A Few Things I Believe About AI: Knowledge Orchestration and the Power of End-to-End Interaction Data

A Few Things I Believe About AI: Knowledge Orchestration and the Power of End-to-End Interaction Data

Artificial Intelligence (AI) has made tremendous progress in recent years, with models like GPT-4 showcasing impressive reasoning capabilities. However, one crucial aspect that still poses challenges is the knowledge base that AI relies on. In order for AI to effectively reason, it requires access to the right knowledge at the right time. This problem, known as knowledge orchestration, is the primary unsolved issue faced by AI builders.

Knowledge orchestration revolves around how knowledge is stored, indexed, and retrieved for language models. Developers are continuously working on expanding the context window sizes to accommodate more knowledge. GPT-4's 32,000 token context window, which is eight times better than previous models, demonstrates the rapid progress being made in this area. Furthermore, companies like LlamaIndex, Langchain, Pinecone, Weaviate, and Chroma are developing tools and infrastructure to simplify the process of storing and retrieving knowledge from various databases.

While knowledge orchestration is important, the type of knowledge itself is equally crucial. One particular type that holds great potential is end-to-end interaction data. This refers to comprehensive data that captures the entire lifecycle of a process. Whether it's a company's internal process or a process that enables customers to perform certain tasks, having visibility into the beginning, middle, and end of the process provides valuable insights.

The availability of end-to-end interaction data allows for techniques like reinforcement learning through human feedback, fine-tuning, and prompting to recreate and improve processes automatically. By steering models with this data, AI can continuously enhance its performance over time. Startups that integrate horizontally over a process, replacing external solutions with their own to gain a comprehensive view, will have a competitive advantage in an AI-driven world.

Replit, for example, is a startup that has horizontally integrated over the process of turning ideas into software. Their developer platform enables users to write and deploy apps directly from a browser window, allowing them to have a holistic view of the process.

Integrating forward over more layers of the value chain is also valuable. OpenAI's decision to develop ChatGPT was driven by the desire to obtain human feedback directly from end-users. By integrating forward, they gained access to valuable customer data, which ultimately improved their models.

On the other hand, backward integration through earlier parts of the value chain can also be beneficial. By having access to the editing process that leads to the final output of a process, AI can learn to recreate or enhance that process. For example, a platform like Midjourney could generate an initial image, offer comprehensive image editing capabilities, and even measure the performance of the resulting image on social media.

However, privacy concerns and internal political resistance may arise when sharing data between different apps in an ecosystem. Startups that prioritize owning the entire end-to-end process and centrally storing the associated data will have an advantage in navigating these challenges.

AI has the power to make predictions about phenomena that may not be fully explainable through traditional scientific methods. For instance, if AI can accurately predict something like anxiety, it implies that it has encoded at least part of the explanation for anxiety within its neural network. This suggests that intuition and storytelling, rather than purely rational thinking, may be effective ways for AI to predict and explain complex concepts beyond our current understanding.

In conclusion, knowledge orchestration and the utilization of end-to-end interaction data are critical aspects of AI development. By addressing the challenges of storing, indexing, and retrieving knowledge and by integrating horizontally or vertically over processes, AI builders can enhance the reasoning capabilities of models. Additionally, prioritizing privacy and data ownership from the outset can provide a competitive advantage. As AI continues to evolve, it may offer insights and explanations for phenomena that are beyond the grasp of our rational minds, shedding light on the power of intuition and storytelling.

Actionable Advice:

• 1. Invest in developing knowledge orchestration systems that can efficiently store, index, and retrieve knowledge for AI models.

• 2. Consider integrating horizontally or vertically over processes to gain a comprehensive view and access to valuable data.

• 3. Prioritize privacy and data ownership to navigate potential challenges associated with sharing data between different apps and stakeholders.