#96 GPT-3 and our AI-Powered Future

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Hello everyone, this is Adele, Data Science educator and Evangelist at datacamp. One of the most exciting, awe inspiring developments of the past few years in data science has been the rise of large language models like GPT3. In case you've been living under a rock, generative models like GPT3 for text, Dall E2 for images have shown the incredible potential for what an AI powered future would look like. Whether it's automatic image generation from prompts, sophisticated code, autocomplete, the possibilities are endless. And this is why I'm so excited to speak with Shubham Sabu and Sandra Kublik, Authors of the O'Reilly book GPT 3 Building Innovative NLP Products Using Large Language Models. Throughout the episode we talk about the rise of large language models, the underlying technology and how it's different. Why GPT3's API interface is revolutionary in machine learning and potential use cases, its risks and limitations, and much, much more. If you enjoyed this episode, make sure to rate and comment, but only if you enjoyed it. Also, I wanted to let you know that this week access to datacamp Premium and datacamp teams is completely free. What does this mean? It means that all you need to do is register to gain access to all of our learning content, access to datacamp certifications, workspace competitions, and much, much more. Make sure to take advantage of the offer with no strings attached. Now onto today's episode. Shubham, Sandra, it's great to have you on the show.

Yeah, pleasure to be here. Thank you so much for having us.

I am excited to talk to you about GPT3, your book on it and what it means for the future of AI and data science. But before we get started, can you give us a bit of a background about yourselves?

Yeah, sure. My name is Shubham and I started as a data scientist initially and during my time as a data scientist I got to work with a fintech firm where I established the entire machine learning and data practices, the technology infrastructure, all from scratch. Then I thought of doing something for the AI community and moved into the role of AI evangelist where I got to foster the ideas and thoughts of the community members throughout the spectrum. Right now I'm working as a senior AI evangelist at Ginaai, which is a neural search company. Around 2020 when the OpenAI API got released, I was in the early members who got the access to the API. I was literally amazed by the things that it can do and have experimented with the API a lot, have been posting a lot about the use cases that we can do, started writing blocks on it. That's how it all started and that's how it all converted into orderly book where I got in touch with an orally editor and she was really excited to have something out on this topic. That's how it all got into the play.

Yeah, I have untypical backgrounds when it comes to AI. I was a liberal arts major and I was always drawn to creative projects. So I used to think that I'll become an academic or a writer. I was experimenting a little bit with movies as well and then I pivoted to startup ecosystem because I always loved tech. I always loved how it enables us to improve our lives, to make them as frictionless as possible. So I was always in love with it and I just wanted to be closer to it. And also I felt that being an outsider at the time, that the breakthroughs happening in AI are something to be observed and to watch closely and I wanted to just get involved in whatever form I could. I started with setting up a hackathon community for enthusiasts. That's how Deep Learning Labs was established. And then it organically grew into an incubator for a startup, Snakeskrit. I also started a YouTube channel just to feed my curiosity and give myself some space to discuss and think through these AI breakthroughs that were the most appealing to me. And obviously everybody here I guess, was mind blown when GPT3 was launched. I was lucky enough to also become the early tester in the beta. And yeah, I guess around the time Shabbam reached out to me, that's how he found me, I think through the videos and he offered me this awesome project to write a book about it, to learn more about it. So of course I just dived into it and for the past year or so I was working for Neptune AI and right now I want to continue on this NLP path. So I just joined Cohere and we are also launching the book. So it's super exciting.

So I'm excited to talk to you about GPT3 and your book on it. There's a line from Ernest Hemingway's the Sun Also Rises where one character asks another how did you go bankrupt? And the other character responds with two ways, gradually, then suddenly. I'm always reminded of this when looking at a lot of the results from large language models like gp. It feels to me like an outside observer, like that the AI community has been doing a lot of gradual improvements to NLP systems and it has suddenly resulted in awe inspiring systems and outputs. I wanted to set the stage for today's conversation by first understanding what makes GP3 so interesting and how is it different from other machine learning systems that we're used to.

So when we talk about GP3, it always makes sense to look at a little bit of history from where it all started and what's the origin of all these language models. So it goes back to 2017 when Transformers got introduced and it has changed the direction of nlp, how the field has been looked, how the field will progress and how things will work in it. So Transformer was one of the revolution reduced of attention, which is basically looking at certain things similar to how a human does. So an AI model which can exactly replicate how a human brain works. That's where it all started. Then researchers at Google, OpenAI, Microsoft, they started experimenting with how we can take this forward and make something that is usable for general public or audience or someone's an engineer or a data scientist and how all of these use cases can be put to use in real world use cases and how businesses can be formed on top of it. And that's how the GPT series got originated. So we didn't directly land it at GPT3. It was a part of generative preteen series. We had GPT1, we had GPT2, then we had GPT3. But what changed with GPT3 and what makes it so exciting and what makes it so revolutionary is this was the first time that we saw that an AI model, NLP model or a language model can do task, can do any number of tasks. It's not limited to a specific task. What we have conventionally seen in nlp. So just to give you some context, like machine learning model, how it works is you give it a training set for a specific task and then you train it on the training data set and that's how you inference it, or that's how it performs a task, a specific task on which it has been trained on. But GPT3, because the data it has been trained on comes from such a big universe of Internet, it can literally perform any number of tasks that you can think of. It is the first time that we have seen a task agnostic or a task independent AI model, a truly generalized AI model that can perform any number of tasks. The other good thing about GP3 is the way you interact with the model. Previously it was a thing that if you have to interact with model, you need to have a technical prerequisite, you need to have an understanding of a programming language, you need to have data set, you need to understand how training works, how inferencing works, how you can deploy it. But GP3 just dissolved all these conventional paradigms and gave you a simple user interface where it is as easy as talking to a human. So you just go to the playground, you give us instructions in simple English and the model will come up with an output. It's like collaborating with a human or collaborating with a buddy or a subject matter expert. So you can also think of GPT3 as a subject matter expert for n number of tasks. So that's what makes GPT3 so special. I'll pass it over to Sandra to throw some light on that.

These are great points. What I would add at the top of that was that introducing GPT3 in the form of an API and giving a broader access to it to developers. And as Shaba mentioned, the interface got so simplified that people without necessary, necessarily heavy use cases. So after a short period of time, once the initial access was released, you could see all these use cases just emerging from the community, testing it, and they were just like mind blowing. Just translating legal documents into a simple language or analyzing the recipe of the product and then translating it into what are the ingredients that are harmful, what are the ingredients that are good for you? All sorts of things really. And I think this is, this was one of these like radically new things that got to interact with this powerful model via a very simply designed API and you got to actually explore different use cases at the top of it. And we talked with open API creators in the interviews for the book and they admitted themselves. For example, Peter Wellinger, VP of Product There he admitted that they themselves, when releasing the API, had no idea what it is capable of and they wanted to give the access to the community so that they can show them actually the boundaries, the limits and explore further. And that was just a brilliant idea and it got us where it is right now.

That's really exciting and I want to unpack all of these elements with you. So let's start off with the technology underlying GPT3. I mentioned a bit here the gradual work that the AI community has been doing to improve the technology underlying GPT3. What are the changes that have happened over the past few years that led to these high performing results?

Shabam has already mentioned that we had this major paradigm shift in the NLP once the transformer architecture was introduced. So we start in 2017 with the famous paper Attention Is all youl Need. Where this architecture was launched and the backbone for transformers is a sequence to sequence architecture. Basically transformer model processes a sequence of text all at once instead of a word at the time. And also has this powerful mechanism that Shabham has mentioned called attention. And transformer architecture is definitely a key thing to highlight when you think about the changes that have led to the birth of GPT3. Another one was that with time in the NLP space language models, initially they weren't so big, they weren't so big and impressive, but they started to become bigger and bigger and the data sets that they were being trained on were becoming bigger with more and more data availability, with open source projects where researchers put together this massive data set and it was just easier to train these models on. Another thing that sort of parallelly emerged was more and more computing power at the hands of the guys that have the computing power. And so it allowed them as well to train more and more powerful models and experiment with bigger and bigger architectures. Another one was that okay, you have this powerful computing power, but at the same time you want to find ways in which you use this computing power in economy efficient way so that you don't run out of it, so to speak. Simply put. And one of the, one of the techniques used in GPT3 was pre training the model and this basically helped to reuse all this initial training, this very lengthy process of training of the model to be applied to other use cases with a little bit of fine tuning or a little bit of tweaking to a particular use case that you had in mind. So that was like a big one as well. Shabam also mentioned that there were iterations of GPT before we arrived at GPT3 and there was GPT1. I think it was introduced in 2018, where it had I think around 120 million parameters. Then GPT2, 10 times bigger, with a bigger data set as well. And then eventually GPT3 which was 100 times bigger and also had 100 times heavier data set. So they were constantly working with opportunity to have bigger data sets and also with opportunities to have more computing power. And also seeing that scaling leads to the emergence of more powerful language capabilities. And these models were actually being able to do more and more perform better at variety of tasks. They went this path and that's how we arrived at GPT3 with which hit the sweet spot.

That's really awesome. And I'm excited to talk about the scaling aspect here of GPT3 and where do we hit a wall when it comes to scaling. But you mentioned earlier in your chat here, Sandra, especially the API model and how transformative it is democratizing access to working with such powerful models. The API model of GPT3 is definitely interesting and I think it does introduce a paradigm shift in how we interact with complex AI systems. Even I, someone who hasn't necessarily coded a lot in the past two years, found it very intuitive to work with GPT3. How does the API mod change how we interact with ML systems? And can you walk us through the concept of prompt engineering?

So we have seen like in 2020 it was the first time somebody has introduced this API based approach and previously it was all hosted locally. You train your model, you host it, you get your own data sets and that always lets you hit up wall on the task that you want to perform. So you can only reach to a certain limit or certain level of accuracy when you are training on your own data set, when you're hosting on your own, because there are technology limitations, infrastructure limitations, hosting limitations and whatnot. But what OpenAI decided and what I think made it revolutionary was giving GPT3 access in the form of API. It allowed people who don't understand coding, as you mentioned, right? You haven't coded for quite some time, but when you use GP3 it is very intuitive. You don't feel like you're coding something or even you're interacting with a sophisticated language model. So all of these things doesn't come into picture. When you interact with GP3, it's as simple as talking to someone and getting the output by giving the input. And input is as simple as natural language or simple English. And you get the output of whatever you want. It can be generative output can be search output, it can be a number of cases, classification, entity recognition, all the things that is possible with conventional nlp. And the process of giving this kind of input and getting the outputs, which is a kind of organic or natural process, which is as close to giving an input in English is prompt design. So it is an intuitive process for people without any ML expertise or ML background that they can give a textual input to model in simple English and get the desired output and whatever you want. So let's say if you want to write a paragraph on NLP, what you have to do is simply ask GP3, can you please write a paragraph on NLP? Or please write a paragraph on NLP with this and it comes up with a paragraph. It is as simple as that. Some tips on prompt design and engineering that you have to keep in mind While interacting with GP3 is to understand what GPT3 knows about the world and giving the input in such a manner to leverage the knowledge of GPT3. So GPT3 is not great at giving you the factual answers because it generates things on the fly. So it is really good when you have to complete something, when you have to create something, when you want to go creative and when you want to put abstract things out in reality. Right? Because we have seen a lot of artists, lot of illustrators and people from design background getting attracted to GPT3 and getting and using GP3, because all of these people had a lot of abstract ideas going through their mind and they didn't have any idea of how to represent it or how to put it to put that into execution. Then they came to GP3, they gave it as input in the form of prompt and got all those output for those abstract ideas. So it's basically like acting as a sounding board for these kind of people and making it easier for them to understand what they actually want to do and helping them with their creative and design process.

Yeah, maybe what I think it's a great take on prompt design. Maybe what I would add to some tips when it comes to interacting with the model would be that you need to realize that GPT3 is like super, super good at storytelling and it's going to continue in the same fashion as you would prompt it in. If you start with like science fiction novel, with a few lines of a science fiction novel, it will continue in the same way. If you will start with a line that looks more like a love letter, it will continue in the same way. It's just. It's incredible at being able to move between different styles and mimicking and continuing in the same fashion. So the most important would be to making sure that the initial input that you give it hits that checks of that requirement. If you're going for a certain genre, just make sure to give it enough of an input so that it can continue in the same way. Yeah, and another thing would be that if you find yourself getting inconsistent messages, inconsistent outputs from GPT3, just make sure that you give it enough of a context to make it consistent. One example that comes to my mind would be like question and answer use case where you try to get sort of trivia style questions and answers. You give it a question and it gives you an answer or you ask it to create both questions and answers. And without enough of a context it might get, it might give you some answers that are non factual that are just made up because it has all this, all this data at its hand and it doesn't necessarily think in logical factual ways. However, if you ask it to be factual, if you say okay, write a few trivia questions with factual responses, then you're going to get the factual responses. So it's as simple as that. Just like giving it enough information of what you're trying to achieve in order to arrive at the desired output and thinking about it as if it's a one metaphor is just like talking to a friend in a bar and trying to be as simple and concise in your messages as possible so that the other side understands what you're going for and then you should be good.

I love that last part, especially on the question and answer style prompt engineering. You know, one example that I've seen, which is one testament for the intuitiveness of the question and answer style prompt engineering as well as for the emergent capabilities of models like GPT3, is creating jeopardy style questions and answers. I saw this example recently. 5, 7, 8, 10 years ago, you would need to train a model specifically on Jeopardy questions to be able to reach that level of parity, but just with a few prompts. For example example on GPT3, it has been able to blow that specialized model out of the park. Right, just through that prompt engineering. It's really interesting in a sense because it showcases those emerging capabilities of GPT3. It's not trained on that task, but it does really well just with two or three prompts.

Yeah, exactly. As we mentioned, it's extremely good at very quickly figuring out what you would like to achieve. And as long as you give it enough of a context, it should get there. At this point it's like extremely generalized model that can be applied to so many language based tasks. I think there is a reason for why we are still waiting for say the next iteration, GPT4. Let's see what will happen in the future. But at the moment, GPT3, it's already so usable and so appliable to different types of tasks that you can really achieve a lot with it, with just a little bit of a nudge in a certain direction.

So we talked about scale and in a lot of ways scale in the terms of the data ingested and the number of parameters of GPT3 has been a massive factor in why it's so good and why it's so easy to use. How important is scale as part of GPT3 success? And what I'm trying to get at specifically here is reaching generalized intelligence. Truly a matter of scale.

First of all, when you look at OpenAI's mission, that what they are striving for is all their projects basically that they engage in is arriving or facilitating the development of AGI that is benevolent and beneficial for as many people as possible. So with their experiments with the models, they certainly are trying to arrive to as general intelligence as possible. So with initial expense to GPT3, the scale was extremely important. It was crucial. They were being taken aback by how much the model capabilities change when you add scale to it, when you leave the same sort of architecture, when you leave the transformer as a backbone, but when you just make it fatter and bigger. And the iterations that followed GPT1, GPT2, GPT3, they weren't that different in terms of the architecture. It stayed the same. But what they were doing was they were increasing the number of parameters, increasing the data sets. And this is how they were trying to see whether it changes and whether it gets better at certain benchmarks or general language based tasks. And it proved to be true. So that's why they were incentivized to go in that direction. Having said that, with scale and with scaling of computing power, there also come costs and there also come concerns, for example, related to the environment. And as OpenAI was scaling their models, there were more and more research showing that we should actually be more aware, more careful of how we are using this computing power because of the economic and ecological, most of all footprint. So one of the research papers that I saw that basically compared how much carbon footprint GPT3 generated compared to, let's say, cars, it showed that the initial training phase that lasted a couple of months was comparable to a lifetime of Five cars, five passenger cars that generate a certain carbon footprint. So it's massive when you think about it. Right. It's a lot. They are aware of it and they are trying to address it. And we no longer think that only scaling, blowing things out of proportion and reaching bigger and bigger levels is necessarily the answer to arriving at more generalized intelligence. I think we are looking at experimenting with more techniques that are trying to achieve the same level of performance, but on a lower scale, which is like some tweaks on the architecture. And I think we also are not only scaling language models, but also involving other modalities like audio visuals in order to arrive there. And I think this will be more of a direction where we will go to in order to achieve this more and more generalized intelligence.

Following up on what Sandra said. Right. The other way to look at generalized intelligence apart from scale would be how we can make changes in the architecture and use the same number of parameters or maybe, yeah, along the same lines. So the other way to look at it would be how we can combine different modalities and how multimodality can be brought into picture. Because GP3, if you see, even in all its glory, it still works on text. It's just a text based model which uses text as input and gives text as output. As simple as that. But if you think about combining different modalities, combining text with image, audio, video, how we as humans perceive things, it's not just images, it's not just audio. So it's a combination of what we see and what we hear, and that's how we perceive things and that's how we make sense of things. So to get to generalized intelligence, which is similar to human, we need to take into consideration the component of multimodality. And I do think that we are moving in that direction in the next iterations of future futuristic language models. Because if you see, recently Dall? E2 got introduced, which basically takes text and images and it generates images much better than any artist can do in that given time. So within seconds it comes up with brilliant images given the text prompt. And again, your text prompt can be as abstract as possible. It will still come up with images because it has been trained on billions of images. So futuristic language models can be a combination of Dall? E and GPT3, where it combines different modalities like text and images, and then can make sense out of it. If we also look at the other research in the same areas, we will get to know that Google's DeepMind has released Kato. So Gato is a generalized agent which again combines multiple modalities and not just text. It combines text with audio and video and it is a multimodal, multitask language model. So what it can do is it can use the same weights and it can play Atari, it can caption images, can chat, and even use a robot arm to do a number of tasks. So this is the direction that we are moving towards in the future. And again, a very interesting example that came to my mind is Sekan. So Sekan, what it does is it combines the advancements in language model with robotics. So you have this understanding of language model universe and you combine that knowledge with robotics and then it's as simple as giving command to a robot and the robots become smarter in doing all the tasks that you want them to do. So multimodality is definitely the direction that we are moving forward and this is where the generalized intelligence can come.

I definitely agree with that notion, especially on multimodality and in some sense reaching a form of generalized intelligence. And I use here air quotes for generalized intelligence is both a research problem but also a system architecture problem of how can you combine different task oriented AI systems together. And I do think that even if the to a certain extent on the research side, like the goalpost for what defines generalized intelligence moves, we will to a certain extent in the future see useful generalized models be actually used in real life. And I think this marks a great segue to discuss some of the greatest use cases that you've seen GPT3 produce. There's a lot of actual startups and tools right now that are built on top of GPT3. Can you walk me some of your favorite use cases of GPT3 so far?

Definitely. It is very exciting to see like how a next wave of startup has started on top of GP3 or built on top of GP3. So while researching for our book on GP3, we had this section where we discuss about the startup ecosystem, the corporate ecosystem and entire effect on economy for language models like GP3 can have. So we did came across a lot of different use cases and it would be right to say that GPT3 actually acted as a launchpad for these startups. Some of the use cases that I really like that I have to point out would be viable. What it does is it is a feedback aggregation tool and intelligent feedback aggregation tools which can combine all the sources that you have, your customer feedback, your internal documents, all the insights that you are getting from different sources and it puts it all together and gives you a proper user Interface or simple user interface where you can just ask questions and get simple answers on how it works. So you can simple questions like what's frustrating our customers about the checkout experience? And the application may respond like customers are frustrated with the checkout flow load, as simple as that, some data and analyzing it like what went right, what went wrong and then coming, making decisions or drawing out inferences out of it. That entire curve has been reduced to a simple question that you can ask to this AI model to this GP3 based application. So it has really simplified the life of product managers, founders, customer success teams and all the front end side of the teams and the people who are working in these startups or these companies. So that was one of the very interesting use case that I had that I think had a lot of value in real world and can be definitely used for conventional data analysis instead of doing that. The other interesting use case that I came across, it was a very recent use case that I came across is Super Meme. So it basically uses GPT3 to generate memes. It was one of the most interesting and funny use case that I came across. So what it does is it takes input from you. What do you have in your mind? It allows you to select a template and it runs GPT3 and comes up with different kinds of memes in a matter of seconds. So you just select a template, gives whatever is in your mind. It comes up with a lot of memes. So now it's very easy for anyone to be a memelot in the world of GPT3.

I'm actually looking at examples right now and they're pretty hilarious.

Yeah, Meme Assistant is a golden use case, I think. But yeah, I mean generally my favorite use cases are also around the creative use of GPT3. I think it's just such an incredible storyteller that is just made for these use cases. It's very natural for it to create a story behind a certain character. You have all these examples like AI Dungeon for example, where you have just text based adventure game where GPT3 is the powering engine behind all these characters and stories that you engage in and you create as you go. One use case that we got to actually explore deeper in the book was Fable Studio. Fable Studio is like this pioneering VR studio that is creating a new genre of stories using new technologies, using virtual reality, but also using AI. And they have experimented with GPT3 to basically create the messaging, the content behind its character Lucy. They created this Emmy award winning movie called the Wolves in the Walls and they have this character that is eight year old girl. Many appearances. For example on Twitch where Lucy was like just engaging with the viewers or was singing a song or just like telling a story and they could engage with it. And they told us that 80% of all these Lucy appearances were powered by GPT3. So that's just incredible that you can basically create a character with the help of the model. I think there is a big potential there and so I'm very excited about use cases like that. Another one also related to writing was basically creating copy that allows you to, I don't know, sell a product, create a nice social media post. GPThere is also incredible at that. So there are many use cases such as Copy AI or Jasper or copysmith platforms where you are able to literally within seconds generate very nice social media posts, articles, what have you, whatever you need, YouTube video titles, YouTube videos, scripts. It's just incredible how much it can be helpful with variety of texts when it comes to the digital realm. So that would be also one of my favorite use cases and also we type into it in the book.

That's really great. And what do you think are use cases that will be truly transformative in the short term?

Short term I think is key here. I think we can already see how all sorts of assistants related to the creative work are transformative in the sense that they allow you to do more, faster and maybe in a more fun way. GPT3 is incredible for writer's blog. When you're writing something and you just feel stuck and you want to generate a handful of paragraphs to choose from or just to keep the creative juices flowing. That's a great one. And it's already available now and I think it will impact our comfort and our creativity of writing texts, that's for sure. Another one would be coding assistants. So GPT3 is not only trained on human language, but also on programming languages. And so it can be used to power, for example coding assistants where just like with GitHub Copilot and I think they use Codex, which is a descendant of GPT3. It's basically like a younger brother of GPT3. They are using it to help you either learn how to code or to fix certain problems that you arrived at when you're coding. I know already people that are using GitHub copilot and I think this use case will be growing in the future and will definitely again change the comfort and sort of the creative process of coding.

Yeah, I definitely agree on that. Assistant type, use case there's also an adjacent use case that I've been excited about, which is very educational. Like one thing that I've seen is explain your my code or explain this piece of code type use case, which I think is going to be really great for democratizing education and programming, data science, etc. On the flip side, it's also very important to also acknowledge the limitations of GT3. Large language models are definitely far from perfect and can make at best some pretty basic mistakes and at worst some very harmful ones. Can you walk us through what the limitation and risks for systems like GPT3 are?

Before we look at the limitations and risks that GPT3 poses, it's very important for us to understand that GPT3 is, is not a truth teller, it's a storyteller. And if you think of GPT3 as a minute replica of human brain, just like all other humans, it is also poised to make some mistakes and it can't be perfect, right? We also do a lot of basic mistakes in our day to day lives. Similar with GP3, if the prompt or the input given to model changes or varies or contains some harmful keywords, it can come up with a response that can be harmful in nature. That can have pretty basic mistakes. If you ask for factual questions that goes beyond the time of the training, that goes beyond the time when the model training has stopped, it will definitely come up with wrong answers or make mistakes. And it's poised to make mistakes there, right? So it is perfect when we talk about the generative or the creative capabilities of GPT3. But when it comes to a factual answer, we cannot think of it as something that can be perfect and other potential risk that it poses. And the biggest one that I can think of is misinformation because it is capable of generating a vast amount of data, a vast amount of information just through simple prompting. And the fact that anybody can do it, it poses a very big risk of a huge level of misinformation that we can see on Internet. It can give rise to a lot of propaganda bots which can just spread information within seconds. And that which can spread a large amount of information within seconds, it can dilute the quality of information that is already present. Because in today's world it's very difficult to verify the sources. And before you verify the source, this information spreads like fire. So it is very difficult to control the quality of information that's available online. And even with GP3, we have seen this example where somebody has created a clickbait blockchain post and pushed it on Hacker News and then it just went viral. It came on the top of Hacker News and yeah, so just like the spread of misinformation bias and all these things it can do.

Yeah, I would add to the misinformation but we actually did some research on the research out there involving GPT3 and ITS. There is this really cool report by Georgetown researchers released I think within the past two years. They are basically taking GPT theory and looking at all these different use cases that are. Well, you know, let's put it, let's put it like this. All these possible ways in which GPT3 can power misinformation and in which, in which they can go wrong. And this report, I was really struck by how easy it is to generate stuff like for example, tweets that propagate a certain idea in the certain light, targeting a certain group of people. GPT3 is actually very good at that. And you can do it easily, you can do it fast. And then as Shaban mentioned, you can have an army of bots on Twitter that just take your agenda forward. So it is an actual risk. It is scary how good it is at giving sort of the powerful voice to anybody. And in this group we include people that have some sort of agenda, political agenda, what have you behind it. Misinformation is a huge one, I would say another one bias. Here's the thing. When your data sets was created, the guys weren't exactly thinking that all the Reddit or the Order Reddit posts, all the subreddits that they were scrapping from the Internet to curate this data set to create this sub sample of humanity or what have you. They will have all these ideas that are extremely racist, extremely sexist, extremely xyz. They will be propagating certain, certain stereotypes, political, social, economical, all sorts of stereotypes. And what happened was that because this data was in the data set, GPT3 for some reason is able to now amplify them in its responses. So it needs to be curated, it needs to be filtered in order to prevent these responses. Initially it was a big challenge for the OpenAI team and they were working super hard on addressing it. They introduced the content filter for the responses so that they are safe to use. And they also introduced this, it's called Process for Adapting Language Models to Society. That's the name. So essentially what they did was how do we make these models that have these crazy amplified biases that we did not expect and we do not want to have nicer, more adapted to society, better models Basically they came up with this process where you create a set of values for the model to follow and it actually is able to adapt and to follow and become more, more usable in the society, so to speak. So that's another limitation of the model which comes from the fact that in this data set these stereotypes were included.

That's really great. I really appreciate the holistic answer. I also share a lot of the concerns that you have, especially on the potential for misinformation and bias and also creating personal bubbles for people on social media. If a lot of the content that tailored to your rated and is tailored to your preferences, we could have the risk of supercharging social media's capability of creating political bubbles as well as social bubbles. But even to the concept of personal bubbles where all the content is tailored to you and it's auto generated for you. And I'd love to discuss that at the end of our chat. A lot of the concerns that you mentioned, and also connecting back to the concern of environmental economic costs as well, what do you think are some of the research solutions or safeguards that are being developed right now to be able to fix these problems in the long term? And how do you think in the short term teams using GPT3 will have to reconcile or work their way around these limitations?

The one good thing about with GPT3 was that OpenAI was aware from the very beginning that these can be the potential limitations and can potentially a potential risk that a language model with a scale of GP3 can have. So they had a dedicated team working on AI, responsible AI and defining an AI policy to safeguard the end users from these kind of potential risk and harms. So the thing that they had is along with the language models that they have built the different variations of GP3, they also built in parallel a content filtering model. So whenever you give an input to GP3 and it comes up with an output, there's a clear lining where if the output is safe, it is highlighted in green. But if the output contains some harmful keywords, it is contains sensitive content or contains content which is sexist, which is racist, it highlights and it gives a warning that this content is harmful and not good to use. So that's the quick fix that OpenAI came up with. I'm not saying it is perfect, but it is at least something. And we are moving in the direction where we are thinking about responsible AI, we are thinking about AI ethics and how we can tackle those challenges and in the short term, what teams can do or what end users who are using GPT3 can do to avoid these risks is to go about their prompt smartly. When I say smartly, they can be careful about the prompt that they are giving to GPT3. They can see that whatever keywords that they are giving to GP3 does not prompt the model to generate some harmful response or some sensitive content. One of the very good example, or we can also call it as a misuse of GP3 that end users said was of AI Dungeon. So it was a stick storytelling experience, was entirely virtual experience where you give certain inputs and the stories and different worlds get created. And it was a kind of very realistic game. But then people started using it for sexist things, racist things. And because the model doesn't have a ball, there is no thin line where model can differentiate what is good and what is bad. It is again a machine which will do whatever you ask it to do. So there needs to be some safeguards that need to be put from OpenAI who has designed the model, but also the end users need to understand their moral obligations and basic duty when they are using these kind of models or techniques. That's where the AI policy comes in and that's where the AI as a subject comes in. And I do believe these will be burgeoning field going forward and in long term we'll see a lot of research on AI ethics and AI policy and how these models can be used. One good example which I can give you, like in current times you have seen a lot of talks about value targeted data sets. So as Sandra correctly mentioned, none of the AI or language model or an NLP model by its origin is biased or has. Yeah, is biased or have misinformation or capability misinformation about the data set it has been trained on. So it's a data set that has been generated by humans. It's the data set we get from Internet those contains bias and that bias gets propagated to the language model. So recently we have seen this concept of value targeted data sets where data sets are adopted to how the values of society are. So data sets adopted to the values of society, what we think is good, what we think is bad and adapting those and training those models on the value target data set. And GPT has good feature tuning where you can just take few samples, like 100, 500 samples and call it a small data set and can fine tune the model. That's where you can use a value target data set. So for your domain specific application, it makes it highly reliable and it assures that you don't get those kind of sensitive, harmful response in the end when you train, when you fine tune the GPT3 model on a value targeted data set to know more about it, you can definitely check out the book. We discuss it there in detail of how targeted data set look like and how you can fine tune gpt3 for your use case to avoid these limitations and risks.

Yeah, I just want to add one more point in which it shows that OpenAI is continuously working on its models and is improving the API. A few months back they have released a series of models called Instruct GPT. And what they do is basically they're models that are trained to be much better at following your instructions, they are much better at giving factual answers and they are much better at filtering the unintended, abusive, violent, what have you content. So I think they also not only give tools to the community to be able to curb these negative, potential negative outputs coming from the models, but also they are working on making the API safer. Is that when you're using the API in order to launch some sort of application, you have built a product, you want to give it to the world. You're going through a process where you need to explain what this is for. They are looking at it in depth, they're looking at the type of use case that you're using it for, and then they decide whether it's safe to be released to the world or not. So they give themselves the opportunity to put a stop to something they just wouldn't like this tool to be used. For an example that chopin mentioned with Aadungeon was that when both the AI dungeons creators and OpenAI folks realized that the model is being used for creating like sexist, racist content and so forth, they have used much bigger filters to the content and there was like a big push coming from the OpenAI. This will stop because they are monitoring how the API is being used and they are being able to, I would say they are definitely caring a lot when it comes to the safety. And they are. Of course the models aren't perfect, but they're continuously working on it and we can expect, as Shobha mentioned, a lot of research coming making these models better, safer to use.

That's really great and we're reaching almost the end of our episode, but we've talked a lot about the short term use cases as well as the value of models like GPT3. But I'd love to talk about the future a bit as well. The paradigm shift assured by large language models and the transformation transformer Architecture, I think it's truly something. You know, we saw this with the recently released GATO system by DeepMind, the many different large language models developed by Google, Microsoft and Meta, and the same vein of how the advent of the smartphone ushered in tools that we didn't thought or apps that we didn't thought were possible before the smartphone. Think Uber, Airbnb, et cetera. Where do you see the future of NLP systems heading and what are some of the ways or unexpected ways that you think they will change our lives?

So language models like GPT3 has completely changed the way we see and perceive the world. So if I have to put it in simple words, it has just opened the imagination of what is possible and it has just changed the realms of what is possible. We are living in very exciting times and we have a very exciting future ahead of us because GPT3 has the capability of models like GPT3 has the capability to replace the way people find and search for their information on Internet. So it can allow you to access customized and concrete information that is to the point for whatever you are looking for. It's similar to replacing what we actually do with Google today, right? We search for information, we get a lot of results, then it's up to us to make sense, go through all these different web pages and then find what, what's the information that we are looking for. So let's say finding something or researching about something takes 30 minutes to find the concrete examples and make notes of it. What GPT3 can do us is can give us an exact to the point information in a matter of seconds. So the 30 minutes of yours get converted into seconds and that's all the time you need to get the relevant information. Another important concept that I want to touch here is procedural web. That's something that I think we will be adding in the future. So what procedural web is, it is a kind of Internet where content will be adapted to the users, content will be personalized to the needs and users queries. So what it can have is let's say instead of me going to Google and searching for different results and it comes up with the rank number of pages and I go to different pages. What it will do is I'll search for something and rather than searching from a select set of databases, it will generate things on the fly, just like a human does. So if you search for something, it will generate that thing on the fly and you'll get concrete to the point information. So it's as simple as asking questions and getting the answers. So that's the kind of future we can experience and we can get with the progress in NLP systems and large language models and moving towards more generalized AI, getting information on the fly and just removing the time that we spend on research. Because research is a part of, it's a very big part of every job, right? It's not limited to engineering, data science or data professionals. Everybody who does any kind of job has to invest a lot of time into research. And the only medium of research is Internet. And it can just completely change how we look at things conventionally and can create a future where things are more sorted, more well defined and more streamlined and we can get information on our fingertips. That too very concrete and to the point.

I think these are incredible points. And adding to that, I think not only large language models will change our relationship to information, how we consume and how we benefit from it, but also will make it more fun basically to engage in searching for information. What I'm thinking of is for example, being able to talk to virtual assistants that are powered by these large language models that are able to have a really nice small talk with us about all sorts of topics and then moving on to certain more effective information exchange. But having this really nice human touch in interactions with the machines would be, would be one of my bets that I think this will definitely increase our comfort with talking with chatbots with virtual voice assistants will get better and better. Right now it's already fun, but I would say it's still pretty limited and we can feel that when we're talking to our Alexas and so forth. Actually my Alexa just woke up. But yeah, it's just, it's going to be, it's going to be better and more fun. That's one thing. And another thing following up on this coding assistant use case, I think LLMs will allow us to create also more effective sort of communication with the computer where it will be much easier to translate from human language into coding in which it will be also possible to translate from voice commands, natural language voice commands into coding. I can definitely see a future where I am talking to my machine and my machine is creating a game for me based on what I just described. That I want a certain world with certain characters in it and with a certain storyline. I think it's definitely going to be possible and also I think it's going to make people without coding skills, for example, more and more engaged in this process and just, it will just democratize access to it so that myself coming from non coding background, I will Be able, I am already able to create like super basic games with codecs for example. I think the opportunity there is incredible and we are actually exploring this bigger trend of combining the no code approach with large language models. In the book we are talking to Babel in the book, to the Babel co founder who tells us how he sees this, this aspect moving forward. Yeah, I'm really excited about this one as well.

Yeah, these are super exciting use cases. And adding on top of that, I think there's also a potentiality for even people with low digital literacy or not really high ability to use computers to leverage this voice command to be able to use machines generally. You know, there's one example of a step AI which is I think had former OpenAI engineers working on it as well. And what they do is that they let you use your computer through, through voice commands, hey, download Excel, do this, do that. And I think the potentiality of having a Jarvis like assistant is going to be super exciting. So as these models get better, what are you most excited about and what are you most worried about?

So I think the excitement is obvious. Like you, you have this super powerful tech, you see all these different ways in which it can be like awesome. And what I'm just looking for is to seeing it more and more in the real life. Right now I'm talking about the potential, the different like early signs of where it can be really good at. But what I would really want to see is to be surrounded by all these applications, to be talking to my Alexa and having a blast, having an awesome conversation or to be able to create this game. So I'm really looking forward into actually moving from discovering the possibilities into actually bigger, more wider adoption coming from not only startups but also enterprises that we use these products on a daily basis. I'm just looking forward to that for sure.

What really excites me is how this language model like GPT3 has the capability to bring in people from different backgrounds into the AI ecosystem, which wasn't a possibility, which wasn't even near possibility before because AI used to be such a big buzzwords. People hear it and they were like, oh, that's not for me. I don't have the technical prerequisite. I am not a technical person, I am not a research scientist. But with GP3 it is getting much and much easier and more and more organic and natural for people to understand what it can do and come up with different applications or use cases in these hours. And like we already saw in case of Replica and Fable Studio, of how they actually came from design background, film background, and they combined GPT3. So what replica did is it combined GPT3 with a virtual assistant, personalized virtual assistant, giving you a personalized experience of chat experience. And what Pebble Studio did is it combined GP3 with metaverse, like creating one storytelling and all these things and how it worked flawlessly. That person with an engineering background or a data science background would have found it hard to do. So it is very exciting to see how people who are not even related to technology can come and put their ideas into execution and the products that we get to see. So I do feel, and I'm very positive about it, that in next one or two years we will see a wave of these startups, a wave of these products which will just blow our mind. And coming to what worries me a lot about GPT3 is again going back to the bias and data sets that we have that the model has been trained on because it is nearly impossible to eradicate those biases completely from the data sets because those have been existing for decades, for years and they have been there and we don't have an option to create data sets from scratch. So that has been there. But what we can do and what I am really looking forward to is coming up with research solutions like content filtering model or something which again streamlines whenever there is a bias or highlights whenever there is a bias or there is a sensitive content or comments on the output. So I am positive that we are going to resolve and tackle all these challenges with the research that's going on in the field of AI ethics and AI policy. So yeah, I hope to see this problem getting resolved soon. Very soon.

Yeah, I realized I forgot to mention what worries me as well. So I would definitely second Shabham in terms of the bias, but also what we have mentioned before about the potential for using this tool for political propaganda. I myself am based in Poland right now, so it's really close to Ukrainian conflict, to Russian aggression and I am seeing on a daily basis the campaigns which are designed at finding all these bots that are spreading misinformation within the context of the war and taking down these accounts and just imagining how GPT3 could power these accounts just scares me. Honestly, that would be my biggest concern at the moment.

Definitely. That's a really great list. And to your points Shubham, especially on the combining the metaverse with a lot of AI generated worlds to a certain extent, if VR matures and multimodal model matures as well, we can have the potentiality of a lot of AI generated worlds where people have their own unique personalized experiences. And to your concerns here as well, the risk of misinformation and bias are really huge, especially once you combine that vision of AI generated worlds and what that could. What could that mean? My biggest concern when it comes to large language models as well as like image creation models like Dall E is the potential for I mentioned this slightly in our conversation for personal bubble filters where people really lose reality of a collectivist reality to a certain extent and don't have a shared experience anymore simply because their feed is curated for them by auto generated content that is created personalized for them. Do you see that as a risk in the future by any chance?

I think personally that it's already happening. A friend of mine has a couple of Twitter accounts in order to be able to tap into different communities and based on this little experiment that any of us can do, you can see how different your feed experience will be like we are basically existing in these echo chambers already. I think we are already there. It's still not in the context of the metaverse, but it's metaverse will be basically translation of the reality into the meta. So going to meta. I think we will face the same problems as we are facing now unfortunately. But yeah, we just need to be better at designing the algorithms that are taking us maybe purposefully out of these bubbles, being able to create personalized experiences, but at the same time having a dose of just throwing us out of the comfort zone and experiment with that, see if it works and keep on improving. Because I don't think we have much choice otherwise we will just end up in our own eco chambers in these closed communities that only are able to talk to each other and don't have the language to talk to people outside of the certain group where they are.

Finally, Sandra and Shubham, I really enjoyed our conversation as we're closing out. How can people access GPT3 and how can people read your book?

So of course our book will be out in mid July. It will be available in the ebook version and it will be available in the physical paper version at the end of July. You can already actually order it via Amazon. We have released a couple of digital resources along with the book. One of them is a sandbox powered by Streamit where we help users to create their own application and we have resources on that both on GitHub and also on YouTube. We have also started releasing the conversations that we have mentioned here in the podcast that we had with the many different sides of the ecosystem stemming from startupers, co founders of startups powered by GPT3, people that built the API influencers in the space. We try to talk to as many people as possible that are involved in creating this emerging ecosystem. So we are releasing these conversations as well via YouTube. You can check them out already.

Just to add, we also have this GPT3 club where you can just go to GP3 club like Premiere into get an updated Premiere into what GP3 is, how you can quickly get started. What are the steps to take in? Just simple 2 to 3 steps is all you need to get started with GPT3 and it also highlights what do we cover in the book. So a basic overview of what GPT3 is, how you can get started, how the ecosystem looks like, and where we are moving forward in the future. So that's what GPT3 club covers as well.

All right, that is awesome. Shubham and Sandra, thank you so much for coming on Data Framed.

Thank you for having us.

Thank you for having us. This is up to you. You've been listening to Data Framed, a podcast by Data Camp. Keep connected with us by subscribing to the show in your favorite podcast player. Please give us a rating, leave a comment and share episodes you love. That helps us keep delivering insights into all things data. Thanks for listening. Until next time.