The Sixth Wave of Automation's Impact on Talent and Society

The Sixth Wave of Automation's Impact on Talent and Society

Published 8th January 2018
Sara R Moulton

Editor of HQ Asia

Published 8th January 2018

HQ Asia speaks with machine-learning expert Amr Awadallah who is Co-founder and CTO at Cloudera about how automation impacts society and work, what work still needs to be done in machine learning, and the six waves of automation throughout history.

Hopefully you have seen Hidden Figures, a thought-provoking film released in late 2016. One of the main themes is about the threat of electronic computers replacing humans. Set in 1961 and during the space race to land on the moon, the three main characters Katherine Johnson, Mary Jackson, and Dorothy Vaughan work at NASA as human computers. Vaughan spends a lot of time peering at the new IBM 7090 electronic computer as if it is the enemy.

The unofficial supervisor of her fellow human computers, Vaughan decides to learn how to use the IBM so she can pivot her job and the rest of the human computers’ jobs. This eventually leads to her promotion to supervisor of the Programming Department for the IBM.

Aside from being an excellent movie, the film shows that throughout history we have feared technology. If Amr Awadallah, Co-founder and Chief Technology Officer at Cloudera, could give the human computers in Hidden Figures advice, he would tell them that “automation should not scare us. It can relieve us of mundane and repetitive tasks”.

He shares an example of effective automation: a major US bank was able to train an algorithm to do 360,000 hours of work done by lawyers to perform that task in seconds. A lawyer’s first thought might be that this algorithm will take their job. While machines may replace some tasks that lawyers perform, it will be low-value tasks like searching documents for case research for the lawyer to interpret and use.

What does this mean for talent?

Awadallah is quick to note that machines will not replace all jobs. Rather, the easily replicated aspects of jobs can be done by machine, which will allow humans to focus on more high-value risks. He does note that there are a few exceptions:

  1. Humans who are at the top of their field.
  2. When machines can’t handle an issue. For example, when a lawyer finds a sophisticated argument for a case.
  3. Humans who train the machines.

Automation’s impact on society

The six waves of automation are communication; food; discovery of math, physics, astronomy, etc.; making stuff; process; and decisions. “Each time a wave passes, people lose their jobs and have to readapt because of automation,” explains Awadallah. Just like in Hidden Figures, this is not the first time technology has augmented or replaced part of a job. But this means there is an opportunity to reskill and upskill.

Awadallah shares two ways this impacts society:

  1. Academia is still catching up. He shares that the skillset to work on autonomous     decision-making is still rare. He notes that while it is a 10-year-old industry, it has only been taught in academia for the past five years.

    “We need talent who knows how to leverage this technology. There are three solutions to get there: education and training, tooling, and being part of a rich partner ecosystem,” he says.
  2. Societies will benefit from adopting a universal basic income (UBI). Already a topic of interest at the 2017 World Economic Forum, a basic income would improve universal quality of life as well as assuage concerns around machine learning (ML) and the future of work. In an article for the World Economic Forum, writer Scott Santens best defines UBI:

    “Consider for a moment that from this day forward, on the first day of every month, around $1,000 is deposited into your bank account – because you are a citizen. This income is independent of every other source of income and guarantees you a monthly starting salary above the poverty line for the rest of your life [in the US].

    What do you do? Possibly of more importance, what don’t you do? How does this firm foundation of economic security and positive freedom affect your present and future decisions, from the work you choose to the relationships you maintain, to the risks you take?”

    Advancements in technology can benefit society: as machines replace jobs or parts of jobs, a UBI can give people the opportunity to contribute to society, which includes buying the products and services that machines make instead of humans.

    As automation becomes mainstream, individuals will have more control over how they spend their time.

What to watch for

Awadallah warns against getting caught up in “innovating for innovation’s sake”. As we move through this sixth wave of automation, Awadallah cautions against getting caught up in technological advances. He advises to remember that “technology is a means to an end. Ask yourself what is the end? Begin with this in mind”.

When asked for an example, Awadallah shares about a major hospital chain in the US which has a finite number of AED carts. Rather than depend on doctors to discern which patients are at high-risk (and the locations where the shock carts should be placed), the hospital uses machine learning to analyse patients’ charts and data. The algorithm then identifies the patients who are at risk of a heart attack, and the best locations to keep the AED shock carts.

This kind of machine learning is, in many cases, lifesaving; it is not frivolous innovation. “Replace things with technology where human error is a big mistake,” advises Awadallah.

He offers this advice for talent and teams working with machine learning: be aware of training the algorithms enough.

Whereas a human can be taught to quickly recognise an apple from a pear, it may take the algorithm 1,000 tests to be able to correctly identify the apple. Individuals and teams also must be aware of their own biases, and that they do not train the ML to have these biases.

A few recent examples where teams have not trained ML enough mostly have to do with race. Automatic hand soap dispensers, Xbox Kinect, and Google Photos have all misidentified or not registered dark skin. Machines need to be taught to identify diversity.

Machine learning has the potential to improve our lives. From taking over mundane tasks to identifying critical illness and mitigating risk, it is our responsibility to train the machines properly.

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