By JEFF VESTAL
Learn how Elastic is at the forefront of the large-scale language model revolution – helping users take LLM to new heights by providing real-time information and integrating LLM into search, observability, and security systems for data analytics.
The iPhone social shift: the dawn of a new era
Once upon a time, not so long ago, the world was a different place. The concept of a "smartphone" is still a novelty, a cell phone primarily as a means of making calls and maybe sending the occasional text message. Yes, we have "smart" phones, but they are simpler, geared toward business users, and mostly for telephony purposes. Web browsing? It's there, but light, not something you need to spend hours doing.
In 2007, a huge wave hit the shores of our daily lives, and the world has been turned upside down ever since. That wave is the iPhone. Despite initial excitement, skepticism, and even outright dismissiveness about the iPhone (remember the BlackBerry reaction?), the iPhone quickly became a game-changer for society.
iPhone is more than a phone; it's also a cell phone. As Steve Jobs said: "An iPod, a phone, and an Internet communicator... y'all get it? It's not three devices, it's a phone." Soon after, the launch of the App Store opened Opening up the possibility of Pandora's box, triggering a digital revolution. It didn't take long for the iPhone to become ubiquitous, glued to the hands of users around the world. Suddenly, the world is within reach. Do you have complete Internet knowledge in your pocket? This is no longer a dream, but a reality.
But social transformation isn't just about technology, it's about the way we interact with the world and with each other. The iPhone has changed the way we consume media, shop, work, and even date. It turns us into hyperconnected beings, able to connect with anyone, anywhere, anytime (for better or worse).
Fast forward to today, and it's hard to imagine a world without smartphones. They have become such an integral part of our lives that we often take them for granted. But just as we've gotten used to this technological marvel, another revolution is brewing that promises to be as transformative as the iPhone, if not more. This revolution is led by large language models (LLM) and chat interfaces like ChatGPT.
LLMs follow a similar pattern: The next big wave
We've been living in the prelude to an LLM revolution for quite some time, just as early "smart" phones paved the way for the iPhone. Over the past 50+ years, humans have increasingly gravitated toward intuitive methods of interacting with information systems. SQL provides a way to describe what a user is searching for. Natural language processing (NLP) provides a way to extract context from natural human questions, allowing programmers to key in specific entities. Today, generative artificial intelligence allows humans to interact with information systems using completely natural language.
Of course, machine learning (ML) models have been around for decades, and while they may seem "simpler" compared to what we've experienced over the past few months, they've been doing their job, steadily learning and improving . Most models are designed for a specific task -- whether it's classification, regression, or something else.
In the years before ChatGPT, deep neural networks (DNNs) became very powerful, especially for image tasks. In NLP, gated recurrent neural networks, although not transformer-level accurate, have been used by most state-of-the-art NLP systems. Also, these advanced models are often used by organizations rather than individual users.
Before ChatGPT came along, individuals could use simpler versions of bots. Think Siri and Google Assistant. Or Microsoft's earlier efforts with the short-lived but notable Tay . These "AI assistants" can answer our commands, answer our questions, perform simple tasks like finding the weather, and even perform preconfigured automated tasks. But they are only the precursors to something bigger.
Then ChatGPT came along, and with it came the dawn of a new era. For many, this is the first time they are interacting directly with LLM. The general public can now interact and chat with LLM through a free web browser interface. Sounds like a small thing, right? But this seemingly simple interaction marks a sea change in the relationship between humans and artificial intelligence.
Like the iPhone, initial reactions to ChatGPT have been mixed. Of course, everyone was excited. Tech enthusiasts and futurists couldn't hide their joy. But there is also skepticism and, in some places, outright fear. Could this just be a fad? Will the LLM replace jobs? These issues continue to feature heavily in public discussion.
But like the iPhone, interest in LLMs continues to grow. Google, Facebook, and other tech giants are releasing their own LLMs and chatbots. Azure is a close partner and funder of OpenAI (creator of ChatGPT) and is integrating ChatGPT into its full suite of products. Open-source LLMs are growing rapidly, with some insiders even hinting that they may eventually surpass Google and OpenAI.
So, we are standing on the precipice of another social change. The tide of the LLM revolution is rising and is about to hit us. LLM is about to change our lives in ways we could never have imagined, just as the iPhone did more than a decade ago.
coming social shift
The growing interest in LLMs has led to the transformation of various industries. However, this in turn raises concerns about bias, inaccuracy and safety. In the next few years, the standard for people to interact with data will be to use search boxes, chatbots, and prompts built to execute workflows. The transformation has already begun.
Large-scale language models aim to understand and generate human-like text from the syntax, semantics, and context of natural language. Although not comparable to human performance in all cases, the latest version of ChatGPT has shown comparable results in a variety of professional and academic settings. Launched in March 2023, it stands out from other models with features such as visual input, higher word limit, improved reasoning, and manipulability.
These large models excel at understanding and creating human-like text across a wide range of topics and applications, leading the field in natural language processing. They excel at tasks such as sentiment analysis, text summarization, translation, question answering, and code generation. However, the challenges they face go beyond content generation. For example, past data collection times have no new information added to the model. This hard stop of knowledge dates can lead to inaccurate responses, especially when information changes over time. They also grapple with bias in the training data, resulting in outputs that can perpetuate harmful stereotypes. Another issue is the potential for inappropriate or offensive content. Furthermore, these models are computationally intensive and require significant resources to run efficiently. They also come with limitations like token limits, which affect the length and complexity of the content they can generate. Finally, the operational and usage costs associated with these models can be high, making access and scalability a concern for many users.
These challenges require more research and development to alleviate these issues and unlock the full potential of these models. Future iterations are expected to have enhanced functionality and fewer limitations as the technology advances. Thus, this will pave the way for their wider acceptance and integration into the social fabric.
As these models become more accessible and affordable, they are expected to revolutionize every aspect of life and work. In academia, LLMs can serve as invaluable research assistants, browsing the vast literature, providing succinct summaries or proposing innovative research trajectories. In the commercial world, they can empower customer service chatbots, providing 24/7 support and managing customer inquiries with almost human-like fluency and understanding.
The range of potential applications of LLMs is even wider. In education, for example, they can provide individualized tutoring that accommodates each student's unique learning pace and style. In healthcare, they can assist in the analysis of patient symptoms and medical literature to support clinical diagnosis and treatment decisions. Incorporating LLMs into our lives not only increases our productivity, but also enriches our experience in numerous areas of life.
Given these developments, society must adapt and evolve along with these technologies. This includes developing regulatory frameworks, codes of ethics, and educational programs to address the impact and implications of large language models. The way we work, communicate and interact will change dramatically over the next few years, underscoring the importance of preparing for the coming societal transformation.
To effectively navigate this shift, it is critical to leverage the expertise of organizations at the forefront of this technology wave to unlock the potential of LLMs. Elastic® is one such organization. As a builder, I like to think about new things we can build, and as a longtime user of Elasticsearch®, it's interesting to think about the role Elasticsearch will play in building new tools to exploit the potential of LLMs.
Grasping the Future with Big Language Models
Known for its innovative and powerful search capabilities, Elastic is well positioned to capitalize on society's shift to large language models. As the LLM continues to revolutionize fields, Elastic is not only keeping pace with this transformative technology, but is leading the way in developing strategies to help its users leverage the full potential of these models.
The Elasticsearch Relevance Engine™ (ESRE™) is a best-in-class document retrieval system that pushes the boundaries of what LLMs can achieve by facilitating access to real-time public data. Elastic extends this capability to proprietary information as well. Through secure and customized solutions, Elastic enables LLMs to help internal employees navigate complex documents with ease, while maintaining privacy control over their data through features such as role-based access control (RBAC).
Elastic also enables users to centralize their observability and security data and integrate it with the reasoning capabilities of LLMs, opening up a new world of possibilities. The transformative potential of this integration can be glimpsed in a recent blog post demonstrating the one-way way Elastic can be integrated with LLM, or this blog post discussing how to integrate Elasticsearch with open source LLM. Also, a recent contribution to the LangChain Python library shows how to use Elastic to generate vectors, and this PR allows Elastic to be used as a vector database to store embeddings and perform approximate kNN and hybrid searches.
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