Mischa Dohler 00:11
We have new stuff coming out now in technology. And I think this envelope between accelerated fabric like neuromorphic, and quantum is one, AI is another and robotics, specifically soft robotics, which are quite useful in medicine.
Camille Morhardt 00:27
Hi, I’m Camille Morhardt, host of InTechnology podcast, and today I’m going to have a conversation with a VP of Emerging Technologies at Ericsson. His name is Mischa Dohler. We are going to talk about the intersection of emerging technologies with telecommunication systems, globally.
Misha has a very interesting past. He was actually born in East Germany, and he is half British-half German. He lives in the United States now and he used to be, for a decade, a professor and chair of the Wireless Communications Department at the King’s College in London. He’s an IEEE fellow, a Fellow of the Royal Academy of Engineering, also a fellow of the Royal Society of the Arts and advisor to the FCC, an author, a pianist, and probably a bunch of other things I’m forgetting, but very passionate human being. Really looking forward to discussing a bunch of different topics across technology.
Welcome, Mischa.
Mischa Dohler 01:29
That’s absolutely correct. And I did a lot of other very exciting things we can talk about with the arts and with health and music.
Camille Morhardt 00:27
Oh, well, let’s start with that. What very exciting thing did you do with the arts?
Mischa Dohler 01:29
I always maintained that we shouldn’t be doing technology for the sake of technology. It really should be for people for society in the end of the day. So I spend a good deal of my time in the UK with people trying to understand how would they use future technology. So in the early days of 5G, I would go to surgeons, I would go to artists, I would go to musicians. And I would ask them, “Hey, if we threw out a technology called 5G by 2020, how would you use it?” And I learned so many things. I worked with Rob del Naja from Massive Attack. Some of you may know him. I work with the National Theatre with the National Gallery. And really, my highlight was actually to work with our surgeons at King’s College in London on this concept of 5G telesurgery. So you would essentially have the surgeon the patient separate with a 5G link, and save lives simply because you can operate much quicker.
My highlight of this week was I just returned from Florida, we’re talking February 2024. I was with 200 surgeons in Orlando; there were 10 sitting presidents of medical societies, representatives from the White House, FDA loads of folks that robotic surgery companies, myself, and we demonstrated the world’s first 5G live operation over 10,000 kilometers. So we went from Florida, Orlando, to Dubai and to Shanghai, we did two operations. And it was really a stunning milestone moment for the community. And we really believe we can make a difference there using technology we are developing as an ecosystem.
Camille Morhardt 03:12
What were the operations?
Mischa Dohler 03:15
This specific operation started going live with a chicken wing, okay, presumably out of the supermarket. The next one was on a banana, so you could actually test the soft tissue issues. And the third was on a pig. Previously, we did actually operations over shorter distances on humans because the regulator allowed for that. So it’s a really exciting development. And we have now a path forward and how to use that in the US and globally to save lives.
Camille Morhardt 03:41
Oh, that’s interesting. I was going to ask you how far along we are. But it’s good we’re operating on fruit. So I have a sense of it. And I prefer that as a first start.
Mischa Dohler 03:51
You know, there’s some cases, of course, you know, which really require an urgent intervention that’s called actually in an emergency, we’re really highly urgent intervention. If there’s no intervention, the patient would die. So we’re really looking at cases related to stroke, to heart failure. So these are the type of use cases we are often at the moment.
Camille Morhardt 04:11
I assume it’s a certain kind of robot or machine that’s performing the surgery?
Mischa Dohler 04:17
Yes. And that’s a universe on its own. It was pioneered here in the US, maybe 40 years ago, by Rick Savato. He came up with the idea, let’s use robots to do the intervention because they’re so much more precise, you know, patients recover much quicker. Then 30 years ago, Fred Maul founded a company called Intuitive Surgical which actually built a commercial product around this. Then Jack Marisco, 20 years ago did the very first teleoperation from France to New York. Then 10 years ago I pioneered that 5G telesurgery concept and today we have companies building the actual products which are a able to operate over long distances. So the answer is yes. And it’s a growing and thriving ecosystem.
Camille Morhardt 05:01
Interesting. So let’s move to the arts. What is 5G going to do in the arts?
Mischa Dohler 05:08
Ah that’s a great question. You know, I’m actually a musician. So I really was trying to understand how can we bring this emotional bond between artists and the audience? How can we enact it remotely the same way as if you go to a concert? So if you go to a live concert, you listen to somebody—Beyonce– you know, these emotions are very strong. And I was trying to understand what triggers that? It turns out that latency is very important. So when we communicate over the internet, the time it takes for signals to reach each other, you know, is often hundreds of milliseconds, simply how the video codecs work, security works and all that. Whereas if you’re not in the same room, I have about a 20 or 10 millisecond latency. So my emotional response in the brain is very different. So we started to use 5G technologies and ultra low latency technology to kind of shorten that distance and therefore recreate that emotional bond.
So we did the world’s first 5G concert. And that was me playing the piano in Berlin, under the Brandenburger Tor, and my then eight year-old daughter, Noa, singing in London. And we were able to connect each other at 20 milliseconds. So we didn’t get it down to 10. But joining us is remarkable. 20 milliseconds, you know, I really struggled not to cry during that performance, because the emotional bond was so strong. I have my daughter 1,000 kilometers from me beamed in, and we’re giving this joint concert. So these are the type of questions we’re trying to answer back then.
Camille Morhardt 06:37
And what about holograms for concerts? Wasn’t ABBA working on that?
Mischa Dohler 06:43
There was a lot of work done in London when I was there, with Madonna with Mamma Mia, so ABBA. So there’s a lot of stuff actually being tested. I think now with the release of augmented reality devices on virtual reality or passed through virtual reality, like the Vision Pro, Quest3, and later, we will have maybe the Ray Bans and more advanced stuff, Magic Leap, you name it, is that huge swath of glasses coming out, suddenly, you are able to project essentially, these experiences right into your eyes. So you may want to have a hybrid experience, you may want to go to the concert, actually, and then have an overlay, you may want to be at home, you may want to be with friends. So I really think the next 10, 20 years will completely change the way we engage with the arts. And therefore I also believe our arts will be prepared for change.
So just think back, you know how, let’s say, the Greeks or the Romans invented the theater stage, which was an innovative step on how to do theater. And then a lot of creativity has happened over the last 2,000 years. But there was no real disruption on how we consume that art. So I think you know that stage consumption will change. And, you know, the reason I brought let’s say, the National Theatre with my engineers together is because I maintain, the techies are very innovative, but not very creative. And the artists are very creative, but not very innovative. What happens if you put them together? And the answer is, first day, it’s a disaster, and second day, it’s magic.
Camille Morhardt 08:05
Yeah, that’s interesting. So what I imagine is, if everybody showed up in one of these headsets, then everybody would have front row seats and probably perfect audio tuned to their own preferences.
Mischa Dohler 08:17
Absolutely, you’re right, these are the opportunities. And then that also means as an artist, you need to rethink on how you procure art, what type of art you produce, and how do you deliver it.
Camille Morhardt 08:35
Do you think it will change television or movies that are already coming through digital interface?
Mischa Dohler 08:42
So you know that our viewing behaviors have changed, if you think about it, you know, you know, maybe 100 years ago, went to the cinema, so the screen was twenty meters away from you. And then TV was introduced, and suddenly it was at two meters from you. And then we invented the smartphones and suddenly it was twenty centimeters. The trajectory is very clear. You know, as a techie, I predict the future by just looking what’s the trend in the past. And so therefore, we will now stop consuming content, which is about two centimeters from the eyes through these glasses. And, you know, the Vision Pro is just a testament that this will truly happen. Apple has partnered with Disney to produce entirely new content. Disney, I think, I believe in epic have partnered to build metaverses, gamified experiences. So we’ll start producing it much closer to the eye. And it’s not difficult to predict what comes next. Because the two centimeters go to two millimeters, the moment you start putting on contact lenses, so that would be the next step.
Camille Morhardt 09:32
So in our vein of running through different technology categories, if we were to pause on AI machine learning, what, from a telecommunications perspective, are you most interested in in that space?
Mischa Dohler 09:44
Yeah, telecom was always very interested in the general families of AI. So we have been using whatever computer scientists came up with–whether it’s Convolutional Neural Networks, cognitive neural networks, RNNs, implementable version of LSTMs; we’re going this roadmap of networks which were tuned very manually to networks, which are now tuned by AI simply because the degree of freedom you have in the network is so large, there’s no way you can do this as an engineer. But you know, the future now going forward becomes really interesting because we are introducing generative AI capabilities into our networks, which opens up a whole swath of new applications.
And maybe the lowest hanging fruit in a sense, is an opportunity which is very close is ready-to-use kind of chat bots to engage for the likes of the telco operators to engage with their customers; they can scale operations. But also for us as Ericsson because you know, we have a lot of gear out there, which is fairly complicated to operate. So whenever our operator customer as a question, rather than calling a human Ericsson employee, they first can consult essentially, a chatbot, which has been trained on Ericsson data with Ericsson employees. So that is a real opportunity there, low hanging fruit.
But it goes on that we use it, you know, for writing code, we can use it to emulate large systems rather than going out in the wild and driving around and testing things, you would use generative AI to recreate these scenarios; it becomes much cheaper to launch new ideas. And of course, the third one then, which is the holy grail is to use gen AI to help you design these systems. And that’s actually been used in the semiconductor and the chip industry quite a lot. So if you talk to Nvidia, if you talk to Synapsis, you know, these companies have been using AI to generate their next generation chip fabric for quite a while. Why can’t we do that? So that’s a natural question. And we are quite excited about using this generative AI capability to really design telco networks of the future, will be co-designed with a human engineer, of course, you know, nobody will get rid of us as engineers; but at least our capabilities will be 10x really kind of empowered will be like the magic one for us to go forward.
Camille Morhardt 12:03
So you’ve essentially walked me through what is a pretty basic use case of using gen AI or large language models to look through internal databases and then help people pull out answers like a chatbot. I think that’s probably one of the fastest growing use cases. But that all the way through sort of this very future, very interesting and complex concept of actually using artificial intelligence to design and balance and orchestrate our critical infrastructure. And across that continuum, I wonder if you could help us understand how security and privacy is going to play a role as we transition from kind of very proprietary localized data collection through critical infrastructure that’s often automated.
Mischa Dohler 12:51
I think it will be easier for us to really cater for security and privacy issues, because the same way as you would train a gen AI construct on designing new things with your data, you can train it to investigate security weaknesses–to examine your end-to-end system from a privacy point of view. This becomes hugely important in systems which have a huge degree of freedom. So where you have a lot of attack surfaces, a lot of ways of interconnecting things using very different protocols to interconnect things using very different code families internally to arrive at essentially these applications.
You know, we will not use it as the sole decision-maker in that process, we will use it as an amplifier so that the design will always be a co design, it will always be AI helping the human engineer to build better systems from a performance point of view, but also from a security and privacy point of view. And I maintain you know, AI will not replace our human jobs. But the people who know how to use AI will replace those who do not. So that’s the future we need to prepare for.
Camille Morhardt 14:05
I’d love to spend just a minute on you as human being. You’re so well-versed in so many different technologies, is that a tactic that you use? Do you spend a certain amount of time each day on different technologies? Or how do you become so knowledgeable about so many different things?
Mischa Dohler 14:23
You know, a) I am a very curious person; I am also a person who likes to deep dive a lot. So I’m not satisfied just to understand the cheerleading high level stuff. I do upskill a lot, so you will be surprised. My day is actually literally one hour on upskilling and because my day’s so full as VP of Emerging Tech, I get up at five o’clock and I’m in the office 5:30, and I have my 6am-7am, I’m dedicated route to upskilling trying those than what’s out there using new tools. It has really been quite helpful to me to understand anything very basic from quantum technology and neuromorphic, blockchain. I also spend a lot of time on system thinking. And I’m thinking a lot about policy. So I’m on the board of Ofcom, which is our UK regulator, Spectrum regulator and on the tech Advisory Committee of the FCC, so a lot of policy thinking, as well. And I spend a lot of time thinking about humans, as well–that very emotional relationship of people with people, people with technology, what’s the role? I never forget that.
I’m thinking about my children, how will my daughters grow up into that tech century? What will 2050 look like? What can I do today to make this a better future? So that’s a little bit my thinking, how I tick internally.
Camille Morhardt 15:39
Explain neuromorphic computing to us.
Mischa Dohler 15:42
It’s an entirely new compute paradigm, right? So in the computer science world, we talk about a Von Neumann architecture. So Von Neumann introduced that architecture, saying, “Hey, let’s have the compute engine, which we call CPU these days, decoupled from the memory where you store your information, and then connect it with a little bus there.” And that takes a lot of time to actually get this information forth and back between memory and compute. It costs a lot of energy.
Now, along comes neuromorphic, where you have actually completely new materials, which allow you to put computing and memory in the very same instance. So you don’t need to ship all that information forth and back, you can do it in many ways. One way is just to use very new material, kind of meta materials to make that happen. And it turns out by doing this, you save a lot of energy, because you know, you can suddenly just maintain part of the little chip infrastructure, which you need to do certain calculus rather than keeping everything powered at the level of ones and zeros as we deal with our traditional infrastructure.
So it turns out that bringing this memory and compute together, we save a lot of energy. Then people came along said, “Hey, why don’t we build entirely new ways of calculating things?” And the neuromorphic compute fabric allows us to do operations without using energy for multiplications. And multiplications, we need that a lot. Right? So we roughly have additions and multiplications. Now multiplications take about a 10th of the energy today in neuromorphic. Put it all together, and suddenly very complex operations like AI consume a million times less energy than our traditional CPU fabric and GPU fabric. And everyone was, “Hey, why don’t we use that?” And everybody got very excited about this, of course, loads of technology challenge this, like the very early kind of CPU years in a way.
But you know, companies like Intel, really pushing this very hard and as a great fabric, and other companies out there. And I’m trying to understand, where are we commercially? Would that make sense to implement that? You know, and our gear, we’ll have 6G gear, which we’ll have by then at the end of this decade.
Camille Morhardt 17:56
So how is neuromorphic computing and a roll into 6G?
Mischa Dohler 18:00
So we still don’t know; we’re still investigating as a community. I’m not saying Ericsson per se, but as a community trying to understand where will it be. What we are starting to see, 6G will really be about a lot more antenna elements. So we call this ultra-massive mime, whatever you want to call it at the moment, we may have a 64 elements on the roof. And then maybe you know, you have like six maybe in the phone, “Hey, what if we scale this up to 1,000 antenna elements on the roof?” And then suddenly, you start thinking, “Hey, you know, if I have to power all these 1,000 elements, and connect all the processing, in addition, my bandwidths are getting wider. More users are coming on. My compute energy, you know, will just go through the roof.” And we’ve done the calculus, it’s really crazy. So there’s no way we can do that. So we need new ways of dealing with that energy increase. Neuromorphic comes along. It’s one of the contenders. So it’s not the only one. There’s other stuff as well, we’re looking at. But neuromorphic essentially gives you the ability to really bring down this energy envelope, whilst not jeopardizing the performance on that.
So it turns out that neuromorphic cannot be used for all algorithmic families. So we’re trying to understand what can be done, what cannot be done. Should it be really integral as part of our stack to process data? Or should it sit on the side as we like to do it today? You know, this is publicly available, then we just call certain acceleration functions when we need it, and then continue with processing. So a lot of question marks, and that makes it so exciting because we need to take very difficult strategic decisions very quickly, to make sure we remain competitive towards the end of this decade.
Camille Morhardt 19:44
So tell us about quantum computing. I know you’re looking at that also.
Mischa Dohler 19:48
You know the whole quantum construct is super interesting. Quantum has so many different branches. So the one which is most unknown at the moment is quantum computing. Rather than having CPUs which deal with ones and zeros sequentially, you would build something called qubits, which allow you to essentially have ones and zeros kind of together and then at the end through smart processing and algorithms to try to find the right answer.
So quantum at the moment, where are we? The interesting thing about quantum is nobody believes it would happen. And yet year on year, engineers delivered on the roadmap they had promised before. We have companies like IBM, who have been in this for a very long time, you look at their roadmaps and how they built in a number of qubits over the years, they always deliver on these qubits. So there’s no doubt or somehow the doubt in me has, you know, it really diminished down to zero. They will deliver on that roadmap forward. So ’26, ’27, ‘28 will be the year where we will have quantum computing fabric, which if we played well will be much more powerful than what we have traditionally, even if we have very, very big supercomputer centers. And the beauty of processing data in parallel is you can actually solve exponentially difficult problems in an optimum way. So the classic one is the salesman problem, as you know, you decide on what’s the optimum way to go for different cities; 24 cities in our traditional compute fabric, no computer on the world can do this. And quantum can do this in a few minutes.
Having said that, quantum works really well for quantum problems, ok? So if you have a material science problem, which is a quantum problem, and you have a quantum computer, they can help you to solve that very quickly. If you want to use quantum for digital problems–such as finding the optimum antenna tilt, or during certain digital tasks–then it’s not that optimum. So we still need innovation and research on the algorithm side, not only on the fabric. So that’s quantum computing.
But then we have quantum radios, we do have, you know, quantum networks, we have quantum key distribution is a security element, there’s a radio element, there’s a networking element. So quantum is just such a fascinating fabric, and it’s all evolving. The only downside it has at the moment is it’s extremely energy consuming. So contrast that with neuromorphic, which consumes almost zero, quantum is you need to cool it bring it down. So we need a lot of innovation there. And we also need to make sure that if we use a quantum computer, the problem is so hard that we would need like trillions of years to do it on a normal fabric because then the whole energy story makes sense. It needs to be sustainable.
Camille Morhardt 22:36
It sounds like rather than a consolidation of compute technologies, you’re looking at a heterogeneous mix of compute technologies, depending on the function or the workload, is that accurate?
Mischa Dohler 22:46
Absolutely. It’s a very great observation, Camille. That’s exactly what we’re looking at. And you know, specifically in the heterogeneous setting of quantum and traditional compute, we just published a blog on how we use quantum and traditional compute as a hybrid solution to find the optimum antenna tilt. It’s a very hard problem when you have loads of antennas, many users; we work with heuristics so far, so heuristics are algorithmic approaches, which aren’t optimum, but try to get as close as to the optimum, we can do that. With a quantum solver, suddenly, you get much closer to the true optimum in a much quicker time, and we’re able to do that much better than just a non-heterogeneous solution.
Camille Morhardt 23:24
If you had just a huge amount of funding to look at something that’s of personal interest to you, what would it be?
Mischa Dohler 23:30
You know, I would probably try a little bit what we tried to do in London, push the envelope on both, really. So you know, try to understand how can we bring the innovative element of technology together with a creative element of the arts? And really get both communities start thinking, how can they disrupt their own ecosystems; it’s a very general view, but you know, usually it comes out when you bring them together. And we have new stuff coming out now in technology, and I think this envelope between accelerated fabric like neuromorphic, and quantum is one, AI is another and robotics, is yet another, specifically soft robotics. So it’s not only about hard robots walking but actually soft robots which are quite useful in medicine, many other applications. So that’s the technology envelope, and the connectivity connecting it all–5G, 6G, etc.
And then on the artistic side, we have new ways of procuring the arts–whether you use let’s say, glasses, new ways of stages, haptic equipment, you know, creating immersive experiences, creating emotional bonds, creating a digital aura in arts, which we couldn’t do before, right. So before you would go in an exhibition, there is nothing before going exhibition, great experience going out of the exhibition, and then you forget about it. So building these digital aura trails, I think, you know, this is where technology can really help.
So loads of opportunities there. It would really bring arts back into the curriculum, bring it back into schools, bring it back into universities, make it an integral part of our educational process. That’s really what I’d love to see.
Camille Morhardt 24:58
What is the soft robot?
Mischa Dohler 25:00
A soft robot is a robot which mimics the way how, let’s say an octopus walks. It’s very soft. There’s no hard element there. And we love to explore that world because, you know, nobody can be very close to real big robots. So I’m not sure you’ve ever been close to one. I had one at King’s ABB. These are beasts, these are industrial things, you know, you don’t really trust it. If somebody hacks in there or something happens, you know, they just swing and you’re just basically toast. But the soft robot can really enable that coexistence I think with humans, use it in surgery. So the ability to control soft tissue, you know, like an octopus, I think or a snake. That’s the title of inspirational biological phenomena we use to design that.
Camille Morhardt 25:42
Well, Mischa, everything from octopuses to Apple Vision Pro to neuromorphic computing. Thank you so much for your time today. It’s been a fascinating conversation.
Mischa Dohler 25:53
My pleasure, Camille. Thank you for having me.
The views and opinions expressed are those of the guests and author and do not necessarily reflect the official policy or position of Intel Corporation.
