AI Will Enhance Us, Not Replace Us
This article originally ran in The Australian on June 26, 2018 (link here).
When I was young, way back in the 1970s, my school teacher asked us what we wanted to do when we grew up. My friends and I all wanted to have exciting jobs like being a pilot or a fireman. Then one friend spoke up and told us that computers were going to take our jobs. For the next several years I heard similar comments, but then something changed. It seemed as if all of a sudden, new jobs were created to work with computers, and there was a shortage of people to fill those jobs.
Travel forward a few decades, the Fourth Industrial Revolution is upon us and the key technology behind the industrial revolution is artificial intelligence (AI). Yet when I hear people speak about upcoming changes, I feel that history is repeating itself, just with “computers” replaced by “artificial intelligence”.
We will see more and more AI-enhanced human roles.
AI will create jobs. A recent study by Gartner concluded that AI will create 2.3 million jobs over the next 2 years, with a net increase of half a million jobs. Other studies have made similar conclusions. And contrary to what you may expect, these jobs won’t just be for computer geeks. We will see more and more AI-enhanced human roles.
The key reason that AI won’t simply replace humans is the well-known economic principle of comparative advantage. David Ricardo developed the classical theory of comparative advantage in 1817 to explain why countries engage in international trade even when one country’s workers are more efficient at producing every single good than workers in other countries. It isn’t the absolute cost or efficiency that determines which country supplies which goods or services. It is the relative strengths or advantages of producing each good or service within each country, and the opportunity cost of not specializing in what you are best at. The same principle applies to humans and computers.
If a task is repetitive, frequent or common, has a predictable outcome, and you have data to reach that outcome, then automate that workflow.
Computers are strongest at repetitive tasks, mathematics, data manipulation, and parallel processing. These comparative strengths are what propelled the Third Industrial Revolution, which gave us today’s digital technology. Many of our business processes already take advantage of these strengths. Banks have massive computer systems that handle transactions in real time. Marketers use customer relationship management software to store information about millions of customers. If a task is repetitive, frequent or common, has a predictable outcome, and you have data to reach that outcome, then automate that workflow.
Humans are strongest at communication and engagement, context and general knowledge, common sense, creativity, and empathy. Humans are inherently social creatures. Research shows that customers prefer to deal with humans, especially for issues that generate emotion, such as when they experience a problem and want help solving it. Don’t replace human interactions with computers. Don’t force customers to “Press 1 to hear your account balance. Press 2 to change your password, etc.” when they just want to hear a human voice and talk to someone who will fix their problem.
The AI revolution will free up humans to be more human. It will take away the mundane and the procedural and empower humans to be creative and social.
Trustworthy AIs will learn to explain themselves. For the AI revolution to be successful, AIs are going to have to learn how to earn our trust. Already we are seeing humans demanding that AIs explain their decisions in a human-friendly manner. In the European Union, the General Data Protection Regulations (GDPR) include a consumer right to receive an explanation for algorithmic decisions. Consumers are becoming more demanding, expecting that they are treated fairly, reasonably, and transparently. Researchers have developed techniques that make such human-friendly explanations available, and these are now becoming available to businesses. For example, rather than simply rejecting a loan request based upon a black box algorithmic decision, lenders can create AIs that explain which data values led to that decision. After all, rather than simply turning a potential customer away, isn’t it a much better customer experience to tell a customer what they need to change to be accepted for a loan in the future? These same tools can be used to identify and remove unfair and discriminatory biases in algorithms.
How can you prepare for the AI revolution? If you are an employee, future-proof your job security by strengthening your human skills: your soft skills like common sense and your ability to communicate and connect with others. If you are an employer, change your workflows so that computers enhance human interactions. Hold your AIs accountable for the decisions they make, using modern techniques that provide human-friendly explanations for those decisions. Change your hiring and staff KPIs to focus on human skills rather than the ability to do procedural work. Free up your human staff to interact with customers, and creatively generate more sales and higher profit for you.
The future isn’t AI versus humans. It is AI-enhanced humans doing what humans are best at. Free up your humans to be human!
About the Author:
Colin Priest is the Senior Director of Product Marketing for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. Colin is a firm believer in data-based decision making and applying automation to improve customer experience. He is passionate about the science of healthcare and does pro-bono work to support cancer research.
Colin Priest is the VP of AI Strategy for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. Colin is a firm believer in data-based decision making and applying automation to improve customer experience. He is passionate about the science of healthcare and does pro-bono work to support cancer research.
We will contact you shortly
We’re almost there! These are the next steps:
- Look out for an email from DataRobot with a subject line: Your Subscription Confirmation.
- Click the confirmation link to approve your consent.
- Done! You have now opted to receive communications about DataRobot’s products and services.
Didn’t receive the email? Please make sure to check your spam or junk folders.
Belong @ DataRobot: AAPI Heritage Month with the ACTnow! CommunityMay 25, 2023· 3 min read
Deep Learning for Decision-Making Under UncertaintyMay 18, 2023· 5 min read
Getting Value Out of Generative AIMay 10, 2023· 3 min read
Many companies are experiencing mounting pressure to have a generative AI strategy, but most are not equipped to meaningfully put generative AI to work. For AI leaders, there are deeper questions you need to ask as you consider your path with generative AI.
Discover the challenges and benefits of big data in AI, downsampling, and smart sampling techniques to reduce data size without losing accuracy.
DataRobot 9.0 helps organizations scale the use of AI to create value enterprise-wide. Discover how it simplifies ML production, automates deployment, and manages model drift to maintain business value.