Julie Edwards: And welcome. I see you starting to join us. Thank you so much. Come on in and feel free to get settled. I'm Julie Edwards with Niche Academy. We will start here in about 4 min, but feel free to take some time and settle into the zoom room. If you would like. You can hop over to the chat and say, Hi! Let us know where you are from.
Julie Edwards: We do save and publish the Chat transcript on our blog. So you don't have to use it if you don't want to. But you are very welcome
Julie Edwards: to say, Hi, if you would like Hello, Texas coming in first. Hi, Texas! Hey?
Julie Edwards: Philadelphia! Hey! Colorado! Wow! Everybody's coming in now. St. Petersburg, hey? Indianapolis, hey? Trinidad and Tobago. I'd love to see our international librarians.
Julie Edwards: Indiana, hey? From the Bronx, hey, Georgia, we have a this is a huge webinar today, and I. It has been a personal goal of mine to try to get someone from all 50 states, and maybe we'll do it today. Big numbers here. Come on in and say, Hi, hey, Massachusetts, my home state.
Julie Edwards: This is one of my favorite parts of all the webinars. Just to see where everyone's from, to see everyone joining us from all over. I love it.
Julie Edwards: hey? South Carolina, hey? North Dakota? Nice. hey? Virginia, Maryland. by Texas!
Julie Edwards: So nice to see everyone, hey? From Minnesota. We're just talking about Minnesota.
Julie Edwards: Hey, Prescott?
Julie Edwards: Nice to see you, hey? Denver? Hi, Wyoming! I started my library career in Wyoming.
Julie Edwards: So we're getting some of the little States or the are the big States with little populations. We've got both Dakotas and Montana and Wyoming and Nevada
Julie Edwards: so nice to see everybody. We need somebody from Rhode island in here. I don't know if I have seen anybody from Rhode Island, but if you're just joining us, welcome to this webinar, we're going to get started in a few minutes. We open the room up a little early today, just cause there are
Julie Edwards: so many people joining from all across the country. Hello, Alaska and Louisiana, opposite corners of the country, Florida, definitely opposite corner of the country.
Julie Edwards: more joining from Trinidad and Tobago. Welcome, welcome! I don't see any of our New Zealand librarians on here yet, but every now and then we get somebody
Julie Edwards: from New Zealand. Hi, Canada.
Julie Edwards: so nice to see all of you joining us feel free to get settled in. We will get started here in just a couple of minutes. But we wanted to let everyone come in and get settled before we start
Julie Edwards: the webinar officially.
Julie Edwards: So nice to see so many of you. Hi! New Mexico! That's one of my favorite places on the planet.
Julie Edwards: This is so fun. I've traveled a lot across the Us. And lived in several different states. And so I really like II have so many happy memories from so many of these places. Can you hear me? Okay. I see some somebody saying the volumes low. Okay, good. Great thanks.
Julie Edwards: Awesome. Happy holiday. Cincinnati, hey, Connecticut! I went to school there so nice to see you.
Julie Edwards: This is so fun, so great to see everybody Hi! From the beautiful Oregon coast. Great! So one thing to keep in mind with the chat. So we do record the Chat Transcript with this many librarians in the room.
Julie Edwards: There are is often great information that's passed back and forth in the chat. If you want to chat with everyone, please just make sure that your chat settings are set to everybody. Sometimes messages will come only to me that I think you mean for the whole group. So just check your
Julie Edwards: your chat settings and make sure that they
Julie Edwards: are great, and we are right at the top of the hour, so keep on coming in. Hello, everybody! We are going to get started here. So to officially start. Welcome. My name is Julie Edwards, and I am a librarian and instructional designer with niche Academy, and I am so happy to welcome you to this last webinar of the year Chat Gpt, engaging with technology in the generative AI era.
Julie Edwards: So let me start by briefly introducing Niche Academy. We are an easy to use all in one learning platform to help libraries create and deliver effective training and measure learner success. So our platform is the perfect tool. If you are looking to increase e resource usage, move your programming online, deliver information, literacy, instruction, deliver and track staff training.
Julie Edwards: We can help you with all of those things. If you're interested in finding out more about our e-learning solution, chatting with a member of our team, or hearing what your colleagues in other libraries have to say. I'll drop a link in the chat and you can check us out
Julie Edwards: mit Ctl. And we are recording this webinar, and I will make the recording, the slides, the Chat Transcript and the audio transcript available on our blog after the webinar. So usually in a day or 2 you will get an email from me with all of that information, and you can always check our blog. I'll drop the link in the chat box to that as well.
Julie Edwards: please continue to chat with each other, using the chat box if you would like, but if you have a question for the presenter, please put those in the question and answer box it's not unusual for us to get many hundreds of people in these webinars. We're already way over 600. So questions do get lost in the chat. If you put them in the QA. Box, I will do my best to moderate those at the end of the Webinar
Julie Edwards: mit Ctl. And this Webinar series is part of our commitment to solving bigger problems together, providing continuing education in the library space. I really love hosting these, and I'm so thrilled to see so many of you here today.
Julie Edwards: Mit Ctl, and we do have some exciting webinar news, as many of you know, over the years we've built this series, keeping things practical and accessible and focusing on tools, tips, and techniques that you can immediately implement in your libraries in 2,024, we're expanding our reach to include webinars for human resource and training professionals 120
Julie Edwards: mit. Ctl. And so each month will also offer a webinar on topics that talent managers most need to help them recruit, train, and lead vibrant workforces. So if you love our webinars, you can help us spread that love by sharing this news with your Hr. And Lnd. Managers. Let them know that the quality and topics that you enjoy are now going to be available to them. I'll drop the link to the upcoming webinars on our blog, and you are 123.
Julie Edwards: Welcome to share that out with anyone.
Julie Edwards: So without further ado, our presenter today is Bo Rui Zhang
Julie Edwards: Borri is the natural language processing specialist at the Health Science Center libraries at the University of Florida. She earned a doctorate degree in linguistics and a minor in computer science. In the same year, at the University of Minnesota. In the Twin cities.
Julie Edwards: She offers consultations in machine learning, and does collaborative research that uses foundational models in different domains, such as clinical notes and natural history collections. She works as the lead in her library department, AI team and is currently teaching an undergraduate AI course to students from various backgrounds. I feel really honored to have her with us today, and I will turn the webinar over to her.
Borui Zhang: Thank you so much for the introduction, Julie. Hello, everyone. I'm bree, John, I'm A librarian from University of of Minnesota, of Florida. And today, let's talk about chat Gpt, and what's going on with this software and how we engage with technology in the generative AI era.
Let's get started.
Borui Zhang: So Chat Gbt is apparently pioneering the error of generative AI, if you haven't heard about Chat Gbt yet. By this title, I hope you have the sense of chat. Gvt probably is a great example of what a generative AI is. And throughout the presentation I will explain step by step, what is generative. AI and
Borui Zhang: and some examples applications.
So let's first to look at some of the bios about chat. Vt, here are some numbers and fax that I collected hopefully is the most updated information. So Chat Gpt was developed it by Openai in November. Actually, that was on November thirtieth, 2022. So it's been a year that this application came out, and it quickly gets so many users
Borui Zhang: to test out this chat bot. So to this month it has already gained
Borui Zhang: 108 million users, and I just quickly checked the abstract I gave for this webinar. I said that was 100 million users that was in the last last summer. So apparently there's another 80 million user gained in the in the second half of this year.
and also 1.7 billion website visits just in the single month of October this year. So what Chat Gbt are trying to do is to have a conversation with you with users towards a desired lens that user can accept. And the format user likes and the style. And then language that a user prefer
and just some
Borui Zhang: Report from the news. Some organization has found that Chachi Bt. Has passed some US. Medical like licensing exams and some schools. Universities reported that Chat Gt. Has passed some law or business schools, say exams, not
Borui Zhang: usually like a plus students, but like a B or a C plus, or something definitely better than the students perform the lowest in those units. And so what in general, what Chat Bt can do is it can answer questions obviously, because they pass all these exams, and it can also generate
Borui Zhang: data, analyze data and even code.
Borui Zhang: currently, I collect this information about Chatbot can support 50 plus languages. And I, because we all make mo most of us in the Us. We probably use English, but just let you know that. There are other languages might support this chat system as well. And lastly,
Borui Zhang: It's a fascinating fact that because of how Chatbot and General AI these days is the the designing behind it. It can lie sometimes, too, unfortunately. So we will talk about that, too.
Let's quickly look at what's the interface got? Look like, and I will show you some examples.
Borui Zhang: So the Chat Gp, also, if your tech person always follow about Gpt model version, Chat Gp is also called Gpt. 3.5 3 basic means. That's the third version, maybe between the performance is between Gpt 3 and GP. 4,
Borui Zhang: very simple interface. And you start your question your instruction. All sorts of conversations right here at this error from their website. It kind of give you some prompts idea of. Oh, this is a
Borui Zhang: very intelligent chat bot! To what can you do with it? It gives you some suggestions like, I can do creative writing for you. I can answer, or a sort basic things out for everyday life. And I can give you some tech support and maybe answer a question about how to use some software or some devices.
Borui Zhang: And so also can explain some games, not play games with you yet, but explain some games. If you're wonder how those game works.
Borui Zhang: Okay, let's say something. Let's start looking at some examples. When I first started using Chat Gbt, maybe just 2 days after open AI launch this device. I'm I'm a fan of language. I always like to learn new languages, and at that time I was really interested in learning a Swedish, and I asked, Chat gp in this prompt.
So this in this prompt
Borui Zhang: and chat Gpus answer involved 3 different languages. I asked in Mandarin Chinese about this English sentences to translate that into Swedish
Borui Zhang: and Chatty Vt. Really nailed the answer down to a very simple answer about which is a correct translation in Swedish.
Borui Zhang: So the mandarin part, I ask, please help me translate this sentence, which is a English tongue, twister to the other one, and the this picture down here. I just wanted to show you
I was goes curious about how Chat Gvt behave differently from the traditional search engines like Google being. And this is my Google screenshots. After I put the same question into it as you can see. It doesn't give me the translation in Swedish, II
Borui Zhang: directly at all. But it start talking about how this Tom Twister come out. There's a Wikipedia about it, but that wasn't really my question. So I will say I'm very satisfied about this performance. I gave very. I was given a very clear result that I needed without going through all this listed results to click on and see a translation.
Borui Zhang: Okay?
Next. I was also curious about this interactive smarter conversational partner will behave for my personal question. So I just ask a not very fair question. I say, Okay, I have.
Borui Zhang: So when I whenever you see you, that's me giving a prompt and chat. Gpt. Give the answer. I apologize for these. If this founder look a little small on your end, but
Borui Zhang: II try to highlight the keywords that I want to ask to pay attention to so hopefully, this will solve some of the reading difficulties.
So I say, Ok.
Borui Zhang: it's December. We? I might have a 2 days sustain Orlando, but I only have $20 $200 for the trip. Can you plan something for me to spend on 2 days.
and I was also fairly satisfied because Chat Tbt. Right away recognize that the budget is probably not enough to visit all those beautiful Disney Disneyland Park or universal studios and those things it says, a budget of this can be a little challenging. So that was good. And if you take a detailed look of this plans, and it's
Borui Zhang: pretty much every activity is suggested. Had to show some like to save money. Go in the morning. so it's last crowd.
Borui Zhang: and try to have some picnic lunch. So you don't have to spend money in restaurant or things like that. And on day 2 it also mentioned. Oh, you can have fun and have some free attractions. So saving money. So I feel like, okay, this is pretty pretty involved with my question. II didn't give me a a generic answer like, Oh, okay, you have 2 days in Orlando. Here we go. You must go visit these places.
Borui Zhang: but this to me this is really it's it's good. II will be really threatened surprised if it says.
Borui Zhang: Oh, you know, based on how I understand you. It looks like your your mom's birthday is coming up. Maybe you should just spend this $2,000 to buy expensive gifts for Mom, and she will sponsor you a fabulous 2 days trip. But yeah. So this intelligent hasn't reached the level. That kind of threatening me yet.
Okay, let's see another example.
Borui Zhang: As we work in the library. Sometimes we're asked for, or we wanted to create a book list either for ourselves or for clients. And here it goes. My,
Borui Zhang: the either interest domain or research domain.
Borui Zhang: I just asked, Chat gt, okay, can you come up with a fun book list about linguistics to recommend to this new friend that I just made, and and
Borui Zhang: I will say, if also fairly satisfied, it says, Okay, here's a fun engaging book in the on linguistics that you front might enjoy. So so first of all, I checked. These books are all they they do exist and author information are are correct.
Borui Zhang: and I will say is because this a beginner level course and try to use less jargon and really explain things. I think it can be fun. But so, because this is a conversational AI, you can always ask a follow up question after it gives you some preliminary answer. So I ask another unfair question.
Borui Zhang: can you add some fun? Theoretical linguistics, books to me? Theoretical linguistics. Books are my favorite, but I'm pretty sure that's not some people's up preferred options.
Borui Zhang: So here it says, Okay, sure, I will have some fun linguistics, book, and all these ones are my favorite. But maybe these are just
Borui Zhang: popular enough. I'm not sure if they are fun to general audience, but at least it tried to give me some new book list and
Borui Zhang: at least I'm satisfied. But you should try out your own request to request a book list for yourself or for your friends.
Borui Zhang: Okay?
And another really cool thing about Chat Gbt, is this it? It can. Maybe it's still limited, but it starts showing the potential to help people learn how to code.
Borui Zhang: So here is example. I ask. You know, I wanna have a very quick demo of machine learning about coding, programming and just create a small data set and the format that and then use this machine learning python package to show a demo.
and it
Borui Zhang: it did a pretty pretty good job on getting the data set and show really produce the code in the chat interface and for you to do again just a reminder that you can always ask a follow up questions from their output.
I didn't attach my second question. But if you like, you can always ask about oh, I don't understand even the first line of code. Explain that to me, and it will do a fairly good job. But over the time I also heard that it's
Borui Zhang: depending on the popularity of the programming language. This one I use python Python is considered, if not the most popular 1, one of the popular programming languages. Maybe that's why it does a pretty good job. And if you change to a rare programming language. You might not have a neat output like this. But you should definitely check out this function, too. It's pretty cool.
Okay, so let's summarize a little bit about comparing chat. Gp to traditional search engines.
Borui Zhang: I hope. By this point I have convinced, convinced to you that catch Vt. Will at least make you feel like is the output is more personalized and the interactive through conversation, getting your answers and asking follow up questions the flexibility make you feel like that's something that
Borui Zhang: traditional search engine didn't give us.
And
Borui Zhang: also but on the other side, the good thing about traditional search engine is. It's
Borui Zhang: usually after you ask a question in the search bar. It provides you a very specific list of results, and you can really evaluate
Borui Zhang: and select the links that you wanted to click, and you will probably find more
resource citation. About the information you acquired rather than the current version of chat. Gbt.
Borui Zhang: also chat. Tbt generates answers versus the traditional search engines retrieve information. So the oh, just a simple, simple way to answer. This is
Borui Zhang: the generated answer, you see, is not the same as another person might see. Sometimes, even if you ask the same question, depending on how you organize your question, you might get a slightly different answers. So all these generated answers are through language models, generated language models.
Borui Zhang: and the but the traditional engine search engine will trying to find out the the keywords or the information you're looking for, and take a guess, and directly give you something that's already an Internet or in their
Borui Zhang: in their database to provide you, that piece of information is not generated. Okay? And another not little bit less technical. Fact is, currently I don't know, was our chat. Gvt has inserted any advertisement. But, as we all know these many years by using the traditional search engine. As advertisements are everywhere.
Borui Zhang: Okay?
So
Borui Zhang: yeah, chat Gbt is a example of generative AI. But what is generative? AI. I'd like to answer this question by first to discuss a little bit about what is AI.
Borui Zhang: So here's a definition. Surprisingly or not surprisingly, the definition of AI is actually coming, not the recent years when Touchy Vto got famous or other application, AI application got famous. It's back in the forties, 19 fortys. So
Borui Zhang: the this.
Borui Zhang: this first definition says, it's a system. AI is a system which amplifies people's own
Borui Zhang: knowledge and understanding.
Borui Zhang: So after reading this, you you might feel like, Oh, okay, this is a little bit
Borui Zhang: conceptual. What's the concrete implementation? Do you have example of showing AI it's little bit hard to get to that point.
Borui Zhang: And now we have to in to know the second concept which called machine learning. If you if you Google, if you're chat, Gp, this question that you will find there's so many people trying to offer this definition of motion learning. I'm just picking my favorite one from Andrew on.
Borui Zhang: so.
Borui Zhang: machine learning, Ml, is ace, the science of getting computer. Okay, I know the devices computer to act without explicitly programmed. Okay?
Borui Zhang: Programmed. Okay? I sort of know, but explicitly what is explicitly and what is what's the meaning of non explicit then?
Borui Zhang: And I will show you a example in minutes. But I also want to
Borui Zhang: show you that it.
Borui Zhang: So the concept of AI and machine learning is currently being the common understanding is machine learning is a sub field of a I, and is also it's. It is a fair question to ask.
Borui Zhang: what's other? What what besides motion, learning? What are the immediate immediate subfield of AI.
currently. No. There are other type of
Borui Zhang: structures that inside of machine learning. But parallel on the same level with machine learning as a subset. Of AI, currently. No. So also, that's why these days people kind of use this to terminate interchangeably.
Borui Zhang: And okay.
I wanted to explain a little bit more about this explicitly programmed, so that to help us understand, what do we mean by without explicitly programmed, based on AI.
So think about you need a dog sitter. and
Borui Zhang: you, the this new person doesn't know how how. How the emotion of your dog and you, and luckily you you have this notes very. About
Borui Zhang: what will make your dog happy? What! Make your dog sad? So you keep all these notes
Borui Zhang: and so explicitly programmed means you have all of these situations, and why they're happy were set in a list. So when that babys, Doc Sitter is here, and they can check on the situation to see whether that condition will make your doc happy or sad.
Okay, so that's explicitly programmed. That means you need.
Borui Zhang: To figure out all of these situations that can make your dog happy.
and as we can imagine, the list can be very long, and maybe still not long enough to include all of the conditions, to make a prediction, to be helpful for your
Borui Zhang: new dock sitter.
So what AI is doing? Explicitly. Learn from model to trying to solve the same problem is
Borui Zhang: it will collect all the existing information from you
Borui Zhang: and formatted like I showed this pipeline here to do some pre-processing, to just make sure these information turn out to be a understandable way for a machine to learn to train itself to really understand, not line by line, but have a general understanding with this words or information around and produce a model.
Borui Zhang: And what's after that is to predict a new.
Borui Zhang: we'll be able to predict a new condition. This condition doesn't have to be already listed in your
Borui Zhang: in your notebook, but this model hopefully, is smart enough to tell you whether your pad is happy or sad. So that's basically what the implicit means from AI
Borui Zhang: learning also this word interchangeably with learning and training process look like, so in explicitly learn, it might not be so. What it learned might not be extremely direct and translatable for a human to understand. But the task you we can see the output being delivered to us are either sad or happy.
So many of the AI applications are doing this predictive task, making a prediction. helping people make a decision or give you a recommendation.
Borui Zhang: So in this case is helping you to let your new Doc sitter to get a better idea of your docs emotion.
Borui Zhang: So
Borui Zhang: after knowing how AI learns is a good, hopefully, the train model are good learners. and what are the popular learners? Popular models has been existing.
I have 3 examples for you today.
Borui Zhang: The first one I want to. Say a little bit. Is this alpha goal?
AI learner, if it learns
Borui Zhang: the any game. So it happened to be the go game. It doesn't matter if you know this game or not. It's a board game. But what I wanna emphasize is
Borui Zhang: AI trying to learn the rule of this game and try to come up with a strategic way to play to as a as one side and against a component. What's really, this has happened in 2,016. So this
Borui Zhang: AI learn learner, this model after it got massively trained on this game. It's it sent i imitation to the world. At that time the world champion Lacey Dong. And they had this human champion versus this
Borui Zhang: model, and apparently the model win the game, or maybe lost one and went to, but eventually that
Borui Zhang: that's game made Alphago a famous. So here is an example of
Borui Zhang: once we learn game rules, and we made we could make AI a good player.
Borui Zhang: and AI can also be a good artist. We're we're good depending on how you how you review art on your own opinion? little bit later. This in. I think this is not 2,016, I think, started from 2021, 2022, and 2023. This 3 years. Also from same from Openai, where the Chat Gpa developed Dolly was a computer vision AI which takes some
Borui Zhang: text prompt, prompt, or we're we're not, but it analyze image data. And so this is example of if we say, red appenda scientist mixing chemicals, and it draws a picture like this for you.
So so this model is also getting smarter in the past 3 years. Now we have this Dolly 3 version, which is enabled in Gpt. 4 version as well. So if you are a pay, the user of Chat Gbt, you could access this function as well, and give. Give the prompts, and see how that this device will draw a picture for you.
So that's what we called a smarter learner, for as an artist.
Borui Zhang: and the third one, of course, is the language model large language model because it's so big as Chachi bt, a conversational partners, it really trying to learn human languages and trying to see what you want to do. And how can I output the thing you really wanted, and in a accessible way.
Borui Zhang: And I here, I wanted to emphasize that
Borui Zhang: computer vision, this one Dolly, and chat Gvt or GPT. 4. This, too, are generative ais.
but not the game players. So.
Borui Zhang: as I described earlier, right when when it gets some instruction to draw a picture that's generating
images. And when we when they get
Borui Zhang: prompt to
Borui Zhang: have a conversation with you, that's also generate some information, either text or some other
Borui Zhang: structured things that can be expressed by natural languages. But game playing is mainly using AI to make a decision, to make a move, make make a move on the board, and not necessarily generate anything new.
Borui Zhang: And
Borui Zhang: so what? Ha! What these celebrity learn AI learners models have in common is is is hard to be. A celebrity, is hard to be a good model. So
it turned out, they all. They all used this special
Borui Zhang: machine learning technique called the deep learning or neural network models. To implement, to learn how to play game, how to draw a picture, and how to have a conversation in a very intellectual way.
Borui Zhang: don't wanna say too much about deep learning, but just to let you know deep learning is considered a sub field of machine learning.
Which is, we just learned that machine learning is a subfield of AI. And so what deep learning this idea is also not very new. It's actually started around 1960 s.
Borui Zhang: When, as the original thought is inspired by human, how human brain works
Borui Zhang: and trying to see if neurons can all work together, split the tasks to acquire some information or gain some skills to perform a complex task tasks. And but it didn't get implemented right away in the sixties is because the computer power and how quickly we can collect data are not that impressive at that time, but.
Borui Zhang: needless to say, these days computer power are getting very, very cheaper and faster. And the way we collect data are much sufficient in the I would say, recent 20 years, too.
Okay, so what good deep learning is good at?
Borui Zhang: It's good at understanding complex data set.
Human language is really hard to understand.
Borui Zhang: And join picture is hard to understand. I'm not a good drawer, so I can. I really rely on something can draw a good picture for me
and large, it can also handle larger scale data. So the traditional statistic model. Usually they can handle
Borui Zhang: data, say, in the scale of the entire Internet
but deep learning, because of the architecture built in that way can handle this massive amount of data. Particularly, I wanted to mention this
Borui Zhang: particular model in deep learning. Neural network is called transformer model.
The transformer model is really good at handling sequential data. Sequential data means
Borui Zhang: it's the data set is a sequence such as languages. The sentence we say, right? There's for example, in English that has to be subjects object verb object. So those sequence sequence matters. Data set.
Borui Zhang: Sometimes is a challenge for AI to learn. But it turns out that transformer has been doing really good about this, just to give you a quick example of why, transformer, how transformer.
Borui Zhang: very high level to show you how transformers learn a language.
Borui Zhang: This is probably not the way we learn the language. But that's the way transformer trying to learn.
Borui Zhang: So let's say this sentence, the animal didn't cross the street because it was too tired.
Borui Zhang: So the input of the model. So is this sentence, and the output is the sentence itself. So it tries to learn from itself.
Borui Zhang: And it's also trying to learn the relationship between words and tokens. Say so here. This little figure means
Borui Zhang: in some of the layer. In this model the word animal is associate much closer to the word, it not the word, because not the word, the, but it.
Borui Zhang: And so II hope, as in many many of us are English native speaker. You should also agree that maybe the it means animal. Maybe is. It's extremely case. It means street but is less
Borui Zhang: likely to have this. It means something like a cross. So basically, this is how well transformer started handling.
Borui Zhang: how to understand human natural languages. And here.
Borui Zhang: in a nutshell, the simple goal of having a language model, because these days we keep saying a large language models and what does it mean? Right? So the large language models
Borui Zhang: our language models. It just is is larger. It handles trying to process more data set. Try to be smarter, trying to be able to handle more application tasks, and the they all share the goal. The goal is pretty simple to predict the next word token. Some languages don't have this concept of words. So that's why I quote unquote the words there
Borui Zhang: in a in a very naive way, if we don't have these smarter models.
Borui Zhang: we will just what's the most predictable next words in English, it's gonna be those d it and those last meaningful the words, because they usually occur many times in a document. So those are. The words are very likely to occur right next to other. If we only count the frequency of words, but that's simply not true. For how human! Think
Borui Zhang: what should be the next words. So over the years, I will say, in the last 50 years, people are trying to figure out how to train machine learning model to understand human language better. And today the top performers are transformer model.
Borui Zhang: Okay?
So I think now, we have all the ingredients we need to actually
Borui Zhang: interpret what Chat Gpt is.
Borui Zhang: And I suggest that we read, Chat Gbt, this
Borui Zhang: this name. In the back whores way.
Borui Zhang: So Gpt is short for a generative, pre-trained transformer. We just learned that Transformer is a type of deep learning model.
Borui Zhang: Deep learning is a subset of machine learning and transformer models are particularly good at handling sequential data like languages.
And so next.
Borui Zhang: So that means this model uses transformer architecture.
Borui Zhang: Pre-train, pre-trate means I or it's it's a model that already trained on various language, natural language, processing or language tasks like summarization, or
Borui Zhang: ex especially, which word coming, the the next of depending on the previous word or previous context. So a model that has trained. So that's
Borui Zhang: one of the steps they trained. This transformer.
And another
Borui Zhang: modifying word for this term is a generative like we said
Borui Zhang: generative. You ha should be anything that should be anything that can generate something. It can be data
Borui Zhang: text data or image data.
Borui Zhang: And my guess is. Yes, there is not no video yet. Either. You're using chat Gpt, threefive or 4. There's no video yet. But I think maybe let's see if they in in the future they will do this. Type of
Borui Zhang: data generation. And eventually a chat
Borui Zhang: chat must be conversational based. That means it has to figure out a way to remember the questions you have asked
the output. It has provided you to remember enough context to follow along the conversation.
Borui Zhang: So that's where it got pretty
Borui Zhang: talented.
Borui Zhang: Also, just to show you that Chatbot is not some all ab application, all of a sudden drop on the earth of the planet, and then people start using it. There is a very long concrete history of building language models.
Borui Zhang: There are many language out models outside of a chat. Gp family. I'm not including those today, but just to show you a little bit about the development of Gpts.
Borui Zhang: The number 3 is not a random number chat, Gt, 3.0 is not random number. They open and I picked it's actually started with GPT. One back all the way. How many years now?
Borui Zhang: 5 years!
Borui Zhang: To start with! Gpt. One where the parameter is already 110 million. It's already a pretty big number.
and it's only doing some limited tasks.
Borui Zhang: that relevant to natural languages. Not as today. How intelligent it can they can understand or even provide you a joke or something to the 2,023. Gpt. 4. If you wonder about
Borui Zhang: what does parameters even mean that really cause these are all deep learning models. So there are many types of parameters.
And you can simply
Borui Zhang: understands this as collective neurons. And, let's say, neurons are detectives, and a big gathering of detectives that
Borui Zhang: trying to work together to tackle some specific language, natural language, related tasks
and the more
Borui Zhang: higher number of parameters you see, that means.
more
Borui Zhang: heavier, more complex tusks they are trying to figure out, and of course the more number is shows the longer training period or longer.
Borui Zhang: A more cutting edge technology needed to train this model. So GPT, 3,
Borui Zhang: because 3.5 and point 4 are not open source. So we don't really know exactly the the number of parameters. But the last update is 175 billion. So compare just 2 years from 2022. Is more than
Borui Zhang: 10
Borui Zhang: wow! Or even 20 times about parameters, and it handles more and more complex. Language, natural language-related tasks.
Borui Zhang: Okay? And being said, all of these, actually.
Borui Zhang: the secrets. The secret sauce, for how there are so many large language models out there. Why, Chatch, vt get outstanding and openly, I did say, why, that's the case. Without really letting you letting you know all the details. But they did mention this nuance technique they're trying to get.
Borui Zhang: which is called reinforcement learning from funer human feedback short for RLHF. What? That the concept on the definition is a little bit theoretical.
Borui Zhang: It's so. It's a policy model that governs the generation of outputs within a given environment by controlling the state. What does even mean? So
Borui Zhang: think about
Borui Zhang: the same dog case? Right?
Borui Zhang: Life is complex. Life is difficult. Sometimes. You don't just wanted to make sure you know, exact the emotion that your dog will have either sad or
Borui Zhang: happy. You also want to know all the behaviors this dog can have once dog gain really good intelligence. The behavior is sometimes not most of the time, not in your control. So you're trying to
Borui Zhang: provide an environment, a principle, general principle for your dog to behave well, to be a good family member. So it's hard to really label each action from your dog that's good or bad it has to be. It's usually a a sequence when you can tell. That's a bad
Borui Zhang: behavior, and then you would start telling your dog. No, don't do that. It's harder once once if we see this from the timeline. So a policy model basically means in a large, like a large language model is
Borui Zhang: really try to control the output, to make sure this output is not not a violence. It's it's ethical output
Borui Zhang: to to not be unambiguous to to be friendly. And all these are really hard. All these principles are hard to label. So what open AI did is to hire human evaluators to go, to go look at these tasks
and rank this tasks, based on their preference to help create an environment for
Borui Zhang: Chat Gbt to behave well and not surprise people in a really bad way.
Borui Zhang: And
Borui Zhang: and yes, so you don't. You definitely don't wanna make a mess at your home. So that's probably also why hire a dog sitter is much cheaper than hiring a dog trainer. So right trainer has much more job to do than sitting a dog.
Borui Zhang: Okay? So
Borui Zhang: here is just my limited tips of how do we do a little bit?
how we can improve our use experience by using chat Gbt on our end. So
Borui Zhang: W, we definitely need to know the challenges of a current, large language model is to really react to it as as a user.
Borui Zhang: And ma, many of you may have heard about this challenge already. Right and large language model lies, unfortunately, sometimes hallucination. So if you so say the book list, example
Borui Zhang: it you. It gives you a list, but you really don't know either those exist or not, if you don't check on it, because, remember, the simple goal of a large language model is just to predict next likely
Borui Zhang: occur token a word token is not really checking things. Oh, is this a is this a concrete book? Or is this a rather a valid website to to give back to you. It's it's a language model is focusing on giving you a great experience on having a conversation. There's there's also no fact check on the policy model. I just said II don't. I don't know if there's
Borui Zhang: fact checking human being says, Oh, this this model! This task is good. Pass out that one is not a fact, then rate it down it. The principle II don't think it has included fact checking bits, and but lo! The all these large language models are trying to co tackle this question and trying to make the situation a little better.
Borui Zhang: Some more serious concern.
Borui Zhang: about privacy like your user. You have to give your have have to write get an account, and how much? And the prompt that you made will be automatically saved by
Borui Zhang: those these companies, and how you how they will handle your data, your data, and how their policy will change over time is a little bit uncertain these days, and also focus on training
Borui Zhang: materials. These smarter models are always trying to collect as much as data as possible, and some of them might hit the copyright com complication, even though the AI, the content AI generated are considered a new piece of information, a a new piece of
Borui Zhang: the way to put language together, but because it's learned from human intelligence. And how should we handle that question? It's still unsettled. It's a trendy topic. And
Borui Zhang: the last challenge, I
Borui Zhang: I think, is also relevant is because these the current models like Chachi Bt are not fully open source. So we really don't know the source code. And so if you ask
Borui Zhang: analysis question is really hard to evaluate the quality of that analysis, because you don't know what's behind, how they train that analysis, how you get to your results? Based on this unknown training process.
Borui Zhang: So what's the general suggestion on this?
Borui Zhang: Especially to try to
Borui Zhang: get the answer you you need in a good way is to ask question more specifically, provide a clear instruction as possible. Don't just say, like plan a trip plank trip 2 days in Orlando. You can. You can say a little bit about maybe your where you're from? What are you interested?
Borui Zhang: And it might give you more
Borui Zhang: satisfying output from your request and provide relevant content.
So probably how happy your dog is is irrelevant to how you wanna
Borui Zhang: have a 2 days trip in Orlando, so make sure that the yes, the more contact information, more information, is better for getting a better result, but also make sure those contacts you provided are
Borui Zhang: are relevant. Of course, avoid ambiguity. Human languages are highly ambiguous. I know it's really hard to reduce this level. But we should try to
Borui Zhang: ask unambiguous question, especially the topic you care about might say a term that term might mean it's totally different thing in different domain. So be aware of that.
And
Borui Zhang: also this. But here research policy in your you in your University or Research Institute. This is more for researchers. If you make sure that you understand all these are web web based. So that means there's a server and anything, any.
Borui Zhang: any. Information with the data set you're trying to let online server to analyze, will expose your data to some party that you might know.
And last, but not least, is this.
Borui Zhang: golden rule is no fixed golden rules I got at my library. I got a lot of question about. Oh, you kind of give me the best prompt ever to boost my work. Efficiency. It's it's not really real for so many reasons. 1 one is these model, even Chat Gbt itself. It's trying to learn more information from the Internet or could collect new data. They keep evolve.
Borui Zhang: And even you have the same question you asked in different months, you might get totally different answer. So it's usually not reproducible
and to
Borui Zhang: to to have a chat. Bot is really to give us opportunity to ask questions and simply taking a
Borui Zhang: fixed rules. You're kind of lose the opportunity to come up with your own question, because, remember, it will rem. It will help you get deeper and deeper. You don't have to ask a perfect question from the first shot. You can always ask a follow up question. So my general suggestion is, don't be too obsessed with finding the best golden prompts which might just not exist.
Borui Zhang: Okay? So finally, what should we expect in generative AI, these are all my already happening. I'm just summarizing the current observation. So model competition. While I was preparing this slice I think Jim and I came out and Google had a really good a press conference about how Jim and I is good, and
Borui Zhang: so we might in the future we might see a lot of nuance. Generative models came out, and they will compete. And I guess that's a good time for users. So we just we should just try it out hopefully, in a affordable price to be a user.
Borui Zhang: It's it's a little bit. We don't have the control, but that's how it is right now. And so in business, large language models are very smart right now, and also with the computer vision, the drawing models. So I was, I would say, many devices like our traditional theory, Alexa, all these models, my getting this smart model in their current application and provide you a better experience with these different applications.
Borui Zhang: A more relevant area is education for all of us. I do really think, even in my current teaching, I do think these
Borui Zhang: smart models generative AI models will reshape teaching and learning environment. Needless to say, students could just take a exam question to ask Chach Gbt. And put their answer
Borui Zhang: in the homework, and then for you to grade. That's probably not the ideal way anymore to assign a homework. So it's it's a time for educators to start rethinking about how to teach how to test the students understanding of of knowledge.
Borui Zhang: And also on the learner's side, too.
Borui Zhang: technology tech field. So II showed you a little bit about how chat Vt can help coding. And this is very potential. It's not just to learn the basic coding. You could also. So in in natural language, processing domain. A lot of pre-processing tasks are can be
Borui Zhang: can be done by using large language models depending on the specific task. But many of them in these days can be done in that way, which is very, which is a very promising trend in the future. AI development, isn't it. Isn't it fascinating? You use AI to develop AI in the future.
Borui Zhang: And lastly, user experience adapting over the last 12 months, this Chat Gvt period. It's the opening. I changed quite frequently about the payment of our user, wh, which model you can access how to pay for it. So again, this is a little bit passive on our side. We we just have to agree their agreement if you wanna use it. But currently even you are a paid user.
not very stabilized the server, especially if you use the voice speaking feature about
Borui Zhang: giving. Ask question, invoice and receiving answer. Invoice that one is particularly bad.
Borui Zhang: Okay, here's a summary. Thank you so much today.
Borui Zhang: to spend this hour with me. We started with by reviewing how Chat Gt functions. And I showed you some simple data from my personal experience. And we discussed, what is AI, what is machine learning and deep learning how they're related with each other, and why Geneva AI is a sub domain of machine learning. And we ha!
Borui Zhang: After that we talk some details about chat, Gpt, model architecture. What's the goal of a language model? And what's the challenge? And future expectations in general, in large language, model computer vision model and as well as the general. Chat. Gp, generative AI models. Okay? And that's it. My last message is, this is the best time to learn, because you you do. It's not perfect. But you do have a system to assist you. If you wanna learn something new.
Borui Zhang: Okay, I'm ready to take questions. I'm sorry I I went over a couple of minutes. No, that's great. Thank you so much. You're getting a lot of thanks in the class or I'm calling it a class
Julie Edwards: webinar. People are saying it was so insightful, so valuable, interesting, informative. Thank you very much. There are a lot of questions. We probably won't get through nearly all of them but one. There's there's a general, there's a general concern about the lying and the hallucination? And is it actually lying? Is it lying to teach itself?
Julie Edwards: How do we? How do we deal with it? And how do we help our patrons or our students understand that everything it's giving back to us is not we shouldn't just trust it blindly. So it's an it's an information literacy question. Basically a critical thinking question, any tips that you have from working with the librarians at your university or instructors or your students?
Borui Zhang: Thank you for that question.
Borui Zhang: I think, for now, because we just stepped in the first year of Chat Gp, not even other language models, I will say the safest way is to not use
Borui Zhang: chat. Gbt for very serious fact-checking tasks.
Borui Zhang: Chat. Gbt is currently doing amazing job on creative writings like, Oh, come up with a book list and templates about the holiday messages. Those are really good and some minimum tasks that you you have to be a user that exactly know what you're expected
Borui Zhang: from the output. Once you ask
Borui Zhang: those questions, if you don't know about that, that's a little bit dangerous to
Borui Zhang: just say, okay, here's my data set. Analyze it. And I will say Hallucin. But on the other side, hallucination, they're trying to
Borui Zhang: develop new models, actually to tackle hallucinations hopefully in the future it will be solved, but definitely, not right now. Again. This is a from a learner's perspective.
Borui Zhang: Do really think about what are the questions you should ask. What you are trying to gain from it. If you're if you want to find
Borui Zhang: the concrete
Borui Zhang: answer that can track all of the resources. The current chatty team might not be the ideal one for you right now.
Borui Zhang: That's an excellent answer. Thank you so much.
Julie Edwards: There have been several questions about the differences in the paid versus the free version of Chat Gp, can you speak to those differences.
Borui Zhang: Mo. The highlights one in my brain. But I think open AI website has the best explanation of what's the pay version? Was the non pay version. So the pay version definitely has the Dolly, the computer vision model embedded. That means
Borui Zhang: you don't. So chat. Gbt, right now you you can't, you say, draw a picture for me. This is, draw a picture of teacher teaching in a classroom it can do that. But Gpt. 4, the paid version can do it, and lately the GPT, 4 version, can O also have this
Borui Zhang: tiny applications that can let user to create your own application. Put some information. Be careful on all of information you put in to come up with your personalized chat. Bot! That answers your question.
Borui Zhang: And what else? Oh, and the the pay the version. Also.
Allows you to upload the files so you can upload a a page of text and
Borui Zhang: and it even you don't ask anything. It probably will tell you what this thing is trying to say. Yeah, up upload file and analyze files is also possible. Now, but again, this is our highlights I have in mind. The website might offer you more. However, the paid version only allow you to have 40 questions with within every 3 h. If I recall this number correctly, so be be careful. Don't waste your questions.
Julie Edwards: That's great. Thank you. And that brings us right up to the top of the hour. Bori, thank you so much for this, webinar. I people are just so happy. We really appreciate your time and your expertise and giving us this broad but also deep overview of what this technology is.
Borui Zhang: Thank you so much. Thank you for having me happy holidays.
Julie Edwards: Thank you all for attending this last webinar of 2023. We really appreciate your presence today. The chat has been fantastic. I am saving it, and it will be published on the blog so that you can share it with your colleagues, and go back all over all these great notes and resources that you've all shared with each other. Love it. When a bunch of librarians get in the room. Thank you all again. We'll see you in 2,024. Have a great rest of the day. Everybody bye, bye.