AI adoption is growing faster than many had predicted. Research from a recent Global AI Survey by Morning Consult and commissioned by IBM indicates that 34 percent of businesses surveyed across the U.S., Europe and China have adopted AI.
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That number far exceeds estimates from market watchers last year, which put adoption rates in the low teens. And the examples of AI at work in the business world are vast and varied. For example, a major European bank was able to reduce costs while enhancing productivity at its customer call center with an AI-powered virtual assistant. A healthcare provider in the Midwest used AI to create a program that could help it better predict which patients were most likely to develop sepsis.
Some industry analysts may attribute the rise in AI adoption to the surge of new tools and services designed to help lower the barriers to AI entry. Those include new ways to fight data complexity, improve data integration and management and ensure privacy. While all true, I think even bigger forces are at work.
In fact, I’d suggest that the major drivers of this revolution are the same ones that helped propel the original Industrial Revolution: language , automation and trust . Forged in factories of the mid-18 th century, all three forces are playing a unique role in tempering AI for widespread use today.
Organizations like the World Economic Forum include AI, along with other technologies like mobile, robotics and IoT, in what is referred to as the 4 th Industrial Revolution . But we at IBM believe AI itself is the heart of the new revolution—the AI Revolution.
One difference this time around, compared with the 18th-century Industrial Revolution, is that the infusion of language, automation and trust into AI is deliberate—not the byproducts of trial and error, abuse and remedy. In the AI Revolution, language, automation and trust serve as guideposts for AI providers and practitioners to follow as they design, build, procure and deploy the technologies.
Critical to the Industrial Revolution was the construction of quasi-universal languages. Vocabularies formed that included words to describe new parts, new products and new processes to enable producers, traders and distributors to facilitate trade and commerce at home and internationally.
In fact, the idea of a shared commercial vocabulary can be traced even further back, to the Middle Ages when the term lingua franca arose to describe a pidgin language used between Italian and French traders. But with the Industrial Revolution came terminology around such life-changing innovations as steam-powered machines, processes like assembly lines and new modes of transportation, like “train,” that would remain relevant two centuries later.
In the AI Revolution, though, it’s not necessary to create languages to adapt to the technology. Instead, the technology can adapt to human language. The AI technology known as natural language processing (NLP) uses computational linguistics to provide parsing and semantic interpretation of human-language text. Whether the AI system accepts audio and converts it to text or takes text directly from a chatbot, for example, NLP enables computer systems to learn, analyze and understand human language with great accuracy, as it understands sentiment, dialects, intonations and more.
This language capability advances AI from the realm of numerical data to understanding and predicting human behaviors. With NLP, data scientists can build human language into AI models to begin improving everything from customer care and transportation to finance and education.
The keys to widespread adoption are in the technology’s ability to be customized for particular projects, to support more languages than just English and to understand the intentions of a user’s query or command. NLP, for example, can leverage advanced “intent classification” that automatically discerns the intention of a question or comment to quickly give chatbot users accurate results. […]