So, I am very excited to introduce our CSE Distinguished Lecture Speaker, who is Professor Monica Lam. Professor Lam is the Faculty Director of the Stanford Open Virtual Assistant Laboratory, which is creating open virtual assistants. So this is, I think, really important work, you know, virtual assistants, you know, I think like Alexa and Siri and so on, they're increasingly ubiquitous in our homes and even in our workplaces. And in many ways they're still in their infancy, right? And I mean that in two senses of the word, there are a number of technical problems related to AI machine learning, natural language processing, HCI, programming languages, systems and so on. And equally importantly, these systems, of course, have the ability to listen to us. And so now is really the time to make the kind of critical progress on privacy and openness and democratic control over this sort of infrastructure. So I'm very excited to have Professor Lam speaking here today. I don't think there's really many people I can think of who might be more qualified to work on this kind of big multi-disciplinary design problem. Professor Lam has published over 150 papers on a wide variety of topics including NLP, machine learning, architecture, HCI, programming languages, compilers. You probably know some of her work on compilers. She's a co-author of The Dragon Book, which is the definitive text on compilers. And so I will, I think that, you know, all of that speaks for itself. So together with my co-host Joyce, I would like to welcome Professor Lam to Michigan CSE for her distinguished lecture on the Open Virtual Assistant Initiative. Thank you very much for your kind words. So maybe I should share the screen. So, well, first of all, thank you very much for inviting me. It is great to visit even though this is virtual and one day we get to see each other in person soon again. But what I'm going to talk about today is our work on the Open Virtual Assistant. And to start, I just want to make some adjustment here. And to start, I want to talk about how exciting this is for computer science researchers today. And that is because the computers are going to get a new interface. This is the first time since many years ago when we have the workstations and the personal computers where we acquire the mouse and the bitmap display. That was that many years ago. And now it is a new interface and it is the human interface. This is the interface that we use and now we get to a chance to use that with the computers. And this is all made possible because of natural language processing and machine learning. And this is really exciting. It changes many things. It changes how we interact with the computers. There are three major ways. The first here is that when we ask for information on the computers, we no longer just think about keywords. We can just ask it like you would ask a teacher. You just say, here is what I want to know with all the qualifications or the things that you care about. You just say that in English. How wonderful would that be? The second thing is that if you look at all the programs that we use, it's going to acquire yet another modality. Okay, it's not necessarily replacing it, but now you can interact by voice. And before long, the younger people would just think that that is the normal way in which you interact. Interacting by speech is so much better than just software, than the menu-driven approach. Because now you're not talking about a limited set of choices. You can just express yourself and there is a dialogue and ongoing back and forth between you and the computer. It is so much more fluid. And finally, what is really exciting is that there is the third interface, which is the programming interface. In the past, we all have to be educated, formally writing, learning programming languages to get the computers to do what you want. Just imagine now everybody can automate their tasks without having to hire a developer. I'm not talking about very fancy codes, but things that you do on a regular basis, you may be able to automate it by yourself. And this opens up the use of computers for a lot of less profitable applications in a sense. I have talked to teachers and they said, I wish I have software that does X, Y and Z and so forth like that. But imagine that we can now just use natural language and have the computers perform tasks on your behalf. This will completely change what the computers can do for humankind. So this is very, very exciting in general when you see the voice interface. The voice interface is going to be embedded in one of these most interesting applications that we have shown up in the last few years, and this is the virtual assistant. So instead of you going and visiting all the different websites, searching for things, looking up apps, you just talk to one entity, the virtual assistant. So you can, you know, instead of remembering all the passwords, the virtual assistants can keep all your credentials. You have now a uniform way to talk to all the different, all these different resources. And what happened now is that the virtual assistant has a holistic understanding of what you do. All right, imagine the assistant can look at your calendar.  And it might just say, can I arrange a zoom meeting. And it says a zoom meeting and can I order you salmon from Tamron and this from for your mom and we can have a virtual dinner together. And they say, okay, because this is because the assistant knows this is the kind of things that I like to do on a Friday evening, for example. Okay, so things will take on a very different feel when you don't have to think about search engines and purchasing all in different places because you can do everything for you. So what we, we know that there are a lot of really big companies are very influential today, and we can imagine the virtual assistant when it matures is really a combination of Google, Amazon, Facebook, all combined. Okay, this is that powerful. And what is concerning is that we're seeing an emerging duopoly. Alexa has 70% of the US markets today. And Google Assistant takes up pretty much the rest of it between the two of them they are way over 90%. I'm talking about the speaker, the smart speaker assistant. These assistant don't just let you shop at Amazon or search in Google. They are creating a voice web. Alexa has 100,000 third party skills, 60,000 compatible iot devices. Okay, so you are now accessing the iot and the website through assistant, like you used to do, you know, like what we normally do is we access is we access web pages through the browser. So the assistant have these are open and open platforms, but it's open but it is also proprietary. Okay, you cannot confuse the two it makes a huge difference. Today they can say I'm open to everybody. And tomorrow they can change their mind. And because they have the full control you are using them it's just like the Apple device, you know, Apple charges 30% for all the purchases made on the mobile devices. Can you imagine Microsoft saying oh if you use the operating system I'm going to charge you charge the company's 30% for everything that is spent. It is just in, in, you know, it is, it is a lot of power that these companies have. It becomes kind of like the voice. This is the voice web, and this is your interface to the digital services. Then this is the, the assistant becomes an intermediary between you and all the businesses and they have control over where you are buying your groceries, where you're talking about everything because why do I really care, right, the assistant can serve it to you. So for example, imagine Kroger's or Walmart, you know, it's like today, they are open, you're allowed to sell through them. Tomorrow they can say, Oh, I'm going to take a cut, or I'm just not going to let you in. Alright, this is the part that they have because it's their system. It also has a ramification on the society because if you, if you don't have to open competition, innovation is going to suffer. And if they amass a lot of information about many, many people, they have a lot of power over how people think how people can communicate with each other and all the information that is being shared as we see lately in, you know, what happens with Facebook, for example. And on the other hand, you know, voice is, and in the meantime, you know, voice is very, very pervasive. It is very important. What does it mean that all the voice technology is being delivered through these couple of companies. What about the low resource languages, languages that are, you know, they're not as much purchasing power that is associated with certain languages are these companies going to support them. What about the nonprofit causes who is going to build those interfaces for these people. So there are a lot of issues that can come up when you have a single a couple of companies that are controlling the voice interface. So our goal here with the open virtual assistant initiative is to advance the voice technology make it available in the open domain. This is still early stage and we think that this is absolutely the right time to get going on this. So just talk about technology. I really, we really want to let user to have an alternative, which is to have an assistant that honors privacy that they can use. It's a little bit like Firefox, right. Today you have Firefox, you can at least there is a, there is an alternative, for example, to Chrome. The prep, you know, some of us are using Firefox, but the fact that there is alternative also keeps Chrome honest. Okay, there are things that they wouldn't have done if there is no alternative. So this is very, very important. So that is our overall overall goal. All right. We just have a little problem. And that is, we don't have any real users. We know that natural language processing means that we have to have training samples. How are we going to get training samples if we don't have real users, this is the advantage of Google and Amazon. And on top of that, we don't have billions of dollars. Alexa has 10,000 employees, and many of them are annotating the data, they take the natural language sentences, they annotate it this is what it means and then you use this to train the neural network. We don't have any of these things and a lot of people are just writing us off. It's like, you're not going to make it. I've been struggling with this problem. I mean, I've been working on this for five years in the first few years, we really are struggling, and then we realized some very important aspects about this annotation based machine burning. Annotations are just not good enough. Right. Alexa Google they have been added for a few years and the NLP is still really limited. Okay. It is much better than before, but we still have a long way to go. And the reason here is it's the natural language sentences. These are, these are not natural phenomenons. These are sentences that we engineer every sentence that I put up that I make right now is something that I engineer, I use to describe what I'm thinking right now and it is not just a matter of understanding what everybody like to talk about. You have to understand a new sentence. Okay, you have to understand what I am saying because this is a new sentence that I've never put together before, and nobody else said that before. All right, so it is not just a playing the frequency games like what are the most common things that people ask about, because that will not help to serve everybody's needs. And language is compositional. That means that there are exponential number of combos in the sentence. And when you put them together in a dialogue there are exponential paths. So how many dialogues can you possibly annotate. There is a very famous open benchmark called multi was it just a multi multi domain transactional data set you go there and you pretend you're buying restaurant buying plane tickets reserving restaurants and so forth all those things that we cannot do today. As a matter of fact. Okay, so that's a multi was it's an open data data set. And it has been annotated, but it has been re annotated twice. Amazon spent money re annotating it because the first annotations were deemed to be too incorrect. Google came around said that that's still not good enough, let's re annotate it again. And we analyze when we analyze the errors, there are about 30% of the dialogues were incorrectly annotate it is lacking in consistency. And that you feed the feed the neural network with a 30% error in annotation, it is not going to do a very good job. Okay, you're confusing it. Limitations have a lot of limitations. And maybe we can handle some of the most common things, but seriously, can you put the entire web up by having Amazon and Google annotating all the sentences there are lots of things out there on the web. And there are also many, many languages. Okay, so this kind of reminds us a little bit like Wikipedia. Okay, you really need a lot of people to put that information up in order to create the largest knowledge base and creating creating this largest voice interface is, I don't actually think it is doable by just a couple of companies. So, so we have a have we decided that we really need to take a different approach. I cannot make it with just applying the standard paradigm, we changing it. We do not send annotate, but we mostly synthesize. So what we realize is that first of all, I'm not trying to do natural language understanding between two humans. It is between a human and a computer. And the computer's capabilities are limited. We know what the computer can do. What it can do is to get, you know, is to issue queries to knowledge basis, run API's and then in the best case scenario, they can run programs. Okay, they can, everything they can do can be represented by a set of programs with these primitive operations. So what we can do now is to look at what the computers can do and say we are going to cover what the computers, computers know, not what people are asking today, what are the most popular questions, but we say here is the database. These are the questions that they can answer, and we synthesize accordingly and we synthesize primitive operations, we synthesize compound sentences, putting things together. And when we synthesize them, they are cut correct by construction. All right, I don't have an annotation problem at all. Right. But you can say that synthesis is always just sent, you know, it is still artificial. So what we propose is that you just throw in a few shot of real data to prevent overfitting. That's our overall strategy, because we can't, we just cannot afford this annotation style. It's also which I discussed is limited by itself. The second idea here is that we encapsulate our know-how in tools. We are not going to build it ourselves. What we're going to do is to take everything we know and make it available in tools so that other people can use this to build the interfaces. So the tools that we have today allows the individual to say, here is what the computer knows. This is the database. This is a schema of the database. And I will also show you some representative data. And they say, generate me a dialogue agent. Okay. Given the schema, we automatically go through these tools, we just synthesize tons and tons of perfectly annotated training data. And we use this to train a neural network. This is a semantic neural network. What it does, it takes natural language and then translate it into a formal representation. We have created this language called thing talk. It's for representing what the virtual system do. It is, you can think of it as a DSL domain specific language for virtual assistants. It's an executable program. You can have a, we have a compiler. It absolutely just take that and executes it. Okay, there is no intermediate representations, because what we found is like let the neural semantic parser figure out what the representation is. The advantage of this is that now you can go into other natural languages. They all have the same representation. We're not trying to capture the, the, the structure of the natural language at all. We kind of go backwards, starting with what the computer can do. And we just do it into a translation from natural language to code. So you know that, you know, I've been doing work on compilers. I kind of look at it as nothing but a compiler. It is a compiler for the highest level programming language, because we're translating natural language into, into a lower level code. Okay. So that sounds fine. But the question is, how do you get good enough training data. And that's time it has for quite a number of years is like, as I said, we started working on it and by the third year is when we kind of figured out how to go about it. And what we do here is a combination. We have templates that we created to generate a large collection of basic sentences. The important thing here is that these templates are domain independent. I don't know what I am saying, but this is what the constructs open. And you supply, so the users domain information provides us with the words for the domains. We put them, we throw them in, we generate sentences, we synthesize sentences. Many of them are very clunky. And we take that, and then we generate paraphrases, so that it will smooth out as normal language based on that particular domain you want to introduce domain specific terminology, which is a sentence by sentence. Now there is previous work on this and we were, and previous work uses human paraphrasers, and they will rewrite the clunky sentence into natural language sounding sentence. And what we have done is to take that to a next level, we automated it. And we observe that there are a lot of very, very good pre trained language models up there. All right.  And you cannot even distinguish it from real life from human written language sentences or human written stories. But the problem with these pre trained language models is that they don't really know what it is talking about they know the correlations of work, work of words. They say this is equivalent to that or this is similar to that. It knows a lot about all the relative, you know the relationships between words, but they are not grounded. They don't know what they are saying. So what we are doing here is that we generate the sentences these awkward sentences but grounded in the semantics because the semantics given by think talk. And then we use the pre trained networks to find all the equivalent sentences that we can get and add that in as the training data. So that's how we can generate a good data set. So we encapsulate these things in tools, and then the third step in our strategy is to scale, and is that we have to enable reuse. Right. It is not me building up one domain at a time. And the reuse is, it's all just what we have been doing computer science, we want to standardize representation so we put you know we have been working on think talk, we want to propose this as a standard. If everybody annotates their semantics using think talk, then we can share. Right. And on top of that we open source all the tools or the training data or the models. If we created a skill for music and for Spotify, you can take the same set of languages and then you apply it to another, another music skill.  So reuse is how computer science has been taking care of the issue of software engineering productivity, and we do the same thing. It's like, imagine that we didn't have Java, where you have, you know, and all the tools for web servers that are built using the Java library, we have so many useful libraries and so forth. We're going to do the same here. We need to standardize on the languages and tools. And our goal here is to empower the 20 million plus voice interface developers out there. I picked that number because there are 20 million plus web, web developers up there today. Okay, they are doing website designs and serving that. And what we see here is that there is a need to interface to just about everything online. There will be imagine 20 million plus developers, and these are not people who are going to learn AI, and we don't want these people to annotate, they don't, you know, that's not even enough people if you stick with the annotation regime. All right, but so this is a very tool based approach. So that's the high levels. Let's get into the more details, the technical details. Oh, before I do that, I want to show you a little bit of preview of the results to give you a sense of where we are now. So what we did is that we take a very, very popular domain, which is restaurants. And we have, we saw crowdsource a bunch of com commands in, in particular, they, in particular, we are not looking just for the popular commands, we ask people to come up with more of the compound, more complex questions. So for example, if I want to ask for a Spanish restaurant that is open at 10pm in Palo Alto. Okay, that is a compound sentence that includes three different fields. The cuisine, the time, and the location. But as you can see here, you link them up in one sentence and it just is very, very common, very, very smooth. And we take those questions, and we test it out on all the top popular assistance, and we see that we actually beat them all in this, in this experiment. And what I show on your on the right here are some of these examples and you can see that these are reasonable sentences like show me restaurants rated at least four stars with at least 100 reviews because we all know that reviews with very few ratings with very few reviews are totally, you know, reliable. Okay, and as you can see here, these are all the examples that Jimmy can do, and only a small number of these questions are actually answered by these commercial assistance. I see a couple of hands up maybe I will. This is a good time to answer some questions. Hi, you might be covering this later. But I just want to make sure I understand what the neural models doing so the training data is natural language and then, like, and then natural language to think top code. And then the model is learning how to basically transform natural language into think top code. Is that correct. Yes. Okay, thank you. And then to answer the questions you just execute the thing top code or answer, you know, if you open the garage door it will just open the curve you execute it it will open your garage door. I see. I see. Okay, thanks. Okay. There is another hand. I have a similar but not quite the same question. So you talked about other annotate annotation based learning algorithms not having considered the structure of natural language. But I didn't quite understand how you incorporated the structure of natural language into your algorithm. Well, this is what I'm going to talk about next. If you still don't understand it at the end of this part. Let me know. Okay, thank you very much. So this is just the preliminary. And let's get into the details to start I want to talk about think top what what is think talk. We said that the interfaces, the voice interfaces are good for queries for dialogues to queries to knowledge dialogues to pro software, as well as automation I'm going to show you an example of each one of them. So, for example, I say I like a Spanish restaurant in Palo Alto, and you see that that's the query that we that that's in think talk. The thing talk is basic as far as queries are concerned, they are roughly equivalent to SQL. But we have a different semantics. It is because we know we're going to synthesize a lot of questions we want a language that it is easy to compose. And secondly, we know that we're going to translate from natural language we also want the, the target to be an easy target for neural networks. So it has been tuned for those two purposes. Okay, and what about into dialogues as interfaces. So instead of just mapping the question into a query. We also capture the result of the query so that we can communicate it back to the users, and the state of one sentence carries to the future sentences. So when you say please book a table for two. I know that you are booking that particular restaurant that I am proposing. Okay, so now you can see that this is a representation that captures the semantics of an entire dialogue. For automation. Here's an example, when I leave the house turn off the lights. And this is the construct we have we say we can monitor the GPS, when the location is not home, then you set the power of the light bulb to off. All right. So these are API's, and we have implementation that executes this code for you as you can see, this is an event driven programs. You know, it's, it's a pretty, you know, it's not an easiest program in the world from a computer science point of view, but now we are saying that in English. All right. And what I want to point out here is that all our tools are based on grammars. It is not fixed to think any particular thing talk language. These is the grammar that we found and we have tested it and tuned it so that it is easy to synthesize to you. But now everybody else can add to the thing talk to capture more information. So for example, we have access control in natural language, and we take the automation sentence and we add the person who are allowed to do these operations. We throw them in and we have a very good active, we have access control a remote request of operations. And we just change the grammar and the tools will automatically run. Okay. And this is what I mean by I want to encourage we a lot of people working together to build up the, the semantics of what the, what the assistant can understand. Now let's take a look at the training data, it's all about synthesizing the right training data. As we mentioned, as I mentioned before, you start by having the developers provide the database schemas and representative data. And you. So for example, this is a schema from schema.org schema.org is a standard schema that has been defined and a lot of websites have meta information that puts, you know that they put all that information using schema.org in their metadata. And the reason why they are doing this is that they want to, they want to support search engines, the search engines using this information can help people locate a restaurant, find out what its phone number is, and so forth. So what we're saying that is that we can scale by working on the standard representations so that we can now be a voice agent for all the websites out there that use this metadata. So if we connect these two, I think more and more people will put more information into the schema.org. And by creating one assist one voice interface, you can serve all the different websites in the same category so this is the standard power of, you know, the power of standardization. So as you can see here here is a restaurant and for example there is a cuisine and so forth. Right. So the next step here is that we notice that even though we are talking about cuisine there are, you know, it's just, for example, Spanish, right. There are many ways of just talking about cuisine. You can, in many different parts of speech, you can say, what is a restaurant that serve a Japanese food, or we can use it as an adjective what is a Japanese restaurant I don't even talk about the word cuisine I don't want to talk about I mentioned the word food. And there are many different ways of expressing the same field in different parts of speech. So what we do here is that we go to the pre trade networks. We ask them for paraphrases. And when we see that they find, you know, they represent cuisine in different parts of speech, we record that verbs, adjectives and so forth. So if we have the basics for each field, then the next thing we do is that we put them into sentences using grammar templates. So for example, we have a gram template that says which blah serves, which blah is the table name verb, which is going to be pulled up from the, the, you know, I'm looking for a verb you can look at the field and look at the part of speech and say the service is the word. The value is Chinese, and then food is just how you what these are the words that we learned from using the pre chain networks to figure out what the primitives are. So we have templates that turn them into four sentences. And we have templates that give you compound sentences. We understand, for example, comparisons of types. So it's a greater than or slower than or heavier than it is all based on types. So the templates are now based on parts of speech and types and not on the domain information. So now you can substitute the domain and we can generate a lot of sentences for each language. So for English, we have 800 domain 800 templates. You may say that's a big number, but you may do it once and for all. Okay, instead of asking for thousands and thousands of annotations, we came up with these templates we're now generating many, many basic sentences. But they're not good enough because these are very clunky sentences because they're not domain specific. The next thing is that we use the pre train networks to get paraphrases to turn them into more, more fluent sentences. So, and I'm going to expand on this because this is not so obvious how you get good domain specific paraphrases, but this is how we generate a lot of lot more sentences. The next thing here is that we throw it and we want to generate dialogues. So we have a dope, again domain independent models, and we just generate thousands and thousands of dialogues you give me a table of restaurants, I talk restaurants. Okay, you tell me you give me a table of basketball, I talk basketball. Okay, and this is how we are going to get the scale. Finally, you want to have other languages. We base we rely on neural machine translators to generate languages in, you know, sentences and other language as languages and we train a semantic parser in that native language. We don't translate back and forth at the edge. We have native semantic parsers. So that's the full, the full tool chain that we have created. So, as I mentioned the domain specific paraphrases are not so easy to get the reason. So the basic idea is that there are the, you can take a sentence, and then you can use a neural model like a seek to seek to generate an equivalent sentence or the paraphrase of the original sentence. And there are existing general purpose paraphrasing data sets that you can use. So you can just do that, and then you add a level of fine tuning and then you can generate paraphrases for what we need. So here, they are just paraphrases based on the neural model, and the sentences may not be correct. And they may not be so good. So for example, here are some, some things we pulled out from our experiment, search some cafeteria that I that have greater star, than three and do not have smoking. As you can see these sentence are very, very clunky, because this is synthesized based on words that we pull out. And when we send it through the paraphraser, it says search for a restaurant that has more than three stars that sounds good, and doesn't spoke. And that is not working. Okay, so this is not a good sentence. And here is another one find restaurants close to my home. As you can imagine the paraphrase can say five restaurants near me, but that's not identical because I may not be home. And then, for example, search for people who are employed by Stanford. These are various questions that you can get by tuning, turning up the temperature. The very greedy one looks good point three looks good. And then it starts doing something different find people at Stanford that's not good. You know, currently employed you can argue maybe it's good maybe it's not good. But the point here is that when you generate these extra training data, you can actually get it wrong. And if you train a neural network was wrong data you don't have a good parts semantic parser. So what do we do so we use a self training. So what we do is that we start with a synthetic data we train a semantic parser the first version. And we take the synthetic data we send it through the paraphraser. And we get noisy paraphrasers for paraphrases. So we take the paraphrases send it to semantic parser again to get the logical form. And then we compare the logical form and see if they are correct. If the paraphrase gives you the same logical form, according to the semantic parser, we take the sentence when we throw it back in and use it for paraphrasing. And then you generate the next version of the semantic parser, and you can repeat, and this is called this is an auto paraphraser. And what you note here is that the sentences that I throw in our sentences that are already recognized. But by throwing them in, it's going to stretch the semantic parser to include more sentences that are close to the ones that are being accepted through this process, and it will grow the number of paraphrases that can be accepted. So this is how we do that. And this is a, and what we, in this experiment, we use a BERT encoder LSTM decoder. And let's see the result. We tried it on six domains. We're using schema.org databases. And these are some of the standard, you know, there are lots of websites of these different domains. Here are some statistics on that database, the 18 properties per domain. We come up with these automatic annotations. We synthesize sentences, about 300, we paraphrase another 300. Okay, so this gives us a lot more variety. And on top of that, the dev set and the test set are not, this is very important. These are not generated and paraphrased. If we take generated sentences and paraphrase them, we can get very, very good results while having a very poor semantic parser. It's absolutely important that you crowd source, that you get natural sounding sentences directly from humans. And this is what we use for the dev set as well as the test set. Let's take a look at the result. So we tried it on all the different domains. And we ran some ablation studies. And what it shows is that if I just generate sentences using templates, it's not so good. And once we throw in the auto field, the few annotators, you know, finding new field, different parts of speech, different ways of saying the same thing in different parts of speech. It increases it because when we do the template generation, we have a lot more variety. Now we are close to 60 on average. We talked about a paraphrase or automatic paraphrase. If we don't do any filtering, the result is actually worse. Right. But when we do the self training and paraphrasing, it brings it all the way up. Everything is above 60%. And what is actually interesting here is to compare this with a auto with a manual annotator and paraphrase data set. And we see that there is only a 6% gap. So this automation takes us a long way to getting the performance and here, all without any human annotations. So, and I'm just going to briefly talk about the other approaches, the other parts of the tool chains. And when we come when we try to do multi-lingual, we really do not want to do everything that we have done for English for every single language.  We don't know the other languages. And it's a lot of work. And so we generate the training data using machine translators. But one thing that is very, very important to know is that when you transport, you know, when you port it to a different language, you also have to port it to the terminology, the entities off that different language. So in America, you may be talking about burgers, but in a burger king and so forth actually is pretty international. But if you are in other countries, you want to talk about their local dishes, their local restaurants and so forth. So what we did is we put, we put, we built a novel neural aligner that allows us to substitute the entities from English to local entities, and that makes a huge difference. This is absolutely necessary in order to do this internet internationalization. So here's the results. We talked about restaurants before the same data set. And we show that across all the 10 different languages, the results look pretty good. And these are all tested with human translated test data. Okay, this is not translated using the computer. So for Genie, our experiment is a few shot training, which is that we tick synthesized synthesized translated aligned data, and we throw in a few manually translated sentences. And we compare with the state of the art, and you see that there is a pretty good difference. So the result with this, with this approach, we are now talking about just doing this multilingual question answering dial answering agent in one day. And what we needed to do primarily is to hire professional translators to translate the depth set the test set as well as you know as well as the few shot training data. That means that it is relatively cost effective and time effective. I see a hand up. Maybe I can take a question now. Yes, I had one question regarding your previous slide. You said that this you actually the translators are going to translate the entities in English to entities in the other languages. Right. Ah, Yeah, and for instance, I'm from Iran, and I know that burger is very different from DJ kebab. So this doesn't really serves and preserves the correctness of the translation, because it seems that some errors will be generated in this. Yeah, I can get into the details more. It is, I kind of, let me show you this and then it will probably be very clear. So what we do is that we take the English sentence. And whoops, I need to play. Okay, you can see it. No, I can't. You cannot see my window. Oh, I think let me make sure that it is shared. Yeah, so the whole idea is that we really need to substitute with local entities. As you mentioned, whereas we make the translation here is an example of English sentence and a thing talk, we put it in. And what we actually wanted in the end is really not talk about burger in Italy. And instead of this place called woodland pond, we want to refer to it, refer to a local place like Venetia. Okay, so we are totally want you really want to substitute the terms with local terms. And the way we do this is that first we went through the translator. And as you can, as you can. As you pointed out, the way the burgers and woodland and places are translated through a translator is, is, is literal. And, and they do not go, they do not look like the original English words. All right. And so what we do is that we have a neural liner that says the word burger corresponds to hamburger woodland pond is now this thing that says place by the lake or something by the lake by the woods. Okay, and we straighten that out because we you know this doesn't even match the logical form that we have because that's not a place name that is understood in in Italian. All right. But once we identify those locations, then we just go and substitute back the original terms so we can see that that now the Italian sentence referred to burger referred to woodland pond because these are, you know, the the parameters the entities. And then we do the substitution, and then we put in the right, the right words, the words that we want the place that we want. And this is how we change the sentence. I see that makes sense. Thank you. Thank you very much for the question. So, so that's how we got these results. And, and it is very important that we lower the cost of translating it to other languages, especially low resource languages. So, so that's what we do with multilingual and for dialogues. Again, you know, we have a domain independent template. And we have these English sentences that correspond to what the user says when what we say, and these are all blanked out for the domain. So we substitute into the domain information. And now we have many, many more sentences and we go through the paraphrase serves. And that means that we have even more natural sounding sentences. And this is what we do the training for. And we did an experiment on zero shot chance for learning. What it means is that we take the standard benchmarks. And once you get the result for attraction, we withdraw all the data on on attraction, and we just substitute it with synthetic training data. Okay, and then we train with the rest of the training data for the other domains. And what we, and here is the experimental results. The first result is done by who at all and they use a trade model and you can see that except for taxi everything is below or around 20%. So our first our approach, using trade, just jumps it up by a substantial amount. And when we train change the training model, I mean the the neural model using some tea which has better pre train network information, it brings it up quite a bit. And on top of that you throw in a few shot. Again, it goes up. And it is actually interesting to compare this with a with the full data set. And you see that we are within really reasonable bound, even though we have not a single bit of training data for each of these domains. So if it is zero shot we are like at 70% of real data training for 1% few shots, we are at 80%. So we drastically reduce the cost of annotations. So in summary here we have built this. This is the tool chain that we have created. So we talked about privacy. And let me just explain how we managed to provide the privacy. And as we have an open source virtual assistant, the assistant actually can be its open source you can run it at home. And so, all your IOT information can just be be made available to your assistant at home and it never leaves the house. The neural, the language translator right now has to be in the cloud. And the way we. So, so for example, if I ask what is my checking account balance, the translation is done in the cloud, but now you have a think talk representation that is executed locally. Nobody knows what you're checking account balances, except for your own personal device. But, you know, not everybody wants to run their own local device. What we want to support is to give people choice. We want to do in a way that is exactly like email. You know, like Hillary Clinton you want to run it at home you can run it at home. But if for other people they may want to use the cloud but what is important is that it is open source. And then you have a choice of vendors. Right. And having a choice of men vendors create competition. And the assistants are interoperable. And in, and we all know that we're, you know, because of sharing we all give up our data to Facebook. And so what we have done is that we also created sharing, we allow the assistance to communicate with each other. We use think talk and remote think talk as the protocol. You can share all kinds of things that you can do with a virtual assistant with people that you want without letting them have your passwords. Okay, so I think it's just about time that we improve our sharing capabilities. In the last few minutes, I want to talk about how we make open assistance real and I want to make this real. But this is really hard. Right. I mean, we, we write papers, they are not product ready. And we write these, you know, we put it out there. And typically what happens is that the large companies will take all the good ideas put them into their proprietary products and a lot of ideas on how to protect privacy they just never get implemented. So we need to change that and we're very, very fortunate we have convinced one foundation so far, which is offered piece loan to put money to, you know, to invest in the technology for consumers. So they put up the first, but first bit of money for supporting the productization so we are able to hire a few engineers, and we hope to raise more money in order to make this dream a reality. So we are putting a lot of effort in building Thinkpedia. Thinkpedia is the opening encyclopedia of ways interfaces to IOTs and web services. So it, you know, Alexa has a third party repository. Google has its own. And what we're saying is that we need an open one. We actually have built this for three years now but this is, but we have now better technology and we also have engineers to make this real. And what we are working on is that by the second half of this year, we're going to have skills for the top 10 domains are reasonably good. One of the hardest things are the IOT devices. The good news is that we're partnering with Home Assistant. It is a open source community. They have already 100,000 users that connected to over 1000 IOT devices. And they have meta interfaces for the different specific IOT devices. So we provide the voice to the meta commands and now we are connected to thousands of these devices. And they have already been, and they have already adopted us and we have been shipped with Home Assistant as their voice interface, but it wasn't very good, frankly. But now we are making it, making, we are improving it with all the resources and the new technology we have. And so by the middle of 2021, we hope that this is something that we can use and we have a starter community of developers. And we have been working on the music. We partner with Smart News for News, Yelp for Restaurants, and so financial data with Alpha Vantage. We are using the technology to do Q&A for Wikidata, and then there are the table stakes like weather, jokes, timers and alarms. We plan to put out in the middle of this year, and there will be open source virtual assistant that can work with these Wikipedia devices. And this is the first step. All these things are built by us. Then the next step here is to hope that we can attract skill contributors. And the value proposition here is that if you put your third-party skill in Alexa, all they are going to do is to do intent classification for you. There's not a whole lot of AI behind any third-party skill that they provide, but we will give you a better AI technology. And once you build a skill, you can put it on Alexa, put it on Google, and you can even put it on your own companies, phone service, and web services. And we believe that there is a reason for these companies to use this technology because they do need voice interfaces. They may not know, they may not believe that we can make it yet as an independent platform, but I believe that this is useful to them nonetheless. So in summary here, we talked about three sets of open source software, the tools, Thinkpedia and the assistant itself. And what we see is that we are putting the first complete system together with some early adopters with the help of home assistant. But we really want to create a community, academia, companies, open source communities and users. And we want to invite you to join us. There is a lot of very, very interesting research opportunities. Once we get a few real users, the kinds of research you can do will take on a different level. So here is the information, go visit our website, join us and collaborate.


Hi, I'm Ian Schnuer, executive director of the Financial Modeling Institute, the FMI, the world's only financial modeling accreditation body. I am thrilled to announce the partnership between FMI and D. Brown Consulting. To support the career advancement of African finance professionals, D. Brown Consulting has launched the Financial Modeling Academy, the FMA. At the FMA, candidates will receive financial modeling training to prepare them to sit the FMI's Advanced Financial Modeler Accreditation Exam, that's the AFM exam. The Financial Modeling Academy also teaches deep modeling skills to prepare candidates for a career in finance. The Financial Modeling Institute offers the world's leading accreditation in financial modeling, and the AFM is our flagship program. In order to attain the AFM accreditation, you need to demonstrate on the exam that you can build an integrated three-statement model of a company from scratch in under four hours. The Financial Modeling Institute is the only accreditation body that offers rigorous, proctored financial modeling exams. Attaining the AFM accreditation means you have validated your tremendous proficiency in financial modeling. We look forward to welcoming you into this unique program and helping you gain the skills and the accreditation that you need to access financial opportunities globally. We are very excited about this partnership between FMI and D. Brown Consulting, and I hope you take the opportunity to develop and demonstrate your financial modeling skills. I look forward to welcoming you in our growing global community.


Welcome to the complete collection of Bill Russell's greatest stories told by NBA players and legends. This week Bill Russell passed away at the age of 88, and with many of us unable to watch him during his NBA playing days, this video serves to showcase Bill Russell's greatest stories on the court, but also as a civil rights activist off the court. I won't keep you waiting with this one, if you enjoyed this video though I'd really appreciate it if you could leave a like, subscribe if you are new for NBA content and without further ado, welcome to the complete collection of Bill Russell's greatest stories. I'm here tonight to recognize and salute the legacy, the heroism and the courage of Bill Russell. But before we talk about that, we have to recognize something else, that Bill Russell is the greatest winner that sports has ever known. Bill the competitor will live on forever, and the things that he did on the court enabled his team to win 11 world championships.  And my mother would say, well, we shouldn't feed Bill so well the next time. You know, whatever, I'm like, yeah, well, I'm gonna do something about that. Look at what his peers thought of him, and how they sort of genuflected before him. I'm talking about Jerry West. The greatest winner that ever played in basketball, Bill Russell. And Elgin Baylor. Bill Russell won 11, I think a 13 year championship every year, and the year he didn't win a championship, he was hurt. Julius Irving. He talked to me about his life and his feelings about things, and became very much a father figure at that moment for me and probably one of the most special people in my life. And the list goes, Kareem. That extraordinary contribution to the sport is one thing, and then his extraordinary contribution as an activist is something else.  Bill Russell won 11. And magic. Everybody knows without your rebound, there's no fast break. And Charles Barkley. What he's done for civil rights in his country is unmatched. Thank you. It's everything you've done for me and my family. Bill Russell, the man is someone who stood up for the rights and dignity of all men. He marched with King. He stood by Ali. When a restaurant refused to serve the Black Celtics, he refused to play in the scheduled game. 55 consecutive wins in college, back-to-back NCAA championships, 11 NBA championships and 13 seasons played. An Olympic gold medal in the Presidential Medal of Freedom. Speaking of legendary Celtics, Bill Russell. What he's done for civil rights in his country is unmatched. Him and Ali will always be, to me, my heroes as far as that goes. It's easy to be a social justice guy now when you've got $100 million, you're making $30, $40 million a year. But those guys did all the heavy lifting back in the day.  And being drafted by him was a biggest honor. Honestly, he's the advocate basketball player. So he always would have an opportunity to teach you about life. He was my first coach. You got him fired. No, I didn't. Reggie Thiers got him fired. I had to sit next to him. He had a role. I had to sit next to him on every plane ride and every bus ride. And every pregame meal. So I have a million Bill Russell stories because of that. So I sat next to him and heard every single Boston Celtic story. So what they thought was going to be rookie Hazen actually turned out to be the best moments of my life. Would you tell a story about when he was telling about guys sitting on the bus? And so I heard every story you could imagine. Well, we had a terrible team, Shaq. So Bill Russell just came in the bus and he said, Kenny, I tried to move. He said, no, Kenny, you got to sit next to me. I said, coach, I can't move back there once.  You want to be a champion one day? Sit next to me. I'm the only one out there who knows how to win. My favorite Bill Russell story was when he got stopped by the cops one time. And the cops started giving him a lecture, he said, hey, hey, hey, give me a ticket or a lecture. Can't give me both. Hey, give me both. I was sitting there in the rookie of the car going. I'm going to jail. I'm going to jail. I think that you're going to win at least two or three championships here, OK? And if you don't, but I see you play the way you should play. I'll share one of mine with you. This is a genuine friendship thing I'm talking about.  You have no idea how proud I am of you. I couldn't be any more proud of you than I am of my own. Kendrick Perkins joining us now as well. A little 2008 Celtics reunion in the house, nodding and smiling. Well, first of all, Malika, hey, look, don't have me crying on TV. I remember Bill Russell, you know, to have KG attention the way he just did, KG was actually listening. KG was actually embracing the moment and hearing them out. KG actually almost got emotional as Bill Russell was talking to him. And you don't get that from Kevin Garnett a lot. Like Kevin Garnett, you don't get his undivided attention. Not Doc has, but I will say this. When you think about the great Bill Russell, I remember Doc used to always say, I rather perk you take charges than the block shots because all you're doing is blocking shots and they're going out of bounds. The great thing about Bill Russell is when he defended and he blocked shots, he kept them in play. It started fast breaks. It actually started to where he kept, they got the position back. And when you talk about winning at the highest level, he is one of the most unselfish greats to ever play the game in my eyes. The true definition of what Doc used to always preach, losing yourself in the team. Michael, we can spend time discussing Bill Russell's basketball legacy in a moment, but I'd rather ask you, what is Bill Russell's impact on American society? Wow. That's a great place to start. Every generation has a responsibility to teach the next. One thing I learned about Bill was how he drew inspiration from his grandfather, whose model was, a man has to draw a line within himself that he will not allow any man to cross. Bill learned from that and added his own model. You disrespect the line, you disrespect me.  Bill's dissatisfaction with the injustices of the world never changed. His hope for a better tomorrow, Bill has led the way that inspires all of us, the next generation, to follow his lead. I was a huge fan of NBA history and I read all about that whole era and Bill Russell and the all-star game where the players decided to strike in two hours before the game for better rights. It wasn't just about civil rights, it was about union rights and player rights, but it all sort of blended together. I'd like to use the word and think of the thought of activism even more than pioneering and he was both. We talk about civil rights and activism and pioneering and Bill Russell endured. He endured a lot. At the time he was dealing with a lot of racial issues in Boston and stories, people were throwing things at him during the game and yelling the craziest things from the court. I said, well, how did you deal with it? He said, well, I internalized it and I felt like the best thing that I could do is use that as fuel as opposed to just simply having an emotional outburst to them. I decided to use that as energy to enhance my performance, right? So even though it seems as if he's not, he's tuning out the noise, he isn't. In today's game, if some of the things that were being said that he was telling me, I mean there would be riots, there would be fights, but some of the intolerance that they had to put up with, some of the comments that was made towards them, that was a predominantly white audience, white fan base, and to break through and basically have to turn the other cheek because they had to sacrifice so we could sit on this couch, make millions of dollars, talk about basketball, have a great life. We can't forget the people that laid the ground for us because a lot of those players didn't have a voice. And a lot of times I feel that we're speaking for a lot of those players in the 40s, 50s and 60s and that's what I'm so appreciative of and every time I see them, I let them know that, that thank you, thank you for everything you've done for me and my family. I would say the most important and impactful or one of the most important impactful athletes that ever lived. He's certainly the most impactful basketball player and I don't say that because he won at every level, which he did. I don't say that because he won 11 championships in 13 seasons, which will never, ever, ever be duplicated. I say it because in the face of all that was so much to lose, John. Bill Russell may have been in the toughest position of all because he was working and living and trying to play for a team in Boston. A place that was certainly not friendly, not hospitable, usually, to African American men. The Boston Celtics was the first NBA team to start five black players. Now they were rewarded for being so progressive. With titles. Yes. But the city did not appreciate it. That means that there was really no connection for me between the fans in Boston and the Boston Celtics. This man was a fearless civil rights activist at a time when our country was really very racially divided, particularly by the way in the city he was playing in. Playing a game in 1961 and getting the key to the city and then they go back to the hotel and they're not able to stay there with the other white players and Bill Russell goes and wakes up the mayor in the middle of the night at two or three in the morning and gives him back the key to the city. We have to realize that everyone that hates another person, another race is not the same and I think Red Arbuck did a great job of showing Bill Russell that. You know Bill Russell has always been my favorite pioneer. He told me a story one time after the game they all went to a hotel and of course they wouldn't let the black players there and Red Arbuck had enough and he took the whole team and he moved to a different hotel. So you know I just think that people like Red Arbuck showed us that everybody is not the same. We know about 1961 when the Celtics were playing an exhibition game in Kentucky. And I went to a restaurant down the street and the preparer asked me where I wanted. I said this is a restaurant, isn't it? I'd like to get something to eat. He told me no. We played a game and we were booked to play a game in Lexington, Kentucky. Casey went down to the restaurant in the hotel we were staying and they would not serve it. I decided and the other guys decided to go with me not to play. So I told Red we were leaving. I said because it's important to me that everybody everywhere knows that the black players decided they have to stand up for themselves. And that was it? Yeah. And two of his black teammates were refused service in the coffee shop of the hotel and Bill Russell went up to Red Arbuck's room and knocked on the door and said Red we're going home. And they didn't play in that exhibition game. And if you can imagine this John there were actually people that wrote. Although one of the newspapers in St. Louis wrote that the black players embarrassed the Celtics and they should be suspended and fined. You know we talked about pioneers and we talked about Bill Russell who was my first coach who drafted me in the NBA and I'll never forget the story because he was an eye opener in terms of cultural experience for me. We were talking and debating about European plans and flux into the NBA. I was watching him you know looking at a script figuring out what was going on. I'm like what's that he's like oh some foreign players I'm looking at. I said coach did you tell me there's not a great basketball player in Alabama that you got to go all the way to Europe to get this guy. And all of these guys coming in and then I was saying well you know how they're taking jobs they're not really that good. They're not true big men. All of those things that we were talking about. He stopped me and he said Kenny. He stopped me he said grab me and he said never say that. Coach what are you talking about? He said as a black man you could never talk about not inclusion. And I was like whoa he took a small basketball moment as a jest and taught me a life moment about inclusion and about life. So in there learning like everyone talked about these basketball lessons you learn life lessons from guys like that that went through things that you would never think you'd ever go through. And when you talk to those older black guys who did all the heavy lifting of us they always talked about the only time they felt comfortable when they were around their teammates because they couldn't eat together. They stayed at different hotels but one thing about sports I've never been on a team that had any racial issue because like you love your teammates. You're like I never said old dude's white, old dude's Jewish, dude's Hispanic. Like when you play sports you're like... Can he play? Can he play? Sports really has changed to me society because it's been an example of hey those two guys can hang along. I really think young kids take from those lessons. I really do. Elgin Baylor here on The Rich Eyes & Show. The best player you've ever played against? Against? Well you know it depends on what you're looking for in the player. If I had to pick one player to start a franchise you take a guess who you think I would take? Of all time in the history of the game? Of all the players in the history of the game. Wilt? No. Russell. You're right. Bill Russell. Bill Russell won 11, I think a 13 year championship every year and the year he didn't win a championship he was hurt. I mean the guy was just incredible. And think about it see people look at the score, look at statistics. The guy is incredible. I mean the way you know the way he played you know and people were just shocked they said Russell and they're just looking at his stats and see the guy there with 12, 13 points again. Sure. But looking at intangible things hey he's going to save you 30, 40 points. Well I think the most underrated part of Bill Russell that people don't talk about enough that he was a gigantic mentor to four or five generations of NBA superstars. Obi Bryant spent hours on the phone talking with Bill Russell. When I was still playing and searching for ways to get better I decided to give Bill a call. What I've learned since is even more than I imagined possible. I learned how Bill was so masterful at winning. You cold called him. I did. Why? Well I wanted to learn. I think the most important thing that we can do when you want to learn is actually pick up the phone and ask and so I picked up the phone and I reached out to Bill Russell that began a great relationship of mentorship and he had a lot of great stories to share with me. When Bill Russell was a player the great Will Chamberlain you know could score a million points that Bill Russell admitted to Kobe that often when the game was in hand he would allow Will to score. My game plan was to get him in a rhythm that he's comfortable with. Now that may require he gets 45 points but if we play it right he gets 45 we win the game. So Will would feel better about the matchup they had just had and walk away thinking well I did my part. I mean amazing things. I can't tell you the amount of NBA superstars that have shared the impact that Bill Russell has had on their life. People know Bill for his cutthroat nature and he definitely has a cutthroat nature but what they don't know is that Bill won by relying on his team to do what they do best. For example, he said when I played I could handle the ball. He knew he could handle the ball with the best of him but he knew that Cousy was the best of him at handling the ball. I could score the ball and I could pass the ball but it was really important in order for us to win I had to look at my teammates and say okay there's other players here that do that better and what I do best is defend and rebound so I'm going to completely focus on that. That is the definition of true leadership. Vision, trust and sacrifice. That's what lies at the heart of a champion and a small sliver of what's inside the mind of this basketball genius. When I watched you guys play it was always built up like you guys were such bitter rivals not only on the court but off the court too but then I read somewhere that every time you played against each other you would pick one another up from the airport. You know, you would have thought if you saw the two of them before a game that he was talking to his loving brother. When I was a rookie, Welp was a freshman at Kansas. We played a Sunday game in St. Louis and he drove to St. Louis from Kansas and looked me up and we spent the day together on Saturday. And you'd never met him before that? No. And nobody knew that we knew each other. I cannot begin to describe to you as an opponent how good he was. But I had to show up. The other guy that you're most associated with and always have been something you almost don't even want to talk about anymore but I'm going to bring it up anyway is Bill Russell. People also theorize that the reason there has been a kind of thin feud between the two of you, if at all is because that Russell was always considered the more sensitive ball player, the more intelligent ball player, the more team-oriented ball player and ultimately, of course, the more successful ball player. The media portrayed the duel of the two giants as a struggle between good versus evil. Russell, the selfless team player against Chamberlain, the greedy individual star. The rest made those guys out to be bad guys in terms of hating each other but I think those guys liked each other and I think they liked to play against each other not because of Conrad or because of the experience of both of them. Chamberlain reveled in the spotlight unlike Russell who chose to maintain his privacy. You become the ultimate winner and the ultimate teammate and willed as this guy who had all these records but we all know winning is the only thing that matters. Did you feel like he was starting to resent you at all? No, I don't think so. I don't think so at all. I don't think either one of us ever resented the other guy. And they always chose to believe that you cannot be as good as we both were, apparently, doing our thing and really like you. I had to be envious of him. The only thing I was envious of him was the fact was that people would from time to time call me a loser. You always need the best to bring the best out in you so I was always fortunate to have to know that I was going to play against William Felt and Russell. Winning is only important if you beat the best. My friend Wilt, Norman Chamberlain was far away the best player I've ever played against. Wilt Chamberlain was indeed the most dominant player ever but I was taught from guys like Isaiah and guys like Mike that it's all about the championships. So Bill Russell has 11 championships that will never ever be matched again. I think the closest to that is Mike with six so I probably have to go with Bill Russell on that. Interesting, the 60s, the Celtics was really one of the first teams to integrate. So everyone in my family and around the community, we were Celtics fans because they had all the brothers. And you know, my father and my mother was like, hey you got a rupee of Celtics because, you know, they're breaking down racial barriers and everything else and even though we were in Chicago, the Celtics were the team they told us to root for and so when we went outside and played basketball and you ran a fast break, they would call it a Boston. How about this one? 1987 Eastern Conference Finals, John, Isaiah Thomas is about to beat the Boston Celtics defending champs on their own floor. Instead, throws the ball away. We also remember that, Bird stole the ball. When I threw the ball away in game five, 1987, we're playing the Celtics, got a chance to beat them. The next day, he's sitting at home in his house. He doesn't want to talk to anybody.  People are calling. And the phones ringing, people are trying to call me. I'm not even thinking about going to practice. And his wife said, you got to take this call and Isaiah said, no, I don't want to talk to anybody. She said, no, no, you got to take this call. She goes, no, I think you should take this call. I go, no, really. And she looked at me as only she could. And she said, no, I think you should take this call. And I got on the phone and it was Bill Russell. It was Bill Russell saying, young man, get back on the horse and start riding again. Get back out there. It's a tough loss, but keep going. And prove to everybody who you are. And, you know, he said, I see you back here game seven. That was it. And here's Bill Russell. It was a legend for the Celtics. And that's, that's how he and I became friends. And over the years, we've always stayed in touch. He's always been a mentor, always given great advice. And, you know, we just had a day for the NBA. Now that he's gone, but he touched so many people and he touched so many of us doing his time here. You know, when you sit here and you say, well, you know, in 1995, they voted me as like the best or the most popular athlete, you know, was ironic that I'm the youngest of the three. So in essence, it's all related on who is watching now. If you ask 20 years from now, I'm pretty sure LeBron may beat me. There was a debate and Michael Jordan answered because I think LeBron is the best player now, but five is more than one the last time I look. So I'd have to take Kobe. Kobe or LeBron? In terms of dominance of the game of basketball at this stage, it is LeBron. Championship-wise, Kobe-Bron. Between the two, that'd be a tough choice to me, but five beats one. Where does championships rank as far as how good a player is or their accomplishments? Well, you know, I've tried to stay away from that. And the reason is I've never asked a player to play against a ghost. Past, present, and future. We could only play against the guy that showed up while we were playing. I won six championships. Bill Russell won 11. Does that make Bill Russell better than me and make me better than him? No, because we play at different eras. So when you try to equate who's the greatest in all time, it's an unfair parallel. It's an unfair choice. I would never say one is greater than the other. That's me, and that's my opinion. You, Michael Jordan, and Will, do you have to pick one person? Who do you pick? Well, uh... You pick Bill Russell. That's right. That's right. For the reason, he would never distort you deep, and I play games in my peak. Where? I get six shots. I get closed out in the whole front court. The way I play, my team wins. Oh, I love it! I was distorting the offense! That was the most profound statement I've probably heard in a long time.  That was one of the smartest teams I've ever heard a basketball player say. I mean, that was awesome. What was it like playing the Boston Celtics in the 60s and early 70s? Well, Michael, you know, you played them a lot of times, and there weren't that many teams in the league, and they were battles. I mean, I respected them. What made them so good?  You could not, if you didn't see him, you better look to pass the ball. And so that took away a lot of layups, a lot of really close attempts to the basket, and he always seemed to, he was an amazing shot blocker. Michael, he was like the plastic man. He could drive pictures of him in the air, his body is like flat, and he reaches over, and your enormous runner really fast, wasn't a great shooter, but he could do everything that a team needed to make up for the mistakes that these other players made, particularly defensively, and his rebounding ability allowed them to run up and down the court, and that's why they scored a lot of points. By 1969, Russell had secretly decided he was done. This would be his final season. I knew before the season started it was going to be my last year, but I didn't tell anyone. Because throughout my career, I was always very careful not to say things that would change my teammates' For the sixth time that decade, Boston faced the Lakers in the finals. This time, the creaky Celtics were heavy underdogs. The Lakers didn't just have Jerry West and Elgin Baylor, now they had Will Chamberlain too. Fittingly, it came down to a Game 7. The Lakers had home court advantage, the Celtics had Russell, somebody who had never lost a Game 7. But Game 7 at home gave the Lakers owner Jack Kent Cook to celebrate, albeit a little too early. I want to set the game up for you so maybe you understand my attitude. I knew what he was going to say. Even though Russell owned them all decade, the Lakers were so confident before that Game 7, they stuck 10,000 balloons in the rafters to be dropped right after they won. I saw the owner of the Lakers coming down to stay us early before anybody had come into the gym along with the commissioner. He gave me a voice that said, Sam, I want to give you one of these sheets. It says, when the Lakers win the championship, the USC band would play happy days here again. And they had these seats behind the baskets where the USC band would sit. And the balloons would come out of the rafters and there must have been thousands of balloons. I had never seen, I didn't look up. Before the game, Russell found a copy of those celebration plans. So I took this sheet back to Bill Russell. I said, Russell, I'd like for you to see this sheet. And I want you to read it. The first reaction was to laugh, you know. I said, look at this. And guys, this is what I know. There's not a remote possibility or chance for the Lakers to win this game. I said, if this team played the best game, any team in history's franchise play, they would still lose. I said, it's impossible for them to win tonight. But then he says, we're going to change our game plan. We're going to run, run, run. We're going to run them into the floor. The key is defense rebound and outlet passing. I am the best that ever did that. We're going to run them out of the gym. Russell shoved it through. We're 17 points ahead going into the last quarter. Within about the last 30 minutes, they started making a comeback that you could not believe. They were scoring on every position we were missing. The Celtics miraculously celebrated one last title. And no, they never dropped those balloons. At the end of that game, you got interviewed by Jack Twine, and you literally couldn't speak. I'm here with Bill Russell. Bill, this must have been a great win for you. Exactly. I'm going to say what's in your mind right now, Bill. It must have been a great win. This is such a great bunch of guys, you know, and it's just been so fabulous the way they played for me. It sounds all funny, you know, talking like that. I told these guys before the game, I don't care what happens. I would trade you guys for the guys in the world. And I hope you guys enjoyed this video. If you did, please help me out by leaving a like, subscribe if you are new, and hit that notification to date with all the new episodes. Here are two new episodes that you may enjoy and I'll see you next time.


As a former catcher, Girardi was probably flummoxed by the fact this guy didn't do the most basic thing of catching, which is catch 16 pass balls. And the Yankees wouldn't let him do it because I still believe there were a lot of you could talk about the post game press conference and being more even keeled and not being a tension convention and the dugout in the clubhouse. I think the way he went after Sanchez was a major reason why he was let go. So they obviously have gone moved mountains to try to make sure this kid is in the right head in the right frame of mind. Now look at Hicks last year. Did Hicks cost them games last year? We were having how many conversations? Why is he out there? He can't hit and they kept putting them out there, putting them out there. It may have cost them games and now we're seeing that you know what they were thinking about the future because they thought Hicks was really good and they turned out to be right. Maybe they're doing the same thing with Sanchez. Leave him out there. If we lose the wild card game because of a pass ball, we'll live with it because he's going to be that much better next year and he's going to be our next golden boy. I don't necessarily agree, but tell me I'm wrong. Well, we'll see how it plays out because if they start him in a wild card game and he ends up losing the game because he can't catch the ball, they've got a lot of questions to answer. And again, we put in to get Brian on the air today, Brian Cashman, and we were told he's not doing any interviews this week and that's cool. He has every right to do it, but I mean at some point I'm sure Boone will have to answer to this when he has his press conference today. How long can you continue to go with Gary because his offense is not justifying, you know, holding your nose at the defense. The sabermetric world we've been called on it to. They don't care about one of you seven. He's got 16 home runs. They extrapolate out out to a full season. A guy's hitting 30 something. Yeah, but you know, Austin Romine has 10. Austin Romine's not the automatic out he used to be. Austin Romine, in their eyes, is not a second away from being Gary Sanchez from 2016 and Gary Sanchez from last year. They're banking on that all of a sudden it's going to click and he's going to be Mike Piazza, that he's going to be hitting the cover off the ball and you'll live with the defensive liabilities because he's going to win you games with the bat. That hasn't seen it. You haven't seen it yet, but they believe it's going to happen. But the other reason I disagree and I agree with you and I kind of disagree with the way they're handling it is, I think if you leave him out there where he cost you a playoff game, I think that does more damage than sitting him down. And remember that the thing that turned into a meme when one of the sacks had Eli had and he was sitting there on the turf. Yeah, yeah. And he kind of had this daze look on his face. Take a look everybody at the ball that got past Gary that Zach, by the way, Zach Britton is locked in that Zach Britton threw for the strikeout. He had that same look that Eli had. We had a close-up of him on yes. I think that this kid right now doesn't know what's going on. Not a bad guy, wants to be a great player, wants to be a great catcher, but it's not happening. Now they have 12 games remaining. Would I continue to run him out there? Absolutely I would. And you know why? Because I want him to become the Gary Sanchez that he can be offensively and then you can justify playing him. But if he's going to hit 187 and not hit the ball out, you can't justify taking a chance at a one game and go home game.


Hey everyone, welcome to today's day trading recap for Thursday, October 21st, green day today, plus $525.  I had a winner in Microsoft for 128. And then on the runners four for four, Disney was basically a scratch plus 14 Tesla nice winner 640 a b and b 160 and zoom plus 205s. Let's check these out. I'll show you what we did. Let's start with BA. BA started coming down had a little volume pop here. So it got long here and then tried to add right down here and it just kept going. So I ended up cutting out. We got another volume pop here right at yesterday's open. So I re entered and got some of it back took that out of it. So took a loss overall in BA. And then Microsoft Microsoft was a quick one. I didn't get much follow through had this push up to the pivot with with nice volume got short right here and caught got out of half my position here for a nice profit. And then when it bounced up acting like it was going to be strong again, I just cut out of the last half, which ended up being a good idea because it did trail higher on me. So plus 128 in Microsoft and then Disney Dizzy Disney didn't get much follow through at a Disney. So I ended up scratching the trade came down to yesterday's high got long right here caught this bounce got out of half my position. And then when it kind of started rolling over, got out of the other half for a scratch plus 14 overall. Tesla was a nice one. In fact, it could have been much nicer. I did not get in on this initial big spike in volume here, which is a tip, typical area just didn't give much pullback. So I never even entered. But that would have been a nice one. I waited for this pullback here got long and caught this run up here. So plus just couple contracts on that plus 640 air bnb air bnb had this big volume that came immediately came down. And so when it bounced back up to unchanged, I got short, pushed up a little more on me, but then finally flushed down the bid ask spreads and air bnb on the options were super wide today. So when it got down here, I just ended up cutting out and getting out of it and booked a small profit of 160.  So when it pulled back, got long here and got out of some on this push held and got out of the rest right up here. So nice trade in zoom, zoom. So plus 525 on the day will be streaming live tomorrow on Friday. See everybody then


Hey PsychDigoers, do you remember the last time you felt sad, anxious, or scared that you were somehow off? You're not alone, even if it may feel like it. The National Alliance on Mental Illness, or NAMI, reports sometimes a funk or rough patch can be a sign of something much deeper. This video represents 10 mental health symptoms you shouldn't ignore. Before we begin, we need to warn you that this video may be triggering for some. If you feel triggered by this video, please take care of yourself, even if it means clicking off this video. With that being said, let's talk about the 10 mental health symptoms you should not ignore. 1. You're having feelings of sadness or depression that last longer than two weeks. All people experience sadness at one time or another. However, most normal feelings of sadness get better with time. The American Psychiatric Association reported abnormally intense sadness lasting two weeks could be a sign of depression. The National Institute of Mental Health stated, not being able to snap out of it or sadness being heavier than normal should be taken seriously. 2. You're having extreme mood swings and don't know why. You find that your mood seems to switch super quickly or randomly. Tlamp and others studied how often the average person's mood shifts on any given day. According to the National Center of Biotechnology Information and CVI, this study showed people tend to experience positive emotions, such as joy or love, more often than negative ones. But it's totally normal for someone's emotions to change daily. However, the National Alliance for the Mentally Ill, NAMI, warns especially dramatic mood swings that cause huge changes in your behavior and energy can be a sign of bipolar disorder. Studies such as the American Academy of Pediatrics say the more red flag signs are experiencing sadness, anger, or feelings of excessive euphoria for most of the day, especially if it has nothing to do with what's going on in your life. 3.   According to the National Institute for Mental Health, it may be a sign of an anxiety disorder. Other signs of anxiety disorders include having problems relaxing, insomnia, racing thoughts, unexplained aches and pains, or being easily irritated. The Anxiety and Depression Association of America suggests that if you've ever felt this way on most days for at least six months, you should consider talking to a mental health professional about your symptoms. 4. You've started withdrawing from others or isolating yourself. Taking time away from others can be positive for your mental health and physical health. But what if you constantly make excuses to avoid socializing? Mental health pros such as Raymond Starr Jr. and Howard Dubowitz say avoiding people or events you used to enjoy can be a sign of mental illness such as depression, anxiety, or a psychotic disorder. 5. You may have noticed you're having delusions or hallucinations. Kumari and others describe the two aspects of psychosis, hallucinations and delusions as a sense of perception or belief that creates a lot of urgency in the person experiencing it, despite evidence the perception or belief isn't real. The National Alliance on Mental Illness reports up to 100,000 people in the US will experience psychosis every year. Nami further stated three out of every 100 people will have a psychotic episode as a symptom of a physical or mental illness such as schizophrenia or schizoaffective disorder in their lifetimes. 6. You're having more and more difficulty dealing with normal life situations. Have you ever gone through a time in your life when nothing seemed to go right and you just couldn't deal? The Australian Department of Health stated a rough patch that you just can't get past one that hurts your ability to function for more than two weeks to two months may be a sign of depression or anxiety. A major sign that what you're experiencing is more than just a rough patch is when you have a hard time functioning in normal situations for several weeks, according to Mental Health America. 7. You're sleeping too much or too little. The Harvard University School of Medicine believes that 10 to 18% of the general population has problems sleeping. However, Harvard's med school stated either sleeping too much or too little is two or three times more common in people who suffer from a mental illness. The Harvard Med School stated about 50% of people who live with ADHD, depression, anxiety, or bipolar disorder suffer from insomnia or hypersomnia, which means sleeping too much. 8. You've started abusing drugs or alcohol. According to MentalHealth.gov, 1 in 4 or 25% of people who live with the symptoms of a mental illness use alcohol or drugs to self-medicate things like anger, anxiety, or mania. Students believe individuals who suffer from depression, anxiety disorders, personality disorders, or schizophrenia are more likely to use drugs and alcohol as a coping mechanism than people diagnosed with other mental illnesses. 9.  Do you ever feel anger that's almost impossible to control or way too much for the situation? If so, your anger may be warning you about your stress levels, unresolved grief, or your anxiety. MentalHealth.gov reported extreme anger outbursts can also be a sign of unresolved trauma, bipolar disorder, alcohol abuse, obsessive-compulsive disorder, intermittent explosive disorder, or depression. If you wonder if your anger is healthy, experts recommend first recognizing your physical and emotional signs of anger. 10. You've started thinking of self-harm or suicide. The MentalHealth.gov reported up to 10% of people under the age of 30 have thought about self-harm or suicide. There are many reasons people consider self-harm or suicide, such as abuse or neglect at home, a major loss, trauma, or a catastrophe that you can't control or avoid. Brodvik 2018 reported approximately 90% of people who have completed suicide also suffered from a severe mental illness, such as bipolar disorder or depression. If you find yourself thinking about self-harm or suicide, we would very much like you to reach out to a mental health professional and get the help that you need and deserve. If you can relate to any of these symptoms on this list, you're definitely not alone, nor are you bad for feeling this way, but we want you to take care of yourself, which means reaching out to a mental health professional if you've been experiencing any of the symptoms on this list. As always, any information provided here is for educational purposes only. If you need mental health counseling or treatment, please contact your insurance company, Medical College's Student Counseling Clinic, or your county crisis line. Keep watching the Psych2Go channel for more information on mental illness and mental health, and remember, help is out there. We'll see you in the next video.