Increasingly, the words ‘Artificial Intelligence’ (AI) are not limited to the realms of science fiction. They’re being used more frequently and interchangeably in the domains of technology, business, and even our everyday lives.
The Hype and Its Ramifications
The term “AI” is buzzing, selling newspapers, software, and technology. Before long, it might even be promoting breakfast cereal. The hype cycle is upon us. This term is thrown about as a catch-all phrase. The incessant chatter revolves around its potential applications, how it’s revolutionizing industries, and its potential to solve the world’s most complex problems. While some of these assertions are undoubtedly true, this approach can be overly simplistic and, at times, potentially wildly inaccurate. AI is a blanket term that encompasses a vast array of techniques, and I find the broad usage increasingly frustrating.
Understanding the Nuances of AI
At its core, AI relies on mathematics and mathematical techniques to determine outcomes. However, the purpose and nature of those outcomes matter significantly. By lumping them together, we risk undermining and conflating their capabilities. So, when we refer to AI, what exactly do we mean? Decision science? Predictive modeling? Machine learning or generative predictive text? Are we aiming to forecast future events or merely predict the next word or image/letter based on historical data? Understanding these outcomes is crucial.
Another intriguing aspect is our evolving relationship with computers. Historically, we’ve perceived computers as precise, engineering tools that operate error-free, in contrast to fallible humans. Although these new AI techniques run on computers, their outputs have become more probabilistic, imperfect, and, in a sense, more human. However, our language still clings to the old paradigm where “the computer is always right.” This perspective might need updating.
The AI Assistant
Personally, I view these new Generative Predictive Text models as helpful assistants—aiding, streamlining processes, and enhancing efficiency. Yet, they still require oversight to ensure the quality and accuracy of their outputs.
In the long run, perspectives will evolve. But for now, let’s ensure we’re not solely chasing the hype and begin to speak more precisely. After all, precision is where the real benefits and productivity lie.
