For non-Internet based businesses predicting where your next sale would come from or who would place a support call would be invaluable. We have the data we just need to better understand the patterns it represents. <\/p>\n\n\n\n
By Bernard Marr<\/a> – Artificial intelligence<\/strong> (AI<\/strong>) has been hugely transformative in industries with access to huge datasets and trained algorithms to analyze and interpret them. Probably the most obvious examples of this success can be found in consumer-facing internet businesses like Google, Amazon, Netflix, or Facebook.<\/p>\n\n\n\n Over the last two decades, companies such as these have grown into some of the world’s largest and most powerful corporations. In many ways, their growth can be put down to their exposure to the ever-growing volumes of data being churned out by our increasingly digitized society.<\/p>\n\n\n\n But if\u00a0AI<\/strong><\/a>\u00a0is going to unlock the truly world-changing value that many believe it will \u2013 rather than simply making some very smart people in Silicon Valley very rich \u2013 then businesses in other industries have to consider different approaches. Companies in, for example, healthcare, agriculture, manufacturing, or logistics simply are not going to have millions (or billions) of people signing up to freely share volumes of data, in the way that they do with Google or Facebook \u2013 the models of interaction between consumers and the businesses are completely different.<\/p>\n\n\n\n For the last decade, it\u2019s generally been assumed that AI<\/strong> \u2013 and in particular,\u00a0deep learning<\/a>, which relies on complex deep neural networks \u2013 requires enormous volumes of data in order to surface the insights needed for genuine transformation. But what if we could get insights from smaller volumes of data? Read On:<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"