What we do
So it doesn't matter what you’re using, whether it be a dating application or a spell checker, they're all somewhere using some level of machine learning nowadays, and so as a developer you're more and more faced with the challenge of, “how do I integrate some of these things into my application?”.
Zac Abbott: Hello and welcome to Explain IT brought to you by Softcat; the show for IT professionals by IT professionals that aims to simplify the complex and often overcomplicated bits of Enterprise IT without compromising on the detail. I'm your host Zac Abbott and over the next 30ish minutes I’ll be challenging our panel of experts to take an area of the IT ecosystem and of course, explain it. In this episode we’re going to be taking a look at some of the key emerging technologies that everyone seems to be talking about, such as blockchain, quantum computing and machine learning. We’ll be taking a look at what they are and how they might be utilised by organisations today and their potential impact in the future. Joining me today to discuss this is Craig Lodzinski, Softcat’s chief technologist for data and emerging technologies and Martin Beeby, principal advocate at AWS. Thank you for joining me today. Now before we get on with the show it's time for the most important question that you'll be asked today. If you could only listen to one album for the rest of your life, what would it be and why? There is a winner. Craig.
Craig Lodzinski: Are you going for me first? This is going to be super awkward as well because I'm wearing a t-shirt of a band and I'm not going to pick one of their albums so that's going to get super awkward. It would be Songs For The Deaf by Queens of the Stone Age.
Zac Abbott: Good album. Martin. Desperately searching iTunes library, other libraries are available...
Martin Beeby: The problem is I can't actually remember any album titles immediately, as you asked that question I'm like, I really can't remember what an album title, I don't listen to albums anymore, and I've completely lost all... it will be something by Incubus, but I'm really not sure what it would be.
Zac Abbott: Right, ok, so…
Martin Beeby: The Best of Incubus…!
Zac Abbott: Because you've not picked an album, your default choice is Now That's What I Call Music 45, which means…
Martin Beeby: I'm happy with that, that's a classic, great compilation.
Craig Lodzinski: Is it on cassette?
Martin Beeby: Only on cassette.
Zac Abbott: So I’m going to have to go with Craig because he picked an actual album, but The Best of Incubus, well, we’ll Google that maybe. Cool. Easy winner there. So Craig we've heard the word thrown around but what exactly is blockchain?
Craig Lodzinski: So with a lot of emerging tech the reality vs the hype and the marketing behind it is a little bit different, so blockchain came really to the fore in terms of cryptocurrency, that was where it started getting mass attention, but effectively blockchain is a set of technologies where you have a distributed Ledger system, effectively. So one of the key factors in blockchain is the way that you share it, the way that everything is put down in the Ledger, so by creating blocks that are literally a chain of blocks, and by how you write that, you create something that's designed to be immutable, it's designed to be single source and has certain benefits and certain advantages within areas such as cryptocurrency.
Zac Abbott: And it's had major implications for the world of cryptocurrency, as you say, but how would it affect the wider tech industry?
Craig Lodzinkski: Firstly, it’s an emerging technology and it’s difficult to establish exactly where it is because outside of cryptocurrency it's not seen major impact but we've seen, for example, HPE is trying to use blockchain to work improving their supply chain, it's being used in things like guaranteeing supply chain in things like medicines. But blockchain in itself and blockchain derived technologies have a wider impact, so I know I'm going to deliberately hand this one over to Martin - there’s certain applications were actually database is just better, and we’re using blockchain where it doesn't really belong and I know AWS guys worked and launched at Reinvent last year QLDB?
Martin Beeby: Yes so, QLDB has a lot of the features of blockchain technology in the fact that you're creating these blocks which are cryptographically secure and they are chained to one another in such that you can't then go and change any of the history of the transactions. So people are actually using this for things like cryptographically secure logs or logging systems and what you often find when you speak to a customer is they say, “I want a supply chain,” or “I want a log of something that can't be changed by anyone, that I can guarantee hasn't been altered or edited in any way,” and what they find is that they don't actually necessarily need a blockchain as such, but they need something that's cryptographically secure and can guarantee that someone can't edit the transactions, or the things that have gone into this database effectively. And so often people start off thinking, “Well I need a blockchain,” and they realise very quickly that something like the quantum Ledger database which is a service that we offer actually offers the sorts of properties that they actually want in their system. But that's not to say that there aren't applications and commercial businesses which require blockchain, there's lots of different kinds of blockchain. One of them which I've worked with quite heavily is a thing called hyperledger fabric which is a type of blockchain which doesn't have some of the properties that are required by cryptocurrency, so it doesn't have proof of work, for example, in the same way, it uses less electricity, it's less... but it has some of the properties and it allows you to add what we call smart contracts or pieces of code to the blockchain, so it's a distributed Ledger which can execute code as well as just storing information and transactions. So there's some really interesting technologies around blockchain, which lots of commercial businesses, specifically in supply chain, but in lots of different places can use.
Zac Abbott: Blockchain itself as a whole has been touted as the most disruptive technology since the internet itself. Do you guys agree with that statement or…?
Craig Lodzinski: No I don't agree with that, I think every new technology that comes out is the best thing since sliced bread. The internet fundamentally has enabled something much broader, something that can never be predicted from the outset and first of all you can't make a prediction so early on in any technology stream, the guys at DARPA weren’t looking around at what they were building and same at CERN, and going, “This is going to be ginormous, this is going to be THE internet,” tech predictions don't work like that. Predictions are difficult to make and to say that something is going to be a seismic, as transformative as the internet just isn't the case and also I think when you look at blockchain it's not a universal platform in the way that the internet is, it's not something that’s a utility service in the same way, so the breadth of applications just aren't there, in my opinion anyway.
Martin Beeby: Yeah I completely agree, it's another way of storing information. It’s fundamentally a database. I mean if you want to get really excited about databases then you can, but I don't think it's going to change the world necessarily. It’ll change the kinds of applications that we can build, but someone's going to be building other something on top of it maybe, or utilising it somehow, which creates new ways of doing it, but blockchain in itself, at the moment, a serious of go applications is not something that's going to change the world in itself.
Zac Abbott: So Martin, you mentioned that yourself and AWS are familiar with aspects of blockchain and Craig you said that there are potential use cases within organisations already, so what is the actual state of blockchain today, is there anything that's going to happen over the next few years that will make blockchain a fundamental technology for business?
Martin Beeby: I think the state of it today is that it's much simpler to use than it was just two or three years ago. So when I first got involved in blockchain and was using hyperledger fabric you had to start building machines, you’d have to go and download the Open Source Code, then you’d have to create the network, then you’d have to create a Consortium, you’d have to get other people to create that infrastructure to join your Consortium, there was lots of... you’d have to choose all the different sort of technology aspects of it, it was really difficult to set up, it took me about 4 weeks to set up my first hyperledger Consortium, now it's offered by AWS and other cloud vendors as a service, hyperledger fabric, and so if you wanted to start experimenting today, it's a case of going onto the console, creating a network and you can start working with it and start coding against it. And then also if you wanted something which is more like a Ledger, a distributed Ledger, then we have the quantum distributed Ledger service which again is a service that you can just go use and you can use it just like you can any other database service from AWS. And so the state of it today is that it's much more accessible than it was previously and we’re starting to see companies embracing it and starting to use it in their different applications.
Zac Abbott: Would you say, are there any disadvantages to blockchain, perhaps?
Craig Lodzinski: Like anything it's not a defined thing that's binary, so the risk is of getting really hyped up about blockchain and deciding to go all in full bore on it and certain fintech companies have gone in there and decided that they’re going to be a blockchain company and it has to be very defined with what they’re doing, but actually they might be better off with any number of different database services. They might be missing the really key factor, it's all about use case and certainly, as blockchain’s become a lot more accessible and a lot easier to get to grips with, that opens up the wider use cases because it's reducing that inertia. But that doesn't necessarily mean that it's the right solution for every application, it has to be taken on a case-by-case basis.
Martin Beeby: One of the big misunderstandings that I get from lots of people when they talk about blockchain, they’ll say, “But doesn't blockchain use a lot of electricity?”. Like, you can't use blockchain because the computation required to mine the blocks is so intensive that it costs an awful lot of electricity, and that's true of certain blockchains. So cryptocurrencies, where they require proof of work, that’s something that they need to do and it does require a lot of computational effort and therefore electricity, but then other blockchain technologies have different consensus algorithms, ones which are not proof of work, which do not require so much electricity, so some of the assumptions that people make, “Oh well I could never use blockchain in my company because it would require tons of electricity,” I hear that a lot from customers, it's a misunderstanding about different blockchain technologies. Not all blockchains require that or have that. So there are a lot of misunderstandings and most blockchains are very very different from each other and the way that they operate so you have to be very careful and clear about which one you are using.
Zac Abbott: Ok so if we’ve had a look at what blockchain is, where it can be used, what it’s currently being utilised for and potentially some of the advantages or disadvantages of using it within a company. Moving on from blockchain let's take a look at machine learning. Can we quickly define what machine learning is, Craig? Are you going to say it's machines that learn?
Craig Lodzinski: Yeah it’s teaching computers. Yeah it’s how we teach computers and the differentiation in machine learning is you're taking a training data set or historical data and you’re teaching machines information without discrete programming and discrete instructions.
Zac Abbott: And Martin, from what you're saying why is machine learning so prevalent and influential at the moment for developers?
Martin Beeby: You start to see that machine learning is becoming part of every kind of application. So it doesn't matter what you're using, whether it be a dating application or a spellchecker, they're all somewhere using some level of machine learning nowadays. And so as a developer you’re more and more faced with the challenge of, well how do I integrate some of these things into my application? And that's becoming easier and easier as well as models are developed by big companies like AWS which make it easy to just access those models as services, you can start doing really interesting things like taking an image and recognising what's in that image and seeing faces and stuff. So the services are becoming more and more prevalent and you can do more and more things with these services. But what I find is becoming more and more interesting is it's not just a case of me providing a piece of data to a trained model and it giving me back some information, we’re getting to the point now where we are also able to give our own historic data to services and then those services intelligently make predictions based upon our own data without me requiring any machine learning knowledge myself. So we have, for example, a service called a recommendation service where you can basically build an amazon.com style recommendation system. So if you bought this, then you’ll be interested in this, you can build that whole thing just by using a service by providing your sales data and then it will make predictions for you and all you need to do is call an API you don't actually need to learn anything about machine learning. So what I think is happening for developers, it’s becoming simpler and easier. And then getting into more interesting avenues and niches. So for example something I'm really interested in at the moment is this, a new service from AWS called CodeGuru which is a code review system. So I write a piece of code as a developer, I’m a software developer, now ordinarily I go and speak to someone else on my team once I've finished it and checked it in and they do what we call a code review, they look at the code, they ascertain whether it's bad or good, what needs changing and CodeGuru allows me to check it in and in a computer effectively looks at my code and makes recommendations about how I could improve it. And that is closing the loop and tightening that loop for developers and making us more efficient and highlighting a problem that we might have in our code, things that we can improve upon. So what I think is becoming more interesting today with machine learning is that it's becoming easier and easier to use it inside of applications and it's starting to benefit people in all kinds of different niches and different avenues.
Zac Abbott: And obviously you've been through a bit about how the developers of today are using it, so is it just a time-saving tool really, or are there more things they can do with it?
Martin Beeby: I think it's a superpower. Not just a time saver, it's a literal superpower, so I can do things today that I physically couldn't have done previously because I can put the world's computational power to solve a problem. I can't, for example, look over a billion records in a database and make predictions about what those things mean, but a computer can. So it's giving me capabilities that I didn't previously have. So now a developer using something like CodeGuru is now a better developer, so you get to the point where actually it's impossible to do your job without utilising this service as well and I think that's where the power of artificial intelligence and machine learning comes. When it combines the creativity of humans with the incredible power and the superpowers that machine learning can give us, so it's that combination which allows us to do that things that we wouldn't have been able to do previously.
Zac Abbott: And are there other organisations that can give the same services and things like that?
Craig Lodzinski: So a lot of the foundations of machine learning and AI technologies go way back and we've discussed those kinds of stuff before in terms of the origins, but it all comes from the scientific community, from the research community and is based all in mathematics and sometimes we potentially, as an industry, suggest that things are machine learning and AI when they're actually just linear regression, but it add a few zeros on to a paycheck for some people, so that does happen. But there's definitely universality and there’s a lot of open source and community driven projects in the ML and AI space, but for organisations it's about accessibility. So absolutely, you can go and build it yourself, you can use services from a wide range of organisations, or you can plug into embedded services which is increasingly what we’re seeing, particularly on the inference side, so making those decisions, being able to look at historical data rather than fully building the custom models. We're starting to see, now we mentioned databases in the blockchain segment, I do predict that these types of services are going to become these underpinning application services in the same way that a database is, in that if you currently have CCTV cameras, you'll be able to tap into computer vision services that may be offered by that vendor, by your cloud provider, by existing software that you already have.
Zac Abbott: Ok and then how can organisations adopt and implement machine learning today, is that something they are able to do easily?
Martin Beeb: They're probably using it already. I mean if you're using a Saas application that's been built in the last 5 years, you’re using machine learning some way. I was writing a blog post this morning, I use Grammarly heavily, Grammarly’s a great AWS customer and when you're writing, basically Grammarly makes grammar suggestions and spelling suggestions. They are doing all of that by using an AWS service called SageMaker and they’ve built a custom model which they can basically use the historical grammar information and spelling information to generate predictions on what you were meant to be typing, or the spellings that you're meant to be using. Great customer, thing that I use all the time and it's just part of my workflow, I didn't need to know that it was machine learning, it just is and you find that all the time, if you're using a laptop, if you’re using a phone, if you're taking photographs on your iPhone, you'll notice that they are all being tagged, so if you do a search on your photos, it's using machine learning to do all the tagging and metadata information. It's all around us. So I think you'll find that most organisations are using it all the time and it's becoming more and more common place and then people can make decisions about, you know, I've got a specific use case where it would be great if I could see a little bit more about that image or understand a little bit more about the context, and they can use machine learning to do that. So for example, Disney have got this huge back catalogue of video and animation and they've recently done a project whereby they use machine learning to catalogue and create metadata on all of that content, so that their animators and their producers and directors can easily search by tag name to find other examples of that work previously. So if they wanted to know something specific about how Finding Nemo, how Nemo moves in Finding Nemo, they can search for all the scenes where Nemo's in and where he's doing a certain movement and then they can use that to base the future animations of their other work. So they’re using machine learning to do things that would take years for humans to do but now putting at the finger tips of those of designers, of those animators, those directors so that they can use it to create better work in the future.
Zac Abbott: Wow so if that's how it's being used now, looking towards the future, how do you see machine learning evolving, will there be any crossover with perhaps blockchain, or how do you see it?
Craig Lodzinski: So I think the possibilities of machine learning and AI as a set of technologies, because it's a fundamentally different approach to how we interact with machines and how machines become more cognitive, more human and more useful to us as humans, this is only going to get broader and broader and there will be peaks and troughs, there are certainly certain ethical implications and considerations around the applications of the technology, but the fundamental principles and the building blocks are going to continue to spread and be very very important.
Martin Beeby: I would say with machine learning and blockchain that's a really interesting segue and intersection, but there's probably, I genuinely think most technologies don't exist in bubbles, they all often are made part of bigger systems and you can absolutely see someone, for example, using machine learning in conjunction with something like blockchain to list and log all of the ways a machine learning algorithm might have made or come to a decision, because that's one of the ethical problems with machine learning is that we're thinking about how did we come to, or arrive at a specific prediction or output and there's lots of work and lots of companies which are going into trying to make that auditable, so that would be some examples where you might use blockchain and machine learning together but it would be kind of niche.
Zac Abbott: Ok so we’ve looked at machine learning a little bit deeper there. Craig, something you mentioned in our tech predictions episode earlier this year, episode 1 of this season, you mentioned that we might be moving closer to the potential of a commercially viable quantum computer, where do we stand with quantum computing at the moment?
Craig Lodzinski: In terms of quantum computing, there’s been potentially a lot of hype and a lot of information about, ‘have we achieved quantum supremacy?’ from Google, Honeywell have come up with some really interesting hardware recently, there's been some great papers, there is a risk that we're over hyping these technologies and I'm aware I’m not helping that and potentially telling you about what we call a ‘quantum winter’. There are companies out there like D-Wave, like Rigetti, like ionQ who are building commercially available quantum computing technology, are making it available to individuals, this is no longer trapped in purely laboratories and purely in research institutions, we are starting to see commercialisation of quantum tech.
Martin Beeby: And with all of those three that you mentioned, D-Wave, ionQ and Rigetti, we actually have all of those available today in AWS. So if you want to you can go into the AWS console, you can build quantum algorithms, you can test them against simulators and then if you want to move to that next stage you can actually run it on those physical pieces of hardware, physically which we operate and maintain in super chilled rooms or near vacuum conditions. So you can play around with quantum computing today, but the reality is, before you get too excited about that, is that we’re probably a good 50 years away from actually having real tangible benefits from this, I think. It's a long way off, this is not a blockchain or a machine learning conversation, quantum computing is much further away. It’s going to require a ton of investment and research and my company, AWS, we’re doing a lot of that at the moment. But for the reality is, for most people, there won't be direct commercial applications that are acquired for a long time I don't think.
Zac Abbott: So quantum computing - not necessarily having a big impact on organisations now, outside of quantum computing tech organisations?
Martin Beeby: Yeah, precisely. Unless you're, maybe, I mean there are certain companies which are really going to be interested in what it could do and where it would change things massively so if you've got a problem, if you're trying to solve fluid dynamics, you've got a real computationally complicated problem then you would be looking at quantum computing thinking “how can I utilise this?”, but for the vast majority of businesses up and down the country and around the world they're not going to be finding a solid application how they can integrate it with their SharePoint system, it's not at that level yet. But it's definitely something we’re, at that kind of research stage and there’s interest going on and you can start playing with this stuff but it's very theoretical and far too academic, in my opinion, when you get into it, when you start using it. I've tried to build quantum algorithms and have not really... I've done, followed the tutorials, I don't really understand what I'm doing, I don't really understand necessarily the implications of it and every time I try and pretend, I realise my knowledge gets holes poked it very quickly because it's a very complicated thing to try and understand.
Zac Abbott: We saw from our Softcat business tech report last year that quite a lot of organisations are slightly hesitant to embrace emerging tech and new tech. How do you think something like, obviously as you said we're way off, but something like quantum computing can become relevant for organisations of all scales, not just the biggest tech companies or the biggest tech consumers?
Martin Beeby: I think the biggest challenge that you're going to get with something like quantum, initially for customers is going to things like security. We’ve mentioned blockchain, we've mentioned most companies require some kind of public private key cryptography. All of that is potentially undermined by the ability to factor large integers and so if quantum computers make that really easy and someone has access to a 200 qubit quantum computer then they can undermine a lot of the current way that we do security but with that said, at AWS we already have quantum resilient cryptography, so if you look at some of the stuff in the key management service that I was looking at yesterday, we have examples of quantum resilient algorithms which can, you know... There's security companies, people like AWS are already thinking about these sorts of things and making these things available, so it's not necessarily a problem but it's definitely something interesting and if you're a technologist like me it's a super interesting thing to look at, but I don't think the broad applications are going to be that big for a while.
Crag Lodzinski: Yeah agreed and I think a lot of the work that we’re seeing done on quantum is, at the moment, getting ready for quantum. One of the big things that's going to hold back with emerging tech and we're seeing organisations struggling to adopt areas like blockchain and to do really clever stuff in data science and machine learning quantum we are way further down the line with that, as Martin mentioned, you have to keep these things in near absolute zero condition temperatures, they require cryogenic wiring, there's no Microsoft server quantum edition, you can't just go… I dread to think what the licensing cost for that would be. I’m sure our Microsoft team at Softcat will have a lot of learning to do on exactly how you license... presumably on a per cubit basis, but that availability, that commercialisation of being able to switch it on, we're starting to get there, but we’re still a huge way away from this being something that you can have in a regular data centre, that you can do conventional computation on.
Martin Beeby: I think that's maybe one of the most interesting things, this is a technology that I don't think you'll ever have in your own data centre. I think it's only hyperscale providers that are able to provide that kind of, those super chilled conditions, or the near vacuum conditions that are super computer, sorry a quantum computer requires. So it's really interesting from that point of view is that it might be the only time which we ever see that sort of computing which isn't possible to do at home, or isn’t possible to do in your own data centre.
Zac Abbott: So we’ve had a quick blast through blockchain, machine learning and quantum computing, is there anything else on the horizon that you think is going to take over, or that organisations will start to utilise to benefit them?
Craig Lodzinski: I'm going to go on my personal passion opinion, and this is something I've mentioned and will continue to do so, is that I think we are starting to see a revolution in how we are using video. And a lot of that comes from computer vision and trends in machine learning, but the use of video as a universal, ubiquitous sensor is, in my opinion, is going to explode over the next few years. We’re seeing it being used in things like shelf replenishment in supermarkets, in looking at desire lines, looking at advertising and creating intelligent billboards that use video as a sensor to detect people, to detect images, to detect conditions. The ability to take vast streams of 4K, 8K etc video to identify everything that's going on in there and to make assumptions, predictions and derive information from that, is going to be really significant and I think organisations are going to have to have a look at how they are designing their topologies, how they’re designing their Edge networks, how they are moving dataflow because video is big data bandwidth, it's big file sizes and that's going to have an impact on how we interact with all of our platforms as well as in our day to day.
Zac Abbott: And did you say that organisations are already using, or starting to use this?
Craig Lodzinski: Absolutely, yes, there's... take a look… I know we're going big on Amazon but they are quite a significant company
Martin Beeby: And I'm right here so…
Craig Lodzinski: We have Martin, so we're going to go through all the examples but if you look at Amazon Go, the supermarket, and Alibaba have a similar projects out in China as well, this ability to just stroll through a supermarket and walk out, that’s all based on the intelligent use of video as a sensor and we're starting to see, for example, in autonomous vehicles, video and lidar are absolutely critical in that.
Martin Beeby: I love that we actually have a thing in our team with the Amazon Go store, which is, it's right next to all the Amazon buildings and whenever we’re in Seattle, my team has an effort to do this, where we take our Amazon Go mobile phone application and we have to go into Amazon Go store and get a Diet Coke and a chocolate bar in the shortest period of time because it tells you when you go in and then when you leave how long it took and so I think our record currently stands at 36 seconds for going in, getting a Diet Coke and getting a chocolate bar and getting back out. But we basically take turns, it's like a little race.
Zac Abbott: Sounds like you guys have loads of fun.
Martin Beeby: Well, you know…!
Craig Lodzinski: Are you hiring?! Because I am quite a quick runner and I do like Diet Coke.
Martin Beeby: So it's really interesting, but we see this all the time, video is used a lot more. I was looking at project recently by Sky News and they were using some AWS services during the royal wedding when Sky News were filming all of the different people arriving and with the royal wedding, some of them are really famous people, but some of them are kind of famous but you're not sure who they are, and so the editors were trying to find a way of making it obvious to the viewer who was showing up. And so they were using a real-time stream of the recording and overlaying information, information about who the different people arriving at the royal wedding were in real-time and so that improved what the viewers were looking at. And we've seen it in all sorts of different scenarios, where you’re taking real-time video streams and then overlaying information. Six Nations rugby is an example where AWS again were trying to overlay statistics about the game, based upon the information they'd retrieved from video streams during the Six Nations rugby games. So it is, you're completely right, video is becoming more and more important. I'm working with video a lot more, as a developer, than I ever was. Fundamentally splitting up into images, then working with images, but generally it's still trying to do that in near real-time is really difficult and made easier by some of these cloud services.
Zac Abbott: Well we're about done with…
Craig Lodzinski: Technology.
Zac Abbott: ...With the episode. Well, with technology in general!
Craig Lodzinski: We've solved it lads.
Martin Beeby: Completed it.
Zac Abbott: I think before we start maybe it would probably be useful, we've been through what these things are, blockchain, machine learning, quantum computing in quite some detail, but Craig, if could you give us a quick 10-second summary of what they are and then which of the three is going to have the greatest impact on organisations right now.
Craig Lodzinski: Sure. Blockchain is a group of distributed Ledger based systems that are cryptographically defined and that organisations are using in a variety of ways, but still in its infancy. Machine learning is a different approach to how we programme systems without using discrete programming, but using information and historic data sets. And quantum computing is an alternative to classical computing whereby you use quantum bits rather than conventional bits and that has some very interesting impacts on how you can deal with factoring large integers and running computational tasks. Machine learning is the most impactful right now and has had the broadest impact and will continue to do so. Blockchain is a little bit further away from prime time, although there has been a lot of hype, we’re now getting past the cryptocurrency phase and into commercial reality and seeing a lot of organisations taking blockchain or similar systems and similar technologies and using those in anger. And quantum computing is still a long way from having a very broad impact on organisations, but thanks for the work of people like Peter Shaw, we are starting to see preparations for it and starting to see commercial organisations like AWS working on quantum systems.
Zac Abbott: Perfect. Well that's it for another episode of Explain IT. Craig, Martin, it's been great talking with you, thank you very much for your time. If anything on this show has piqued your interest or you have any questions, please do get in touch [email protected] Also don't forget to click subscribe so you can stay up to date with all the latest episode of Explain IT wherever you get your podcasts. Thanks for listening to Explain IT from Softcat.