This is Think Tech Hawaii, Community Matters here. It's Monday, and you know what that means, research in Manoa. I'm Jay Fidel here on Think Tech. This is our premier scientific show, and we have an old friend come back, that's Andrea Gabrieli from HIGP. Nice to see you, Jay. Nice to be here. Yeah, nice to see you. Now, Andrea is born in Cincinnati, wasn't it? Oh, that is quite, that's close, that's close, close enough. Are there many Italian restaurants in Cincinnati? Oh, yes, born in Italy, but where they have plenty of volcanic activity in Italy, and that is a connection there. And he's a research assistant at HIGP, the Hawaii Institute of Geophysics and Planetology, working under Robert Wright, who is the director of that institute right now. Yeah, that's fine, that's fine. And he's working on sensors in geology and the like, and that takes him on trips to see geological phenomenon. Hawaii is a wonderful place for this kind of research. And recently, you went on a trip to a lake. What tell us about the lake? Could I sail on the lake, Andrea? Of course, but just once, and only for a moment. Only for a moment. So we're talking about a lava lake on the summit of Kilauea Volcano. That's the lava lake that's within the crater, which is within Hale Maomao. And so we went there on a permit with the USGS and the National Park Service to collect data and study the gases and the dynamics of this stunning phenomenon. Lava lakes are extremely rare. There are only five lakes, active lakes, on the Earth. Because you need to have a combination of various things to actually gather a molten pool of rocks and so have a lava lake. The danger is too, aren't they? Oh, yeah, absolutely. You try to sail on a lake like that, and only once for a few minutes, and it's finished. Yeah, yeah, yeah. And in fact, this is why we had to go through the USGS and the National Park Service granted as a permission to actually go there. Because we remember, again, the floor of the Kilauea is closed. Due to ongoing volcanic hazards, and particularly the gases that are released that are extremely toxic. Yeah, yeah, so it's not just that you could be burned up. It's the gases that could hurt your respiratory system. Absolutely, absolutely. And that's actually even the source of the fog that sometimes we experience even here on Oajo when the winds are from the south. Well, I want to examine exactly what you did and learned in the lake. But to make it relevant for us, I want to take a quote that you sent me. This is a quote by the British poet William Wadsworth. And it stands for the proposition of a lake carries you into recesses of knowledge, especially if you're doing a PhD on sensors and geological phenomena, recesses of knowledge otherwise impenetrable. And just to put it in perspective, Alexa, who is William Wadsworth? William Wadsworth was a major English romantic poet who, with Samuel Taylor Coleridge, helped to launch the romantic age in English literature with their joint publication lyrical ballads. Thank you, Alexa. Seems to be right. Seems to be right. And so why is a lake, a lava lake, the kind of lake that he's talking about in terms of the fact that it could give you the recesses of knowledge that were otherwise impenetrable? Let's see what a lava lake is. Let's take a glimpse. Let's peek into the earth interior. And I think we have our first video, and we can have a glimpse of what a lava lake is. Oh, here it is. A lava lake, as you can see here, is a pool of molten material that comes back in a cycle. And we can see the surface of the lake is divided into pieces, plates, if you like. And we can see the red boundaries of these plates. We can see two surfaces of spattering at the end of the lake. And this lake is actually, we're looking at the summit of Kilauea volcano. And this is the largest lake in the world now, because this one recently overtook the width of the Niagara-Gongolava Lake in Africa on the African Rift and became the largest 300 meters in diameter, more than 300 meters in diameter. So that helps in terms of understanding. You were at the top of the rim of the lake. We were at the rim, that's right. And you looked down, how far below you is the floor of the lake? The floor of Halemao-Mao was about 100 meters below us. And then, but the lake was at, when we were looking at, when we were there at the end of July, the lake was about 30 meters below the floor of Halemao-Mao. So that's the, the two levels, the floor, and then below that. That's right. So let's see our next picture. And we can see, that's right. Here it is.  It's kind of an overbuilt jacuzzi. If you want, it's like a sink. It's a sinkhole. It's a sinker, because it's a pool of material, which is connected to a tube, to a conduit, down below, which goes to the magma chamber. And like a sink, if you like, it goes up and down, the level goes up and down. It recently overflowed about a year ago. And the only difference with the sink is the temperature. This is about 1,000 degrees C. So it's a little bit hotter than the water that comes out our sinks. And also it goes the other way. So it doesn't flow from the top, but it comes up from the bottom. So it bubbles. And particularly, you can see the source of this material on the right-hand side. You can see the red material coming out. And then you see there is like a track. There is like the material is being carried away from that source, away from it. You can see there is sort of a pathway. The color is different, sort of silver. And then the material sinks as it becomes colder and denser. So this is a cycle. It goes up and down, up and down. And it's sort of like a convection. It's like a boiling soup of potatoes in a cold winter. Here in Hawaii, we have cold winters. No, I'm just kidding. But anyway, yeah, that's really what it is. It's a convective mechanism that brings the material up and then downwards, upwelling regions and down. So when it's warm, when it's hot, in the red area, but the glowing area, it's going to rise. As it cools, it's going to go back down into the pot. It's going to sink. That's right. So if I look down sort of as a silo, I see this lake as a silo of material. I mean, how far would it, and not that we would swim here, but how far would it go in this molten form? Does it go to the center of the Earth? Where does it go? Well, as I said, this is connected to a conduit that then goes down to the magma chamber. But when this vent, this lake, started to form back in March 2008, the lava lake was not there. There was a violent explosive eruption at the summit of Kilouea. And then what happened was that slowly the eruption began to fill this feature, this vent, and fill it and then form the lava lake. When this was beginning, the depth of this vent, this cavity, was 200, 300 meters. So that's some sort of gives you an idea of what we're looking at within. And lava lakes are particularly important from a scientific point of view, because for example, the movement of the plates on the surface, which sinks and rise, can be compared with what really happens on the surface of the Earth by looking at the plate tectonics. For example, the subduction. Subduction. They have that a lot in Washington. And they have it in Hollywood, too. In the morning paper, it was a case of a fellow who lost his job over subduction. Subduction over company. Never mind. Here we're talking about rocks sinking. If you think about what is happening, for example, on the Cascade Range in Washington and Oregon states, that's really the wonderful plate going underneath the North American plate. And that's create volcanic, that's create volcanism. Does it also create earthquakes? Earthquakes, as well, associated with the rising of magma. It's different from the San Andreas fault, for example. That's a different mechanism. It's not related to you. Is it the San Andreas fault? Yeah, it was my ancestor. No, this is really, it's a fault in California. That creates earthquakes. But that's a different mechanism. Here we're talking about sliding plates. There we're talking about subduction. But a lava lake, as our friend William Wordsworth at the beginning was sort of reminding us, although he was talking about something else. A lava lake can give us an idea about the mechanisms that really happen within the Earth, and really try and give us an understanding of what phenomena that we can't really think of. So can you give a moment about your research, your science here? You're taking readings. You're getting data. What readings? What data are you getting when you stand at the rim of this lake? And what conclusions do you draw from that data? Let's bring, I think we have a picture that shows us standing on the rim of the lake. So we can actually see what we were doing. Oh, there it is, yeah. So on the right, we have my supervisor again, Robert Wright, and Casey Hannibal, who is my friend and co-worker. And then we can see Iniello, Matt Patrick, who is one of the geologists at the Hawaiian Volcano Observatory who came with us. We're pointing down at the lake imaging spectrometers to try and detect the gases that are released by this volcano, and particularly what we're trying to do, Casey is doing it in the mid-wave infrared. I am doing it in the thermal infrared, so different regions of the electromagnetic spectrum. What we're trying to do is detect carbon dioxide. Now, as you know, there is a lot of carbon dioxide in the atmosphere. So it's particularly difficult to separate, to identify the carbon dioxide which is released by the volcano and can tell us what is happening to the magma chamber down below from the atmospheric carbon dioxide. So how do you do that? That's right. We're testing these kind of sensors. We're testing this kind of sensor on detecting these gases to see if we can actually detect these gases. Because volcanologists have used sulfur dioxide for a while now to interfere about what is happening in the magma chamber. But sulfur dioxide is sort of easy because there is not a lot of sulfur dioxide in the atmosphere. So you can immediately identify the volcanic one. But carbon dioxide is the most tricky. But why are volcanologists interested in carbon dioxide? Why is it better? It's more difficult, but why is it better? Yeah, well, you know, this is a really perfect time. It's what I call a cliffhanger, literally. Don't jump. Don't jump. We're going to find out the answer to that question right after we come back. And right now, we're going to have a break with Andrea Gabrielli of the HIGP research team that was at the Lava Lake in Mauna Kea. I'll be right back. This is Think Tech Hawaii, raising public awareness. My friend, mother, what big eyes you have. She's sad. All the better to see you with my dear. What are you doing? OK. Research says reading from birth accelerates the baby's brain development. And you're doing that now? Oh, yeah. This is the starting line. Posh. And this is over. You're dead. Read aloud 15 minutes. Every child, every parent, every day. I'm Ethan Allen, host of a likable science on Think Tech Hawaii. Every Friday afternoon at 2 PM, I hope you'll join me for a likable science. We'll dig into science, dig into the meat of science, dig into the joy and delight of science. We'll discover why science is indeed fun, why science is interesting, why people should care about science, and care about the research that's being done out there. It's all great. It's all entertaining. It's all educational. So I hope to join me for a likable science. OK, we're back. We're live with Andrea Gabrielli of HIGP, the Hawai'i Institute of Geophysics and Planetology. He's a research assistant there. And he's studying sensors in geological formations. And I was only joking when I said Manakea. It was Kilauea, where you were. That's right, Kilauea volcano. Kilauea volcano. Yeah, a really spectacular place. And really, I mean, you could get up there. I can't get up there. But maybe I'll go with you next time if I can breathe all right. Did you have trouble breathing up there? Well, luckily we didn't. But only because we were wearing respirator with HEPA, particle pre-filters, to actually prevent the sulfur dioxide to get into our lungs and everything. It really injure you. So you were talking about exactly how you discern the kind of carbon monoxide that's in the air in general and the kind of carbon monoxide that's coming out of the carbon dioxide. Pardon me, it was a test. The carbon dioxide that's in the air generally in the carbon dioxide that's coming out of the lake in the volcano. So how do you do that? We use this thermal light perspective imaging sensor. But this is still a test. We went there actually to test our abilities with these sensors to measure this carbon dioxide. But we stopped before the break with the famous question that the suspense. And we were saying why are volcanologists interested in carbon dioxide? The reason is because carbon dioxide starts to be exalted, starts to be released, be separated from the silicate melt that is rising. When the melt, the magma, is still really deep in the volcano. So basically, this is the reason why. It could give us warnings, early warnings, about what is happening in the volcano. Whereas sulfur dioxide is easier to be detected. However, it is released by the rising magma. When the magma is already really close to the surface. So just to give you an example, there is a volcano in Alaska and Mount Redoubt. And scientists were monitoring. That's near Juneau. That's near Juneau. Is that the one that blew up in the 50s, I think it was? I think Mount Redoubt is more recent eruption. But anyway, the volcano, before it blew up, it began to exolve carbon dioxide a couple of months before the eruption happened. Whereas it started to release sulfur dioxide a few days before the eruption. This is why volcanologists are really interested in carbon dioxide. Because it could give us warnings. And this is very important when trying to warn the populations living nearby, active volcanoes, and to try and prevent death and future death from natural disasters. So you have to have a device that can read this remotely, because you can't be standing there all the time. We don't want to go too close. You don't want to be close, right? And it has to send you a message to say there's too much in the air here. Absolutely. So it takes some action to warn people. So the Hawaiian Volcano Observatory is interested in what we're doing in this possibility. Because we haven't really detected carbon dioxide yet. We're still testing it. But the Hawaiian Volcano Observatory in the USTS was interested in our sensors, in the sensors that Paul Lucy, Robert Wright, have developed. Because they could give us these kind of warnings. And that's exactly the reason why they granted us a permit, as well as the National Park Service. But lava lakes can really give us lots of information about the gases and the activities. But let's maybe watch one more video to see the activity of lava lakes. So I think we have the second video I think about. I think there it is, yeah. So here we were standing on the rim. And this is a much less serene view than the other one. We can see a huge spattering fountain here and waves in the molten material. This fountain, this spattering source throws material up to even 10 feet into the air. So what we are looking at is actually the top of the spatter. That's the top of the, yeah. We were looking at it from above. We were standing on the rim, looking down, looking down. So if you were down at the same level, you would see it jumping 10 feet in the air. But you wouldn't see it for too long because you would be burned to a crisp. Absolutely. And I think, let's see one more picture so we can talk about more the dangers of this active lake. Let's see the bird picture. This is the bird picture. This is a video. And you can see there is a bird flying over the surface of the lake. It's hard to see if it's up in the upper right-hand corner.  Maybe let's watch again what you think. So we can really see. And then we can make some comments. Yeah, let's see that one more time. Yeah. So the thing about the bird is that the bird is in the fog, in the fog, if you will. In the sulfur dioxide gases. And the bird doesn't do well at that. The bird is not any better able to handle it than we are. Not really. The bird is a coiaquea bird, one of these long, white-tailed, tropic birds, about two feet, the wingspan. So they're large seabirds that nest on the walls of Hale Maomao. They fly to the ocean during the day. And they feed on fishing. And they come back. They nest there because there are no predators. However, usually the adults fly high above the thermals generated by the lava lake. Fly on the thermal, yeah. On the thermals, way above the thermals. However, we think this one was a juvenile that got inexperienced, got too close, and was blinded and intoxicated by the hot gases rising from the lake. And it was forced to land, as we could see in the movie. It was forced to land on the surface of the lake and was roasted. What a tragedy. Did you see him land? You couldn't see him, though. The smoke was too dense. The smoke was dense. But we could actually see the bird burning whenever. So it was a horrible scene. But this really tells us about the dangers and about 2,000 tons per day of sulfur dioxide are released by that crater alone. Then there is Pu'u'u'u'u, that's the other vent on the southwest rift zone, southeast rift zone. But that's another vent. Only Halema'u'u'u puts out 2,000 tons per day of sulfur dioxide. And there is an interesting story. We were standing while this poor bird met his destiny. We were talking to Matt Patrick, who is one of the geologists there at the HBO. And he was telling us that he was reading some papers about by Thomas Jagger, who is the founder of HBO. And he was talking about these birds that fall into the lake sometime. But he was mentioning that after Jagger, nobody really witnessed these birds. Because it's really usually the crater, the caldera floor is closed, and people aren't allowed to go there. There was a special permission that was granted to us. But so we saw this after Thomas Jagger. So that was an interesting thing, although very sad. But again, reminds us of the dangers of this gas. Yeah. So when you get the data, what's the form of the data? And how do you interpolate the data and come to scientific conclusions so that you can, I guess, get a system going to know when you're reaching danger levels? So these sensors gather image. And for every pixel, every spot in the image, we can gather spectral information. So what it means, basically, is that there is a source of light, in this case, it's infrared light, coming to the sensor. Now, the source of the light, in this case, is the lava lake. The gas within absorbs part of the light at specific wavelengths. Now, what we look in these sensors is the depth, the missing light, if you want, at specific wavelengths. And the depth of these features, how much light is missing, if you want, gives us the amount of gases that is in there. So by looking at the depth of these absorption emission features in the spectra, so the missing light, we can tell how much gas is in the plume and then try to infer what is happening. So now, when you look at one pixel, if you don't have a high resolution, that pixel could include a number of gases, couldn't it? So are we saying that one pixel is one kind of gas? I mean, you can only identify one kind of gas. Or can we identify multiple gases in the one pixel? For one pixel, basically, we get a spectrum from 8 to 14 microns. And each gas absorbs light at specific wavelengths. So sulfur dioxide, for example, absorbs most of the light at 8.6 microns. So we look at that particular wavelength, and we say, OK, this is sulfur dioxide. Other gases such as carbon dioxide, carbon dioxide is a nice feature at 14 microns. So basically, for each pixel, we can retrieve a spectrum. And the spectrum tells us stories about different gases. So we can look at various gases released from the volcano. And again, most common gases are carbon dioxide, which is the one we talked about. Sulfur dioxide also mentioned. And also, don't forget water vapor, because volcanoes are also a great source of water vapor coming from beneath the mantle. What is H2O? H2O, water vapor, just water. How about just oxygen? Can you see that, too? Is there any oxygen coming out of the lake? Well, no, there's no oxygen coming out of the lake. But oxygen in the infrared part of the spectrum, oxygen is not particularly active. So it doesn't even absorb and emit light. So you don't care about it? No. But if we remember William Wordsworth and his romantic lakes in the Northwestern part of England, those lakes really emit oxygen. Because the vegetation, the algae that are in there can release oxygen. Interesting, yeah. We'll see we. So it comes back to him. It comes back to William Wordsworth. So when you look at all the pixels, you're putting it in a spreadsheet or database somewhere and evaluating the relative density of this kind of gas versus that kind of gas. So if I give you a lot, for example, of sulfur dioxide and not so much of carbon dioxide, what is that going to tell me? I'm looking for proportions, right? I'm looking for relative presence, right? Absolutely. And again, carbon dioxide and sulfur dioxide are exalted, released from the magma, at different depths. So if you find more of this or more of that, it basically tells you, you don't really are 100% sure because then you have to put it in perspective and even consider earthquakes or deformation or other geological and geophysical factors to actually effectively monitor volcanoes. However, it gives you an insight of what is happening in the magma chamber within the plumbing system of the volcano. And really, when you put it in perspective with other geophysical measurements and everything, it basically foster our knowledge so we can really try and predict volcanic eruptions and try to enthear what is happening below. One last question, Andrea. So you're working on trying to identify what's there? What kind of gases are coming out? But do you have a model to compare it against her? What kind of gases would be coming out with a prospective eruption happening? In other words, do you have a profile that you look at to make the comparison? This is more a geological, that's the ultimate goal that all the volcanologists have. Yes, by looking at what we call solubility laws, we can infer where the magma is and try and understand what is happening. But my research at HIGP is mostly on building, retrieving algorithm to actually gather this information and also to develop sensors that can be used, in this particular case, we were pointing at the lava lake, but in the past we were also looking at the sky and so trying and look at these gases against the cold background of the sky and also monitor, for example, air pollution and fog. So here we're talking about gases but these imaging spectrometers in the thermal infrared hyperspectral sensors can really give us even a lot of other informations about gases and pollution as well. But in terms of volcanoes and lava lake, it really gives an insight to peek into the earth interior and try and understand what is happening below. Thank you, Andrea. Andrea Gabrielli, a research assistant at HIGP, the Hawaiian Institute of Geophysics and Planetology in the southwest at UH Manoa. Thank you so much. Thank you, Jay. Aloha. Aloha. Okay.


But the next speaker is Harshit. He will tell us more about Firebase Machine Learning Kit.  Good afternoon. So first of all, a quick question. Quick show of hands. How many of you guys are mobile developers? Any mobile developer in the house? Mobile dev, working on Android, iOS. All right. And how many machine learning experts here?   OK, better question. How many people in the room are not really very experienced with machine learning as in like you are fascinated by the idea of it, but somehow like you are reluctant to try it because of the math involved, because of the statistics involved, and all those things. So how many people in here? Good. I'm in the right room. Makes sense. All right. So today I'll be talking about Firebase ML Kit. So it's to modify the title a bit. So it's not exactly machine learning made easy. It's mobile machine learning made easy. Because as time progresses, the laptops or the devices that we use are getting smaller and smaller. So our mobile phones, the things that they can do today, even a computer would not be able to do those things a few years back. So tech is getting more and more mobile. We have smartwatches in upcoming time. We might have smart chip implants as well. So right now the fight is to implement machine learning in as smaller form factor as you can. So that's the crux of my talk today. I'll be talking about how you can implement machine learning in mobile, even when you are not an expert machine learning developer. So about me, I'm an Android developer. So I work in a startup in New Delhi. That's coding blocks. I work as an Android developer and an Android instructor. And I'm also an open source enthusiast. So in my free time, I love to work on open source technologies and tools. So machine learning as a mobile developer. You can replace this as a web developer or as a non-ML developer. So this is what you see. If you go on to an online course by Andrew Ng, this is what you see. Because this is an actual snapshot from one of the deep learning initiatives, the course that Andrew Ng has recently launched. So I tried doing that course. I stopped at this slide. So I mean, this was my expression. So it's some sort of elevation. I mean, I used to learn these things back in high school and college. But I mean, as you start working as a software developer, you really encounter these things. So summation, integration, theta, alpha, beta, gamma, all those things. We don't really use them in our daily lives. But as a machine learning engineer, all the courses that you have online, they start with these things. I'm not saying this is not important. It's important. But it's overwhelming when you start. So yeah, so this is me when I start. So what to do? Because I mean, machine learning is going to be the future. And you need to know. Because if you want to, so there was a talk today morning, so how machines are going to replace your job as coders. So if you are concerned about your well-being and you want to earn money when you get old, I mean, I'm not that old, sir.  So when you get old, if you want to earn money, then you have to modify yourself. You have to learn new things, keep on learning. So how should you do that? So machine learning is one answer. But again, to do machine learning, you have to do all these things, which is sort of a feedback loop that you cannot get out of. So yeah, so what to do? So this is something that can help us get out of the loop. So the title of the talk or the crux of the talk will be implementation. So I am a practical person. I am more interested in practical implementation than theory. And a lot of course online, they focus on theory. Apart from a few courses like FastAI, other courses that you see online are more focused on theory. Not that that's wrong, but I'm not good out for it because I love practical things more than theoretical things. So yeah, so if you are like me, if you like to make things and learn by making those things, rather than learning stuff and then making things at the very end. So this is something that can help you. So who knows about Firebase? Firebase, anyone? So Firebase is basically a product built by Google. It's around mobile developers. So this used to be back-end as a service, but now they have grown their product stack and they have a lot of functionalities available. So you can implement auth in your app. You can send notification. You can do some sort of machine learning stuff on your apps. And ML Kit was recently announced at Google IOC 2018. And this allows you to implement machine learning features in your app, both offline and online. So we can use this to do some practical hands-on learning and get the joy of machine learning. So there's a very common philosophy that I like to follow. So we do something only when there are two motivating factors. So first is money. Second is we feel like doing it or we get joy by doing it. So let's say we are working at a 9 to 5 job. So either you are motivated by your work, you find joy in doing your work, or you're getting money. Apart from that, there's no third factor. And if I'm learning machine learning, so I'm not getting money for doing that. I'm paying money to learn machine learning from an online or an offline source. So the only second factor that can motivate me to learn machine learning is if I'm enjoying the process. And if I have to solve differential integral equations all day long, definitely that's not a joy for me. So building stuff, that's how I get joy. That's something that's interesting that intrigues me. So yeah, that is Firebase. That's something that Firebase ML Kit allows. So Firebase is basically it was announced at Google by in Google IoT 2018.  And it also allows you to host a custom TensorFlow module onto Firebase. And iOS as well. It supports both Android and iOS. And I think web support is still in works. So probably in the upcoming future, ML Kit will also support web apps by using TensorFlow.js. So what all do you need to start if you want to use Firebase ML Kit? If you are just learning, then you probably don't need these things. So for example, you don't need an app or an idea that needs some machine learning features. So you can simply start. So you need to know basics of Python and TensorFlow. This is optional later on, not right now. To start, you just need to know basics of app development because right now it's not available for web apps, but it's only for mobile apps. So you need to know the basics. OK, so how do Android apps work? How do iOS apps work? You need to know basic Java, Swift, Kotlin, all those languages. So that's something that you need to know, and that's it. Rest of the things you learn as you start exploring the APIs or playing around with the APIs. So what all the available APIs are? So first is the text detection. You can detect a text from an image. Second is face detection. You can detect faces, emotions, facial contours, all those things. It also allows you to scan barcodes and extract values from them. It has image labeling API available that takes an image and gets some info from that image. It also has landmark recognition API. So for example, you take a picture of a famous place. It tells you what that place is without using the metadata of that image. Lastly, again, it allows you to host and run custom TensorFlow models. Let's say we'll see that example later on. Let's say I want to make an app that detects whether object is a water bottle or is that a pen. So I can train a custom TensorFlow model. I can host that on Firebase. And Firebase will allow me to use that model as well. So it's very flexible in that regards. So the first five APIs that you see are custom prebuilt APIs that you cannot tweak or modify. But the last one, you can tweak and modify to your own content. So there are two types of APIs again. So first is on-device API. So that runs on your mobile. It doesn't require an internet connection. It doesn't require anything else. It runs on your mobile phone. It runs without internet. So for the next billion, so this was a very popular tag pushed by Google. So supporting or building for the next billion. Because recently in India, we got GEO. So by the inception of GEO, more than a billion people came online for the first time. So if you want to build apps for them or you want to build features for them, so they have to be tailor made and custom made for them. But the con of this on-device API is that it has a low accuracy. The accuracy is lower. The results that you get are limited. But it also has the cloud API. That's paid. But again, for the first 1,000 calls per month, it's free. So let's say you're playing around with the APIs. You don't need to pay anything. It needs internet connectivity. So that's the rest billions. And it has higher accuracy, obviously, because it runs on Google Cloud. It uses models that are trained on Google Cloud and all those things. So let's jump directly into the action part of it. I'll show you some code snippets. I'll show you some working examples of how I played around with ML Kit and built some apps. Some of those apps are live on Google Play Store. People are using it. And yeah, all these things are done within a time frame of, let's say, three to four months. So I am not an ML expert. But again, I learned things by doing them. And yeah. So first is text recognition. So again, on the left side, you see the API. And on the right side, you see an example app built that uses this API. So for the text recognition API, I built a credit card scanner app. So you see this in many apps. For example, you're using PayPal. So PayPal, if you want to enter your card, it asks you to scan your card. It automatically takes the details from your card, and that's it. So I made a clone of it using Firebase text recognition APIs. So just take a picture of the card. It will recognize the card number and the expiry date. It gives me other things as well. It gives me the city rewards and all those things. But I don't need them, so I skipped them. But you get the general idea. So it can extract text from devices. It has both cloud and non-device APIs available. So the benefit with cloud APIs is the drawback or limitation of on-device APIs is it can only detect Latin characters. Let's say you want to support Hindi, Gujarati, and other non-Latin languages, you have to use the cloud API. So you can do that. You can definitely do that. If you want to use support non-Latin languages. But yeah, cards normally have Latin characters, so on-device API worked for me. And so the implementation or the steps involved. Even if you don't know how mobile development works, the steps involved are very simple if you look closely.  So Firebase Vision Image is a class within Firebase ML Kit. So you create an object of the Firebase Vision Image class. On this object, Firebase performs all the inferencing, all the ML inferencing. So it's a single line code. Is the code visible to everyone? So the important code is highlighted. It's like in red. So you first create an image. This language is Kotlin, by the way. JS developers might find it somewhat similar. If you're a Java developer, it's again very similar. It's not at all strange from Java. It's quite similar.  It's a single line code. Just pass in the bitmap, and it automatically takes care of converting that bitmap into a Firebase Vision Image. Then next, you get access to the cloud or on-device text detector. So you get access to the text detector that actually going to detect something on this image. And that's a single line code as well. So this is the Firebase Vision class. You get an instance of that class, and you get the Firebase Vision Cloud text detector. For example, I use the cloud variant of the text detector. If you want to use the on-device API, you can simply call Vision Text Detector. That API is documented very well. So in case you want to learn how the API is working, also take a look at the documentation, that's something that you can do. So once this is done, the next step is run the detector on the image. So again, so Firebase Vision Text Detector, dot detect an image, pass in the image, and attach some listeners. So I have a success listener. I have a failure listener. There are more listeners. There is a completed listener. So all these are lambdas. So for example, in the success listener, I get a single string. So that string contains all the words inside this text document. And all the new lines are separated by a new line character. That's backslash n. So simply go ahead, split the single string at backslash n, and perform some registrations on the word that you get to detect if that's a card number, or is that an expiry date, or is that the user name. What's that? So again, I mean that's it. Again, doing this won't make you a machine learning engineer. Obviously, that's something that this API won't be able to do. But what it'll do is it'll arise your curiosity on what all things are possible using machine learning. So again, there's a custom model part that I'll be outlining briefly. So in that, you can train a custom model of your own. So once you train your first custom model, you sort of know. So this thing is possible using machine learning. What else is possible? But because if you don't do this thing, you probably won't know all the applications that are possible using machine learning, or using TensorFlow, or using by training your custom models. But once you get into the process, but once you get your hands dirty in the process, then you know, OK, OK, so I did this. Now this is possible. Now this is possible. It's very similar to other programming languages. If you don't know app development, you don't know what sort of apps you'll be able to make. But once you make your first app, once you know the basics, OK, OK, this is how app development works, these things are possible, then ideas start coming to you naturally. So that's something that I wanted to induce into myself. So just learn the basics. Learn what all things are possible. And once you know how the basics of things work, then you can probably start brainstorming ideas. You can search online to see, OK, is this thing possible or not. But again, that excitement, that drive to know the basics, that was not available on online courses, because they all start from the very stats and maths and probability part of things, which I don't personally like. So this was the text detection API. This was very simple three lines of code. And the best part about five bases, all the APIs are very similar. So if you know how one API works, the structure of all other APIs is very similar. It's exactly the same. You just have to change one or two things. So last is extract text from the response. That's done over here. So I get the list of words. I split them in backslash n. And I just print all the words. What all words did I get? So that's almost it. So inside onFillere, I displayed a message saying, something went wrong. Please try again. It was just bookkeeping. Next up is barcode scanning. So this allows you to scan and process barcodes. So this is part of Google's vision API. So this has only onDevice API available. It runs barcode processing on your device. It doesn't require internet connectivity to do that. So again, there's an example app on the right side. So I scanned this QR code. It showed me the data of this QR code is this, this, this. So again, steps involved, exactly same. So create a five base vision image, single line code. So there's an optional step as well. That's not available in all the APIs. But it was available in the barcode scanner API. So you can determine what all type of barcodes we want to scan. So there are more than 21 type of barcodes. So I also learned this when I was making this app. That's something else that I learned. So let's say I want to scan only QR codes. I don't want to scan other kind of barcodes. So I can specify that in this options object. So I created this options object, and I set the barcode formats. Put all formats you want to scan. And inside this line of code I wrote, I want to scan all the formats. So it's essential. It's very similar to not passing this options object. But again, you can specify what all barcodes you want to scan. Only want to scan the QR codes. There are square in nature. Do you only want to scan the barcodes that are rectangular in nature? Or there are other kind of barcodes as well. So there's NV21 type of barcode. There's Aztec format. There are more formats. Let's say you want to scan Aztec format barcodes. Then you can specify that format inside this options object, and your app will run much faster. So this is optional. This is not required. If you don't pass this optional object inside this barcode scanner, it will detect all the barcodes. That's what it does. So next, get access to the on-device barcode detector. That was, again, single-end code. Lastly, run the detector on the image. Again, so FirebaseDetector.detectInImage, pass in the Firebase Vision Image, and attach some listeners. So on success or on failure listeners. And extract data from the response. So this is how you extract the data. So depending on the type of barcode, so I said, OK, if the barcode contains a URL, get the URL from that barcode. If the barcode contains a contact info, get the contact info from that barcode. Let's say I want to store this contact info. If it's a URL, I want to open that URL. If it's a driving license, I want to see if that driver license. I want to validate that with an API. If that's Wi-Fi, I want to connect to that Wi-Fi. So these things are available out of box with Firebase MLK. So that was the barcode scanner API. Next up, you have the image labeling API. So it works like Google Lens. So how Google Lens works, not exactly like Google Lens, but it's quite close to how Google Lens works. So you take an image and this API returns a list of objects or items or emotions or colors or features that it thinks are available in the image. For example, I took an image of my headphones. So it gave me a list of items. It says, OK, this image probably contains headphones or it's related to technology or it has an electronic device or there's an audio equipment and all these things. So again, this has both, so it identifies objects, location, activities, animal species, and all these things. So this has both cloud and on-device API available. So cloud API has more objects that it can identify. The on-device API has limited number of objects that it can identify. So steps involved are the same. So create a Firebase vision image. You can optionally specify how many labels you want to return, how many items you want to detect. Let's say I only want to restrict this API to detect 20 items. So it'll give me 20 top items. Then again, get access to the on-device or cloud image detector, run the detector on the image, and extract the results. It's again, it's exactly the same as other APIs that we just saw. This is the crucial part of this API that I have just on outline. So I got the name of the label and I got the accuracy or confidence of that label. So as you see, the confidence is listed as well. So accuracy is probably 96%. So it gives you both accuracy and the name of that label. So after this, you have landmark detection API. So that sort of detects the landmark from an image. So as you can see, so I took a picture and it says, this is probably Golden Gate Bridge. So latitude is something, longitude is something, and the accuracy is something else. So this thing does not extract the metadata from an image to determine the location, but it tries to identify the popular landmarks in that image. So this works with the Taj Mahal or Colosseum or Marina Vesants. If you try it, you'll probably get a proper response from that image. So this identifies the popular landmarks from an image.  But it works with all the famous landmarks. I think it can detect more than 30,000 popular landmarks. And this only has cloud-only API available. So there's no on-device API available. Or let's say you take it. Perfect. Yeah, for the one box, please. First thing, let's say you took a picture for like six months back. At that time, you didn't have your location enabled. So how it should group those pictures? So it doesn't rely on the metadata. It detects the image from the landmark. It detects the location from the image available. I mean, there's no way, but if you're passing Latin long, you can use the reverse geocoder API so you can extract the location from the Latin long. You can get the name. That's something you can do. So no need to pass the image. If you have the Latin long, simply go ahead and extract the name from Latin long. OK, it's back. All right, so yeah. So I think I was at the landmark detection API. So again, steps are the same. You, 10 minutes. 10. OK, let's see. So you get the Latin long available with the response.  So that tells you whether the object in the image is smiling or is it angry, what's the dimensions, or what are the X and Y coordinates of the face. So that's Lord and savior Elon Musk. So again, steps are the same. I'll just skip to the code snippet directly. So it's this. So you get the right eye open probability. You get the left eye open probability. You get the smiling probability. You get the angry probability, the joy, the surprise probability, all these things. So you can make an emotion detection app using this API. So lastly, it's custom model study. So I won't be covering how I made this app, because first of all, there's not enough time. I only have five to six minutes, thanks to the person over there. Secondly, it's not easy to summarize in a talk. So I have written a blog post on this. I'll share the links with you. You can go through the learning process or the creation process of this app. So this custom model, what TensorFlow, or what Firebase allows you to do is you can sort of create a TensorFlow model, or a TensorFlow Lite model, host it on Firebase, and Firebase will automatically serve that model to you. You don't need to include that model in your app. That will increase the size of your app. Just have the model on Firebase. Firebase will automatically download the model, run the model, and give you output from that model. So tips for creating a good model, I'll give you some tips. So have a lot of image while training your model. Argument your data by modifying the images that you already have. And account for real-life use cases by adding noise to a data set. So these things might not make sense right now, but once you go through the blog post that I'll be sharing, they'll make more sense. So sorry about that. I could not expand on this because of the lack of time. But again, so this is something that I'll share resources with you. You can go through those resources. So takeaways, machine learning is hard, but it can be fun if done right. So it's hard. Definitely, it's not easy. So by using this API, you will not become a machine learning engineer. That's for sure. But it's fun. And it's easy. And it's fun if you do it or you tackle it the right way. So it was fun for me. It was relatively easier for me because I used the practical implementation sort of things with machine learning. I didn't go through the theoretical way. So tools in APIs available make the onboarding process super easy. So there are a lot of tools available so that make the process of onboarding or learning machine learning very easy. So for a custom model, having a good data set should be your primary focus. If you're using a custom model, make sure that your data set is proper. It's properly documented. It has proper images and all these things. So what's next? So after this talk, what should you do? So ML Kit, the thing I just showed you over here, it has a lot of code labs. So have you heard of Google Code Labs? Anyone? Google Code Labs? So basically, it's a step-by-step guide that Google has. It has starter code. It has what next steps you should do. And it has an end code. So what you can do is you can try this code labs. So it'll give you a starter code, download that code. Then there's a step two. It'll tell you, OK, now add this code. This code does this thing. Then add this code. This code does this thing. So it's a step-by-step guide to making an app that uses ML Kit. So there are various code labs available. So first is there's a barcode scanner code lab. You can take the links. You can snap some pictures if you want. That's fine. So after that, you have image labeling API available. So there's a code lab for that. You can have a Google Lins clone. There's text recognition code lab available. That you can go ahead and try and play around with. So there's a code lab for running a custom model with TensorFlow or ML Kit. That's available. Also, the next steps would be, so you should try this code lab. This is a code lab that you must try after finishing with ML Kit. So that's TensorFlow Poets. So this takes you to the process of creating your own custom model or your own Firebase API. So the Pokemon app that I just told you about, right? So I made that app by following this code lab. So it will walk you through the entire process of collecting the data set, training your model on that data set, and using that model onto Firebase. So definitely do this. It's one of the best code labs that Google has so far. So something else that you can do is you can go through Google's machine learning crash course. So this is MLCC. This is meant for developers. So this takes you through the mathematical aspects of machine learning, TensorFlow, and all these things. It's, again, it's just four-weeks course. It's relatively easy. It's practical. Then if you like what you just saw, so the apps that I just showed you, all the screenshots. So they are part of an app, a sample app that I made. That uses all these APIs. It's on my GitHub. You can go through that app. You can play around with that app. The app is open-sourced. You can go through the source code and see how the app was implemented. So there's another app that I just made using machine learning Firebase. I'm like, that was talking about. So it's Pokédex. So this is, again, open-sourced. The data set is available on Kaggle. And it's live on Play Store as well. So if you take a picture, it'll tell you which Pokemon that is in the picture. So again, there's some proof of the process that I followed, that it works. So by following the process, I was able to make an app that uses machine learning that does not depend on the pre-built APIs that user custom model and it's available on Play Store. It's live people are using it. So again, that's the validation. So initially I had the ideation and this is the validation. So you need to prove that this thing might not work. So here's the proof that it works. Yeah, so I think that's it from my side. I have blog posts on all of these APIs on all the things that I just showed you. They are available on my Medium account. You can find me on Twitter and that's my GitHub. If you have any questions, right? Do we have time for questions? Okay, yeah. So you can use image magic. So image magic, so it's a photo editing tool but they have a command line script for it, right? So I used image magic to sort of like scale and rotate my images. So initially I had only 10,000 images, right? So I scaled and rotated and cropped every image. So for a single image, I had 21 more images. So at the end of the day I had like 21, I think 210,000 images, right? So image magic, you should use image magic, yeah. Over in API, so Firebase has built an API to call a model that is on Firebase. Yeah, it's possible. So the only reason I used Firebase for this was because I don't know to bloat my app because the model is often 15 MBs, right? So it won't affect, I mean in case, in fact like deploying it on mobile phone, it's better because if you deploy it on Firebase, right? On Firebase's console, for the first time you launch your app, the model is downloaded to your phone, right? But after that it'll be used locally. It'll be used from the local downloaded model. But if you have the model on your phone, from the get go, let's say you include that model in your app's source code, from the get go, it'll be much faster for you. The first launch will be much faster, right? It'll be like, it'll be hassle free for the user. Is that, was that your question? The only battery life that you should be concerned about is the camera that's always running. Because I mean, so a lot of new phones, they have special dedicated CPUs for machine learning. So the neural net APIs, if your phone is running Android version, Marshmallow and above. So they have a specific APIs for machine learning. So they have the neural net APIs. So they reduce your battery life, sorry, they reduce the battery consumption significantly, right? Before Marshmallow, yeah, it uses your CPU that might affect the battery life. But after Marshmallow, that is Android 6.0, your battery life is like much better by when you're using these models. But again, battery life is one of your least concerns. Your main concern should be memory usage because the camera is always running, right? And yeah, that's about it. Okay, let's thank Harish for a nice talk. Thank you. Thank you.


Capital-based macroeconomics, I've used that term for a number of years and it's began to soak in and other people are using it too, that it doesn't come from a particular country. Well of course it did, with Minger and all that, but it certainly applies to many more economies than the Austrian economy and that's why I put it that way. Now what we're going to do today is look at the difference between sustainable and unsustainable growth. This is adapted from my book Time and Money. Start with the elements of this story and start with a production possibilities frontier which most Austrians don't like to use that and in fact it's not very usable by itself but with some of the other graphics it works very well. Loanable funds market and that's a market that we have what we think of in the widest possible sense. I know Murray Rothbard in Manneconomy and State doesn't much like the loanable funds market, the graph of the loanable funds market, but he looks at it in a very narrow sense so I think I can get away very easily with using it but in a very broad sense. The structure of production, that's what you saw in the first lecture that I gave which is one of the more unique parts of the Austrian theory and therefore I did it as a separate thing just to show you that that's what's going on in this theory and labor markets well but this time stayed specific labor markets which is differentiated from the normal neoclassical view and I won't save my message to the end but I'll just show you right here that what we're looking at is sustainable growth which has to be founded by savings and on the other hand unsustainable growth which is triggered by credit creation so that's the that's the distinction that we're going to look at all through this show. A methodological point and this is paraphrase from Hayek he says before we can even ask how things might go wrong we must first explain how they could ever go right right now in business cycle theory that little maxim is usually flouted especially in Keynes who seems to think it never goes right so there's no way of showing how it might and what you'll see in this PowerPoint is that even though it's about business cycles that's a whole stick here but it turns out that we probably will use about two-thirds of the lecture showing how markets work right and once you show how they work right then you're in a good position to show how they can go wrong with federal reserve policy for instance okay so capital based macro features consumption and investment as alternative uses of the economy's resources and here what should strike you is that this is this is very different from the Keynesians because Keynesians add them together C plus I plus G and here we're putting C and I on opposite at opposite levels there consumption and investment so under favorable conditions a fully employed market economy allocates resources to both uses making the most out of the trade-off and what I say favorable conditions I mean the market is allowed to work okay so we're on the production possibilities frontier shown like so okay so the PPF is often used for emphasizing the concept of scarcity as illustrating the implied trade-off and the expositing theories of capital and interest economic growth and international trade but the PPF is rarely appears in macroeconomic constructions this show though is gives heavy weight to the possibilities frontier so investment here represents gross investment which includes replacement capital and we show you here there's gross investment and replacement capital is a pretty good part of gross investment but not all of it which leaves room for net investment okay and that investment is what allows the economy to grow so with positive net investment the economy grows the PPF shifts outward from year to year permitting increased levels of both consumption and investment now here what you see with the graphs is that if you're looking at one PPF then yes consumption and investment are alternative ways of using resources but over time from period to period if the PPF shifts outward then you have consumption and investment moving together okay so this investment based outward shifting of the PPF represents sustainable economic growth based on savings okay what's the economy grow okay so poor four periods of growth are shown with consumption as well as savings and investment increasing each period the actual rate of expansion of the PPF depends upon many factors and first and foremost a change in the savings preferences which provokes a movement along the initial PPF affects the rate at which the PPF expands outward okay so we should be able to see this suppose people become more thrifty more future oriented they reduce the current consumption and save instead so if that happens and we're moving along the PPF get something going on there okay with the increased saving and investment the economy grows at a faster rate we'll see if that works what's the economy grow okay it's doing its it's doing its job okay it's the increased saving that makes the difference so you see when you have growth then sure enough investment and consumption are moving together it's just when you have when you're on a given PPF that see and I move opposite directions of one another okay so let's compare the high growth economy with the original low economic growth we can do that with no initial increase in saving the economy grows at a modest range you can watch the arrow there on the left and with the initial increase in saving investment increases at the expense of consumption after which both consumption and investment increase dramatically from period to period what's the right side okay and if you look across there you can think you see now that okay the economy is out performing what it used to be before you had the increase in saving now let's look at the loanable funds market the loanable funds market was a staple in pre-Keynesian macroeconomics and here we're just plotting the interest rate against saving and investment and it goes like this saving constitutes the supply of loanable funds there this supply curve is and demand reflects the business community's willingness to borrow and undertake investment projects like so okay so with the interest rate serving is a price loanable funds theory is a straightforward application of the marshalian supply and demand analysis so if the market is working then we get an equilibrium point which equates savings with demand for investment so loanable funds theory was closely identified by Dennis Robertson a contemporary of Keynes and a critic of Keynes's alternative theory which was the liquidity preference theory of interest so this is just my little plug to Dennis Robertson I liked him okay on the suggestion of Roy Herrod who was a sympathetic expository of the Keynesian system Keynes included in his general theory page 180 a graph rendering of the loanable funds market and now it turns out that Keynes did put it in but only to emphasize that he was throwing it out okay he didn't like that loanable funds theory and we'll see why as we go along if people become more future oriented they increase their saving causing the interest rate to fall and thereby encouraging the business community to undertake more investment projects so watch the saving curve shift rightward increase in saving there it is with a given technology the equality of saving and investment is prerequisite to genuine sustainable economic growth it's saving that finance the investment the loanable funds market and the PPF tell mutually enforcing reinforcing stories so we just look at these two graphs together like so now you have to look at both graphs at once okay notable funds market shows how the interest rate brings saving and investment in the line with one another the PPF shows how the trade-off is struck between consumption and investment the market adjustments and output prices and wages and other input prices keep the economy functioning on the PPF so there's a assumption here at this point that the market is working and the prices change if they need to change so wages and so on so these two elements of capital based macro economics show the pattern of movements in consumption saving and investment and the interest rate that are consistent with the change in intertemporal preferences that is with saving so as before we let people become more future oriented they save more which transmits a signal a lower interest rate to the business community so watch the saving induced increase in the interest rate and the corresponding movements along the PPF here we go so we've got it we've got the thing working right right the lower rate of interest establishes a new equilibrium in the loanable funds market and the economy moves along the PPF in the direction of more investment and less current consumption now most other macro economists will just balk at that that somehow you're you're getting less consumption but more investment and we'll show you why because Keynes looked at it the other way but it turns out that at lower interest rates then lots of investment projects become affordable and investors are eager to use those resources which will give output in future years or future months whatever it is even the possibility of a market economy could work in this way is at odds with Keynesian theory you get this get the flavor of Keynesianism here note that more investment is undertaking as consumption falls that's that's the Austrian view okay you can see that on the graphs but according to Keynes any reduction in consumer spending would result in excess inventories which in turn would cause production cutbacks worker layoffs a spiraling down of of income and expenditures the economy would go into recession and the business community would commit itself to less not more investment that's Keynes paradox of thrift so in other words from that PPF Keynes sees that the that the market is just going inside the PPF it's it's going with less consumption and less investment so you get a depression right there for Keynes now the key is about the retail inventories the rep their representative investments according to Keynes if retail investors retail inventories were a representative investment then Keynes would be right here the drive demand effect dominates reducing the consumer spending means reduced inventory replacement in general late stage investments move with consumer spending that's what Keynes would say however the interest rate effect dominates in the long term or interest stage investments a lower interest rate can stimulate industrial construction for instance or product development so you get that trade off okay so to keep track of the changes in the general pattern of investment activity we need to consider the structure of production and the stage specific labor market and that's what we looked at a couple of days ago the structure of production and it's so unique of the Hayekian theory the Austrian theory that I had a separate take on that a couple of days ago okay so capital based macroeconomics disintegrates capital inter-temporally consumable output is produced by a sequence of stages of production the output of one stage feeding in as input to the next the temporal temporarily defined stages are raised graphically from left to right in the output of the final stage constituting consumable output alright so an early stage process would be something like product development looks like that's going on there and the late stage would be inventory management the guy needs some customers actually but there they are okay so late stage investment activity is exemplified by inventory management for pedagogical convenience the initial capital structure is shown as having five stages of course there are lots of stages in a market economy but we're making do with five here with growth of the number of stages can increase although all five of these stages are in operation during each time period resources can be tracked through the structure of production over time and we'll see if we can do that together the sequence of stages form the Hayekian triangle the summary depiction of the economy's inter temporal structure of production in a growing economy the triangle increases in size along with the outward expansion of the production possibilities front here now we'll see let's watch this and now we're going to link up those two graphs so watch the PPF and the structure of production expand together okay so that's just ongoing growth no change in the saving preferences but people are saving all along and that gives you ongoing growth so when people should choose to save more they send two seemingly conflicting signals to the market and they're seemingly conflict conflictive because people are thinking in terms of Keynesian term so decreased consumption dampens the demand for the investment goods that are in close proximity with the consumable output this is the drive demand effect in other words that they're saving more they're not spending as much on consumer goods and so inventories drop people put holding so much inventory because people aren't consuming that much but a reduced interest rates which means that lower borrowing cost stimulates demand for investment goods that are temporarily remote from the consumable output this is the time discount or interest rate effect the drive demand and time discount are in conflict only if investment is conceived as a simple aggregate as it is in Keynesia C plus I plus G so investment no matter whether it's consumption or in or early stage is is considered investment okay in capital based macro capital and hence investment is conceived as a structure so changes in the demand for investment then can add differentially to or subtract differentially from the several stages of production that make up the structure Keynes theorizing in terms of aggregates rather than in terms of structure underlies care high-exclaim that Mr. Keynes aggregates conceal the most fundamental mechanisms of change and that that was it that's what Hayek needed to say at that point that was the problem increase saving results in an allocation of resources among the stages of production the two effects derived demand and time discount have their separate and complementary effects on the capital structure let's watch that and here just for emphasis let me say this slightly differently that a decreased demand for consumption goods dampens investment activities in the late stages of production that would be inventory replacement that would be dampened reducing the height of the Hayekian triangle and with time discount the reduced rate of interest stimulates investment activities in the early stages of production increasing the base of the Hayekian triangle okay so watch the structure of production respond to an increase in savings note the emergence of the sixth stage of production increased saving results in a reallocation of resources among the stages of production the two effects derived demand and time discount have their separate and complementary effects on the capital structure okay now we can use these two graphs together and show how it works increased saving then has an effect on both the magnitude and the investment aggregate and the temporal pattern of capital creation so what's the economy respond to an increase in saving okay so it didn't fall into the interior of the PPF like Keynes thought it would it simply changed the activities from the late stage to the early stage in view of the increased saving and of course those increased savings are what the people operating in the early stages are using they're borrowing those savings in order to you know in order to have those early stage activities so the PPF shows that more saving permits more investment see the arrows the Hayekian triangle shows that capital creation in the late stages such as retail inventories is decreased while the capital creation in the early stages such as product development is increased you get that kind of a twist of the structure of production and all this is by way of showing how markets can work okay without being hobbled by a central bank the structure of production is given a more future orientation which is consistent with the saving that made the restructuring possible that is people are saving now in order to increase their future spending power that's why they say okay they don't save for fun it's not fun okay and and so they plan to spend it and take some entrepreneurial judgment to figure out what they're going to spend it on but that's that's what the apartment part that's what the economy is all about okay note the increase in the growth rate this the sound is kind of lags what's going on but we'll we'll make do okay so now let's look at it first I think we look at the PPF as tracked by both the PPF and the Hayekian triangle consumption is seen to fall as the economy is adapting to a higher growth rate after which consumption rises more rapidly than before and eventually surpasses the old projected growth path so look at the PPF you get a little you get a little bit of a fault and then a big bounce you get the same thing let me do that again watch the graph down and then up okay and now down below here I've I've got a graph that shows consumption over time and it would look like this and that's exaggerated showing it goes way down and it typically just still goes up but at a less a lesser rate but I want the people in the back room to be able to see it so I'll make it go down all right and then you can compare the the dashed line with the solid line and see the saving implies giving up of some consumption in the near future and you'll see that there okay saving implies the giving up of some consumption in the near future in order to enjoy more consumption and the intermediate and far future and that that there's where you see the far future is getting more getting getting more consumption okay while most macroeconomic theories deal with the labor market and the wage rate capital based macro allows for state specific labor markets with the change in the interest rate stage specific wage rates change in a pattern rather than change uniformly and we can see that we put some labor markets down here although a labor market for each stage could be depicted the pattern of changes I ask wage rate gradient is revealed by distinguishing between the early stage and the late stage labor market so there's a early stage labor market and there's a late stage labor market so what's the economy respond to an increase in saving just looking at labor labor markets what you'll see well let's do it and we'll see let me do that once more if you didn't see it so what's it what's happening here is that workers are leaving the retail markets and going into the investment markets because that's what the economy responding to increased saving involved so here we go so one demand curve shifts left and the other one shifts right and then we can even show what Hayek called the wage rate gradient that there's a movement in relative wage rates which is the very kind of market signal that causes workers to drop out of the retail business and get into the investment business now we've got it all together here loanable funds market production possibilities frontier structure production stage specific labor markets looks like that okay so now watch the economy respond to an increase in saving and you can see it's poetry in motion I think it would be the right word but here here it goes I'll do it a couple of times okay so you don't get Keynes sliding into depression you just market mechanisms work like market mechanisms are supposed to and that's it I'm going to show this again if only because it took me a long time to make it look so there it goes okay now we're going to shift gears totally we were really talking about business cycles as opposed to how the economy works so now we're talking about policy induced unsustainable growth the macroeconomics of boom and bust and I take this from Steve Hankey because he has a much more stern luck than I can muster but he did I have to say he didn't read time and money before he wrote this so he wrote with interest rates artificially low consumers reduce savings in favor of consumption and entrepreneurs increase the rate of investment spending and then you have an imbalance between saving and investment you have an economy on an unsustainable growth path this in a nutshell is the lesson of the Austrian critique of central banking developed in the 20s and 30s so we'll see how this goes this is from Hayek I think I'll take time to read it because some people have claimed that in his late years he didn't he didn't approve of that business cycle that he had done earlier but here is he's in his late years okay and he says booms have always appeared with a great increase in investment a large part of which proved to be erroneous mistaken that's because people weren't saving the interest rate just went down because of the Fed that of course suggests a supply of capital was made apparent which wasn't actually existing the whole combination of a stimulus to investment on a large scale followed by a period of acute scarcity of capital because people weren't saving is consistent with the idea that there has been a misdirection due to monetary influences and that general scheme I still believe is correct okay now look what we've got credit expansion increasing increases in the money supply enter the economy through credit markets the central bank literally lends money in existence the new money masquerades is saving that is the supply of loanable funds and tails shifts rightward but without there being any increase in saving okay so what's the opposing movements of saving in investment as the central bank adds money Delta M to the supply side of the market for loans no this clues you off this is a different kind different kind of structure there you go so so he increased the money supply but that didn't increase saving it reduce saving because saving the interest rate is lower now and so people save less they're not so eager to save at the at that low rate of interest responding to a lower interest rate people actually save less and consume more right so they're consuming more the result is not a new sustainable equilibrium but rather a disequilibrium that for a time is masked by the infusion of loanable funds okay now pumping new money through credit markets drives a wedge between saving and investment investors move down along their demand curves taking advantage of the lower borrowing costs you see that arrow okay and savers move down along their unshifted saving curves in response to the weakened incentive to save that is that lower interest rate so the discrepancy between saving and investment is papered over literally isn't it papered over with newly created money which is self-represents no investable resources and there's that arrow so that's what's going on in the labor market much of Hayek's writing on money is aimed at shifting focus away from the bedrock relationship between money and general level of prices and toward the intertemporal discoordination that is caused by credit creation now look what happens upstairs here favorable credit conditions spur on investment activity which suggests a clockwise movement along the PPF in the direction of investment like so but income earners are actually saving less and hence consuming more which suggests counterclockwise movements along the PPF in the direction of consumption so what you're looking at here is some very serious disequilibrium the wedge between saving and investment translates into a tug of war between consumers and investors okay noting the investment dimension of the clockwise movement and the consumption dimension of the counterclockwise movement we see that credit expansion pushes the economy toward a point that lies beyond the PPF the PPF is that is the sustainable levels of consumption and investment it can go beyond the PPF but then it's in the unsustainable region that's the whole point all right so we got consumers pushing upward and investors pushing rightward and the result is the economy going out beyond the sustainable PPF into an unsustainable region that's how that's working okay okay now look at production possibility the stages of production the triangle the low interest rates consistent with future orientation stimulates investment activities in the early stages so you can see that to the left there but without sufficient resources being freed up elsewhere many of these investment projects will never be completed that's why you have the dotted line doesn't quite get completed compounding the intertemporal discordination increased consumer demand draws some resources toward the late stages further reducing the prospects of completing the new capital structure in other words people are consuming more rather than saving and you get a distorted triangle in that direction the dynamics of booming busts entail both overinvestment shown in the PPF and malinvestment the unsustainable lengthening of the hierarchy and triangle so you get mal investment overinvestment you get overconsumption as shown in both the PPF and the triangle so Mises repeatedly used the phrase malinvestment and overconsumption to describe the business cycle okay the tug-of-war pits that consumers against investors pushes the economy beyond the PPF unsustainably the low interest rate favors investment in increasingly binding resource constraints keep the economy from reaching that extra PPF so look at that PPF diagram and you can see that you're moving towards but you don't get there all right because resources are scarce much more scarce than than was thought and so the temporally conflicted structure of production dueling triangles John Cochran in Birmingham gave me that phrase dueling triangles I like it and so the economy can fall into depression and so if you look at that orange line probably no use orange and blue because that's all colors you know but look at that orange line and you see that's the only thing that Cain's picked up on you know he just saw the economies falling into the interior and there it is okay now real quickly because we're almost out of time you got a wedge between saving and investment you've got a tug-of-war between consumers and investors here we go padding the supply of loanable funds with new money drives a wedge between saving and investment papering over differences between saving and investment gives play to the tug-of-war between consumers and investors pitting early-stage against late-stage distorts the hike and triangle at both directions the temporal coordination eventually turning boom in the buzzed so watch the economy respond to credit expansion let's see if this works and you're all too young to know who that is you know who it is who that's that's Joe the flummer that gave these people hard time back in an earlier presidential election okay I don't have time for the rest of this so we'll quit right here thank you very much


The administration block The administration block is a fixed block you'll see everywhere in Moodle. It might be on the right as here or on the left according to your theme. What you see in the administration block depends on your privileges on the site and also within a course. As a regular course teacher within a course you'll see a course administration link expanding to give you options to help you manage your course for example editing the settings, enrolments and grades. We look at these in separate videos. You also have a link to switch your role to different roles, primarily a student or learner as we've called it. Clicking here would then display the course page or activity from the point of view of that role. This is useful as a teacher if you want to check how things work for your students. A student in a course would have a different view of the administration block as we can see if we go into the teaching with Moodle course where our user Sam is a student. There she sees a link to her grades for that course and the option if enabled by the administrator to unenroll herself. Thank you.


How men fall in love. Hi, this is Helena Hart from commitmentconnection.com and in this video I wanted to explain the process through which men fall in love. I do get asked about this all the time from women and it's really interesting. You know, it's a little counterintuitive so I'm so happy to talk about this with you today and of course I'd love to hear your thoughts on this as well. So a lot of people talk about like a mind-body connection when it comes to having a man fall in love and that's actually not where it's at. Like I said, it's a little counterintuitive so let me explain this and just go into a lot of detail here. So if you've ever tried to connect to a man or make him fall in love through like a body connection, like if you've ever tried to make a man commit or solidify the relationship or further the relationship through sex, you know that absolutely does not work. A man can have a great sexual relationship with a woman who he sees no future with. Maybe he's really attracted to her and the sex is really good but that's not what makes a man fall in love, the kind of deep love that makes him wanna commit to you forever. So, you know, trying to just connect to him through his body is the wrong way to go and then trying to connect with him mentally is also the wrong road to love as well because men can have a great mental connection with a woman and just see them as a friend or a buddy, you know?  So the way to a man's heart, how he falls in love is through a heart to heart connection. It's through connecting to his heart. You know, of course, physical attraction is important and if the sex is great, that is amazing. That's like icing on the cake and if you have a lot in common and have a mental connection, that's amazing too. You know, the three mind, body and heart can work together but the way he actually falls in love is through connecting to his heart, that deep heart to heart connection. So how to connect to a man's heart? It's always through your own heart first. That's really important. I'm gonna just repeat that. The way to connect to a man's heart is always through your own heart first. So you can't connect to a man's heart if you're not connected to yourself, if you're not in touch with your feelings and in touch with who you are, you know? Having that authentic confidence and trust in yourself is so important. That's what makes a man feel safe. Like, okay, she trusts herself. She feels safe with herself. That's how he knows that it's safe to come emotionally close to you and connect to his heart. So creating a deep heart to heart connection is really important and we should definitely talk some more about this in future videos. I'll give you a couple tips here that you can try and I'd love to hear how this goes for you. One way to do this is by expressing how you feel to a man. You know, if you're feeling a certain way and a lot of people take this to mean talking about the man and the relationship. And of course, you can do that down the line but that's not where you should start.  It could be about the weather or your day or something that happened at work or your plans for the weekend or what you did last weekend. Talking about how things feel to you is really important and it sounds very basic but most of us women are kind of going, rushing through our day, kind of going through the motions and we're on autopilot and there's these opportunities to connect with a man's heart and we're missing them all throughout the day. Maybe we're stuck in our masculine energy or we're just so busy that we forget to slow down and really get in touch with how we feel and then express that to a man. So next time a man asks you how was your day or what are you up to really get in touch with how you're feeling. Like, oh, I'm feeling so relaxed, laying here on the couch, work felt so overwhelming today, it feels great to be home just hanging out. Something like that, very simple or if he asks what you're up to saying, oh, I'm feeling so sleepy, I'm about to drift off to sleep. Rather than the typical response of, oh, my day was fine or I'm about to go to bed, really get in touch with how you feel and express that to him. So that's one way to connect to a man's heart. And again, he needs to feel that you feel safe with yourself and you trust yourself and your own feelings. That's how he knows that it's going to be safe to come close with you and connect with you on that level. And then once you get good at this, you can start going deeper and expressing more and more things like how you feel moment to moment or how you feel when you're with him. So that's really important when it comes to connecting with a man's heart. Another way to connect with his heart is when he knows that he can make you happy. And the last part is when you respect him as the masculine partner. And we can talk some more about that in future videos, but really quick, a man needs to know that he can make you happy, that it's easy to make you happy. That's how he knows that it's safe to connect with you emotionally and get close on that deeper level. If he feels like you're constantly disappointed in him or he can never do anything right, he will start to pull away.  Nothing he ever does is the right thing and it will block that heart-to-heart connection. And then also like I mentioned, he needs to feel respected as the masculine energy partner that will compel him to want to keep coming towards you. And again, it creates that safe space for a real connection to happen. So I hope that was helpful for you. I'd love to hear your thoughts on this and I hope you're having a great week. Thanks for watching. And if you'd like to discover exactly what to say and do to get the man and relationship you've always wanted, click the button on the right side of this video to visit our website and I will talk to you soon.