A generation ago, the rise of the World Wide Web ignited the Big Data era and helped drive the AI revolution we are seeing today. At first, more data translated to deeper insights, leading to advances in risk modeling, targeted advertising, business intelligence and other data-driven innovations. However, despite the world’s data doubling every three to four years, experts now say AI models are running out of data, which will significantly hamper their growth and effectiveness.
The reality is AI is able to ingest and synthesize data faster than we can generate “new” data it hasn’t seen before. For example, once AI has absorbed all the knowledge in a scientific textbook, no new insights can be gained until a new edition is published. Even then, the subject matter is largely the same, so AI knowledge expansion is incremental. Although the amount of data increases, the lack of variety and novelty is what’s holding AI back.
