An Acrobat's Take on Tech
Why AI’s Superpower is its Ability to Adapt: Lessons from Einstein
Exploring adaptability as a measurement of intelligence.
Albert Einstein once observed, “The measure of intelligence is the ability to change.”
Let that sink in.
The measure of intelligence is the ability to change.
In a world where intelligence has become a buzz word and a new-found fascination for many, this statement deeply resonates with me and offers a tantalizing lens to interpret much of the hype coming from recent tech headlines. Inherent to an ability to change is the implication of adaptability, resilience, and open-mindedness. Adaptability in revising knowledge rather than just accumulating it, resilience in flexibly moulding with external shifts rather than remaining rigid, and open-mindedness in curiously adopting new perspectives rather than closing to the possibility of incomplete understanding.
While there is surely lessons to be learned in one’s own human application of ability to change, viewing this as a core measure of intelligence also offers a lens through which to interpret the artificial intelligence revolution.
From Rigid Systems to Adaptive Intelligence
Early AI systems, bound by rigid, rule-based programming, were limited in scope and flexibility. They excelled at specific tasks like chess, but failed to navigate unstructured environments or learn beyond their explicit instructions. Now, with the proliferation of machine learning and deep learning, modern AI models exhibit flexibility in recognizing patterns, making decisions, and—crucially—improving over time through data exposure. They stretch beyond predefined rules, learn from their environment, and adapt to new data inputs.
This flexibility is AI’s superpower.
Take transfer learning, for example. In transfer learning, an AI model trained on one task can apply its learned knowledge to a new but related task without starting from scratch. This approach is powerful in real-world applications: AI models used in healthcare, for instance, can quickly adjust to new medical findings or data shifts, personalizing treatments in ways traditional systems could never achieve. Similarly, in finance, AI models adjust to shifting market conditions and volatility, continuously learning from new patterns to predict trends and manage risk.
Of course, no hero’s journey is complete without obstacles, and AI’s quest for adaptability faces its own set. Modern AI systems, despite their advancements, still struggle with flexibility beyond their immediate domain. Most models require extensive retraining when faced with entirely new tasks—what’s known as catastrophic forgetting, where AI forgets previously learned knowledge when it encounters new data. While transfer learning offers some solutions, we’re still far from developing AI systems that can seamlessly adapt to multiple diverse tasks.
Humans and AI: A Dynamic Duo
Einstein’s wisdom also speaks to the unique dynamic between human and machine intelligence. While AI can automate repetitive tasks, solve complex calculations, and even assist in creative problem-solving, humans bring something AI lacks—context, curiosity, ethical judgment, and emotional intelligence.
At least for now, this has kept us in the driver’s seat of defining AI’s goals and boundaries. By setting clear objectives and providing continuous feedback, we ensure AI systems evolve in ways that (hopefully) benefit society. It’s a collaborative tango where both partners learn and adapt, creating a harmonious blend of human intuition and machine precision.
But yes, with great power comes great responsibility. It is my belief that along the way of developing such adaptive technology, core values such as transparency, accountability, inclusivity, and sustainability must guide development.
Without a firm ethical framework, AI’s adaptability could become its Achilles’ heel, especially as it begins to influence critical systems such as law and healthcare. Ensuring that AI’s ability to change remains aligned with human values is perhaps the most significant challenge we face in the AI revolution.
The Future of Adaptive AI: Learning to Learn
Though I am but a humble product designer (and yes, amateur acrobat) and not an expert in complex AI models, here are a few emerging areas of research that may indicate greater machine adaptability, and therefore intelligence:
Continual Learning: One of AI’s current limitations is its need for retraining with new tasks. Researchers are exploring continual learning, where AI can learn new information without overwriting previously acquired knowledge. This would enable AI to adapt like humans do—building on past experiences rather than discarding them.
Meta-Learning: Often dubbed “learning to learn,” meta-learning focuses on creating systems that can learn new tasks with minimal training. This makes AI systems quicker and more adaptable to unfamiliar problems, much like humans who can apply general knowledge to novel situations.
Explainable AI (XAI): As AI’s adaptability grows, so too does the need for transparency. XAI aims to develop models that not only make decisions but can also explain why those decisions were made. This will be crucial in ensuring AI systems are aligned with human expectations and ethics.
Hybrid AI Models: Combining symbolic reasoning (rule-based logic) with neural networks could lead to more adaptable systems. While neural networks are great at pattern recognition, symbolic AI excels in logical reasoning, offering a more balanced form of adaptability.
Human-in-the-Loop Systems: Keeping humans actively involved in AI’s learning process will ensure AI continues to evolve in ways that reflect human values. Human-in-the-loop approaches allow AI to benefit from real-time feedback, creating a symbiotic evolution between human judgment and machine learning.
Embracing Change as a Measure of Intelligence
In a world where technology is evolving at breakneck speed, adaptability—rather than sheer computational power—has become a meaningful marker of AI’s intelligence. As AI systems continue to develop, their ability to adapt, learn, and change in response to new environments may be a key element in defining their role in society.
Yet, as we push AI toward greater adaptability, we must also ensure it adapts responsibly. Human oversight and ethical frameworks will be critical in guiding AI’s evolution toward systems that not only learn and grow but also serve humanity’s broader best interest.
Ultimately, Einstein’s words serve as both a challenge and an aspiration—for AI to achieve true intelligence, it must learn not just to adapt to the world as it is, but to change in ways that align with the world we wish to create.
