As organizations grapple with an ever-growing influx of data, traditional data management methods often fall short.
Manual processing is labor-intensive and often impossible at pace or scale, and conventional automation tools like Robotic Process Automation (RPA) and Optical Character Recognition (OCR) require extensive training periods and are typically confined to specific use cases.
In this landscape, EmergeGen's AI innovation stands out.
Understanding how our technology works is the first step to understanding how it will redefine data governance and operational efficiency for data-intensive organizations in finance, insurance, pharma, and more.
At the core of the AI revolution are Small Language Models (SLMs), Large Language Models (LLMs), and Generative AI, each playing a unique role in transforming how we interact with data.
Read on for our explainer of this trifecta of AI tech and to appreciate how the EmergeGen algorithm is changing the game - or download our full paper on the topic now.
The Trio of AI Tech Changing Our World
While LLMs and SLMS have unique advantages and applications, and their roles are often complementary, the advantages of using SLMs alongside Generative AI are clear when you're looking for heightened efficiency, accuracy, data privacy, and reduced cost.
Let's explore their unique characteristics.
Large Language Models (LLMs)
LLMs, like the well-known GPT series, are the hefty generalists of the trio. Equipped with vast knowledge and the ability to generate human-like text across a wide range of topics, they have transformed how we now research, ideate and create.
However, LLMs' vulnerabilities are embedded in their training time, cost, computational power requirements, and memory constraints.
Google's 530B parameter model PaLM's training cost is roughly estimated anywhere between $9M to $23M. Anthropic's CEO, Dario Amodei, has forecasted that by the end of this year, we might see models whose training costs exceed $1 billion, potentially rising to $10 billion by 2025.
The initial training phase is particularly power-hungry and carbon-intensive: recent estimates highlight that the carbon footprint associated with training these models is akin to the electricity consumption of a US family over 120 years.
Small Language Models
If LLMs can be something of a blunt instrument, SLMs are a precision tool, bringing both accuracy and efficiency to the table.
A small language model is a type of machine-learning algorithm trained on a smaller, more specific dataset and often of higher quality than those used for LLMs. These models possess significantly fewer parameters—a measure of the complexity of the model—and a simpler architecture.
Despite their size, SLMs are capable of understanding and generating human-like text, similar to LLMs which are trained on vast amounts of data - but with fewer, if any, hallucinations.
SLMs are instead tailored to specific industries or tasks, often resulting in higher accuracy and relevance in their outputs. They can incorporate domain-specific knowledge and terminology, making them invaluable for applications that require precision and expertise.
They also cost less to create and require less computational power. They demand significantly less training data and can be trained relatively quickly—often within minutes or hours—compared to LLMs' need for vast data resources, with training times spanning several days to weeks.
"Small language models can make AI more accessible due to their size and affordability. At the same time, we're discovering new ways to make them as powerful as large language models." Sebastien Bubeck, Machine Learning Foundations Lead, Microsoft Research.
Sometimes, less is, indeed, more.
Generative AI
Imagine a world where your digital assistant doesn't just answer questions but understands your industry-specific jargon, anticipates your needs, evolves its thinking as the data it reads changes - and seamlessly integrates into your daily tasks.
This is possible when you have the synergy of Gen AI alongside an SLM.
Generative AI is something of a creative genius among our AI trio. It goes a step further by not just understanding and responding to inputs but also creating new content, from drafting emails and articles to composing music and designing graphics.
Combining the precision of SLMs and the creative capabilities of Generative AI revolutionizes what organizations can do with their data: from risk management to fraud detection, policy underwriting and client reporting, fusing creativity with domain expertise holds significant potential for multiple sectors.
"Generative AI will have a significant impact across all industry sectors. Banking, high-tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI. Across the banking industry, for example, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented."
McKinsey, June 2023
Download our full article here for a more in-depth explanation of EmergGen's AI innovation and how we use static and dynamic models to deliver unsurpassed unstructured data governance.
AI: The Future of Data Governance
The trio of AI technologies is about more than enhancing productivity; it's about redefining the boundaries of what machines can do.
"Generative AI's greatest potential is not replacing humans; it is to assist humans in their efforts to create hitherto unimaginable solutions."
Harvard Business Review, August 2023
Gartner estimates that 80% of enterprise data is unstructured, but until now, structured data has been the go-to for most analytics and models as the sheer variety of unstructured data - think images, audio clips, videos, and all the data created by machines like website logs, server records, and mobile apps - a substantial challenge for traditional data processing methods.
Organizing this data—let alone leveraging its intelligence—has been restricted until now as it cannot be processed and interpreted at scale.
EmergeGen bridges the gap between the data your organization creates and the intelligent information your data platform needs and steps into the marketplace with an alternative to the existing imperfect LLM products.
The unique Small Language Models (SLMs) we build are nimble specialists who search, retrieve, and parse all data types, standardize the data, and then build relationships to existing data in your organization.
Consider a global bank that partnered with EmergeGen to streamline its processes. By implementing EmergeGen's AI solutions, the bank automated data retrieval, standardized the information, and efficiently added crucial details.
The impact was staggering: mortgage applications that previously took 5-7 weeks to process were completed in just 2-3 days.
This not only boosted accuracy and efficiency but also significantly enhanced customer satisfaction.
Gartner's 2023 survey highlights that 55% of corporations are either piloting or implementing LLMs. However, the streamlined and cost-effective nature of SLMs is likely to drive even higher adoption rates for those with the foresight to leverage them.
EmergeGen: Unlock Your Data's Potential
EmergeGen's solutions offer a unique blend of efficiency, customization, and security. Our SLMs are designed for specific industries, ensuring higher accuracy and relevance. Moreover, the reduced computational requirements of SLMs allow for local deployment, enhancing data privacy—a crucial factor for sectors like healthcare, finance, and legal.
In an era where data is the new oil, leveraging its full potential can give your organization a competitive edge. EmergeGen's AI-powered solutions transform data into actionable intelligence, driving innovation and operational excellence.
Ready to explore in more detail how EmergeGen can revolutionize your data governance?
Download the full whitepaper for an in-depth look at how our AI solutions can unlock the insights in your organization's data goldmine.
Or, if you’re ready to get your data to work for you, with our AI at your side, speak with us: Sales@emergegen.ai