Hi, my name is Christopher Michael Stewart and I'm going to be presenting today on data science, human language technology and natural language processing. Okay, so just a little bit about me. I did a PhD in French linguistics at the University of Illinois, then worked for a few years as an assistant professor of French linguistics at the University of Texas at Arlington in the modern languages department. I then left that job and worked as a voice engineer in the text to speech research and development division at Nuance, which is now called Serence in Belgium. And while I was there I developed the Siri voices, took on a major tech face and major customer facing role, and had a lot of good beer in Belgium. Then I left that job and got two jobs as a senior data scientist first I worked at Tata consultancy services in in Arkansas, developing a machine learning pipeline for Walmart, and then worked at a tiny startup called narrative wave, doing time series modeling of things data. I now work at Google as a computational linguist. And my team works on detecting policy violations at scale best practices for crowdsourcing tasks and I build automated data quality reports before I go much further I'll mention that I am not speaking on behalf of Google here and only talking about my experience and background. Okay, so I've set myself up the impossible task of in 45 minutes talking about data science human language technology and natural language processing, each one of these is an entire careers worth of information and there's no way I could possibly do much justice to all three of these fields in 45 minutes. So what I've aimed to do then is hit the sweet spot between these three fields so what do these have in common. And there I'm going to tell you that what I think that they have in common is something that I'm here calling predicted probability. So these are all fields that are all about modeling statistical modeling in particular machine learning and trying to trying to figure out something about make some sort of inference about the future and use data to predict that so what does that look like. So here's I have predicted probability and then ml so machine learning or deep learning deal and read more deep learning than machine learning. So under that I put the short names for these and I'm going to be using DS HLT in an LP, and I put some examples of what's called conditional probabilities. So a probability is denoted by P open parentheses, the probability of some event right so the probability of a fair of a fair coin coming up heads is point five. We also condition probabilities on things. So in data science if you're working as a data scientist at for instance at Tesla or somewhere like that. You might be interested in predicting what is the probability that an object going that's that appears in front of a car will be a human. So you're, you know, it's a predicted so will be a human. And it's probabilistic what is the probability this object is a human, given data coming from thousands of sensors historical data metadata, all sorts of stuff, right.  So just in predicting what is the probability that a segment is good for a text to speech synthesis context, given a language models spectral specifications duration focus, you know narrow or broad focus part of speech etc. If you're working in LP you might be interested in predicting what is the probability that a tweet a tweet will have hate speech, given metadata about the tweet, the actual content the language in the tweet, the time, it was tweeted the place it was tweeted who retweeted it how many retweets did they get all the sort of information. So this is this is the sort of world that you will live in and so I'm going to start this talk, talking about probability and predicted probability and machine learning. And this is probably very different from the other talks that you've been that you've been to because I'm really going to start by the part that you might not know very much about and work back in the second half of the talk to an actual language context. Let's start with predicted with with talking about probability and machine learning and things like that. So if you don't like that, I'm sorry, but it is the common thread here amongst these three subject areas. So, before I start though before I go to in depth here I just want to emphasize that if you have never taken statistics or have no idea what machine learning is, or are afraid of equations or you know whatever the case may be. So if you don't worry, just try and sort of absorb the ideas here. So like for instance, this conditional probability event. You can imagine what is the probability that's going to rain on on Friday this coming Friday right. You might have some sort of naive probability but if I tell you that it's Monday, you might have a very different prediction than if I tell you it's Thursday, closer to the time we have a better idea of what's going to happen right. So try to engage with the ideas, even if it's, you know, sort of foreign and seems odd or hard to get, just try and get some of the ideas don't worry if you don't get all of the particulars. Okay. Think to yourself, have you ever taken a class in statistics. I can only see a few people raise your hand if you have a statistics costs. Okay, if you haven't had a statistics cost no worries, not a problem. But the interesting part for our purposes about about statistical modeling is that so statistical modeling is when you take a set of data and you try and build a model to do something and you know the famous quote all models are wrong but some are useful. So the point is to try and figure out something with this model right. In social sciences we build models, we collect data right language data in this case for instance. And we build a statistical model mathematical model that of that data. And we assume that this model tells us something about what is going on in the data. So if you have a regression model, you will have parameters in your regression model and it will tell you something about what, you know, what, what is the, how predictive is, you know, someone's language background of the language that they use or, you know, something like that. So we assume that these models reflect some sort of underlying process, because of that we're very interested in the parameters the parameters the important parts that's the interesting parts about the model like, you know, does someone's age tell you more than their L2 or you know whatever about some sort of linguistic behavior. Because we're interested in parameters work we prefer simpler models in this statistical modeling culture. And this is prevalent in causal research but not so much in engineering I forgot to mention that this, I'm taking this from a paper by a statistician named Leo Bremen called statistical modeling the two cultures and it's it's helpful for understanding how these two cultures differ. So that's data modeling if you've taken introduction to statistics you've talked about things like this, about t tests and a novos and regressions and logistic regressions and all that sort of stuff right. So there is a different cultural statistical modeling that Leo Bremen in this paper calls algorithmic modeling and algorithmic modeling we do not assume that the model that we build reflects some sort of underlying process there's no assumption that what comes out of the model is about reality. We're not really interested in that what we're interested instead in is prediction, we're interested in predicting what's what's going to happen with this data that we have in the past. Because of this we don't really care about the model being simple, because we're not really interested in what's going on in the model all the time we're more interested in prediction. So not afraid of black box models bottles that we don't really know what's going on necessarily in every tiny little part of the model. There's lots of training data and lots of computational power, typically. Now there is, if you've never heard of this algorithm modeling sort of world and deep learning and machine learning and all that sort of stuff. But you have taken a statistics cost there is an intermediate between these two as a field called statistical learning. And there is a book written by hasty and tips Ronnie called introduction to statistical learning that I really recommend here, if you've never read it. Okay, so let's talk a little bit about machine learning so machine learning great buzzword what does it mean. So in traditional in traditional programming you take input, you make a program and you get results up for instance, and a popular coding problem is to say, I'm right a function where you give this function input a number. The number is divisible by five you output fizz and then the number is divisible by seven you output buzz or something like that. So you have input and integer a program, this thing that says, you know, is this number divisible by five is this number divisible by seven and output something does something. Machine learning is a little bit different because we take input and results, and we put them into machine learning model, and what comes out of it is something like a program. So this isn't this and this it's important to know that this has nothing to do with language, you could be this is basic binary classification so you could be looking to classify. You know this is in this I have like this funny sort of toy example of is this a chihuahua or a muffin. But you know you could be looking to like predict is it going to rain or is it not going to rain. Is this person likely to be likely to click on this link or not click on this link or whatever any sort of binary classification. So for the purposes of this little demonstration we're going to talk about this funny toy problem in visual binary classification, which is is this picture chihuahua or muffin, which I took this picture from an article where you can see that actually chihuahuas and muffins are not always that easy to disambiguate, which is kind of scary. So we want to build a classifier with us what will we do we need labeled data. So we need an image one and image two and image three and image four, where we where we indicate is this thing actually a dog or a muffin or a muffin or dog or whatever. And we put that into a model and the program that comes out of it will just call for now dog or muffin. So we build a classifier and then we use it so we start with these this was called training data and these four instances here image 123 and four. We do a new an unseen image into this and use the classifier so tell me what I asked the classifier, not literally but we have the classifier predict is this a dog or a muffin. For instance, in one case it could say, I'm 95% sure that this is a dog. Right. We look at the expected results so if we if we put another image image six and it says look I'm 55% sure so not really all that sure, but 55% sure this is a dog. So we look at the expected results. Oh, it's a muffin. So I wasn't real the mission, the model wasn't real confident that it was a dog and that's good because it wasn't a dog was a muffin. So we can do this over and over again image seven image eight image seven 64% chance it's a dog image eight 92% chance that it's a muffin. We're wrong with image seven and we're right with image eight. And you can go through this over and over and over again. So this is kind of the basic intuition behind how this works and you can see that what we're interested here is predicting so I don't really care what's going on a dog or muffin. I don't really care that the model is looking for years in an image or, you know, how bright or dark the images or whatever it doesn't really matter. I'm really interested in how accurate the model is that predicting whether the image is a dog or a muffin. And just to reiterate, first of all, trigger warning the next slide does have equations so if you're scared of betas and X's and Y's and things like that. And Greek letters of all sorts of your eyes on the next slide. So in the data modeling culture we're most interested in understanding what the model tells us about the data generating process.  Right. So you're interested in the data generating process. All data goes in the model, and you're not really concerned with prediction. If you go to publish an article. You're never going to come to you and say, Hey, look, I found this other the speaker that you didn't talk to who has these characteristics, please put those characteristics in the model and tell me how predictive they are of this person and I'll tell you if you got it right or wrong. That will never happen because that's really not the point right. You're really interested in what's going on in the in the in the data generating process so an algorithmic. The data modeling culture were primarily concerned with prediction. So your priorities change. So in this, in this sort of world, some data could help you make better predictions and some data can in fact help you make worse predictions. You can end up in instances where you have not very many observations, but millions and millions and millions of variables like genetic arrays and genetic testing is a good instance of this. Sometimes you only have a few samples of a gene, but you know you have as many. I don't really know that much about genetics, but as many of the little, you know, pairs or whatever of gene do dads. It's sorry it's a long it's a it's late in the day I'm not very eloquent here but you get my point. So sometimes you have more observations than more variables than you do observations, and that might be a problem for your model so what might you want to do. If you were one of the person, one of the people who earlier said that they had had introduction to an introduction to statistical modeling class, you will be familiar with this data modeling equation y equals beta not as beta one x one beta two x two beta all the way to beta p xp. And if you've never seen this and this looks confusing and crazy. Don't worry about it. Imagine that you want to predict height given weight. Right, you if you have you'll have an x and a y axis on the x axis might be height and the y axis might be weight or vice versa doesn't really matter.  And I'm going to put you know this one point in here on the what on the y axis, and I want you to tell me what the x axis will be well that's what you have here right.  And the betas are the weights on those things and what you want to do obviously is you want to draw a line that minimizes the distance between all those points and that line. Right, it's called the line of best fit. So that's what this minimizing RSS you want to minimize the residual sum of squares. So let's say that you have so much data that you actually have a problem because some of those x's are not very useful so you want to be able to adjust these betas right. It's very simple this this whole thing is the exact same this is machine learning this is called Ridge regression. And the equation is the exact same except for you add this little penalty term here at the end. This says look don't let don't let my, my betas get to my squared betas get too big. And this this lambda parameters upside down why allows you to sort of turn a volume knob on all these betas right. It's so it's very simple it's a very simple thing and that that has been taken you from statistical modeling sort of the data modeling world to machine learning. Okay, now, obviously that's not really the state of the art the state of the art is something more like much more complicated algorithmic modeling. Here you have like a basic neural network. And this is what the state of the art is. So this is what the people who actually, you know, sort of build these production models at big companies this is what they do, which is, you know, it's just a one iteration of slight bit more complicated but my what I'm trying to impart here is that this you can you can start it if you understand ordinary least squares regression this top equation, you can easily go to statistical learning which is this next one just by understanding what's going on in the model. And then once you understand that with a little bit more work you can go to this algorithmic modeling world. So the intuitions here again are very simple you can have a very a model that doesn't do a good job of predicting it's just kind of makes random predictions right. Or you can have a model that's very in tune to this training data that really understands that the the all those chihuahua and muffin pictures that we showed to it. But if I show a new picture, it doesn't know it has no idea that it's a chihuahua or muffin. So you want some sort of sweet spot where you're not making completely random predictions, but your predictions aren't so tight so closely tuned to that training data that it doesn't generalize. You want to you want to find this minimum error here and that's this is referred to as the buying bias variance trade off. So we're going up our discussion of machine learning here how effective is modern machine learning well deep learning. Yeah, I put a few links here you're welcome to copy them down and find the articles. It's not always great. I mean, as you probably have experienced in life, but but it is quite good. There's a massive need for labeled data and who labels this data and how do we ensure that their labels are good. Now we're getting to our world linguists are actually pretty good at getting data from humans right and so linguists often work on this in industry. Okay, so this is, this is, you know, maybe more than you have sort of taken on in your explorations of statistics. So how can you start learning about machine learning well. One thing is to remember that all approaches, even the most complex deep mind model that you can possibly imagine has X's and Y's it has things you want to predict and things you already know. It has weights that go on the X's that predict the wise, and it has air the model is wrong, and you want to quantify how wrong the model is. So you want to remember that all these approaches have these ingredients. If you want to start learning about machine learning you can develop some, develop some statistical intuitions. Remember that that basic inferential statistics will serve you well going forward. Do your own analysis as much as possible make sure that you understand ideas like statistical assumptions normality is a normal test model assessment. So just go to stack overflow and enter in some R code that someone tells you builds a linear mixed effects model it's not that that will not serve you well. Read an introduction to statistical learning if you want to go beyond inferential statistics. If you're already there and you're looking for something more complex. Find data, Bayesian data analysis and if you want more complex than that, then you're in the wrong place because this talk is not for you. So that was the part about machine learning. Thank you for sticking with me through statistics. Let's talk now about about natural language processing. So now we're going to turn to language. So how are these modern, these machine learning models used in natural language processing. So this section is just a brief peek into the kinds of consideration that go into an LP pipeline. This is not state of the art in LP. This is like, you know, if you want to learn Python and you start and they say, Okay, you know define a variable that's a string, and now capitalize the string or you know whatever that's kind of this equivalent so I'm not I'm not proposing to you that this is like state of the art incredible in LP, you know knowing these things is not going to get your job but it is a peak into this world. Okay, so what, what is involved in natural language processing so this is just a sample sort of pipeline. Things you might want to do if you were building a natural language pipeline with two sentences Moscow has also Moscow also denounced what it described as the rise of quote nationalist and neo fascist sentiment in Ukraine's western areas where it said Russian speakers were being deprived of it is repeatedly expressed concern for the safety of Russian citizens in Ukraine. So the first thing that we might be interested in here is defining a sentence boundaries. Right. The sentence boundary you could very naive approach would be to say anytime you see a period that that's the end of a sentence that's going to be problematic. Right, because if we have something like AD, I don't some sort of an abbreviation or USA or something like that. You could have periods that are not sentence boundaries. So again, you're going to need to train a probabilistic model that tries to predict when sentence boundaries are, and you know sometimes be right and sometimes will be wrong. Okay, the next thing you might want to do is to divide all this this sentence up into tokens. Now what are tokens tokens or something like words, but importantly, they may not always align with what you think of as a word boundary. So for instance, one one interesting thing that I see right away here is this Ukraine. So Ukraine apostrophe s most people would say that's, that's a word, right. But for the purpose of tokenization, we might be interested in saying that this apostrophe s which indicates possession is a separate token. Okay, so there's, again, this is something that you're going to have to model probabilistically after this part of speech tagging. I have a speaker note here but I can see it but I think it's part of speech tagging the state of the art in English is something like 98% correct 90% accurate. So again, this is an instance where you would build a model and probabilistically predict what these parts of speech are. I'm sorry if you're a syntactician but this is, I'm just going to skip over this in the interest of time and move on to entity detection so going. So I've started at sort of a very low level tokenization, then part of speech tagging going up further syntactic parsing parsing and then even further up than that to entity detection so you can see that you want to be able to model the fact that some of these are sort of related to it. So, Moscow and it have some sort of relationship, etc. So this is called entity detection or named entity recognition, you might want to cluster them right so that Moscow, it are custard what and rise, Ukraine, etc, etc, etc. These are just the kinds of considerations that you would want to take into account if you're going to model if you're going to have a computational representation of what's going on in these two senses. So again, all of these are predicted using deep learning models and state of the art natural language processing. And if you're interested in deep learning models. If you're this guy at the bottom here, that's a visual representation of a deep learning model. This and subsequent steps are typically implemented by research scientists and computer engineers. It's not often the case that linguists, because we are not really trained in, you know, math and computer science and things like that are writing these models from scratch. And more, a lot of a lot of this used to be the case that hyper parameter tuning and things like that you had to do by hand but increasingly even that is is solved by the computer by just grid search and things like that. So this model will contribute domain knowledge to identify rules and identify areas for improvement. Lingus have a lot of obviously a lot of experience with, you know, what language should look like and so when the model gets something wrong are pretty good at saying hey I think that this is what the problem is. And once you have enough of those you can identify rules right you can say hey look it looks like the model is not great at dealing with possessives or you know whatever the case maybe. And to provide and linguists also work to provide annotated data that train these automated processes. So you might think oh wow this is all pretty impressive. So NLP is pretty much what people call a solved problem right. Well not necessarily. You probably recognize that you know a lot of, a lot of machine learning can kind of go wrong and do wonky things and the other day I was working on something and tried to get some sort of digital personal assistant to play a song and I said the name the name of the song in Spanish so like, please play the song. And to the thing and it says, here's what that is in Spanish or whatever and then just translated whatever I said in Spanish so why didn't it get it right. So there are some things that are pretty challenging for natural language processing so let's consider this passage really quickly. The police arrested Mayor John Smith yesterday. He was suspected of stealing garden gnomes the latest breach and create and a crime way of rocking the sleepy town of Springfield. So if we think about abstract representations of these sentences. We can start to see that there are things that are a little bit difficult right so those things look like this. The fact that we have co reference, right to entity so like Mayor John Smith and he. The police are tied to this, the town of Springfield, we have. If we look at this sentence ontologically. How do we want to represent something like Mayor John Smith. Is this an instance of a mayor whose name is john Smith or should it be john Smith and his occupation as mayor, what is the sort of preferable representation here in terms of salience. We have these event relations so the fact that we know as humans and language users that suspected comes before arrested, normally someone is suspected of something, and then they're arrested right but a computer doesn't know that you have to you have to sort of engineer that. These are inferences that require real world knowledge of criminal processes. Also the fact that there, there is something like subjectivity right that someone can be suspected of something that's sort of a subjective call that's a subjective. Consideration right and you know we we understand that but computers don't really understand that ditto or even for things like a crime wave that rocks. You know how do how do we represent the fact that this is all this is metaphorical usage, and that the town of Springfield is not actually sleepy. It doesn't it's not that it wants to take a nap. It's a way of saying that a town is calm and you know sort of be called ditto from the time. So yeah, these are challenges for for for modern natural language processing. Okay, so starting to wrap up here. There's all been a lot of information I'm sorry to sort of. I've gone through it relatively quickly. I don't know if we do questions. Is there a question period does anyone know I'm not done with these yet. But I guess we'll see at the end of this. I think there is right because you guys can put questions in the chat. In any case, I'm happy to go back through parts that might have been on the top but let's talk just briefly before that about what you can start doing now so let's say you're in a master's program and you think oh this NLP stuff seems kind of cool you know I've I've taken an introduction to Python and and I took statistics 101 or whatever what what should I do next. This should be yourself sorry. So the first thing is learn, you're going to do a lot of independent learning so there's this is not the sort of thing where you turn up and you know someone gives you a job and you know immediately you have a job for 50. There's a lot of sort of you have to invest a lot of your own time into learning things here. So learn how you learn best. That's the first step. You know, a lot I was in the office hours the other day and you know several people said, hey, you know I took, I'm taking an introduction to Python, and I'm sort of interested but it's boring, and it's hard to stick to. And I just don't really like it that much. And my advice was it will then find a project, you know figure out some sort of project that you're actually interested in like I'm interested in. I don't know like I studied Georgian or something and it has this very interesting syntactic property. And I mentioned I want to build something that can predict that or I wanted to suggest a better way for my favorite software package that handles to better model, you know, whatever it is, find a project and contribute to that project a lot of natural language processing software is open source so you can you can find the people that develop it. Take the code is called forking the code so you just make a copy of the code. Look at it change the part that you want and send the change back to the person who develops the code that's called a poll request and say look, I really admire your work and think it's very cool. But I found some I found something that I think can be improved. Please take a look and see what you think. How do you measure progress, you know, learn, you're going to have to present what you've done in the past and what you want to do in the future, and sort of give some sort of indication of the progress that you've made on your various projects. And that's going to be extremely important when you go into industry. This works really well for technical skills right you can say, I take an intermediate Python, and I, I have, you know, this change this change and this change that I made to the natural language toolkit package that are now in production or whatever so it works really well for technical skills. You have to do side projects so find problems like I mentioned and fix them. You know, it's not always the most glamorous and interesting work but guess what it's what you're going to be doing if you get a job on these fields. And so yeah find a problem and fix it. There's paid work so big tech companies for instance higher paid interns every summer. It's volunteer work. I mean, you know what I said about working on something like an LTK that's, that's free.  Maybe the most important of all of these things is to keep records of what you did. It's nice to tell people I know about statistics and I know about programming and I know about this I know about that, but it's much more convincing. If you can point to things you actually did. If you want to be able to point to things you actually did the way that you would do that, if you make a web page, right, a personal web page, you make a, what's called a repository a code repository, and you point from your web page to the repository and the repository has project one project to project four project five, and the web page does a beautiful job of explaining. Here's a project one does. Here's a project to does here's a project three does. And for each one of those projects, you can point to all the code that you wrote to do the project. So you really want to be able to prove, you know that you, you have whatever kinds of experience that you have. So it's really important to keep records really really really important to keep records. So one of the first chances to use new tools be sophisticated about data so this goes back to the slide where I said, look, make sure you understand the models that you're building. A lot of people build statistical models and have no idea what they mean. Don't be one of those people be sophisticated about data apply for an internship, like I said, there are a lot of companies in tech that hire interns to do. They have access speech synthesis and automatic speech recognition and natural language processing and all sorts of stuff like that. So apply for an internship automate your drudge work so you, you know, is it the case that you are working on an R script that is now 3000 lines long that has all of your dissertation research in it. So, stop, stop doing that, figure out ways to write functions and then call the functions and write unit tests to test your function so that you know that they're working as intended. Automate your drudge work and finally prove it make artifacts and understand impact so if I can just impart one thing from you from to you through this session. If you're interested in, in this sort of line of work, make sure that you can show people your work, make sure that's very, very important. It's nice to say I learned Python, it's a lot better to say, look at all these contributions that I have to nltk. It's very easy to sit there and sit through an introduction of Python costs. It's much more difficult to go and contribute to a software package. But one will show people that you know what you're talking about and others is kind of like, well, maybe they know something about Python. Maybe they don't who knows. So, I think that that's where I stopped here. But I thank you for your time and we have about 10 minutes left for questions. Hi, Chris. Thanks again. So I was just wondering for those of us who have taken the compositional semantics course. What kind of keywords how can we probably that into relevant language that you know people understand what we actually worked on. From a computational semantics course. Compositional semantics. Compositional semantics. Compositional. Oh, okay. Okay, that's okay. I think it's very related. It's like, you know, identification and everything. Well, well, so one thing I could I think that compositional semantics is about is ontologies, presumably you've worked on ontologies and embeddings and things like that. Okay, so that that's kind of the currency of the realm in natural language understanding. So natural language understanding is kind of the province of like the digital personal assistance and things like that. Right. So if I tell my whatever Google home or, or, you know, Alexa or whatever. I love my mom. Why don't we call her now. You know, you need it some sort of represent an ontology that says mom is referred to as her you know whatever however you want to envision it. So natural language understanding is a good and has the sort of added benefit of that it's it's a very hot field right now. They're hiring lots and lots and lots and lots of linguists to make lots and lots and lots and lots of ontologists. In other words, you can you can look for a zontology. Yeah, I wish I knew more about compositional semantics. Yeah, I think it's like very helpful for it. Translation into working with competition because there's, you know, you've landed lambda functions in order to like throughout how it's related to everything. I think it's great. Any other questions in the chat. First, would you suggest to put academic output together with side projects on our website. Yeah, for sure. Definitely. And yeah, there's so there's nothing wrong with with talking about what you've done in academia. So my first job outside of academia was in Texas Speech Research and Development, and the director of the order whatever that I was joining sort of said yes and me all your, you know, academic articles I'd love to read them and then I turned up for the interview and he said, Oh, thank you for coming. By the way, I didn't read any of your articles. So, you know, don't expect that people are going to read, you know, 750 page articles, because you're interviewing with them. They're not. But there's nothing wrong with putting the fact that you wrote an article that's in language or, you know, journalistic linguistics or whatever on your website. Okay, it certainly is impressive. I mean, points that you can, you know, you can make deliverables, but it's probably not realistic to expect everyone to read all of your articles. It's just not how industry works. Next question. Is it necessary to learn Python Python for NLP work. I've done some Java at beginner level. Can I use that. It's certainly not necessary to learn Python. Python is nice because it's very simple. It's a scripting language a high level scripting language so it has the benefit of being relatively easy to read compared to Java which I'll be honest. I look at Java and I think public class this public cost I have no idea what all that stuff means.  It's certainly not necessary to learn Python for NLP work. Right next question. In your experience, are there opportunities to work on these kinds of projects with the team strictly as a linguist without having coding knowledge. Yeah, I get this question all the time do I need to know how to code. And you know that the, the truth is, you don't need to know how to code, but if you are not willing to interact with. If you want to work with engineers so if you're going to work in tech, you're going to work on a team you're going to work with engineers, there are engineers and every engineers the ones like should do the stuff. They write the code that you know makes things work that people use. So if you're not willing to engage with the ideas and code and sort of throw your hands up, you're going to make a rod for your own back so you don't have to strictly speaking know how to code, but it is always going to help you, it will never, ever, ever hurt you to know how to write code. And generally speaking, you don't have to know it, but it will always help you. The next question asks, where would I go to look for side projects for linguistic annotation that might be a few hours per week. Yeah, so this is a great question. I mean I don't, I don't really know I've not done it but at the question the companies that I've worked at we hired we contracted with with vendors who sourced or who hired people who did the annotation so when I was at nuance I think that we worked with either was up in Butler Hill or is now up in Butler Hill so that's one lion bridge I know does this too. And there are a variety of companies that do this sort of thing. On the next person would like to know the name of the statistical learning book that you mentioned earlier in your presentation. Yeah, so the authors of that book are hasty and Tib Shrani, and the book is called introduction to statistical learning. And that's sort of the, the, the, the nice reader friendly part, there's also one called elements of statistical learning that is much more in depth. I think there's someone else added the chat of that. So yeah, introduction to statistical learning. And next question will linguists become more or less relevant in the future of NLP. This is a great question. I mean I don't I don't have a crystal ball and obviously don't really know. But I think that the, I think, as, as NLP ventures into more and more uncharted territory. They become more and more relevant, because linguists understand how humans use language right and that's kind of our, our superpower, and you know the more we get into difficult questions of, of language and wanting to model that the more linguists skills will linguistics, a linguistic skillset will be valuable. So my prediction is more, but it's a probabilistic thing. I don't really know, right. The last question that's in the chat at the moment is, I missed it. I'm a linguist and I'm learning data science how can I make sure that my new knowledge of data science makes me eligible for jobs in computational linguistics at companies like Google. How can I make sure that my knowledge, my new knowledge of data science makes me eligible well. I mean, to some extent, like this, this distinction that I've made here between natural language processing data science and human language, language technology is artificial. So you have data people whose job title is data scientists that work on language things and people that do human language technology who are engineers who don't really know anything about linguistics. So I guess what I'm trying to say is, if you are learning about building models and being statistical models and being sophisticated about using data, then there's no way that your, your work here can not make you competitive for jobs or not making more competitive more eligible for jobs in fields like computational linguistics at companies like Google. Next question. Any exciting projects you're working on right now. Any suggestions for YouTube channels or Twitch channels for watching coding live streams. So I can't really talk that much about what I'm working on now. It's highly proprietary. But yeah, I mean I can tell you that I work in the ads org which is probably the most profitable machine on the face of the earth right now. So that's kind of interesting. Any suggestions for YouTube channels. I don't know I'm old I read books and Twitch, I, I almost don't even know what that is so I can't kind of be really occur in. And we have a hand from way way live. I enjoy your talk very much so thank you for the talk. I'm wondering because you mentioned that you come from, you're doing jobs that are related to voice in the first place and then you switch to Google and other places that might deal with text as well. So I am a mathematician and I am wondering what are the, how is the job opportunities look like, how do they look like for speech scientists versus an natural language processing side of the linguistic opportunities. Well, so speech sciences is great because it gives you a lot of experience with kind of the more technical aspects of language. And yeah if you want to work in a field like Texas speech synthesis, you're working on that all the time or ASR automatic speech recognition, you're going to be working on that all the time so it's just, it's a little bit different kind, you know, sort of work. In terms of are there more robust jobs. That's kind of hard to say it's really really difficult to say which one has more jobs. So we have about two more questions to get through we are at time. The next question any introductory material you can remit recommend for anyone interested in learning about these topics. Yeah, that the book that I mentioned introduction to statistical learning is is a great first stop. There is a so that the software packages I mentioned the natural language toolkit in LTK is all open source and it has a book that's all online for free. It only demands your time and attention. So there is in addition, there are a number of good textbooks one written by a guy named Jacob Eisenstein who used to be at Georgia Tech and is now Google, who just came out with a new book on natural language processing and intro to natural English processing. I had to confess I don't know the title, but I'm sure if you look up Jacob Eisenstein book natural English processing will pop right up. There's a lot of nice introductory material, but keep in mind that it is it's not always written for you know I for people who are not technical.  Just try and wait through it you know do your best to understand what you can. And our last question. At least our last question right now how relevant is a graduate degree for NLP slash computational linguistics work in tech. Yeah, so I get this question, question a lot too. And so sort of the market reality is that is that it, you know, if if a company can afford to pay someone who has an undergrad degree the same salary, which one will they hire. Obviously they're going to hire the person with a graduate degree, right.  Almost invariably right. So, you know, don't forget that these, these fields obey market forces and so the, the applications within natural language processing human language technology now that that are hiring the people will be the ones are the most profitable. And if you want to. You know it's not the case that you need a graduate degree to work in an NLP or computational linguistics, but it is the case that you need a skill set that will allow you to work in it. And it is the case that there is unfortunately a glut of linguistics knowledge in the world, and you know not that many jobs. And so, you know, just keep that in mind. It's important to remember to be realistic about, you know, sort of the job. Great, I think those those were all the questions for you so Thank you for your time. Have a great day.


Transcript of an interview with Terry Smith regarding something known as the Highway Man Game dated September 26th, 2021. Interview conducted by Jane Daniels for the benefit of the Spectre Archive. All right, we're rolling. You're recording. Yep. So, about the Highway Man Game, that's what it's called, correct? Well, that's what I call it. A lot of people call it other things. Some folks call it the Candle Wax Game, the Highway Game, or just the Backroads Game. Some people don't even call it a game at all. For them, it's just survival. I see. So what exactly can you tell me about the nature of this game? A lot. But can you answer some questions for me first? Um, I can certainly try. Questions like what? Why are you interested? No offense, but you don't seem like the sort of lady who'd be interested in this sort of thing.  I guess what I'm wondering is, what's your angle? I guess I've got a couple. I've got a friend. She asked me to keep an eye out for any information on something pretty similar to this. And the organization I work with has an interest in this sort of thing. I guess you could say I'm trying to kill two birds with one stone. But that doesn't answer my question though. Why are you interested in this? I suppose it's because I'd like to understand more about the parts of the world that we don't always see. You ever actually seen anything? Yes. Yes, I have. More than you'd expect. Shit. Well now you've got me curious then. Tell you what, you tell me about the game and I'll tell you about some of the things I've seen after the tape stops rolling. Deal? Alright. Yeah. You got yourself a deal, Mrs. Daniels. So the highway man game, what can you tell me about it? What's the point? The point is to win. I guess part of it is the rush. When you're in the car and the game is on, the danger is real. At the same time though, it's not quite as risky as some of the other games out there. It's dangerous, sure, but it's a little more friendly for the inexperienced. So long as you remember the rules and keep your head on right.   But if it weren't for that harness, well you're basically just hanging on the very edge of the tower past the point where you'd normally have fallen off completely. You've heard of that, right? Yeah, I've heard of it. It's exactly like that. Only you get something at the end of it. It's a rush. It's risky. And the reward you get is usually worth it. As long as you take the proper precautions, well, you should be completely fine. Granted, if you end up playing the game without planning on it and you're not prepared, well then you're SOL. How exactly does one end up playing the game without planning on it? It's a lot easier than you think. The chances are slim. You need to be in the right place at the right time and even then it's a long shot. A guy could drive the back roads all night, every night, and never run into the game while another guy can venture onto those back roads just once at the wrong time of night and never come back. How do you play the game if the chances of even starting it are so slim then? Well, you gotta stack the deck a little bit. If the highway man knows you've got an offering, you're more likely to end up in the game, like a lot more likely. But it's never a sure bet. I've gone out on the roads plenty of times with the right offering in the perfect conditions drove until morning and didn't see a single thing. Again, it's all up to chance if you ever actually start the game or not. I see. All right. So tell me about these offerings. What have you got to do to increase your chances of playing the highway man game? What you need is a candle made of tallow. Some people swear by other offerings, but my friends and I always have had the most success with a tallow candle. You should also be on the road before it gets dark. You technically don't have to, but it's best to start in the late afternoon before dusk sets in. That way you can get far away from anywhere too populated. The back roads are the best, especially the ones with heavy forest, although I ended up in the game while driving through farmland a couple of times too. Once the sun starts going down and it starts getting dark out, pull over to the side of the road and light the candle. It should have a particular smell to it as it burns. It's not a great smell, but I'm pretty sure it's the scent of the offering that draws him in. Let the candle burn for a little bit until it starts to look melted and the wax is running down the sides. Then you take some dirt from the side of the road and pour it over the fire until it goes out. Once it's snuffed, you light the candle again, put it in your car and keep driving until it's either dawn, the candle burns out, or the highwayman comes. And that draws him? It makes your chances of drawing him a whole hell of a lot higher. Maybe, I don't know, one in ten maybe, as opposed to one in a million. Like I said before, it's never a sure thing. There's a good chance that he won't come at all. If morning comes or the candle burns out, well, then the game's basically over. Honestly, you'd probably best hope that morning comes first, because if your candle goes out and you're in the middle of nowhere at three in the morning, that can put you in some real shit. That's when the game is the most dangerous, because if he comes for you, you won't have anything to offer him.  Gas station, hotel, McDix, doesn't matter. Just get off the back roads and don't drive again until it's morning. I see. I take it not having an offering is a bad thing, then. It's a really bad thing, and I'll tell you why in a minute. See if you're lucky and the highway man comes for you, then you'll know it. The first thing you'll probably notice is how your car goes all wonky. The radio, the clock, and the dial stop working, right, and start going all over the place. You might get some cell reception, but don't count on it. You might also notice that the road gets really dark, too. Be trees on other side of the road, street lights are few, far between, and always on the left-hand side. Even if you turn around, they'll still be on the left-hand side. And under those street lights, that's where you'll see him. The highway man, yeah, he might be out in the darkness at first, but he'll be there. If you wait around, he'll probably show himself. Best not to wait around at the first street light, though, or the second. Just keep driving until he comes out on his own. He'll always be there. Under every street light you pass, sooner or later, you'll see him. I see, so I assume that you've seen the highway man, then, right? Could you tell me what he looks like? Yeah, yeah, I've seen him. He looks mostly like a man, from a distance. The skin isn't quite the right color. He doesn't wear clothes, not that there's anything to see, and I don't know. Maybe he used to be a regular guy or something, but his features, they look warped. Like they're melting off his face. Everything's in the wrong place. I don't like looking at him for too long, honestly. Even the noises he makes, these tinny, rasping sounds. Shit, usually he stays by the side of the road under the street light. Sometimes he'll just watch you. Usually, he'll wave, like he's saying hello. I see. Anyway, once you start seeing him under the street light, keep count. The road you're on, it sort of loops. It doesn't turn or anything, but it keeps going. It doesn't change much. Occasionally, you might find a wrecked car or something, but no matter what, you'll always come back to the street light, and you'll always come back to the highway man. Anyways, you have to keep going once you see him. When you see him the first time, you have to pass him. Same drill when you see him the second time. It's when you see him the third time that it's important. See, once you've passed him the third time, you've got to turn around. You never pass him a fourth time. Never. May I ask why? I was told that he doesn't like it. I don't know if that's true, but it's what I heard. When you pass him the third time, you turn around and drive until you see the street light again. It'll be on your left side, even though you turned around and he'll be there. That's when you stop. When you stop, he's probably going to approach the car. He's slow, but you've still got to be quick. See when you stop and you see that he's coming, you've got to take the candle out of your car and place it in the middle of the road. Then you get back into your car and wait. If your candle burned out, well then you're screwed and there's nothing you can really do but keep driving and hope to find a way out. Not that I think there is one. I've never heard of anyone who came back without giving him an offering, but I don't know. Maybe there's a way. Anyway, he'll take the candle. Sometimes he'll just pick it up, turn around and walk back into the woods. But I've also seen him swallow the thing whole, flame and all before he leaves. That's how you know that your offering was accepted. And that's how you know you're allowed to leave. And how do you leave? You've got a window. See the highway man is supposed to still be waiting for you. The next time you pass the streetlight in either direction, so you've got to get off the road before you get there. My advice is to drive slowly. Sooner or later, you'll see a narrow path through the trees. There's no light. It's hard to see and it might be blocked by a busted old car. But so long as your offering was accepted, it'll be there. That's your ticket out. You take the path and you keep driving until it ends. When it ends, you'll be on a different road. No highway man, no more glitches in your car, and it should be dawn. So you're safe to drive home. That's it. That's how you win the game. That seems like a hell of a lot of trouble to go through just for an adrenaline rush, if you ask me. It's not just the rush. Like I said before, there's a reward for it. Every time you make it out alive, there's a reward. You'll find it when you get home, waiting right outside the door to wherever it is you live. What kind of reward? It varies. But they say that it's always something that was lost. Sometimes it's something of yours that was lost. Something valuable. A relic of your childhood, a keepsake, some sort of personal treasure. But other times, it's something that belongs to someone else. Sometimes it's valuable. One time, I got this stuffed dog I had when I was a baby, Rufus. Another time, I got a set of solid gold cufflinks. Those brought me a half decent paycheck. Once, I just got a hat. Although I guess it was a hat that was important to somebody. It's hit and miss on what you get, but played enough times and you'll make some decent money. Still seems like a lot of trouble to me. Hey, it ain't for everyone. Like I said, if you're smart and you take precautions, everything should be just fine. My friends and I, we play all the time. We've all got stories. I'll bet, have you ever lost anyone? Have we? Well, yeah, there was Nick a couple years back, we, he went out one night, didn't come back. We figured that something went wrong. Either his candle had gone out or he'd passed too many times. Something, I don't know. And that doesn't worry you. When you deal with this kind of thing, there's always a risk. The world we can't see, it's not a safe place. There's no lifeguard on duty. There's just rules. People make mistakes. Nick screwed up and he, look, I didn't wish anything bad on him. None of us did. It could have just as easily been me and Nick would have said the exact same thing.  Yeah, yeah I do. But I also don't take risks like that. I'm sorry, I'm not judging you, I'm really not. I suppose, well, I suppose I just don't think I'd be as calm about it as you are. Yeah, well, every few years it's someone, usually someone on the forums. As you see their cars out on the road, doors torn off, windshield smashed, I recognize some of them from the pictures they posted. I stopped a couple of times, picked through them, tried to see if there was anything they left behind, letters, phones, something you know, to get to the people they cared about. I don't usually find anything. Did you ever see Nick's car out there? I… yeah, yeah I've seen it, couple of times, never in the same place. The road shifts, everything moves, I try to make sure it reminds me to play it smart. Remember the rules, don't get cocky, and stay focused, yeah, yeah that's it. I see, that's all I had. That's for your time, Terry. Yeah, yeah, no problem. Hey, you said that if I told you mine, you'd tell me yours. What exactly is it that you've seen? Right, a deal's a deal. Here, let me just turn this.


Hello everyone, as you can see I'm flying solo today because I'm doing an impromptu review of Ghostbusters Frozen Empire. I originally was not going to do a review on this movie. First of all, there's too many reviews out there, it's saturated, there's so many Frozen Empire reviews, it's crazy.  But I decided to do one for two reasons. I'm a huge Ghostbusters fan. Ghostbusters 1984 is my favorite movie of all time. I love that movie to death. I've seen it more than any other movie ever made. So I'm invested in Ghostbusters.  And I put it out there to see if anyone was interested in my thoughts on the movie. And enough people said, yeah, go ahead, shoot a review. So here we go. It's noon on a Monday. I'm drinking Danakroyd's Crystal Skull Vodka, which is fitting because Danakroyd is the brainchild of Ghostbusters. So here we are drinking at noon, doing a Ghostbusters review. It can't get much better than this. So before we get into Ghostbusters Frozen Empire, just to recap what I thought about Ghostbusters afterlife. I did a review on that, but if you don't want to take the time to watch that, that's fine. In broad strokes, I liked the idea of Ghostbusters afterlife, of continuing the story with the Spangler family. I hated the fact that they relied on an old villain and too much nostalgia in the movie. Then the trailer for Frozen Empire comes out. And I thought, man, that's really the movie I wanted afterlife to be for the most part. But it's just a trailer. It's hard to judge from the trailer, right? But I was excited. Early reviews started to pour out from people who are able to see the movie early and it didn't look good. People were really critical of it. I didn't really read or watch any full reviews. Just the titles alone were enough to give me an idea of what people thought. And I was kind of dreading the worst. I was like, Saturday before watching the movie all day, I was like, oh man, am I going to hate this? Is this really going to be like a bad experience? Because the last thing I wanted it to be really is mediocre. I think mediocre would be worse than bad. And I was already kind of planning a review in my brain thinking that would be the tagline. It's worse than bad. It's mediocre. I'm sitting in the theater before the movie starts and all these kids are around like kids, like four and five year old kids. Can it be a kids movie? Is that what they're marketing this towards now? Because the first movie kids liked it, but it was marketed towards kids. And then the trailers start playing before the movie all the coming attractions and it's all kids movies.  And I'm like, man, are they priming me for a kids movie? Because usually the trailers before the main feature are cut of the same cloth of the movie you're going to go see. And I was prepared for the worst. And I was thinking of all these things in my head like how I'm going to tackle a review, like little routines like, you know, I got a jumpsuit right over here. But this movie isn't even worth suiting up for. So I'm not going to bother. Then the movie started. And I'm going to tell you right now. I'm going to suit up. So now that I've suited up and taken the time to suit up tells you that this movie is worth the price of admission. And I think personally worth your time. My fears of this being a very kiddie movie steeped in the whole Marvel trend of editing and action instantly subsided with the opening sequence of this movie, which is a flashback. And I'm not going to go into too much detail about the flashback. And with this flashback they instantly kind of set the tone for this movie, which is dark. And a dark sense of humor. I laughed at something very dark right in the opening sequence of this movie. And instantly I felt okay. I think things are going to be okay here. I like the way that they're taking this movie. Quick summary of the plot without getting into too many spoilers and then we'll go into what I liked about the movie, what I didn't like, and also why I think this would have made a better Netflix show in the long run. And Ghostbusters Frozen Empire, they've reopened the Ghostbusting business, which is being financed by Winston Zedmore because he is a multimillionaire now. Gary Gruberson from the first movie along with Callie, Trevor, and Phoebe Spangler are basically the Ghostbusters. And you get a sense of what life is like in the firehouse. Now like there's a great scene where they're doing laundry next to the containment unit.  And I always thought that it was silly that a miner would be allowed to be a Ghostbuster. And they tackled that right away. Right off the bat, Walter Peck, This man has no dick, who's now the mayor of New York, calls the man and says, Well, she can't be a Ghostbuster. This is too dangerous for a miner. And I love how they tackled that situation right away. So Phoebe is banned from Ghostbusting.  And he's having real problems with enforcing discipline with Phoebe. Because Ghostbusting is now flourishing again, well Ray's occult books run by Ray Stance is also flourishing. And he's been purchasing all these haunted rare artifacts that are then taken to the research facility funded and run by Winston, Where they do experiments and research on these haunted items. One of the items Ray buys is this mysterious orb, which we find out later, Is actually like almost an old school ghost trap, which contains an evil spirit called Garaka, Which is our main villain of the movie. Phoebe feels really left out about not being able to be a Ghostbuster, And she's lonely and she actually of all things befriends a ghost, Which I thought was a really interesting plot twist and a little bit of a spoiler, But I'm gonna leave it at that because a lot more evolves with that storyline.  That in very broad strokes is the plot of Ghostbusters Frozen Empire. So the first thing I'm gonna say about Ghostbusters Frozen Empire is I actually did like it more than I liked Ghostbusters after life. And the main reason being is this is what I personally wanted out of a Ghostbusters movie. I wanted that kind of real good balance between the new crew and the old guys, And I really feel like we got it in this, and we got a new story, we got a new bad guy, And it moved the whole plot and all the character stories got to move forward. It's not that I hated afterlife, I just thought a lot of it was unnecessary, But you do need afterlife to get to Frozen Empire, so it does have its place. Another thing I really liked about this movie, again more so than afterlife, It had a much darker tone, it had scarier moments, and all the humor was way better, And much darker, which is what we want in the Ghostbusters film. Ghostbusters, at its essence, is pretty dark humor, and I thought we got that here, And not only dark, but more adult humor, it wasn't really kiddy. Sounds like you got at least two people in there already, might be a little crowded. A lot of the jokes were adult jokes, much like the first movie, I really don't think that this movie was made for kids, Even though there's a lot of elements that kids would enjoy and appreciate. It is legitimately funny, unlike afterlife, I did laugh out loud watching this movie, Multiple times, I don't think I laughed once during afterlife. And a lot of the humor does come from one source, And that is Kumail Nanjiani who plays Nadim, The character who sells this orb to Ray Stance. He really steals the show as far as the humor goes in this, And I'm completely fine with that, I'm fine with one person being the silly character, Leaving room for everyone else to kind of play it straight, But for one character to be kind of the humorous focal point, To me, works a lot better than having a whole cast of funny people. He very much so fills the shoes of Rick Moranis in this movie, Which I felt was missing a lot in Ghostbusters Afterlife, And I think they noticed it too. And this character has a lot more to do with the plot than the trailer leads you to believe, So I'm just gonna leave it at that. Of course McKenna Grace nails it again as Phoebe Spangler, And steals the show again, I'm totally fine with that. She's the star of these movies, The same way that Bill Murray was kind of considered the star of the first one. The whole cast is really pretty solid, Like there's not one bad performance of course in this movie because they're all killer actors. If anything the weakest link in this movie I think is Finn Wolfhard, And we're gonna get into that a little bit more when I talk about the negatives. Another thing I really enjoyed about this movie, And I think it's one thing that a lot of people disliked, Was they really took their time to flesh out these characters. There are a lot of subplots in this movie, And you need that, you just can't have a movie where it's all action, And it's all just fighting the bad guy because too much action gets boring too. The trailer really leads you to believe that this movie is gonna be a lot more action packed than it really is. And again I'm fine with that, I want to learn more about these characters, I want to feel for them and see what happens to them. Most of what you see in the trailer is all in the final act. Everything else in the movie was new to me and refreshing, Because let's face it a lot of these new trailers you see the trailer, That was the movie, I don't need to see the movie, I just saw it condensed into three minutes. That's not the case at all with this movie, They left a lot out of the trailer, And they actually put a lot in the trailer that's not in the movie, Which again I was fine with, These little subplots give a lot more substance to these characters. There's a great little subplot with Ray Stance and podcast, Where they are the ones doing the investigating and doing the research on what this orb is, And they kind of go off on their own and try to figure out what this is, And where it came from. And that does a great job of building those characters and expanding on even someone like Ray Stance, We've known for decades. Brings us to like a great scene that I loved in the movie, Where Winston is confronting Ray about going off where it's dangerous, He's like you're too old for this, you could have been killed, What are you doing, you know this is our golden years, We should be enjoying it, you shouldn't be out there still chasing ghosts. And it actually was a bit of a tear-jerker scene, I really love that scene between the two, Because you didn't really get that in afterlife, You didn't really get the old crew interacting with each other so much, And seeing how their relationship has changed, You get that in this movie. But at the same time, the old crew doesn't overshadow and steal from the new crew too much at all. The way that they share their screen time between the old and new, I thought was balanced very, very good. I also appreciated the fact that the nostalgia wasn't so shoved in your face, Like it was in afterlife. It's there, and then it's gone pretty quick. It's pretty minor. Another thing I loved about this movie is how much the cartoon series inspired it, And it's pretty blatant. And for me, it's welcomed, Because the cartoon did such a good job of capturing the spirit of the movie, But then expanding on the universe, And even expanding on these characters to the point where you kinda do believe It's the same characters from the movie, Even though they're voiced by a completely different cast. Incorporating elements of the cartoon into this movie really made it feel like it's all come full circle. There's that one character, Lars, You know, if you've seen him in the trailer, Obviously inspired by Egon Spangler's design in the cartoon. He's got the blonde hair, He's got the blue jumpsuit, He's got glasses on, It's pretty much Egon. The whole opening sequence where they're busting the Hell's Kitchen sewer ghost, Is right out of a Ghostbusters cartoon, Introducing all the new gadgets, Very much out of the cartoon. There's a scene where Ray hops on a motorcycle with a side cart called Ecto-C. Again, very much like the cartoon where they had all these different vehicles they'd have. You know, they had the helicopter. If you recall the cartoon, there's a couple of kids, Which were called the junior Ghostbusters, Who were part of like a fan club, Who'd follow the Ghostbusters around and help them every once in a while. Some people hated them, I didn't mind them. But Walter Peck says, What is this, the junior Ghostbusters? When referring to Phoebe, So I liked that little nod to the cartoon. The score from this movie I really liked too, And a lot of people were complaining about it, Saying it sounded too much like the original. Well, didn't you see Ghostbusters after life? That score was exactly the original. This is different. It is an original brand new score, Which takes hints and notes and cues from the original, But it's now doing its own thing with a new movie, So it sounds familiar, But it sounds new at the same time. As opposed to after life, it just sounded rehashed. The character development, the story, All the subplots, The way they balanced the new and old crew, I thought worked very very well. So overall I did really like the movie, Most of it I really enjoyed, And I had very little complaints, But there are some, So let's talk about some of the complaints. Much like Ghostbusters after life, This movie did have too many characters, And because it's only a two hour movie, You can only spend so much time with all these characters, So some characters do suffer to lack of screen time, And lack of development, Because there's just too many to keep track of. Finn Wolfhard's character Trevor, Like he could even not be in this movie, And it would have been fine. His whole little subplot with trying to track down The firehouse ghost, Slimer, Could completely be axed from this movie, And would have helped the pacing a bit more, Which I felt was a little off, Because there's so many characters, And so many subplots where it hurts the pacing, And gave more screen time to characters which needed it, Characters like Cali Spangler, I thought was almost completely left right out of this movie, Her character didn't move forward or develop much at all, Winston Zedemore should have been more in this movie, He was in it more than he was in Ghostbusters 2, He was in it more than he was in Ghostbusters Afterlife, But I still felt like he should have been in it more, I felt like he should have had equal amount of screen time, As Dan Ackroyd does, And Dan Ackroyd has a lot of screen time, And it's used very well, Like don't get me, that's not wasted screen time at all, But I thought that Winston should have had just as much, And giving that character the screen time it deserves, And there were some editing issues I think in this movie too, Where it was kind of structured in an odd way, Where they tease you that something was gonna happen, Which is fine, So like Lars puts his hand on the orb, And it freezes his hand, And then you see like ice start to form on New York streets, And you're like, Okay, Garaka's gonna bust out of the orb now, Like shit's gonna hit the fan now, But then it doesn't, And I thought the final act was a bit rushed, Like by the time Garaka gets out of the sphere, And they actually gotta do battle with this demon, The movie's pretty much over, I'm completely fine with all the time spent on the characters, Developing these subplots, But they should have got to the shit hitting the fan a little bit earlier, So there's a little bit more action near the tail end of the movie, And I think that was some people's complaints, Was there wasn't enough action and ghost busting, Which leads me to kind of my final thoughts, Is that this movie really should have been a long form Netflix style TV series, If they wanted to put all this stuff, All these characters, all these ideas, All these subplots into one thing, They should have stretched it out over a TV show, Which I think is what the franchise should do next, And if you would have taken all these subplots like in Frozen Empire, And kind of expanded all those into like a 45 minute episode, Where everyone gets their time to shine, And you get to expand on the story more, I think it would have made for a lot more compelling viewing, And I think at the end the viewer would have been more satisfied, And you can do the story more justice, In a long form episodic type series. So that's it, that's my thoughts on Ghost Busters Frozen Empire, I thought it was pretty damn solid, I thought it was better than mediocre, Did I love it? No I didn't love it, Did I like it? Yes, I'd say I liked it a lot, But I didn't love it, so if I were to give it like a star rating, I'd say maybe 3.75 stars out of 5 is probably what I'd give it. I liked it because it gave me time with the characters that I love, And it showed respect to those characters, And I think it respected the initial tone and vibe of the original movie. One problem with Ghost Busters now is it's become such a big thing, The fan base has grown so damn big over generations now, Like different generations of people love Ghost Busters for a different reason, And they all want to see something different out of a new movie, And I think that is kind of the problem, Because with a fan base so big with so many different age gaps, And you're gonna make a 2 hour movie and you're gonna narrow what you're gonna do down to this, What's the chance that everyone is gonna like it? It's gonna be pretty slim, there's just too many people to please at this point in time with Ghost Busters. Where the franchise goes from now, I don't know, there is an animated series in development, Which I'm looking forward to and I'm hoping they could really expand on the world a bit more, And give the movies a break for now. We've had two back to back and I think maybe it's a bit too much. I did like the movie a lot, I liked it more than Afterlife, And I can tell you that because when I saw Afterlife, I went to bed torn. I had trouble sleeping, I was torn on the movie, I didn't know what to think. When I came back from the theater from this one, I went to bed satisfied and slept pretty damn good, Not being torn about what I thought about the movie, it was pretty clear I liked it. And I am pretty critical of movies, you know, like I was pretty critical of Afterlife. I love Ghost Busters, Ghost Busters 84 is my favorite movie of all time. So yeah, I'd say go check out the movie, let me know what you think, And until then, keep drinking Crystal Skull Vodka!


You need a financial advisor with the freedom to focus on your specific needs. Whatever your goals, Reid Potter can create a game plan tailored to you. Call Reid at 432-0777 at Pikeville, Kentucky to learn more. The Paintsville-Johnson County Trail Town Committee will be hosting the second annual Van Lear Trail Ride Saturday, August 6th. The ride will begin in the Van Lear Days Parade and will close with a hog roast, bonfire and live music. Anybody can be involved in this ride. It's going to be right here in Van Lear. We're going to start right here at Web Store that morning. They're going to be taking everybody's info and everything, starting registration. So just anybody that wants to come, has a side-to-side fool or whatever have you, they can be involved. The ride will cover Butcher Haller and will last six to seven hours. We're going to also have music up here on the mountain as well that night during the pig roast. So there's going to be tons of activities going on. I mean, it's just an every year kind of thing. It is free to register for the ride and t-shirts will be available for $25. For more information, visit Paintsville-Johnson County Trail Town Committee on Facebook. For Mountain Top News, I'm Brianna Robinson.


Good afternoon, people. To give you a quick introduction to Grape, well, actually, we're not going to build a REST API with Grape on five minutes. So the point I tried to make is to give you a quick introduction to Grape. My name is William. I'm from Kanatama, a Ruby on Rails company in Indonesia. So what is Grape? Ruby is a REST-like API, a micro framework for Ruby. It is designed and run on REC, and it provides a simple DSL. This is a usage example of Grape. As you can see, there are a few DSL we can use, like prefix, resource, and get. You can catch the idea. We divide the resource, and we divide the method behind the resource. A few of Grape features I'd like to present today is only three features, but there is a lot more features of Grape that you can see at the GitHub. First is racing exception, versioning, and authentication. And racing exception, you can use a helper, an error, and then you can divine what code you want to return to the client. But also, you can return JSON instead of message. And then we have versioning. There are four versioning strategy that provided by Grape. The first and the default is path using path like this. And then using header, it's simply like this, like using header and vendor. And then the exception header, and then the parameters. As you can see, on the blue line over there is an AP for v1, but you can change the AP to whatever you want. Then I'd like to introduce you to API formats. Well, Grape provide us with the four formats, JSON, XML, and also TXT. Here, we define the response always on JSON. And then also, Grape support for the JSONP. You can use a Wreck JSONP. Also, Grape provide us with REC course, so you can use course on your REST API. The last part I want to introduce you with Grape is authentication. Grape provide us with basic HTTP authentication, digest authentication. And also, when you want to use Oout, you can use Word in Oout or REC Oout. That's all for me. And I hope you consider Grape in your next REST API project. Thank you. Thank you. Thank you.


What's up everybody Jason here for jazby's case brace calm 2012 13 timeless treasures basketball sold out. This is a five box break random teams number one now. I'm going to show you guys the sell sheet item description page that That's on the website number two because number one sold out. So number two is already down To 22 left, but this is number one. I'm just showing you guys this so there it is Chase and potential Colby Brian autographs in here from Kawai Kyrie Irving Anthony Davis Andre drumming Campbell Walker And many more and these are huge rookies as remember that 2012 13 class was like combined together So you have some big rookies in here, and of course there's big veteran autograph and Hall of Famers So remember this is time of treasure number one Let's get this going Roll the dice just two times snake eyes. Okay from Patrick down to Scott one Two, I know right just when we just only did online. We had no store Adam down to Oliver Two times one Two Oklahoma City Thunder and Denver Nuggets So Adam you got the okc Thunder Joey with the Knicks jay suit of the Kings Adam with the Hawks and they're the magic shang With the Lakers Matthew you have the New Orleans Pelicans Cameron with the Bucks Michael Hughes with the T wolves Derek give the Pacers Derek Brown that is Juan You have the Maverick Scott last one multiple pistons Matthew with the Hornets Maverick you have the Bulls Steven you have the Grizzlies Maverick with the Suns Anthony with the Spurs leave out with the Celtics Roger with the Clippers Mike with the Houston Rockets Adam with the Nets Gregia the 76 just calling with the Warriors Micah with the Wizards Steven you have the Cavs Alexander with the Blazers Patrick with the Heat Brady with the Jazz Richard with the Raptors and Oliver you have the Nuggets It's me off the top of this I'll give you guys time. I'm making any trades She doesn't make any trades Brazy saying jazz up for trade and Leave us in any offers for Celtics. All right guys. I'm gonna close the trade window then. Sorry looks like nobody wanted to Make any trades That's funny. All right guys TWC No, he He bought the Cleveland Browns right now leave us.  All right, so it looks like 10 boxes per inner two innards per case. So Let's open this up. So this is a full master Yeah, I didn't sound right, but I was just trying to clarify Anybody else thinking what what is going on over here What's up with the Anderson's today One two three for this side four five six for this case three One two three. So these will be for maybe round in teams three and four So now let's quickly open up this inner and we'll also Roll a die to see which five We'll use for this break here So again one two three for the left side again four five six for the right one Two three. So these will be for number two. He's over here And I think each tin has a new player on it, which is pretty cool All right, just remember all the Kobe packs will be going to the Lakers. So Shang you have the Lakers in this break So all Kobe pads will go to you and we'll leave all those packs last Michael Beasley Carl Landry Roy Hibbert Andre Iguodala that's six out of 25 Nuggets edition these ones are just blank base. I mean got Steve Smith timeless signatures for the Atlanta Hawks and That's 80 out of 199 Atlanta Hawks going to Adam Kelly. We do have a Laker and It's Devon a banks. That looks like Trevor Arisa Doesn't it doesn't that look like Trevor Arisa? That's crazy unless this is what Devon you banks look like but Either way to one I ran for the Lakers. That's going to Shang Swear this looks like Trevor Arisa, but maybe not Ah, and we got a redemption. Good luck. We got glass rookie autographs gold car number 191 Jeremy Tyler I have no idea So let's quickly pull up pin me.net slash checklist And we'll look up and see where it's supposed to go and car number 191 Jeremy Tyler at the Golden State Wars and these are all out of 10 Golden State Wars It is Devon. Okay, a little extra Arisa there for a second. All right, so they go Golden State Wars calling with that one All right, next one again all the Colby packs will be going to the Lakers Whenever it sells out Liva, uh, we're it's not sold out yet. So Whenever it does the Bible Savalosha and Jason Richardson Chris Bosch and Derek Favors All right, we got glass rookies. That's Myers Leonard for the Portland trailblazers You out of 499 Portland it's going to alexander And we got another glass rookies. That's Charles Jenkins another one there for the Warriors number to 476 And we got palgasol to 99 a little jersey relic out Daniel Gibson J. J. Hopkinson or Hickinson Roy Hibbert and Marcus Thornton And we got our mark eaten for the Utah Jazz on car number to 199 Brayden with that one try to trade the jazz couldn't trade mojo And we have the last rookies of nullan smith trailblazers another one for alexander to 499 Then we got a All-star patch Manu Ginobli. That's pretty nice Number one out of 49 That I'm going to the Spurs Anthony with that one Yeah, I think you're thinking of absolute football jeff where it's one per case absolute has the glass rookies or glass cards I guess either just call it glass because you know, they may look like it's glass But I know what you're talking about jeff absolute football gives you a one glass per per case Uh, don I know the I don't know if you heard joe talk about this, but I know a couple weeks back Uh YouTube announced that they were going to lower the streaming quality for certain people or certain I don't know. I don't know the specifics, but there was going to be times where you may have to See the stream at lower than what you can normally do just to help out since everybody's at home I don't I don't understand because we're paying for our own internet, right? But yeah, they were going to stream they were going to like lower the stream quality For some users or some some streams But to my knowledge it looks like Minus to able to stream at 720 But it looks like it's taking a little too long to load. So It was at 480 But yeah, I'm not too sure how it's affecting every single person but For the most part Thomas Robinson to 349 for the kings kings Jace And we got shelvin mac to 499 Washington wizards going to michael we got another redemption and we got Validating marks card number 25 Jordan Crawford there you go Jake crawf now. I want to make sure to see is this clippers jordan crawford or where because he's bounced around So it's like a validating marks card number 25 And that's actually washington wizards jordan crawford just to show you guys Validating marks and card number 25 Washington wizards. So there you go There's another one for michael. All right, last box guys again. Number two is in the store and I'm sure we can run that back As well, we haven't hit a big rookie yet. So I assume people are going to want to give that chase a shot montelis patrick paterson devon harris ramon sessions And look at who we got here glass rookie kairi irving That's 24 out of 3 99 So there you go. Actually there's one of the bigger rookies in this class Look kairi And that's going to steven with the cleveland cavaliers And then we got jared cunningham for the mavericks glass rookies to 499 Mavericks Going to juan And right here is diago splitter So 149 a little piece of the jersey All right, now let's quickly ribble with all these kobe packs. And again, this is all going to shang You know, I've only seen twice in my life a numbered out of eight or 24 in an autograph I've only seen it done twice But there is autographs out there r.r.p. One of the greatest Just got inducted into all the fame yesterday Yeah, I know they do exist. There's just really tough to pull All right, there you guys go. This was thomas trezzler's rattle team number one number two's in the store We can get the next one going jaspy's case breaks.com. Appreciate it guys