Thank you for the opportunity to speak today. I'm going to talk a little bit about conversational language understanding, just wait for it to come up. Actually, it's going to be a bit of a review of what are some of the approaches that people are doing. But first, I'd like to kind of motivate this area of research and this type of work by giving a demo. Actually, it's a recorded demo. So this first one is me trying to find a restaurant in Edinburgh with a view of the castle. Is there any way that has a nice view of the castle? I have a review of the outsider saying, did any table with a view of the castle as well worth it if you can? What's it like that the other say that? One review of the outsider said, fun, good food and friendly service. Do they have vegetarian options? According to one review, however, the restaurant had a good choice of vegetarian options. Can I pick a table for two people? Sure, let's make a reservation at the outsider. What date would you like his book to work? For tomorrow. What time would you like the reservation for? For 7 PM. OK, so he won reservations for two people at 7 PM on the 20th of June, 2018 at the outsider. I'll take those slides again. OK, let's start again. Cool. So there you could see that I was able to hone in on a particular restaurant, ask kind of free farm questions about it, and get satisfactory answers, I think, and then go into booking where it needed for me some information, like how many people, when, as a time and as a date. So just to demonstrate the open questions that you can ask here's another quick clip. What was that cafe where JK Rowland wrote Harry Potter? According to one review of the Elgin House, a lot of cafes say that they are where JK Rowland wrote Harry Potter. Does this place have good service? One customer wrote, this place is rather crowded at the service was rather slow. And what's their address? According to the address of the Elgin House is 21 George to 4th Bridge. OK, can I have a look at the table? Sure, let's make a reservation at the Elgin House. What day and time and for how many people? Cool, so that hopefully is some motivation of the type of stuff that we're building, Poly AI. I'm now going to go into conversational language understanding type of technology that powers these spoken language systems. What is conversational language understanding? Well, for me, it's some method of deriving a representation which is then used by a spoken language, spoken dialogue system to decide what to say next. And that representation could either be some explicit representation, like the food has to be French, it cannot be Italian, and it needs to be in the King's Cross neighborhood. Or it could be something like a vector in a vector space, an implicit semantic representation that's a hidden layer somewhere in the middle of a neural network that's trained on a more entwined task. I'm going to talk mainly about three paradigms for doing language understanding in the context of a conversation, slot-based systems, ranking systems, and generative models. So first of all, slot-based systems. So here's a dialogue in a slot-based domain. It's finding a restaurant in Cambridge. On the right-hand side, you can see the dialogue state, which is composed of slots and values. So at the beginning, they're asking for cheap price range in the central area. And you can see that in the second term, they can add to their goal, they can change their goal from food equals Indian to food equals Chinese. And you can have special slot value relations indicate that they're requesting the phone number at the end. So that was an example of a dialogue from the dialogue state tracking challenge, which has slots with categorical values. So five areas, three price ranges. And there are many more food types. But you might be sort of thinking, is that really covering restaurant search in a city? I would maybe ask about the atmosphere, if there's good service, if they have vegetarian options and stuff, like in the demo. But I'll come back to that topic later. Table reservation is maybe more of an obvious slot value domain where you need to constrain the date, the time, and the number of people. There, the values aren't really categorical. There's a whole different range of times, for example, and a whole different range of ways to refer to those times. Flights is another example that might be slot value-based. We need to query a database at some point to see if there is a flight from this airport to this particular point on this particular date. So how do people track the dialogue state? Well, one method is word-based recurrent neural networks. Here, the recurrency is through the dialogue turn. So each step, the network takes some input, which is text or spoken language from the user. And what the system had just said, it then updates its state internally and then produces distribution over the slots and values at that turn. So that way, it learns to jointly understand the latest input and to update its state and then to give a new value. And then typically, you'd have one of this type of model for each slot in your domain. One of the big issues that you come across with the slot-based stuff is that the label space is very sparse. So in the training set, it might not contain examples of labels that you will then see in the test set. There are many different food types, and in the dialogue state tracking challenge, there were several food types that didn't appear in the test set. But also, there are lots of different ways that you might specify that you want that type of food. So if you did it naively, you would need an example of each different way for each different type of food. And that would mean you need a lot of data, which you just can't get. So one way of dealing with that is what's called de-lexicolisation, which is where you replace mentions of slots and values with some generic tag. So here, all of a sudden, these first three sentences look very similar to each other. And what this allows you to do is to generalise across different slot values. It allows you to track new values that you've never seen before in training, so long as you can de-lexicolise. And it also lets you bootstrap to a new domain. So you might have a model that's trained on restaurants. It might do something for the slot values that are important in rising hotels. Another way of getting the generalisation to deal with the sparse labels is to exploit pre-trained word embedding spaces. So this is a diagram of the neural belief tracker. And the key idea here, it's not super important to understand the diagram altogether, but this d equals c times r. That means that it's comparing a representation of the context c with a representation of candidate slot value r. So long as you're able to provide a vector for a food type, like, say, Persian, and a vector for a word in your sentence, like Farsi, you hope that in the pre-trained vector space, they would be close together. And you'd be able to identify that this was a mention of that slot value. And it would generalise in that manner. So I mentioned the label space. When you're developing a slot-based dialogue system, you need data to train the natural language understanding component. There are three main ways to get this type of annotated data. The first is just to launch some system and to log the interactions it's having with people and to label the data. Obviously, that assumes you have some initial system that you can launch, which might be an issue. Another idea, number two here, is that you could actually simulate fake conversations. So I have a computer talking to a computer in the label space, so in the explicit semantic annotation space. And then higher mechanical Turk workers are similar to translate from the labels to natural language, so like real English or whatever language you're using. And then you end up with dialogues that are in natural language that are fully annotated. And the last idea is you can do a Wizard of Oz type setup. So you have one worker paired with another one. One worker is playing the part of a user, say, for example, trying to find a restaurant in Cambridge. And the other is playing the part of the system, so they've got all the controls that the system might have. And you just record everything they do. And you end up with fully annotated dialogues. Actually, it gives you some idea of what a human might do in that situation, so it might help you to train a decision-making component as well. Some of the challenges of slot-based dialogue systems, I think the main one for me is that it can impose a artificial structure on dialogue that's suboptimal. So I mean, if you were to look at real conversations about restaurants, I doubt that you could annotate them with the slot-value pairs that are mentioned in, for example, a dialogue-state tracking challenge. While it's useful for connecting to APIs and database queries, it's maybe not exactly how people talk. And in order to really exploit the system and make it useful for you, you kind of need to know what the slots and values it has, what kind of things it can deal with ahead of time. So how do you let the user know? We deal with area, but we don't deal with the quality of the service at a restaurant. Another issue with the large label space, apart from generalization, is that usually the way people deal with it is to factorize the dialogue state into different components. And then have a model that tracks or classifies different aspects of the state. And that just means you've got a lot of models to take care of, which is an engineering problem. Every time you want to deploy something new, you need to check that each model is getting good enough accuracy and that they all play well together. And also resulting from this difficulty in finding data that you just don't end up having as much data as you would in other areas. And you can apply machine learning to this task, but machine learning excels when you've just got millions of examples. And here you're not going to have millions of examples. So the next paradigm in terms of conversational language understanding I'm going to talk about is ranking. I'll explain this in context of the smart reply system, the power smart reply in Gmail. There it's using a model that scores input emails and responses together. So it'll give a high score to a response that goes with this email and a low score to a response that looks random in the context of this email. And actually that score is a dot product between two vectors, one vector that represents the input email and one vector that represents the response. And those vectors are the final hidden layer in some deep network. So there are a couple of reasons why factorizing it as a dot product is a good idea. One is during training. So if you imagine that these x and y are emails and responses that go together. So x1 is a vector representation of the first email, x2, the second, and so on. And then y1 is the vector representation of the response that goes with x1 and so on. Then this matrix here is the n by n matrix of all possible scores in a training batch. It's just a fast matrix multiply away from competing the x and y. We would want the diagonal to be very high for scores of emails and responses that go together to be high. And then the off diagonals below, which we can learn with a softmax loss. It gives us a lot of signal in our learning. It's very efficient. We'd manage to do n squared comparisons but kind of linear complexity in a way. Also during inference, we can pre-compute all the vector representations of the responses and then just do a simple search. So here, training data is much easier to collect. It doesn't need any special annotation. We just need pairs of, in this conversational context, this was the response. And we learn what responses are plausible in different contexts. It's very easy to constrain the output of this because we control the candidate set of things that it's ranking. So we can ensure, for example, that each sentence was written by a human, that each sentence has been approved maybe, if you're worried about what it can say. And also, a big thing is that it has implicitly learned its representation of meaning. So it's learned that vector space. It wasn't hand engineered like in the slot-based systems. Instead, it was directly optimized as part of the learning process on the data for the task at hand. So the last paradigm I'll talk about is generative models. So this would be sequence-to-sequence. The first application of this to dial up would have been Oriel Vinyals's neural conversation model. The idea there is that you train a neural network to encode a conversational context and then produce responses word by word in a generative fashion. So here, you've fed it, how are you? And you give it some special transition token. And then it generates a language model. It generates fine thanks, word by word. So this is how state-of-the-art translation works, where you'd feed it, say, French, and then it would output English. But I see translation as a bit more of a slightly simpler mapping than the mapping of inputs to responses. And as a result, these models, obviously, they just model the data that they're fed. They can have this issue of learning a kind of blurry model over the data, so that when you go to generate from them, they will just produce very common responses. It doesn't seem to depend on the input. So there are special tricks to overcome that, but if you didn't do anything, it would just say, I don't know, or I love you, or whatever is common in the data. Also, of course, this is complexity that depends on the sequence length. So you have to run the RNN for each sentence that you're outputting, whereas ranking is sort of just one pass through the network. And it's hard to constrain the output in the same way that you could constrain the output for the ranking system, so it's kind of difficult to apply this to a useful task. The demonstration of that might be if you were to talk to this model, if you trained it on some conversational set, you ask it, what's your name? It would just sample a name from the distribution of names in the data, like I'm Bob. If you asked it again later, there's no guarantee it would be consistent with itself, but come with some other name from the data, possibly. So if you want to train it to have a particular personality or do a particular task, it's difficult. So I'm going to summarize the pros and cons of the different paradigms. First, we have the slot-based systems. It's really good if you want to have explicit structure, if you need to do a particular API call like Bucca Table, for example. However, that structure that you impose can lead to artificial dialogues. Who says that that's how people will approach your system? It's an engineered label space, and you don't end up with a lot of data. So for a ranking, you can use a lot of data. You can constrain the output of the system to make sure that it's like human-written and constrain it in any other way that you like. And it's learned a simplistic semantic space. On the other hand, it's challenging to connect it to an API call. So how would you extract some explicit representation in the same way that you can from the slot-based system? So for generative methods, it has the one advantage over ranking that it can generate new sentences. But that's also a disadvantage that it can generate new sentences. If you're a company that cares about your image and you have an assistant that represents you and it can theoretically generate any possible English sentence, there are some obvious issues with that. This issue of domain adaptation is kind of tricky. So there are ways to tweak your model that's trained on general text to have a particular personality or something that's very difficult to measure and it's very difficult to get correct. So thanks for your attention. So we're representing Poly AI. We're a new company. We're about 12 people. Two people are in Singapore, including me. We're working on conversational systems. So if there's something that you're interested in, then please talk to me. Thank you very much.


You need a financial advisor with the freedom to focus on your specific needs. Whatever your goals, Reed Potter can create a game plan tailored to you. Call Reed at 432-0777 at Pikeville, Kentucky to learn more. The Appalachian Center for the Arts loaded in their new art exhibit. So we've got a new gallery coming in in March, and it is the works of Tim Smith and Tina Oosley, and they are from Big Sandy Community College, and they're bringing their stuff in, they're going to be showing in March. Yeah, this exhibit is an exhibit of artworks which we've worked on for some years now, some's newer and some's older. Tina Oosley, my fiance and me, we work together as artists. She works mainly with oil paint, and I work with many mediums including sculpture, printmaking, painting, drawing, and other things. And we just really enjoy making art together. The exhibit will open to the public March 1st and is free to view, but select pieces will be up for sale. Tina works predominantly in the expressionistic style with her loose brush strokes and sometimes arbitrary colors and plays of scale and things like that. It can fall in some of her works as more abstract than expressionism. Me, on the other hand, I have no specific style, so to say. I'm about to switch constantly back and forth, and I just enjoy trying a lot of different things. The gallery will be open Monday through Friday, 1 to 4.30 p.m. or by appointment. Me and her both are natives of the region. I've lived in Pikeville and over at Raccoon Creek, Kentucky, growing up and so forth. And it's just great to see the arts being promoted in this region much stronger than it has been in the past. And we both are very supportive of that, especially at this place. We're very excited that Pikeville has got a great venue for the arts needed for a long time here. Reporting from Mount Top News, I'm Ethan Miller.


I've never seen almost a bigger sigh of relief out of Sonic Fox after taking that match So the crowd in the back said mercy and he said And one of the biggest threats in this top eight but right now Here we go This is Sonic Fox round number one. Let's fight Cetrion and Yes Queen Cassie This is what Sonic Fox Surprise pick is and it's all about that shoulder right there getting some amazing punishes against Cetrion zoning Well, you can see Dragon was ready to punish it He's definitely ready here Oh looking for that big jump nuke That's a bulk of the damage that you're gonna see from Cetrion not normally known as a damage Look at this, the range is perfect here I expected to come in from Sonic Fox Dragon absolutely ready for it Close to sealing the deal Got it! On the movement! Sonic Fox getting a little ahead of himself What a start out of Dragon Ready for the aggression of Sonic Fox You talked about ready for the advancement has those one ones ready Ready to punish the shoulder Which he said is a massive problem in this matchup Okay, well we'll see putting that to the test Jumps over and that's not a punish but counter it instead He does go for the knockdown Yeah, that was interesting that we saw this particular variation You know, not trying to go for the knockdown I guess that's on the player there Good positioning here We're looking at that pillar to start a crush it can Knockdown, here's Sonic Fox trying to approach Yes, ready for Dragon's approach here And no, hold on, here's the punish No implication on that, it's gonna be a big punish And we're going coast to coast here Corner to corner That makes it gonna be just a neutral jump kick Trying to put some massive active frames out there Sonic Fox in pressure here Keeping it safe Yes And now anything's gonna do it I went in for the shoulder again The idea was right You're gonna have active frames out there with the one one That was just beautiful timing And a Sonic Fox to get in between it Starting towards the corner here But of course that's how you can get out There you go, see it Just like that, you let her breathe This Elder Goddess is out of there And crushing blow here Not one of the launchers But an officering that has quick start up And missed the plus frame I love seeing that boulder sail through the sky You know the threat of that boulder That huge crush it could meet somebody in the sky And it's an even bigger threat As they have recently found That it launches What's that makes it be out of the corner Look at the OS to wake up potentially That's what a lot of central players Like to do in the corner They like to low prop out in the down forth Or attack the ball But here we go The jump right out of there Look how ready Dragon is Sonic Fox is in trouble right here There's no way to escape Ready The flawless blocks on the way in from Sonic You saw them do multiple boulders The previous rounds as well That doesn't mean you take less chance Get ready for the grab of all things And in there Sonic Fox He needs more work though I'm certainly will this is going to be close To about tying us up here Yeah Get very close and deep But here she is Oh my goodness He did wake up jump again The savagery Look at Sonic Fox is laughing at you Why would you do that But it works back to back for Dragon here No smile or reaction from Dragon He's ready Well I don't know if that cast He went exactly as Fox expected Or hoped Is he going to stick with it Yes he is Believing in his choice And he's got more breathing room Here in the top eight Than he did in order to get here You know those first to twos Second guessing yourself Early really happens a lot But here you've got some breathing room Sure Yep you can experiment just a little bit But now of course He needs to win Three of the next Four? Potential four matches Yeah That was just a phenomenal start For Dragon right there And you know he's done a phenomenal Great job of keeping Sonic Fox away And he's Stopping approaches here Getting huge damage himself Sonic Fox just hasn't gotten You know Against the Okay That can launch You can do a full screen Geyser into about 500 to 600 damage It's so sick As a zoner to get that level Of satisfaction from a combo It's rare Oh no No Put us out of Sonic Fox And Dragon It's just running away with this right now I gotta say it does seem like that Dragon seems absolutely ready for everything He's ready for counterpokes He's ready to control the screen with zoning He's ready for punishes And it doesn't seem like Fox is quite as prepared Punish not quite Close though Oh Nice crossover I'm feeling that if Kasim Kidd Was actually in front of Setsuron That may have antired This is exactly where Fox wants to be Looking for the big damage Did wanna see that Dragon Wow Nice to confirm And out of the corner first Not a ton of damage afterwards So ready for it And I like what Dragon did right there He ended with a wall Because he would have ended a geyser And a breakaway would have occurred Would have been in a punishable situation Who didn't expect it But Dragon expected that SNS has been against Lovely with punish right on through He is so scared of that shoulder What an absolute animal Nice side switch as well Big damage Back to full speed We go just a raw geyser And the fatal blow Is it gonna be enough She survived though Not very effective Okay But still a lot of control And he hops on forward Dragon Controlling things Not just with the projectiles But with the normals as well And you don't expect that At a Setsuron in an exact position Who's gonna try to run away Try to zone you out With that last little bit of damage And Dragon says you know what He's gonna expect this Forward dash of pressure And there is Sonic Fox Can we see a 3-0 Against Sonic Fox How many times did we see Sonic in his position At combo breaker though He was down 0-2 I think twice in that top 8 I was able to turn things around But in those cases He switched characters And he did not do so here He's just gonna have to run it back With Cassie Cage the whole way Off to a good start here Doesn't get great Okie off of her throws This Cassie Oh my goodness There's a rush all the right there No crushing blow off it Interruption right there Sonic Fox Trying to get the corner here Dragon out At the cost of one defensive bar Something you'd rather see Dragon do this matchup as well Is do anything into the Amplified wall on block Because she's one of your Characters that can stand 1-1 Through with that 7 frames very quick Whip on it See what he gets He wants the corner for himself Or at least maybe He wants all the stage to himself Yeah he's backing up a little bit Hoping to see some movement Oh my goodness Dragon's so smart Trying to make these reads That was the first one That may have not worked out That's not an easy thing to do Got it Hold on now Safety Staying away They both have failed Oh he got it And that is going to do it It's match point Dragon threatening to 3-0 Sonic Fox He said that if He feels like if he plays The way that he knows He can play He's confident it's every Single person here Especially here against Sonic Fox Look at the way That he's playing right now See how confident he was To teleport close To go for the grab But Sonic Fox was ready Oh Big good punish there Not a lot of damage But good positioning And a good okie there Off that control tackle Oh here we go The re-stand for Dragon Here and on the rock wall We're going to mix things up And every time that he's Staggering with a stand-up One like that is Dragon A downforce is waiting for him Fox is ready for the Stagger Not ready for the anti-air But Dragon trying to back off And then start something up Trying to get the anti-air Sometimes it's good Not right there Especially once those jump Kicks are active Very hard And kind of on top of her head Dragon trying to move He got punished He got punished for waking up Jumping into the game That's right Here's the whip punish Now there is defensive bar He's going to save it See if Dragon can control the screen You're bringing down to a scary Hell trap Trying to take up space here Anything in the guys are right And now he's ready to Jumping along Dragon is going to take it 3-0 Over Sonic Fox Desperately trying for the shield But it's not going to work And Dragon doesn't He's already stood up He knows what happened There you go He makes it into Winter Spinals Over Sonic Fox


Question is from JP94, how do I grow my traps? Oh, good old traps. Grow them traps. You know, the most common exercise for traps is our shrugs, but I'm beginning to believe that shrugs are not the best exercise for the traps. And I learned this because of Map Strong. We wrote Map Strong, there were two exercises in there that I'd done before, but that I didn't do in regular rotation. I'd throw them in every once in a while, but I didn't do them consistently. The high pulls. The snatch grip high pulls. Snatch grip deadlift. Yeah, snatch grip high pulls mainly, and heavy farmer walks with a trap bar. Man, I started doing those in my routine, and I did them regularly because in Map Strong, they're in their weekly.  Yeah, that was what centered around that. When we were, I remember when we were riding it with Robert Oberis, one of the things, and one of the ways that when we create a program like that, a lot of it is us still steering the programming, but we ask a lot of questions from the athlete. Like, what are some favorite moves, or what have you found well? And one of the things that when we were looking over a lot of the stuff that Robert was doing, all of us right away were like, God damn, a lot of upper back stuff here. I mean, and he's like, yeah, no. I mean, for the lifts that you have to, and it makes sense. It makes sense why he was doing a lot of that. And so that's part of what is unique to that program is we made sure to include, that's not something we would, that high of, that much volume in the upper back and trap area is probably not something we would do in a normal standard program, but because that is a supporting area for a lot of overhead pressing and lifting that they do, it's crucial. So if you're somebody who's interested in growing traps and you haven't followed Map Strong, to me, that's gotta be a must in your rotation. I'll tell you what, traps is one of those muscle groups that, you know, when you see certain muscle groups really developed on a person, you know like that's a strong. You know they're strong. Yeah, that's a strong person. Like you see a lot of developed biceps. Doesn't necessarily mean they're a strong person. You see a dude with developed traps. Usually it means they're pretty damn strong. It's just one of those body parts. And it really is an important muscle group. It stabilizes the entire shoulder girdle. The traps are very important for overhead presses and for rows and just for giving you strength in that upper back area. Traps like any body part respond well for most people if you train them about two to four times a week. So on average about three days a week they respond well to different rep ranges just like any other body part. Anywhere between low reps, three reps to higher reps maybe as high as 20. And they also respond really well to different angles. Shrugs, overhead shrugs, snatch grip, high pulls. And then tension movements like heavy farmer walks. Like when you're walking with really, really heavy dumbbells or a heavy trap bar and you're standing tall and maintaining good posture because you're using so much weight. Cause when I did a trap bar, you know, farmer walks I got up to over 400 pounds. And although I'm not doing a shrug with that weight just a simple fact that I'm supporting it and staying stable with my upper traps in my upper back. I mean, they responded like crazy. Well, it's such a great contrast between that and then doing an explosive movement where you're getting that fast twitch response out of the muscle, which stimulates it completely different ways. So now you get your traps to develop, you know both directions. Yeah, I know what's funny about traps too is a lot of people get tight in their neck and they, you know, they'll, they get that tension. And a lot of times people think, well, don't work that area out because you're really tight. And I used to believe that. Now, what I'm about to say isn't the opposite.  So I would get clients who would have really tight trap areas and I would work on correctional exercise for their shoulders and offset the overactivity of those muscles. But then I'd have them do some really light full range of motion shrugs really, really light just let them come all the way down let them squeeze all the way up and they'd get all this relief in that area. So just because something's tight doesn't mean it's too strong. Sometimes it could be tight and weak. Oftentimes it could be tight and weak. Now I know a lot of women avoid training their traps. I think this is a mistake. Now of course there's an aesthetic component. If you're somebody that has really well-developed traps and you don't want them to get too big, I get it.  It's not gonna happen. It takes a long time to develop any muscle including the traps. Still train your traps because here's what's gonna happen. It's gonna make your mid-back look better. It's gonna give you better posture if you train them properly. It's gonna make your overhead presses stronger. It's a very important muscle for overhead presses and if you wanna develop good shoulders you also need to have strong supportive traps.  Worst case scenario you get to the point where you look in the mirror and you're like, okay I think I'm happy where my traps are. Then you can really scale down but it won't happen overnight. I wouldn't freak out over it. Don't avoid the trap.


COVID-19 is causing economic havoc around the globe and undermining efforts to end extreme poverty, hunger and malnutrition for millions of people across the globe. But this pandemic may only be the tip of the iceberg, exacerbating economic slowdowns and recessions already present in recent years. An FAO study in 77 countries revealed a rise in hunger between 2011 and 2017, with 65 of those countries already experiencing economies that were slowing or contracting. COVID-19 has accelerated these concerns. The world economy is expected to lose 11 trillion US dollars over 2020-2021 and up to 28 trillion US dollars over 2020-2025. This is the equivalent to losing the entire GDP of the United States of America. This contraction of economies is translating into skyrocketing unemployment and declining wages and incomes, seriously compromising people's capacity to access food. Sadly, the poor and most vulnerable who already spent the largest portion of their income on food are most at risk. Access to nutritious food and healthy diets is even more urgent during times of economic turmoil. Before the COVID-19 pandemic, more than 3 billion people were already unable to afford healthy diets. The effects of the pandemic could nudge this figure up by approximately 17%. Governments have responded quickly to this pandemic-induced economic crisis, releasing stimulus packages with fiscal spending that has reached unprecedented levels. These efforts have helped maintain jobs for workers and softened the economic blow of containment measures. However, many low and middle countries lack the tools to deploy the necessary liquidity injections and public spending commitments to stimulate their economies and save lives. Now is the time for countries to implement smarter macroeconomic stimulus packages. These packages would address immediate needs while laying the foundations for building back better. A critical focus area is the transformation of agri-food systems and related infrastructure needed to achieve affordable healthy diets for all. Using policy and investment of agri-food systems transformation to the country implemented macroeconomic stimulus packages is an important first step. Governments also have an important role to play in prioritizing affordable healthy diets within their agriculture, trade and social protection policies and investments. To achieve this, macroeconomic stimulus packages must keep the food supply chains functioning while promoting more nutrition-sensitive value chains. Nutrition-sensitive investment in food and agriculture is critical going forward. Trade is a key ally in diversifying diets and lowering food price volatility, making nutritious and healthy diets more affordable. Restrictive trade policies must be avoided while trade-facilitating practices and intra-regional trade are promoted. Policies supporting decent rural employment opportunities and rural finance, including for women and youth, are more urgent than ever. An emphasis on nutrition-sensitive policies will assist countries in ensuring access to nutritious foods while increasing affordability of healthy diets for the most vulnerable populations. COVID-19 has exposed the fragility of hunger and food security and of our agri-food systems, but together we can implement smarter macroeconomic stimulus policies that transform agri-food systems with an aim to getting back on track to end world hunger.


Hello everyone today I am going to give you seven reasons why it is so hard to lose belly fat which is an important fat that leaves a diabetes that's why you're in diabetes channel but this channel is not only about diabetes we talk about weight loss metabolism and so forth I am Dr. Ahmed Ergen I'm an under chronologist and metabolism expert and a diabetes specialist so let's dive into why it is so freaking hard to lose this damn belly fat now number one the belly fat is actually a hormonal gland yeah you heard it right it is actually the fat cells in our body especially the belly fat the visceral fat we call this which is the fat around your organs that are pushing out from your belly the visceral fat actually works like a hormonal gland they create a lot of enzymes and cytokines and proteins that actually creates insulin resistance they actually protect themselves especially in that area where you are doing everything you can but that belly fat once you develop that fat well it will go but I'll tell you that but it is very hard to remove that fat because it is protecting itself with a lot of cytokines so the best way to avoid or best way to prevent that problem would be not to gain belly fat we'll talk about that in a second but I'm telling you that the belly fat is a hormonal gland that protects itself that's number one number two well we all kind of like the sweets and the alcohol right so the social gatherings you know the parties you know the birthdays and this day that day you know all sorts of holidays we are always subject to you know making this hard decision or what eating not eating drinking not drinking it is a difficult decision and it's surrounding us everywhere so every time you say oh it's a one time it's not gonna hurt me it's gonna hurt you and that thing that you eat the alcohol and any sweets and the carbs goes right to your belly your belly fat will accept it graciously and will not let it go so that's number two well it's number three low estrogen levels lead to belly fat so no wonder my office is full of post-menopausal women trying to understand why they're gaining weight although they have not changed anything in their life and I don't blame them because when the estrogen levels go down unfortunately your body shifts the weight from your buttocks and from your thighs to your belly and sometimes a hormone replacement helps but most of the time even when you take hormone replacement most women will see an increase in the size of their abdominal area due to change in their life and number four the diet only will not get rid of the belly fat you have to exercise now a lot of people have a tons of exercise excuses not to do exercise right but you have to if you're not doing cardio if you're not getting your heart rate up to 120 130 for at least 30 minutes to an hour that belly fat is not going to go away so yes you have to diet right avoid all the bad carbohydrates the sweets the alcohol but at the same time you have to learn to make a priority to exercise every day and especially cardiac exercise to get rid of the belly fat and number five is the cortisol now cortisol the stress hormone and unfortunately makes fat cells thrive especially in that visceral area in the abdominal area so if you're stressed out or if you just stress about everything or if you just have a difficult life you know that's just not going to help you to get rid of the belly fat so do some relaxation do some yoga do something that will relax you and try to avoid stressful situations that will definitely help you in the long run to avoid that belly fat but number six sometimes you cannot really beat genetics or actually pretty much never you cannot beat your genetics so if your mom was an apple-shaped person you know if people in your family have significant abdominal obesity sometimes it's just in your genes and don't just be frustrated because everything you did I said and you still didn't lose everything in your belly you'll lose it but you may still retain some and that may be just simply genetics and lastly and most importantly the added sugars now most people think that they have a great diet but they don't mind putting all sorts of ketchup on their food for example well these ketchup's are full of added fructose and sugar that you may not be aware of so every time you consume a food try to make a habit of looking at the nutritional facts especially high fructose corn syrup is heavily used in United States in many developed countries as well and there is great evidence that the fructose especially those high fructose corn syrup not the fructose from the fruits but these processed fructose goes to your liver gets trapped and turns into fat immediately so you need to make sure that you are staying away from everything that is processed that gets on your table so guys I hope this helps and I hope you like this video give a thumbs up share and we'll see you in the next video. Hey guys I hope you're enjoying this channel so far and I hope you subscribed already if you didn't do it and if you did watch this video right there I think that will help you too


Ladies and gentlemen, the President of the United States, accompanied by Mr. John Plendenin and Mr. William Colbert, United States of America. Thank you very much. Thank you all, Secretary Bohn and ladies and gentlemen. Before we begin, I have a brief announcement. Today at my direction, Ambassador Mike Blitman, our INF negotiator, placed on the negotiating table in Geneva, our new draft treaty, calling for the elimination of all U.S. and Soviet ground-based INF missiles. This new step by the U.S. reflects the fact that the Soviet Union recently agreed to my original proposal to ban this entire class of U.S. and Soviet missiles. Ambassador Blitman also presented at the Soviet Union today a protocol to our draft treaty that calls for the most stringent verification regime of any arms control agreement in history. This reflects my long-standing insistence that the most effective verification possible is needed in order to ensure that an INF agreement marks a lasting contribution to our efforts to build a safer peace, and I will not settle for anything less. In short, our new proposal calls for eliminating all U.S. and Soviet INF missiles and launchers within three years, a ban on modernizing, producing, or flight-testing any INF missile system, a comprehensive verification regime tailored to a double global zero outcome. With these new actions taken up by the U.S., it's now up to the Soviet Union to demonstrate whether or not it truly wants to conclude a treaty eliminating this class of U.S. and Soviet missiles. In regard to verification, I have to tell you I'm not exactly a linguist, but in my most recent meeting with General Secretary Gorbachev, I had mastered a phrase, a proverb dated in Russian, Dovayai no Provyai. It means trust, but verify. Well, now to the business at hand. I always try to keep my remarks short and will today, though when I run over I like to remember something President Eisenhower once said. One good thing about being president, he said, is nobody can tell you when to sit down. But it's a pleasure being here at this meeting of the National Alliance of Business. Since its founding 20 short years ago, the Alliance has been an American leader in addressing a tragedy that concerns all of us, structural unemployment. Never has your leadership been more effective than in the last six years. With Bill Kohlberg as your president and now with John Clendenin as your chairman, for spearheading our country's first public-private partnership to prepare for the future Americans who thought they had no future, the job-training partnership. It's easy to forget that when we came into office, federal job programs had become a national scandal. For 40 years, government-sponsored training had achieved little more than to give leaf raking a bad name. One study in the late 60s found that the programs of that period had, quote, not helped business find qualified employees, and the largest percent of businesses said this was because training was given in the wrong skills. It was impossible, a follow-up study found, to track individual trainees through the system. Information on the potential labor market was inadequate. The management system was bloated with salaried staff, end quote. Well, if anything matters got worse after that, the CETA program of the 70s didn't settle on mere incompetence. It added corruption. In many cities, CETA positions were parceled out as political patronage, and CETA spent millions on such worthy projects as building an artificial rock for rock climbers to practice on. The worst-hit victims of these federally-financed fiascos were the trainees themselves, most genuinely needed the simple but basic skills of showing up punctually, performing a task that others valued, doing it well, and getting it done on time. Instead, they got laxly supervised make-work. The message came through loud and clear that honest effort will not be honestly rewarded. Well, thanks to you, all this has changed. When we came into office, we took one look at this mess and said it had to stop. We said that it wasn't enough to get better managers and stricter accountants. We wanted a revolution in approach. The Good Book tells us that excessive pride is a deadly sin. Well, the failing in these programs started with governments excessive pride. Over five decades, every federal training effort had been run by government because government believed it knew what was best for the unemployed and for the businesses that would hire them. I've always thought that the common sense and wisdom of government were southerned up in a sign they used to have hanging on that gigantic Hoover Dam. It said, government property, do not remove. Well, we said it was time to bring the grace of humility to this prideful city. So we turn to you with your help and guidance. The job training partnership is business run and locally run, not Washington run. And unlike all the many programs before it, and despite so many voices saying when we started that business would not rise to the challenge of helping the unemployed, you met the challenge. The hard to employ are getting trained. They're getting placed. They're keeping good jobs with good pay and good companies. They have reclaimed the American dream. And they're not alone. Not everyone needs special job training, but everyone is part of a growing economy. In November, America will set a record, the longest peacetime economic expansion in our business cycle history, 59 straight months of growth. Never before has this happened. After a decade-long rollercoaster ride down in the 70s, the American family once more has seen its income rising strongly and steadily since our recovery began. And the net worth of American households, assets minus liabilities, has been growing right in line with income. This is not a question of the rich getting richer and the poor poorer. No, in fact, if you include pension and social security rights, the gap between rich and poor has steadily narrowed, and we expect this to continue. Too often in the past, blacks lagged behind others as others advanced. But in the last five years, black employment has shot forward twice as fast as white employment. Since 1982, the real income of black families has increased almost 40% faster than white family income. And the share of black families in the highest income brackets is up by over 70%. This August, the percentage of blacks employed was the highest on record, as was the percentage of all Americans employed. Economics colonist Warren Brooks looked at this record and concluded that on every front, jobs, income, even household wealth, this 1981 through 1986 has been the best five economic years in black history. Yes, we still have some distance to go, but our economic policies are the headwaters from which economic justice for black Americans will, like a great river, flow. Yes, all of America has come a long way from the malaise days of the late 70s. And while getting here wasn't easy, it was simple. We simply got government with all its failures and reasons why it can't be done out of the way and let the American people take a crack at putting things right. Now I know that many still look at our cuts in taxes and regulations and scoff. Sometimes they dismiss the strong economy as a sign of my luck and then keep right on with their old talk of new programs and more spending. Well, maybe they ought to take a moment to look at the facts. I know that may come as a surprising suggestion to many of our critics. I'm not saying they're hostile to the facts. Just apathetic about them. Recently, the U.S. Chamber of Commerce examined the facts in the major industrial economies of the world. Their conclusion, more government beyond a point really does mean less growth. As the Chamber's Chief Economist Richard Ronn concluded, for many countries, economic growth rates significantly decline and unemployment rates begin to increase when total government spending exceeds the range of 15 to 30 percent of gross national product. Well, today the United States is around 35 percent. And that's why I'm determined that the way to close the budget deficit is not by raising the American people's taxes but by cutting the federal government spending. I've talked a great deal in these last few months about an economic bill of rights for America, including a line item veto and a balanced budget amendment. And if Congress isn't ready to do what's right, I'm ready to take the message to our state legislatures. Before the decade is out, we owe it to our children to see to it that the Constitution of the United States of America includes a balanced budget amendment. I feel so strongly about this because I believe that we have it within our power to continue an era of unprecedented growth that will transform America's future and the world's. In industry after industry, America can lead mankind into the 21st century. As the Los Angeles Times reported recently, in every part of the country, innovative companies are putting to rest the popular misconception that the United States is de-industrializing. Our determination to lower tax rates and increase incentives is paying off. Investment as a share of overall economic activity has risen since our recovery began and is now one-sixth of gross national product. Even while in Europe and Japan, the investment rates have been falling and this investment is ushering in a new American industrial age. But some would end this new age of opportunity before it ever begins. In the next few weeks in Washington, you will see the challenge before us. On one side, you'll find those who support what used to be called Reaganomics. Of course, our critics stopped calling policies Reaganomics when our policy started to work. On the other side, you'll find, if I may coin a phrase, pit bull economics. May look harmless, but let it loose and it'll tear America's future apart with higher taxes, new and costly programs, and protectionist trade policies. It's dangerous. I can't help but interject here about the fellow that knocked on another man's door and when he came to the door said, do you own a black pit bull? The fellow said, yes. Well, he said, I have to tell you it's dead. He said, what do you mean it's dead? What happened? He says, my pecanese killed it. He said, your pecanese killed it? How? He said, got stuck in his throat. Our opponents talk about the trade deficit and saving American jobs, particularly in manufacturing, even though the foremost authority on job creation and loss has reported that in statistically significant terms, we haven't eliminated a single manufacturing job. In more than two decades, some manufacturing industries are down, but others are up. And in fact, throughout the economy, in all sectors, we have created a record 13 and a half million jobs in the last five years. And industrial production is surging strongly forward. The issue here is not over jobs or family income. We know how to create more jobs and better incomes, the way we've been doing it. We must have compassion for those workers who have been displaced as their industries have become more efficient. We must help them develop new skills and find new lines of work. We have proposals to do this before Congress, and you've been leading the way in this area, too. But that's not the issue here in Washington. No, the issue is control. Should Washington have more control, or should the American people? Well, let me be clear, I'm not questioning motives. Many fine and well-meaning people still believe that control is best and most wisely held in the hands of our central government. The way they talk about American people, I find myself remembering a story I once heard about a great baseball manager, Frankie Frisch. One day, he sent a rookie out to play center field. The rookie dropped the first fly ball that was hit to him, let it go up ground or go between his feet. And when he did get his hands in the ball, he threw it to the wrong base. Frankie stormed out of the dugout, grabbed his glove, and said, I'll show you how to play this position. And the next batter slammed a drive right over second base. Frankie came in on it, missed it completely, and fell down when he tried to chase it. He threw his glove down and yelled at the rookie, you got center field, so loused up, nobody can play it. Well, that's how many critics may think, but in choosing between big government and the American people, I'm old fashioned. I stand with the men who wrote the glorious constitution whose 200th birthday we celebrate this week. I put my trust in the people. In the months ahead and in the battles to reduce federal spending, prevent destructive protectionism, keep taxes down, we'll decide whether the American economy will retain the vitality that gives our future so much promise. I hope I'll have your support in these battles. Let me mention one area in particular that I know you care about and where the central issue is, once again, Washington's control, welfare reform. 23 years ago, Washington launched a war on poverty, poverty one. But from that failure, we learned many lessons about poverty and getting out of it. We learned that work should be more rewarding than welfare because work is the only genuine path to self-respect and independence. We learned that welfare should sustain and not disrupt families because intact self-reliant families are the best anti-poverty insurance ever devised. And we learned that the federal government doesn't really know how to apply these and other lessons to the day-to-day problems of the welfare family that's trying to lift itself up. Successful reforms have been virtually, without exception, those that were homegrown in state capitals, cities, and neighborhoods. And that's why we propose to allow expanded experimentation at the state and local levels. Experiments that work will be tested more broadly. This is just plain common sense. And yet there are those who believe that the federal government should once again parachute a single set of reforms on the nation. The history of just one more reform from Washington is bleak. For example, a few years ago there was an idea here to replace welfare with what amounted to a guaranteed minimum income. We were told that this would cure the problems in the system. And among other things, it was argued that families would no longer have to break up to receive assistance so they would stay together. Then somebody said, maybe we should test this in one or two cities before we do it in the nation as a whole. So they did. And they found that people worked less and families broke up faster under that plan than they had before. As in so many other areas in welfare reform, it's time to get the federal government to learn some humility and admit what it doesn't know and put its trust in the American people. At the very least, we should insist that the government will not do more harm than good when it acts. It's time for us to look carefully at the full range of government activities and ask which ones make it harder for the poor to escape poverty, which cut off rungs in the ladder of American opportunity, which make it more difficult to realize the American dream. America is at the dawn of a new age of hope and let us resolve that all Americans will know the promise of the new age and the new century. Now, before I close, let me add that Washington is, I hope, about to have a great debate. I have nominated one of America's most distinguished legal scholars and jurists, Robert Bork, to the Supreme Court. Well, tomorrow the Senate opens hearings on his confirmation. Judge Bork believes laws should govern our country. And if you want them changed, you should convince elected legislatures to change them, not unelected judges. This doctrine of judicial restraint shouldn't be controversial in our democracy, but it is. And I hope it'll be fully debated in the weeks ahead. I say I hope because too often character assassination has replaced debate in principle here in Washington. Destroy someone's reputation and you don't have to talk about what he stands for. Well, I hope that Judge Bork's critics will be candid about why they oppose him and not fabricate excuses for attacking him personally. That way we can have a full and open debate on an important constitutional principle. And when the votes are counted, America will win. Thank you and God bless you. Mr. President, in the words of one of America's best known mayors, you've made our day. And we thank you so much for honoring NAB, this audience, this board of directors, and several who are on the board who arrived and are not here at the head table. We genuinely appreciate your coming. Ladies and gentlemen, I'm sure that those of you who for the first time today went through the security arrangements set up by the Secret Service Department were impressed with their thoroughness. They do a superb job. One of the roles that they have to do is now to get the president back to other quarters safely. And in order to do that, I would ask that all of you remain seated while the president and his party depart from this auditorium. And again, Mr. President, we genuinely appreciate the time that you spent with us. We genuinely appreciate the message that you brought to us and we accept the charge to do all we can to lead this nation toward the 2000 year and start the 21st century. Thank you very much. Thank you.