Redefining Intelligence

Recently, I’ve often found myself in discussions about "intelligence," especially in measuring the capabilities of a particular artificial intelligence engine and comparing these engines to human intelligence. And while these conversations are fascinating, they often seem to overlook a key issue: What exactly do we mean by intelligence? As humans, we struggle to define it clearly, yet we confidently label machines and people as intelligent—or not—based on standardized metrics. The issue here is that we’re trying to measure something we haven’t adequately defined.

For too long, I believe we have equated intelligence with specific outcomes—solving problems, scoring well on tests, or demonstrating expertise in certain areas. These measures, while useful in some contexts, only capture a fraction of what intelligence really is. To understand it better, we need to expand our definition to reflect the true depth and versatility of intelligence.

Here’s my definition of intelligence:

"Intelligence is the capacity to continuously learn, adapt, and synthesize insights across interconnected systems, allowing for both creative problem-solving and the development of new understanding in dynamic environments. It reflects the ability to integrate feedback from experiences, adapt to new challenges, and generate insights that respond to evolving needs, while recognizing the emergent behaviors within a broader system."

Now, let’s break this down and explore what intelligence really means:

Capacity to Continuously Learn and Adapt

At its core, intelligence isn’t a static trait; it’s a continuous process. It involves the ability to keep learning, adapting to new circumstances, and adjusting to change. Unlike what traditional IQ tests might suggest, intelligence isn’t simply a measure of what you know in a single moment. Instead, it’s about how well you can integrate and apply new information, regardless of whether you’re solving an immediate problem or simply gaining a new understanding.

Think of an artist experimenting with new techniques in their work. They might not be trying to solve a specific problem, but they constantly learn, adapt, and synthesize different ideas to create something entirely new. The ability to evolve and expand one’s knowledge, even when not faced with a challenge, demonstrates a key aspect of intelligence.

Synthesizing Insights Across Interconnected Systems

One critical dimension of intelligence, often overlooked, is the ability to draw connections between seemingly unrelated areas. In systems thinking, intelligence is not confined to one domain but involves seeing how different systems interact and how insights from one area can influence or enhance another.

For example, take a scientist studying ecosystems in the Amazon. They’re not just learning about plant biology or water systems in isolation—they’re recognizing how these elements interact with broader environmental, social, and economic systems. Intelligence is about seeing the bigger picture and synthesizing insights from multiple interconnected systems. The same is true for someone in business who recognizes how marketing strategies, consumer behavior, and emerging technology intersect to create new opportunities.

This synthesis of insights isn’t always about solving a direct problem. It can also lead to deeper understanding, innovation, and the ability to predict or respond to changes in dynamic environments. The interconnectedness of systems requires an intelligence that is flexible and able to pivot, even when no immediate problem demands it.

Creative Problem-Solving

Of course, problem-solving remains a crucial component of intelligence. But as we expand our understanding of what intelligence is, it becomes clear that solving problems is just one part of the equation. True intelligence doesn’t merely apply learned formulas or known strategies to familiar problems; it involves creatively navigating new and unpredictable challenges.

Imagine an entrepreneur in a rapidly changing industry like technology. Their success doesn’t come from knowing all the answers ahead of time. Instead, it comes from their ability to creatively solve problems as they emerge—often in ways that others haven’t considered. This is where intelligence becomes a form of applied creativity. It’s the capacity to respond to shifting circumstances with novel solutions that come from integrating insights across different domains.

Developing New Understanding in Dynamic Environments

Perhaps the most crucial shift in this expanded definition is recognizing that intelligence isn’t just about responding to problems. It’s also about developing new insights, knowledge, and understanding even when there isn’t a straightforward problem. Intelligence is a proactive force. It allows individuals to explore, learn, and innovate in dynamic environments to survive, thrive, and grow.

Consider a researcher exploring the possibilities of quantum computing. They may not be tackling a specific, immediate problem, but their work is about expanding our collective understanding of how systems could work in the future. They’re learning, adapting, and generating new insights that will eventually change how we solve problems—but intelligence is at play long before a specific challenge is presented.

Machine Learning and AI: Learning and Adapting Over Time

In the world of AI, particularly in machine learning, intelligence isn’t judged by how well a model performs at a single point in time. Instead, we evaluate a model’s ability to learn, adapt, and improve over time. Machine learning models are trained on datasets, make predictions, and adjust based on feedback. Over time, they become more accurate and effective at solving problems, but their intelligence is demonstrated by their ability to improve rather than by an initial score.

For example, an AI model designed to predict retail consumer behavior might start with limited accuracy. However, it refines its predictions by processing more data and integrating feedback from real-world interactions. Just like human intelligence, it’s not the initial performance that matters most but the model's capacity to continuously learn and adapt.

Can Intelligence Be Measured?

Given this expanded view of intelligence, can we measure it? And if so, how?

Traditional metrics like IQ tests or standardized exams fall short because they measure intelligence as a fixed trait, focusing on specific cognitive abilities at a given time. But intelligence, as we’ve now defined it, is dynamic, context-dependent, and deeply tied to one’s capacity to adapt and grow. Measuring such a process would require tools that assess how well someone learns, adapts, and synthesizes insights over time—not just how they perform on a single test.

For instance, instead of measuring intelligence through test scores, we might look at someone’s ability to navigate complexity and uncertainty in real-world scenarios. How well do they learn from feedback? How effectively do they integrate new insights into their understanding of interconnected systems? The true measure of intelligence lies in the processes people use to reach those outcomes, reflecting their ability to adapt, learn, and synthesize information over time.

So, How Intelligently Are You Living?

Intelligence, in this expanded definition, is not just about solving problems. It’s about continuously learning, adapting, and integrating insights from interconnected systems to both solve challenges and generate new understanding in dynamic environments. Whether we’re an artist, a scientist, an entrepreneur, or an AI model, intelligence is an ongoing, evolving process.

Perhaps the more important question isn’t how intelligent someone is by conventional metrics but rather how intelligently they are living their lives—how successfully they learn, adapt and grow within their environment.