Hello and welcome back to another lesson. Now, what I wanted to do within this fairly brief lesson is actually to summarize Macbeth, the entire play in five minutes. This lesson is actually going to be very useful for you. If you're studying this entire play in advance of your mock exams or the final GCSEs, having being able to take a step back and look at what the sequence of events are within the play in a nutshell is really useful in terms of your revision. Okay, so let's get started. The play starts off with Macbeth winning a war against the previous Sane of Cordo. It starts off with us knowing that there's a war in Scotland caused by the Sane of Cordo, but Macbeth, who's very brave, valiant and very loyal to his king, he wins this war. And as he's heading back from this war through the woods with his friend Banquo, who fought alongside him, they meet the witches, the witches tell Macbeth, hail Macbeth, Sane of Cordo, hail Macbeth, king hereafter. In other words, they prophesy to Macbeth that he's going to be Sane of Cordo and then king. However, they also tell Banquo that you're not going to be king, but your children will be kings. Okay, then the witches disappear. And Macbeth, who's really taken by these predictions, these prophecies, then writes to his wife, Lady Macbeth, who's in the castle, and he tells her of these prophecies. And separately, we realise Lady Macbeth, who's now at their home, their castle, actually wants to, wishes for Macbeth to become king. Okay, so she really plots. She's really happy to hear about these prophecies and she wants to plot in order to influence Macbeth to kill the king in order so that she also can be queen. Then she successfully influences Macbeth when he returns. Okay, so she tells him, look, Macbeth, you've firstly been promoted now, Sane of Cordo by King Duncan. So he's coming over. This is a perfect time to kill him. And she manages to minute and influence him to kill the king. She uses his masculinity against him. And once Macbeth agrees to this plot to kill the king, he feels quite guilty and sees a floating dagger. However, this floating dagger does not deter him. This dagger actually leads him to King Duncan's room where he murders him as he is sleeping. And once King Duncan is killed, his sons don't trust the situation. They feel like treachery is still around. Therefore, Malcolm and Donald Bane, his sons flee, leaving Macbeth to take the crown for himself. And once he is crowned, he decides to have Banquo murdered by killers. However, Banquo, even if he's murdered, his son flees on escapes, which further triggers Macbeth's paranoia. And he later, during his coronation, sees the ghost of Banquo, which is his second hallucination. Separately, we learn that Macduff distrusts Macbeth and he flees to England. Now Macbeth at this stage is very consumed as his ambition is quite corrupt. So he goes to see the witches to know about his future. And they show him three apparitions. The first, which is an armored head, tells him beware Macduff. The second apparition, which is a bloody child, tells him that no one or born of woman can harm him. And the third and final apparition, which is a crowned child with a tree in its hand, tells him that he can't be defeated until Great Burnamood moves to High Dunstanane Hill.  However, these killers find his wife and son who they murder, but Macduff is up in England. Therefore, Macduff's family is killed, but Macduff is a survivor. Separately, Macduff in England ends up plotting a rebellion with Malcolm against Macbeth because they realize he's at the root of all these problems in Scotland. And he later learns that his family has been killed and this gives Macduff additional inspiration to kill Macbeth. Then we find that Lady Macbeth, as Macbeth becomes increasingly consumed as an ambition, she then becomes sidelined. So Macbeth even forgets about her and she gradually becomes quite guilty about all the terrible things she's done and she starts seeing hallucinations of spots of blood on her hands. This leads her to really reflect on all the killing she has caused. She can't live with herself because of the guilt and she commits suicide. And the play ends with a massive war between Macbeth and his troops as opposed to Malcolm and Macduff's troops.  So that's really it when it comes to understanding the play Macbeth. And especially the key events. So I would suggest try your best to really memorize the sequence of events, especially if you're revising for this play and you don't have very much time. Thank you so much for listening and I hope that helped.


I would like to wish all of you a happy Mother's Day on 14th of May 2023. Mother's Day is celebrated to remember that Mother is the most important thing in our lives. Once a Mother, always a Mother. And Mother is the representative of God on Earth. We all know that. So whenever all the females who are planning to become a Mother, just remember, educate yourself well and eat well. Do good exercises so that during pregnancy and postpartum, even when you have to take care of your baby, you are healthy. So take your vitamins regularly, go for a preconceptional counseling, meet your doctor regularly and do your exercises. This is all other aspect but fitness is also very important and mental health is very, very important.


Hey everyone, Drew with GeoArm, and I am back to talk to you about the 2GIG GC2e and how to program in the PIR1e-345 wireless encrypted motion detector. Now this is an encrypted device, remember this, so if you're not looking to program an encrypted device, this isn't the video for you. But this is the encrypted version, so we're going to go ahead and proceed on here. First thing we're going to do is you're going to go ahead and hit security on the screen. Then you're going to hit menu, and then go ahead and hit toolbox. Once you're in toolbox, it's going to ask you for your installer code. Now the installer code on my panel here is factory of 1561. Yours might be different, but mine is 1561. At this point you'll see a new menu up here. You want to hit the right arrow in the bottom right hand corner twice. At this point go into installer toolbox, and then system configuration. Now when you get in here, it's going to ask you what sensor you want to program this to. My first available is zone 1. I'm not going to program at Derek's, that's just too easy, so I'll scroll over, we'll go to zone 5. So you press the right arrow until you get to zone 5, and then you press the down arrow. At this point it's going to ask you for the sensor type. My sensor type is going to be an interior follower, which is what you would set a motion to that you don't want to delay on. That's going to be an instant motion, and by the way it only will activate in a way mode. In stay mode it will not arm, so that way you can move around the home. Now if you do want to delay on it, so that way it gives you a little bit of time before it goes off, then you're going to set it as an interior with delay. But in this case interior follower, both of those zone types by the way are both going to be non-existent in stay mode. So the motions will not be active in stay mode as long as you program it as an interior type. So interior follower is what we're going to go as, and we hit the down arrow. It wants to know what the equipment type is. It's going to be a motion here, so you just go ahead and hit the down arrow. Then it's going to want to know the equipment code. This is I believe 2869 if I'm not mistaken, and it is. 2869 is the equipment code for the E-Series PIR motion. So this device, 2869 is the equipment code for it. Hit the down arrow after you've put that in. Now it wants you to learn the device in. So what we're going to do is we're going to go ahead and hit shift, and then learn. Now what it's waiting for is an RF transmission. So I move my hand in front of this motion and it actually caught me. Usually you kind of have to tamper it if you've been moving in front of it because it's not prepared to capture motion at that point. But what I'll do is I'll go ahead and I'll open it up and we'll confirm that that serial number is in fact correct. It's going to be one second, let me get this screw out of here. Okay, got it open. So let's verify that that is our number, 0298638, and that is our number. This is our motion. So we got this guy in there, you can go ahead and put your cover back on. You'd be able to put your screw back in there at this point as well. We don't need to do that because it's time consuming. So we'll just move past that, go ahead and hit okay. After you've hit okay and you verify that serial number is correct, hit the down arrow. Now it's going to ask you for the equipment age. This is a brand new motion, so I'm going to leave it brand new. You can change it to existing if you like, but it's new. So now it's going to ask you for the loop. The loop in this case is going to be a loop one. Just leave it at loop one, hit the down arrow. Now it's going to ask you for that dial delay again. We don't want an additional dial delay on here. We want this thing to transmit immediately. If you do want a dial delay on there, you can do so, no problem at all. But I am going to go ahead and disable that by pressing the right arrow. Hit the down arrow at that point. It's going to ask you for your voice descriptor. I'm going to go ahead and hit insert and keep hitting the right arrow until I come up to motion. It can take a little bit of time here because there are quite a bit of descriptors in here. Apologies on the amount of time it's taken here, but like I said, there are quite a few descriptors in here. So let's see what we have. Motion, boom. All right, hit the down arrow after you've put in your zone descriptor. And you use the right or left arrows, by the way, to navigate and choose which word you want to do after hitting insert. So either direction, it'll take you towards. At this point, after you've hit the down arrow, it'll say select RF sensor reports. This is where it's going to ask you if you actually wanted to transmit its signal to the central station. I definitely want that. If you're trying to set something up locally and you don't want it to transmit, that's fine. Then you just hit the right arrow and you disable it. I do want it to transmit, so I'm going to leave it and then hit the down arrow. Now it's going to ask if you want this device to be supervised, meaning if this device ever falls out of RF supervision, it will alert the panel and let your panel know that there's something going on. I absolutely want that. I'm going to leave it, press the down arrow. Now this is where it asks you if you want to chime.  So probably not the best idea for a motion unless that's what you're looking for. But I'm going to leave it disabled and then hit the down arrow. At this point, it will give you the wrap up of all the zone information that you programmed in. You can edit it or you can edit the next zone by clicking edit next or you can skip altogether, which is what I'm going to do. I'm going to hit skip because I have nothing else to program, then I'm going to hit end. At that point, you've got a whole list of things that pop up and you'll see a little save changes box at the bottom here. If you don't want to save the changes, you made a mistake. You just uncheck that box and then you hit exit. I do want to save these changes, so I'm going to go ahead and leave that box checked and then hit exit. At this point, it will go through its reboot process. So we're just going to wait for this to finish. Once that's done, the motion will be learned in and I'll show you that it's learned in by tampering the device. All right, wait for this screen to come back here. All right, there it is. Now we'll go ahead and we'll tamper this device and it should appear on the screen. There it is. And we have our motion tamper there. Go ahead and close it back up and that tamper should go away. There it is. All right. So that, ladies and gentlemen, is how you program in the PIR1E from 2GIG onto the 2GIGGC2E. If there's any questions or comments, please leave them below. Other than that, you guys take care and have a great day. Make sure to subscribe to our YouTube page and click the Show More tab underneath the video where you can view valuable links pertaining to this product, similar how-to videos, and our low-cost, no-contract alarm monitoring services.


Hi everybody. My name is Georgie Benardete. I am the CEO of the Impact Investing Marketplace Alliance 17. And it's such a pleasure to have today Susan Graham and Simon Mokehi who are joining us to discuss everything about innovation systems changing and what does it take to scale the STGs. So we have a very short and but exciting 30 minutes. So we'll get started with both Simon and Susan spending time first to you know introduce yourselves from your perspective and also share your perspective and the way you define the word innovation. And thank you so much for being here with us today. Shall I kick it off? Sure. Fantastic. Well thanks obviously for having us here and Simon I look forward to having a good discussion. As some background the CEO of Dendro Systems and we really started because of a challenge.  And that for us is increasing carbon dioxide in the atmosphere year on year and a loss of biodiversity 10 times higher than it's been in the last 10 million years. And you know there are very few challenges in this world which have their own deadline, their own clock. We often have to artificially create that deadline.  And here we've been given one for the whole of the world to take up as an innovation challenge. And that's the environment talking to us and setting that deadline. So that's what we've taken on at Dendro Systems and spend all our energy coming up with solutions for it. And then Simon Mulcahy, Chief Innovation Officer at Salesforce. And for those of you who don't know Salesforce, it's a 21-year-old company now, barely just out of being a teenager. And the fastest growing enterprise software company. And really our job is to bring customers in there and the companies and their customers together. So imagine creating a single source of truth around your customers and enabling you to engage them. And for us innovation is actually a value that we have. It's one of the core values of Salesforce. So there's innovation happening at every level. And we like to think of it much more in the context of kind of applied innovation. How you use technology in the context of creating real meaningful outcomes. And we'll talk more about that I'm sure. Thank you so much Simon. So you know as I was preparing for the session, I found I'm here in Seattle right now. There is around 1,175 books with the word innovate. So it'd be tempting to think like Harvard Business School things that we should not use that word. And however, Susan from your perspective, you definitely understand the power of innovation, not just as an innovation in technology for the sake of it, for innovation of the service of the world. And also one thing to remind ourselves, you and your company were chosen one of the top three, you know, ideas and realities in support of up links, one trillion tree challenge. So I think what's interesting for me is to understand what was a thought process by which you were able to finally, you know, to finally, you know, figure out a way to get restoration of ecosystems done right in a way that we could get to that deadline that you so well described. Yeah, it's interesting. I feel like I'm almost a mix between two people. I'm someone who's incredibly impatient. And a lot of the people who are in our team have that lack of patience, that sense of urgency in everything that we do. And one of my favorite quotes from Tom Chi is doing is the best kind of thinking. And so we've got that kind of an attitude. And then that has actually collided with what I picked up from Oxford when I did my PhD there, which is when you come up with an idea and you've got that urgency, we were very much encouraged to, you know, go sit under a tree for a while and just think about that, which is obviously in complete contrast to getting something done and executing. But it's so important to reflect on the why and reflect on the problem because, as you said, there's been lots of innovation on earth. And sometimes we've innovated in the right direction. We've spent, you know, years and millions of people's lives and dedication towards innovating something. And then perhaps we've reflected and said, did we go in the right direction? We've gone in that direction. But did we go in the right direction? And so when I think about specifically around carbon, and obviously the problem we're tackling around restoring biodiverse natural ecosystems is much more than carbon. But when we look at carbon, we have a carbon cycle. And at the moment, we're not in a carbon cycle, we're on a carbon path. It's as if we've set ourselves in motion on this path that's not sustainable. And we need to get back down to a state to be back in a carbon cycle. And so it is about asking this question, what problem are we solving? And are we creating another problem for the future? And that's why we take a holistic approach to say we're not just solving for carbon dioxide. Because if we were, we might plant monocultures over the whole earth. How terrible would that be if we just destroy the world's ecosystems and the potential for biodiversity on earth because of solving one just in its own little discrete corner. So we very much take a holistic approach to say, there's a better way of doing this. We can actually solve multiple challenges all at once if we can just find the solutions and work with the right partners to solve the bigger problem. So that when we reflect and sit under a tree, we can be happy about what we've seen, but we can go fast and solve those problems. Thank you so much. Now, I'm a neuroscience geek and neuroscience support your idea of pausing before you start. So and that's where the best ideas seem to come out. Simon, I love what she's mentioning of kind of thinking about innovation in the right direction. Right. And once again about not just sake of it, but what are the elements that ensure that innovation is indeed going into the right direction. I understand from our conversations in Salesforce that you have thought about this and that you have a very specific process by which the full company, all of the employees, the value system and the understanding of why you're doing innovation makes sure that the results are positive, viable and bring success to your clients. Could you discuss a little bit about that process? Yeah. So we have a process which is effectively how we ensure that everybody in the company is aligned. It's what we call our V2 mom and I'll break that down in a minute. I think it's maybe worthwhile just pausing though first and talking a little bit of kind of to follow on from what Susan was saying. In order to have, in order to innovate, you kind of need to be really, really clear on what you're trying to achieve. And I think all too often people innovate thinking that all they're doing is improving the system that they had before them. It's an incremental style of innovation. That's fine, but all you're going to ever do then is you're leaning on the behaviour of the past, the thinking of the past. And if you look at the world that we live in today, much of the world that we live in, we've accepted, but the people who shaped it are dead, have been dead for a long, long time. And we just continue to absorb their thinking as if it's immutable and we shouldn't change it. So actually a lot of innovation has to be now not about translating what they did into a modern sense, but actually pausing and thinking about how you might, with a beginner's mind, look at the problem. And that for us is step one. It's a beginner's mind. And if you don't have that beginner's mind, all you're going to do is basically just pick up where everybody left off and haul it, like whatever, you're going to turn the wing nut, a couple of cranks to the right, a couple of squirts of oil is the best you can do with any system. That's not good enough in a world where change is accelerating. So even a system that I'll explain to you is irrelevant if you're just incrementally approaching it. And that does mean the beginner's mind is fundamentally important. How are you really, really looking at the world and going bearing in mind everything I know and everything I have at my fingertips, what can I do to make this system better? So I think that's kind of the first thing. And that beginner's mind is something that you'll hear Mark Benioff at Salesforce talk about all the time. And it's what's allowed us not to just keep innovating in an incremental improvement way, but to actually pause and reassess and then look at everything that we have and then move forward. And when you do it like that, you still need to then get everybody that you've got around you and go, okay, we've got a new plan. Beginner's mind has delivered this amazing insight. We're going to go now go over there. How do you get everyone to follow you over there? And everybody else like, oh, hold on a minute, I'm on the other story. I'm going in that direction is a really, really important question. And so we have this process where you have a vision, which aligns everyone, what are we going to achieve? Everybody has a vision. And that starts with a CEO, the vision for the whole company. What are the values then what matters to you? So Salesforce are values are trust, customer success, innovation equality. So that then orient people that as we're going in that direction, we must always align to those four kind of cultural values. Methods, how are we going to achieve that? Obstacles, what will prevent us from achieving that? Metrics, how will we know when we've achieved it? And we still, it's kind of a cascade. Everybody in the company starting with a CEO, we have a vision for the corporation, we sit down with our top leaders and go through this, is does this V2 mom work? We beat it up in management meetings. And then it's good enough. We go, right, that's it. There's no other plan. It's just this V2 mom. And then everybody writes theirs on the back of that and the cascade model. What that does is it does ensure that everybody goes in that direction that everybody has the same values that the budget, what we, how we spend on our money and resources is aligns to that. And if stuff's not on the V2 mom, it doesn't happen.  And that's nice and crisp and clear. And that allows for momentum and efficiency of effort. That's amazing. I can, I can sense how exciting it must be to have everybody going towards that vision and how and achieving that vision makes it more probable. Susan, you mentioned about partners. And so Alliance 17, number 17 is a not to partnership for the goals. So the notion that the only way we're going to achieve the promise of the goals is by working together. Usually in some audiences, when I'm, when I'm more daring, I use the word radical collaboration and just radical thinking, radical collaboration. And I think to Simon's point is new thinking, not trying to replicate what people who are dead now taught us. So how do you see that happening in your space, Susan, that is you're bringing so many actors to the table? What is the thought process that makes that just like Simon has internally in Salesforce, you have across the network of partners and clients and vendors to make things happen? Yeah. And I think that the question around partnership often makes you ask that second question around time scale. So sometimes I was giving a talk about five years ago, and we were reflecting on apps and would be, would the iPhone exist in five or 10 years? You know, it was a question and it was uncertain, you know, am I creating an app, right, with this functionality that might be on a platform that fundamentally doesn't exist in a short, in a relatively short time period. And so when we think of partners, you know, I often acknowledge the traditional custodians of our land. And that that is to acknowledge all that has come before us, and the people and the communities who have built what we what we have today, and then to acknowledge the future, and that the future is long. I'm an optimist, I believe that the future is very long, and beyond our imagination for time scale. And so then when we bring it back down to today, and the urgency of the challenges that we have, it becomes quite clear that you need to partner to solve this challenge, because we are not looking to create change in a blip. Some challenges, we hope fingers crossed are a blip. COVID is a natural disaster. And it will relatively in on reflection be shown as a short one. Where we're looking at our climate and and our biodiverse ecosystems, this is a trend. This is a long term challenge at the moment, and long term responsibility going into the future to live in a sustainable way and innovate and have the technology to support restoration of these ecosystems. So when we think about partnership, it's very much an acknowledgement of the timeline, and then in terms of capacity. And so at Dendra Systems, we provide technological services to enable our mission is to enable scalable restoration of the natural world. It's not to do scalable restoration of the natural world. Of course, we do we have a an aerial seeding service, which is a drone that goes and plants trees and shrubs and crosses. And so of course, we're doing it. We have we have a AI system that goes and analyzes every blade of grass and every animal, every whisker on a kangaroo. But but really what we're doing is enabling others. And so that, you know, is core to to who we are because when when we think about this, it's at this, there's a hyper local scale, where you've got local communities and stakeholders, you've got regulatory bodies, you've got finance groups that are all looking to to execute and we support that as almost an operational intelligence layer. But then when you look at that even broader scale, you're enabling complete system change. And this is where we've been described as an innovation catalyst. So we have we have an innovation, but we're catalyzing innovation around us. And that is in terms of markets, that's the way that people think about how to invest in ecosystem restoration, how we think about a sustainable world, not just from an environmental perspective, but from a financial perspective. And so so this is where you've got this hyper local partnership, very direct. And then you've almost because being an innovation catalyst, there's these knock on effects. And so you require other people or rather other people do change their behavior. And so we need to consider how are we changing their behavior and their systems by by what we do. So so partnership is is very important to us. And we, you know, you know, contracts might last for five years, but the partnership is really for 20 or 50 years in an acknowledgement that that's how long it takes to restore an ecosystem. And, and so that's the way I think about partnership. And obviously, it's critically important because otherwise, a, we might get it all wrong and b, it won't last very long. Mm hmm. That's fascinating, right? The power of words, like how how the solution changes when you see yourself from, you know, do or versus enabler and also kind of the having empathy for the users, having empathy for the others that are coming to the table in order to maximize again the potential of achieving the mission. And so as I think of the mission that we all have gathered here, people within the World Economic Forum, I understand you personally as well. You know, again, I come back to the SDGs and the mission critical that we have together to achieve it. And I understand from your perspective, Simon, that now you're spending something that from a number was kind of shocking that you're spending 75 to 80% of your time thinking about sustainability and acting about sustainability and also thinking. And so it's, it's a very unusual thing to think because many corporations, right, would imagine or many people would imagine like Salesforce, you know, why is Salesforce thinking about sustainability? And me personally, I think every corporate should be thinking about sustainability, if it thinks wider and more nuanced. But I would love to hear from your perspective, Simon, what are you thinking about? What is Salesforce thinking about? And what does the future look like as companies start thinking about sustainability? Yeah, I mean, it's just a very, very important subject. It's just something I've been spending a lot of time on recently, not least because we have a great partnership with the World Economic Forum, the uplink platform is built on Salesforce. But maybe more broadly than that, we have a compelling event of the SDGs pointing to by 2030, if we have not really got our act together, then two thirds of the biodiversity, the natural capital will have been, you know, killed by a world that is not designed, a business world which is not designed to fit into this natural world. And we know that there's this kind of critical issue that we need to address, but the world is still marching as business as usual. I spent a lot of time focusing on digital transformation, how organizations which are still actually analog by design need to transition to digital. COVID has massively accelerated that. So the logical question is, okay, well, what happens after digital transformation? And it turns out that the next biggest change probably bigger, much bigger than digital transformation will be this major shift to what has to be a low carbon economy. If not, then we might as well just forget because there'll be no future story anyway. So this is big shift to the low carbon economy that has to happen. That is already impacting the capital markets. Massive, trillions of dollars are already moving towards more sustainable businesses. Unsustainable businesses are already being penalized. Regulatory systems are already pointing to, if not already moving towards at least getting companies to divulge their carbon footprint and it'll go beyond that. Business leaders are already moving in this direction, not just individually, but in groups, the business round table, the world economic forums doing. There's this major revolution that is potentially about to happen, but it might not. It could fall flat unless there's real momentum from business and that's an opportunity for us. It's an opportunity for all of us. How do we connect the business, how do we connect sustainability to the growth agenda? And if we get that right, it's a growth opportunity for everyone. So it's natural that we'll be thinking about it how can we help the world's future retailers, bankers, manufacturers, etc become sustainable, because those that don't will be relevant in the future. It's a growth opportunity, so that's why we're focusing on it. And one very important point, it's the right thing to do as well. And it reminds me now that you mentioned about the right thing to do. I remember meeting Mark Benioff in San Francisco, actually in a YGL summit and a professor Schwab asked him to define leadership and he said, leadership is love. It blew everybody's mind, but that's the most succinct is we're doing this and this leadership and business leadership because of love and that's the why and that's the right thing to do. So we have very few minutes. I wish I could stay here. What I would like is for each one of you, so what's next? You guys have done, have had incredible experience in transformation at the systems level, at the company level, at the global level. How does somebody start? What's the first step? Somebody who just really doesn't see themselves as innovators, as creative, as systems thinker, what would be one recommendation that people can take from your experience perhaps? Yeah. I love that question. Firstly, it's just do it. It's that easy. You're born as an innovator, you've just got to do it. But something I always like to remind myself, especially in conversations like this, it can be very serious what we're talking about. The importance of the why, very serious. When we talk about the how, it can be very serious. It can be involve regulation, governments, incentives, shareholder action. They can be very serious topics. And for me, the how is so important. Why are we on this earth? What's happening for you today? What's happening for you tomorrow? How is so important? And so having fun, I know that it's often not something that we talk about, but having fun is important because we are humans. We're not innovation robots. We're not here to innovate. We're humans. And when you talk about love in leadership, it's a very human quality. And having fun is a very human quality. And so I think that for me, our data scientists love playing in our data sets. We have data sets on natural ecosystems that nobody on earth has ever seen before. It's an AI engineers playground. You just get to, you get to have fun in it. Like, look what we can see. And whether it's a snake and trying to figure out, correlate whether a human walked past that snake and how close did they get, it's about having fun. And so when people think about starting innovation, either within a large organization or a government or starting a startup, it's about saying, well, choosing the right topic, the right path of why, choose the right problem. Don't go after a problem that is too small. Just be brave, go after a big problem that has meaning. And then choose the right people to have fun with and the right solutions to have fun with. I'm an engineer. So of course it's like, let's have fun with this tech.  So that's, that's my advice is, you know, choose the right problem and choose the right people to have a good time with to solve, solve these big problems. Thank you so much. I appreciate it. Simon. My Susan, I would kind of follow that up by, I don't know why it's important. Like, be really clear what problem you're trying to solve. Really, really, really clear. Get that, get that right. Get the narrative in place that describes the problem you're solving, how you're going to do it so that you can communicate it to others and get others to join you on your mission. If you can't articulate it as a compelling narrative, as a story, it'll be hard to do it because you'll just be on your own. And then get going. Tactics drive strategy. Don't build an all-encompassing, high-faluted strategy. Just get going. Tactics, your tactics, the feedback you get will drive your next steps. That's incredible advice. And I need to summarize it, but I think everything you have said, it's just wonderful. Susan, one of my favorite quotes, and I'm going to butcher it, it says, do not invite me to your revolution if I cannot dance. So I agree with you on the fun part of the creation and playfulness, but to sum it up, basically, I think we all share, and I think the audience obviously share, and anybody who's been around the World Economic Forum, like we have been, there's a true sense of urgency, right? This knowledge that there's these challenges, there's a deadline, and it's a possibility, and it's a solution. The solutions exist. We know what we have to do. We need love. We need, you know, obviously, innovation, and we just need to get going. I like what you said, Simon. We know the future story, right? So now it's up to us to make sure that it doesn't go the wrong way. Having a beginner's mind is crucial. Understanding the power of words and how do you see yourself in the innovation chain, so to speak, from doing to being, to enabling, to catalyzing, and the moment is now. So it is very much a pleasure to have been throughout this whole week with you, Simon, and Susan, getting ready.  So thank you so much.


Namaskar. I am professor Devati Purkayastha from the Indian Institute of Technology Bombay. Welcome to my course, Business Fundamentals for Entrepreneurs Part 1, Internal Operations. This module is called Manufacturing and Quality. This is the sixth module. But before I get into the module itself, let me again explain the flow of the course. So I started with sharing what is a business, what's a company, types of business, and then the big picture of a company, vision, mission, strategy, goals, culture, etc. In the last modules, I talked about how companies innovate and design new products. Once I've designed a new product, the next topic of course is how do you create millions of such products or millions of liters or millions of tons of products? And how do you do it with quality, which is today's module? And just so you know, in the future modules, I'll talk about finance, accounting, leadership, human resource management, and ESG. Now today's module, you may think that it's only important for science students or engineering students. But in today's world, students need to have a broad view of business, a broad set of skills, a broad perspective across functions. So to encourage especially the non-engineering, non-science students to participate carefully in this module, the arts students, the commerce students, the humanities students, if you're going to go for a career or start a company, I would encourage you to think about how do you manufacture in large numbers and how do you ensure quality. Today's module, like all modules, will have six topics. And there are two reflection points where you can pause for three minutes and try to write down in a journal everything that we have learned and a few questions that I will ask you. Let's start with the first topic. What is mass manufacturing? Mass manufacturing simply put is making millions or billions or crores of copies of the product, the new product that a company wants to launch in the market. Basically it uses a few concepts called economies of scales, specialized workers, and a continuous set of assembly stations and machines. So how did it come about? So in the early days, and if you're making a simple product, a pen or a pencil or a toy, one person could finish the entire manufacturing. One person could make a pot or a pencil. But if you look at a complicated product like a phone or a car, one person can make it but it's very difficult and it will take him a long time and probably the one person will not have the skills to make the entire product.  Break it up into small steps and small stations where sequentially the car or the phone or the machine is built step by step by step. And I'll talk a little bit of examples later on. So what are the advantages of mass manufacturing? First, it's high precision rate because what happens is in one mass manufacturing station, one assembly line station, one step of the process with specific workers, the focus is only one step, one component, one assembly step. And the worker is trained and is super specialized on that one step. He may know nothing about the other steps but he's a specialist in that one step. So you get high precision rates and because the same person is repeating only the one step, he can do it very fast. So the production cost goes down and the production level goes up so it's higher efficient. But what's the disadvantage? The disadvantage is you have to invest a lot of money in creating that assembly line examples of which I'll show you later. It takes money. Also it needs frequent upgrades because as you upgrade the product, you have to upgrade your assembly line. And of course it's not good for the worker or the employee because he's doing the same thing a hundred times, a thousand times on the same day. And he may think, what is my value contribution? Because only doing one small piece of manufacturing over and over again on the same day. So the morale of the interest level of the employee can come down. So let's look at a few examples. Before you get into the actual pictures, let us also understand what are the different elements of manufacturing because manufacturing is not just one simple process. It has got many pieces to it. Manufacturing starts with first the product specifications and certifications, which is what I covered in the last module. A company will innovate and test out and create a few pieces of a new product. And they'll write down all the specifications and they will get all the certifications to make sure that the new product works. Before they create millions and billions of copies of the new product. So that's the first key element. Now getting into the manufacturing itself, there are several parts. The first part is if a product needs 10 components or 100 components or a car may want 1500 components, those components have to be procured from many different suppliers and brought to a central assembly point. So that's procurement. Apart from the components, you may also require, depending on what your final product is, commodities. For example, if you're a chocolate company, the commodities that get into a chocolate manufacturing is a lot of sugar, cocoa, which is cocoa powder, milk and some flavorings and some fruits and nuts. That's what goes into making a chocolate bar. So then you have to procure the sugar in trucks, truck loads of sugar, container loads of milk, cartons of cocoa powder and the flavoring, etc. So you need to procure the components for a product like a car or a phone or commodities like sugar, milk, cocoa for a chocolate bar. And then you have to procure packaging material. Imagine the chocolate once is made, a small bar, it needs to be packaged or if it's a liquid food product, like a beverage, it needs a bottle. That's called packaging. So that needs to be procured. So that's another piece of manufacturing. Then you get into the actual manufacturing once you've got all the components and the commodities and the packaging material together. You have to assemble them if it's a hard product, like a car or a phone. If it's a metal like steel or food or chemical, then there are no components. There are chemicals which have to be mixed together or commodities which have to be mixed together and processed to create the final product. And then once you've made it all you have to package it. You'll probably understand a bit better when I show you some of the actual case studies. But keep it in mind that you have to procure the product, you have to assemble them or process them and you have to do all of this keeping in mind the quality standards because you cannot have a chocolate bar with let's say insects in it that will destroy the goodwill of the product and the company and people will fall sick. And you cannot have a car which keeps breaking down or a phone which doesn't work. So the other part of manufacturing is quality standards and quality control and we'll talk about it. And then the last part of the manufacturing, all the things that happen in a manufacturing plant would be logistics because you have to bring in all the components and commodities and the chemicals and the iron ores or the coal to bring it all into a factory. And once you produce the product, the final product, you have to take it out of the factory and give it to where the customers are. So all of these is what goes into a manufacturing process, let's say in a manufacturing plant. So what does it look like in real life? Let's start with what a product specification, what is a specification? This is the first part I said of manufacturing. Product specification can be very detailed, book, manual, data sheet, etc. And you should cover these elements. First you should define the use case. What is the use case for the product? If it's a motorcycle, it is a certain use case. If it's a car, it's a certain use case, some other different use case. If it's a small car, a cheap car, it is a use case. If it's a very expensive, big car, it's a different use case. Motorcycles, cars, and then trucks is a business use case. So whether it's a motorcycle or a car or a truck, the business cases are very different. But for the company to define the specification of a car, it's not enough to say, oh, we'll build a four-wheeler because it has to make sense ultimately for the customer. And therefore the company in the product specs will probably have to write down and say, okay, these are the cars that the customer already are using. But here's what our car, our company car will be unique about and will be better, etc. So to define the use case, you can see what are the options and the specs that you have to include under the use case. You then have to write down the features. And the features are, what will the product look like? Is it small? Is it big? Is it heavy? Is it waterproof? Is it dustproof? Is it round? Is it square? What will it look like? What will it feel like? How will it work? Will there be buttons on the side or buttons in the front, buttons on the top? Will there be a jack here, a power cord there, the voltage? So what will it work like? What will the quality look like? If I drop it, is it shatterproof glass? If the car, God forbid, crashes, how safe is the car? How good is the quality? How many miles will the car run before you need to replace certain things? And above all, what is the cost? So these are all the features. The use case, the features. And then you get into the benefits. So if you put all of this together, what are the benefits to the customer? Because you can't just look at it from a company point of view, you have to look at it from the customer point of view. How will our company product be faster, cheaper, better, more satisfying and what are the quality certifications? So some phones may not be waterproof or dustproof. Some phones may be dustproof but not waterproof. And some phones can be dustproof and waterproof. And some phones can be military grade. You can put it in water, you can throw it, you can drop it, the phone will be okay, it's a rugged. So those are all certifications. IP67, IP68, phone, dust and water specifications. And all of this is captured in what are called design documents. So if you go to a plant, the workers need to know exactly what is the product all about. Because in a big plant, there may be 1,000 workers, a 5,000 worker or 10,000 workers. And those 10,000 workers must all know what is the product all about. What are the design of the product? And of course, what is my step of the product? What do I have to do in the product? So if you go to a plant, a manufacturing plant or a factory, you will find very detailed design documents which will be drawings, manuals, spec sheets, blueprints. You'll also see that there are samples of a screw, of a wire, of a liquid. There may be 3D printings. So all of this together is a product specification. But if you look at it from a customer point of view, let's say if you go out to buy a car, you will see a car. So this is an example of a Toyota Glanza car. So when you go to a Toyota showroom, you will see a car. And you'll know what it looks like, what it feels like, what is the quality of the paint or the shine or the tire, how tall is it, how big is it. But you may still want to know what are the specifications of the car.  It is from the Toyota website, Toyota India website. And it will tell you the features or the specifications of that product, which in this case is a car, will be listed in that brochure or the website. So you can see the engine specifications on the screen. How big is the engine? Is it petrol or is it diesel? What are the kind of variants? What are the dimension? How long? How wide? How high? How is the suspension? What kind of suspension it is? And then if you look on the screen, you'll see what kind of brakes does the car have. And how heavy is it? So these are all technical specifications. As a customer, you may want to know it. But in a factory, in the Toyota factory, there may be 10,000 workers, 5,000 workers. So they will need the specification in far more details. And they will have the documents for that. So these are specifications. And then features. As a user, more than the specification, you may want to know what the features are. And you can see a lot of the features of the car in the datasheet. And I encourage you to start looking at cars and phones or laptop specification as a business person, not just as a customer. So features, you can read it on the screen. The rear seat, how comfortable is it? Is it leather? Is it not leatherette? The steering wheel, how comfortable the sound, the music, the air conditioner. These are all the features. So this is typically about product specifications. So once you've got the product specification, remember the chart I've shown you, then the next part of manufacturing or the next part of activity in that factory is to get all the components. So a car that I showed you can have 1,500 components, maybe more. Five tires will be there. There will be lots of screws and rubber parts and large body parts which are made of sheet metal. And electronics and the engine blocks and the piston rod and the batteries and the wipers, things that you can see and things you cannot see because they're in the engine or they're below. And same with the phone. There are many parts on the phone. You can only see the screen and the body. But inside the phone, there may be 1,000, 2,000 parts, microprocessor chips, memory chips, boards, PCB boards, wires, which you cannot see, temperature controllers, et cetera. So procurement is a process of a company obtaining all the parts that goes into the company's products. And the company may not make all the products themselves, so they look for suppliers. So if you buy a phone from an Apple or Samsung or any of the big companies, the phone company will not make all the components. So the screen may be made by someone else and LG or Samsung may make the screen. The chip itself can be made by someone else like Qualcomm or NVIDIA or whoever. The memory chips may be made by someone else. In a car, the manufacturer who's making the car, assembling the car, will not make the tires. They'll buy the tires from someone and the battery from someone else and the steel for the body from someone else. So that's the procurement process. And of course, a car manufacturer or even a beverage manufacturer or a food manufacturer will have many suppliers. And you have to have relationships with the supplier and look at some examples from food and chemicals as well, apart from cars and phones. And as I said, procurement can be for components, if it's a phone, car, et cetera. It can be for agriculture commodities, if you're making food of average. It could be for chemicals, if you're making a washing powder, and of course, packaging material, et cetera. So how do you procure? Is it as simple as that? Now, if you're buying, let's say you are buying a phone for yourself and you understand that it's quite a complex process. But remember, the phone manufacturer doesn't have to buy one piece of that chip or the screen or the memory module. He may have to buy 10 million pieces, 1 crore pieces, 100 million pieces, 10 crore pieces. So procurement in such large numbers is very complex.  But the manufacturer of chocolate has to buy tens of millions of litres of milk and tens of millions of kilos of sugar and millions of kilos of cocoa and millions of metres of the wrapper with which you cover the chocolate.  So it's a complex process. So what are the steps in procuring? First, the procurement department and most big companies will have a procurement person, which in many ways is the reverse of the salesperson. So in my next module, in my next course, I will talk about salespeople, sales processes. So sales processes and salespeople are selling to the customer. But the company's procurement processes and procurement people are buying from the suppliers. They have some similar but also different processes. So first of all is to understand the requirements of the company. How much of which component does the company need? If I'm making a thousand cars a day and a car has 1500 components, I have to buy 1500 into 1000 components. That's 1.5 million components per day. And then you multiply it by days and weeks and months. So you have to identify the internal requirements, the components, the numbers, the delivery schedule, etc. And then the procurement person has to go and identify suppliers. Where will I buy the tyres from? If I make 1000 cars a day, I'll need 5000 tyres a day and then so many tyres per week or per month. So which tyre manufacturer do I buy from? So you have to identify the external supplier. And then you have to place the order. But before you place the order, there's negotiations on the price. So I'm going to buy 1 million tyres. What is the best price? So there's negotiation. And then the order is placed. And the order may not be for a million tyres, which is over a year. It may be I need 10000 in January and 10000 in February and I need 500 every day. So the order is placed like that. And then the order is placed. The supplier will start feeding into the plant. Remember, one plant may receive goods from 100, 200, 300, even 1000 suppliers. And therefore goods receipts itself is a very complex process because link to the good receipts is the payment that has to go out. And if a company is selling every day 1000 cars, whose value is let's say $10,000 each, so that's 10 million every day. That's about 100 crores of rupees. But the component values that's coming in every day may be half of the sale value, which may 40, 50 crores of components are coming in every day. And someone has to keep track of all of that. And then the quality has to be checked. If you are getting 5000 tyres every day or 1000 batteries every day, you have to make sure that the quality is correct. And finally, of course, someone has to keep track of all of these and make the payments. So therefore, many of you may want to look at manufacturing as a career, but my suggestion to you is manufacturing has got many parts to it. And you can choose a career. For example, they are professional procuring specialists. Just like they are professional sales specialists, they are professional procurement specialists. And there are highly trained professionals who run factories or assembly lines. So as you understand each of these processes, and you see the examples with each process, you can choose your career option of which career interests you. So now we have talked about the product specifications and then procuring the components or the commodities. Now that you have looked at how the product specifications are defined and how the components are procured in a factory, let's now go inside the assembly process of the factory.  If you can't enter, at least look at the factory from outside. And you can go to any part of India in an industrial area, planet-based visit there, and look around all the factories. And if you know someone, enter the factory and go and see how all the steps that I'm describing to you are actually happening. It'll give an appreciation of business and it'll broaden your understanding of how to choose your career. And one of the most interesting aspects of manufacturing is the assembly line itself, where you can see step by step by step how products are being manufactured. So let's look at what does an assembly line look like. So this is how a typical car assembly line will look like. It's not a very sophisticated assembly line because it's a manual assembly line. So you can see the empty shell of a car, which is one step in the assembly line. But when it starts, there's nothing. So it may start with pieces of the body being welded together. That's maybe the first few steps of the assembly line. And then once the body is made, you will find that different components are being screwed on to the body, as you can see in this picture. And if you look carefully, you will see that there's a lot of parts which are kept on the side. So this is what you'll see if you enter an assembly line, which is undergoing manual assembly. But a lot of the modern plants are robotized. So there is actually no human being putting in the components. They're all robots. And you can see a picture of those robots. Now these robots are not the ones that you may have read about or looked at pictures of robots who look like human beings. No. These are still robots which are arms, which are coming in and assembling different parts of the car. They may be welding the body itself. They may be screwing on a component. They can be hard flipping or flitting or fitting two plastic parts to each other. And the yellow ones that you see on the side are the robots. And you can see the assembly line itself, which are the cars. It's a moving line. The cars keep moving. And at each steps, the robots come in, assemble something. And in a manual assembly line, the human workers will assemble something. But the car keeps moving from when there is nothing to when there's a finished car. Let's look at a phone. So again, this is how an assembly line or an assembly factory or a factory can look like. And you should go on field trips and have a look at such factories. And if you'd go inside one of these factories, and I'm going to show you the inside of a phone factory. Remember, this phone has very small parts. And they have to be either welded or screwed of plastic fitted, tightly fitted to each other. And on the assembly line, you have to fit in the parts from the sides. So this is what it will look like. It is dust free. It will be pollution free. And the workers are assembling the phone. Till you get the phone on your hand. Now, I would now encourage you to reflect a bit about what I've talked so far. Think of a car or a phone that you're using. And write down in your journal the components that you can think of. So if you're riding a bus or a car, look around you and see what the components are. The seat, the battery, the body, the window, the rod, the handle, the steering wheel, the dashboard, the plastic. Think of the components. Think about where the car manufacturer got the components for. So if you look at the body of the car, it's made of steel. Someone would have supplied the steel, maybe a Tata steel or a JSW steel to the car manufacturer. Look at the tire and think about who could have supplied the tires to the car manufacturer. Study the car or the phone and think about how it was assembled. How the body parts are welded together. How the tires are screwed to the axle. How the dashboard, the plastic dashboard is fitted. How the seat is put into position. Or if it's in a phone, look at the phone and see how the screen is fitted to the side chassis. And if there's a button on the side, how is the button fixed to the body of the phone? And think about how they could have been assembled. Try to visualize and draw in your journal what could be the steps of an assembly line of a car or a phone or any product that you choose. And think of some way that if you are an entrepreneur and you want to set up your own assembly line, how you can improve on it. As always, have your journal, do the reflection points together one by one. Keep the notes so that by the end of the course, you'd have assimilated the learnings better. So take your time.


Right, thanks for going to talk. First of all, can everyone hear me? Okay, it's like wave at the back, you know. Cool. Okay, so I'm Emma, I am the lead python developer at Cambridge Medical Robotics, we're a start-up working on developing the next generation in robotics for keyhole surgery. That is pretty much as cool as it sounds, to be honest. That is pretty much as cool-as-jobs it sounds to be honest, so if you are interested, we are hiring, I'll put a link up at the end. So, how do we get from that to the topic of this talk? I'm also on the organisers of the Cambridge programme study group. We meet up about once a week to study topics in computer science. About a year and a half ago we were looking at machine learning and in particular genetic algorithms and how we could use those to evolve some code. Having managed to get that to work, I pretty much instantly became fairly concerned about my job security. So, if we think about it over the course of history, the kind of way we've lived our lives and the kind of work we've done has massively changed the result of technology. So, for instance, in agriculture we used to have large majority of the population working in the fields, sowing seeds, harvesting crops. We developed things like this and now we've kind of freed up most of us to work on other things. A more recent example, I'll be walking into a supermarket. I'm sure you've all seen rows of these self-checkouts where you might have had lots of people working at Till's. Now you can just have a single person staffing these. You might think, OK, well, that's quite repetitive work, things that are easy to automate, but increasingly, with artificial intelligence, we're seeing even higher-skilled work coming to the firing line. So this is IBM's Watson machine. If you've not heard of this before, it was developed a few years ago as a supercomputer to compete at the US quiz show Jeopardy. This is a contest in which the contestants are given the answers and they have to respond with what the question was. So figuring this out involves kind of a mixture of natural language processing, having a big store of information or general knowledge that you kind of retrieve data from, and also kind of applying some machine learning techniques to figure out kind of connecting all this together. So this is definitely one at Jeopardy. It's defeating the human champions at that. And it's now been applied to some other areas as well. So we have things like medical diagnostics, where there's a huge volume of information. So new clinical studies being published all the time, journal entries, the patient's own medical history. This is a lot of data for a doctor to deal with when diagnosing a patient. This machine can instantly have all the latest journal articles. It can read them all in a time that a doctor simply wouldn't have and have all that information available to it. So it's potentially able to make more accurate diagnoses, particularly for uncommon conditions that a doctor may not have come across before in their working lives. You can also apply it to things like legal work. So preparing a case for a trial, you have lots of prior judgments to trawl through, lots of legal studies, that kind of thing. It can also be used as a teaching assistant in the classroom, because of its natural language processing abilities. You can understand questions that students are asking and come back to them with their answers. So now we have people like doctors, lawyers, teachers, also having their jobs coming into Fire and Iron and this rising technology. It's all about our software developers then. So you might think, okay, hopefully we're okay, hopefully we're needed to write the code that makes this work. But as I said, it's looking at ways that you could generate that code rather than write it yourself. So how might you go about that? So one approach you could take is something called a genetic algorithm. So this is a type of guided random search algorithm. So it's opposed to a systematic search where you've got your kind of data structure and you're going to look through it, this starting one end of going through until you find what you're looking for. Or a random search where you might say, okay, I'm going to look over there, now over here, maybe over there again, have I found it yet? Nope. A guided random search is where you look in a few places and then based on what you find there, you make a decision as to where you should look next. So this makes some assumptions about the data being in some way sorted or continuous around the points of interest. Okay. And in particular, as a genetic algorithm, it's a guided random search algorithm in biology, particularly from evolution, where we have this idea of natural selection and that if you have some characteristic that makes you more like to survive, say you're quicker at running around from tigers or something like this and we have survival at the fittest, you're more likely to pass on your genes to the next generation and those features will get propagated. So how do we apply that to a genetic algorithm? So if we take the example of evolving a string, so not programmes yet, just a regular text string. So say we want to evolve one containing the title of this talk. Okay. So what we're going to do first is we're going to generate some initial population strings, so 100 of them, 1000 of them, just completely random strings or different lengths, different characters. We're then going to evaluate those and see how much they look like the target we're trying to get to. So this is where our analogy of evolution has somewhat completely gone out the window because we now have an end goal in mind and we're going to construct our fitness function so we are steering this algorithm towards reaching that. Okay. So having done that, we're going to select which members of this population should survive and pass on their genes. So which initial strings looked the most like the target? Okay, and naively we might just say we're going to take the half with the best fitness score, just the top half and throw the rest away. We're then going to cross them over so this step is going to be analogous to you getting half your chromosomes from each parent. So again a simple solution we might just say I'll take the first half of one string, the other. There is then a mutation step, so in the same way as some genes get a random new mutated during reproduction to introduce new characteristics, we're going to pick a random character in this string and then just change it to a different one. Okay, so we've now got a population consisting half of the fittest individuals from the previous round and half of their sort of children as it were. We want to now check how we reached our target. If so, great, we can stop. If not, we're going to loop that round. Okay, and that's the general flow of it. So let's try and run that then. Okay, so what we're going to see when we run this is that for each generation we're going to print out some statistics about sort of the fitness scores that we've got and we're going to print out the best string that we've got in that generation. Which to start with is probably going to be something that's about the right length because in a collection of random strings that would be the best. Okay, so as this goes we should see some features that start to emerge, the spaces go in the right places, the characters get correct and indeed we've got to their talk title. Okay, so that's all well and good for strings but now so we want to evolve programs. So there are some extra challenges here. So whereas with string evolution I could just dump some random characters into a string and it's a valid string. It might not be very close to what I want but it is a string. With program evolution if I just put a load of random characters into a file the odds that's going to be a valid Python program a valid C program or what have you are pretty slim. So there are some rules about syntax and things that we're going to need to conform to. So you might think okay well I'll have some templates like this thing looks vaguely like a four block and I'll have some blanks and I can fill those in and I'll have templates for if statements and that sort of thing and I'll combine them like that. And fair enough you will generate your initial population will have lots of valid programs in and that'll be fine. But when you come to cross those over and to mutate them you'll very quickly get back to invalid stuff again.  Okay and that might work the problem you're going to have there is that we want to be able to program arbitrary things in this language that we're going to be evolving we don't want to limit what we can create. So what we need is for the language to be something called Turing Complete and the idea and the language that you kind of naively make may not be so. So what do I mean by something being Turing Complete? Okay so Alan Turing did a lot of work on the theory of computation and in particular the idea of reprogramal machines that could be used to compute arbitrary stuff. And so you count this concept of a Turing machine as a sort of model to think about this to help him mathematically reason about them and all it is is it's an infinitely long piece of tape it's divided up into a series of cells you have like an arrow that points at the cell that you currently look at and you can move that arrow backwards and forwards and in each cell you can put a symbol in there you can read out the current symbol or you can change the value of that symbol. That's all it is. It's a very simple device but it can be shown mathematically that anything that is computable and not everything is there are certain things that are mathematically impossible to compute but if it is computable it can be computed on this device. Okay and when we say a language is Turing Complete all we mean is that it can be used to simulate any single-taped Turing machine aka anything that's computable you can compute in that language. So it enables us to program whatever we like basically. So those are our requirements then we're looking for a language that's very synthetically simple and it is Turing Complete. So at this point I thought well that probably already exists let's not reinvent the wheel so like any good developer I googled it came across this site Cory Becker site primaryobjects.com this is a great site she's got lots of articles on machine learning one of them was a project where she'd been working on pretty much exactly this problem so evolving code rather than writing it. So what is this language used? So it's got only eight characters and it's pretty much a model of a Turing machine. So the interest of not getting myself thrown out the conference for swearing repeatedly for the rest of the talk I've left the name of it slightly as an exercise to the reader but I'm sure you can guess. So the first six characters are pretty much just a Turing machine so this is moving the pointer backwards and forwards with the value at the cell reading it in and reading it out and putting a value in. The last two characters, the square braces are where things get interesting. This is what gives us our control flow. So the ability to say based on the value I've got here I'm going to make a decision, I'm going to keep looping or I'm going to break out of that loop. So that's what allows us to give these languages power. So what does a program in brain F look like? So this for example is Hello World so your classic first program just print Hello World to the screen. You can see this is a fairly steric language you wouldn't really want to write anything complicated in it particularly when you get past just printing output it gets rapidly quite complicated keeping track of what you're doing. You certainly wouldn't want to try and debug or maintain any code written in this. But to a computer this is just as intelligible I'd say if it's a Python code, if it's a C code it's fine like this. From our point of view we already know how to generate strings we already know how to cross them over and you take them and this is just a string so we can apply all the same techniques. So let's try then and evolve a program in this. So I was going to do Hello World but I realised there's probably not time in this talk for that to converge on a solution. So we're just going to go for a slightly abbreviated one it's just going to say Hi. Okay. So what we're going to see is for each generation we're going to print out the output of each program and we're also going to print out the program strings so we're going to have the actual brain F code itself for the best individual in that generation. Okay. So we have some new issues to contend with here. So we now have the potential to have an invalid program so while brain F has very few syntactical requirements you do need to match up those square braces so if those aren't matched the program's invalid. We also have the potential to time out so we can have a valid program that gets stuck in an infinite loop so we need some cutoff point after which we judge that as an error as well. We also because real machines don't have infinite amount of memory we have to use a finite length tape rather than the model's infinite one so our program might go off the end of that memory in which case again we need to notice that so all of these cases we spot we give them a very low fitness score so we try and weed them out so they're not selected to the future generations. The next thing to note is that we get quite close to the target output and then don't seem to get very much further on the string evolution where the thing we're evolving and the thing we're measuring it's fitness of all one and the same here we're measuring the fitness of the output but we're actually evolving the program string so while the output might be very close it might require quite a big jump in the program code to get to where we want so it might be stuck in a slightly awkward loop or require quite a big mutation and so to try and help with that we've increased the number of times the mutation will allow so rather than just having replacement you can now sort of have mutations or delete them and in terms of the replacement one rather than just being a single character that we might flip we're kind of going to go over the whole program string and maybe change any of those we also need to change our selection metric so if we just selected the top half that would be a bit too elitist we'd be very narrowly driving towards something that looked like the output start with and sort of cutting off avenues that didn't look so promising originally but would come back round so what we want is some chance to include a sort of lower fitness score so that we can increase our chance of getting out of these local minimum work that's stuck in so what we're going to do is rather than selecting the top half we're going to have sort of like a roulette wheel spinner so if you imagine like a pie chart where the size of each wedge is going to be bigger if this individual has a higher fitness score and then we're going to spin it and sort of select one so lower fitness individuals may still be selected but with a lower probability and so hopefully we will then actually get to the solution running a random demo is a bit of fun on that's fun a bit of fun on a presentation one thing you will notice is that some of these programmes are quite suboptimal so you'll see lots of pluses in a row followed by lots of minuses so clearly there's things that we could do to make that more efficient so we have high so we have our programme so it's worth saying that this can be taken further so Cary Beckasite she took this and she made evolved programmes that generated the first few Fibonacci numbers or that took user input and added those numbers up so more interesting things can be done I think it might also be quite interesting to rather than evolving Brenneth programmes to say take the absent syntax tree of Python and try and evolve that and see if you get that to work so I certainly did some stuff with function evolution modelling those as trees and that did work so I think that could work so if you're interested all the code for this talk is up on github I hope you found it interesting I'd like to thank again Cambridge Medical Robotics for enabling me to be here and give you this talk as I said we are hiring so if you too would like to work with knife-warding robots it does make you sound quite interesting at parties and in all seriousness is very rewarding work and we're interested in Python developers software test engineers if they're any embedded to developers in the room or if you could do any of those it would be great more than one even better but yeah alright thank you very much Emma do we have any questions hey thanks so you're generating lots of programmes and then running them is there any danger of this doing anything weird on your computer or I don't know what's included in this brain F library in terms of APIs what it can do on your system yes it can do arbitrary things so it could potentially do anything yeah that would be bad questions hi thank you for your talk so what I got from this is that you sort of revolve a search space and you set the software to find it now in the highest the high version probably it wasn't very efficient but like performance wise would you sort of go for this in order to make also evolve programmes or go further I mean that do you have proof that these genetic algorithms really are worth dealing I mean going deeper into that so I didn't quite hear that have you actually tried it somewhere was that a question about how you could make the performance better I didn't quite hear no it's like do you have any actual sort of scenario where you have tried this and you got a better result on your own sorry I still quite hear you have I tried it do you have an actual scenario where you have used this oh in real life probably not it was mainly for fun and interesting educational value of trying it out I guess if you could improve the performance it would work better then yeah why not certainly if for some reason you wanted to write brandf programmes you don't want to have to write those on this you can certainly write them quicker than I can so for dealing with something like that then yes this would be better but why would you do that normally but I think it's certainly interesting more from a sort of academic point of view of kind of what could you do if you push them out of this rather than any practical applications right now hi great talk are you using generic algorithms for robotics development no I'm not using them at work this was just a side project yeah I think when it comes to surgical robots we probably want them to be slightly more deterministic but yeah thank you have you ever experimented with evolving the syntax tree of the programme instead of the text representation maybe this is much more efficient I don't know yeah so I haven't tried that it's one of the questions that came up when I gave this talk at Pycon UK last year and certainly I think that would be a really interesting thing to do with this so it did look at function evolution so evolving polynomial functions which model as trees in kind of a similar way that you would have syntax tree and that did work for evolving them so I think you could definitely do it quite tempted to try any more questions oh hi any specific reason that you use genetic algorithm instead of any other optimisation techniques not particularly so we were just studying a range of different topics in machine learning and this happened to be the one that I was doing at the time which is why I used this I'm not not for instance saying this is necessarily the most optimal way of doing this but it was just interesting good right if there are no more questions oh yeah why wouldn't you use genetic algorithms or any kind of for the robots because I mean we have self-driving cars that kind of go in that direction I mean I understand that you want deterministic behaviour but I guess you could achieve good results so why would you use them in general I think if you're a useful search algorithm so if you have like I said data that is in some way sorted that you can tell from looking in one area if the thing next might be useful it can be a more efficient way to search a large space I don't think you would I'm not aware anyway of any applications for self-driving cars but I think it's more searching large amounts of data that it might come to play ok right it's lunchtime everybody thank you very much