Humanity Elevated Future Proofing Your Career - Decoding, Language, Principles, Applications and Future of NLP

Episode Date: January 6, 2025

Discussion from the topics of the book on NLP that is released on Amazon in December 2024. This book helps uncover the fascinating world of Modern languages evaluation and discovery of Modern LLMs and much more.

All right. Strap in, everyone. We're going deep on this one. Deep dive time. Exactly. A deep dive into NLP. Natural language processing. Yeah.

NLP, all that good stuff. And, you know, you guys sent us a ton of sources on this one so clearly. Yeah. It's a hot topic. It's a hot topic. You want to know not just how it works, but where it's going. Where is this all headed? Right.

We're going to be talking about how these models are evolving. What's the potential for encoding information in DNA. Oh, yeah, DNA. And even tackling the question of which languages will dominate in the future. Which languages are going to be the language of the future. That's a big one. It is a big one. Yeah.

It's a fascinating field. It is. Especially now with the rise of multimodal NLP, which is basically like giving these systems extra senses. Beyond just text. It's really pushing the boundary. Yeah. So let's start with the basics because I remember the days of like that clunky translation software. Oh, for sure.

Where it made you sound like a robot from like a bad sci-fi movie. How did we get from that to AI that can write sonnets and code like a Silicon Valley prodigy? Well, the journey of NLP is a testament to how much our understanding of language and computer science has just evolved over the years. It's come a long way. It really has. In the early days, think way back to 1954, the Georgetown-IBM experiment was a huge milestone. 1954, okay. They managed to translate a grand total of 60 Russian sentences into English using a rule-based system.

60 sentences. That's like... That's it. That was huge back then. That's like a baby's first words compared to what we see today. Absolutely. What made those early systems so limited? Imagine you're trying to teach a computer to understand a language by handing it a massive dictionary and a rule book

thicker than a phone book. Yeah. That's essentially what those rule-based systems were like. They couldn't handle the nuances, the ambiguities, the beautiful messiness of human language. The beautiful messiness, I love that. It is, it's messed. It's like trying to explain a joke.

By dissecting the grammar and vocabulary, it just doesn't work. You just kill it. Yeah, you kill it. When did things start to get less robotic and more well-human? The real game changer came with the statistical revolution in the 80s and 90s. OK. This is when we started feeding computers massive amounts of data and letting them figure out the patterns themselves. Hidden Markov models, HMMs, were a key player here. Hidden Markov models. They're great for modeling sequences, like figuring out how likely one word is to follow another. So instead of rigid rules, it's more about probabilities and patterns. Exactly. Like the computer is learning to anticipate the flow of language. Exactly. And this led to the rise of Enogram language model, which could predict the probability of a word based on the previous words. Think of it like predictive text on your phone, but on a much grander scale. Oh, OK. This made tasks like speech recognition and machine translation significantly more accurate. But then deep learning entered the scene and it was like upgrading from a bicycle to a rocket ship. Okay. Deep learning. Now that sounds intense.

Yeah. How did that transform NLP? Deep learning uses these artificial neural networks. Inspired by the structure of the human brain, this allows computers to learn incredibly complex patterns from massive amounts of data without needing us to explicitly program every rule. So we shifted from hand crafting rules to letting the data do the talking. You got it. I like that. Yeah, that's the beauty of it. Okay, but hold on.

One of the things you sent us was about these word embeddings. Word embeddings, yes. But sound like something out of a spy movie. They do. What are they exactly and how do they work? Word embeddings are a way of representing words as vectors in a multidimensional space. Yeah. Think of it like a map of meaning where words that appear in similar contexts end up clustered closer together.

Okay. So king and queen would be close neighbors while king and banana would be miles apart. So it's like each word has its own unique GPS coordinates in this vast semantic space. That's a great way to put it. That's pretty mind-blowing. It is. Yeah. These word embeddings really capture the essence of meaning and they power a whole range of NLP tasks. Okay. And this leads us to the rock stars of the deep learning era, models like BERT and the GPT series. Ah yes, the big names everyone's talking about. The celebrities. I keep hearing about how amazing BERT is. Pretty impressive. What makes it so special?

BERT which stands for bi-directional encoder representations from transformers. Okay. Was a breakthrough because it reads text bi-directionally. Just like we do. Gotcha. So it considers the words that come before and after to understand the full context. Okay. Earlier models were kind of like reading with one eye closed. They missed out on a lot of crucial information. Context is key for BERT. Just like for us humans trying to understand a conversation.

Absolutely. It's not just about the individual words, but how they relate to each other. Exactly. Gotcha. And this ability to grasp context led to huge improvements in tasks like question answering. You've got the GPT series developed by OpenAI, which has been making waves with its ability to generate incredibly human-like text. GPT-3. GPT-3. It can write stories, poems, even code with a level of fluency that's pretty remarkable.

I've seen some of the stuff GPT-3 can do, and it's honestly a bit unnerving. It is, yeah. It makes you wonder where the line is between human and machine creativity. Right. Yeah. Are we just machines? Right. We got all we are.

It's pushing the boundaries. Yeah. And it's raising these profound questions about the nature of language and creativity. And it's just the tip of the iceberg. Speaking of pushing boundaries, what about this whole DNA encoding thing? DNA encoding. That sounds like something straight out of science fiction thriller. It's definitely on the bleeding edge. Okay.

Some researchers are exploring how to encode information directly into DNA molecules, essentially using DNA as a storage device. So instead of hard drives and servers, we could have data stored in tiny strands of DNA. Yeah. That's wild. It is. It has some incredible potential advantages. Like what? DNA is incredibly dense, meaning you can store vast amounts of information in a minuscule space.

Okay. It's also incredibly durable. Think about all the information we've gleaned from fossilized DNA. We're talking about a storage medium that could potentially last for a millennia. Okay. That officially blows my mind. It's pretty amazing. But let's come back down to Earth for a minute.

Back to reality. We've talked about how far NLP has come. Right. But what are some of the biggest hurdles it still faces? Human language is notoriously tricky, right? It is. One of the biggest challenges is ambiguity. Ambiguity, okay.

Words can have multiple meanings depending on the context. Take the word bank. Are we talking about a financial institution or the edge of a river? It's like those optical illusions where you see one thing and then suddenly you see something completely different. Yes. I can see how that would be a nightmare for a computer. It is a nightmare. Researchers are developing increasingly sophisticated techniques to tackle this, like using contextual embeddings to capture those subtle shades of meaning.

Okay. But human language is full of curveballs. There's sarcasm. Sarcasm. Irony, humor, things that rely on understanding not just the words but the intent behind them. So it's like teaching a computer to read between the lines. Yeah. To understand the unspoken parts of communication.

That sounds like a tall order. It is a tall order, but we're making progress. Okay. One of the most promising avenues is multimodal NLP, which combines text with other forms of data like images, audio, and even video. It's like giving these systems extra senses to help them navigate the complexities of human communication. Okay. Multimodal NLP definitely sounds like something we need to dive deeper into. But before we go there, you mentioned that there are some new platforms emerging that are making NLP more accessible. What are those all about? There are some really exciting platforms emerging

that are democratizing access to NLP. One that you asked about specifically is Amazon Bedrock. Yeah, Amazon Bedrock. What's the deal with that? What makes it so special? Yeah, what's the deal? Well, think of it as a one-stop shop for all your NLP needs. Okay.

It gives you access to a suite of powerful pre-trained models. Okay. Including their own Titan text and Titan embeddings, which are optimized for different NLP tasks. Okay. But what's really cool is that it also provides access to models from other leading AI companies like AI21 Labs, Anthropic, and Stability AI. So it's like a buffet of cutting-edge NLP models all in one place. That's a great way to think about it. I like it. So it's like a buffet of cutting edge NLP models all in one

place. That's a great way to think about it. I like it. Yeah, it's a buffet. And you don't need to be a coding whiz to use it. No, you don't. So even if you don't have a deep understanding of the underlying technology, you can still build amazing things with NLP. That's the goal. That's amazing. So you're telling me I could potentially build my own chatbot or text generator without needing a Ph.D. in computer science? You absolutely could. Wow. And Amazon isn't the only one playing in this space. Oh, no, not at all.

Google also has some fascinating offerings, particularly in the world of multimodal NLP. They do. Tell me more about Google's multimodal projects. That was something else you mentioned we should look into. One of their most intriguing projects is something called PARME. PARME, okay. Which stands for Pathways Language Model Embodied. Okay.

It's essentially a multimodal language model that can control a robot. Hold on. Did you just say a language model that can control a robot? I did. That sounds like something straight out of a sci-fi movie. It is pretty futuristic. Yeah. What's groundbreaking about Pall-Emmy is that it can process both language and visual information from the robot's cameras. Oh wow. So you could give it a command like, bring me a

bottle of water. Okay. And it can actually understand what you mean, navigate its environment, and carry out the task. So we're not just talking about robots that can follow simple instructions. No. But robots that can understand natural language and interact with the world around them in a meaningful way. Exactly. That's the goal. Okay.

I definitely need a minute to process all of this. I know. It's a lot. It's incredible to see how far we've come from those early clunky systems to AI that can write poetry and control robots. It is amazing how far we've come from those early clunky systems to AI that can write poetry and control robots. It is amazing how far we've come. It is. It's a testament to human ingenuity and our relentless pursuit of pushing the boundaries

of what's possible. And the best part is we're only just scratching the surface of what NLP can do. I'm excited to dive deeper into this, but I think that's a good place to pause for now. Agreed. Let's take a breath and then come back to explore the future of NLP and the question of which languages will dominate the AI landscape. Sounds like a plan. We'll be back in a flash to continue this deep dive. Okay, so we've talked about the basics of NLP, how these models have evolved from those rule-based systems to these deep learning marvels, and even peeked into that mind-blowing potential of DNA encoding. Right. DNA is like a hard drive. Yeah. Wild stuff. But now let's talk about the future.

Okay. The future. What's next for this rapidly changing field and what role will different languages play? That's the million dollar question. Will English with its massive online presence remain the dominant force in AI or will we see a more multilingual landscape emerge? Yeah, I mean, it's a bit like the Tower of Babel, isn't it? Kind of. We've got all these amazing NLP tools, but will they be able to bridge the gap between different languages and cultures? It's a challenge, but a crucial one. We don't want to create this digital divide where some languages and cultures are left behind.

It's not just about communication. It's about access to information, opportunities, and even cultural preservation. So how do we ensure that NLP benefits everyone, regardless of what language they speak? One approach is to develop more multilingual models. We talked about BERT earlier, right? Well, there's MBERT, which stands for multilingual BERT. MBERT, oh. It's trained on data from over 100 languages and can transfer knowledge across them. So it's like MBERT is learning the universal grammar of language,

the underlying structures that connect all these different ways of speaking. That's a great way to put it. And there's another model called XLMR that's also making waves in multilingual NLP. XLMR. These models are showing that multilingual NLP. XLMR. These models are showing that it's possible to break down language barriers and create NLP tools that can be used by a much wider range of people. That's really encouraging, but I imagine simply translating data isn't enough, right? Each language has its own unique quirks and nuances. You're absolutely right.

Direct translation often misses the cultural and contextual subtleties that are so crucial to understanding language. So how do we capture those subtleties and ensure that NLP tools are truly inclusive and culturally sensitive? It requires collaboration. We need to work closely with native speakers and language communities to create data sets that accurately reflect the diversity of language use. This means going beyond just text and incorporating cultural knowledge, idiomatic expressions, even humor. So it's about combining the power of AI with the richness of human experience and cultural understanding.

Exactly. It's about recognizing that technology alone can't solve these challenges. We need human expertise, cultural sensitivity, and a deep appreciation for the beauty and complexity of language. This makes me think about the potential impact of NLP on endangered languages. Oh, that's interesting.

Could these tools actually help revitalize languages that are at risk of disappearing? It's definitely a possibility. Imagine using NLP to create digital dictionaries, translate traditional stories, or develop language learning apps. Yeah. These tools could make these languages more accessible to younger generations and help keep them alive. That's a really powerful idea, using AI to not only understand but also preserve the incredible diversity of human languages.

But let's shift gears for a moment and talk about the practical side of things. Okay, practical applications. How is NLP going to impact our everyday lives, I mean? You're interacting with NLP more than you realize. Really? Every time you search on Google, use a voice assistant like Alexa or Siri or get recommendations from Netflix, NLP is working behind the scenes. Wow, I never thought about it that way. So it's like NLP is the invisible engine powering so much of our digital experience. Exactly. It's shaping the way we interact with information, technology, and each other. Okay.

But it goes beyond convenience. NLP is being used in some really profound ways. Give me some examples. How is NLP changing the game in other fields? In healthcare, NLP is helping doctors analyze patient records, identify potential risks, and even personalize treatment plans. It's like having a super-powered research assistant that can sift through mountains of data and provide insights that humans might miss. So AI is becoming the doctor's new best friend. You could say that. It's augmenting human capabilities, allowing doctors to make more informed decisions and provide better care. Wow.

And it's not just health care. In finance, NLP is being used to detect fraud, analyze market trends, and provide personalized financial advice. That's okay. In education, it's powering intelligent tutoring systems that can adapt to individual students' learning styles. It sounds like NLP is this incredibly versatile tool that can be applied to almost any field. It really is. What are some other areas where you see NLP having a big impact in the near future?

One area I'm really excited about is personalized learning. Personalized learning. Okay. Imagine a world where every student has access to a virtual tutor that can understand their strengths, weaknesses, and learning preferences and adapt its teaching style accordingly. Okay. NLP is making that a reality. That would be revolutionary.

It would really democratize access to quality education. But with all this talk about the amazing potential of NLP, are there any risks we need to be aware of? Of course, every powerful technology comes with its own set of challenges. Right. One of the biggest concerns with NLP is the potential for misuse. Misuse? Like how? Give me some examples. Well, imagine NLP being used to create highly convincing fake news or propaganda.

OK. Yeah. Or to manipulate people's emotions through targeted advertising that plays on their fears and insecurities. Right. These are not farfetched scenarios. The same technology that can be used to improve lives can also be exploited for malicious purposes. That's a sobering thought. It's like we're at a crossroads with this technology. We are. We have the potential to create incredible things. Yes. But we also have a responsibility to use it wisely and ethically. Absolutely. The future of NLP depends on our ability to navigate these ethical considerations

and ensure that these tools are used for good. Okay. That's a lot to digest. It is a lot to think about. We've talked about the potential of multilingual NLP, the impact on endangered languages, and even the ethical challenges we need to grapple with. I'm starting to feel like I need a bigger brain to hold all this information. I understand NLP is a vast and complex field and it's constantly evolving. Right.

But I think we've covered a lot of ground today and giving you a solid foundation to build on I agree but before we wrap things up I want to circle back to something you mentioned earlier okay those new platforms that are making NLP more accessible right we talked about Amazon bedrock but I'm curious to hear more about what Google is doing in the multimodal space you're talking about Paul M eM-E, right? The language model that can control a robot. Yes, that was mind-blowing.

It feels like we're on the verge of a sci-fi future where robots are our companions and helpers. Well, Pall-M is definitely a major step in that direction. Yeah. power of combining language with other forms of data, like visual input, to create AI systems that can understand and interact with the world in a much more sophisticated way. So it's not just about robots following instructions. It's about them truly understanding our intentions and being able to assist us in more complex and nuanced ways. Exactly. And I think this is just the beginning. As multimodal NLP continues to evolve, we're going to see AI systems that are more perceptive, more adaptable, and more capable of collaborating

with us in a truly meaningful way. It's a really exciting time to be following this field. It is. I'm ready to see what the future holds, but I think that's a good place to pause for now. Okay. We've covered so much ground today, from the basics of NLP to the mind-blowing possibilities of multimodal AI and the ethical challenges we need to address. It's been a fascinating journey. It has. But this conversation isn't over yet. Okay. We still need to explore what this all means for you, our listener. How can you leverage these incredible advancements in NLP? That's a great point. Let's take a quick

break, and when we come back, we'll dive into some practical tips and resources that you can use to explore the world of NLP and discover how it can enhance your life. Okay, so we're back. Back again. And ready to wrap up our deep dive into NLP. Lots to unpack. It has been. It's been quite the journey. Quite a journey. So. Yeah, but before we say goodbye, we want to make sure you leave with some actionable takeaways. Yeah. It's all well and good to talk about these advancements, but what does it mean for you, our listeners? Exactly. So let's get practical.

Okay. Let's get practical. We've talked about these amazing platforms like Amazon Bedrock that are democratizing access to NLP. How can someone with, let's say, limited coding experience actually start using these tools? Well, one of the great things about these platforms is that they often have pre-built templates and workflows that you can use as a starting point. For example, with Amazon Bedrock, you can create a chatbot without writing a single line of code. Really? No coding at all? No coding at all. So it's like those drag and drop website builders exactly but for nlp but for nlp that's amazing it's pretty incredible so you can choose

from a variety of pre-trained models customize the conversation flow and even connect it to other services like messaging apps absolutely so you could create a chat bot for your business your personal website or even just for fun all without needing to be a coding expert. Exactly. It's for everyone. That's pretty empowering. It is. But let's say you are someone who wants to go a little deeper. Okay. A little deeper.

To understand the nuts and bolts of how these models work. Yeah. Where would you even begin? There are some fantastic online resources available. One of my favorites is the Natural Language Toolkit or NLTK for Python. NLTK. It's a library with tons of tools and data sets for working with human language data. Okay. So if you're comfortable with a bit of coding, NLTK is a great place to start experimenting with NLP. And there are also tons of online courses

and tutorials that can guide you through the basics and beyond. For sure. Coursera, EDX. Yeah. Even YouTube has some excellent resources. Well, yeah, YouTube's a goldmine. So it sounds like there's something for everyone, whether you're just dipping your toes into the world of NLP or ready to dive headfirst into the code. Absolutely.

And don't underestimate the power of simply playing around and experimenting. Oh, for sure. Try out different tools, see what you can create, and don't be afraid to break things along the way. Break things, make mistakes. That's how you learn. That's how we learn.

It sounds like that's really the heart of NLP. It is. It's about exploration. Embrace the curiosity, the experimentation, the joy of discovery. The joy of discovery, for sure. It sounds like that's really at the heart of NLP.

Absolutely. NLP is a constantly evolving field. And the best way to keep up is to stay curious, stay engaged, and keep exploring. Who knows what amazing breakthroughs await us just around the corner. Well, on that note, I think we've given our listeners a really solid foundation to build on. I hope so. From those fundamental concepts to the practical tools and resources, we've covered a lot of ground in this deep dive. Yeah, lots of ground covered. It's been a pleasure sharing this journey with you.

Likewise. I've had a great time. And hopefully you've gained a deeper understanding of NLP, its potential, and how you can be a part of this exciting revolution. For sure. And to you, dear listener, thank you for joining us on this exploration into this fascinating world of natural language processing. Remember, the future of language technology is in our hands. Let's shape it wisely and creatively. Couldn't have said it better myself. And until next time. Until next time.

Keep exploring, keep learning, and keep those questions coming. Keep them coming.