Humanity Elevated Future Proofing Your Career - Large Language Models Course from Deep Learning an Overview

Episode Date: January 13, 2025

This podcast delves into the fascinating world of Large Language Models (LLMs) powerful AI systems revolutionizing how we interact with language. It explores their intricate architecture, highlighting components like Transformers and Attention Mechanisms. The podcast also covers the massive scale of LLM training and diverse applications, from text generation and translation to question answering and code generation. Crucially, it addresses the ethical considerations surrounding bias, misinformation, and societal impact, along with future directions in the field.

All right. Get ready, because today we are going deep into the world of LLMs. LLMs? Large language models. Yeah. We're not just talking about, like, you know, your run-of-the-mill AI here. This is, like, sci-fi stuff, but it's happening right now. It really is. AI that can, like, whip up a short story, translate a super-dense legal document, maybe even write code for you.

It's mind-blowing how fast all this is developing. You know, what was once just theoretical is like becoming an everyday reality. Exactly. And to kind of walk us through this whole LLM scene. Yeah. We've got LLM course from deeplearning.pdf. Okay. It's a course summary that promises to like give us a no nonsense look at this whole thing.

Right. The goal here is to give you a really clear understanding of what LLMs are, what they can do, and how to make sense of it all. Yeah, think of it as like your crash course on LLMs. By the end of this, you'll be able to hold your own in any conversation about this cutting-edge tech. I love that. Yeah. Okay, so let's get into it.

Sure. What exactly are LLMs? We've all heard the AI buzz, but this feels different somehow. It is. What sets LLMs apart? I think you're right to feel that difference. Okay. It's like comparing a basic calculator to a supercomputer, you know?

Okay. LLMs are like a whole different breed of AI. They can do way more than just recognize patterns. Okay. They can actually understand and generate human-like text, which makes them super versatile. So we're not talking about like a simple chat bot here. No. These things are like the real deal.

The real deal. How do they even work? What's the secret sauce? At the heart of it all is something called a transformer. Okay. You can kind of imagine it as the super efficient system that can analyze an entire sentence all at once, figuring out how all the words relate to each other. Wow. It's like having this like bird's eye view of the entire linguistic landscape.

So it's not just getting like bogged down in the individual words. Right. It's kind of grasping the meaning of the whole sentence, the whole paragraph. Exactly, precisely. And that's just one piece of it. LLMs also use something called an attention mechanism. Okay. You know how you can focus on certain parts of a conversation and like filter out the background noise?

Yeah. That's basically what attention mechanisms do for LLMs. Wow. It lets them zero in on the most important parts of a piece of text. So they're like the ultimate listeners. Yeah. They're really good at like picking out the important stuff. Exactly.

Wow. And then there's the encoder-decoder framework, which is like the engine that drives the whole process. Okay. Imagine like translating a book, right? Uh-huh. the encoder takes the original language and turns it into this universal code, and then the decoder takes that code and turns it into a whole other language. Wow. It's kind of like what's happening inside an LLM.

It's like they've cracked the code of human language. It's really incredible. But I've heard that training these LLMs is a monumental task. It is. We're talking about feeding them huge amounts of data. Yeah, massive, massive. Like crazy amounts of data. You're not kidding.

Imagine training an AI on a data set the size of Wikipedia, like two times over. Wow. We are talking about books, articles, code, everything. It's crazy. And that takes serious computing power. Like think server farms that could power a small city. That's wild. It is. But they don't just, like, throw all this information in and hope for the best.

No. There's got to be some method to this madness. Yeah. What is it? It all comes down to two main phases, pre-training and fine-tuning. Okay. Pre-training is, like, giving the LLM a foundation in the basics of language, you know, grammar, vocabulary, a little bit of common sense reasoning. OK, so that's pre-training, like laying the groundwork.

Exactly. What about fine tuning? So fine tuning is like specialization. Once the LLM has this general understanding of language, we can train it on specific tasks. So let's say we want it to be a master translator we'd feed it tons and tons of translated text helping it learn all the little nuances of different languages so it's like taking a student who aced their general ed classes yes and then sending them to grad school to become like an expert in one specific field exactly i love that

analogy yeah that's that's a really good one. And this whole process of pre-training and fine tuning. Yeah. That's how we create these LLMs that can do those, those amazing things we talked about. Yeah. The writing, the translating, even the coding. Okay, cool. Yeah. Right. So we've, we've laid the groundwork. We know what LLMs are, how they work, the sheer scale of like training these things. Right. But let's get to the good part. Yeah.

What can these LLMs actually do? Right. The possibilities seem endless. They really are. I mean, we're talking summarize a dense report in like seconds. Right. Saving you, you know, hours of work. That's what I'm talking about. Time is money. Exactly. And these LLMs sound like a real productivity booster. Absolutely. But let's get specific. Sure. What are some like real world examples of LLMs in action? Okay. So so picture this you're a doctor and you're

like struggling to keep up with all the latest medical research. Yeah. An LLM could analyze a ton of studies and pinpoint the most relevant findings helping you diagnose and treat patients better. That's that's incredible talk about a game changer for for the medical field. It is. What about other industries? Yeah. I've heard LLMs are shaking things up in the creative world too. Oh, absolutely. Imagine an LLM working with a musician

to write a symphony. Wow. Or helping a filmmaker develop a screenplay. That's crazy. We're already seeing early examples of AI-generated art and music, and the results are pretty amazing. So are we on the verge of like a world

where AI is creating alongside humans? Yeah. Blurring the lines between human and machine creativity? It's definitely a possibility. And it's a question that causes a lot of debate. Right. But I think it's important to remember that LLMs are tools that can help boost human creativity. OK. You know, not necessarily replace it. It's a good point. It's all about how we choose to use these powerful tools. Right. But with all this talk about AI and creativity, I can't help but think about the impact on jobs.

Are we headed for a future where like robots are writing our novels and composing our music, leaving humans out in the cold? It's a question a lot of people are asking, and it makes sense to be concerned. But I think it's more complicated than just like robots versus humans. LLMs will definitely change what jobs look like, but they'll also create new opportunities. Think about the people who will be needed to train, manage, and refine these LLMs. So it's not about being afraid of the machines, but adapting and evolving alongside these new technologies.

Exactly. The people who are willing to learn new skills and are open to the possibilities of LLMs will be in a good spot. So adaptability is key. Yeah. But let's talk about the players in this LLM game. Okay. Who's leading the charge in developing and using these powerful

technologies. Oh, it's not surprising that the big tech companies are at the forefront. Right. You know, companies like Google and Facebook are putting a ton of resources into LLM research and development. And then there's OpenAI. They're making huge waves with their GPT model. Right.

Right. Those names are almost like synonymous with the AI revolution. They are. What about smaller companies, startups? Oh, yeah, absolutely. Are they getting involved too? For sure. There's a whole ecosystem of startups and smaller companies popping up.

Wow. Each with their own approach to LLMs. Some are focusing on niche applications, while others are developing tools and platforms to make LLMs more accessible to everyday users. So it's not just a playground for the big guys. Nope. There's room for innovation and competition all over the place. Exactly. And that competition is leading to some really amazing progress.

That's great. But we can't forget about the cost. You know, access to these powerful LLMs isn't free. Yeah, that's a good point. Right. All those massive data sets and powerful servers. Yeah, they don't run on good vibes. They don't run on good vibes. What's the financial situation like?

Well, the pricing varies depending on the provider and the LLM you want. Okay. Some companies have free tiers with limited use, which is great for just trying things out. Others have subscription models with different tiers based on usage and features. So it's kind of like choosing a phone plan. Exactly. You pick the option that works for you and your budget. Right.

Okay, cool. But what about the actual management of these LLMs? Yeah. It sounds like a pretty complex thing. It can be, and that's where MLOps comes in. Okay. Think of it as like the operations manual for AI it's a set of practices and tools that helps

organizations manage the whole lifecycle of an LLM okay from development and deployment to monitoring and maintenance so it's like having a team of AI mechanics yeah making sure that everything runs smoothly. Exactly. And as LLMs become more important for businesses, good MLOs practices are going to be essential. All right. So we've covered a lot. We have. From the technical stuff to the business side of things. Right.

But for those who want to dive in and start exploring this LLM world. Yeah. What are some of the key tools and resources out there? Well, we've already mentioned some of the big names like TensorFlow and PyTorch, powerful deep learning libraries that give you the building blocks for creating your own LLMs. So if you're a coder and you want to get your hands dirty, those are the go-to tools. What about options for people who aren't like coding wizards? Oh, don't worry. There are plenty of user-friendly options. Platforms like Hugging Face offer a huge

collection of pre-trained LLMs that are ready to use for different tasks, all without writing any code. That sounds much more approachable. It is. It's like having a library of ready-made LLMs just waiting to be used. Exactly. I imagine there's still a learning curve though, right? Yeah. How steep is it for someone who's brand new to all this? Well, it depends how deep you want to go. If you're comfortable with basic programming, you can start experimenting with pre-trained

models and APIs pretty easily. But if you're looking to build your own LLMs from scratch, you'll need a stronger foundation in deep learning and natural language processing. So it's like learning a new language. You can start with the basics and then work your way up to fluency. Exactly. And the good news is there are a ton of resources to help you along the way. Okay. Online courses, tutorials, communities, all sorts of stuff. That's great. Yeah. But let's be real. A lot of people are probably thinking, will LLMs take my job? Yeah. It's a valid concern.

It is. Anytime there's a big technological shift like this, there are going to be anxieties about job security. Yeah. What's your take on all this? I think it's important to remember that history has shown us that technological advancements often lead to new and unexpected job opportunities. The key is to embrace change and be willing to adapt. So less about fearing the robots and more about understanding how to work alongside them. Exactly. LLMs are tools, and it's up to us to figure out how to use them effectively and ethically. Think about it this way.

LLMs can free us from those boring and repetitive tasks, allowing us to focus on more creative and strategic work. Okay. That's a much more optimistic way to look at it. It is. It's not about humans versus machines, but humans with machines working together to achieve greater things. Exactly. Okay. So we've talked about the tech behind LLMs. Right. The companies developing them and even how they might affect jobs. Right. But let's zoom in on some specific ways these LLMs are already being used to solve like real world problems. Okay. One area where LLMs are making a big impact is customer service.

Oh yeah. Think about those chatbots you see on websites. In the past, they were pretty limited in what they could understand and respond to, especially when it came to like complex questions. Right. But with LLMs, we're seeing a whole new kind of chat bot that can have much more natural and nuanced conversations. It's not just about like answering FAKs anymore. No. These LLMs are actually getting good at solving customer problems. Yeah.

Almost like talking to a real human. That's the goal. And we're getting closer and closer. I like that. And it's not just about keeping customers happy. LLMs can also handle a ton of inquiries at once, which frees up human agents to deal with more, more difficult cases. So that's a win-win for everyone. Yeah, everybody wins. But what about applications beyond customer service? Sure. I'm curious about how LLMs are changing things in other fields.

Well, one area that's really exciting is content creation. Okay. Imagine an LLM that can help you come up with ideas, write marketing copy, or even generate scripts for videos. Wow. We're already seeing companies use LLMs to create personalized content for their customers. And the possibilities are really huge. That's incredible. It opens up so many possibilities for writers, marketers, anyone who works with content. Right.

It's like having this tireless creative assistant at your fingertips. Yeah, that's a good way to put it. And it goes beyond just writing. Like imagine an LLM that can create music for a video game. Wow. Or realistic sound effects for a movie. That's crazy. The creative potential is just, just starting to be tapped.

It's amazing to think about how LLMs could like transform these creative industries. It really is. But I have to ask, what about the ethical side of things? Right. Tools is powerful. There's always a chance for misuse. Absolutely. That's why it's so important to have these open and honest conversations about the ethical implications of LLMs. Okay. You know,

one concern is the potential for bias in the content they generate. Right. Since LLMs are trained on these massive data sets of human language, they can pick up and even amplify the biases that already exist in that data. That makes sense. We wouldn't want these tools to like perpetuate harmful stereotypes or spread misinformation. It's like any powerful technology. It's all about how we use it. Exactly. We need to be aware of the potential downsides and work to develop safeguards to minimize those risks. It's an ongoing conversation that involves everyone, researchers, developers, policymakers, even the public.

Okay, so ethical considerations are super important. Yeah. But what about the future of LLMs? Right. What's coming next? What are the next big advancements? Well, one area that's creating a lot of buzz is multimodal LLMs.

We've talked about how LLMs can understand and generate text, but what if they could also process images, videos, audio? That's the idea behind multimodal LLMs, to create AI that can interact with the world in a much more holistic way. So instead of being limited to just text, these multimodal LLMs could understand and create content in all sorts of formats. Yeah, exactly. That's incredible. It is. Imagine an LLM that can watch a video and then write a summary of it. Wow. Or an LLM that can generate a realistic image from a text description. That's mind--blowing your possibilities are pretty much endless it really sounds like it yeah that paints a pretty amazing picture of the future it does but as these elements

become more powerful and more complex uh-huh how do we make sure that they stay accessible and sustainable right we've talked about the huge amount of computing power needed to train these models yeah it's a valid concern but luckily researchers are already working working on more efficient and scalable training methods. Okay. One promising area is developing new hardware that's specifically designed for deep learning. So it's not just about software advancements. Yeah, right.

We need specialized hardware to keep up with these MLMs. Exactly. And there's also a lot of progress being made in algorithm development, looking at new ways to train LLMs more efficiently and with less data. That's good news for the environment and for our budgets. It is. So as we wrap up this deep dive, I want to go back to the human element. How do we prepare ourselves to thrive in a world where LLMs are becoming more and more a part of our lives? That's a great question. I think the most important thing is to stay curious and be adaptable. Don't be afraid to play around

with these new tools, learn how they work and explore what they can do. So it's not about being scared of the machines, but embracing the opportunity to learn and grow alongside them. Exactly. The future of LLMs is ultimately up to us. If we approach this technology with curiosity, with creativity, and with responsibility, we can really unlock its full potential and create a future where humans and AI work together to solve some of the world's biggest challenges. That's a great point.

It is. It's a call to action for all of us. It is. So to our listeners, we encourage you to keep exploring this fascinating world of LLMs. Absolutely. The journey is just beginning and who knows what incredible discoveries

are waiting for us in the future. Who knows?