Global Data Science Forum

Three Guiding Principles for AI Technology Development

By Nir Kaldero posted Thu October 25, 2018 12:22 PM

  

The following is adapted from Data Science For Executives

Nir_Kaldero_5G0A9443_resized.jpg

Some people fear that the Fourth Industrial Revolution will make humans obsolete. They fret that robots will become so adept at doing the jobs humans currently do that we will find ourselves redundant. I want to assure you that this is not the case: artificial intelligence (AI) and machine learning will become increasingly useful as extended-intelligence tools, meant to augment us, rather than replace or supersede us.


We are living in an era of Human + Machine. The technology is here to augment and extend your intelligence. It is not trying to replace you or make decisions on your behalf. Simply put, it’s a “brain helper” in an era of a wealth of data and unbounded opportunity. Embrace this technology, as it can compliment you and help push your organization to the next level. Data will enable you to learn, and better, cleaner, and more reliable data will help you to learn exponentially. Data is critical, and its care and cultivation are ultimately your responsibility.


The Fourth Industrial Revolution will completely change the way business is done and companies are run in the next five to ten years, just as the Internet has done in the last ten. The transformation will be bigger than any previous revolution has brought about. To compete in the new landscape of the Fourth Industrial Revolution, organizations will be required to drive value by leveraging the vast amount of data they already possess with sophisticated machine-intelligence modeling techniques.


AI is not merely a business advantage, however. Microsoft CEO Satya Nadella once said, “We are pursuing AI so that we can empower every person and every institution that people build with tools of AI so that they can go on to solve the most pressing problems of our society and our economy.”


Data Science and AI are top-down initiatives. If you start at the top—if you establish the right culture, process, and change management to augment the power of the technology—the message will amplify throughout the entire organization.


Maybe your organization will solve a crucial problem, whether it’s cancer or another disease, warfare, economic crisis, hunger, or homelessness. There are many ways that you might reshape society, leveraging machine-intelligence techniques, to make the world a better, safer place for the next generation. It may not be a simple or easy journey, but it is possible.


Principles for AI Technology Development and Developing an Inclusive Workplace


As I write these words, we’re clearly a long way from fulfilling the potential of AI and machine learning. The current skills gap is enormous.[1] But over and above the skills gap, we need to acknowledge that the Fourth Industrial Revolution will change every one of our jobs in the near future. To make this revolution work for the benefit of our economy and society, we need to be inclusive: everyone in the workforce can and must be able to participate, not just people with the “right” degrees or credentials. Technology will always continue to change over time, and we are going to have to continue to reskill and invent new skills so that no one is left behind.


While this technology provides opportunities, it also introduces big new challenges. Trust and transparency must be at its core. Working with leaders at large technology enterprises—the ones that create the chips and infrastructure that enable us to deploy machine-intelligence models—I learned that technology development is based on the following guiding principles:


  1. Purpose: The technology’s purpose is to augment our intelligence and help us do what we do, not to replace us.

  1. Trust: Your data belongs to your organization, is your competitive advantage, and must be fully protected.

  1. Transparency: Data scientists and other technologists must do all we can to explain and help you to understand how these sophisticated models generate predictions, shedding light on questions such as how these models are trained and what data was input into them to generate their output and predictions.

The message is very consistent among all major players in the field. We must embrace this technology, trust it, and employ it to our benefit.


No other industrial revolution can match the speed of change and the technological advancements we’re currently witnessing. The world is more complex and volatile today than at any other time in our history.


In addressing AI, it’s impossible to ignore the common fear that machines will take our jobs, replacing us. Research indicates that automation and smart-learning systems will likely eliminate around 10 percent of today’s jobs and that 100 percent of job responsibilities will look different as we progress in the Fourth Industrial Revolution. Job creation is also a critical part of the equation, however. New skills and new jobs will emerge, and unemployment isn’t expected to rise.


The current skills gap in the use of machine intelligence in the industry is enormous. But over and above the skills gap, we need to acknowledge that the Fourth Industrial Revolution will change every one of our jobs in the near future. To make this revolution work for the benefit of our economy and society, we need to be inclusive: everyone in the workforce can and must be able to participate, not just people with the “right” degrees or credentials. Technology will always continue to change over time, and we are going to have to continue to reskill and invent new skills so that no one is left behind.


To ride these waves successfully, we must understand and acknowledge that artificial intelligence is no longer a future fantasy or a chapter in a computer-science textbook. We are at a crucial point in history when everyone should know and understand how to enable, deploy, and implement this technology to solve the fundamental economic, social, and environmental issues we are now facing. The guiding principles of purpose, trust, and transparency can light our way.


[1] LinkedIn Economic Graph Team, “LinkedIn Workforce Report | United States | August 2018,” August 2018, https://economicgraph.linkedin.com/resources/linkedin-workforce-report-august-2018.

- - -

For more guidance on how to start the journey to successfully transform your organization to become data- and model-driven enterprise, find Data Science For Executives on Amazon.

1 comment
60 views

Permalink

Comments

Thu October 25, 2018 01:35 PM

Congratulations, @Nir Kaldero on getting the #1 spot on the Inc. list "10 Leadership Books That Should Be on Your Radar Going Into 2019"! Here's the excerpt from the article: 

There's a reason why data science is the second fastest-growing sector in the U.S. job market: Every enterprise needs data science, yet few leaders actually understand it. "Data Science for Executives" examines how businesses can implement data science and AI initiatives. Written by Nir Kaldero, vice president and head of data science at Galvanize, the book tackles myths, provides practical strategies and explains why data science will be essential to every type of business.