How Machine Learning in Oxford Is Transforming Medicine Worldwide with Lionel Tarassenko, President of Reuben College

[00:00:04] Lionel Tarassenko: Machine learning algorithms using deep learning are as good as radiologists in analysing images, for example, mammograms or to detect breast cancer or other forms of CT scans or MRI scans for early cancer detection. Performance of machine learning algorithms are equivalent to those of the best human experts. The interesting thing is with these new techniques. We can go further.

[00:00:33] Susannah de Jager: Welcome to Oxford+, the podcast series that takes you deep into the myths and truths of the Oxford investing landscape. I'm your host, Susannah de Jager, and I've spent over 16 years in UK asset management. My guest today is Lord Lionel Tarassenko, CBE. Lionel is a world leading expert in the application of signalling processing, artificial intelligence and machine learning to healthcare, with a strong track record in translation to clinical medicine. Professor Tarassenko's work has had a major impact on the identification of deterioration in acute care and on the management of chronic disease.

[00:01:09] Lionel was born in Paris in 1957. He received a BA in Engineering Science in 1978 and the DPhil in Medical Electronics in 1985, both from the University of Oxford. After a period in industry working for Rakel Electronics, he was appointed University Lecturer and Tutorial Fellow in Oxford, St. Hugh's College in 1988. He was elected to the Chair of Electrical Engineering, and to a professional fellowship at St. John's College in 1997. He was the driving force behind the creation of the Institute of Biomedical Engineering, which he directed from its opening in 2008 to October 2012. Lionel was elected to the Fellowship of the Royal Academy of Engineering in 2000 and to the Fellowship of the Academy of Medical Sciences in 2013.

[00:01:57] He is a Director of the University's wholly owned Technology Transfer Centre. Oxford University Innovation and has founded four university spin out companies. He was the head of the Department of Engineering Science from 2014 to 2019 and is now the founding president of Reuben College, the University of Oxford's newest college. He was editor in chief for the Topol Review, preparing the healthcare workforce to deliver the digital future. Lionel Tarassenko is at the core of translational science in the University of Oxford, and with his new leadership of Rubin College, where we are recording today, I'm very privileged to be able to discuss his views on how he sees Oxford evolving further.

[00:02:36] Lionel, thank you for joining me today.

[00:02:38] Lionel Tarassenko: Glad to be able to chat to you today.

[00:02:41] Susannah de Jager: So, you have an extraordinary history with Oxford, I'd love to hear some of your experience where you've translated research out of the lab and into a practical application in the real world.

[00:02:54] Lionel Tarassenko: Yes, well, I've been very fortunate to be in the right place at the right time. I was appointed as a professor back in 1988, then elected to the chair of electrical engineering in 1997 and that election was partly due to the fact that I'd done some translational work with established companies, with Rolls Royce, with Sharp, who were on the founding company of the Oxford Science Park in the 1990s, but after the appointment to the chair in 1997, instead of working with established companies, I got involved in setting up spin outs. In fact, my first spin out was in 1999 with two ex students of mine who'd ended up in Cambridge, and they came back to Oxford so that we could do a spin out together and at that time when you were doing a spin out in Oxford, it was still slightly frowned upon. Then within a decade it was tolerated and now it's positively encouraged.

[00:03:54] So I've been through all the stages from colleagues sort of frowning upon me doing all this kind of commercial activity to now saying, how can you help us do the same? So that's been a very interesting kind of progression in the last 25 years. But the reason why I did it in 1999 and did the latest one a decade ago was really as an engineer, passionate about translating from the lab into a domain where it can be used by as many people as possible and the two areas I've worked in was further work with Rolls Royce on engine health monitoring. So, if you've flown on an Airbus plane recently, you're very likely to have been monitored by software that was developed by one of the spin-out companies until it was brought back in by Rolls Royce.

[00:04:42] They got slightly concerned that they were relying on this software, but it was an SME and SMEs do go down as well as up, so they thought it'd be safer to bring it back into Rolls Royce. But mostly, since those activities finished, it's been mostly healthcare and I'm passionate because what it means, its not just a research paper, it's not just the results of a clinical trial, it's something that makes a difference on the hospital ward or in somebody's home as they try to manage their diabetes and so on and that's what's really driven my passion for doing spin outs, is the fact that you can reach so many more people and a spin-out is a good vehicle for doing it, because when I worked with Sharp or when I worked with Rolls Royce and Oxford Instruments, we're one out of maybe 50 projects.

[00:05:30] When you do a spin-out, you're one out of one projects, maybe one of two in the second year of the company, but there's a real focus on taking it from the lab into the commercial arena in order to diffuse it as widely as possible. I used to live in Headington quite close to the John Radcliffe Hospital and one thing that got me out of bed in the morning and on my bike to go off to the Institute of Biobank Engineering was the fact that as I rode past the hospital, everybody in the hospital that morning was having their risk of their disease getting worse assessed by an algorithm that we developed in my research group and that's the kind of thing that got me up and that's the kind of thing that drew my passion for doing spinouts.

[00:06:14] Susannah de Jager: And so there you're talking about OBS medical and patient monitoring, and it's one of the first FDA approved technologies that actually use machine learning.

[00:06:23] How did you come to pioneer in that area?

[00:06:26] Lionel Tarassenko: What I usually say is that I was doing machine learning before it was even called machine learning. I had come back in 1981 to do a doctorate and I'm unusual because my doctorate was actually in pediatrics. I was in a small unit at the John Radcliffe Hospital, an accident of history. It was because the first professor of pediatrics in Oxford was Sir Peter Tizard, his father, Sir Henry Tizard had been one of the inventors of radar. So as a professor of paediatrics, he had huge interest in electronic instrumentation. So there's a small team of us and my project for my doctorate at the John Radcliffe Hospital was to develop methods to monitor the blood flow to the brain of premature babies at the risk of brain hemorrhages known as intraventricular hemorrhage.

[00:07:16] That got me interested in neuroscience and so when I went back to mainstream electrical engineering after finishing my doctorate in biomedical engineering, neural networks, so computing using some models that are loosely based on the way neurons in our brains compute and pass information from one neuron to the other via billions, trillions of synapses. That was called neural networks. I went straight from finishing my doctorate, my DPhil in Department of Pediatrics, to starting to work with artificial versions of those neural networks at a time when somebody else down the road at the Gatsby in London, somebody called Geoff Hinton was doing exactly the same. Geoff Hinton published a key paper on the Error Back Propagation Algorithm in 1986, which underpins the whole of machine learning and I was doing that in the late 1980s and was then appointed as a professor in 1988 and what's wonderful about being a professor in Oxford, you can explore really what you want to explore because I was an electrical engineer, what I originally explored was building special purpose hardware to implement these neural networks.

[00:08:33] I then gradually moved into developing new algorithms and then having developed those new algorithms, moved into applications, monitoring jet engines, and then back to my first love, because the university had asked me to set up the Institute of Biomedical Engineering, back into applying those algorithms to healthcare applications. So you have an amazing journey there with machine learning as a kind of underpinning technology and algorithm.

[00:08:59] Susannah de Jager: That's amazing. So you're one of the absolute first people that was doing this interdisciplinary work and you're completely across both, which is not unique anymore, but at the time I imagine very much so.

[00:09:11] Lionel Tarassenko: It was at the start, as far as I know, I was probably the first person to be doing this. I shared an office in the first six months with one of the world's best machine learning and computer vision experts, Professor Andrew Zisserman and as I was coding this up, he's a far better computer software programmer than I am. There are bugs in my software and Andrew used to sort them out for me. But he would say, look, you're wasting your timeline with these algorithms, they're a complete waste of time. Data driven learning is not the way of the future and I often remind him when he gives, some of these invited lectures at the Royal Society, he's a fellow of the Royal Society, based on the amazing machine learning and computer vision work that he's done.

[00:09:51] So I was a pioneer. Probably, again, in the right place at the right time and I've been very privileged to work with some fantastic people in this field and to apply it to healthcare and you asked me in the previous question about taking it through the FDA. Through the company OBS Medical, we developed a software algorithm to identify early physiological deterioration in patients in step down care or in critical care and doctors are very good at doing pairs of vital signs. For example, if your blood pressure goes one way and the heart rate the other way, they might be very worried about that.

[00:10:27] There are actually five or six vital signs and to think in five or six dimensions, it's quite hard for a doctor, but an algorithm can do that. So we designed a machine learning algorithm that use all six vital signs, integrated the information and then as a result of that, was able to identify deterioration earlier than algorithms or doctors that would only look at one or two of these parameters. We took that through the FDA. We had to have three different existing devices to get approval as predicate devices, but we did and one of the interesting phenomena, I think, is that the FDA has now approved somewhere between 600 and 700 medical devices and software algorithms that rely on machine learning. So it's already happening in healthcare and probably healthcare is one of the leading fields for the applications of machine learning.

[00:11:20] Susannah de Jager: And we all benefit in ways that we're probably not aware of. So you've spoken there about some very specific granular applications of machine learning and AI in healthcare that you pioneered and now are commonplace.

[00:11:32] But one of the areas that still feels like it's very much in its nascent phase is the application to the large sets of data and you spoke recently in the House of Lords about how the UK shouldn't be looking to try and get ahead in large language models and that the horse is effectively bolted there. But that we should be looking at large multi modality models and using our national asset of NHS data. Please can you tell us a bit more about that and how you see that evolving?

[00:12:04] Lionel Tarassenko: Well, that's already a brilliant summary that you've given me here. The House of Lords Digital Committee did an inquiry on large language models throughout 2023 and published its report in February 2024. It's actually a very good report called Large Language Models and Generative AI, and I thoroughly recommend it to anybody who wants to read it. It's a very good snapshot and the debate in Parliament was on the 21st of November on that particular topic, to lead the House of Lords and really, what I was trying to say, because one of the recommendations that they were not quite sure about was whether the UK should develop a sovereign large language model.

[00:12:46] So, effectively, its own capability to compete or to stand alongside GPT 4 or Gemini or Anthropos Cloud Sonnet,but it seems to me that we know that GPT 4 took approximately a hundred million dollars to train and huge amounts of resources, both in terms of the people working on it and the amount of electricity that it required in order to be able to train this hyperscale large language model. GPT 5 is the next one, will probably come out sometime in 2025 and that's probably closer to a billion dollar in terms of the cost. Now, I don't think the UK can compete with that and I think that the time has probably gone whereby a country could think that they might want to set up their own sovereign AI capability to compete alongside those hyperscale large language models from the big tech companies in California.

[00:13:45] So, you have two ways of thinking about how nationally we should deal with that. We could work with those big tech companies and fine tune them for some applications and I think there's probably some room to do that. But more interestingly, you could reflect upon the data sets that you have that are unique to your country, that are an advantage compared to any other country and we have several of these data assets in this country. I think we should call them Sovereign Data Assets. That's what I was trying to argue in my speech in the House of Lords. For example, the main example, the one that I know best, of course, is NHS data, but it's also all the BBC databases, some geospatial databases, the Office of National Statistics. We've got some amazing resources.

[00:14:33] In terms of NHS data, 67 million population, 98 percent of people in this country use the NHS or at least have used the NHS at some point in their life. It is literally a sovereign data asset and as well as making that speech in the House of Lords, I have put down my first amendment to the data bill trying to define with two or three other peers, what a sovereign data asset is so that we can actually control access to those sovereign data assets and my view, for example, is if we develop then, on top of these sovereign data assets, what you quite right, are not large language models, they're not LLMs, they're LMMs, Large Multimodal Models, simply because the NHS data is not just text, it's not just discharge summaries, it's much more than that. It's the vital signs like the blood pressure and heart rate, it's the lab data, your blood, your blood glucose, your creatinine and so on. It's also the pathology lab data, which these days comes in form of images, it's MR scans, CT scans, etc. So, multiple modalities and if you integrate all of that together, you will be able to do things that have not been done before and let me give you a very simple example to try and illustrate that.

[00:15:59] We now know that machine learning algorithms using deep learning are as good as radiologists in analysing images, for example, mammograms or to detect breast cancer or other forms of CT scans or MRI scans for early cancer detection. Performance of machine learning algorithms are equivalent to those of the best human experts. The interesting thing is with these new techniques. We can go further because we can use not just the scans, we can use the whole profile of the patient at the time the scan was done and probably in the run up to the scan, there probably is either some hospital data or some primary care data on that patient before they have the scan and also because we'll be doing this training our models on retrospective data, we'll have the outcome two years from the scan, five years from the scan, maybe even ten years from the scan eventually and because you know the outcomes, the Large Multimodal Model will be able to identify some hidden features in the scan that even the radiologists themselves do not know are the features that affect the outcome.

[00:17:15] So we'll have better analytical prediction than human experts can do and that's a fantastic goal to set for ourselves for those Large Multimodal Models and the NHS dataset is the best in class, best in the world. There are countries like Estonia and so on that have got all this data available, but Estonia's population is one and a half million. It's actually smaller than the number of people that work for the NHS.

[00:17:41] Susannah de Jager: Just taking that, it's amazing and very exciting to hear. You can also see a way that not only would those that are being scanned be captured earlier because the model would pick up on things that the human eye perhaps doesn't even know to look for, but you will be able to potentially create a profile of those that did develop and scan those earlier and therefore potentially put assets and focus on screening for particular cohorts?

[00:18:06] Lionel Tarassenko: Absolutely. It's interesting, healthcare, you sort of work backwards. The first problem you solve is to try and improve the treatment of those who have already been diagnosed based on what you've learned with your Large Multimodal Models, for example, in terms of their outcomes. Then, you might improve diagnosis and reduce the false positives or the false negatives and then, of course, the Holy Grail, you're absolutely right, is prevention and so, once we build these huge data sets and of course, they will be augmented two fold by genomics data and also by these extraordinary data sets. The one that already exists and has existed for more than a decade, UK Biobank and we've just had under the new government, the first permission to link UK Biobank data to those individuals primary care data and the follow on project from UK Biobank is Our Future Health, which has just recruited its 1,000,000th participants and if you integrate all of that information together and build the Time Series, as we call it, the historical longitudinal data set, you'll be able to go backwards.

[00:19:20] For example, already in UK Biobank, and just with UK Biobank data, we can identify from their records 10 years ago, using simple, wearable data, those people are far more likely to have Parkinson's disease ten years down the road and we can start therefore early intervention to try and minimise the risk of Parkinson's or minimise the severity, of the Parkinson's disease. So all that becomes available, provided we have the whole history and we can start doing some of that already. We can start screening people in hospital, for example, we could do it today for colorectal cancer, because we know that there are early warning signs, maybe in your blood profile, two years ahead of you developing colorectal cancer. So we could do this automatically for everybody who's in the hospital. It might only be a fraction of a percent that we detect early, but when you think that there are tens of millions of visits, because some patients go several times a year to the hospital, but if you analysed all the data when there are lab results available, you could screen people for colorectal cancer two or three years ahead of when the first symptoms would appear.

[00:20:34] That's something that we know we could do today. We could do so much more if we build those data sets and integrate all the information that's available. One other thing I should stress is Large multimodal Models are incredibly good at dealing with the variability from one patient to another, because not every patient will have the whole record. Some will only have a fraction of the record. We can still do something in the way that we train Large Multimodal Models, even if we only have 10 percent of the complete data set. So, I think this could be transformational. It's totally dependent on us having access to the data and therefore I see part of my work in the House of Lords in trying to set up the rules for access to this data in order to benefit patients in this country and in order eventually financially to help the NHS so that the NHS budget will continue to grow as we know, but we can't just keep putting money from taxation. There needs to be other ways and this is one way of doing it in a way which I think we can convince the general population is a worthwhile way of doing.

[00:21:43] Susannah de Jager: I hope so. I actually am a participant in MyFutureHealth.

[00:21:47] Lionel Tarassenko: Well done. I'm a UK Biobank participant.

[00:21:49] Susannah de Jager: Taking a slight step away from the UK, which is so exciting, back into Oxford more specifically, one of the big changes that's coming into Oxford is the Ellison Institute of Technology and a lot of the themes that we're talking about here are part of their stated goals of pioneering healthcare and clearly the relationship with Larry Ellison and the Tony Blair Institute speaks to internationally looking to digitise healthcare. I'd love to know how you see the Ellison Institute impacting Oxford and really just to get your views on what it's going to change that's currently here.

[00:22:28] Lionel Tarassenko: So I think it's a fantastic development for Oxford. Some time ago, AstraZeneca tried to come to Oxford. We could not accommodate them, they went to Cambridge. So the fact that the Ellison Institute of Technology has come to Oxford is wonderful for Oxford. That's the first thing to say. Secondly, the agreement which the university has just signed with the Ellison Institute of Technology, I think it's the right kind of agreement. I think we've been very fortunate in that Sir John Bell, who is Regius Professor of Medicine since 2002, has moved away from the university to become the first president of this EIT, Ellison Institute of Technology, in Oxford and because John's come from the university, he has seen what has happened, lower scale, but in similar ways. So let me try and illustrate that. When I became head of engineering science in 2014, Within a couple of weeks of becoming head of department and the same thing happened in computer science, the next door department, two or three of the professors in my department were being offered the opportunity to leave the university and go and work for, it was then, Google DeepMind, or Amazon, or Facebook as it was then, Meta now of course, NVIDIA and others.

[00:23:48] So across engineering and computer science, there was this, what appeared to be a land grab, and it was really an intellectual land grab is about coming in and grabbing the professors, but over a weekend, because it had to be done that quickly, we actually negotiated a deal whereby it was 50 50 and 10 years later, I can say, and if I could mention again, Professor Andrew Zisserman, FRS, ever since 2014, he has had a 50 percent appointment at Google DeepMind and 50 percent in the University in the Department of Engineering Science and that has worked pretty well.

[00:24:25] Partly because the university has a lot of very good PhD or DPhils, as we call them, students, so postgraduate research students who want to come and work for professor Zisserman, because he's so well known, so he has a good supply of PhD students. That's his university lab. But all the postdocs are now employed by these big tech companies, because unless you want to stay in academia, if you've got a PhD, a DPhil, which you did in Professor Zisserman's group, Professor Paul Newman's group, other groups are available in engineering and computer science and stats and mathematics and big Data Institute, then you can command a very worthwhile salary as a postdoc in any of these big tech companies and the resources are so much better. So I worked with Andrew Zisserman and I was head department, trying to get him state of the art GPU cluster for his computer vision algorithms. That took several months, and we went as fast as we could.

[00:25:29] By the time the cluster arrives, it's almost out of date. Whereas if you work in one of the big tech companies, that compute resource is available on your desk tomorrow if you need it and you have these wonderful postdocs and that's the reason why these professors want to work with those big tech companies. It's both a human resource at postdoc level and the computational resource and Ellison Institute of Technology will do the same, I think. So, the idea is that the professors will not all move across to Littlemore and become employees of EIT. The plan is for them to have these 50/50 jobs, again, working similarly across both the university and the Ellison Institute of Technology and the way we should view it is that we should be able to do so much more then we would have been able to do if EIT had not come to Oxford.

[00:26:20] Susannah de Jager: And I think there's a theme coming through the conversation here when you've spoken about large language models, the expense of training them, DeepMind, the expense of putting the data into that is well documented. The research that's available here being funded or co funded by EIT will be a benefit. I suppose that is a very pragmatic view and by the way, for what it's worth, I agree with it. The flip side of that is we often are accused in the UK of having sold companies too soon. I'd love to know whether you think that's inevitable or whether more capital supply with changes such as the Mansion House Compact should or could change that.

[00:27:02] Lionel Tarassenko: Yes, well, before the large language model debate in the House of Lords, we also had a debate on science and technology and its contribution to the UK economy and we only had four minutes each because there were so many peers wanting to speak on the topic, which in many ways is great. That this will be a topic that will be of interest to many members of the House of Lords.

[00:27:26] I started by mentioning the fact that just two or three weeks before, we'd had the wonderful news of Geoff Hinton,having won the Nobel Prize for Physics and then very soon afterwards Demis Hassabis having won the Nobel Prize for Chemistry. Geoff Hinton had done his PhD at Edinburgh. He did his Neural Network Algorithm Development when he was at UCL. Demis Hassabis was a Cambridge undergrad and his PhD at UCL. But they were awarded and Nobel Prizes at a time when the latest developments they'd made of the last decade, they had done for a two trillion dollar U. S. big tech company, Google and so I thought I would do a gedankenexperiment in my four minutes available, which is, what would it take to have a one trillion dollar company come out of the UK in, say, the AI field? And the first one was to have a good supply of UK PhD students in Russell Group universities doing computer science, information engineering, statistics, and so on and there are some dangerous signs here. There's possibly an unintended consequence of the £9,250 tuition fee for undergrad degrees, putting people off from staying on and doing PhDs because they graduate with 50k debt and by the time they finish their PhDs there'll be a 70k debt with the interest rates that we have. So that's the first thing, let's make sure there are enough home PhD students around to create the intellectual capital to be the researchers within such a company.

[00:29:05] Secondly, we have to make sure that there's funding available to create the companies. So these are proof of concept funds. In the budget of the previous week, the Chancellor had announced 40 million pounds for proof of concept fund. I said that was great. It shouldn't go to Oxford, Cambridge and Imperial because those funds already exist. In Oxford, we're incredibly privileged. We have Oxford Science Enterprises, who I know have already featured on these podcasts, so I don't need to explain, but it's a fantastic opportunity to set up new companies in all sorts of fields, deep tech, biotech, AI, et cetera. So that's the second required step that you have.

[00:29:45] The third is scale up funding and really one of those is maybe the mansion has compact delivering by 2030 on the promise that five percent of pension funds will invest in these UK high tech companies and then the fourth one and I'm not an expert in this at all, but there's also been a report done which the House of Lords had looked at by Lord Harrington for Jamie Hunt on foreign direct investment and again, this is all part of the ecosystem because it so happened that Richard Harrington was in the year below me at Keeble as an undergraduate.

[00:30:20] Susannah de Jager: There's a lot in there and the variables are many, but the prize is huge, so we can only hope that some of those things come to bear. Taking a small step back, you spoke about a few individuals there who have won Nobel Prizes and who have done brilliant things for this country, but sadly, more latterly perhaps under the auspices of a US company. William Hague published a comment article earlier in the year commenting on Novo Nordisk and how they've moved their national GDP by, I think, two to three percent and very much focused on very few people will change the outcomes for very many. He is now Chancellor of Oxford University. How do you see his leadership in that role potentially impacting some of these themes that you're talking about?

[00:31:08] Lionel Tarassenko: So again, I think in the Chancellor election we had six and then five in the final round. The six other ones voted for in the first round, five of them ended up in the final round as the final five. I thought all five were outstanding candidates and William Hague is a very clear winner. We used the alternative vote system and every round William Hague, Lord Hague was the winner. So he has a very strong mandate, he's highly respected at the university, he's been very good working with his college, Magdalen, I know one or two Magdalen fellows that speak very highly of him, very much involved, not just with the fellows, but also with the students and I think he'll be the right kind of ambassador.

[00:31:48] He has the right gravitas. I happen to listen quite regularly on Tuesday morning on Times Radio when he features between 9 and 9:30 and I think he says very sensible things. He has worked very closely with Tony Blair and the Tony Blair Institute on topics that we've discussed in this podcast, sovereign data assets, making use of the tremendous NHS data assets that we have, but also thinking about how, because behind the US and China, the UK is very much in third place and there's not much doubt that it is in third place. So we've got this fantastic human capital assets in terms of the AI research across Russell Group universities. It's often said that we have 12 universities in the top 100 in the world.

[00:32:39] What we need to do, what Oxford needs to do, but not just Oxford, any of these other 12 universities need to do, is to go from being the best in the top 100 universities in the world, to being in the top 100 companies in these new technologies as well, you not necessarily a dozen, but half a dozen would be good enough. Now that is quite difficult. People don't realise sometimes why it is difficult. It is difficult, for example, in healthcare, where I've got some expertise, is because if you develop AI for healthcare or a new piece of medtech and so on. If you go to the US, you've got 50 states with roughly the same reimbursement rule, each of which is roughly the size of a European country.

[00:33:24] So there's a huge advantage there with scale. It is quite hard to scale up in the UK because, we are a small country, smaller than Texas, for example, or smaller than Florida. So how do you scale up? We've talked about in the podcast that, we've got the ideas, we've got the companies being spun out, we have the seed funding, I think we have Series A funding. I think what people are really beginning to talk about is the scale up funding. That is missing and in Oxford, we need to do that in partnership with OSE that provides seed funding, provides Series A, contributes sometimes to Series B but if we want to do B and C, we really need to have the funds available and that is where, at the moment, we are failing and we're not seeing, not just the data, but some of these companies as almost sovereign assets. I think, personally, it is a pity that Arm is no longer a UK company.

[00:34:21] I happen to know the person who took it from an SME to a world leader, Warren East, who then went on to be Chief Executive of Rolls Royce, but when he was Chief Executive of Arm, he developed this amazing licensing model. He's a graduate of my department, he's an Oxford engineer. Arm, I think if the right capital had been available, could have remained a UK company instead of being bought out by SoftBank, and it's the Arms of this world that we need to grow and to go back to the exam question, as we always say to the undergraduates, somebody like William Hague, with the stature, the gravitas, the expertise, the people that he knows and his presence on the world stage can help us and that's not the only thing that the Chancellor will do, of course, but can help us in this particular domain, really scale up as a university, to help the companies we've spun-out to go through these stages beyond Series B, Series C, and really becoming, first of all, billion dollar companies, then 10 billion, then 100 billion, for the benefit of the UK.

[00:35:28] Susannah de Jager: It's very exciting and you spoke there about William Haig and Tony Blair having worked together and one of the things that they've also co published papers on is pension capital reform, which we're now seeing obviously the Mansion House side but also Labour looking into local government pension schemes, all of which is very positive mood music, and we just need to see it translate in these ways. So I too am full of optimism for what that might do. There is the other half of that, which nobody ever seems to want to talk about, which you've raised, which is, we still don't have a natural buyer for many of these things at the scale that the US has. Thank you, Lionel. This has been a fabulous conversation and I'm very grateful for your views across your domain areas, we could have gone on for hours.

[00:36:15] Just finally, I would love to get a sense of what you would most like to see developing here at Oxford in the next five years.

[00:36:24] Lionel Tarassenko: Well, we're sitting in Reuben College, so I think I need to mention Reuben College. It's sometimes I describe it as my fifth startup and it's wonderful how the university can be flexible and do new things. We're the 39th college in, above 900 years, we don't know exactly how old the university is. What is remarkable about Oxford is that at times, the bureaucracy can seem overwhelming, but the people at the helm, the Vice Chancellor and now the Chancellor, William Hague and so on, are going to leave the door open for Oxford to be a full participant in the 21st century. I think that there are possibly one or two other colleges in the pipeline, it'd be great to see. I would welcome a 40th and a 41st college, it'd be great no longer to be the newbie, and I think that is important.

[00:37:15] The university continues to evolve. I see the Ellison Institute of Technology as part of that, but I do think that we do need to think about the human capital that we're creating, that we align and I'm talking about a postgraduate level, but of course, when I was head of engineering, I was also equally concerned about the undergraduate level and there's this tension between home and international students. Yes, we should educate international students, it's part of Oxford's mission, it's a global enterprise. But I do worry that we're in danger of not creating enough human capital. So, for these enterprises of the future. One of the interesting things that is happening, I think, at the moment, is the revival of the humanities as a result of what we talked about. So when people are saying that artificial general intelligence is just around the corner, really, what that makes us think about is, what is it that makes us different as human beings from a piece of silicon on which a very intelligent algorithm runs? And I think the answers lie not just in computer science, but also in philosophy, in theology and that's why Oxford is a fantastic place to be able to bring those subjects together to discuss exactly those things and I think we are going to be discussing these topics even possibly one day on an Oxford+ podcast, and what I hope will happen is actually not just what we talked about, companies to flourish at Series B, Series C, but also graduate education, undergraduate education, but not just in the subjects that we require to develop these algorithms, but also in the humanities and so on and my parting shot would be to say, here at Reuben College, we're right in the middle of the science area.

[00:39:09] Above us, we've still got one floor of the science library, the college occupies the middle floor and the grand floor and I don't want it to be known as a science college. It should be, like every other college, a multidisciplinary college, and Oxford will be a fantastic place for these conversations about where does AI go, how should we develop the right kind of ethical framework when we're developing not just AI, but AGI, Artificial General Intelligence, and also these incredibly important conversations as to what the difference may be between AGI. A is in the clue, artificial, and the intelligence, the consciousness, whatever it is, the human soul, if you will and here I reflect my kind of Eastern European background, that is going to be even more important to have those conversations and Oxford Colleges and the university at large is the place and so my greatest wish will be that Oxford continues to thrive, not unbalanced, but balanced across all of these subjects for it to be a place where these really important conversations will take place.

[00:40:15] Susannah de Jager: Wonderful. Thank you very much, Lionel.

[00:40:17] Thanks for listening to this episode of Oxford+ presented by me, Susannah de Jager. If you want to stay up to date with all things Oxford+, please visit our website, oxfordplus.co.uk and sign up for our newsletter so you never miss an update. Oxford+ was made in partnership with Mishcon de Reya and is produced and edited by Story Ninety-Four.

‍