3 in 4 businesses are either exploring or implementing Al.
34% of businesses across the U.S., Europe and China have already deployed AI, while 39% said they were ramping up exploratory phases with Al.*
As market watchers of only last year had estimated that adoption rates would be in the low teens, these numbers far exceed expectations.
With adoption growing faster than predicted, many have attributed the surge to the lowering of barriers to AI entry, enabled by the introduction of new tools and services, including new ways to ensure privacy, fight data complexity, and improve data integration and management.
Yet there are actually far bigger forces at work.
The major drivers of this revolution are the same ones that propelled the original Industrial Revolution: automation, language, and trust.
The 18th and AI Revolutions compared
The Industrial Revolutions of the 18th century sparked tremendous economic activity across a wide range of fields, such as manufacturing, commerce, and transportation.
And just as the essential forces of language, automation, and trust played a prime role in this earlier revolution, all three forces are today similarly playing a unique role in preparing AI for global usage.
This new revolution, according to international bodies such as the World Economic Forum, is being propelled and empowered by innovative technologies like mobile, robotics, and IoT.
But at the heart of the new revolution there lies AI.
And that means that when it comes to the driving forces of language, automation and trust, there is one key difference in their roles in the rise of 18th century factories and their part in today’s revolution.
For whereas they were formed in the earlier revolution as essential by-products of trial, error, abuse and remedy, this is an entirely revolutionary revolution: for the infusion of language, automation and trust is entirely deliberate.
They serve as necessary guideposts for AI providers and practitioners to follow as they design, build, procure and deploy the new technologies.
Only in this way can the AI Revolution drive a new wave of growth.
And by 2030, AI is expected to contribute about $16 trillion to the global economy.
The Industrial Revolution was driven largely by automating previously arduous, labour-intensive and time-consuming tasks.
Large waterwheels, then later even more effective steam engines, could power multiple looms or hoists, through elaborate systems of belts, wheels and pulleys.
The new age of data processing has created its own work-intensive tasks, including the overwhelmingly mundane collecting, sorting, and sifting of ever-increasing volumes of information.
Just as in the earlier revolution, these are chores that leave precious little time for the actual work of creating the models required and building business.
Of course, these onerous tasks are all necessary as they facilitate AI. Yet AI actually provides its own solution through automation.
The data preparation process becomes entirely automated when using IBM’s DataOps suite of services.
Moreover, last year saw the release of AutoAI.
This is the very first technology to streamline the machine learning model-building process, ultimately leading to the automation of building, deploying and managing AI models.
AI is used to build AI.
For the first time, this enables the capabilities and benefits of AI to be extended throughout enterprises to non-data technicians and architects.
The Industrial Revolution was only possible because it had to adopt its own new language of words and phrases that had previously been unimaginable or unnecessary.
How else could anyone be expected to describe the ground-breaking inventions, processes, and new modes of transportation that were being developed at a phenomenal pace?
Steam engines. Trains. Assembly lines.
It was a whole new vocabulary contain the terminology enabling producers, traders and distributors to facilitate trade and commerce at home and internationally.
Ironically, in the AI Revolution it’s not really so essential to ensure language adapts to the new technologies.
Rather, the technologies are adopting human languages as their own means of communication.
Natural Language Processing (NLP) uses computational linguistics to provide parsing and semantic interpretation of human-language text.
It empowers computer systems to learn, analyse and understand human language with remarkable accuracy, even down to understanding sentiment, dialects, intonations and more.
Utilising even more advanced innovations such as ‘intent classification’, AI NLP automatically discerns the intention behind, say, a chatbot users’ query or command, resulting in a swiftly supplied, precise response.
In other words, through these evolving language capabilities AI has moved from the realm of numerical data to understanding and predicting human behaviours.
Before the Industrial Revolution, many customers and producer would regularly meet face to face.
With the increase in automated manufacturing and the expansion of common-language trade opportunities, these day to day meetings were gradually phased out.
The bond with the consumer still had to be maintained, however. Trust in the company and the quality of the product – the ‘brand’ – was of prime importance.
But how can you ‘trust’ data?
AI and machine learning are only as good as the data that go into them, which itself can suffer an unwanted bias due to human norms and processes.
Constantly changing results can also cause the models themselves to ‘drift’ over time.
This results in untrustworthy algorithms that, despite the sophistication of any machine learning model, ultimately produce biased, inaccurate results that are difficult to detect.
IBM Research has developed a remedy for this challenge.
Watson OpenScale alerts developers to bias in machine learning models, while also detecting drift.
What’s more, its results are explained in plain language to ensure enterprises respond with confidence to clients, partners or regulators.
A Global Revolution
Just like the earlier revolutions, the AI Revolution will change the world.
All enabled, as we’ve seen, thanks to the tremendous advances in automation, language and trust.
And IBM is at the forefront of moulding these forces to our advantage.
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