Welcome everybody to this session. My name is Dakshina Murthy and next 45 minutes or so I will be talking about understanding understanding language. So I changed the title from what is mentioned in the brochure just I you know this was a scooter and I felt it was deeper so you know but the idea is you know how do we how do machines understand language and how do we benefit from understanding how they are understanding the language. Post lunch talks are always tricky I will try to keep you all up and running for the next 45 minutes so I will do my best right. One slide about the organization that I represent I am from International School of Engineering. We are AI machine learning research and education institution. We are present in India, in Hyderabad, Bangalore and Mumbai. We have academic partners in France, Ren School of Business in USA, Case Western Reserve University and in Canada Carlton University. 60 plus faculty and scientists all are interested in AI ML working on that. Two products are already launched, two products are in the pipeline 75 plus patents, thousand publications so we are very actively involved in research. Faculty either research on technology areas like blockchain, big data, IOT on analytics or applications of analytics in healthcare, financial markets etc. So we teach and we do research so that is what we do. In that institute I am one of the investigators for AI for business group. So the theme of our research is we do not actually we are not that keen on inventing new models. What we acknowledge is that really smart people are creating really smart models and you know really fast. Every day there is a new model and most of these models are very academic in the sense that because it is developed in a university setting the accuracy, the performance is given a lot of importance but when business wants to apply really accuracy is only one of the indicators you have to look at its productionizability, its maintainability, the stability a bunch of things come into picture. So they need separate metrics and frameworks. Our group tries to create those metrics and frameworks and facilitate that process. So today while we look at a bunch of AI machine learning related models today I want to focus on natural language processing and why is that? I started seriously looking at NLP in 2010-2011 at that time the state of the art models were conditional random fields. You know super cool mathematics I mean log linear models, stochastic gradient descent and it allowed domain experts to input their features in a very cool and smart way sort of machine learning at its best you know you needed domain experts, you needed super tough math, you know ideal setting for a PhD to feel good about and I thought you know oh I can apply it I did a few use cases and I told myself you know I will live happily ever after. We then you know one of my clients called and said you know what two interns we hired for an undergrad institute built something called an LSTM that broke whatever you did for six months man they just gave more data no features nothing okay I was ego got crushed you know for a long time I used to call this Starbucks neural networks you know large short tall medium neural networks whatever you do you know so I mean I was upset obviously but then you know I got to live with that and I said okay I broke up with CRF started dating LSTMs said oh they're super cool so you know they can do all that and by 2013-2014 I said yeah LSTMs I think I can apply I know how to do this and then I saw Google doing something odd you know one day I saw a paper where they said my LSTM is one part of one half of it they said you know there is an LSTM then we will run another LSTM in the reverse direction and then we will stack eight of them one on top of each other and provide a residual connection you know what this is one half we call it encoder and then we have those eight layers on the other side we call it decoder and we'll put something in between we call it attention what is happening here okay I said okay what do I do and it took me some more time to figure out and then then I thought okay partly understood it last 12 months they said Bert they said excellent they said Ernie I mean where am I going here so I said wait wait wait I can't be spending my life fine-tuning hyper parameters of these models I have to understand something more about how these guys do these cool things okay so whenever you want to understand something much deeper it probably benefits to ask very simple questions so I asked myself how do we humans understand language okay is there anything that I can correlate it to the way these models understand the language so tried observing a few kids actually and started taking notes first thing when you're learning a language you learn a few words and you start guessing the meaning of the sentence with that one word or two words that you know okay so that's you know if you have what to pick a completely new language probably that's what you do and once we get there once we understand we start forming some naive rules you know if there is a dollar most likely it's currency if there are 10 numbers probably it's a mobile phone number so we sort of make up these simple rules and probably after some time we build fairly complex parsing we split it into multiple things combine and start understanding the language and then once we become comfortable with the language we start guessing trends we we build beautiful auto regression so the clouds are in the dash you and I probably will tell skies and someone in the IT department will say server room whatever that is okay there somewhere we start filling in the gaps learning these auto regressions trends that's another very important and lastly we seem to be becoming good with what we learn we don't forget for the next task what we learned in the previous task we sort of building so these are the paradigms are dimensions in which humans learn languages and what I found interesting was that whether thinking about it explicitly or not the machine learning community actually went about in more or less the same way to solve the natural language problem and let's see how first thing learning words the naivest thing was the one-heart vector when people in 1950s and 60s the machine learning community might have influenced them you know whenever you'd see a categorical attribute just dummy fight right so whenever you see a word depending on the vocabulary size if your vocabulary is ten thousand words you create a ten thousand dimension vector and put your words in alphabetical order and the first word is one zero zero zero second one is zero one zero zero zero and so on and so forth so a vector of V dimension where V is the dimension of the vocabulary where V minus one zeros and just one hot guy so there is only one one hanging around there so that's one hot but the problem is book library cricket any topic one zero zero zero one zero zero zero one they're all similar to each other equidistant because they're all one they're all just one one and several zeros right Euclidean distance is always root two so they didn't have even the basic semantics needed to understand the language so while this is a representation we invented I don't think it's relevant for any major problem right to solve it I'm going to plot models in a specific graph let me spend a minute on the strategy that I adopted I should work with some visualization experts to come up with something better but this is what I have currently the x-axis I'm going to just write the models in chronological order to the extent possible I'm not a AI historian so if I miss a year or two don't worry but in general the chronological order y-axis is going to be the models ability to understand the words so one hot vector doesn't understand word meanings it's a very dumb representation right so the it's at zero so there's no understanding of the words now the color is how well it does the trend prediction so how well it looks at past words and guesses future words there is no color here so it doesn't do anything the shape number of sides in the thing I want to represent by how well it parses and applies the rules okay this is again a circle with no shape so there's no parsing no rules and overall size is the performance of the model I could have drawn smaller thing but I was worried whether it would become visible or not but this is almost a point so this this is sort of of nowhere you know so I'm going to as we discuss more and more models I'll fill this graph right okay that's where one hot vectors are then in 1960s a bunch of machine learning guys made some really smart hypothesis about understanding words they said hey those words that occur in few documents but when they occur that occur in large frequency are really useful words because if they are all over the place they must be the or than space filler but if they are in a document but only once maybe it's just a fluke but in a few documents but when they occur if they occur a lot of times and then it must be a really useful word so they created what you probably know definitely know are these term document matrices where they took a lot of documents and put them in columns lot of terms and put them in rows and at base level you binary if the term is present in the document one L zero or if you want to scale it by the number of times it occurred across the documents and within the document you can come up with some nice smart scaling factors and they will become some numbers like this okay now this vector of the word understanding is a lot better than the one hot vector because most likely similar terms will appear in similar documents so when the vectors are similar it means that they occurred in similar documents so most likely they are very similar to each other so got better than one hot vector but the problem with this model is this is still extremely it's not as parsed as one hot but very sparse right there are only a few numbers more importantly this is much larger dimensions I mean if you think of a Google this is a billion documents so your one hot vector is probably size of the vocabulary this is size of the corpus so this is scary so what people did is hey you know what this number here is a combination of two things it's there because the document wants to express some concepts and the term is capable of expressing those concepts only when there is a match between the concepts that the document is wanting to express and the concepts that the term is willing to is capable of expressing you see that term in the document so they believe this matrix is because of two sub matrices one is a term and its concepts another one is a document and the concepts that it wants to express so they created a linear algebra guys went ahead and did some really cool things figured out a way to split this matrix into these two matrices term concept and concept document and what is the advantage this can be a very small number this can be 100,000 by million but this can be 100,000 by 300 or 200 this can be 200 by million so your matrix overall will form again so the term concept vectors are much shorter number one and much more denser so that itself is an advantage but really what clinched the deal here is the term documents are very document specific so if there is a document on data science and it doesn't have the word AI I will get a zero there but term concept on the other hand when you factorize it it figures out that you know AI and data science occurred in many other documents together so probably they are similar concepts so even though at this point in this document AI is not there most likely they are relevant so it figures that out so a long range interactions short dimension and sparse sort of clinched so they are very very popular I think in 1990s these were quite popular now okay I understood the words well that's only one part right what else can I do can I parse can I build trends can I build rules these models have inherent problem with building trends simply because I dropped many words that I thought are unnecessary so previous two words concept is not there I lost the order of the words I put them in alphabetical order I am gone for good so the only thing that they could try was building rules and regular expressions was one way where a human was giving those heuristics I don't even think we can call it machine learning right you give rules you are it's not machine learning so the more popular method is you take these term concept or term document matrices they are structured data so you apply your logistic regression or support reclamation to build the rules on top of that so this is the second generation can we take questions after the talk I know tf this is SVD kind of matrix factorization term concept right term tfidf is the original one term b o double bag of words thing okay so so do ML on it you find these simple rules so in my graph now these guys still don't have any trends and hence I haven't colored them a few rules that they identified through ML so I made the shape a bit more complicated so the number of sites is a sort of you know indication of whether what they're doing so they are better than one heart in terms of word understanding and they are better than one heart in terms of the fact that they're combining word understanding with rules still they are not very much there with respect to rules that's okay so in word understanding as you guys probably know the ultimate happened in 2013 right word embedding sking minus man plus woman is queen all that you know Google actually is very good at marketing some of these things I mean there was enough work even before that but somehow Google just became so prominent with it but by the way last year someone really studied this and they found that word to work did something crazy I mean when you ask if father is a doctor what is mother it said nurse that's a different discussion all together but you know what yeah it it became sexist without realizing but anyway let's not get into that but glove and word to work sort of opened a new era glove went with these co-occurrence mattresses they took a vocabulary by vocabulary matrix and they saw every word what precedes it what succeeds it what is two words away what is two words this way and came up with probability distributions of the words and then factorized it that was glove what to work on the other hand is a pure brute force neural network you give one heart vector here you predict the next word and somewhere in the middle neural net keeps creating better and better representations right so you pick the representation that you want so what to work is extremely popular but what to work and now have this problem in the era of Twitter even if I start with a million words someone else invents a new word and when there is a new word my entire word to work I have to randomly initialize again so I have to retrain the entire model so many are many models came up to solve that problem Facebook fast text etc my favorite is that space is bloom is quite good essentially what they do is they embed a portion of the word so Facebook's sorry bloom space is bloom takes the prefix embeds it suffix embeds it and word embeds it if it's not there it will give a random thing and it also creates categories so if there is a capital letter initially it calls them in it cap so all doc words that are started with the initial capitalization or they go under that group all digits for digits all caps like that they created some 10,000 groups into which the entire vocabulary so even if there is a spelling mistake it forms one of those things and they embedded the groups also so the embedding of a word is embedding of prefix suffix the word and the group and so and they add them up so it's it handles the out of vocabulary and spelling mistakes a lot more elegantly but bloom is only one example fast text does that there are many other embeddings that do that but all these embeddings are context free which means if I have a sentence like this he went to a prison cell with a cell phone to extract the DNA cell samples of the inside inmates all cells will have same embedding right because yeah you get one embedding for one word so that's the problem so these are contextless but still having said that this is like a new era and the people were super kicked about it so in terms of understanding words and doing ML on top of that and people did some really simple things and that work beautifully like you know I saw sentiment analysis paper where they took word vectors of each word they added all of them called that sentence word vector and attach the sentiment and build an SVM on top of that and it gives state of the art results I mean so really understanding words can take you a long way and this is probably the ultimate of understanding words now parallely there is another community that was focusing on understanding language only through regression I want to fill in the blanks given the past two words how will I guess the next word so the approach there is you know you compute it look at large car percent say what is the probability that I see this word what is the probability that I see this word given I saw this word what is the probability that I see this word given I saw these two words and so learn all this unigram by gram trigram as they call them probabilities and then use base theorem to predict what is the next word for some time Google you feel lucky was using something like that okay probably now they use neural nets I really don't know what technology they use but this was a workhorse this Markovian model was the workhorse of of regression based language models from 1980s to 2000 also okay and the culmination of that is these here and Markov models which are really smart it's so sad that they lose to neural nets actually and this is like so nicely mathematically developed you know they use base theorem so the a word and its part of speech what is the giant property they really they use base theorem and then they use Markov model on the so I see the words but I don't see the parts of speech so the Markov model is now on the part of speech what is the probability that it's a noun given that the previous one is a verb and the given that the previous one is a determiner so the Markov model is not built on the word here but on the hidden state of the word so there is a base theorem to do giant distribution there's a Markov model now one word let's say there are three possible states it could be a noun verb or determiner with the very close probabilities now I have a sentence with ten words and ten possible parts of each one is the top three guys if I put that is a three to the power of ten combinations that's humongous so they used like really smart dynamic programming kind of counting to actually solve that problem so hidden Markov models are mathematically extremely elegant but anyway there I can get romantic about it but they are they're dead okay well more or less right so but that was very very popular 1980s to 2000 this is a parallel track to NLP where you are only looking at regression to solve the NLP problem previously all the models were they were only looking at meaning of the word so I sort of cheated squeezed it in now again I'm not a historian so this came first or this came first I really don't know I just had to put it somewhere so around this time they came first so I there's no word meaning so I brought it down and there are no rules other than the regression so there is no shape or anything there's no and then but I colored it because trending is beginning to be learned trend color we decided to indicate with sorry trend we decided to indicate with color so this is like sort of it tells you there is a regression in it but no word understanding and no rules in it good 2002 2003 and then came this conditional random fields did any of you study it anyway oh my goodness this was ultimate in machine learning at some point okay but anyway don't worry about it they lost the war okay but conditional random fields are beautiful because they are the first attempt to combine trending with rules two of the four are connected the machine learning model and term document is meaning with rules right this is trending with rules so it had all the things that hidden Marco model could do so what is this probability of this given the previous one is this given the previous one is this it had all that but it also had a very elegant way for a domain expert to add whatever features he or she thought was important so let's say statistical model I want to predict the path of speech of model I can add a feature like his model capitalized is the prefix of the last word statistical ICAL if it's yes or no and these are normally called indicator function in their language but you create these hundreds of features I read somewhere that the state of the POS at that time had hundred thousand features hand coded I mean you need a lot of grad students to do that yeah right hundred thousand features hand coded and they you know that they built it and then it's elegant machine learning right you have here hundred thousand features X1 X2 here are not words they are the features and you learn from data W1 W2 W3 classic machine learning and it can be a negative or a positive number so you do a exponentiate it and softmax it so that's why they're log linear model so if this whole sum is minus 100 also I don't carry to the power of minus and it still is a positive number and when I softmax it I get a probability so conditional random fields had this beautiful trending and an elegant framework to fit the rules and super mechanism of gradient descent to solve the problem really they were state of the art so somewhere around this the they're definitely a lot of trending in it as good as hidden Marko models and the rules are many many more hidden Marko only looks at trend whereas here I can add my rules so I made the shape more complicated still word meanings are not added so they don't have any bag of word or anything it's just words are just treated as entities are categorical right so but performance wise these are anywhere comparable to this so they are they're fine then came the era of for deep learning I said deep learning 1.4 4 4 is the nowadays that's the fact I think you see industry 4.0 web 2.0 so I just thought I would also add a few but this is like 7 years back deep learning right when you started deep learning the first efforts in deep learning was about combining meaning and trending so I have the bag Google word to work or something they go in as inputs now I build it on top of a LSTM which can look at much longer trending than HMM I'm not just looking at two words back but I can look at much longer sequences that's why I called it auto regression 4.0 much larger than HMM so you give you combined meanings and you combine trend hidden Marko model was only doing trend this is meaning come trend and trend is much larger and then they said hey you know what you combine trend from this direction and then you combine trend from this direction and you concatenate them you get trend from both directions so that is bi-directional LSTM even today most people think of this as the most fundamental you need to learn regression based patterns in text right bi-directional LSTM and the meaning is fed from here and then there is a bi-directional LSTM but there are quite a few instances and personally I also experienced even convolution neural nets do a very good job of getting the context LSTM the problem is they go too long right do is it good to actually have such long trending do you want to predict tomorrow's Google price by looking at 15 years back Google price no you may not want to right so convolution neural nets on the other hand are much more closer in terms of looking at the thing so those three W1 W2 W3 formed some embedding here the W2 W2 3 W4 form this W3 W4 W5 form this now these three form this in a typical convolution neural net local connection but if you think now about dub this thing is not only an embedding for word 3 but also includes the context information of 1 2 on that side 4 5 on this side so I am getting a short term bi-directional a context inputs into my word embedding so actually CNN's work very well in practice in embedding by the way people call this encoding when embedding normally is when you just get that word to a kind of a meaning encoding is when you add context to it so do a convolution or do an LSTM to actually give it a shade of the past or future words okay so both are fairly popular though I would say this is probably more known for some reason some of the commercial products actually only use this but they don't publicize that much I don't know why anyway then of course this ELMO is another very popular thing which encoded where it started with one hot encoding of characters and used a convolution neural net to create a word level embedding and then put those words in LSTM to get that encoding which is context rich so a CNN gives a word embedding and the they give context LSTM gives a context embedding that is very very powerful so I mean I am adding meaning and trend in in these models but is very interesting they don't deal with and and entire word they will deal with only a small fraction of the word so even if your vocabulary is 10 million they are able to divide it into 30,000 fractions you can combine them in multiple ways to create as many words as you want so that's also a very powerful representation so these are state of the art so in this phase of machine learning deep learning we are combining meaning and trend right and we almost forgot that last thing that we use right which is we become good with time until now there was no way for us to incorporate that so in language this whole transfer learning was a very late entrant nobody was transfer learning but in the past two years this became extremely popular people use two ways you know how do I transfer learn one is you know train on entire Wikipedia language model language model is a very funky way of saying you know give him the password predict the next word so I give I don't have to tag it it's unsupervised right if I take entire Wikipedia I write it as previous word next word and I try to predict and learn all the weights based on English language then you take your problem and tweak only the weights a bit or there is another technique that people are doing build a neural net keep most of it common only last layers different and learn multiple tasks okay classification language model translation whatever sentiment analysis learn multiple things so most weights get tweaked and tweaked and tweaked because they're learning multiple tasks on a some same data but they have to learn last layers are or what task specific medium be initial layers are all language specific so people did pre-training so bidirectional so trending is covered word embedding so meaning is covered pre-training training is covered so three out of four and we had really powerful models in the horizon almost done if only we can add rules to it we have to figure out a way to add rules to it and we didn't know how to and manually you can but deep neural nets are not very friendly to manual inputs so ashio bingio came up with attention rules rigged and parsing perfected I mean he this is probably he didn't even realize when he proposed it that he was actually building a parser he was building it for at least the paper talks about doing something else that's also a very tough problem but attention essentially is a mechanism where see until now with my word embedding and context thing each word is represented as a vector so in a sentence let's say I have four or five words I'll have four rows one row for each word and I have these vectors now attention essentially is a way to learn for a given task how important are these words so I learned a wait for each of these words h here is h1 h2 h3 right capital h here so essentially I may say hey you know what for this particular at this particular moment this word and this word are very important I can ignore all others now this and this are very important now this and you use a neural net to learn it obviously you don't write those rules right so attention is a very powerful way to learn every vectors importance in a given task and that involves essentially taking elements for this vector that vector combining exactly that parsing and rules that I talked about so the CRF rules are human generated so human reads the text understands the patterns and writes the rules whereas attention rules are much more deepish neural net learns those rules because neural net already understands the embeddings that are highly context rich so attention sort of solved the game and this person he's a founder of a spacey wrote a very influential blog embed encode attend predict this is the blueprint of NLP it actually fits very well with the framework that I talked about until now embed is about understanding words encode is about understanding trends attend is about understanding rules what is missing what is the flaw in this approach if you can think about okay only one thing that I said it not part it's not learning transfer no transfer learning in the model so if only I have to update it I will say pre-train because we said four things are needed for perfect language model right this is 2016 and okay we are three years ahead so we can add this right embed encode attend and predict it's a very powerful model I think pre-train will really improve the performance even further how am I doing with time 10 minutes okay I'm almost done so in my graph now I have embed encode attend predict pre-train at the very end so it is the perfect in word meaning it it's beautiful and it's thick black pitch dark so it really understands trends extremely well and I wanted to draw something more complicated shape but star I thought symbolically it shows it's super cool I so that's why I left it at star but it can actually do really complex rules and patterns and pre-train so it learns and it gets better with each thing if you have time look at an architecture in 2016-17 called hierarchical neural net which is actually built on this model so they take character words and build a sentence level vector through embed encode and predict and then they take sentences to create a paragraph level vector in through encode embed encode predict and then they do a document classification task so very systematically you just write this model so I personally believe for many business applications this probably could be the you know could be good enough I mean yeah this has everything in it so then why but what does bird do differently what does excel net do differently what are they doing you know I'm also surprised so yeah this is what where it stands so it didn't cheat it played well but then when Bert came Google guys more or less said yeah you know this is great we'll do a bit of a training but you know what we will brute force parsing and rules so they created a amazingly complicated shape in my today's this thing you know I mean you each time you do six different rule generators acting independently they called it multi-head attention so each one builds its own set of rules they combine all of them then send it to another six things then combine all of them like that 12 layers oh my goodness that is rules on rules by rules with rules and rules all over the rules you know so obviously you know this guy got even you know you have a good batting team good bowling team good fielder team and you do well you think you're doing well and then you bring Virat Kohli then you lose right you know okay that's that's fine man I mean but still you know this is the way to build a team right so I am still very much with this and then but you know so Joe Butler was brought by Carnegie Mellon they said yeah you know what you attend with brute force I will regress with brute force so this is all colors you know I couldn't put one color because they not only took two words back towards forward they combined all calculated all kinds of permutations and combinations the clouds are in the dash is what the clouds are in cloud or the in the cloud are every possible combination of past to make a prediction and concatenated all of that similarly they brute force Ernie is a brute force on the pre-training I train on many many more tasks than anyone ever did before so yeah I mean so what will be the trend my guess is there'll be some researchers that are going to come up with insane embeddings I mean I will take prefix suffix part of speech named entity or who invented that word when was it written first time in Twitter I can embed every possible type of that and I'll come up with a much better embedding I'm sure there'll be some research it'll be fun to read and fun to test similarly there'll be crazy regressions now you know why only words within a sentence I come up with pattern I'll figure out a way to embed a sentence before embedded sentence after and then come up with all kinds of permutations and combinations at a sentence level to predict I'm sure there are going to be papers on that and then maddening attentions right you know six at multi-head why not now I'll do Wikipedia and whatever the YouTube transcription put together and do probably 15 attention heads and then run it for 25 layers whatever I'm sure and pre-training you know you come up with very smart ways to pre-train the models further use GANs to create more data and pre-train on that I'm sure and then once in a while someone comes up with combining all of these two put together a model hopefully and so the whole idea here is at least I was quite confused with how to understand why these things are working but this framework really helped me to put things and how models in place okay you know what I don't really have to worry about it this is where I focus so machines like humans only need those four things they have to understand the words better they have to build the ability to be predict the trends and they have to create some rules and they have to be pre-trained once you understand each model in these four dimensions you figure out a way to see what you really need for this application more importantly figure out a way when to stop given the computing power given the data huge data nowadays it's difficult to have a data scientist stop and move on to the next problem that extra point not one percent is so tempting so you do that but you know figure out you know maybe you're overdoing on one front end you can just stop and move on to simpler things right anyway that's what I wanted to talk about yeah please go ahead thanks it was nice talk I have one query so you just mentioned before but it was EAP plus pre-trained so if any of the business application where you don't know what new terminology in is going to come in your communication with the customer it's always a new terminology new word right so for training existing corpus let's say we can take it from tree bank or some other thing or which is already available in analytic or something but that is not going to suffice our purpose so considering that situation where you every day your for your every transaction every communication your customer is using new terminology so how this model can work with the new terminology so how to train that yeah no as I mentioned the word understanding even this EAP uses bloom embeddings not the word to work which can handle out of vocabulary that's your question new terminology they don't look at word as word only they look at the prefix prefixes are part of the library I can't run out of it's very difficult to create a new word with completely new prefix completely new suffix completely new word and that doesn't fit in any of the word categories that I have given so people have figured out smart ways to come up with a meaningful embedding for the newer words it's just that word to a can go over the oldest versions but later ones can handle but also has a way to handle so if you just want to take birds word embeddings alone and put it in your EAP if you think that's simple enough model I was talking about that situation where you know same word play multiple POS tagging like noun plus determinator plus because you have LSTM to learn the trending and context this handles that not only word if it's used in a different context it learns the context driven embedding encoding so this works even in that situation thank you by the way I saw few people taking photographs they have collected my PPT I think they will give it they will upload it right yeah it is there so you don't really have to any other questions so I am the one they had they can't upload so just ask me the questions document yeah yeah I wanted to ask you so how would you approach a problem where we have to do document classification where documents have a lot of words maybe on the scale of 10,000 so many sentences how will you approach this kind of problem you know what these guys Jeremy of fast.ai actually did it he first pre-trained because using those document vocabulary only a document text he pre-trained the model and then he took a small fraction of it and classified he got state of the art so I was asking about like I have to classify multiple novels into their genres and novel can have multiple sentences like 10,000 or 20,000 sentence and I don't think the current neural networks will be able to handle this thing right just check out hierarchical neural networks so what they do they start with each word in a sentence and embed that sentence now they take these 10,000 so whatever 10,000 sentences and embed each of those sentences and get one final vector and they use that for classification so the attention is about consolidating all these rules to collapse that entire thing into one vector that respects all the required rules in one go so check out hierarchical neural networks they actually solve this problem otherwise what people normally do take the first few paragraph or first two paragraphs and just solve with that but if you really want to do it the right way and but if you think about it a human also doesn't have to read the entire book to classify it otherwise librarian is the wisest man on the right he has to read the entire library before he classifies any book right you don't do that so you take the first page you take the title you take a few things to make the right guess so that's also not a bad approach but if you want to do the entire thing hierarchical is a good way yes one last question we can take okay thank you very much


Over the past decades, organic and organic hybrid polymers have been applied in different fields, including the adsorption of pollutants from wastewater and solid-state separations. In this review, firstly, these compounds are classified. These compounds are prepared by Solgel method, self-assembly process, mesopores, assembling of nano-building blocks, layered or core-shell compounds, and as interpenetrating networks and hierarchically structures. Lastly, the adsorption characteristics of heavy metals of these materials, including different kinds of functional groups, selectivity of them for heavy metals, effective pH, and synthesis conditions on adsorption capacity, are studied. This article was authored by Babak Sami, Chil Hong Cheng, and Jiao Ning Wu.


and well like Nathan Hill just told you I well it's not really I work on the prefix in of all Chinese it's just that maybe I would like to give you some bank consciousness just to give you some hints what the possible origins of a peak prefix or the P. Presilable might be so the function of the prefix wasn't really clear in Saga 1999 and this was also about criticized by Miyake in the review of the book and I expected that maybe in the new in the latest by accident Saga don't be some some kind of chapter and my point of well all Chinese is still pretty new to me so I'm not really a specialist in all these series and I just skinned the pandas and compare it to other Tibetan languages like like you can see here on the screen and my one of the method I use for this short presentation was the fall leaves model and to take by George Wanderie he doesn't reconstruct a stamp on he just because it's there are various language groups and which are somehow related to each other so and the other method I am still working on is the internal reconstruction means that you have to make analysis the shooting and compare the rhymes and well one of the major benefits of the comparative method is that you have for example drew and now which is reconstructed with the well and there are these brackets and if you compare these these two words in Chinese with other Tibet other Tibetan languages and you somehow get certainty that the reconstruction of who is correct like in or in Tibetan or in Burmese so like I said the function of the p-crafx is still unknown and so one of my first step was to figure out what the logical property of the sound p is and in terms of place of articulation you were aware that the p is a bilabial which is also the M right the only distinction which you can make between M and P is the matter of articulation because M is nasal and P is an explosive but they are both bilabial so I first looked into the finals and maybe some of you might have heard of more organic finals which was already pointed out by Robert Schaefer in the 40s where he recognized the alternation between M and P the basis of the language data of Pozson so how organic finals are also can be found in other or other scholars have pointed this out I think I missed Bortman on the handout but he also talked about this and well I don't use the product to better learn reconstruction by Montesot but I think the example with needle is quite interesting because if you see at these have a look at the needle for example which has the red one here has the phonetic element of can which is reconstructed with that up final whereas needle itself so there you have for example this homogeneic final alternation and so crossing the border and including other languages you see that Tibetan languages have mostly or the other the great majority have the rhymes up I'm not sure about the lepcha example maybe it's right but well you can see the alternation right and if you maybe if we concentrate on words in and her might be possible might be really a relay group and if you compare the examples for example the malam is somehow well to me it's tentatively related to in a cognac which is a more Naga language or lushai which was mentioned yesterday so there you again you have this some organic initial alternation in a a guarantee a language has also this alternation where you see you have made a final home house then close it hot and there is this variation so I wanted to talk about the key prefix so I have to focus on the initials and not the finals so that was the first that was the second step so it I thought maybe if there is a if there are homo organic finals there might be homo organic initials so there is for example Yaka also Kiran the language which has a dialect or variation Marek, Anga, Tumok, Ka which well I mean you can argue that Ang is something prophetic which needs to be added like in school or like a school you have this a and here you have the same but I think that Nga is might be the stem and again if you compare it to other languages like like Lebcha you find these series Lebcha Tibetan and all Chinese which might maybe that's was just thought maybe it's just a similar development which you can observe in Yaka so again in other languages like you mentioned Lebcha or Bahin which is the Kiran the language you have this this alternation between Mak and Miak the I kill where the causative form is formed by a palatalization and not pharyngealization but it doesn't matter I think that Tibetan Mak is common Mak, Lebcha and both of them have nasal, my labial sounds which on the other side you can see Bahin which has the word biaqo for example which starts with a B so with a plosive so the question is which one is first and if you compare for example other words like ripe cooked boiled ripped and you have in all these languages which are discrete an initial end so in Khan a Magaric language you have this this shift or this change or alternation between M and P cook by boiling which has in Khan in proto Khan but also the alternation between M and P and I think that cook is probably a derivation of it or maybe just an alternation but still the same or a related route so I haven't talked about the causative prefix in all Chinese and I think that all the languages also have this this this prefix P for example water bottle languages especially diva where it depends on the source it depends on the source so which you consult but for example diva in the description of Joseph in Berlin 2006 you have this alternation again of moon and pool which is wrong on the handout and depth not 2014 doesn't have the this alternation but what is interesting is for example cream which if you look at another border garland which go for you can see that a run means be dry so we might reconstruct a form like purin and then it by the time it merged together but there was probably P prefix so again in border you have the same here's something similar the master also what about a language which has also put prefixes and the last example fill up make full might have the tentative cognate Chinese then I'm sure maybe there are also you don't see cool also you don't see and Tibetan look and as you see there's this dialect of variation loom and mean maybe it's really so that was the comparative part and it's still the comparative part it's sending something the word sending so the link mean for example might have the tentative cognates with Tibetan spring and in calm language and by me leave only although I'm not really sure about that but you can see that M and P somehow correspond to each other so well the only internal reconstruction I did so far is I found blinks in shooting in a node so and it has some semantics relation and I think that might be related to or have the same route because if we propose the sound change M to P and the sound change into M you might get this form planks so after all I think it's it's like the homo organic in finals the homo organic initials are somehow a common phenomenon which you can observe in languages and so exactly that's that's all I had up I just wrote it yesterday there are these sound loss which you see one's law for example pretty bad and it's true and if it's if we if the sound law sound change to blue is somehow on a some of the same sound change where we can treat it as a sound similar sound change it would mean that Chinese and all Chinese synidic and Tibetan share the same sound law sound change which is a shared innovation which makes them related to each other and we can maybe reject the term sign of Tibetan and I don't know maybe accept to the vermin or transingling so these are some references I have


So I have had a lot of requests to film a bookshelf tour and the thing is I have a lot of bookshelves which is why I've been too lazy to do it and today we're gonna take a step in that direction. I'm gonna start part one of probably three to four parts of bookshelf tours. So I thought I'd start in the bedroom for no particular reason but these are the shelves that you've probably almost never seen. Like you can see them in my vlogs sometimes, but I don't film videos in my bedroom really. So we're just gonna start there. I'll do those today and then you know work my way through. I have I think at least three videos worth of bookshelves for. So anyway today we are doing the bedroom off we go. So this is what we're working with today. This is my bed which I think is self-evident and those are the bookshelves and yeah we're gonna go through them. I try for the most part to keep my unread books in my bedroom because I like the books that are seen on the shelves in videos more often in my living room to be books that I have read and therefore I'm like comfortable or not comfortable but there are books that I'm like yes I have read these I endorse these whereas books that I haven't read yet I don't know maybe maybe I'll hate them and then I'll look back at old videos and be like oh I wish that book wasn't behind me in this video. So anyway not for the most part the books in my bedroom are unread books. I do still have some unread books in the living room but anyway yes off we go. As you can see this is also me this is my bedroom so this is also where I do my makeup. So this is not books but this is the top of my bookshelf. Tada below starting here we have Stranger Times which is a book that I randomly got it sounded interesting when I saw it and there are four I got it. Then I have Demon Voices by Philip Pullman which is in the same universe as the other His Dark Materials books. Then I have all my Natasha Pulley books in a stack. I haven't read any of them yet but I keep collecting them because they're so pretty. So the Lost Future of Pepper Harrow which is actually the sequel to the Watchmaker of Filigree Street so they should be next to each other but they're not. The Bedlam Stacks and The Kingdoms. Then I have some more Philip Pullman books. I had these together side by side but then I just ran out of space so I moved over. So the Book of Dust or La Belle or the Volume 1 of La Belle Sauvage. I have them right together obviously. The Secret Commonwealth. Then I have I used to have here the Queens of Innocentia as well but I did just read that so it got to go to the living room. So I now I just have Lady Hotspur by Tessa Gratton. Then I have The Lost Queen by Cygney Pike. Then I have this trilogy which I've had for years and years One of these days I'll read it. Dreamer's Pool, Tower of Thorns and Den of Wolves. This is the Black Thorn and Grim trilogy by Juliette Morillier. The Liar Thief by Jennifer Fallon, Thousand Chips by Natalie Hayes and then all my Harry Potter books because I don't want them in my living room for reasons. And down here I have I'm always complaining about all the Michael J. Sullivan books that I've collected even though I haven't read them so I have all of the Legends of the First Empire books in hardcover and his newest because it's just not me those were Christmas Nolan which is also in this same universe I think am I wrong about that but obviously these all are. Then I also keep collecting all the books. I actually have Red Ruin of Kings but I have them here together anyway because I haven't read the rest of the series and I probably wouldn't need to reread Ruin of Kings if and when I decided to finish the series. I say if even though I own them all because I didn't love Ruin of Kings but I love all these covers so I want to like this series. So I'll give it there's one more book um so I'll probably get that too. I don't know why. Then I have the first two books in the Queens of Renthia I think is the series yes the Queens of Renthia. Originally this was um I got this probably like five years ago because someone said it reminded them of Patrick Rothfuss and I clearly that wasn't enough of a motivator for me to read it but I did buy the first two books then I have almost unhauled this so many times but every year I'm like no this Halloween I'll read it but it's like a a spooky story that's mixed media. Then I've been I've put this book on the buddy read poll for my patrons all many times but it never gets picked crown for cold silver heard is pretty good like violent grim dark. Then I have the entire Queen of the Tearling trilogy um which is really pretty um I don't understand why the logo keeps going up but whatever and people have I've heard mixed things but I still want to read it. Then I have the first two books in this R.I. Salvatore series um I don't know the series oh uh The Coven Child of a Mad God and Reckoning of Fallen Gods and I have this chunky collection of stories that are edited by George R R Martin. I think Joe Abercrombie yes he did and the reason I got it was because it was edited by George R R Martin and it has a story by Joe Abercrombie in it um but I have not read it yet obviously. Foundry's line have heard mixed things have put it on the buddy read poll it always loses. I soft DNF prior the orange tree one of these days I might finish it it's just again such a pretty book that I want to like and then on the bottom I have doubles of all these books um that are left over from my attempts to find a matching set like matching size um and yeah I just have them all now and then like some of them I got signed not like signed copies of them so I just I just have them all I don't know what to do with them I'll figure it out. Similar situation with this I wanted them all on hardcover but they've stopped um doing the series on hardcover like you can't get the newest books in hardcover at all like you never could and then I got this the paperback signed but I don't want to get rid of the hardcovers so I just that's what I'm doing there. Then I have the inheritance trilogy that is completely out of order because I don't know why. I have read the hundred thousand kingdoms but I just wanted to keep them together anyway so I still need to read these other two and the city we became I pre-ordered the second one even though I haven't read the first one and how long till black future months so this is my N.K. Jemisin section. Black leopard red wolf also keeps losing on the buddy read poll uh godblind also keeps losing on the buddy read poll game of kings I got this for Klaus not for Klaus but because of Klaus this is Klaus's fave I have also put this on the buddy read poll one of these days we'll read it I got this ages ago because it sounded interesting but I also got it so long ago that I don't know what it's about and I've never heard anyone say anything about it it's by a pretty small press hotkey press one of these days I'll see what it's about then I got 9th reign ages and ages ago and now it's like a thing mainly thanks delia brooks and I think the broken binding subscription is doing the series next but I do have the first one in paperback and I got Lovecraft Country when the show was coming out because I found it cheap but I still haven't read it um Ophelia Swam was sent to me by the lovely Evie from She Was Only Evie um and it is like a spinoff about like what if Ophelia didn't drown but swam away so I am interested in reading that and then I feel kind of ashamed um I didn't actually know about this non-fiction book um about um Latvians being sent to this uh concentration camps in Siberia I was talking about this being part of my family history with Jess from Jess Owens and she's the one that discovered that there was this like memoir about it so I got it and I still haven't read it because I already know what it's going to be in it it's going to be super depressing then um I really enjoyed Five Horse alone so I picked up another lobby to Darabuck because I want to read more from him and this cover looks amazing and then I mentioned to somebody at some point that I'd never read any Vonnegut so they were like here's a Vonnegut and I started reading this um non-fiction book about Palestine and then had like just ran out of time and haven't finished it and Echo Wife was given to me by Amanda um this is a a arc of it but I still haven't read it this I have read but I will probably get rid of it this was an arc that was sent to me and I was so upset that I didn't like this book because it looks like it would be my perfect book but I didn't like it and then I have decided that I would like to torture myself by reading a little life someday so I just have it here so that when the mood strikes I'm ready to torture myself it's just sitting here ready for me to embark on this journey and that's this side of the shelves moving on to the tall one on this side I have some books kind of um stuck like it's not like I have a huge amount of organization here I did kind of keep it by author um over here it's by size and type and also I've kind of like put together books that I have various like blog projects in mind for so I've just kind of like put them together so that when I'm ready to do that um they're all ready together plus they're here next to my bed where they like taunt me was like hey hey remember this project you were gonna do yeah do it read us so here at the top um for the most part I keep my unread book at the month club books up here but I needed a little I had a little extra space so I stuck these up here as well I got some wilkie collins books um I just like use editions a lot um so this is the haunted hotel um then I got two different copies of station 11 because I couldn't decide which one I wanted to get haven't read it yet but I have and I don't know why they're split like this the wilkie collins of the station 11's but they are so that's that and then all my unread book at the month club books so keeper of the night million dunes lally willows a ladder to the sky the family upstairs song of the crimson flower fountain of silence this is a journal um sounds of the girls up people anxious people which my patrons refused to allow me to read necessary people gods of jade and shadow a space between worlds early riser the burning lesson magic lessons a fate of the fall and a mexican goth that's blended in the vial and the moors account and I read bills at some point I could try to make like a rainbow of this good night well someday moving down here um I actually got a candle in a box for a book that I just happened to randomly get a car I like got this well okay back up I saw someone doing a special edition of the second one of this and then they were selling also the first one um but only the dust jacket for it so I bought the first one in anticipation of receiving the special those jacket for it and the second one and then I haven't read it yet but I randomly in a book box got a candle for it so I just put them together and then these two books were sent to me as gifts from viewers so The Winter King by Bernard Cornwell and Between Two Fires by Christopher Buhlman that's the author of The Black Tank Thief and I have some vlog project books over here feel free to let me know your guesses for what those vlog projects will be I'm gonna say what they are um so this is a vlog project and this is a different vlog project so I have The Grapes of Wrath by John Steinbeck Billy Liar by Keith Waterhouse um In Watermelon Sugar by Richard Brodigan um 1984 by George Orwell me and the devil by Nick Tosh Toshies Toshies and East of Eden by John Steinbeck and then my other vlog project books I have Bunny by Mora Awad Plain Bad Heroines by Emily M Danforth Catherine House by Elizabeth Thomas The Divines by Ellie Eaton Special Topics and Calamity Physics by Marisha Pessil These Violent Delights by Micah Nemeriver and Ghosts of Harvard by Francesca Serratella and then down here these are just like on the vaguely all the same size which is why they're together this is not these are not any way like a project or anything um so I have Iro2 Mistborn which everyone tells me I'm gonna hate but I've got them and the King of Elfame how the King of Elfame learned to hate stories which is a cruel prince novella that I haven't read I don't know if I ever will read it Empire of the Vampire which is Jimmy and Alex's fault I can't wait for us to get to this and through this so that I can get rid of it uh Little Thieves was a gift The Rise and Fall of Dodo I've had this for ages one of these days I'll read it the trees I got mainly for the cover but also like probably five or six years ago but like look at that cover then uh The Undoing Project by Michael Lewis was a gift a long time ago Hook's Tale is a Peter Pan wheat telling that I'm almost guaranteed to hate because I always hate them Gathering of Ravens which I got a while ago but now I've heard bad things so I don't know if I'll read it um The City in the Middle of the Night by Charlie J. Anders because I really enjoyed their first book um the All the Birds in the Sky is that what it's called I think so I got their second book as soon as it came out but I haven't read it yet Valiant Gentleman I randomly got it at like a huge book sale where it was like two dollars it's how I got it I've been drinking but obviously haven't read it their Welcome to Darkness was also a gift so just don't gift me books because I'm not gonna read it uh Alec and me have masks and mirrors um which I hope to read soon I know Alan has a copy of it too so maybe we can buddy read it Caste which I got because I wanted to read it but I haven't read it Shakespeare in Swahili land um which is also nonfiction and I would like to read soon I kind of have a few nonfictions here Caliphate also nonfiction only part of the party nonfiction um then someone gifted me no I bought this for myself but because someone kept telling me that I would really really like this so I was like all right then this came I think in a book box or I ordered it to match the first one I don't know but I've heard that this is a retelling of Hades Town and I'm seeing that this month so I'll probably read this as soon as I see it then I just get rid of it because I got rid of my other special copy of Angel Mage so I should get rid of this one because it was terrible I have read this and we have some more vlog projects down here um over here I have the Moonstone by Wilkie Collins um Jane Eyre by Charlotte Bronte uh The Count of Monte Cristo by Alexander Dumas and this beautiful edition was gifts at me um for Christmas um Phantom of the Opera by Gaston LaRue this is a very beautiful copy Ivan Ho by Sir Walter Scott and The Moonstone by Wilkie Collins and yes this is two different copies of The Moonstone this is like a fancy old vintage edition um and this is just like a paperback and then more Wilkie Collins I have The Woman in White I have A Tale of Two Cities by Charles Dickens uh Mrs. Dalloway by Virginia Wolfe Crime and Punishment by Fedar Dostoevsky and Persuasion by Jane Austen and down here is again more books that I just they just are kind of the same size I sure I don't know why my standards are split they just are so these I have read I have read all the nevermore books I just put them here to kind of be safe and be out of the way because I ran out of space on my like middle grade and YA shelf which we're not doing today that's in that in my probably my next bookshelf tour will be my YA and middle grade shelf so I didn't have room there so I put them here these are the UK editions which are so hard to find but I found them um and they're gorgeous and then um this was sent to me as well Odin's Child um but I wanted it because I heard just Odin's talk about it and I mean it's it's Norse Vikingy type thing so obviously okay I'm just gonna switch them now just bothering me um I'm gonna just we'll just fix that um so yeah I read Skyward last year loved it um I've heard that it gets subtly worse so star site is not as good and then everyone hates itonic but I have the pretty UK editions because you know God them all are hot um then The Likely Escape of the Ariahepe is by H.G. Perry um and H.G. Perry is the author that wrote the Declaration of the Rights of Magicians which I loved so I wanted to read her earlier book so this was actually kindly sent to me um but I would have bought it anyway and then this caught my this caught my eye just because the title or what you will is the alternate title of Twelfth Night which is why it caught my eye but it actually doesn't have anything to do with Twelfth Night but it sounded interesting anyway but like that's why I looked at it and I was like oh but actually like this also just sounds interesting so that's why I got that. Conversations with Friends is the Sally Rooney book that I have not read yet but I obviously intend to. Flamefall is the sequel to Fireborn and this was sent to me by Alan. These are my Jeopardy winnings right here um then Memory Hold Empire was also sent to me. I've heard really mostly positive things but also some mixed things and I have no idea how I'm gonna feel about it. I've had these books for forever and I'm just I can't stand the idea of getting rid of them but I haven't read them yet so one of these days. Once in Future King I did start reading that and it was really really boring but I'm determined one of these days to actually read it all the way through. Then I got this upon the recommendation of Pierce Brown and it's incredibly difficult to find this hardcover because it's not in print. You can get it on Kindle and you can get a really ugly like edition of it nowadays but you can't get this hardcover edition anymore so I found this used but I haven't read it yet but I'm excited too and like I do love this like bold school cover I honestly do. And then I've been meaning to read Ellen Kushner's books for forever. I did start Swordspoint but I haven't finished it yet and I just saw this bind up and it was really pretty so I got it. Black Fox Rending was a cover by. I don't remember what it's about. And then this is like Viking-y so I got it but I have not read it. Magic of Wyndonist. And that does it for the tall shelf. And there you have it those are the shelves in my bedroom. Next time we'll do some of the living room and then more of the living room and then the rest of the living room. So I hope you enjoyed let me know your thoughts and feelings about this video and if you are like you know what forget it I don't want to see your bookshelves do tell me that because then I won't do any more bookshelf tours. We'll very happily not do any more bookshelf tours. But if you really do want to see more bookshelf tours then also let me know that and I will endeavor to do that sooner rather than later. Whatever you want to let me know. I host videos on Saturdays. Other random apps will be up on Saturdays so I can subscribe. Join my Patreon if you feel so inclined and I'll see you when I see you.


And these four things are actually going to be reflected in a home group report. In a home group report. The first one is I want you to write down hunger for God. Hunger for God. Hunger for God. What is expected of somebody who's leading a home group in our church? What is the number one requirement and I believe the number one reason why home groups fail? Is when a leader of a home group loses. Not his prayer life. Not even his Bible reading. But it's hunger for God. It's kind of ironic being hungry generation and not having hunger for God. But it's easy to have a name and not to have the substance. We started as a church. One of the reasons our name is hungry generation is because we were known and we're still are known for hunger. For hunger. Hunger is a beautiful thing because hunger always breeds and produces humility and humility brings holiness. I want you to remember these four H's. Hunger brings humility and humility brings holiness. If we lose our hunger we become arrogant. We begin to think we know it all. We begin to think that we've done everything. We've succeeded. We got it figured out. And when we lose our humility the second thing that we always because with humility with loss of humility there's always loss of holiness. And so with us guys number one thing that you have to keep in your heart as a home group leader is your hunger. And it's harder for those of you who've been in our church since the beginning. Because you probably have went through waves more than you have fingers on your hand. You have went through movements. You have went through things you've got burned. You've been walked upon. You've been betrayed. You've been backstabbed. You already kind of like sometimes it's easy to go through the motions and everything. But for the sake of people who have never went through what you went through you owe it to them to have hunger. Painted fire never burns. So to pretend to have fire to pretend to be hungry we know how to mask and show that we're hungry. When we've been hungry we know how to show hunger without actually having it. Is that what we're doing is spiritual work and spiritual work requires spiritual warfare. Write this down under hunger for God is a spiritual work requires spiritual warfare. Unlike every other social groups you know even the ones that some of us in here have where we gather people. This is different. This is a spiritual work which requires spiritual warfare. Without having that hunger what we do is we don't show up on the spiritual battlefield to fight the battle. So what am I mean practically by hunger for God? Is that your hunger for God is really shown and seen and is strengthened by your prayer time or your Bible reading time. Without having a strong prayer life as a home group leader not only that shows that your hunger for God is dead but that also shows your hunger for God will never even come back. So prayer isn't the only thing that shows I have it. Prayer is the only thing that guarantees for it to come back. We're doing a spiritual work which requires spiritual warfare. David one time instead of going to spiritual warfare he decided to sleep in and stay home and sleep when the kings were supposed to go to battle. What that led him to is that that empowered his flesh and he started to live according to the flesh. I want you to remember this guys as home group leaders. This is not a pretty truth and there's no better way to put it, there's no nice way to put it but this way. If you find excuses to ignore your prayer life you will empower your flesh period. You can quit home group, you can quit every kind of ministry. If you stop praying you empower your flesh. When David didn't go to a battle, David struggled with the battle he wasn't anointed to win. He was not even supposed to be in that battle. Why was he in that battle? Because he refused to fight the battle God anointed him for. And that's the battle he was supposed to be with his armies. The battle God called you to fight is to be on your knees at least 30 minutes a day. An hour a day whatever the time that God places on you. In the morning or in the evening that's up to you. But if you don't show up to those battles, if you find excuses and simply say Jesus paid for it all. I don't need to pray, I don't need to do all this stuff because I've got it figured out. You will see this in your life is where you are going to be empowering your flesh. Same thing happened to Apostle Peter, John and James. When they were in the garden with Jesus and Jesus says hey guys stay with me, pray with me. And the guys were sleeping, guess what happened? When the temptation came in, the only person who endured the temptation overcame it was Jesus. And the guys who slept instead of fighting, they in the temptation, their flesh was empowered. They couldn't resist it.  But you're going to have a secret. You're going to choose which secret you're going to have. I choose which secret I'm going to have. And so I understand sometimes it's hard to find time to pray. Sometimes it's hard to find a time to focus and pray. But hunger for God, love for God is really the only thing they will keep as like T.P. Jeffery says on the course following Jesus. And so get back to your secret place. Maybe slow down a little bit on the social media every free time. If you haven't been reading the scriptures, but you're constantly on the social media, maybe tone that down a little bit. If you know a lot what's happening on TV, but you have not been engaged in prayer in spiritual warfare, which your home group situation will reflect that. And so you don't have to go very far to ask for it. You just look at your home group and you figure out that, hey, this is a good reflection of that.  I just really would challenge you to get back into the hunger for God. Number two, after hunger for God is maturity. Or we can put it emotional, maturity. After hunger for God and prayer life and good life in the word. The second thing that's not just required, but it's important for us to maintain. And that is maturity. Under maturity, what I mean by that is that there's three levels of maturity. The first level is the level of the child, which needs to be taken care of. The second level is we know the level of the youth, which learns to take care of himself. And the third level is the level of the father, which cares for others. So maturity.  There's two parts of our spiritual growth. One is when we meet the cross. The second one is when we pick up one. And many of us, we only know how to come and gaze at the cross and find healing, forgiveness, all paid for. And that's a believer's. But Jesus switched it, always with his disciples, not with the crowd, but with his disciples. He always said this. He said, after they found out he was the son of God. He didn't say that until they knew he was the son of God. So it means after you have a revelation of the cross, he right away brings you another revelation. If you don't pick up your own, you're not worthy of me. He wasn't saying that to the crowds. He was only saying that to people who are close to him. And this is this part that we sometimes don't like to mention it, even behind the pulpit. And this belongs to us. As guys, our maturity involves, we move from looking at the cross to caring one. What does that mean? Caring the cross, where I grew up in the church, the cross meant your spouse. Your spouse, your husband. The cross that you have to bear. And so, and your husband, literally, even Martin Luther, he was teaching his pastors. He says that this bitter, bitter cross that you have to endure and that is just your annoying, crazy spouse. And so that was the way. Some people think cross is a sickness. That's not, we believe the cross is, is you live your life for the cause of Jesus, without feeling sorry for yourself. I recognize, and I see this in myself sometimes, when you start to do something that you don't get anything for it and you kind of sacrifice yourself and the devil always puts the person beside you who is supposed to be doing the same thing. But at this season or at that time, they're not, it feels like they're not doing it and then you start developing self pity and you start feeling sorry for yourself. Like Elijah, I'm the only one who obeys you God. And Saul did the same thing. He came to the priest and he says, nobody tells me anything what David is. You know, I'm just all alone and everything. And that's self pity. The problem with self pity and the problem with feeling sorry for yourself is that that is very deadly.  What I mean by this, guys, of maturity is that we consciously have to live with this fact. Right is that? Our life cannot be dearer to us than Jesus's life was dear to him. Our life cannot be dearer to us than Jesus's life was dearer to him. What Jesus views as maturity is that you know how to live, not for yourself. If your life is very dear to you, so dear that you shake at the possibility that you're going to give something more than what is really required, then you and I have to mature that we are not afraid to... I know it's not proper and it's not politically correct. We're not afraid to waste our life on the cause of Christ. To waste our life on the cause of Christ. The problem with many of us is that when we become homedocletives, guys, it's kind of like becoming a father. And those of you here who are fathers, you can testify to the fact you can't raise a family with a bachelor lifestyle. You can't be a bachelor and raise a family.  And there's a different lifestyle. You can no longer be a bachelor. Guys, when you become a home group leader, there's a lot of, sometimes it feels like, man, but that's not fair. Not only I'm meeting with the people, not only I have a home group, I mean, now they're like putting me at everything and then I have to be like a prayers and I have to be the example for everything. And I felt this backlash even from some people saying, you know what, I don't think that this is for me or I think this is like too much, I need a break and everything.  You sort of, after you get that, this is when maturity is developed, you recognize, my season changed, my life is no longer mine. And if a home group leader doesn't have that, he hasn't matured. You will feel to a certain degree pulled. And if you're not being pulled, that means you're really carrying a title of a leader but a lifestyle of a believer. You can't raise a family with a personal lifestyle. Maturity means simple, I'm aware of new people. Maturity means that my life is no longer mine. And if they ask me, you know, on Saturday, do we need to clean up the church?  So if you don't have it, it's probably in his hands. It's make sure in his hands, you know, or people just keep asking me to do stuff. Well, last time I checked, you did pray, Lord, make me a servant. Why do you complain if they treat you as such? And I've realized the only reason Jesus was able to serve on the last sub is because the Bible says this, he knew who he was, he knew where he was going. When you know who you are, you're secured to serve. Insecure people can never serve. If it doesn't give him anything, why? Because their world is going to fall apart. And so there's a certain security God will develop in maturing us and in serving other people. Jesus says, my yoke is easy, my burden is light. And he says, learn from me for I am meek and then you'll find rest. It means relax. Don't be important. Serve. Love people. Help people. And you will find rest. Because when you worry about your little kingdom, it's very stressful.  And to get everybody to worship you is sometimes just hard. When it comes to home groups, multiplication, this is the huge part. Jesus outlined that faithfulness is not in preserving, but in multiplying. Preservation always leads to two things, laziness and wickedness.  If we stop aiming in our groups to multiply our groups at least once a year, two things are going to creep in. Laziness. And after laziness, wickedness. Wickedness just means sin. Laziness meaning we become lazy. And then we become wicked. Preserving our group is not the goal of the group. Multiplying our group is the goal. How do we multiply the group? By number one, as we mentioned, our maturity is that we disciple new people. We help the new believers. We try to have constantly new people because if your home group is always the same people and it doesn't have new people after a while, your home group lacks the excitement. When you have new people, the next thing that happens is that your goal and my goal for you and your goal for you is your group has to multiply at least one time a year. At least one time a year. So that means that as you are bringing new people, you're already developing your helper. Your helper. When you cannot make it to the home group, the home group doesn't get canceled. Your helper does your home group. He does the report. When you cannot make it, let's say you assign to this person who just gets assigned that you really cannot make it. You assign your helper. You start to get your helper involved. When your home group becomes so big that you're ready to split it but your helper isn't ready, this is what you do. You do a home group, you can do the lesson, share your relation, whatever. When it comes to the ministry time, you get him prepared. You almost have two home groups meet in the same house until you feel like that helper is ready. Just because the helper has people, that doesn't mean the helper is ready. We cannot make a mistake of releasing people prematurely. But the way you can get him ready is by making your home group in two home groups right in the spot, by letting him minister already to a few of the people right there in the home group. And this way you can have a bigger home group which sometimes numbers attract people because if you only have two people, a lot of younger people will have a hard time coming in. It really is easier. Begin to empower your helper. Sometimes, on purpose, pretend to be sick. No, I'm not saying every other weekend once a month. I'm saying sometimes, and just let him handle it or let her handle it so you can see also how well did you do. Because real success of your home group isn't how many people show up, how many people you bring. It's how many people show up if you're dead. It's how many people show up if you can't make it. That is really how you empower or you are not. And so multiplication is the second thing.  You're not going to be looking for new people. You're not going to be having new people because you're not looking for them. And then God's not going to be sending anything. Why does he need to send something to you if you're not even ready for that? You're not even needing that.  And last thing, mind management. Mind management is that your mind always has to be positive about your group. Just because you sacrifice and carry the cross, they don't need to know that doing a home group is a cross. When Abraham was bringing his son to sacrifice on the altar and his son was asking, hey dad, where is the sacrifice? His dad didn't say, you're the sacrifice. He says, listen son, don't worry. God's going to provide a sacrifice. I was reading that story today in Genesis 22. Abraham was so positive, though he was the most sacrificial. And I realized one time, sometimes when we develop this modern mentality of really dying for Jesus, it starts creating this pessimism within us where we feel like the more sacrificial we are, the more right we have to be negative. You cannot be negative just because you're sacrificial. You actually have to be positive.  Life out of you and out of them. You got to be like Abraham said, Lord, you'll provide.  This whole idea, nobody wants to come. Nobody likes it. Honestly, maybe it started like that, that nobody wanted to come, that nobody is new, that they just, they have no point in everything. Honestly, it started like that, but now the reason why the home group is like that is because of you. You got to get rid of the stinky thing. You can't think like that. You can't say like that. Your feelings about your home group, if you dread that meeting, go to your room and say, Lord, bring me new people, give me new people, change me, them or somebody, but something has, I cannot feel like that. You can't walk into a home group and your home group elevates your feeling. You have to go to your home group. You elevate the home group because what is the home group in your mind always has to be bigger than the home group in your house. Write this down. The home group in your mind always has to be better and bigger than the home group in your house. You got to be optimistic. This is what 4th Dimension is all about. You got to have it in your mind clear and cut. This is going to be great. I have an awesome group, awesome people, even if they're not awesome, they will become awesome. God will send you awesome people, but if you're walking around, this is not working out, this is not for me, do two things, either change or quit. But don't drag on like that. If you can be positive, either you change or just don't do home group. I'd rather have 10 home groups that are passionate. They're literally, they don't want to, and when I told them that we had a meeting on Monday and Larissa got mad. Why? Because she's passionate for a home group.  No, that's it. And why? Because her home group is her passion. You talk to Larissa, she's not looking how to cancel it. She's not looking, she's looking. She loves her home group. She looks forward to home group. And that is the home groups that multiply. Because why? Fourth dimension inside is full of peace. I'm excited for it. And the weird part how it works, it's like a magnet and you attract the kind of people who love it and you multiply it and it could be in the same church and other people literally cannot scrape to get, find one person. Maybe change the magnet inside. It's interesting when Lot left Abraham. Two things happen. The Bible says Lot looked and saw. Lot lifted rise and Lot saw Sodom and Gomorrah and he said this is like the Garden of Eden. Very delusional. Fourth dimension. And Abraham's eyes went down. Lot left him. A lot of people left him.  I want you to notice the difference. Lot lifted his own eyes by his own and saw something in fourth dimension that looked like Garden of Eden. He was actually Sodom and Gomorrah. And Abraham who was brought down, he was discouraged and God came to me and said Abraham, lift up your eyes and look. Look this way, that way because this is the land I'm giving you. Guys, if somebody left, if something didn't work out, God is speaking to you. Lift your eyes. Don't lift your eyes on your own but God is speaking to you. Lift your eyes and just begin to imagine, begin to dream of the home room. People coming, getting saved. You meeting with people. Home rooms being released and all of the goodies that are going to happen.


Hey, good morning team and happy holidays to everybody. I hope everybody's getting ready to secure for a little while and spend some time with family and friends. About 20 minutes ago, we released a Nav Admin message that restores rating titles. And to give you a little bit more information on that, I'll ask the VicPont to tell you all what that was all about, what it means, and what our way is going forward. So again, happy holidays to everybody. And I gotta say, you know, as the Secretary of the Navy, as the CNO, and I have been out and about and having conversations since the initial release of the announcement back in September, we realized that the majority of our time has been spent centered on conversations solely based on a Navy convention, which we believe has probably become a distraction. And taken away from the things that each and every one of us should be focused on every day, our missions, our operations, our war fighting efforts out there. But keep in mind, this doesn't take away from the larger effort that we're trying to do in this rating modernization. As we continue to think about those things that we've discussed since the announcement of rating modernization, which is career flexibility, assignment, we're looking to make, you know, you a life expert to be successful. Yeah, no, exactly. And so the big message for me and the one I want to pass on to you is this is sort of learning in action, right? So we had a plan and we went out and we got a lot of feedback from you. And we learned that we could achieve all the objectives of this plan, the flexibility, the modernization, the ability to understand what our jobs mean, all of the transferability to possible civilian careers. We can achieve all of that without taking away the rating titles. You taught us that. You showed us the way forward. And so we learned and we're going to adjust going forward. As the McPond Center work isn't done, we've got a lot more to do to achieve all of that flexibility. That's something we also got positive feedback on was that that's a good part of this program going forward. So I guess the bottom line is we're going to preserve all the good. We're going to throw all the distractions overboard and we're going to move on and stay on course. So thanks very much for all of that. Thanks for teaching us that lesson. We're making the adjustment and moving forward. That's what the learning cycle is all about. So this is that in action. Again, season's greetings to everybody. Wherever you are around the world, take some time, get some rest, reflect on what this season means. We'll come back in the year, the new year, fit, rested and ready to fight again. Thanks again.


What's up everybody Jason here from jazby's case breaks calm 2019-20 panini national treasures basketball one box break Random players I'm players number 11 just sold out. I actually sold out last night. I think Joe didn't want to check or didn't do the The list randomizer. I think we oversold guys. So the early bird winner here is Gonna win a spot in number 12. So this is break number 11 So we're gonna do the other bird as normal top 30 customers have a chance to win a free spot But instead of winning a free spot in this break, you're gonna win a free spot a number 12 So we already took out that spot. So the early bird winner gets a spot there. Boom. Appreciate you guys So like I said, this sold out last night, but we're doing it right now. So Here you go. Let's do the early bird randomizer. Let's use the same dice roller and it'll be four to one five times five times So early bird winner wins a spot a number 12 not number 11 this one one two three Four and it's Joshua. There you go. Had the odds. So Joshua again, you're gonna win a spot in number 12 So I'm gonna put your name in right now and again You can get the next one going already as well guys. That's how as it has been posted So there you go. Put your name in there. Now we use the same dice roller guys five times here One. Oh, sorry. It's supposed to actually Hold on one second. It's actually times your name by five One second guys. Now we got five random players. So that's one two three four five So that should equal 300 Yeah, that's equal to all the player names right there 350 players. That's right. So there you go. Let's go five times here one two three four and five Kevin and five times here now one two three four Patrick Ewing all the way down to Jerry Stackhouse and I'm not gonna say every player's name But I'll do I will give you guys about 10 15 seconds each to see who you have so that way, of course You guys can still study and see the players you guys have so just as it came out first I'll show you guys and then I'll alphabetize it by cuss for a name But I know Joshua is gonna have a lot of the players So that's gonna make it kind of easier to go through this list So just as it came out all the way down to 350 So Douglas your one spot. I'll say your name players Booker Sam Gosell the Kenbei Motombo Gary Harrison John Starks James Connell Batman the bottle down to name McMillan and in there you have Laurie Markin and Ava Bradley and Other ones are that Tony Coupos John hanga you have Kyrie Shaquille Neal Fred Van Ville Joe Smith and Christian Latner Joshua you have a lot of these players So let's just actually show what you don't have and then everything else would probably be in there You guys have a good bunch 440 players Kevin You have a good amount as well, but There you go from you have Yannis in there you have Lonzo Kevin Porter, Jr. You have LeBron James Ewing Colin Sexton buddy heel Charles Parkley Some good amount of players right there Michael B. Malcolm Brogdon Jalen Rolls the Fawn Marvin Jackson Hayes and Jalen McDaniels Richard Farradas you have a good amount of players as well So from circle Dumboya all the way down to Cameron Johnson Joel and B Alan Iverson Tony Parker Ray Allen Down to Justin Holliday Sean You got Kurt Rambas Jason Tatum Jalen Brown Kareem Abdul-Jabbar in there Um Calauri all the down to Ralph Samson Steven There you go. Excuse me. So you got George Irving. You have a Trey young Kauai Leonard All the down to Louis Dampierre So I'm assuming Joshua Just to show you I'm assuming you have Zion Williamson. So there you go. You have John Morant You've probably had a Tyler hero You have Rui. I mean, I'm pretty sure you have all the all the big ones.  Oh, there's there. You got Bowl Bowl in there as well Kobe White you go John Moran in there Kevin Durant Zion Williamson So you got 440 players Good luck, man Yeah, I don't think there's gonna be any trades, right? I mean, I don't think I've ever seen a trade in this so Let's just close the trade window and What we'll do is and Let's actually roll a die and see which box we're gonna use here So there's a box on the left box on the right. These are the last two boxes of the case So for all the one two three four five six for the right Two one two three. So this box here will be used for the last break, which is already on the website So there you go Let's do this box You jazzy cutter knife blades Boom our first one we got is buddy healed. It's four out of five But I said buddy healed name earlier.  Sorry Chicago Bulls Zach Levine Joshua with that one. We have Brandon Ingram for the pelicans It's a 99 Steven with that one. We have Kenny Skywalker Out of ten it's going to Joshua We have out of three out of five Pages Toyakubish. There you go Another out of five. It's gonna rich. We have a Talon Horton Tucker to 99 Let's go to Joshua We have JJ Reddick 28 out of 49 for the pelicans One of Joshua Looks like we have a redemption Leave that there You have Jared Colver to 99 one of Joshua as well.   Let's see We have rookie patch autographs car number 115 Ignaz Brasdaikis There you go. So Ignaz. That's our true RPA That's one of Joshua. So there you go guys. That was the break last box In the store guys, there could be something monster is hiding I'm not sure what we have pulled in the first couple, but appreciate you guys I got number 12 is in the store and Joshua the early bird winner here got a free spot on the next one And then there still is an early bird spot giving away that one and that one is correctly Done there. So appreciate it guys. Jasper's case breaks calm