The world of finance operates at the speed of light. Every second, millions of transactions, trades, and market data points are generated across the globe. This high velocity data stream is the digital lifeblood of the global economy. Buried within this torrent of information are critical events: a fraudulent credit card transaction, the beginning of a coordinated market manipulation scheme, or a system glitch that is silently costing a bank thousands of dollars a minute.
For decades, artificial intelligence in video games meant a Goomba walking back and forth on a platform in Super Mario Bros. It was simple, predictable, and designed to be a minor obstacle. Today, AI is no longer just a bit player. It is rapidly becoming a lead actor, a world builder, and a personal storyteller, fundamentally reshaping how games are made and experienced.
In a world overflowing with data, we are like detectives at the scene of a crime. The data points are the clues: customer clicks, sales figures, social media comments, sensor readings. By themselves, they don't tell us much.
The conversation around artificial intelligence often feels like a coin toss. On one side, we have a utopian vision of a world without disease, drudgery, or want. On the other, a dystopian future where humanity becomes obsolete or is ruled by its own creation.
In the vast and rapidly expanding universe of artificial intelligence, one star is currently shining brighter than all the others: generative AI. You’ve likely seen its work already, whether it’s the hyper-realistic image of a historical figure taking a selfie, a poem written in the style of Shakespeare about a modern-day topic, or the surprisingly coherent chatbot that helps you with customer service.
Building a traditional software application is like following a detailed blueprint. You write explicit rules and logic, and the program executes them perfectly. Building a machine learning (ML) application is more like raising a child.
The rise of artificial intelligence is not just a technological revolution, it's a societal one. AI systems are increasingly making decisions that affect our lives, from the loan we're offered and the job we get, to the news we see and the medical diagnosis we receive.
In the age of big data, the conventional wisdom for training powerful artificial intelligence has been simple: gather as much data as you can in one central place. Companies collect vast amounts of user data on their servers and use it to train machine learning models. This approach has been incredibly successful, but it comes with a glaring problem.
