In our last article, we explored the AI tech trio doing the heavy lifting of finding, organizing and governing the tsunami of unstructured data in our ever-more digitized world.
We uncovered how Small Language Models (SLMs), Large Language Models (LLMs), and Generative AI, are each reshaping how we handle data - replacing the inefficiencies, and growing impossibility, of manual processing, and moving beyond slow and limited tools like Robotic Process Automation (RPA) and Optical Character Recognition (OCR).
But why should changing how we deal with data be a business priority?
Data is your goldmine. Yet a recent analysis found that an overwhelming 96% of the data housed by the companies studied had not been accessed in the previous 90 days - and that 75% of this data had not been viewed in two years.
What decision-making had been occurring, data-blind, in these organizations?
With the power of these new AI technologies, teams can mine the seams of data, uncovering patterns, insights and revenue streams they previously had no knowledge of to drive business growth.
It's no wonder, then, that 77% of data and analytics professionals say data-driven decision-making is the top goal of their data programs.
With the goal of developing data awareness and understanding of how an AI toolkit works, this article uncovers three more terms that often surface in the evolving AI landscape: dynamic and static models and ‘super ontology’. This is not simply more jargon: understanding these concepts is crucial in appreciating the advanced technology that drives modern AI systems, including our own.
Let's dive into these concepts and explore how they intertwine in our proprietary invention to create more intelligent and adaptable AI that will service your data discovery - and mastery.
Static Models: The Building Blocks of AI Knowledge
Static models, such as language models (LMs), form the backbone of many AI applications we rely on today. These models are 'static' because their knowledge is fixed at the time of training. Imagine them as encyclopedias—rich with information but unable to update or learn new facts after their 'publication date'.
For example, OpenAI's GPT-3, a static model, was trained on data available up until 2021. It can generate content, answer questions, and provide insights but can't incorporate new events or trends beyond its training period: a limitation that poses a key challenge.
According to a 2023 S&P Global Market Intelligence Study, 96% of respondents highlight the importance of data utilization in their decision-making processes, so outdated information within AI models can negatively impact decision-making processes, underscoring the importance of keeping information current and live - possible with tools like EmergeGen's dynamic AI.
Dynamic Models: AI in Motion
Dynamic models are the next evolution in AI, designed to learn and adapt continuously. Unlike static models, these systems process new data in real-time, refining their responses as they gather fresh information. Think of them as a live news feed—always up-to-date and ready with the latest information.
One prominent example is Google's BERT (Bidirectional Encoder Representations from Transformers), which dynamically improves search results based on new data inputs. This real-time adaptability makes dynamic models incredibly valuable for industries that require constant updates, such as finance and healthcare.
Gartner predicts that more than 55% of all data analysis by deep neural networks will occur at the point of capture by 2025: the shift towards live data learning is reshaping how businesses handle data and decision-making.
Super Ontology: Merging Static and Dynamic AI for Smarter Insights
Our super ontology is a groundbreaking approach that bridges the gap between static and dynamic models. It creates an expansive knowledge framework, categorizing and connecting concepts, allowing AI to reason more like a human. Essentially, super ontology takes the robust information from static models and enhances it with the adaptive capabilities of dynamic models.
Combining static AI models with EmergeGen's super ontology creates a synergistic relationship. Here's how it works:
- Foundation: The static model provides a vast base of pre-learned information and patterns.
- Enrichment: The super ontology enriches this base with structured knowledge, including relationships between concepts and categories.
- Dynamism: Layered on top of this, dynamic learning mechanisms can apply this enriched base to learn from new data and user interactions, effectively updating the AI's understanding in specific areas.
Allan Beechinor, Founder of EmergeGen and inventor of the EmergeGen AI solution, explains its potential:
"The marriage of static and dynamic models through EmergeGen's super ontology signifies a leap towards AI that's more adaptable, accurate, and relatable.
By leveraging the depth of static models and the agility of dynamic systems, AI is set to redefine possibilities across industries, making technology work smarter and more in tune with our ever-changing world."
Beyond Code: Real-World Transformations Across Industries
As Allan inferred, this powerful tool could change the game for data-intensive industries like finance, insurance, and healthcare.
For example, in the pharmaceutical industry, where new research and drug information constantly emerge, a super ontology-powered AI could link existing knowledge with the latest findings. This would help researchers identify new treatment paths faster, ultimately speeding up the innovation cycle.
The hybrid approach of a super ontology is also revolutionizing fields such as semantic search, where AI can understand the context of queries and provide more accurate results. In customer service, for example, AI can learn from past interactions to offer more personalized assistance.
Conclusion: The New Era of AI-Powered Data Mastery
If 2023 was the year the world discovered generative AI, 2024 is the year organizations started harnessing it to create real business value.
Alex Singla, senior partner and global co-leader of QuantumBlack, AI by McKinsey, recently shared this insight: "In 2024, Gen AI is no longer a novelty. Nearly two-thirds of respondents to our recent survey report that their organizations are regularly using Gen AI, nearly double what our previous survey found just ten months ago, and four in ten are using Gen AI in more than two business functions. The technology's potential is no longer in question."
The synergy of static and dynamic models, made possible by EmergeGen's super ontology, is at the forefront of Gen AI innovation. We give organizations the superpower of being data-aware in real-time, revolutionizing data governance and boosting operational efficiency for enterprise organizations in finance, insurance, pharma, and beyond.
For more details on how the EmergeGen algorithm is ushering in a new era of data governance, download our full paper.
And, if you're ready to access the true value in your organization's data with our AI at your side, speak with us: Sales@emergegen.ai