Apr 4, 2018
| 3 min read

Podcast #6

Navigating Disruptive Technologies: an Exploration of AI, Big Data, Blockchain and IoT

Our conversation with Bruce Weed covered a lot of ground around disruptive technologies – with a central point that the most significant innovations are coming from the combination of IoT, AI/Cognitive and Blockchain used in combination.  Bruce helps simplify the concepts around IoT, AI and Blockchain, and provides a wealth of tips, insights and resources for individuals and companies starting out on their journey.  Our conversation covers the basics and the nuances of these technologies, tracing the developmental arc of technology and industries, and providing many practical examples of use cases for AI and Blockchain in an IoT context across health care, supply chain, manufacturing and other industries. 

Books 

Leonardo da Vinci by Walter Isaacson 

Extreme Ownership: How U.S. Navy SEALs Lead and Win by Jocko Willink 

Resources

Blockchain for Business (Blockchain & Hyperledger Project) 

IBM Big Data and Analytics

  orange-line.pngWe'll notify you weekly about new podcast episodes, upcoming guests, and news. You can subscribe to the podcast and if you'd like to be considered to appear on the podcast contact us.

 

View Transcript

Hello everybody this is Ed Maguire, Insights Partner at Momenta with another episode of our Edge Podcast, and today we have a special guest, we’ve got Bruce Weed from IBM who is a Program Director of Enterprise Developer Advocacy, but more than that, Bruce is a fixture on the New York tech scene. He and I have sat on at least one panel, and I’ve heard Bruce speak many times, he’s a passionate and articulate advocate of emerging technologies, including IoT, AI Cognitive, and Blockchain which falls right in our wheelhouse, and we’re going to look to explore some of his insights and experiences in this podcast. So, Bruce, thanks so much for joining us. 

You’re very-very welcome, and I appreciate you having me today and look forward to the discussion. 

 

First off Bruce, could you talk a little bit about your background, and what in your experiences has helped shape your views of what we call Connected Industry, or IoT, but this emerging wave of connected products, and the analytics that go with it? 

My background, I’ve got a degree in Computer Science as well as mathematics. I’ve always been interested in technology, particularly bleeding edge technology, and so I’ve had the opportunity over the last couple of years starting back with getting involved in Big Data back in 2010, to take that and morph that into working with these other technology fronts, including IoT, AI, and Blockchain, and we’ll talk more about those I’m sure, as we move forward. But really that’s kind of been my background. 

I’m working with both enterprise customers as well as start-ups around these new technologies, and really leveraging the Cloud as the infrastructure to go out and rapidly prototype or build out these solutions that they’re working on. So, that’s just a quick snapshot on a little bit of my background. 

 

IoT is moving I guess from its childhood to adolescence. Share a little bit of your perspective about how you’ve seen the evolution of Connected Industry, what have we learned as large industrial companies have looked to move from this vision to get to production applications? 

I think when we look at IoT and the metamorphosis of it; IoT has been around for a while if you look at in manufacturing plants there’s always been censored devices, everything from the lights in the building, to the heat, analyzing certain equipment, and other things that they measure and control. But the thing that’s really changing I think is looking at this information on a broader scale, on a broader perspective, and also sub-analyzing some of the stuff more locally, so at the Edge. Historically you might have as I’ve mentioned one of these manufacturing plants, they do stuff locally, what they may not be doing is comparing necessarily in the past data that’s happening in that manufacturing plant, let’s say that’s in Indiana, to a plant that may be running in China somewhere, or in India, or some other country. 

So now through the creation and the analysis of Big Data, and using data weights, we’re able to store that information and look at it across a broader base, so that’s one point that’s changed. I think also the introduction of AI, so really starting to analyse some of this data more locally, and figure out if I’m having a problem with a particular item, is it a critical area, or a critical item that I need to upload to the Cloud for additional analysis, or for notification? Or, is it just more I’m collecting localized data, everything looks more or less in check, maybe the engine is running a little bit hot, but I realize through AI, its summertime the air conditioning is overworking, therefore everything is really kind of normal and where it should be. 

So, I think these are some of the changes that we’re seeing, and the most important one and I’ll continue to mention that as we talk, is really leveraging these technologies together, that’s where the real power comes from. Obviously IoT by itself is powerful, AI is powerful by itself and so is Blockchain, but when you start to use these things together, that’s where the real horsepower comes from. 

 

That’s a great point this idea of combinatorial innovation, or the power of combining these technologies, and AI has certainly been one area where there’s been I would say, maybe a bit of misunderstanding about the power and the potential, but it’s also not brand-new technology either. Could you share a little bit of the background for some of our listeners who may not have the perspective, just how this concept of AI and cognitive computing has evolved, and why it’s different from traditional predictive analytics or statistical methods that would give you just a forecast? 

If you look historically where we started, we had the original analysis which I’ll kind of label as descriptive, where we looked at things, what happened, how often did it happen, so we do some discovery, then we do some analysis of what the actual problem is. Then that elevated into really getting into more of the predictive, where we started to look at trends and modeling. From there we got into more of the prescriptive how can we achieve some of the best outcomes. But when you look at cognitive that really takes you to the next level which asks what is the next best course of action? 

So, as an example, one of the clients we worked with had a dermatology application, and that application has infused AI into it specifically. They’re able to go in there in the doctor’s office and look at different treatments for patients and actually come back with a recommendation, and not just a recommendation but a competence level, so it may come that first recommendation is at 90 percent, the second one could be at 60, and the last one at 30, and then the doctor needs to ultimately decide because they’re responsible for the patient’s health, but now they have something they can use. 

They may end up choosing the secondary solution because for that particular patient it’s a better fit, but it very quickly analyzes that data. What’s nice is, it’s not just looking at patient data for that particular patient, it was looking at a broader set of patient data, a broader set of documents, so maybe they downloaded information from the Mayo Clinic, or John Hopkins, or other places where you can take the PDFs, analyze that information, and really make sure you’re attacking it from the best types of treatments. But when we look at cognitive I think it’s important to understand that really what cognitive technology is, it’s really processing information more like a human than a computer; it’s understanding natural language, its generating hypothesis based on evidence and learning as it goes. So, these couple of aspect of understanding natural language, leaning as it goes, some machine learning, all these things are critical and cognitive.  

But the other aspect is cognitive is really here to augment what we do as humans, and quite frankly this is in line with all technology; when I use my smartphone or my laptop I’m using that to do whatever it is I need to have done, it’s not like the smartphone is running my life, or my laptop is dictating what to do, I’m using these as tools so that’s an important thing to keep in mind. 

 

That’s incredibly powerful technologies, and of course we talked about this combination, and the other big technology is Blockchain which is starting to emerge and distributed ledger technologies. Could you provide a view of how you think of Blockchain and distributed ledger technologies, how does it fit into this whole equation? 

When I look at Blockchain, just to give folks a little bit of a background of what it is, as you’ve already mentioned it’s this concept of a distributed ledger, and in the old system obviously each person would have their individual ledger that they used to record transactions. This is basically a distribute ledger where the parties involved have access to the same information, you have to get consensus from the people involved in particular transactions. They’re able to go back and do auditing accordingly to understand the provenance of where this stuff came from.  

You’re not able to change the records, so everything is immutable, it’s that type of object similar in object-oriented or functional programming, an immutable object is really one that’s unchangeable. So, you have that level of safety, and obviously Blockchain also leverages encryption, and it gives you that advantage of being able to set up the right set of processes to make these transactions more efficient, particularly as it relates to eliminating errors, to be able to trace things back. Essentially there’s four main components; you have the shared ledger which really is made to distribute across the business network. You have smart contracts which are the business terms embedded in the transactional database and executed within transactions. You’ve got the privacy which is really ensuring the appropriate visibility, the transactions are secure,  and then finally that consensus aspect that all parties are in agreement and can verify that network. 

So, as an example, if I were doing some type of auto-leasing, I could have an individual lease car, and basically eliminate having to go to a dealership and do a bunch of paperwork; all that could be done through smart contracts where people privy to it would be people like the leasing company, potentially the manufacturer because ultimately when you lease a car, it’s got to be built and come from the factory. The leasing company would be involved, there may be a local dealer where you may actually pick up the car depending on how that gets brokered, but you can actually simplify that process and make that fairly complete. I think that we will see this combination of Blockchain and other technologies as we move forward. 

 

I know IMB has been working with a number of industrial companies, and Maersk comes to mind as probably one of the most public use cases. Where are you seeing the most interest, and early adoption of Blockchain technologies at least from a standpoint of an industry or use case? 

When you look at Blockchain there’s a couple of different areas where we see this type of usage, and where it’s going. I should mention that the Blockchain industries to give you a perspective, and these are rough numbers that have come back from analysis and industry reports; the market is supposed to grow to 2.3 billion by 2021, that’s basically a compound growth rate of around 62 percent, so we’re starting to see a lot of build-up in that particular area. So, that’s what you’re going to see as far as the rapid growth, a lot of people are doing innovation now. 

So, areas that are broad high level that are probably very immutable to this are everything from food safety, and I’ll talk about that in a moment, to obviously financial-types of transactions, that could be cross-currency payment, it could be bill of lading, it could be retail banking, public records, securities, digital property management, syndication of loans, supply chains. Two examples that I’ll call out, one is one we’ve done with Walmart, they had an issue in China where food was going from the farm to the store, in particular pork, and some of it was going bad. So now they’ve implemented a Blockchain solution that can actually track each participant in the food supply chain and understand what’s happening. So, its sensor data, they’re able to keep track of things like temperature that the meat is kept at, duration of the complete process from farm to retailer, they can look at things like humidity levels, all this stuff with sensor data and put it in a Blockchain to make sure they can go back and edit and look at this data appropriately. So, here’s a combination as I mentioned, where you’re merging the Blockchain and the IoT together to do this type of analysis. 

Another example is internally in IBM we use from a global financing perspective, IBM global finance provides financing for our business partners as they go out and work with clients downstream. So, to that end we’ve implemented a Blockchain to provide visibility and providence across the supply chain. So, what we found out is some of the benefits that’s reduced dispute resolution time by 75 percent, it’s also released additional working capital, and this is something we’ve been doing now for the last year and a half to two years, so it’s definitely paying dividends. 

But that’s just a quick couple of examples in a little more detail to help you understand what’s happening in that arena. 

 

The use cases in global trade for instance and supply chain, seem to be really compelling to be able to reduce the amount of time that vendors, or suppliers can get paid and validated, and of course the concept of providence. I was chuckling to myself as I was hearing you talk about Walmart, I was thinking is this the Internet of pork! We have Blockchain secured bacon which I know a number of people who probably would be very excited to hear that. 

What are some of the challenges in implementing Blockchain solution, how do you look at the state of the market where we are right now? And maybe are there technological challenges or organizational challenges, and I’m talking about the lack of developers has a real constraint, but what do you see as the major hurdles and obstacles for this technology to start going mainstream? 

A couple of thoughts, there are a lot of companies working with this technology inhouse to start to experiment where they get some internal projects going, get some experience. But right now, I would say it’s a little bit skills limited, it’s a new area so obviously everybody needs to get skilled-up, and developers in particular need to go out there and learn, and to work with technologies, like hyper ledger composer, and understand the hyper-ledger fabric, because essentially its really about building out business applications on top of these two arena, using hyper-ledger composers to do that is one tooling example. 

But it’s also understanding as you get into this, it’s not just understanding the actual programming side, its understanding the business side as well. I think it’s very much similar if we look back to 2010 data science, that area had the same sort of aspect, it wasn’t just about, ‘Oh, I know how to program in R, so therefore I’m ready to go and be a data scientist’, the whole concept of a data scientist is this person that in theory is made up of three major components, it’s a person that can program, it’s a person that understands data science statistic analytics, and it’s a person that understands business, obviously more skewed towards the business they may happen to be in. So, if I’m working in the financial sector as a data scientist then I don’t necessarily need to understand the healthcare sector, but you need to understand the industry you’re in to really apply that. 

The similar thing here with Blockchain, as you look to implement these things it really is beneficial to understand the business and how you’re implementing the use case and, have some level of that knowledge coupled with understanding the basic concepts of a distributed ledger, along with the programming aspects that we talked about. Some of the people now are doing programming in go as well for Blockchain, which is another alternative language to get involved in. So, I think there’s that learning curve, right now I would say is the first major improvement. The second one is getting the trust level built up if there’s enough use cases out there where people feel confident that yes, this stuff makes sense, we understand what it’s going to do.  

So, it just takes time, it doesn’t happen overnight, but I think there’s enough endorsement around this technology that has got a real solid future, and its already got some really good business use cases in place. 

 

It certainly is extremely promising. I do think in technology, certainly in the investment community there’s a little bit of a bright shiny object phenomenon, where people get enamored with new technologies, and then they become a bit like the baby with the hammer, where they think everything looks like a nail. If you hear sceptics or pushback on Blockchain technology, sceptics like to say, ‘Well, it’s just an encrypted database, it’s not really anything that special. And certainly, I think they can make an argument that you don’t need Blockchain for everything, it’s certainly not the solution to all the world’s problems. But with that in context, where do you see the most relevant uses of Blockchain compared to what databases can do, and are people thinking about Blockchain maybe prematurely as a replacement for traditional databases? 

That’s a great question, we had the same dilemma with Big Data where there were a lot of people trying to use it for everything, and early-on I talked about Big Data, particularly Hadoop and Spark was not a replacement for traditional databases, or no sequel databases like Mongo as an example. So, I think people need to get clarity on Blockchain as well. So as an example, if I need high-performance transactions in a millisecond arena, Blockchain is not the right answer. If I have a very small organization, no business network, Blockchain is not the right answer. So as an example, let’s say I’m a mom and pop storeowner, I have one store, I don’t really necessarily need Blockchain where I can have my general ledger, I could use some software accounting package and that’s probably the right answer for that small business, where it’s a one shop type of setup.  

Blockchain is not a database replacement, it’s not a messaging solution, and it’s also not a transaction processing replacement either. So, we have other technologies that we use for messaging as well as on the transaction processing some highly developed systems for that. So again, it can be implemented in a lot of different industry use cases, but it’s not a be-all and end-all for every particular business case or use case that you have. 

The other thing I should make mention of, I think at least from the beginning and maybe over time it will change, this technology is probably more apropos in enterprise-type businesses, or larger business versus the consumer market, I don’t necessarily know if somebody has a start-up that’s focused more on a consumer type of app, and when I say consumer I don’t necessarily mean a consumer business-oriented app like home banking just to be clear. I’m talking about other types of fun little applets that pop up on the Apple store every day. So, I think we need to figure out where does this fit, and where is the most applicability and go from there. I’m sure it will propagate down as we move forward, and as the technology becomes easier to use, and more people are skilled on it. 

 

It’s a great point that you made about big data Bruce, because the idea that Hadoop can handle every sort of analytic job, I think was floating around a few years ago when people were just getting into the technology. But even so, I think we have this incredibly diverse and rich array of techniques and tools for data analysis, but when we start to apply it to connected industry, or what we’ll call IoT, we’re still doing some very basic blocking and tackling.  

If we circle back to some industries that are late adopters of Connected Industry, we could say certain types of manufacturing, transportation and connected spaces that are not necessarily working with brand new equipment, but are working with retrofits; how do you approach the challenges of applying the right tools to the problem, as your looking to start to take data from processes, and then start to apply this continuum of analytics, and then ultimately incorporate cognitive? I guess what I’m asking is, what would be a good beginner guide for cognitive or AI technologies in a connected context? 

The first thing to look at is to really understand what I call the cognitive IoT marketplace, just to put a little bit of parameters around, 2020 there will be approximately 25 billion installed IoT devices, and the market potential economic impact by 2020 will be about $3.6 trillion. A lot of this is skewed heavily towards B2B, will be probably be about 70 percent of the market roughly. So, when you look at cognitive IoT, it’s really this emergence of linking the physical and the digital worlds to transform business. I think that’s really the main attribute, and how to get there is really looking at first some examples that we see today, and interestingly enough, some of them are consumer oriented that people use every day and may not make the connection, ‘Oh, yeah this is AI, and IoT’, but things like Google Home, Amazon, Echo, if you look at Tesla and what they’ve done in the automotive arena. 

The other one that people don’t realize is, there’s a huge opportunity – the biggest area… actually that most people don’t know this, is if you ask them, ‘Where’s the biggest area for IoT today?’ ‘Where’s it being utilized the most?’ Particularly coupled with AI, and that is in the security area, so security surveillance, looking at people hacking into various IP addresses, any type of security, a lot of that’s being analyzed through AI, and is being leveraged through obviously IoT-types of devices. So that’s a huge industry opportunity in that security area, whether it’s more the out-bound, like I said, surveillance types of things, cameras, sensors, or if its more from a computer security standpoint, either one of those.  

As far as getting started in this arena and what’s the best way to do it, I would probably look at a couple of things; you could look at IoT and start to immerse yourself in that, we have a thing called TJBot, it’s a little bot that you can get and put together, and it’s based off raspberry pie technology, it’s very small, it’s made out of cardboard. You could control it through your application that you develop on the Cloud. The little bot can move its arm up and down to wave to you, it can flash certain lights, it can do different things, you can talk to the bot etc. So, it’s a neat little thing to get started. To that end we do have what we call developer patterns, you can Google ‘developer patterns’, it’s out on the developer works website, or IBM code area, where you can go in and look. We have different areas broken out by the focus of the technology, so we have different patterns for Blockchain, for IoT, for AI, and it goes on from there, including things like container technology, other different areas too. So, you can take a look at that to get started. 

On the pure AI side, you could look at particular areas depending on what interests you, so maybe its machine learning, maybe its natural language type of work. We even have things where we have services that can analyzing the tone of somebody’s voice or analyzing actual text which is neat to build out a profile on somebody. I actually did this recently, as a demo I put in text which Abraham Lincoln had written, it came back with a profile on him and it was actually pretty darn accurate in terms of what the profile of Abraham Lincoln was. So, there’s a lot of different ways to get started, my only recommendation in general on any of these things, start small, learn in a particular area, and then grow your base from there. 

 

That’s great insight Bruce. One of the challenges certainly with AI right now, I think there are a lot of misperceptions in the market about what it means, I think there’s a lot of dystopian scenarios that people see in science fiction and get a bit nervous about things. What do you see as key disconnects or misperceptions in the market that are maybe not quite so extreme, but when companies are looking to implement and adopt some advanced IoT and AI technologies, what would you highlight, or at least is there something that jumps out at you as a real disconnect that’s probably unfounded? 

I think a couple of disconnects, I’ll first talk about one that gets back similar to Blockchain or some of these other technologies, that AI is the answer to everything, it’s going to solve the world’s problems. As we see, as in the evolution of history, one problem gets solved and then there’s another one that gets created that has to be solved, it’s a never-ending set of problems that we’re dealing with. At one point it was polio was a problem, then we had a vaccination for that. Now, believe it or not the flu has caused a lot of problems, they’ve gone back and asked the government to start to invest more money in refining the flu vaccination, because this year particularly there were a lot of deaths. So, I don’t think we’re ever going to run out of problems, and AI isn’t going to solve them all, so that’s number one misperception. 

Number two, you talked about fear of the technology, and that gets into probably a little bit more on the AI robotics side perhaps, than it does maybe pure AI.  I think at the end of the day, it’s how do you treat this stuff, and how do you augment what you’re doing versus letting technology tell you what to do? Technology comes back to make recommendations, it can give you data, it can summarize data, it can analyze data, it can do a million things to one, but at the end of the day you as the individual running a business, or you as an individual analyzing something, need to come up with what you think is a recommendation based on this information. 

I think it’s a little bit of a cop-out to say, ‘The machine told me to do ABC and D’, I don’t think that’s really where we want to go, I think the machine is here to help. Now, having said that, there may be cases that are very benign and it’s a very finite set, as an example, I go into a department store and I work with a chat bot and I say, ‘I would like a sports jacket’, the chat bot comes back and says, ‘What brand would you like?’ I put in my brand, if they have that then they recommend, ‘This is where its located, these are the colours we have, it’s a fixed finite set. If the particular brand is not there, AI is good enough to recommend a secondary choice, so-forth and so on, and I can decide as a consumer whether I want to buy it or not. There’s not a lot of potential downfall there, because again it’s a kind of benign topic, but very important in terms of productivity for the consumer and helping out in terms of from the store perspective or retail perspective. 

Getting to things like medicine or areas like that, that’s a little bit different, or financial decisions. The computer comes back and says, I shouldn’t be doing that blindly, I should be understanding and making a decision as a human-being based on the best information I have available to me, which is what everybody’s done historically anyway. When people make a decision, it’s based on information that they have at that particular time and make that decision from there. 

 

Yes, it’s funny because I think there’s this debate about whether autonomous systems can have judgement, whether it be self-driving car, basically what decisions to make if there’s an ethical decision; do you hit the deer on the road or do you hurt your passengers? I know a lot of these are issues that are going to unfold over the next several years as more powerful systems start to unfold.  

I just wanted to turn the conversation back to Blockchain and ask you the same question that I’ve asked about AI and Big Data, which is essentially for a novice or for a beginner in a business that there’s a lot of noise out there, and certainly a lot of people trying interesting ideas but, what proof-points do you think users in businesses should be looking for to give them confidence around Blockchain, and are there some simple ways that people can get involved just to get more comfortable with the capabilities and the nature of the technology? 

A couple of things, I would recommend reading up on Blockchain as much as you can, there are a lot of good blogs out there to get you started. I also run a Blockchain area out on LinkedIn, it’s called Blockchain for Business, and I also run one for AI IoT, and I run one for Big Data and analytics as well. So, you can see in there, we talk about things like use cases, about the technology, you can also attend local meet-ups, I highly recommend that because those will go more in-depth on that. If you’re a developer, from there you can take it into going to different workshops that we offer, and then going out to as I mentioned our IBM code site, where you can get involved and download information that you can leverage. We’ve developed code-patterns that have code out on GitHub, that you can download to augment what you’re doing, and make your development faster. 

So, there’s a lot of different thing here, obviously try to do them somewhat in order, because I think you’ll need to understand the basics of a technology before you proceed. If you start to get right into the development and you don’t really understand the concepts that could be problematic. So, that’s something you need to look at as you go forward. 

 

I would say there’s almost a universe of interesting content, and amazing insights and innovation, really in all of these areas in Connected Industry, in AI and in Blockchain.  

Bruce, it’s been really help and insightful. I also like to ask all of the guests if you have any resources or books that you like to share or recommend to people? 

Some of the resources I talked about or things I just mentioned in the last dialogue, in terms of attending meet-ups, if you go to meetup.com you can go in there, put in your city, your location, and find out what technology meet-ups there are. Definitely go out to the IBM Developer works website, you can Google IBM code, and there there’s a lot of rich resources. But in addition to that, as far as from a book standpoint, what I like to do is to talk about two areas, or two books which I would recommend, they’re not technology-related, so I like to recommend different books to people based on the kind of books that I’ve read or have been interested in, which I think have value or give you a different perspective. 

The first one I would talk about is, Leonardo DaVinci by Walter Isaacson. I think it’s really an interesting book because it gives you a perspective on how De Vinci analyzed and looked at things. I think what’s fascinating, and it’s hard to flashback to that time period because in today’s society, at least for most of us, it’s really a very-very fast paced environment, there’s not a lot of what I call spare time so to speak, so the important thread here I think is a little bit more difficult, but it’s still worth retaining, and that is the fact that DaVinci really did take the time to understand and observe. A lot of people are so busy doing stuff, they don’t listen, they don’t observe, they don’t introspect, they don’t dive into something; De Vinci actually dissected humans to better understand the muscle tissue, particularly the way the face and the mouth forms a smile, and things like this. You can see that reflected in his artwork and his paintings as an example.  

So, I think it’s a constant analyzing how do things work, why do they work, how can I make them better. What we now focus on is innovation, and innovation I don’t think happens in a vacuum, it’s really analyzing and understand other concepts and base information, and that’s how you start to innovate. That would be one book I would recommend. 

The other one is just general books on leadership. I think leadership is something we don’t see enough of, I should say good leadership is obviously leaders, whether they’re good or not is another question. So, I tend to read a lot of books like, Extreme Ownership, How US Navy SEALs Lead and Win by Jacko Willink, I look at the military because I think the military does understand leadership at its core, and I think if you’ve ever witnessed like did, during Hurricane Andrew in Florida, the military came in and very quickly organized food and housing for people, cleaned up streets, cleared streets, got things in order, there was a lot of looting at the time, and when the military came in magically the looting stopped. So, they were very efficient in every aspect to what they had to do to get the city back up on its feet. 

That doesn’t happen without leadership, obviously the other key thread there is supply chain management, so that would be another thing you can learn from that as well, but those are what I would put out there as two books. 

 

That’s great Bruce, those are terrific examples, and I think we can all learn a lot from the examples and the amazing ability to execute from the military side, and also the greatest polymath of all time, DaVinci is always an inspiration to us, and for generations to follow.  

It’s been a great conversation Bruce, we’ve covered a lot of ground. I just want to thank you again for taking the time to be a guest on the Momenta Edge Insights Podcast, and I want to thank everybody for listening. Hope to see you again sometime soon Bruce. 

Thank you very much Ed. As I joke to people when I talk about DaVinci I say, ‘If you can’t learn from an under-achiever like that, I’m not sure we’ve learnt’. 

 

Great point, thanks so much.