How AI Is Driving The New Industrial Revolution
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.
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, automationand trust. Forged in factories of the mid-18th 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 4th 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.
Language
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.
Automation
The impact of automation on time-consuming, labor-intensive tasks is not new. In the 1780s, a renowned inventor, Oliver Evans, set out to design a new type of flour mill. Evans built his mill with a pulley system and a bucket elevator to perform the most cumbersome job—moving the wheat from the ground to the top of the mill to begin the process. Until then, the wheat had been carried by hand.
Today, with data being the staple of the modern corporate diet, continually increasing in volume, the onerous chore involves collecting and sifting that information for use in analytics and machine learning. Those chores can leave precious little time for doing the actual work of the data scientist: building models and experiments.
When considering AI, companies must look to technologies that automate the overwhelmingly mundane data collecting and sorting work that is critical to facilitating AI. For example, IBM’s DataOpssuite of services automates the data preparation process.
Last year we released AutoAI, the first technology that streamlines the machine learning model-building process and ultimately automates the tasks of building, deploying and managing AI models. This approach of using AI to build AI helps extend the capabilities and benefits of AI throughout organizations to non-data technicians and architects—for the first time.
Trust
The innovations and inventions of the Industrial Revolution would never have caught on if there hadn’t been commerce based on trust. As automated manufacturing and expanding common-language trade opportunities made it less necessary for customer and producer to meet face to face, trust in the quality of the product became paramount. The “brand” of the company and product became the bond with the consumer.
In our 21st-century AI Revolution, that bond with consumers often centers on two aspects of trust: in the way personal data is handled, and the results of AI algorithms. In fact, our Global AI Survey found that nearly 80 percent of the more than 4,500 respondents said that ensuring their AI output was “fair, safe and reliable” was a critical factor in their use of the technology.
When it comes to managing personal data, many corporations now align their commitments to transparency with rules that include the EU’s General Data Protection Regulation, which went into effect in 2018, and California’s Consumer Privacy Act, which took effect at the beginning of this year. Such adherence helps give people greater confidence in the companies with which they do business. (For more on IBM’s point of view on trust, visit the IBM Trust Center.)
On the second point—trust in the results of algorithms—it’s axiomatic that AI and machine learning are only as good as the data that go into them.
Even the most sophisticated machine learning models can produce biased results. Sometimes this happens because the data going into the algorithms are biased, based on human norms and processes. Models themselves can also change or “drift” over time, based on constantly changing results. When this occurs, models can produce inaccurate results that are difficult to detect. We took a giant step to help remedy this issue last year with tech born out of IBM Research called Watson OpenScale. In addition to detecting and alerting developers to bias in machine learning models, and detecting drift, OpenScale offers explanations of the results in plain language to help organizations respond with confidence to clients, partners or regulators.
To help drive successful AI adoption, we developed an approach called the AI Ladder. The AI Ladder lays out simple yet comprehensive steps for organizations: 1) collect the right data needed, 2) organize the data in the most effective manner, 3) analyze the data and apply machine learning and 4) once this is achieved, begin infusing and implementing AI across the enterprise.
Changing the World—Again
Like the earlier Industrial Revolutions, which sparked tremendous economic activity across manufacturing, commerce, transportation and more, the AI Revolution can drive a new wave of growth. PwC has estimated that AI has the potential to contribute about $16 trillion to the global economy by 2030.
And like the revolutions before it, this one can help change the world—again—thanks in large part to tremendous advances in automation, language and trust.
See original post on Forbes.
The Essential Components of AI
Today, artificial intelligence (AI) is optimizing the way business is conducted, enabling predictions with supreme accuracy and automating business processes and decision making. The outcomes range from greater customer experiences to more intelligent products and more efficient services for enterprises. Just as the auto industry suddenly flourished in the early 20th century after many years of incremental developments and experimentation, AI has reached this point in the 21st century with many of the key technologies and building blocks firmly in place.
AI’s value proposition is now generally understood: It has the potential to make virtually any task or process more efficient and yield powerful new business insights.
These days, organizations with no AI strategy are like businesses in 2000 that had no Internet strategy, or those in 2010 that had no mobile strategy. And yet, for many organizations, AI is still uncharted territory.
In my view, there are six key components that are essential to AI. While they may not all fit in the classical definition of AI, the following represent the core building blocks that are needed:
AI Applications: Packaged applications that solve a business problem (i.e., virtual agents, financial planning)
Data Prep and Cleansing: Make your data ready for AI
Model, Build, Train and Run: The studio of a data science artist to build, train and run models (machine learning)
Consumer Features: Speech, images and vision, primarily used in consumer use cases
Natural Language Processing: The nervous system of enterprise AI
Lifecycle Management: Managing the lifecycle of AI models and understanding how they perform
As companies progress on their AI journey, incorporating all or most of these components into their businesses, trust is becoming paramount. Helping users understand how the AI is working and being able to explain decisions is becoming essential to fostering trust and confidence in AI systems. In fact, 68% of business leaders believe that customers will demand more explainability from AI in the next three years, according to an IBM Institute for Business Value survey.
The truth is, AI is still in the experimentation phase for many industries. But, at this particular moment, the opportunity for the technology ecosystem to drive new use cases and new innovations in a thoughtful and ethical manner is profound.
A prudent approach to AI means making data simple and accessible. It means creating a foundation of business-ready data analytics, building and scaling AI with trust and transparency, and having a coherent step-by-step plan for rolling out AI throughout the organization and governing its use. With that shared sense of mission, we will all benefit from the remarkable economic and societal benefits that AI will bring to companies, countries and citizens.
Read original article on Forbes: https://www.forbes.com/sites/forbesinsights/2019/10/10/the-essential-components-of-ai/#d70e14c1f9d8
This article was produced in partnership with The AI Summit and is part of Forbes’ AI In Action series.
AI is not Magic
For centuries, electricity was thought to be the domain of sorcerers – magicians who left audiences puzzled about where it came from and how it was generated. Although Benjamin Franklin and his contemporaries were well aware of the phenomena when he proved the connection between electricity and lightning, he had difficulty envisioning a practical use for it in 1752. In fact, his most prized invention had more to do with avoiding electricity, the lightning rod. All new innovations go through a similar evolution: dismissal, avoidance, fear, and perhaps finally acceptance.
Today, too many people view artificial intelligence as another magical technology that’s being put to work with little understanding of how it works. They view AI as special and relegated to experts who have mastered and dazzled us with it. In this environment, AI has taken on an air of mysticism with promises of grandeur, and out of the reach of mere mortals.
The truth, of course, is there is no magic to AI.
Read the rest on InformationWeek.
Acts of Omission vs. Commission
The risk of doing nothing is now greater than the risk of doing something (even if it fails). Yet, many individuals and organizations remain paralyzed in thought. Then, they wake up and find that they are no longer relevant; not because of what they did do, but because of what they did not do.
Acts of commission are easy to understand. Simply put, it’s the decision to do something. In 1961, when John F. Kennedy declared that the United States would put a man on the moon by the end of the decade, that was an act of commission. He committed to a goal, and a subsequent set of actions to complete the goal. When you read the history, it is clear that at the time he made the promise, the US had no idea how it would accomplish the goal. In fact, all the data pointed to the fact that they could not: engineering problems with the lunar module, multiple fires resulting in deaths, and numerous failed attempts. But, the act of commission, changed it all.
Acts of omission are less easy to understand...Read the rest on Medium.