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Navigating the Evolution of AI
As we stand at the crossroads of AI’s trajectory, let’s recognize that both our personal growth and AI’s evolution share common threads. So, here are some insights between my own development and Gen AI’s near term trajectory.
A Reflection from Joon Nak Choi
Just as we’ve learned to channel our intelligence more effectively, AI will also find its purpose, and we will learn to adapt.
In my 20’s my IQ was off the charts, but I didn’t really have the wisdom to use it correctly. I made poor decisions after poor decision and made poor use of my potential. As I near my 50’s my IQ is probably a bit more limited, but my decision quality is better. I am making much better use of the intelligence that I do have. Gen AI will be the same way. The performance of these foundational models isn’t going to grow rapidly. However, we are going to start learning to make better use of it.
The rapid advancements in Gen AI models, like GPT-4, have been impressive, but the pace of performance improvements is expected to stabilize. This means that while we might not see dramatic leaps in the core capabilities of these models, they will continue to evolve steadily. The real game-changer will be how we learn to use these models more effectively. This involves integrating Gen AI into various workflows, enhancing productivity, and expanding the range of tasks it can perform. For example, businesses are already leveraging GenAI to automate customer service, generate content, and even assist in complex decision-making processes.
In essence, while the foundational technology may not see exponential growth, out ability to harness its potential will significantly improve, leading to more innovative and efficient applications across different industries.
With Pregrade however, we start with the intelligence, and are adding more wisdom to the AI as we go along.
We utilise advanced AI models that already possess significant capabilities in language understanding, pattern recognition, and data processing. Over time, these models are refined and enriched with domain-specific knowledge, ethical considerations, and contextual awareness. This process involves continous learning from real-world appliations, use interactions, and feedback loops.
Pregrade’s AI systems are designed to adapt to evolve based on new information and changing environments. This user-centric approach helps in building trust and ensuring the AI’s outputs are meaningful and beneficial.