Distinguishing between “data-driven” and “AI-driven” isn’t just semantics. Each term reflects different assets, the former focusing on data and the latter processing ability. Data holds the insights that can enable better decisions; processing is the way to extract those insights and take actions. Humans and AI are both processors, with very different abilities.
It struggles or shuts down once you start to think about the full distribution of values and, crucially, the relationships between data elements–information lost in aggregate summaries but important to good decision makiing. (This is not to suggest that data summaries are not useful. To be sure, they are great providing basic visibility into the business. But they will provide little value for use in decision-making. Too much is lost in the preparation for humans.)
Removing humans from workflows that only involve the processing of structure data does not mean that humans are obsolete. There are many business decisions that depend on more than just structured data. Vision statements, company strategies, corporate values, market dynamics all are examples of information that is only available in our minds and transmitted through culture and other forms of non-digital communication. This information is inaccessible to AI and extremely relevant to business decisions.
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However, the training procedure of knowledge distillation is expensive itself as it requires sufficient training data to imitate the teacher model.
Narrow AI is the ability to perform specific tasks at a super-human rate within various categories, from chess, Jeopardy!, and Go, to voice assistance, debate, language translation, and image classification