AI Strategies – Incremental and Fundamental Improvements  

16 May 2018:

 

Keep in mind that the survey was done with future applications of AI as its focus. What immediately struck me is that these responses are almost exactly the inverse of what we should expect.

 

Hospitality, consumer, and financial services are by far the most intensive users of predictive analytics. Conversely, for the public sector and utilities to give themselves such glowing reports about the effectiveness of their data usage seems – well – not to put too light a touch on it, preposterous.

The way I believe this should be interpreted is that those with the lowest scores see the biggest potential benefit from advances in data science.

To be specific, our bottom three ‘leaders’ (hospitality, consumer, financial services) were all among the earliest and deepest adopters of predictive analytics. This means to me that they have the most well developed teams and deepest history with its business value both strategic and tactical, and are therefore most likely to be open to the newest advances in deep learning (aka artificial intelligence).

Why Limit AI Strategy to Deep Learning

To be candid, the only technologies in AI that are commercially ready today are convolutional neural nets (CNNs) for image/video classification, and recurrent neural nets (RNN, LSTM, GRUs) used for sequence problems like text and speech recognition and translation.

Without CNNs and RNNs we wouldn’t have facial recognition, chatbots, personal assistants, instant translators, Alexa, Siri, or visual search and classification capabilities in our recommenders.

There are plenty of other exciting developments in AI underway but they’re just not ready yet for broad commercial rollout.

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Source: Data Science Central

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