The AI revolution: Is the future finally now?

 Over the last several decades, the evolution of artificial intelligence has followed an uncertain path – reaching incredible highs and new levels of innovation, often followed by years of stagnation and disillusionment as the technology fails to deliver on its promises.

Today we are once again experiencing growing interest in the future possibilities for AI. From voice powered personal assistants like Google Home and Alexa, to Netflix’s predictive recommendations, Nest learning thermostats and chatbots used by banks and retailers, there are countless examples of AI seeping into everyday life and the potential of future applications seem limitless . . . again.

Despite the mounting interest and the proliferation of new technologies, is this current wave that much different than what we have seen in the past? Do the techniques of the modern AI movement – machine learning, data mining, deep learning, natural language processing and neural nets – deserve to be captured under the AI moniker, or is it just more of the same?

In the earlier peaks of interest, the broad set of activities that were typically bunched together under the term ‘AI’ were reserved for the labs and, if they ever saw the light of day, they were severely constrained by what the technology of the day could deliver and were limited by cost constraints.  Many of the algorithms and structures central to AI have been known for some time; rather, previous surges of AI had unrealistic expectations of immediate consumer applications that could never be accomplished given limitations of the data and techniques available at the time.

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