OQLIS Insights Overflow Podcast, Season 2, Episode 4. With Andrew Bosma (OQLIS CTO)

How Large Language Models Are Shaping the Future of AI

Derived from the OQLIS Insights Overflow Podcast, Season 2, Episode 4. With Andrew Bosma (OQLIS CTO) and Andre Strauss (CEO, CohesionX).

The conversation around Large Language Models (LLMs) is becoming increasingly relevant. Recently, on the OQLIS Insights Overflow Podcast, host Andrew Bosma delved into the profound impact of LLMs on various sectors with Andre Strauss and his company’s latest software offering Vectormind, emphasising their transformative potential and the need for responsible implementation.

The Evolution of Consumer Interaction

Imagine a future where shopping becomes as convenient as sending a simple voice note. Andrew illustrates this vision: a consumer could simply say, “I want bread, milk, and bananas,” and an LLM-powered system would not only understand the request but also personalise it based on the consumer’s historical data on purchases and preferences when automatically placing the order. This level of convenience and personalisation is the future that LLMs are enabling.

What should be at the core of LLM systems?

The discussion highlights the development of platforms built around LLMs. These platforms are designed to handle the underlying functionalities of generative AI, focusing on responsible AI use. The core elements include:

  • Ethical AI Implementation: It’s not just about bias; it’s about traceability, data protection, and compliance with regulations like GDPR.
  • Data Privacy and Security: Ensuring that sensitive information remains confidential and secure, especially in sectors like law.
  • Tailored Applications: Platforms are deploying LLMs for specific use cases such as HR automation, contract management, and customer service, making AI accessible and practical for businesses.

Overcoming the Authority Gap

A significant point of concern with LLMs is their lack of an authority model. Unlike traditional search engines that rank content based on credibility, LLMs generate responses based on pattern recognition and similarity. This raises the importance of curating and contextualising the data fed into these models to ensure reliable outputs.

Large Language Model

The Shift to Specialised AI Assistants

The podcast discussion includes a future where individuals, as well as organisations, will rely on multiple AI assistants, each tailored to specific needs like healthcare, finance, and productivity. This multiplication of AI assistance promises to democratise access to information and enhance productivity across all sectors.

Endorsing Responsible AI

The discussion underscores the importance of responsible AI development. As LLMs become integral to business operations, the focus must be on creating systems that are transparent, secure, and aligned with ethical standards. This ensures that the benefits of AI can be enjoyed without compromising trust and safety.

What’s next for LLMs? 

The podcast concludes with a forward-looking perspective, envisioning a world where AI significantly augments human capabilities. The rapid pace of AI advancements, backed by substantial investments, suggests a future where AI assistants become ever-present, driving unprecedented efficiency and innovation.

The global conversations around LLMs are shifting from potential to practicality. As businesses and individuals continue to explore the capabilities of these models, the emphasis remains on harnessing their power responsibly, ensuring that the AI revolution benefits all.