Welcome back, day two of theCUBE's coverage of Remars in Las Vegas, Amazon Remars. It's part of the re-series they call it at Amazon. Reinvent is their big show. Reinforces a security show. Remars is the new emerging machine learning automation robotics in space. The confluence of machine learning, powering a new industrial age and inflection point. John Furrier, host of theCUBE. We're here to break it down for another wall-to-wall coverage. We've got a great guest here. CUBE alumni from our AWS startup showcase, Chris Nogate, founder and CEO of Fiddler.ai. Welcome back to theCUBE. Good to see you. Great to see you, John. In person, look at the remote one before. Absolutely, great to be here. And I always love to be part of these interviews and love to talk more about what we're doing. Well, you guys have a lot of good street cred, a lot of good word-of-mouth around the quality of your product, the work you're doing. I know a lot of folks that I admire and trust in the AI machine learning area say great things about you. A lot going on. You guys are growing companies. You're kind of like a startup on a rocket ship getting ready to go, pun intended here at the space event. What's going on with you guys? You're here. Machine learning is the centerpiece of it. Swamy gave the keynote here at day two and it really is an inflection point. Machine learning is now ready, it's scaling and some of the examples that they were showing with the workloads and the data sets that they're tapping into. You know, you've got code whisperers they announced. You've got trust and bias now becoming being addressed. We're hitting a level, a new level in ML. ML operations, ML modeling, ML workloads for developers. Absolutely, I think machine learning now has become an operational software. A lot of companies are investing millions and billions of dollars in creating teams to operationalize machine learning based products and that's the exciting part. I think the thing that is very exciting for us is we are helping those teams to observe how those machine learning applications are working so that they can build trust into it because I believe as Swamy was alluding to this today without actually building trust into AI it's really hard to actually have your business users use it in their business workflows and that's where we are excited about bringing their trust and visibility factor into machine learning. A lot of us all know what you guys are doing here in the ecosystem of AWS and now it's spending here. Take a minute to explain what Fiddler's doing for the folks that are in the space that are in discovery mode trying to understand who's got what because like Swamy said on stage it's a full time job to keep up on all the machine learning activities and tool sets and platforms. Take a minute to explain what Fiddler's doing that we can get into some good questions. Absolutely, as the enterprise is taking on operationalization of machine learning models one of the key problems that they run into is lack of visibility into how those models perform. For example, let's say if I'm a bank I'm trying to introduce grid risk scoring models using machine learning. How do I know when my model is rejecting someone's loan when my model is accepting someone's loan and why is it doing it? And I think this is basically what makes machine learning a complex thing to implement and operationalize. Without this visibility you cannot build trust and actually use it in your business. With Fiddler what we provide is we actually open up this black box and we help our customers to really understand how those models work. For example, how is my model doing? Is it accurately working or not? Why is it actually rejecting someone's loan application? We provide these both fine grain as well as coarse grain insights so our customers can actually deploy machine learning in a safe and trustworthy manner. Who is your customer? Who you're targeting? What percentage of the data engineer? Is it data science? Is it the CISO? Is it all the above? Our customer is the data scientist and the machine learning engineer. And we usually talk to teams that have a few models running in production. That's basically our speed spot where they're trying to look for a single pane of glass where to see which models are running in their production, how they're performing, how they're affecting their business metrics. So we typically engage with head of data science or head of machine learning that has a few machine learning engineers and data scientists. Okay, so those people that are watching, you're into this, you can go check it out. It's good to learn. I want to get your thoughts on some trends that I see emerging. And I want to get your reaction to those. Number one, we're seeing the cloud scale now and integration a big part of things. So the time to value is brought up on stage today. Swami kind of mentioned, time to value showed some benchmark where they got four hours. Some other teams were doing eight weeks. Where are we on the progression of value, time to value on the scales like, can you scope that for me? I mean, it depends, right? Depending upon the company. So for example, when we work with banks, for them to time to operationalize a model can take months actually because of all the regulatory procedures that they have to go through. They have to get the models reviewed by model validators, model risk management teams. And then they audit those models. They have to then ship those models and constantly monitor them. So it's a very long process for them. And even for non-regulated sectors, if you do not have the right tools and processes in place, operationalizing machine learning models can take a long time. With tools like Fiddler, what we are enabling is we are basically compressing that lifecycle. We're helping them automate model monitoring and explainability so that they can actually ship models more faster. Like you get velocity in terms of shipping models. For example, one of the growing FinTech companies that started with us last year started with six models in production. Now they're running about 36 models in production. So within a year they were able to grow 10X. So that is basically what we are trying to see. It's another issue. We're at Remarch. So first of all, you got a great product and a lot of market to grow onto. But here you got space. Anyone who's coming out of college or university PhD program and if they're into Arrow, they're going to be here. This is where they are. Now you have a new core companies with machine learning, not just the engineering that you see in the space or Arrow space area. You have a new engineering. Now I go back to the old days where, my parents, there was Fortran. You used Fortran, it was Lingua Franklin to manage the equipment. Little throwback to the old school. But now machine learning is companion, first class citizen to the hardware. And in fact, some will say more important. I mean machine learning model is the new software artifact. It is going into production in a big way. And I think it has two different things that compared to traditional software. Number one, unlike traditional software, it's a black box. You cannot read up a machine learning model's code and see why it's making those predictions. Number two, it's a stochastic entity. What that means is its predictive power can vain over time. So it needs to be constantly monitored and then constantly refreshed so that it's actually working in depth. So those are the two main things you need to take care of. And if you can do that, then machine learning can give you a huge amount of ROR. There is some practitioner kind of like craft to it. Like you said, you got to know when to refresh, what data sets to bring in, what to stay away from, certainly when you get to the bias, but I'll get to that in a second. My next question is really along the lines of software. So if you believe that open source will dominate the software business, which I do, I mean most people won't argue. I think you would agree with that, right? Open source is driving everything. If everything's open source, where's the differentiation coming from? So if I'm a startup entrepreneur or I'm a project manager working on the next Artemis mission, I got open source. Okay, there's definitely security issues here. I don't want to talk about shift left right now, but like, okay, open source is everything. Where's the differentiation? Where do I have the proprietary edge? That's a great question, right? So I used to work in tech companies before Fiddler. When I used to work at Facebook, we would build everything in-house. We would not even use a lot of open source software. So there are companies like that that build everything in-house. And then I also worked at companies like Twitter and Pinterest, which are actually used a lot of open source, right? So now, like the thing is, it depends on the maturity of the organization. So if you're a Facebook or a Google, you can build a lot of things in-house. Then if you're like a modern tech company, you probably leverage open source. But there are lots of other companies in the world that still don't have the talent pool to actually take things from open source and productionize it. And that's where the opportunity for startups comes in too so that we can commercialize these things, create a great enterprise experience, so they actually operationalize things for them so that they don't have to do it in-house for them. And that's the advantage working with them. I don't want to get all operating system with you on theory here, on the stage here, but I will have to ask you the next question, which I totally agree with you, by the way. That's the way to go. There's not a lot of people out there that are peaked. And that's just statistical. And that's to get better. Data engineering is really narrow. That is like the SRE of data. That's a new role emerging. Okay, all the things are happening. So if open source is there, integration is a huge deal. And you're starting to see the rise of a lot of MSPs, managed service providers. I run Kubernetes clusters, I do this, that. And the other thing, so what's your reaction to the growth of the integration side of the business and this role of new services coming from third parties? Yeah, absolutely. I think one of the big challenges for a Chief Data Officer or someone like a CTO is how do they devise this infrastructure architecture and with components, either homegrown components or open source components or some vendor components, and how do they integrate? You know, when I used to run data engineering at Pinterest, we had to devise and data architecture combining all of these things and create something that actually flows very nicely, right? And this is why- And if you didn't do it right, it would break. Absolutely. And this is why it's important for us, like at Fiddler, to really make sure that Fiddler can integrate to all varieties of ML platforms. Today, a lot of our customers use machine learning, build machine learning models on SageMaker. So Fiddler nicely integrates with SageMaker so that they get a seamless experience to monitor their models. Yeah, I mean this might not be the right words for it, but I think data engineering as a service is really what I see you guys doing as well as other things. You're providing all that- ML engineering as a service. ML engineering as a service. Well, it's hard. I mean, it's like the hard stuff here, but that has to enable.  What's your vision on that? What is that value? It has to be a multiple of at least five to 10. I mean, the value is simple, right? You know, if you have to operationalize machine learning, you need visibility into how these things work. You know, if you're CTO or like Chief Data Officer is asking, how is my model working and how is it affecting my business, you need to be able to show them a dashboard how it's working, right? And so like a data scientist today struggles to do this. They have to manually generate a report, manually do this analysis. What Fiddler is doing them is basically, you know, reducing their work so that they can automate these things. They can still focus on the core aspect of model building and data preparation and this boring aspect of monitoring the model and creating reports around the models is automated for them. Yeah, you guys got a great business. I think it's a lot of great future there and it's only going to get bigger. Again, the time's going to span as the growth rising tide comes in. I want to ask you while we're on that topic of rising tides, Dave Vellante and I since reinvent last year have been kind of kicked down around this term that we made up called SuperCloud. And SuperCloud was a word that came out of these clouds that were not Amazon hyperscalers. So Snowflake, Goldman Sachs, Capital One, you name it. They're building massive proprietary value on top of the capex of Amazon. Jerry Chen at Greylock calls it castles in the cloud. You can create these motes. So this is a phenomenon, right? And you land on one and then you go to the others. So the strategy is everyone goes to Amazon first and then hits Azure and GCP. That then creates this kind of multi-cloud. So okay, so SuperCloud's kind of happening. It's a thing. Charles Fitzgerald will disagree. He has a platform where he's against the term. I get why but he's off base a little bit. We can't wait to debate him on that. So SuperClouds are happening. But now what do I do about multi-cloud? Because now I understand multi-cloud, I have this on that cloud. Integrating across clouds is a very difficult thing. Right, right, right. If I'm Snowflake or whatever, hey, I'll go to Azure, more TAM expansion, more market. But are people actually working together? Are we there yet? Where it's like, okay, I'm going to re-operationalize this code base over here. I mean the reality of it, enterprise wants optionality, right? I think they don't want to be locked in into one particular cloud vendor or one particular software. And therefore you actually have in a situation where you have a multi-cloud scenario where they want to have some workloads in Amazon, some workloads in Azure. And this is an opportunity for startups like us because we are cloud agnostic. We can monitor models wherever you have. So this is where a lot of our customers, they have some of their models running in their data centers and some of their models running in Amazon. And so we can provide a universal single pane of glass. So we can basically connect all of those data and actually showcase. I think this is an opportunity for startups to combine the data streams come from various different clouds and give them a single pane of experience. That way the sort of the, where is your data? Where are my models running? Which cloud are they are? It's all abstracted out from the customer. Because at the end of the day, enterprises will want optionality and we are in this multi-cloud situation. Yeah, I mean this reminds me of the interoperability days back when I was growing into the business. Everything was interoperability and OSI and the standards came out. But what's your opinion on openness? Okay, there's a knee-jerk reaction right now in the market to go silo on your data to get for governance, whatever reasons. But yet machine learning gurus and experts will say, hey, you want a horizontal scalability and have the best machine learning models, you've got to have access to data and fast in real time or neoreal time. That's the antithesis to siloing. So what's the solution? Yeah. Customers control the data plane and have a control plane that's, what do customers do? It's a big challenge. Yeah, absolutely. I think there are multiple different architectures that have emerged. We've seen like where vendors like us used to deploy completely on-prem, right? And they still do it. We still do it in some case, come customers. And then you had this managed cloud experience where you just abstract out the entire operations from the customer. And then now you have this hybrid experience where they split the control plane and data plane. So you preserve the privacy of the customer from the data perspective, but you still control the infrastructure, right? I don't think there's a right answer. It depends on the product that you're trying to solve. Databricks is able to solve this control plane data plane split really well. I've seen some other tools that have not done this really well. So I think it all depends upon- Well, it's no-flagged. I think they have a- Sorry, correct. They have a managed cloud service, right? So predominantly that's their business. So I think it all depends on what you're trying to, what is your go-to market, which customers you're talking to, what's your product architecture look like? From Fiddler's perspective, today we actually have chosen, we either go completely on-prem or we basically provide a managed cloud service. And that's actually simpler for us instead of splitting the control plane. So it's customer choice. Exactly. However you want to use Fiddler, go on-prem, no problem, or cloud. Right, or cloud, yeah. You'll deploy and you'll work across whatever observability space you want to. That's right, that's right. Okay, yeah, so that's a big challenge. All right, what's the big observation from your standpoint? You've been on the hyperscaler side, your journey, Facebook, Pinterest. So back then you build everything. Because no one else had software for you. But now everybody wants to be a hyperscaler, but there's a huge CapEx advantage. What should someone do?  What do I do? Right, I think the biggest advantage enterprise today have is they have a plethora of tools. When I used to work on machine learning way back in Microsoft on Bing Search, we had to build everything. From training platforms, deployment platforms, experimentation platforms, how do we monitor those models? Everything has to be homegrown. A lot of open source also did not exist at the time. Today the enterprise has this advantage, they're sitting on this goldmine of tools. Obviously there's probably a little bit of tool fatigue as well, which tools to select. There's plenty of tools available. Exactly, right? And then there's services available for you. So now you need to make smarter choices to cobble together this to create a workflow for your engineers. And you can really get started quite fast. And actually get on par with some of these modern tech companies. And that is the advantage that a lot of enterprises are seeing. If you were going to be the CTO or CEO of a big transformation, knowing what you know, could you just brought up the killer point about why it's such a great time right now? You got platform and the source and the tooling to essentially reset everything. So if you're going to throw everything out and start fresh, you're basically building a system architecture. It's a complete reset. That's doable. How fast do you think you could do that for say a large enterprise? See, I think if you set aside the organization processes and whatever kind of comes in the friction from a technology perspective, it's pretty fast. If you can devise a data architecture today with like tools like Kafka, Snowflake and Redshift and you can actually devise a data architecture very cleanly right from day one and actually implement it at scale. And then once you have accumulated enough data and you can extract more value from it, you can go and implement your ML Ops workflow as well on top of it. And I think this is where tools like Fiddler can help as well. So I would start with looking at data. Do we have centralization of data? Do we have governance around data? Do we have analytics around data? And then kind of get into machine learning operations. Krista, always great to have you on theCUBE.  Obviously great success in your company. Been there, done that and doing it again. I got to ask you since you just brought that up about the whole reset. What is the superhero persona right now? Because it used to be the full stack developer. You know, and then it's like, then I called, I didn't go over very well on theCUBE, the half stack developer because nobody wants to be a half stack at anything. No one, half sounds worse than full. But cloud is essentially half a stack.  Now you're talking about a persona that's going to reset, look at tools, make selections, build an architecture, build an operating environment, distribute a computing operating. Who is that person? What's that persona look like? I mean, I think the superhero persona today is ML engineer. I'm just really surprised how much, how much is put on an ML engineer to do actually these days. You know, when I entered the industry as a software engineer, I had three or four things in my job to do. I write code, I test it, I deploy it, I'm done. Like today as an ML engineer, I need to worry about my data, how do I collect it? I need to clean the data, I need to train my models, I need to experiment with what it is, I need to deploy them, I need to make sure that they're working once they're deployed. And you got to do all the DevOps behind it. And all the DevOps behind it. And so I'm like working half time as a data scientist, half time as a software engineer, half time as like a DevOps engineer. Cloud rocket tech. It's like a heroic job. And I think this is why, you know, this is why obviously these jobs are like now really hard jobs and people want to be more and more machine learning engineers. And they're paid commensurately as well. And this is where I think an opportunity for tools like Fiddler Exiles exists as well because we can help those ML engineers, you know, do their jobs better. Thanks for coming on theCUBE. Great to see you. We're here at 3Mars. And good, great to see you again. And congratulations for being on the AWS startup showcase that we were in year two, episode four coming up. We'll have to have you back on. Krishna, great to see you. Thanks for coming on. Okay, it's theCUBE's coverage here at 3Mars. John Furrier bringing all the signal from all the noise here. Not a lot of noise in this event is very small, very intimate, little bit different, but all on point with space, machine learning, robotics, the future of industrial. We'll be back with more coverage after this short break. Thank you John.


This episode was prerecorded as part of a live continuing education webinar. On-demand CEUs are still available for this presentation through all CEUs. Register at allceus.com slash counselor toolbox. Hi everybody and welcome to today's presentation on emotional eating, making peace with food. During the next hour or so we're going to define emotional eating and differentiate it really from eating when to celebrate and when it's a problem and also differentiate differentiating it from eating disorders. We'll explore emotional eating in terms of its beneficial functions and rewards and discuss why restrictive diets don't resolve emotional eating. A lot of times people will say, you know, I have been on this diet for forever and it doesn't seem to be working or I can't seem to stick to any diet that I try. And we're going to look at different reasons why this might be. What is emotional eating? And it's exactly what it sounds like. It's eating in response to emotions and feelings other than hunger.  So eating at someone, eating to forget or distract yourself, eating to feel better because when you eat, regardless of what you're eating, but especially if you eat high sugar, high fat foods, you're going to release serotonin and dopamine.  And like I said a few minutes ago, not all emotional eaters have an eating disorder. And we want to differentiate that. Does it mean that their eating is not problematic to them? No, not at all. If they're telling you it's a problem, then it's a problem. They may not meet the criteria for binge eating disorder or bulimia. But it's important to address it because they understand that they're eating for a reason other than hunger. And they want to stop because they want to eat for hunger but not otherwise. And for us as clinicians, the first thing we need to do is understand why is it that they're eating? Is it boredom? Is it habit? So they need to keep a food log or a food journal over the course of a week or two weeks. And sometimes when people come in for an assessment, especially if that's one of their main presenting issues, I'll start out by just doing a retrospective of the last three days to get an idea of what may be triggering some of their eating episodes. And then we can look at some of the habits or bad habits maybe that they've gotten into and start talking about ways to address those. Remembering that Rome wasn't built in a day, this isn't going to go away overnight. But a lot of times if you give people some tips, tricks and tools to think about implementing when they walk out of your office after the assessment, before the first official session, it provides them some hope and gets the momentum going. And again, you don't have to binge to be an emotional eater. Some people graze all day long. Some people will eat and it's not what would be considered technically a binge, but it's more than they had anticipated. Maybe they'd go back for second helpings or third helpings when they really weren't hungry, but it was good. So why is eating so soothing? There are a lot of reasons. Now, there's obvious, it tastes good. So that's, you know, the big obvious bonus. But thinking about the function that eating serves, we have to eat in order to survive. When you were an infant, eating involved a closeness with your parental unit, which could release oxytocin. And I say parental unit because even if it was dad feeding the baby a bottle, there was that connection. There was that contact which caused the infant and the parent to release oxytocin. This is our bonding chemical. So eating was associated early on with bonding. Food may also have been associated with sleep. If the infant or child was given a bottle every night to go to sleep, then they may start thinking or they may be in the habit of eating in order to wind down or calm down. And we need to help them figure out different ways to do that. As a toddler, what did eating mean? Think about when you went from, well, we probably don't remember that, but think about when your kids went from eating, you know, food out of a jar to even their first Cheerios.  And it involved exploration and mastery. They were discovering all different types of textures and tastes and figuring out what smell went with what taste. And it was a really cool and exciting time for kids. And I mean, think about it. They're like a year old, so it doesn't take much to amuse them. But this was rewarding. Reward equals dopamine equals, let's do that again. It involved power and control. The child at this point was starting to be able to feed him or herself. Was starting to be able to be somewhat independent from the parent when it came to a basic physiological function of eating. So eating itself had its own rewards and it was self-esteem building because the child started learning, you know, how to feed himself, how to ask for what he or she wanted, at least in terms of food. There are formations of memories around foods. Even as early as toddlerhood, you know, we have celebrations, we have birthdays, we have different things and most children have certain foods that they really like. And it could be because the first time they ate that food was a really happy experience or it could be just that's their favorite food and that's all they want to eat. But they remember that food and they remember when they ate it, they felt good. They felt happy.  Now that's how the toddler thought. As an adult, we can understand that chicken nuggets themselves aren't making you happy, but you see the connections that we're making here. There's been an association between happiness and chicken nuggets. Unhealthy foods, especially for children when as adults we're still able to control what they eat. Your sugary foods, your unhealthy foods are usually reserved for treats or rewards. So when you're feeling like you need to be rewarded, when you're feeling like you want to feel good, sometimes you'll resort to those things when you were a kid that made you feel good like chocolate chip cookies or Haagen-Dazs or whatever it was for you. We've talked in the past about associations and conditioning and this is all coming back kind of full circle now because we need to understand that our brain has associated pleasure and reward with food for a lot of different reasons, not just because of nourishment. Looking at the reasons why your patient eats is going to help you understand what underlying issues you may need to address in treatment. Culturally, we associate eating with caring and celebration. Think about birthdays, holidays. What do we do? We get together. We have buffets. We have potlucks. When someone passes away, what do you do? You bring food over. When somebody's sick, what do you do? You bring food over. In our culture, there's a lot of emphasis put on eating and nourishing. That's true of a lot of different cultures. Low blood sugar can cause feelings of depression and anxiety which are quelled by food. If somebody typically doesn't eat well during the day, they go long periods without eating or if they have blood sugar issues to begin with and then they eat, they feel better. When they start feeling not so good, what do you think their first reaction is? Let me eat and see if that helps. Evolution predisposes the human body to crave high sugar, high fat, high calorie foods for quick energy and to prepare for a famine. Our bodies are really cool and really frustrating at the same time because your body takes in these foods and it says we're going to secrete the most amount of dopamine and the most amount of reward for these really high calorie foods because we want to make sure we're prepared in case there's a famine. Back in the day, many, many, many years ago, hundreds of years ago, we couldn't guarantee we would have a meal every day, let alone three meals every day. So the body prepared and it said, all right, we need to get whatever we can when we can. So we're going to make this the higher fat, higher, higher calorie foods more rewarding. Now I said it's also can be a blessing and a curse. Today, there's still a little part of our primordial brain that says if it thinks there's a famine, it will slow down your base metabolic rate, which causes people to gain weight. We see this a lot in people with eating disorders who tend to not take in very many calories or if they take them in, they purge them. So the body goes, well, I can't guarantee I'm going to get enough food, I'm going to get enough energy to survive. So I'm just going to turn down the thermostat a little bit, turn down the base metabolic rate, which compounds the problem for the person with the eating disorder. So it's important to understand that the brain is somewhat reactive to what's going on. So I keep saying we need to figure out what's behind or underlying the craving. First, we need to rule out physical causes. For some people, it's as simple as this. If they've got low blood sugar because they're not eating too often and obviously as counselors, we're not going to diagnose this, their doctor or their nutritionist will. But we can start exploring and go, it sounds like you might need to look at having your blood sugar checked or talk to your doctor about how frequently you need to eat. Because some people, and I know I'm very guilty of it. If I get into a groove doing something, I'll eat breakfast and then I'll get into a groove and before I know it, it's three in the afternoon and I haven't eaten for like a whole bunch of hours. I'm not doing math today.  So it's a real simple fix there. In our society, we are so driven and we are so we can get so caught up in things because that's such a fast pace that it's easy to forget to eat or it's easy to avoid eating. So that's the first thing we want to rule out. Are you eating in response to low blood sugar, which is making eating seem more rewarding? And when you in response to low blood sugar, a lot of times people who do that end up eating more than they normally would because they start eating really fast. It's like, I'm going to shovel in as much as I can.   Why is this under emotional eating? Well, because generally when they go into to start eating. Yes, they're hungry, but they're also cranky and irritable. And most of the time they're not thinking about I'm eating for nourishment. It's I'm eating to feel better. Lack of sleep. And this is so true for shift workers, as well as, you know, new parents and college students and anybody who's not getting enough sleep. If we are surviving on sugar and stimulants, we're going peak and lower valley peak and lower valley and you just keep going up and down until you just crash. Because every time you crash, you crash a little bit lower. So if somebody's on that roller coaster, they're going to feel worse between, you know, eating episodes. They're going to feel tired. They're going to feel fluggish, sluggish, irritable, fatigued. And to a certain extent, maybe depressed. And they may be misattributing those feeling those emotional feelings to emotions versus physical causes. And likewise, we also want to make sure that, you know, we're addressing the emotional causes because there's probably stuff there too. But if they're not getting enough sleep, and they're living on sugar and stimulants, their body is kind of in a state of hypervigilance a lot of times. It's exhausted. So they're going to be tired and cranky. So those are, those are a couple of things that we want to look at. Those are relatively easy fixes, or at least relatively easy things to point out and go, let's think about this. One of the things that I suggest for a lot of my clients is just take a week and mindfully, and it is difficult, but try to eat healthfully.  At the beginning, I'm not going to say cut out anything because that's not realistic and it's not fair. And they're probably already struggling if they're coming in to see me. So if I go, hey, let's just turn your world upside down. And guess what? You're not going to drink any caffeine anymore. It's not going to create a happy person. So I ask them to try to make some small changes and see if that starts to help. Dehydration causes fogginess and symptoms of depression. We want to make sure that they rule that out. And too many stimulants also causes dehydration. So, you know, we're looking at some of the physical causes of irritability and fatigue and cravings. Because again, we're going back to when I felt this way before, not looking at why I felt this way, but when I felt irritable, depressed, cranky, what has made me feel better? And generally, it's food. And generally, it's not good food. For me, it's M&Ms. I love my M&Ms, especially the ones with almonds, but I digress. Nutritional causes of cravings, high carbohydrate and high starch foods cause a greater release of serotonin and endorphins. So if you've got somebody who's depressed for whatever reason, they may crave these kinds of foods in order to increase their serotonin level or increase the endorphins, their energy levels. Chocolate. People who crave chocolate may be low in magnesium. The level of magnesium also affects how much serotonin is available. Again, I keep saying this just for legal reasons. We want to make sure their doctor or nutritionist goes in and makes this diagnosis. But if there are particular foods that they do crave, it's important for them to bring that up with their medical provider. If they're craving fatty foods, now again, fatty foods are just good. I love fried foods, but it also could mean that they're not getting enough omega-3s. Americans typically don't. And interestingly, if they crave soda, they may be calcium deficient. Who knew? So these are things to take a look at, to ask people if they're craving soda, maybe cutting back on their soda a little bit. And see what happens, and or getting blood work done.  They're getting enough sleep, but they're still eating when they're not hungry. We need to rule out habits.  When I was growing up, I would go to the grocery store with my mother. And on the way back home from the grocery store, we would always get junk food.  And it was like a 20 minute drive from the grocery store to our house. And by the time we would get back to the house, we would have put a good dent in those potato chips. That being said, I got into the habit of whenever I went to the grocery store, I would get something out of the bag and put it in the front seat and eat on the way home. Now am I paying attention to what I'm eating? No. Likely am I eating because I was hungry? Probably not. So we want to look at habits. A lot of people will eat when they are watching TV. It's a huge one. So we want to not do that. Or if you're going to eat when you're watching TV, make sure you sit at the table. At least that makes you a little bit more mindful.  Are there particular times that you mindlessly eat? Like I said, when you're driving or when you're watching television, those are both habits and can be mindless because you're not paying attention to how much is going in your mouth. You're not probably paying attention to the taste and you're not paying attention to whether you're full or not. So if you're mindlessly eating, then there's going to be a lot more calorie consumption in addition to the fact that you're not eating because you're hungry. You're just eating to eat. Are you going too long between meals than eating a sugar boost, which leads to a sugar crash? So again, that's a physical cause, but we want to rule out these are bad habits that we can tend to get into. Other things that can be construed as bad habits are eating without putting food on a plate.  So put it on a plate, sit down, try not to watch TV. All the things that your grandmother would have told you. So what do we do about it? Emotional eating interventions. I talked earlier about the food diary. Do a retrospective during the assessment if they really want to get a jumpstart on things, but have them keep a food diary, preferably for the duration of treatment, but at least for a week. What time did they eat? Were they craving just any old food or something that was salty, something that was sweet, something that was sour? This will give you a general idea and can give their medical provider a general idea if there are any nutritional imbalances or if there are particular associations. What emotion or state were you in? And I say state because being exhausted is not necessarily really an emotion. If you're happy, sad, mad, glad, exhausted, drained, whatever state feels like it would work. And then because of why were you feeling this way? It doesn't have to be a dissertation. It can be really short and sweet. But I encourage clients to write down everything they eat before they eat it. During the first week or, like I said, preferably throughout the entire course of treatment, why before they eat it? Because it's a stop. Remember, we've talked before about how we have an urge, we have a craving, we have an urge, and then we engage in the behavior oftentimes without stopping to mindfully think, is this what we want to do? This provides that stop that says, okay, I've got to write down the time and then I've got to think about why I'm eating. And honestly, a lot of clients notice a reduction in their habit eating when they have to do this just because they don't want to record. Keeping that up for the period of a month or two months helps break some of the habit eating that they might do. Like I said before, when they're eating, I encourage them to use a plate, sit down, don't walk around, don't stand at the counter, eliminate distractions as much as possible, and focus on the food you're eating that goes with mindfully eating. What does it taste like? Is it good? Take small bites. When my son was young, and I think I've shared this before, he had gastric reflux. And we would sit down to the table and I would shovel in food as fast as I could get it in my mouth, because he couldn't be put down for too long. Before he would start to get fussy, at least until we figured out that he had gastric reflux and Zantac was just a lifesaver. I developed that habit when he was little, and I kept it up for a while. It took a while for me to learn to go back to taking reasonable bites and tasting my food. And even today, if I'm not paying attention too much, I'll eat my dinner really fast and then I'll sit there and I'll be like, well, yes, I'll taste that a little bit later because I didn't taste it when I ate it. Encouraging clients to be aware of their eating habits. Try to avoid setting up a binge by restricting certain foods.  No. I would encourage people not to do that. But to say, you know, I said for me M&M's is one of my favorite reward foods, if you will. I don't keep them in the house, but I will allow myself occasionally to buy a small snack size pack of M&M's when I'm out. Or I will get a regular size pack and I'll share it with my daughter. So I'm not restricting it. I'm not saying I can never have M&M's again. I'm just not making it available to myself when I might have some unrestricted time.  And when you've got kids, when you've got family, it's not entirely possible usually to not have some of that stuff around. But try to avoid having the things that you particularly use for comfort. Because if it's not readily available, then you've got to focus on, guess what, dealing with the emotions instead of stuffing them with food. Try not to go too long without eating. Like I said earlier, if you go too long, then by the time you get to the food, your blood sugar is low and you're just like shoveling in as fast as you can. Initially, distract. If you know that you're eating and you're like, I'm really not hungry, but I want to eat. Take a bath. Take a walk. Call a friend. Heaven forbid, get on Facebook. Whatever it is you can do to distract yourself for 10 or 15 minutes. If after 10 or 15 minutes you're still going, I really want whatever it is, then you can decide what to do about it then. Most of the time, when people stop and go, I'm really not hungry, let me distract myself. They get caught up in that distraction. And before they know it, they've forgotten about the craving. Identify the emotions. If you know that you're not hungry but you want to eat, then say, okay, what's going on? What's going on with me? It doesn't mean that the person is never going to eat when they're upset because a lot of people do. And is it the end of the world? Probably not necessarily. If they can start reducing the frequency of times that they eat in response to emotional distress, that's what we want. We want progress, not perfection. If it's depression, what's causing them to feel hopeless or helpless right now? If it's stress, anxiety, or anger, remember our big lump-together stuff, what are they stressing out about? Do they feel like they're overwhelmed? Are they afraid of failure, rejection, loss of control, the unknown? We've gone through those things. We want them to identify what's going on with them. And then they can make better choices about how to deal with it. So general coping, help them develop alternate ways of coping with distress, distract. We've already kind of gone over that one. I encourage people, and it's one of those DBT things that a lot of therapists encourage their clients, to keep a list of things they can do to distract themselves. Because it's not always practical to get up and go on a walk if you're at work or it's 2 in the morning. So what else can you do to distract yourself? Talk it out with a friend, with yourself, with your dog. Sometimes you just got to get it out. People who are more auditory will prefer talking it out as opposed to journaling it. Now, if they talk it out with themselves, they can record it if they want to. Or sometimes it's just better to have dialogue with themself. If it worked for Freud, it can work for other people. Journaling, if your clients are inclined to journal, encourage them to write it down. Sometimes just getting stuff out of your head and onto paper will help the feelings dissipate a little bit. So you're not mulling them over and obsessing on them and getting stuck in those thoughts and feelings. Additionally, while you're distracting, talking it out or journaling, this is also your break, your stop between the urge and the behavior. Make a pro and con list of the distress, not the eating. Whatever it is that's stressing you out and how can you fix it? Or what are the pros of this situation and what are the downsides to this situation? Encourage them to focus on the positive. If something's stressing you out at work, you've got a big meeting coming up or something you don't want to do or whatever it is, you can get stuck on focusing on that or you can focus on the positive that you do have a job. That meeting only comes around once a month. It's time you don't have to be doing paperwork. Whatever the pros are for that person, encourage them to focus on the positive. If you're distressed because of some kind of a failure or perceived failure, figure out what you learned from it. Whether it was a relationship failure, maybe you learned what not to do in a relationship anymore. Maybe you learned things that you may have ignored. Maybe you learned what you should have done instead. But how can it be a learning opportunity instead of somewhere to stay stuck? And finally, if something's making you upset, if something's causing anxiety, depression, hopelessness, helplessness, whatever the negative feeling, figure out if it's worth your energy to get stuck here. Is it worth the turmoil? Is it worth, you know, having to pacify yourself with food? Whatever it is. A lot of times people say, you know what? No, it's just it's not even worth my effort. It's not worth moving me away from my goals because my goal is to stop emotional eating. My goal is to eat for hunger so I can go to dinner with people and feel comfortable. I can be at a party where there's a buffet and not feel stressed out that I'm going to go and eat half the stuff on the buffet. That's my goal. So is holding on to whatever this distress is getting me closer to being able to do those things. And generally the answer is no. Develop alternate ways of coping with distress. The ABCs. The A is the activating event. What is stressing you out? Causing the distress. C is the emotional reaction. Angry, depressed, stressed, whatever. B are your behaviors. What behaviors or B are your beliefs? I'm sorry. What are the beliefs that are in there that may need to be addressed? What kind of things are you telling yourself? And how can you counter them cognitively? Eliminate your vulnerabilities. You knew we couldn't get through a presentation without talking about vulnerabilities. If someone is well rested, well fed, has a good social support network, not overstretched time wise, then it will be easier to deal with stress or stressors when they come your way. You'll have more energy to deal with it. So there won't be this overwhelming feeling of I just want to bury my head in a jar of peanut butter. Be compassionate with yourself. Some days it's, you know, you're just going to feel anxious. You're going to feel depressed. You're going to get angry. You can beat yourself up over it. And, you know, a lot of people do. Is that the best use of your energy? Or can you be compassionate? Can you learn from it? And you give yourself a break and go, you know what? I'm having a bad day today. And that's okay. I'm not going to unpack and stay here, but I'm not going to fight it either. Help clients learn how to urge surf. Help them understand that just like a panic attack, just like a wave, just like a lot of other things in life, it will come. It will crest and it will go out again. So they can sort of identify where they are on the energy of that urge. Other tools people can use. Close the kitchen. Once I have the kitchen cleaned and, you know, all the dishes are done and it looks pretty. I hate going in there and finding dishes in the sink again. Now I've got teenagers, so we always have dishes in the sink. But, you know, before I had children, you know, at seven o'clock, I'd finish all the dishes, close the kitchen, and that would be enough motivation for me to not go in there and at least not use plates and stuff to eat. So if we're saying that we're going to only eat using utensils and a plate and sitting and all that stuff that we already talked about, then once you close the kitchen, you're not going back in there. Turn off the light. That also helps so you're not being attracted to the pretty lights and, you know, all the goodies that are in the kitchen. Brush your teeth. This is something my grandmother used to do and it actually works. There's some research behind it. Minty flavors reduce our appetite.  And do you really want to brush your teeth again? Meditate. Sometimes just getting in a space where you're not obsessing about anything can help people get past that urge to self-soothe with eating. ACT for emotional eating. What am I feeling or thinking? What's going on with me right now? What is important to me? So if I am thinking, I want to eat. I want to, you know, just dive into this jar of peanut butter. And then I think about what's important to me. Is it important to me to get control of this? Is it important to me to, you know, be able to fit in my clothes in six months or not? So what is it in what way is controlling my eating habits and eliminating emotional eating important to me? And how does that get me closer to other things that are important to me? And what other things could I do that would get me closer to my goals? So if the goal is to have improved relationships, be able to feel more comfortable around food, reduce the stress around going out to eat and just around food in general. What else can you do when you're stressed out? Somebody also suggested that adding a blue light in the refrigerator decreases the appeal of foods, which is interesting because yellow, red and orange and browns think Pizza Hut are all foods that increase people's hunger and desire to eat. But blue obviously is just a completely different primary color and adding a blue hue seems like that would actually be really effective. So cool. Thanks for that little tidbit there. Holiday help. And, you know, we're coming into the holidays, so I've got to bring that up at every single class. Choose lower calorie foods. If you tend to get stressed out or caught up or mindlessly eat when you are at family gatherings. Okay, you know, cut yourself a break. Know that that's probably going to happen. Fill up on the lower calorie foods, the carrot sticks, the broccoli, the white meat turkey, anything that's available that's not like sweet potato pie or brownies. Keep a water or low calorie beverage in your hand. If you've got your hand full, you can't eat at the same time.  Talk to people.  So if you're talking to people, you're not going to be as inclined to go get something to eat because you're wanting to stay engaged in that conversation. Stay away from the buffet, especially if you know that it could get stressful. Or maybe, you know, for me, I turn into a pumpkin at like 730 at night. I get up at four, but I turn into a pumpkin at 730. And a lot of times, holiday parties and those sorts of things are at eight, nine o'clock at night. And, you know, I've already done turned into a pumpkin. So I know that if I go to those, I'm going to be more likely to eat just to kind of stay away because I'm tired. And it's a bad habit. It's not because I'm hungry. So I know I need to stay away from the buffet during those times. Rehearse refusal skills. If somebody says, oh, you've got to try a bite of this, figure out how you're going to address that ahead of time. Because there's generally probably a lot of really good foods. And you may really want to taste some, but sometimes people who emotionally eat know if they start eating high fat, high calorie foods, they're going to want to eat everything. So if I start with one bite of a brownie, I'm going to want to eat every sweet that's on the table. If they know that, then they may want to choose to not even go down that road at that juncture. Encourage people to stay mindful of their distress meter. Before they go back for another helping, ask themselves, am I hungry? Am I just wanting to taste what's here and how do I feel about that? Or am I eating just because I don't want to be here and I'm bored and I want to fill the time? Have people keep an index card with their coping mantra and two reasons they don't want to emotionally eat. So I need to be here. I can do this, whatever the mantra is that's going to get them through the night, whatever they're telling themselves that it's going to help them plow through and make the right choices. But also two reasons that they don't want to eat or they're going to get around it. Maybe they've got something at home that they can eat when they get home. Eating before they go to the party may also help prevent some grazing. Holidays bring out a lot of emotions for people. Some people struggle with depression, anxiety, jealousy, grief, anger, you know the whole gamut during this time. And during this time there's food everywhere. I mean starting at Halloween when your kids bring home the Halloween candy, which usually lasts about a week in our house. Anyhow, the Halloween candy followed by getting ready for Thanksgiving, followed by doing all the baking or whatever you do and the holiday parties coming up on the December holiday season. There's just food everywhere. So it's really easy to cope, if you will, with stress, with being overwhelmed, with being tired, by not eating enough healthy food by binging on unhealthy and soothing food, if you will. So it's important to encourage people to stay mindful of why they're eating what they're eating. When constantly bombarded with high fat, high carbohydrate foods, people are tempted to eat to feel calm. I challenge anybody to say that they've never eaten and go, okay, you know, I'm just focused on this right now. I'm not thinking about everything out here and it feels good. I'm good. Now, good's probably not the word I should use, but it does help people distract themselves. Sometimes when you eat, especially those high intensity foods, you feel happier. Serotonin's released, dopamine's released, you're like, oh, that's really good. I want to do that again. Or you just feel numb. You can get into a zone where you're just eating and not caring. It's not that you're feeling calm. You're just not feeling anything. And a lot of times when people get into that zone, they're not tasting the food either. They're just kind of in a autopilot. Emotional eating, like most other escape behaviors, never addresses the underlying emotions and their causes. So we need to look at it. Are you feeling anxious? Are you feeling jittery? Are you feeling depressed? Because your blood sugar is low? Because you're nutritionally deficient? Because you're not getting enough sleep? Or because there's something cognitive going on? Or all of the above? Emotional eating often results in physical issues like weight gain, poor sleep, and reduced energy.  But some people can start emotionally eating to feel better. They gain a lot of weight. Then they start feeling less energetic. It starts being harder to move around. They get to the point where they are clinically obese, then they're going, I'm never going to take all this weight off. They feel hopeless and helpless. You see where this is going. So they eat some more. It can cause poor sleep. Sleep apnea. It's also hard to get comfortable sometimes.   Which means you probably didn't sleep very well the night before. And emotional eating often results in reduced energy because the foods we binge on, the foods we eat for self-soothing often end up causing a sugar crash. Some people try to undo emotional eating by restricting other calories, which can lead to nutritional deficits and more cravings. I had a girlfriend when I was in high school and, you know, think back to, I don't know if they still do it, but when we were in high school, there was always some kind of candy sale going on. And she would always forego all other food so she could have two chocolate bars each day. And, you know, we're not going to get into all the other issues surrounding only eating two chocolate bars. But the point I'm making right now is the fact she wasn't getting protein. She wasn't getting, you know, most of her vitamins and minerals and stuff that her body needed to make the neurotransmitter so she could feel happy and she was contributing to a sugar crash. But I also know that it's common around the holidays for people to do this. They'll let go all day without eating because they know they're going to a party tonight and there's going to be a lot of really good food. Doing that once in a while, not a big deal. Doing that 10 or 15 times in a month could start to have problems. Emotional eaters need to first find a way to stop before they eat. So whether it's writing in a journal or adding, there are a lot of apps on your phone that you can put your food in even if you're not writing about your emotions and your cravings and all that kind of stuff. Sometimes it's enough to make people stop before they actually reach for the food. Or, you know, kind of an extreme way to go is to not keep pre-processed or pre-packaged foods in the house. So anything that you're going to eat, you've got to make. Second, identify the underlying reason for your eating. Figure out, do I generally eat in response to? And then address the thoughts and emotions leading to the urges. So if you figure out that the underlying emotions for your eating are depression, then what thoughts are maintaining that depression and how can you address it? Once you address the underlying issues, some of the emotional eating will go away. Some of it's habit. We're going to have to break that habit. And over the course of, you know, past couple of decades of working with people, my experience has been the majority of the time. People don't want to hear, well, once you deal with your emotional issues, the emotional eating will go away. They're there because they want to stop that behavior right now. So yes, we need to work on all the underlying issues, but give them a tip or a trick or a tool, whatever you want to call it, to use before they walk out of your office after every single session. That way they have something else they can put in their toolbox and feel more empowered to have control over what's going on with them and what's coming their way. Having the knowledge of what and why is 80% of helping them get to the recovery point. Now, if there's co-occurring or if the eating issues are more than just emotional eating, there's the person meets the criteria for binge eating disorder, bulimia or anorexia. Obviously, there's a lot of other underlying issues they're going to have to be dealt with. So I don't want to trivialize that. But I do want people to feel like they've got some hope over what's going on. Are there any questions? If you enjoy this podcast, please like and subscribe, either in your podcast player or on YouTube. You can attend and participate in our live webinars with Dr. Snipes by subscribing at allceus.com slash counselor toolbox. This episode has been brought to you in part by allceus.com, providing 24-7 multimedia continuing education and pre-certification training to counselors, therapists and nurses since 2006.


From whom? Bearing a child. It is not that children are wrong, we were all children some time ago. It's not about children being right or wrong.  It's not even a thought, okay? It's a silly thing to think whether they're right or wrong. It is just that there was a time when a woman would easily have a dozen children in her lifetime. And it was normal. Three, four of them died, seven, eight of them lived. That was normal. Now, most women who have children here probably have two. Some of them slipped through and they became three. Gently, most women have… are there lots of women, leave the women who are over fifty, sixty years of age. The younger woman, is there anybody who has four, five children here? Almost nil, isn't it? But if you ask this question about fifty years ago, almost any woman in the region of early thirties would have five children or six children. So you… somewhere you got… got your sense in place, you decided that instead of having six, let's have two or one. So if your sense can work to that extent, right now we are in a place where if we have to feed, clothe and offer a decent life for the seven billion people, we need… for the standards that we are thinking of, for the standards that most pe… most people are aspiring for, we need to outsource our resources from at least another three planets' earths. We need at least three more planets. We could do that. It's just that they aren't there. If they were there, no problem. We could have gone and towered one planet close to us and used that planet. But there isn't any other. So till we find such one, it's good to hold your reproduction. Till we find extra planets where we can export our people. It's good to hold back your reproduction, otherwise nobody is going to live well on this planet. It doesn't matter what technology you bring, it doesn't matter what you do, nobody is going to live well on this planet. If you continue to increase… When I say continue to increase, I want you to understand the volume of increase that we have caused. In the beginning of twentieth century, we were only 1.5 billion people on this planet. In one century, in 110 years, we are reaching towards seven billion people and we are technologically more capable than ever before. What thousand men could do, could do. Today one man can do because of the machines and other things that we have. Now United Nations protects that by 2050, we'll be 9.6 billion people. At 9.6 billion people, even if we live at… even if the earth is exploited at its present rate or much more, each one of us have to live with 40% less resource than we are living right now. When I say resource, don't think of some fancy things, I'm talking about food, water, air. These resources, you will have to live with 40% less. That means nobody is going to live well. Yes? Just a few people who control a few things will be okay. All others will suffer in an immense manner. Just by individual fancy, going on doing things, humanity cannot afford it anymore. It's time, human beings function consciously that just coming to zero percent growth is not good enough. We have to go into a minus. Instead of projecting by 2050, we'll be 9.6. Why can't we determine that by 2050 we'll be 3.5 or 4 billion people? Human being means doing everything consciously. What is this compulsion about having a child? One thing is this, biological. Biologically having a child is not a pleasant experience, let me tell you. If you don't believe me, ask somebody who's delivered a child. It is just the fulfillment of being involved with something. It is the fulfillment of seeing something as a part of yourself. If you are willing, you can know this fulfillment with just about anything in your life. If you are committed and involved with something in your life, that fulfillment is very well taken care of. This is not a campaign against children. This is a campaign for the future children. If future generations have to live well, we have to conduct our lives consciously. Conducting our life consciously, one major part of it is right now, because you can't expand the planet, you have to decrease the population. There is no other way. Either you do it or nature will do it. When nature does it, it's not going to be nice. It's not going to be nice at all.


Hello, welcome to this lecture on digital communication using GNU radio. My name is Kumar Appaya and I belong to the department of electrical engineering IIT Bombay. In this lecture, we are going to take a look at quantization. In particular, this is related to the conversion of real world signals that can potentially take an infinite set of values into a finite set of values that can be represented easily in bits so that you can communicate these using digital communication techniques that you have learned so far. So as we just discussed, real life signals are real valued. For example, whenever you speak and measure the volume of your voice, your voice can be essentially represented as potentially a set of real numbers. And because you can actually have a very wide spectrum of numbers, for example, even if you scale your voice to be between minus one and one, there are an infinite set of values between minus one and one that your voice can take. Of course, one aspect is that you need not represent it 100% accurately. You can potentially make a simpler representation that carries most of the information. For example, your telephone is essentially a device, your mobile phone, at least in modern sense, where your voice is converted to a smaller set of values. Similarly, the lecture that is being delivered to you, the video as well as my voice is being limited in terms of the set of values which it can take. Nevertheless, you are still able to discern what is being said and what is being seen. So the practicality is because we need to send data over finite bitrate channels. That is finite bit channels is finite bitrate channels. You cannot send an arbitrarily large amount of data. You have seen in the context of channel capacity that practically speaking, channels have limited capacity even if you allow for errors, the amount of data you can send is essentially finite per unit second. So the question is, how do you convert this real value data into finite bits while minimizing the error? Now this minimizing the error can have many connotations. For example, in the context of my speech, it could be that you just want to minimize it in such a way that you can discern it. In the context of an image, you may want to capture only those colors or those points in the image that, which when replayed to you, looks very close to the original image even though it has some information loss. So in that sense, the question is, how do we convert real value data to finite bits and that is the context of this lecture. We will look at quantization. Quantization in very plain terms means restriction of the set of values which a particular variable can take and we will look at optimal quantizers, which means this is the best that the quantizer can do given some constraints. So let us take a very naive example. Let's say you have a continuous waveform. This is like a wave which is present and let's say that I ask you to quantize it. Quantize it means you have to limit the set of values which it can take. So in this particular scenario, what we are doing is we are saying, let's assume that the amount of bits you are given to represent the signal at any given time is only one bit and in fact, the choice I have made is minus half and half or my representation. So you can see as we start over here, when it is close to zero, then since it is closest to zero, the value which it takes is essentially zero. So in this case, it's when I say one bit, right? It's actually there's a slight error in terms of judgment. It's actually one bit plus zero. So we are also taking zero as a level. Let's say that we have one bit in addition to zero. So it's more than a bit, but we'll go with it. So as it gets closer to plus half, you set the value to plus half. Then as it crosses the limit and it is closer to zero, you set the value as zero and so on. So in that sense, in this case, it's three levels, three level quantization. So in this three level quantization, you're able to see that it's a very poor representation of the waveform. Now if you allow for five levels, in this case, I've said two bits, which is five levels. For five levels, the values which we're taking are zero. We're taking, I think, one fourth and then we're taking three fourths. So as you can see, even though this is still scaly and wavy, it is somewhat better representation than the previous one, because you're able to capture much of the part where the signal rises over here, and the part where it falls over here. But on the high parts, you are essentially missing it.  As you increase the number of bits, let's say this is three bits, four bits and five bits, you can start to see that your representation is much, much more accurate. If you squint your eyes and look closely, you will still see slight amount of jaggedness. But if you look closely at six bits, let's say, and seven bits, eight bits. At eight bits, there are 1024 levels. And 1024 levels, you can see that this particular waveform is being represented to a reasonable accuracy. In that sense, if you're willing to give more data, then your representation will be better. Of course, there are some caveats on how you can scale the data to make it fit this representation better. But naively speaking, there's this general notion of something like CD quality audio. If you go to the reference and see how CD quality audio was defined, of course, there is relationship with sampling rate and so on, which I will skip. But typically, CD quality audio uses 16 bits of quantization. And a slightly more less ambitious quantization, let's say for your phone, will use eight bits or 12 bits. So eight bits to 16 bits is the range for which you will quantize audio. If you have only speech-related information, typically eight bits or 12 bits, if you have music, then you'll likely go to 16 bits, plus it is stereo, so it's actually 16 bits per channel. So these are some rough aspects to keep in mind, and you'll be able to comprehend the decisions made for these quantizations when you inspect the standards that govern this quantization. Now, the overview of how a quantizer is to be viewed is given here. We want a representation of a scalar source, sorry for the spelling mistake. Typically, the scalar source is taken to be a random variable.  Now you may argue that taking it to be a random variable is a little, it's tricky, but it's like this.  We typically model it as a random variable. The more controversial assumption, so to speak, is that we will assume that the random variables are independent and identically distributed. Now here you will have a problem, because whenever I speak, for example, if I utter a sound, the sound from one millisecond to the next millisecond to the next millisecond is going to be very, very close. Okay, after a second or two seconds, maybe it is different, but the very close in sounds are going to be similar. So there is a high amount of correlation over a short time intervals about what I'm speaking. So in that sense, you may argue that the assumption of independent and identically distributed is not valid. So to address this, typically what they do is people take away the correlation. In other words, you know there is some correlation in my speech. You note that down and then subtract the correlation out so that it looks independent and identically distributed or at least uncorrelated. So the IID assumption is valid when you take into account the correlations and just it's almost like you take a notebook and keep the correlations, remove the correlation so that at the receiver or wherever you're reconstructing it, you just put that correlation back and you get it back. In fact, speech quantization and speech compression is a wide topic which heavily makes use of this. Several others you can check like image quantization also use similar techniques. So the assumption is that the source which you're compressing, we will assume a scalar source is independent and identically distributed. So it's like this, input waveform, correlation removed waveform is passed to a sampler. The sampler essentially samples data. Now what is the rate of sampling? Now to decide this you need to study what type of signal that your sampling is. The signal that your sampling is something like a machine vibration or something which is of a few tens of hertz, you get sampled hundreds of hertz. If the signal that you're sampling is something like speech, then depending on the quality that you want, you can sample it something like eight kilo samples per second or if you want CD quality, you have to sample it 40 or 44 kilo samples per second. This is to get an accurate enough representation over time. Remember, we need to quantize over time as well as the amplitude. So this takes care of the time quantization. You, I'm sure that in digital signal processing you would have seen aspects related to sampling. This is the amplitude quantization quantizer. We restrict the set of values which the vertical axis takes to a set of finite values like you saw in the previous waveform. If there were some in between values, you will then map it to the closest value. So this quantizer gives you set of values which are finite. For example, let's say it is 8 bit. That means there are only 256 possible values. And the encoder converts this to 0s and 1s and 0s and 1s. And we've skipped it, but there's also modulation and all those things. Then there is potentially a channel, we've seen all that. And I'm again encompassing all the equalization, error correction, everything in decoder. And decoder again gives you these bits 0, 1, 0, 0, 1. And then there's a table lookup. Table lookup means there are 256 values. Each value map to a set of 0s and 1s. Now given a set of 0s and 1s, you find out what amplitude value it was. Then you need an analog filter, why? Because these will give sampled values. So let's say sample 0, sample 1, sample 2. You have all the quantized values which the transmitter has sent. And the filter essentially draws the line between and converts this to a continuous time waveform such as speech. So when I am speaking to you, my voice is being sampled by the sampler after this microphone. Then there is a quantizer sitting in the system which essentially quantizes and compresses the values. And in this case, the channel can be assumed to be just the internet.  And then these quantized values are being taken out. They are converted back to amplitudes, the finite set of amplitudes. These amplitudes are now just set of numbers. They are now converted to a waveform. You would have seen in DSP, you have these and you interpolate them using an analog filter and that results in the speech that you are able to listen to. So it is 100% guaranteed that the speech you are listening to is not the same as the speech that I'm speaking right now. But it is extremely close and close enough so that you can discern what I'm speaking without losing almost any word. So since the quantization is good enough, every word that I am speaking is something that you are able to comprehend. Let's now look closely at the aspects related to scale or quantization. Let us say that we want to quantize a random variable x, whose PDF is fx of x. Typically the way we do it is this. We have to decide the quantization levels and quantized values. So quantization levels and quantization values are given in this manner. A1, A2, up to AM plus 1 in the way I have given are the quantized values. Quantization levels are like decision regions, B1, B2, B3. So suppose that your signal falls to the left of B1, the value, meaning let's say A1 is 0, B1 is half. Let's say you get 0.4, it's to the left of half, so you quantize to A1. Suppose the value you get is 0.6, it's to the right of B1 you quantize to A2. So B1, B2, B3 are like decision regions, A1, A2, A3, A4 are quantized levels. Note that the quantized levels need not be uniform and the B1, B2 need not be the midpoints. Because for example, if you think about it in terms of your BPSK demodulation, there you had equiprobable symbols and you had the middle point of the decision region. In the case of quantization, for example, if you study how voice patterns emerge, certain values are more likely than others. So typically voice quantizers are not uniform, meaning they have some different gaps. So the quantization levels, when I say levels, I mean decision regions are B1, B2, Bm, quantization boundaries are, okay, I think, let me see, I think I have mixed these up. The quantization levels are A1, A2, Am and the quantization boundaries are B1, B2, Bm, B is for boundaries, sorry. So you have to choose AI and BJ so as to minimize an error.  Now squared error makes sense because squared error is related to power. It's almost like you're minimizing the energy between what you get and what the quantized signal is. So typically, you have to minimize the mean squared error and you choose by convention B0 and Bm plus 1 as minus infinity and plus infinity respectively because there is a B1 which is the quantization boundary to the left and Bm plus 1 quantization boundary to the right that you don't see, they are minus infinity and infinity. So now, our aim is to minimize expectation of x minus q of x the whole square. q of x is the function that determines A1, B1, A2, B2. Basically A1, A2, B2 are determined by q of x. In other words, for example, if you give, let's say I chose A to be 0, B1 to be 0.5, q of x, q of 0.4 will be A1 which is 0, q of 0.6 will be A2. So q is the quantization function. Now your aim is to minimize the squared error. So if you now expand in terms of the PDF, you have to minimize in integral minus infinity to infinity fx of x, x minus q of x squared dx. And you can split this as this particular summation. In this case, it's summation j equal to 0 to m, bj to bj plus 1, fx times x minus aj the whole square dx. That is, you split it into different boundaries. So minus infinity to boundary 1, boundary 1 to boundary 2, boundary 2 to boundary 3 and boundary m to infinity. And within each boundary, what is the quantized value that will give you the error. Minimize this by playing around with b's and a's. That is the optimal quantization problem for the scalar case. So let's take a practical example and see how this works. Let's take a uniform random variable. I think again, let's not take 0 to 1, let's take uniform minus 1 to 1. Let's take this, uniform minus 1 to 1. Now the question is, how do you minimize the squared error? Let's just do this and just see how it works. So let's say we have a uniform random variable. The pdf is this, this is the pdf. Now I will call this fx of x, x. Now the question which we have is, how do you choose the levels, two levels, one bit quantizer, two levels such that you minimize the squared error. Now here I'm going to basically say, okay, let's say you choose two levels on the positive side. You're going to get a lot of error if the value falls negative. Both on the negative also doesn't work. If you choose one level here, another level on the right side, which is not equally away, then also you're going to have an issue. In this case, because of the symmetry of the problem, you can sort of guess that it's good enough for you to choose minus a and a as the levels. Why minus a and a? The pdf is symmetric. And again, just from intuition, it's exactly half of the time positive, half of the time negative. So whenever it is positive, choose plus a, whenever it is negative, choose minus a. This it turns out is the optimal strategy and finding out the optimal a is your aim. Now this is something which you can verify mathematically also, but I'm leaving it to you as an exercise. What I'm trying to point out is that when you have a symmetric distribution like let's say uniform minus 1, 1 or even Gaussian, then the quantization levels, optimal quantization levels have to be symmetric across the 0, you know, across the y axis. So now what remains is for us to find a. So we want to minimize, okay. We want to minimize expectation of x minus qx squared. Now in this case, it's going to be integral minus infinity to infinity fx of x, okay. x minus q of x square dx, okay. Now this is equal to integral, I'm just going to go from minus 1 to 0 because my boundary is 0 as I just argued, fx of x is half, okay. x and my q of x is a plus integral 0 to 1, half x minus a because the quantization level is a.  So this integral becomes integral and because of this negative sign the integral swaps, I'm going to get 0 to 1, half y minus a the whole square dy. So this if you add these up, you're going to get integral 0 to 1 x minus a whole square dx, okay. Now you have to minimize this by choosing a optimally, okay. Now you can make some guesses and things like that, okay. But let's just, I'm just going to call this, you know, j equal to and let's say dj by d a is equal to integral 0 to 1 2 times x minus a dx, okay. So now if I now perform this integral, I can cancel the 2, integral x dx is half. You can also perform the integral over here and you know, just get it by differentiating but I'm lazy, I just did this. So a is half is the optimal quantization level. So minus half and half are the optimal one bit quantization levels. When we do this in GNU radio, you will see that choosing other levels results in higher error. In fact, there's something we can verify. With 2 bits and this is something I leave to you as an exercise.  So it's like this. You have to take 2 power m minus 1 minus 1 and just keep adding 2 to it, I mean from negative side and divided by 2 power m, you will get the values. Again this is something which you can easily verify by using the symmetry of the distribution and the optimal optimality you can prove. And the mean square error is delta square by 12 where delta is bj plus 1 minus bj. That is the gap between two boundaries or gap between two quantization points also. Let's just find j here. So in this case, what is j? j is equal to at for this expectation of, I can take the positive, it's just x minus q of x the whole square and as we proved here, it is equal to integral 0 to 1 x minus half whole square dx. We proved it over here. So now, if you just evaluate this integral, you are going to get x minus half the whole cube by 3, 0 and 1, what do we get? So now, if you substitute, so you get, so when you substitute 1, you get half cube, when you substitute 0, you get minus half cube by 3. So this is 1 by 8 plus 1 by 8, so you will get 1 by 12. This it turns out is the error and this makes sense because delta is, I have written bj plus 1 minus bj, you can also take it as aj plus 1 minus aj for this particular case, it will be delta square by 12. This it turns out is the error optimal for the scalar quantizer quantization for uniform minus 1 1 random variable. Now let's also do one more case, which is the Gaussian. The case of a Gaussian random variable, we can take x to be n 0 1, you can of course generalize it for any other case, but it's very easy for n 0 1. Once again, symmetric distribution for 1 bit, the optimal quantization points are plus or minus a and the optimal boundary is 0 by the same arguments I made just earlier. Therefore, our aim is to minimize integral 0 to infinity x minus a the whole square e power minus x square by 2 by root 2 pi dx, of course you can get rid of root 2 pi everything. Now how do you do this? In this particular case, let us differentiate, if you differentiate this directly, then you get you have to do this differentiation carefully, you just have to differentiate with respect to a. If you do the differentiation with respect to a, you get x minus a times 2 and set it equal to 0. If you split those integrals, you get 0 to infinity x e power minus x square by 2 by root 2 pi dx is equal to integral 0 to infinity a e power minus x square by 2 by root 2 pi dx. Now the advantage of keeping the root 2 pi is that this particular part this integral becomes half, because it is 0 to infinity of a n 0 1 Gaussian is half. The other side you have to perform integral little carefully, you just have to this x dx here, you let y be equal to x square and you know x you can say x square by 2 then dy will be 2 x dx or you know you can basically make the substitutions and verify that you will get I do not recall exactly what you get, but simplifying gives you a is equal to root I think you will get 1 by root 2 pi and a will be root of 2 upon pi. This in fact is the optimal point and in GNU radio, we will set this optimal point and verify that this indeed minimizes the error. Now if you want to go to optimal quantizers, there is something called the Lloyd-Max algorithm. We will just briefly cover the Lloyd-Max algorithm, it is an iterative algorithm that can jointly obtain both the optimal quantization levels and the boundaries. For example, if you choose the initial quantization levels a 1, a 2 up to a m plus 1 and we set b j's to be a j plus a j plus in the midpoints, then we set the new a j's to be the conditional means given x belongs to b j to b j plus 1, why is the conditional mean? If you remember your probability, the conditional mean always minimizes the mean squared error. The conditional mean always minimizes the mean squared error and therefore, setting x to the conditional mean is the right thing to do. That means you get new a j's, again find the new b j's, again find the new conditional means and keep repeating this process. If you keep repeating this process, you will find that after some time, the values of a j's and b j's does not change much and the mean squared error does not reduce. At that particular point, you say, okay, I have the optimal quantizer. This algorithm is more than, I would say, 30 or even 40 years old and it's proved to be optimal under many circumstances. Because the algorithm captures the spread of the random variables, see, every time by setting it to the conditional mean, you are minimizing the squared error and every time it just does slightly better, slightly better, slightly better at reducing the MSE till a point where the MSE stops reducing and that's your optimal MSE, minimum MSE. You can go refer to this algorithm by the papers by the several authors which are there right from the 80s. But one aspect I want to mention is that these are closely related to K-means clustering in the sense that in the case of K-means clustering, it's a very common algorithm used in pattern recognition and machine learning. There you are essentially performing this conditional mean by finding the closest and it's very, very related. If you study the algorithms of Lloyd-Max and the K-means clustering, you'll find a lot of connection between them. One more aspect which I wanted to mention is that rather than quantize scalars like X, you may want to quantize tuples like groups of random variables X1, X2, XN jointly. The advantage of doing this is that it is more efficient at exploiting correlations within vectors. For example, let's say that you have a particular image and you do a transformation to get some vectors. It could be that some vectors may have a lot of correlation among them. Quantizing them independently may result in loss of the benefit of the correlation. So you just want to quantize them as vectors rather than perform this quantization operation in the scalar case. You just perform this on the n-dimensional space and this it turns out the results in Voronoi regions. That is if you have these kinds of quantization points, then the all points that fall within this region are mapped to this point. All points that fall in this region are mapped to this point. And this kind of separation of the space into several regions, this is Voronoi regions. These Voronoi regions determine where you should quantize to and at the optimal point you are again going to minimize some error such as the squared error. Vector quantization is very, very popularly used in several scenarios where you have large amounts of data with correlations inbuilt that you are able to capture. So summary, quantization is an essential step to get your real values to finite precision representation. But remember always whenever you have any practical signal, there is quantization error and an optimal quantizer minimizes the quantization error. Now a scalar quantization approach is of course suitable in many applications. But you may want to use vector quantization in the context where you want to take groups of data and quantize them rather than scalar in geometry you can go for higher dimensional spaces. For further reading on this topic, this is a deep and you know mature topic, you can look at block quantization, rate distortion theory and entropy coded quantization. You can refer to these in various sources on compression information theory and understand more about this topic. In the next lecture, we are going to perform these operations as a simulation on new radio and observe how the errors manifest in histograms. Thank you.


Hello Manchester. I am heading your way on the 4th of February bringing my live event seven levels of authentic communication To your beautiful city and part of the UK. If you want more information, you can send me a message or go to the link in bio I hope to see you for the manchester. You're up next Australia than America This will be the only thing I'm doing in the first half of this year in the UK So I hope to see you guys there. Love you guys. Cheers


I'm sure there are people who don't like this one, but in my opinion, if you have trouble sleeping, if you are not getting quality sleep It's your fault. There are a few things that you can do about it that will massively change how it happens, reduce how often it happens and make sleeping much easier for you. So don't worry I'm going to explain exactly how to do it and in my opinion, this is stuff we can all do. Sleep cycles You've got to remember sleep cycles and habits are patterns. They're basically repeated patterns of things we do So when you go to bed late at night and then you wake up early in the morning or wake up late in the morning That's a habit. That's a pattern that you keep repeating. So if you are currently not getting good sleep You need to change the things you're doing that result in you getting bad sleep. Sounds pretty obvious, right? If you're doing something that results in a bad thing, stop doing the thing that makes it happen, right? So in this case, the thing that makes it happen is your sleep cycles are all messed up And you're not doing the basic things in the morning and evening that will, you know, the low hanging fruits I say. The things that are the easiest to change, but they have the biggest impact I'll explain what they are, but it's probably nothing new that the chances are you knew this stuff And you just haven't been doing it That's why I said in the title a bit provocatively I said it's your fault because the chances are it is your fault All right, so firstly the stuff you know you shouldn't be doing, right? So drinking alcohol too close to bedtime, watching scary movies, having, you know, overthinking in the evenings, getting into arguments in the evenings Scruggling through your phone, watching stuff, you know, just before going to bed and drinking energy drinks past 12 o'clock in the afternoon That's all bad stuff. So if you're doing any of that, stop that. The next thing is in the morning, our bodies need to be moving and absorbing light within the first half an hour of the day So this means doing things like exercise, bouncing up and down, press-ups, squats Or even better, go for a walk, go for an hour long walk before you have breakfast even and then you're getting the benefits of intermittent fasting You're getting sunlight, fresh air and even grounding if you walk barefoot. Most people don't do that Most people wake up and they go sit in their cars, drive in traffic, stressed out to go and sit in an office all day So they're not getting movement and they're not getting grounding So you need to get that stimulation. You need to move and get light first thing in the morning You can combine that with coffee because then you boost your cortisol in your adrenaline And that also helps to kickstart your body in the morning So you're actually, you're setting your circadian rhythm up properly and then on the contrast of that in the evening In the last three hours before bed, you should be not watching screens You should be not moving, not eating, just doing really really relaxed, slow stuff like yoga or reading And to be honest, that is it. Like if you do those things, you will probably not have insomnia You will probably not have trouble sleeping and your sleep will probably be good as well. So give it a try