The term 'Artificial Intelligence' has become so common, and so used in everyday conversation that it has made its way into the vocabulary of just about everyone—from your local school teacher all the way to the tech entrepreneurs of the Bay Area startup scene.
With a history going back to its height decades ago to the invention of cruise control in 1948, the debut of auto-correct in 1993, and the now familiar virtual assistant Siri turning 13, AI is hardly "new." But here's the thing—what you've seen so far are most likely just simple fine-tuned models or surface-level skins.
Ideas, the AI research consultancy, is working on something genuinely different with Retrieval-Augmented Generation, or RAG for short. They're not AI-washing or using AI as a buzzword to mask incremental change. They're using RAG to change the game, leading companies away from the overused path of generative pre-trained transformers (GPTs) towards something far more valuable: Generative Business Intelligence.
Breaking New Ground with RAG
While many are familiar with the capabilities of fine-tuned GPT-3 models or the functionalities of ChatGPT, RAG leads a new era of intelligence. Drawing from various data sources keeps the company updated with the latest information and organizational knowledge, eliminating the limitations that are traditionally associated with AI models.
RAG's main advantage lies in its ability to access a private database for more relevant and up-to-date responses. This is crucial, as conventional generative AI tools often hit a knowledge cut-off date, limiting their outputs to the data available during their training. By utilizing RAG, organizations are able to ensure their information is up to date, enabling more informed outputs that go beyond classic training data limitations.
Semantic Search: The Game Changer
A key feature of RAG is its use of Semantic Search. Unlike conventional keyword or Boolean searches that treat terms independently, Semantic Search is able to understand the relationships between words. This nuanced comprehension enables the relevance and context of the generated outputs, ensuring alignment with the user's true intentions.
The Future Is Actual Intelligence
As impressive as RAG is, it's just a starting point. The ultimate goal is the development of Actual Intelligence—systems that can autonomously learn trend patterns, understand audience behaviors, and tweak data models without explicit training. This leap toward Actual Intelligence signifies a shift from AI's historical applications to its future potential.
Moreover, David Griffiths, Founder of Ideas, says, "Our approach utilizing RAG and journey towards Actual Intelligence sets us apart in the marketplace. Having access to extremely in-depth data sources that can learn in every country and language moves the needle. It allows us to deliver solutions that don't just incrementally improve on today's intelligence processes, but genuinely break new ground."
When it comes to Generative Business Intelligence, the focus is on developing solutions that have true longevity and practical, real-world utility. As we go through this transition in the upcoming months and years, it's important to understand that AI, in its conventional form, isn't anything new. The real game-changer is thinking about Actual Intelligence—a shift that promises to completely redefine how we understand and apply AI in business and beyond.
