Series 2: What have we learned?
This series has been a wild ride! We've spoken to some of enterprise tech's brightest brains this season, and one thing they weren't short of was opinions! Innovation is coming thick and fast, and we've discussed 5G, supercomputers, AI, computer science, digital transformation, zero trust, blockchain, IOT, energy innovation, quantum computing, mixed reality, and everything in between. But there were a few topics that cropped up time and time again. So... what have learnt this season?
Michael: [00:00:00] Another series of Technology Untangled is drawing to a close and ,for me at least, it's been a pretty wild ride. I have had the amazing opportunity to speak to over 40 technologists, scientists, academics, developers, researchers, futurists, and IT generalists worldwide to untangle innovations that are on the cutting edge of enterprise tech.
Andrew Emerson [00:00:25] We're doing things now, which would have been considered inconceivable even just two or three years ago.
Englim Goh: [00:00:31] Each drone today is given instructions as to how to respond and make decisions, but in the future, they won't just be given instructions, they will be able to learn on their own. Imagine that!
Mark Fernandez: [00:00:43] The secondary mission is when we push that edge further out to the moon and to the Mars.
Michael: [00:00:50] Things that sounded incredibly futuristic just a few years ago are already a reality, and so today, for a little change of pace, in this final episode of the series, we've pulled together some of the most important themes of this exciting year.
In over 60 hours of interviews, we've talked about 5G, supercomputers, AI, computer science, digital transformation, zero trust, blockchain, IOT, energy innovation, quantum computing, mixed reality, and everything in between. So, like any good project or science experiment, it's time to draw some conclusions. What have we learned…?
Back for just one episode before the Technology Untangled team have a well-earned break, I'm Michael Bird and this is Technology Untangled.
SFX: [00:01:40] music sting
Michael: [00:01:41] In the second series of technology untangled, we featured an abundance of incredible tech innovations. We've explored what edge computing looks like in outer space, how super computers are helping us to find COVID, and how new strides in 5g and augmented reality might fundamentally change our workplaces. But there was one particular topic that kept cropping up…
Matt Armstrong Barnes: [00:02:08] AI is really starting to make massive inroads into both healthcare and life sciences.
Puneet Sharma: [00:02:15] AI driven data compression.
Colin Wood: [00:02:16] Using wireless cameras and AI to look at cattle health as they enter milking parlors
Jacob Balma: [00:02:22] AI is being used to predict the properties of new materials.
Toju Duke: [00:02:27] We have emotional intelligence chatbots now
Jordan Appleson: [00:02:29] Machine learning models that can make predictions based off historic data… massively important.
Rasha Hasaneen: [00:02:35] You know what? AI is going to take over the world. That's what's going to happen.
Michael: [00:02:42] Yes from deep learning AIs producing the human like text to swarm learning in industry, AI, machine learning, and automation are the words on everyone's lips.And for good reason! In regular organizations, AI is being used to make our jobs better by abstracting away the mundane tasks that we love to hate.
Matt: [00:03:05] A lot of the times these are tasks that it's physically impossible for human beings to process because of the volume of data that is coming in. Organizations are hit by a tsunami of data on a daily basis and, as a result, they're needing to incorporate new tools and techniques that are going to start to evaluate that data and work out where the highest of value, both from the value of the data and the time that human beings can spend looking at that data.
Michael: [00:03:36] Data without insight is a bit like my quantum cheese souffle. Nice to look at, but somehow lacking in substance. Artificial intelligence, particularly automation, has come on leaps and bounds, letting us crunch huge amounts of information and arrive at actionable insights all in our quest for efficiency. As HPE’s John Frey explains…
John Frey: [00:04:01] So, when we look at studies of data centers around the world, what we find is about 25% of the equipment in the average data center is powered up and running but doing no useful work. And then if you look at that other 75%, it's being operated down in the 10 to 30% of its rated capacity. So, we have a lot of equipment sitting idle, and then we have a lot of equipment that's really underutilized. So, one of the things that automation brings to the table is that allows you to see those inefficiencies in real time
Michael: [00:04:34] By now, automation is so commonplace that half the time we don't even realize it's happening. And as useful as it is, this isn't really the part of AI that has piqued everyone's interests. In fact, when we spoke to Ray Beausoleil about quantum computing, he said that over the next decade it's machine learning that is going to make the biggest waves.
Ray Beausoleil: [00:04:56] Artificial intelligence is a false label. When we say AI now, we generally mean machine learning, but I guess ML doesn't sound as interesting. But machine learning is also a misnomer because the machine hasn't learned anything. What we have done is teach a computer, how to solve a problem using software that's too complicated for us to write. The thing that I find really super exciting about those techniques is the ability to use optimization, to use data, to discover the governing equations for phenomena that are so complex that we can't really figure them out, they allude our intuition. We look at them in awe sometimes and say, oh my god, you know, like climate and you say, my god, this is so complicated.
Michael: [00:05:46] Machine learning and deep learning have what seems to be an infinite number of applications, but because these types of AI are relatively complex and expensive to develop, we often hear about them in the most out of this world contexts, like, as we heard from HPE’s, Eng Lim Goh, telemedicine in space…?!
Eng Lim Goh: [00:06:10] Why does AI have a pivotal role in mission critical situations in space? Today, on a space station, they carry with them ultrasound scanners. You have to do medical scans. So if you're on a space station, the delay is very short right between the space station and earth. So, the astronaut can do the scan, medical scan, and then they can do telemedicine with medical doctors on earth. That, that one works, Right. But imagine telemedicine.Now, if you are, if you're having tens or minutes of delay in communication, right? You do the scan, you say, hello, doctor on earth, what should I do next? Then you wait 10 minutes for your “hello, doctor” question to reach earth and the doctor for you. And other tens of minutes to, for the doctor to reply, you know? So, so there'll be times where you will need AI augmented telemedicine.And this is where you need computing power locally with you, right? And you connect your ultrasound scanner to the local high-performance computing and let the AI system give you an immediate answer, provide you guidance while you're waiting for the answer from the doctor on earth to reach you.
Michael: [00:07:20] Yeah, so the possibilities are incredible and they're not really just possibilities anymore.In episode three, academic Jacob Balma explained to how using machine learning alongside the most powerful computers on earth has fundamentally changed the way that we study the world.
Jacob Balma: [00:07:40] I think what nobody was anticipating... we were talking about exascale before machine learning. So, when machine learning got added onto what you can do on an exascale computer, the types of problems that you can solve with the systems now are actually very different. If you think about simulating a single cell, it's made up of, you know, hundreds of thousands or millions of individual proteins and atoms and simulating full quantum mechanics of even of a single cell isn't possible on the largest supercomputer.
Right. But, if you have a machine learning model that can do some of these approximations for you, or at least run the quantum mechanics in a highly efficient way, then you open up the possibility of being able to simulate, not just a single cell, but multiple cells.
Michael: [00:08:23] So, yeah, it's safe to say the scientists are pretty excited.But for the regular organization where automation is abundant flexor being raised about the extent that AI is being used in the workplace. In episode 4, Mary towels from the UK’s Trade Unio Congress shared the findings of their report ‘Technology Managing People’.
Mary Towers: [00:08:44] We were looking at the extent to which AI is being used to make decisions of that nature and of that degree of consequence across the whole of the employment relationship from beginning to end.And what we found was that AI was being used to make decisions in almost all of those areas.
Michael: [00:09:03] AI, bias and ethics are very real concerns, but thanks to the likes of the TUC, we're seeing organizations not only taking note, but in the case of Jen Hawes-Hewitt at the Smart London Board, stepping up to the plate and setting an example.
Jen: [00:09:19] Things that, the Smart London Board are really focused on as well is about, looking at the ethics of AI and understanding that. We've done some great work around making sure that there’s an open conversation with citizens, about the application of AI into public service provision.
Michael: [00:09:34] Almost every guest we interviewed this season mentioned AI at some point, usually when they were asked about future plans. AI's infiltration throughout our lives is set to continue and the effect it's likely to have in our workplaces will be profound.
Leslie: [00:09:53] We're actually going to be having more. More computing, more robotics, more automation, and all of this kind of thing and that will necessarily change the way that organizations are and how they behave. And I know that there's a lot of people concerned about, you know, job loss and all that sort of thing. But, you know, this isn't the first time that humanity has gone through this.
Matt Armstrong Barnes: [00:10:15] The job landscape is fundamentally being changed. It was being changed by AI as an accelerant. That accelerant has significantly increased with the advent of COVID global lockdowns. You could argue that the traditional fresh and high street retailer is in decline, whereas grocery retail is increasing. So, the job market is shifting, people are moving from fashion retailers, into grocery retailers, and over time they would have made that natural progression anyway. But what we are seeing is people can really transition into new roles and AI is actually creating new roles and that can be performed as people move either across industry sector or to new sectors that are being created.
Michael: [00:11:04] It's hard to think of an industry that isn't going to be using AI in some form or anothe so it's no wonder that we're asking some big questions around it. These changes are inevitable, but rather than fear mongering, our guests said that organizations should look for opportunity, albeit with a healthy dose of ethical consideration.So where are we with AI today? HPE’s Matt Armstrong balance gives us the lowdown.
Matt Armstrong Barnes: [00:11:35] AI and jobs. The most successful implementations of AI as are when they're, when they're augmented with humans. So, a human and AI is more successful than a the human on their own.
Robotics is an exploding field. AI and robots are really tackling a lot of those mundane challenges. And they're really moving into some of the dangerous fields where you don't want human beings to go anyway,
AI and science fiction. We're not there yet. We are anywhere between the next 10 and 90 years away from achieving generally available artificial intelligence. That's what the academic community says.
AI is a black box. I don't understand how it operates. It is true. AI is a black box, but there's a lot of work happening now to simplify how that black box operates and allow you to understand how complex deep learning algorithms are arriving at decisions. This is available today. So, you can apply this explainability to the black box today.
And lastly, AI and ethics. There's been a significant amount of work I'd say. And thanks to organizations failing at AI and ethics and recognizing that they failed to meet the ethical needs of an AI. There has been lots of work to tackle some of these ethical considerations of AI.And some of them are really big. Some of them you're starting to apply human level ethics to an AI where we have ethical considerations ourselves as human beings. So, we're trying to apply a set of ethics that relate to us to a mathematical algorithm, which is a step too far. So, we need to sort of zero in on where we can tackle some of these problems when it comes purely to an artificial intelligence, you know, this mathematical algorithm, that's crunching a whole bunch of data and making a prediction.
Michael: [00:13:40] Cheers, Matt. We are certainly going to continue reporting on new moves in the AI space in our next series of Technology Untangled. Although, I guess that it's not long before we start talking about AI as a separate field. As organizations continue to employ AI in the drive for efficiency, we're probably going to treat it as an intrinsic parts of it as a whole. However, as HPE’s Kirk Bresniker noted, we can't lose sight of its environmental impact.
Kirk: [00:14:12] Being able to sense and predict and then to modify and to dial in that behavior, that's a big sensing task, that's a big computational task. And, of course, neither one of those things comes for free either. So understanding, if, say, we say we'll throw some machine learning at it. It's like, well, yeah, but you realize the kind of models that people are building today, even the small machine learning models, a 200 million weight model. Well, that's the same as five internal combustion vehicles, same amount of energy operated over the whole lifetime to train the model one time. So, yes, we want to retrain machine learning models. We want to sense this information. We want that efficiency and speed to gather and then analyze and then make decisions and then be able to take action in a time that matters. But all of that has to be sustainable. Because if it's not sustainable, we can't afford for everyone to be able to use it. And that's not an equitable solution either.
Michael: [00:15:13] Yes, we want to use machine learning and AI to do incredible things, but not at any costs! As Matt very eloquently puts it…
Matt Armstrong Barnes: [00:15:23] Just because you have hammer with the word artificial intelligence on it, don't think of everything as a nail. Artifical intelligence needs to be a tool in every organization's toolkit, and when it comes to addressing a specific business problem, make sure you are using the most appropriate tool.
Michael: [00:15:43] Which brings me on very nicely to the second thing our guests kept saying to us time and time again, and it's the thing most organizations want to avoid more than anything… using tech for tech's sake. Chief Technologist Ian Henderson explains...
Ian Henderson: [00:16:03] I visited a customer a few years ago and I was in the car park and there were lots of bits of broken black plastic around the car park. I said, oh, what are these? And I said to the customer w”hat's that?”. And they said “Oh, those are our smart parking sensors, you know, we've got a parking challenge with everybody driving into the office”. And this would tell them when spaces were free or occupied. And it was great, um, until it snowed and the facilities people cleared the car park with a snowplow and snapped the top off all of the parking sensors! You know, so for me, that was just, it was just a great example of they already had CCTV it, isn't difficult to write an algorithm that looks and says, is there a car in that space or not, right? Rather than a battery powered sensor!
Michael: [00:16:46] Now, almost every new tech has its detractors and evangelists, but by getting overexcited by a potential solution, we risk creating answers to problems that don't really exist.
Matt: [00:16:59] Most people are consuming the video that they wanted, but they were unhappy with 3G, over smartphones. How many people are really watching true 4K video on their smartphone? You know, can you tell the difference between standard def and high def on a smartphone? The other thing is, codecs have become really good. So, this is the how you compress the video that's coming off the internet when it starts to hit your mobile device. To really get 4K you're talking about 20 megabits per second. There's been a number of experiments to say, actually, if we limit that down to 1 megabit per second, which is well achievable inside the 4G spectrum, do you spot the difference? And actually, they don't.
Michael: [00:17:46] So, what's really driving organizations to make the wrong choice around tech? HPE solution architect, Florian Buehr reckons It could be down to a bit of buzzword bingo.
Florian Buehr: [00:17:59] You know, if I use blockchain and… what else probably multi-cloud and zero trust. And if you put all these terms together basically people start listen to you because they don't actually understand what's behind the terms. And blockchain is a lot of like that. I think if you start with the very initial question, do I need blockchain? Maybe not. That is very helpful because if you then end up with an implication, well, I might still need it. That it is a good reason to use it. If you just think that, Hey, let's come up with an architecture to use blockchain just for the sake of blockchain. That's probably not a good reason...
Michael: [00:18:43] Responsible and reasonable use of tech requires the ends fit the means. We heard about a great example from Jimmy Chion from the New York Times’ 5G Lab with their game changing app for photo journalists.
Jimmy Chion: [00:18:59] We don't want to reinvent the wheel. So, a lot of what we did was looking at existing technologies and seeing if there was something that we could use that was more plug and play. So, there actually were systems and boxes that take a lot of SIM cards and provide a robust cellular connection over 4G LTE at the time. And that is used often in trains or buses as a way to provide wifi and have it be both a big internet pipe as well as reliable and consistent, no matter where you are. We were evaluating whether it should be like hardware, where there should be a combination of hardware and software, and whether it should be all software. Ultimately, what we went with was creating an app.
We built the app beam. In a way that it uses a cell phone and cell phone data. So, if you're in 5g it takes advantage of that. If you're in LTE it uses that, if you're in WiFi it uses that.
Michael: [00:19:57] The New York Times’ solution was about creating a genuinely useful tool. And according to digital transformation expert, Dan Broomham from Tquila Automation, this is precisely the point. Because bringing a new technology to an organization isn't really about just having a shiny new toy.
Dan Broomham: [00:20:16] It's really the adoption of technology and knowing that it works across the business. So, it isn't just about applying technology, but how integrated is it into everything that you do and what is ultimately the experience of your customers? What is their satisfaction? And there's something that a lot of companies have spent a lot of time focused on over many years. So, if that experience is good online and remains online, given where we've been with the pandemic, most things have happened online. That's great. I think the other element is around the employee element of that and the employee experience in the same way. And I think the things that have manifested for companies that have thought about their employees and their experience of working remotely and working online, that's really where it's good. They put as much value on the employee experience almost as a customer experience. And then the ones that have struggled haven't necessarily thought about the employee first. And it's really, although I say tech savvy, it's not tech for tech's sake. It's not about providing a really funky laptop that just happens to have all of this stuff that's all around the side. It's about how it works and how it allows an individual to get on with their day job.
Michael: [00:21:11] Whether it's automation or blockchain or quantum cryptography, tech innovation should be about making our lives better - and by extension our working lives better. But, as Mary Towers explained, that not always a given.
Mary Towers: [00:21:28] One of the things that happens is that often these tools are put in place, but actually they don't match how the employment relationship works, they're not matching the human resource requirements. So, employers need to communicate with technologists about that.
Michael: [00:21:43] Using tech for tech's sake can be a waste of money at best, but it also risks eroding trust in the workplace. And as HPE, John Frey explained, there are some serious sustainability considerations too.
John Frey: [00:22:00] One of the things that we find is technology can be a force multiplier as it relates to climate change. So things that really need technology solutions to either measure, for example, climate modeling takes high performance computing to model and do well and we still don't have the resolution that we need. So, as we use technology to really drive climate impact forward, its use of power goes up at the same time that the world's trying to decarbonize all around it. So, if that's happening, we've got to use technology responsibly and we have to make sure that our technology is being used efficiently.
Michael: [00:22:39] It's important that we as organizations consider the impact of the solutions that we employ from all angles. But through all of this, we also still need to somehow maintain an open mind to new technology. The tech world is changing quickly and, as HPE’s Dave Strong explained, those who have their fingers on the pulse stand to benefit the most.
Dave Strong: [00:23:02] Look, what has happened in the last year, right? Get over that stat of failure, what people have done. There are organizations siting out there saying they have done two years of change in 10 months and there's some big organizations saying they've done that.Is it because they've had the time to do it or is it because they've looked at their business models and realized that if we don't disrupt our business model now, if we don't change what we want to be as an organization, If we don't want to change our customer experience, then we don't exist.And I think it is as brutal as that. And if you look at the, you know, the boards of many organizations, they will be looking at technology to do that as well. And I don't think technology has ever been more important as it is today with chief executive officers as it is now. And that goes down and therefore they're looking to how they unlock that through digital transformation.
Michael: [00:23:54] The global pandemic encouraged organizations to challenge the status quo and explore innovative ideas they might never have considered before, which is great news in fields like augmented reality, which, according to Dimension Studio’s Lauren Dyer, is finally getting its time.
Lauren Dyer: [00:24:13] Time's precious and to keep on creating things that people aren't excited about or where people have to do 1,000,001 things to get to the point to where they need to be…It needs to be slicker. It needs to be better. It needs to have reason for and purpose for existing I think a lot of marketing execs and agencies and brands have really had to rethink their communication with people because people are just smarter. And that's the element, I suppose, of moving away from tech for tech's sake and it being a gimmick and it existing just for the sake of existing.
Michael: [00:24:48] On the one hand, organizations want to avoid picking a tech solution because it's this year's hot new thing, but equally it's about realizing the benefits that go beyond the mere nuts and bolt… even when that technology is the much maligned blockchain.
Tony Costa: [00:25:08] What's fascinating about this story we don't own any part of that supply chain from the fisher, all the way to the finished goods. Those are all partnerships. And you quickly realize that when we were doing this project, it's as much about partnership and communication and lot than it is about blockchain. And don't get me wrong cause we're a huge proponent blockchain. I, you know, I'm a technologist at heart, right? And so I, I wanna, you know, drive our company in a direction that embraces innovation and technology, but I also want to provide value. And finding that value is equally important to me than pushing any technology agenda.
Michael: [00:25:55] To go beyond using tech for tech's sake, it's all about the added value, which was demonstrated everywhere from Bumblebee Foods innovative use of blockchain in seafood sustainability to 5G Rural Dorset's impressive stack of use cases.
Colin Wood: [00:26:10] The reason really is because once we understood that 5G is not just a new radio antenna, it's a bundle of independent technologies with the potential to unlock new ways of living and working. This is not just about telecoms companies and mobile phones providing better handsets and coverage, it's got the potential to really impact on those sectors of the economy, whether that's an urban economy or a rural one as the internet of things develops the opportunities are really endless. And this idea of being able to use one network for multiple purposes, really struck a chord with us.
Michael: [00:26:43] Picking the right tech that's fit for purpose means starting with the problem and then looking for a solution, not the other way around.And it's also about keeping an open mind about what the innovation game throws up next.
Leslie: [00:27:04] I think our digital selves are going to be increasingly important and I'm not saying autonomous cause I, I, I, you know, some people talk about, oh, having a digital self that has trained on all of the things that you do and say, and then you can set it free to wander through the world of digital stuff and act the way that you would. Ooh, I don't, I don't, I don't really care for that. Maybe it's going to happen. That's not a future I'm excited about,
Michael Bird: [00:27:32] I think that's a black mirror episode. I don't know if you've seen it, there’s a…
Leslie: [00:27:38] Everything I look at is a Black Mirror episode! It's extremely important that we have things like black mirror to show us what we need to avoid because you get the shorthand. Um, you know, if you say big brother, everybody instantly knows, okay, that's, this oppressive overweening government that we don't want. And if you say Black Mirror, that's like, okay, exploitative human -educing technology that we don't want. And so, it's great to have that shorthand so we can actually say “Woah! Too Black Mirror for me. Let's take this to a different place!”.
Michael: [00:28:08] A word to the wise from Nokia’s Leslie Shannon about being careful what we wish. Because, and this is bringing me on to my third and final point, for every tech we think about employing, we need to keep asking questions.
Leslie: [00:28:24] The place where people have been concerned about 5G. And it is a legitimate question is a new area of spectrum band, which is a really high frequency band called millimeter wave. And this is really, you know, significantly higher wavelengths than we've been working with a mobile telephony before.And so, yeah, definitely. Let's ask a question. Is this, is this a good thing?
Michael: [00:28:45] The tech world is moving at an incredible place and plenty of our guests were quick to point out that that poses a challenge, no matter what the technology is.
Matt: [00:28:57] Retraining into inter core AI skills really requires a scientific background.
Simon Wilson: [00:29:08] Zero trust is about making a better decision you know, the complexity means you probably do need to invest in some more skills and certainly skills in security are in short supply and things that are in short supply tend to come at a premium. So, there is going to be a cost to this.
Sarah McCarthy: [00:29:21] Coding up your algorithms, or if you're deciding what products to buy, ensure you have someone who has an understanding all of the security levels that you need to be targeting and making sure it's relevant to your use case.
Dave Strong: [00:29:42] We rely on a very small skills pool around digital. We know that, you know, it's recognized that a UK level, that digital skills are in great demand, but there's not enough of them. And you're focusing all of that delivery and change on a very small pool of people. They're just overworked, and therefore they're hitting fatigue.
Michael: [00:29:59] To effectively and successfully bring in new tech, organizations need to understand it. And to do that, we need specialists, but we also need generalists… which does come with its own set of difficulties.
Dave Strong: [00:30:13] I think for a long time, we've had very unique in disciplines. And what we're seeing actually is it's a lot more focused on knowing a little bit about a lot of things, no matter where you are. You might say, does a project manager need to know about artificial intelligence? And actually they do that because what they touch it absolutely influences and determine some of the outcomes around artificial intelligence. So, we're seeing a lot more of that requirement to understand a lot more. It's the breadth rather than just the depth and that comes from its own challenges.
Michael: [00:30:46] To bridge the skills gap, worker education is key in all levels of organizations. And due to the challenges of remote working, getting the new generation of recruits up to speed has been particularly different.
Chris Dando: [00:31:00] I really worry about people who are coming into the industry, you know, graduates. So much of our education is hands-on learning, talking to others, building opinions, and it's, you know, you don't do that by zoom meetings. You need to build relationships...
Michael: [00:31:21] Even though we think of younger generations as being generally more tech savvy, the vast array of complex new technologies on the horizon means that computer science and IT education was top of mind for many of our interviewees
Tony Stranack: [00:31:37] The algorithms that you need for AI today need mathematicians, physicists to do. In quantum computing, you're talking about a subset of those guys who can do it. One of the challenges that quantum computing has, and one of the things that will slow it down is getting enough people throughout the education process who really have that depth of knowledge around how the quantum world works in order to be able to operate and program these things.
Matt Armstrong Barnes: [00:32:07] Do think there's a different challenge that needs to be addressed, and that is education.We need to get education started as soon as possible. We're seeing a decline in the number of computer scientists that are coming out. It's seen as a geeky discipline, fewer people want to get involved in it.
Michael: [00:32:33] A boom in new technology and a decline in computer scientists sounds like a disaster waiting to happen. So, what are we to do?
In episode four, looking at AI and the Future of Work, we had an illuminating conversation. with Microsoft's Simon Peyton-Jones, Chair of the Computing At Schools group and the National Centre for Computing Education. And he is deeply involved in tackling just that.
Simon Peyton Jones: [00:33:08] There's a famous quote from Arthur C. Clarke that said any sufficiently advanced technology is indistinguishable from magic. So, the thing I'm trying to avoid is children coming to believe that their sleek phones are essentially magic devices. They're made by wizards in other countries inaccessible to them and if they utter the appropriate incantations, then they will do good things. But since they don't know how they work and they cannot modify them in any way, they're essentially sort of disempowered users rather than being actively engaged and empowered citizens of the world. That's really, I think that's really quite important.
Michael: [00:33:46] We spoke to some incredibly knowledgeable academics this series like post quantum cryptographer Sarah McCarthy, who proved that many of the disciplines we assume as purely academic are anything but!
Sarah McCarthy: [00:33:59] At school, I loved mathematics. Then I decided because I love mathematics I would study mathematics at university and then I really got into the abstract mathematics. If you think of mechanics, that like cars or things flying at certain velocities and different angles. It's really tangible. Then statistics is lyou have a bunch of data and you want to do some analysis on it. Pure mathematics is just like, you're imagining these weird mathematical, like, things in your head. It almost makes your brain turn inside. Right. So, it's like, great. I've just chosen the one, which is like, I don't really see where it can be used.And then I discovered that it can be used as the basis of post quantum cryptography. And I was like, whoa, I've actually got an application for it. So that's why I jumped into the computer science side.
Michael: [00:34:50] And we are very glad you did! So how can we make sure there are more Sarah McCarthy's in the world?
Simon Peyton Jones: [00:34:56] Computing as a school is not established. It's a seedling that has, you know, got green shoots above the surface, but it does not have deep roots.So I want to see computing embedded in a way that no senior leadership team could possibly say, “oh, maybe we just won't do any computing in year eight”.
Michael: [00:35:15] In episode four, Simon Peyton-Jones told us about the important changes in the British national curriculum for computing at school. It seems like the seed has been planted, but there's still a very long way to go. On the other side of the pond, physicist Ray Beausoleil also had education on his mind.
Ray Beausoleil: [00:35:35] My hope is that all this fascination with quantum computing is going to encourage schools to teach quantum mechanics to undergraduates. Because I imagine that 20 years from now, people who are getting their PhDs currently will become research managers, people who have to make decisions about the teams that they lead and the R &D they're going to pursue. And they're going to need to understand quantum mechanics in order to make those decisions.
Michael: [00:36:02] So, although some might argue that there's currently a little bit of “tech for tech's sake” surrounding quantum computing right now, the excitement could be harnessed to scale up the next generation of decision makers. And that's all well and good. But how can organizations contribute to educating the next generation right now?
Simon Peyton Jones: [00:36:22] I'd love to see more involvement from employers and for it professionals. There's this big pool of goodwill and expertise and I'd like to find ways of connecting that up to what's going on in schools in a sort of deeper, richer way than the occasional careers talk.
There are thousands of companies around this country that are they're well known and not so well known and they're doing genuinely interesting and innovative things. And they're going to help sort of explode some of the myths about the, you know, socially challenged male geeks, that the only ones that do computer science.
Education is a big, complicated thing. It needs all of us. It needs teachers, it needs schools, it need senior leaders at schools, it needs the professionals and it needs employers. And we have to actually pull together. And if I think if we do in this kind of collegial way, we can really make a difference at national level, and actually then I think at international level, I'm excited about that.
Michael: [00:37:12] And on that note, it's about time to wrap series 2 of Technology Untangled! AI is here, now, and it's just about everywhere. So, we better get used to it…and use it wisely. New technologies are coming thick and fast, but the most important skill we're going to need is the ability to start with a problem and find the appropriate solution, not the other way round And what it ties and what I said it all together is a strong need for education - from computing in schools and engaging university students, to educating members of the workforce about the tech they're using and upskilling and promoting specialists in emerging fields. And the endless stream of innovation can mean only one thing.There's plenty of material for another series of Technology Untangled!
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Michael: [00:38:08] You have been listening to Technology Untangled, I'm your host Michael Bird. And a huge thanks to our amazing guests that joined me for an interview this series. This episode was written and produced by Isobel Pollard with sound design and mixing by Alex Bennett and production support from Harry Morton, Alex Podmore, Sophie Cutler and Tom Clarke. Technology Untangled is a Lower Street production for Hewlett Packard Enterprise. Make sure you're subscribed to wherever you get your podcasts, as we've got some exciting things planned for series 3, plus something special dropping later this year.So do look out for that!
A huge thank you to our very talented producer, Isobel Pollard. It's her last show as she moves on to new and exciting things. So, Isobel, thank you so much for everything! Also a huge thanks to Alex Podmore and Tom Clark, who are also moving on to new and exciting things – huge thanks to you guys too. Right, so that's it for series two! Thank you so much for tuning in and I'll hopefully see you next time.