. Well, it's been a long journey. Go commit to a decade. In September. So we began looking at how you can manage a massive corpus of information. At the same time, people are looking at developing AI, so it's quite an interesting journey. But our view was we wanted to be able to look at really huge sums of unstructured data.
00:01:27:10 - 00:01:50:21 Unknown Of course, the starting point. What is huge? We had designs on hundreds of petabytes and looking around to see what technologies existed, particularly in terms of database technologies, thinking about graphs and vector. No one had really ventured into anything like that scale. So, Neil, I think the key thing with this is that essentially it was all about scale.
00:01:50:23 - 00:02:12:15 Unknown So you spoken about the scale. This is someone out there who doesn't operate in the field of research. What's the problem you're solving and who are you solving it for typically. Research is about finding information, and it's also about connecting the dots. So the number of dots that you can connect is entirely depend on the size of the corpus you're looking at.
00:02:12:17 - 00:02:34:22 Unknown So we allow researchers to connect more dots across for more information than probably any other tool. Right. So today these researchers we have access to limited amounts of information, shall we say, in research. And suddenly you just open that up for them. What are the other sort of benefits that it has for them? If you think about typical research, think about universities higher education.
00:02:35:00 - 00:02:58:00 Unknown As people develop their skill set, they tend to narrow their focus. As you get further and further up the scale, that focus reduces, the depth, increases, but the focus reduces. So university projects typically have, if you want to draw a sort of a picture of a Venn diagram or vice of people there, and you can say, well, the this group of people may have different skill sets.
00:02:58:01 - 00:03:19:02 Unknown There's some overlap. There's lots of diversification. The same is true with data. So if you're looking at a corpus of information that's quite narrow, then you're going to find certain things out. But there might be things in relate to fields of science or even unrelated fields of science that could answer things that you're looking at. You could be looking at electrical process.
00:03:19:04 - 00:03:40:14 Unknown There might be something inside of fish that actually might be relevant to what you're doing, but you might not think to look there. With corpora, we connecting more of the dots across more of the domains easily. And will corpora understand the context of what is being asked and what's being researched. And go and find almost those adjacent pieces of information automatically on behalf of the researcher?
00:03:40:17 - 00:04:06:00 Unknown Yes. And he points to the interesting point, because, all the new tools, particularly if you look at, say, the transformer technologies that became ChatGPT, these sorts of toolsets lend themselves now to the fact we become a bit lazy. We say we're looking for this, and then we go a step further. We talk about where we think they're likely to find information that narrows down the scope.
00:04:06:02 - 00:04:23:18 Unknown So we have a saying that when using corpora. Brevity is king, right? Do not try and tell us where to look. Give us the challenge. Tell us the problem. Let us decide where all the information that might be relevant that exists. And then you can choose to decide which elements of that you want to follow. You want to use.
00:04:23:18 - 00:04:42:22 Unknown You want to ignore. This is a fundamental shift, right. Because I think what you're saying is where I was in the past, I had to understand my problem and know where to look and then go look for it. But now you're just saying, tell me the problem. Copy will figure out where to look. And there may be places where you haven't even considered and bring that information back to you.
00:04:42:22 - 00:05:00:10 Unknown Is that. That's exactly right. And I'll give you a simple example. So let's say I'm looking at a medical problem. So I might want to look at a new method of treating this form of cancer. Now I probably would look in PubMed. I would look at most of the medical journals. I probably wouldn't look inside SEC filings.
00:05:00:13 - 00:05:26:06 Unknown No. But the SEC filings might include really important information on an 8-K or some other document in a filing that relate to exactly the problem you're looking at. But if you didn't think to look there or you didn't know to look at that, you'd never see it. So you might waste time going to file a patent for your new medical invention, and then realize that the 8-K, the SEC filing, became a piece of prior art that would invalidate your patent.
00:05:26:07 - 00:05:54:08 Unknown Interesting. Wow. Okay. And what what is the process look like? You know, there is sort of an understanding the problem, but how does it talk me through? Sort of like the process of how this works. Well, we decided that part of the challenge was that people particular with tools like ChatGPT, the transformer technologies, tend to want to provide this almost so copious definition of what the problem is.
00:05:54:10 - 00:06:20:08 Unknown And in so doing, they're actually proffering part of the solution. At the same time. And that effectively now is going to narrow down again what you're trying to do. So with corpora we would advise starting with a simple statement. All right. The example the same would be something like the challenge the let's call it as well let's say the the harmful biological, physiological and systemic impacts of human dialysis.
00:06:20:10 - 00:06:40:01 Unknown That would be a perfect statement to start with corpora. You would then ask it to produce a problem definition, which would actually probably probably be with 20, 30 pages of all of the different things that might constitute those sorts of challenges. You would then get that problem definition. You can review that, you can add to it, you can change it.
00:06:40:03 - 00:06:57:01 Unknown Then you can take the problem definition and you can then say, okay, so that's that's a problem. But what's the solution? And you can literally say now produce a solution. And it will idea to solution that goes against that problem definition and gives you a chapter and verse about how it can be solved by looking at what the problems are individually.
00:06:57:02 - 00:07:18:14 Unknown Maybe there's 10 or 20 then taking each one. Understanding the state of current convention. What's the current methodology used? And then you can then see the idea, the solution to overcome the challenges with that. That must be invaluable to that. To the researchers. Right. Not only having access to the information, but having an indication as to how valuable this information is to them.
00:07:18:18 - 00:07:51:06 Unknown Yes, it is, and in several ways. So it almost inverts the model. And this is really a key to how the value of corpora really comes to the fore. And what I mean by that is that in most research projects, you start out with an idea or a notion of what you want to do. You invest a lot of time doing research behind that lab, testing whatever it happens to be, mathematic modeling, etc. but it's going to be a fair time before you get to a view of what your solution is going to look like.
00:07:51:12 - 00:08:22:22 Unknown It might be one year, two years, three years, or even longer. And the cost of that increases with time. But but other things also happening at that time. It's no longer just a static scenario. Other people will probably be looking at holes or parts of that particular solution, and therefore you're in a race against time. So if it's now taking you a couple of years to get to a point where you can define your invention before you can think about finding a patent or protecting it, there's a significant exposure with corpora.
00:08:22:23 - 00:08:57:11 Unknown We invert the problem. We start with a simple statement of the challenge. We come up with an idea, the solution, which a scientist can easily look at. Review and endorse and say that really works, or we need to do this. That happens literally within days of starting a project, actually within minutes of running the product. So in this scenario, you can go and you can file patent applications, provisional personal applications right up front, protecting your invention, allowing you then to talk freely about what it is that you're working on to pre-market what you're doing.
00:08:57:13 - 00:09:24:01 Unknown So in that scenario, you can publish on arXiv and other publication tools, and that gives you the chance to promote what you're doing while you've actually now preserved the intellectual property. So it's inverting the model. So the value of this thing is in more clear that problem definitions fully worked out idea to solution, fully validated ideate solution. And then there's a range of other tools in that that you can work on from there.
00:09:24:03 - 00:09:42:19 Unknown So one of the things I feel I have to ask you about is the whole area of sovereign AI. Okay. That's the word that's on everyone's lips at the moment. Do you have a view on that when it comes to corporate AI? I do. Firstly, we need it as a country. We need this. We we've got a strong history of being innovative.
00:09:42:21 - 00:10:07:22 Unknown The challenge is how we monetize that, how we actually generate real value and real growth. On the back of that, the new opportunities that are coming from the government now, the DC sovereign AI program is one of those 500 million going in to help companies utilize AI to further science and innovation. It's a great initiative, and those things will help for projects that are already defined.
00:10:08:00 - 00:10:39:09 Unknown But I think we need to go further. We need to be able to help inventors create ideas faster. I call it sort of innovation velocity. We need to increase that. And in order to do that, we need tools like Cooper and probably others as well that can effectively give us this asymmetric advantage. If we can innovate faster, we can protect it, protect it faster, and we've got a government back into further those programs, then that really does go towards making Britain probably a superpower in terms of this use of AI in science and innovation.
00:10:39:10 - 00:11:05:03 Unknown Absolutely. And Kapoor. So AI is of course a UK company, right. So the IP is pretty much all been generated here. Yes, in the UK. Absolutely. And I think it's also further than that because the way we design the application, let's say universities, commercial organizations of scale, if they want to run the entire system in-house, we can let them do that in that model.
00:11:05:03 - 00:11:26:20 Unknown Then they can use open white models. They can use a copy of corporate internally and their entire ideation, all of it. Then it's within their own boundaries. It's a real definition of sovereign and this is actually quite important because, you know, we all know the statistics of how many AI pilots fail. It's more than 80%. And universities have a strong track record for innovation.
00:11:26:22 - 00:11:50:07 Unknown But with respect, they don't always have a strong track record of commercializing or protecting their IP and innovation. I mean, do you have a view on that or just this two things that are the basically sort of, if you like, are are the impediments to us being more successful? The first one is it takes too long into a cycle before we can actually crystallize the IP, before we can actually get to a point where we have patents that can be filed.
00:11:50:09 - 00:12:09:18 Unknown That's a big cost. Also, by the time we get to that point, other people may have beaten us to it, in which case most of the R&D work we've done is sunk cost. So the failure rate is what kills us. We have to find a way to be able to innovate faster, protect our innovations much faster, and then actually bring that to market much more quickly.
00:12:09:20 - 00:12:27:10 Unknown We're talking about an exponential change in the cycle of this thing here. And I think that's something that I believe we are a part of that. I think that these these funding probes, that the government are a part of that will be all hard to make these things work together. Now, I mean, we've managed to get through this interview so far without mentioning AI once.
00:12:27:12 - 00:12:50:01 Unknown Okay, I think I said it at the very, very start. What has AI changed? Because, you know, you've been Cooper as an idea, as a company has been sort of in your mind for ten years now, right? If you were doing this five years ago, that that would be an incredibly complex thing to do. So what has AI changed in terms of how you are able to deliver this service to your end clients?
00:12:50:03 - 00:13:14:06 Unknown Well, I recall a lot of the work going in Google, the time when Transformers were being brought into into play. And, I think the paper was entitled Attention is Everything. That's a paper. It's a seminal paper. Yeah, we saw that coming out. And Joe, my co-founder myself, looked at each other and said, this is significant to change the game.
00:13:14:08 - 00:13:37:15 Unknown And I said, you know, we have to think very carefully about how do we play in this space, but more importantly, how do we leverage the wonderful things that this transformer technology is going to do? And so now Cooper is the fusion of, wonderful database technology for unstructured data, together with be able to harness literally any piece of AI, literally any model you want to use it.
00:13:37:15 - 00:13:58:03 Unknown I say, okay, and what about acceleration? You know, a couple of years ago, for the data sets you're working with and what you're generating in real time? I mean, you know, today we work on projects where we run simulations and we go, come back, go, go home, come back the next day. And if we're lucky, the simulations finished, right?
00:13:58:05 - 00:14:15:16 Unknown And that's using GPUs. I mean, what sort of speeds are we talking about for the return of information? It's orders of magnitude, because a large part of and separate out two pieces of AI are here, what I call computational. I was a very different feel to what we operate in. We're operating in the field generative AI.
00:14:15:16 - 00:14:39:00 Unknown Yeah. Okay. Now, in generative AI, things happen quite slowly. People think it's fast. That's the Gemini or fast open AI, GPT or I've gone too close to anthropic to say or matter to say. Produce me report. Doing this, they might take 20, 30, 40, 50 minutes. And they look at it go, wow, that's fantastic. Yeah. But we often find those reports are quite surface level.
00:14:39:06 - 00:15:00:01 Unknown They're not deep in the way that we would CD because they haven't had time to access. Going back the same challenge, how do I find all the information? How do I then condense that into a form that I can process efficiently? And that's still hurting the whole deep research products that are out there today? Because they have to be commercial.
00:15:00:03 - 00:15:22:00 Unknown People won't wait five days for the report, so they have to do a certain amount. So they compromise on the amount of searching they can do within the time available. We've inverted that problem so we can search literally 100 times much information in a 20th of the time it would take for them to do their job. So the answer is with corpora we're talking about answers that take seconds to minutes.
00:15:22:02 - 00:15:52:10 Unknown Wow. Okay. So would it be fair to say that the the frontier models are almost horizontal? General purpose models can be applied to almost any situation. Cooper it's almost more of like a vertically specific, very, very deep, highly optimized, model. I think that that probably is underselling Cooper because I think so broad and deep. Okay, okay. So we have far more data than any model could say.
00:15:52:13 - 00:16:11:09 Unknown Right. Yeah. Right. So the breadth is there. We're also able to find more information and get it down so we can go deeper as well. So we go both broad and deep compare with the life sciences models. And we have full attribution to where the information came from. Can I just switch the conversation more to talk about the human element?
00:16:11:09 - 00:16:37:04 Unknown Okay. I mean, where's the expertise coming from? I mean, who are you typically hiring? What does the computer team look like? Is it full of data scientists? MLOps engineers? Yeah, we call it our ambassador program. So we have some really world class experts on board. People like Doctor Alexander Rape, who's a material scientist, Professor Paul Stuart, who's very much into the aerospace and engineering control side of things.
00:16:37:06 - 00:16:59:06 Unknown Doctor Andy Pardo, who is a, a professor in, I at, Port University. Aki Batty, who's, the medical director for a big medical operation in the UK. So we have a lot of domain expertise. This is the bit that I find quite fascinating, this combination of, you know, computational science with, research or domain experts.
00:16:59:06 - 00:17:21:02 Unknown Do you mind if I ask you about the business model? What is the business model for corporate AI? Is it subscription based? How does that work? So we wanted to go with a model that was more exclusive. And we started working with start up companies, working on really extreme frontier science projects. And the results are outstanding. So our model is not based on revenue.
00:17:21:04 - 00:17:49:03 Unknown It's based on building up, stakes in companies leveraging our technology to help them build IP and then leveraging the value of that. So if someone those our revenues that that that almost zero. But the value we're building is in building IP that we're sharing the value of with the companies we're working with. So you're building this this network of IP and actually some stakes in these startups, as they grow, you grow as you grow they grow.
00:17:49:04 - 00:18:10:04 Unknown Yep. So you've almost got this symbiotic effect, right. It's a virtuous circle. Yeah. Building is key. And we've got plans now to to tie that now up to the universities to be able to show how we can actually let their office share in this same type of capability, which would transform the capital model for university operations. Right. Okay.
00:18:10:05 - 00:18:26:03 Unknown So if it's okay just switching over, I'm going to do a quick fire round. Okay. And then after we've done that, maybe I'm going to come back and ask you for a little take off, a little bit of advice, to anyone starting a business. I regret this then, but go for it. I think you my definition, dash.
00:18:26:08 - 00:18:55:17 Unknown Okay, so I think the plan is there's, 5 or 6 questions. You got to answer as many as you can in 30s get. Okay. Now thinking time. So here we go. One word for AI today transformational. Brilliant. One word for AI in five years. Metaphoric beautiful AI agents hype or real shift. In between the biggest risk in AI right now, people fail to understand how good it is.
00:18:55:20 - 00:19:22:17 Unknown Very good. Biggest opportunity is people actually leveraging it. Excellent. And one thing leaders consistently get wrong. Scale, scale. Okay. You did very well. There was one last question. I'm still going to ask it a bit like, you know, a mastermind I've started. So I'll finish. One AI term you wish people would stop using.
00:19:22:19 - 00:19:47:13 Unknown Panacea. Panacea. Brilliant. Okay. Thank you very, very much. Okay. I prompt of the week. Mel, do you have a favorite prompt? You like a prompt for the week that you would encourage, viewers to use? I do, it's a bit of a double edged sword, this one. So instead of putting your prompt directly into the model, ChatGPT wherever, try the following.
00:19:47:14 - 00:20:17:03 Unknown Change this into a concise prompt for feeding into ChatGPT, and then put your prompt after it. It's masterful, very, very cute. Right. So I think we've covered more or less everything. Well, that's not true. We could we could go on talking for hours, I'm sure. Now, but one of the things I didn't reveal about to you, I said, you're a serial entrepreneur, is that you are actually the chairman of King Games, which, of course, is responsible for Candy crush.
00:20:17:05 - 00:20:46:15 Unknown Probably the most successful mobile application ever to have existed. If you were speaking to an anyone that was starting a business or trying to scale an idea or business in a corporation, is there any advice you would give them, having learned the hard way? I think King's a great example. And, I would say that the perseverance, the tenacity, the stamina, because King was an overnight success.
00:20:46:17 - 00:21:06:04 Unknown The early years were brutal and, no one wanted to back the company. None of the VCs at all. We we scoured Europe talking to no end of X. No one wanted to know. All right? We'd probably got to $55 million a year in revenue. I think it was still with any external. It's it came in all right.
00:21:06:04 - 00:21:24:14 Unknown And of course, can of course put it on the, on the map. But don't forget, Candy crush was probably ten years into the company's, existence. So, so there were the key things, but and then the other one is people. People who in a startup environment can get on with one another but have the complementary skills to make it work.
00:21:24:15 - 00:21:55:00 Unknown And the team at King was exceptional, right? Excellent. And the one thing people always ask about is scale. Okay. Looking back now, across many of the businesses, was there anything that you've learned now that you realized was an accelerator to helping scale? Yeah, I think first, and Cooper is a great example here. I mean, if you look to some of the things we thought was, I, I go to market plan three years ago, it was rubbish.
00:21:55:02 - 00:22:12:00 Unknown It was rubbish. It simply was never going to scale, you know, not without putting in millions into marketing or whatever. It was never going to work. And we had several attempts at that. And I think, you know, quite rightly, the people we looked at that we thought we might as it's still backing who passed on, they were right to pass on it.
00:22:12:02 - 00:22:33:02 Unknown Okay. But that is a favor, because how we have gone down those paths with the backing we do for become a mediocre business in each of those different markets. And so I think sometimes to spend the real time to figure out not how you go to market, plan, but one that can actually work with the so capital that you have available to support it is critical.
00:22:33:05 - 00:22:57:11 Unknown Yeah. Very good. Okay. So we've come to the end of our session. You know this series is entitled I Changes Everything. And my last question to you is is there anything that I shouldn't be being useful or it's not fit for purpose in your in your view? I have concerns along with the rest of the AI brigade in terms of its malign use and areas like cyber.
00:22:57:13 - 00:23:23:23 Unknown I think there's concerns in terms of the mental health implications, particularly of sort of, you know, the so GPT style sort of offerings. So I think we have to be very careful with that. I think there's going to be an acceleration of innovation, and that's going to bring along a whole raft of challenges. We'll have medical solutions that probably are going to be deemed to not necessarily have to go through the same hurdles to get accreditation.
00:23:24:01 - 00:23:46:03 Unknown We have to be careful with those. We shouldn't stop doing those things. We just have to be mindful that it will make mistakes from time to time. And yeah, we have to be careful. Those mistakes aren't to too huge in terms of the cost of lives or whatever. But I think also on the other side, there's people with diseases today that have no options, and they are potentially offers great solutions to those people to find cures.
00:23:46:05 - 00:24:03:10 Unknown So we've got to think about those sides to it. But I think that uses of the technology in a malign form is very dangerous. You can think of warfare. Every nation is going to have to use it in warfare. All right. But we also have to think about how can we be innovative about the defensive side of that.
00:24:03:12 - 00:24:21:23 Unknown I have lots of concerns. Yes, sir. Well, we've come to the end of our discussion. Thanks very much for the session today. I've really enjoyed it. I want to wish you and the AI team the very, very best of luck. Thank you. Really enjoyed it. Thank you. So if you're interested in watching more go to our Qualcomm forward slash I changes everything to see more episodes.
00:24:22:05 - 00:24:46:12 Unknown Thank you for watching.
