Morgan Templar’s Post

LLMs like ChatGPT have taken the world by storm, but for enterprises, they are only half of the equation. Knowledge Graphs (KGs) are the other half, providing the reliability and structured understanding that LLMs lack. 🔵 Transformers - Continuous Knowledge: LLMs capture the fuzzy, probabilistic nature of relationships between concepts, allowing us to navigate the semantic landscape fluidly, gradually shifting from one concept to the next in a continuous flow. However, this fluidity is both a blessing and a curse. Continuous knowledge representations can be unreliable, leading to hallucinations and ad-libbing, which is problematic for business. 🔵 Knowledge Graphs - Discrete Knowledge: Knowledge Graphs, on the other hand, offer a discrete, trustworthy counterpart to LLMs. They represent data as nodes and edges, explicitly defining relationships in a way that ensures logical consistency. While KGs are among the most expressive formal data structures available, they have their own challenges. They can be rigid and far less dynamic and flexible than their statistical counterparts. 🔵 KGs + LLMs - Continuous and Discrete Knowledge Combined: The magic happens when these two forms of knowledge representation come together. KGs and LLMs, when combined, create a powerful tool capable of safeguarding and exploring organisational data. This hybrid approach enables organisations to thrive in an AI-driven world. In its fullest realisation, this KG + LLM approach should function like a protective membrane, providing a unified semantic layer that encapsulates proprietary information safely while still leveraging the power of AI. Simply put, it’s a survival strategy for organisations entering the age of AI. ⭕ Vectors & Graphs: https://lnkd.in/eQnecChR ⭕ Semantic Layer: https://lnkd.in/eFJwKYW8  ⭕ VectorHub Article: https://lnkd.in/ecPWJFkc

AI agents are a $5+ billion business that will grow about 40% in the coming years, says Gartner expert Daryl Plummer. Learn about the emerging world of AI agents in this video. Download the 2024 Emerging Tech Impact Radar: https://gtnr.it/3ZzuKNB #AI #EmergingTech #BusinessGrowth

The root of leadership isn't power. It's wisdom. Here are 10 important lessons from Nature: 1. Resilience blooms in adversity. Identify your core strengths.  Build inner resilience through reflection. 2. Change is survival's paintbrush. Embrace change as an opportunity.  Adjust your approach as circumstances evolve. 3. Partnership fuels mutual success. Map your network of relationships. Consider the ripple effects of decisions. Act with broader awareness. 4. Diversity creates a stronger whole. Seek out diverse viewpoints.  Champion inclusivity in your sphere. Create space for all voices. 5. Renewal is Nature's promise. Reflect on past setbacks.  Identify lessons and growth opportunities. 6. Efficiency thrives in scarcity's embrace. Audit your resource use.  Turn 'waste' into valuable resources. 7. Balance sustains life. Schedule dedicated time for work and relaxation. Honor both. Practice the art of saying 'no'. Protect your time and energy. 8. Success takes root in time. Break long-term goals into small, daily actions.  Cultivate patience in your pursuits. Trust in steady, consistent effort. 9. Simplicity creates profound strength. Simplify complex ideas into core principles.  Communicate with clarity. Focus on essentials. 10. Sustainability safeguards our future. Envision the long-term impact of your actions.  Make decisions that matter for generations. Which lesson is most relevant to you? The cost of ignoring Nature's lessons is the future you could have shaped. Be a catalyst for change. Build a legacy that lasts. For more on Purpose-led Leadership, follow Dr. Elizabeth Lindsey.

Let's be clear, Generative AI is not AI. It’s just one of many types of AI models. In all this hype and pressure to adopt ChatGPT and Agentic AI capabilities, we seem to have lost sight of this. Sure, these models are doing some very cool things, wowing us with their ability to engage in seemingly intelligent conversations and tackle complex tasks. But, while their predictive ability may be impressive when it comes to drafting emails, summarizing documents, or even brainstorming creative ideas, they also have the propensity to generate false information with absolute confidence (the famous Hallucinations). In many applications, particularly in heavily regulated industries, this isn't just a minor inconvenience, it can have serious implications. And a RAG, which many are pitching, won’t solve the problem. Most use cases of AI will require a combination of models, a "Hybrid" approach, in order to truly get the accuracy most organizational use cases will require. This Hybrid approach assumes a combination of models, such as a traditional machine learning model trained on vast amounts of proprietary data (e.g. medical, financial, etc.) with a generative AI component. For example, in the medical field, where the accuracy of a diagnosis is key, the machine learning model could handle the intricate task of analyzing symptoms, test results, and patient history to generate a diagnosis with a high degree of accuracy. Meanwhile, the generative AI could step in to explain the diagnosis to patients in clear, understandable language, answering questions and providing additional information as needed. The result? A more accurate diagnosis coupled with better patient communication and understanding. So, for those organizations that are struggling with where to begin, stop looking at implementing Generative AI to solve all your problems. First, start to understand better the problem you want to solve, then determine what capabilities are best suited to solve it. It might not be a one-size-fits-all solution - and it doesn't need to be. Source Article: https://lnkd.in/gDcZ48f8 #innovation #AI #generativeAI #future

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