The Perceptron was the first single-layer neural network that became the birth of cognitive science in 1957. It’s creator, Frank Rosenblatt, was a Project Engineer with Cornell Aeronautical Laboratory, of Buffalo, NY. Since then, society has become more interconnected. Data now is more ubiquitous, and it’s no surprise that machine learning has experienced a resurgence in attention. Anyone with basic programming and software engineering knowledge can run ML processes to unlock valuable insights from data. Now that more economical computing power is available, machine learning is accessible to everyone and no longer restricted to the domain of data scientists with expensive hardware.

Since 2018, we’ve witnessed the prophecy unfold. Large language models like GPT-5, Claude Opus, and Gemini have moved AI from the realm of prediction and classification into generation and reasoning. We’re no longer just training models to recognize patterns—we’re building systems that write code, analyze complex documents, generate images, and engage in nuanced dialogue. Generative AI has democratized capabilities that once required teams of specialists, placing sophisticated AI tools directly into the hands of business leaders, developers, and knowledge workers. The economic implications are staggering: McKinsey estimates generative AI could add $2.6 to $4.4 trillion annually to the global economy. Yet with this unprecedented capability comes intensified urgency around the questions I posed years ago—questions of ethics, transparency, bias, and control.

The difference now is that these aren’t theoretical concerns debated in research labs; they’re immediate challenges facing every organization deploying AI. As enterprises race to integrate AI into operations worth billions, the conversation has shifted from “what can AI do?” to “what should AI do, and who decides?”