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How Artificial Intelligence (AI) Is Driving The New Industrial Revolution?

Acceptance of AI increases faster than most had forecast. Research by Morning Consult and commissioned by IBM from a recent Global AI Survey shows 34% of firms surveyed throughout Europe, the US, and China have adopted AI.

This is far higher than last year’s market watch figures, which put adoption rates in reduced adolescents. And AI works extensively and that in diversity throughout the business world. As just an instance, with an AI-enabled virtual assistant, a European central bank has reduced costs while improving productivity from its customer call center. A Midwest health provider was using AI to develop a program to better forecast which sepsis patients will be most prone to developing.

Increasing AI acceptance may be attributed to a rise in the use of the latest services and tools to lower the barriers to AI. These include innovative methods of combating data complexity, enhancing data integration as well as management & guaranteeing privacy. Although everything is true, we think that even greater forces are working.

We would indeed say that those who have contributed to propelling the Industrial Revolution have become the main drivers of such a revolution: automation, language, and trust. Forged in mid-18th century factories, all three forces play a pivotal role in tempering AI today for broad use.

In what has been known as the Industrial Revolution 4, organizations such as the Forum of World Economic involve AI and several other technologies such as robotics, mobile, or even IoT. At IBM, however, we think AI on its own is now at the heart of the new revolution, the AI revolution.

One difference this time around because the infusion of AI in automation, language as well as trust—not the by-products of mistake and experimentation, correction, and abuse—are deliberate compared only with the industrial revolution of the 18th century. The AI Revolution provides AI practitioners and suppliers with the guidance they need to build, develop, procure as well as deploy the technologies through automation, language, and confidence.


The construction of quasi-universal languages has been critical for the Industrial Revolution. Vocabulary formed, including words, describing new ones, new procedures, or new products that will facilitate international and domestic trade as well as trade between traders, producers as well as distributors.

Indeed, the idea of a common commercial vocabulary is traceable back to the middle ages, whenever the word lingua franca has been used to define an Italian-French pidgin. However, only with the Industrial Revolution, terms like “train,” processes other than assembly lines as well as new modes of transport, including such steam-powered machinery, came about that would stay relevant two decades later.

However, throughout the AI Revolution, languages to evolve to the technology are not essential. The technology could instead be adapted to just the language of humans. Computational Linguistics are used to provide parsing as well as semantic interpretation of a message as only a human language by AI technology based on natural processing of language. For example, NLP allows computer systems to learn accurately, analyze as well as understand the human language as it understands feelings, intonations, dialects, and much more, out whether AI system accepts audio as well as translates everything into text or needs to take text directly from the chatbot.

This language skill helps AI understand and analyze human behavior from its realm of its numerical data. With NLP, human language could be integrated into AI models by data scientists, starting to improve anything from customer service through transport through finance as well as education.

The keys to broad-based implementation can be adapted to specific projects, assistance in more languages than English as well as fully comprehend the user request or command’s intentions. For instance, NLP could use advanced “tempt classification” to detect the intent of even a query or comment automatically in order to quickly deliver accurate results to chatbot users.


The impact of automation on labor-intensive tasks that take time isn’t really new. Throughout the 1780s, Oliver Evans, the best-known inventor, set up a new form of flour factory. In order to do the most lourd job, to move wheat from its base to the top of the mill, Evans constructed his work with a poultry system and a bucket elevator. The grain was carried by hand until that time.

The costly task today is to gather and screen information for analytical as well as machine-learning purposes, with data the key to a modern corporate diet as well as increasing steadily in volume. These tasks can give the data scientist a valuable little time to do his real work: building models as well as experiments.

Companies need to look at technology to automate the most comprehensive collection and sorting of data which is critical to AI facilitation in consideration of AI. IBM’s DataOps service suite, for instance, automates the process of preparing data.

Last year we set out to release the first AutoAI technology to streamline the model building process for machine learning, ultimately automating the deployment, construction, and management of AI models. This method to AI building helps extend AI’s advantages and capacities to non-data architects and technicians across organizations for the first time.


Industrial Revolution’s innovations, as well as inventions, never would have been achieved unless trade based on trust existed. Since producers needless to face automated manufacturing as well as expand common-language trade opportunities, confidence in the quality of products becomes more important. The company’s as well as the product’s “brand” has become the consumer connection.

This relationship with consumers almost always focuses in our Revolution of its AI in the 21st century on two factors of confidence: the treatment of private information and also the outcomes of AI algorithms. Actually, in our Global AI Survey, we found that almost 80% of 4500 participants said that it was a critical factor to ensure that their AI output is “fair, secure and affordable.”

Many companies have now aligned their commitments to transparency only with EU General Data Protection Regulation, effective in 2018, and California’s Consumption Data Protection Act, which came into effect only at the start of this year, with respect to the management of personal information. This adherence helps to increase people’s trust in the companies those who are dealing with. 

The second statement would be that AI and machine learning are just as nice as the data they contain. It is axiomatic to believe the outcome of the algorithm.

Even the most advanced learning models of machines can produce partial results. This tends to happen because sometimes the data entering the algorithms are partial, based upon human standards and processes. Models can, however, start changing or “drift,” predicated on continuously shifting outcomes over time as well. When this happens, models could even produce imprecise, hard-to-detect results. We took a huge step to solve this problem with IBM Research-born technology named Watson OpenScale last year. OpenScale provides explanations of outcomes in simple language to help companies react in confidence to customers, partners, and regulators, in combination with the detection and alarming of deviants in machine learning models as well as the detection of drift.

We have developed an approach named the AI Ladder to promote successful AI adoption. The AI Ladder provides for an organization with simple yet comprehensive steps: 

1) organize the data as efficiently as possible, 

2) analyze the data as well as implement machine learning 

3) once these are accomplished, start to infuse & implement AI across the company.


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