Data is the coal to the AI industrial engine, except we need our coal to be from diverse regions and backgrounds.
Artificial intelligence. Do we think it will replace our workforce? There are generally two sides to the discussion – broadly captured as optimists and pessimists.
Optimists tend to use past industrial revolutions as examples and highlight the growth in prosperity and quality of life as well as overall job creation.
For pessimists, there’s a fear of mass unemployment. Jobs that are lost in easily automated lower skilled areas are replaced by new roles in higher skilled areas such as data science and psychology.
Whilst society will see an overall improvement in quality of life – this may not be the case for everyone. I’m concerned for those people and it’s important we have open conversations around how we want companies and governments to manage this change.
With the current wave of disruption, I see a couple of key differences from past revolutions that we should consider:
The rate of disruption is much faster.
The first two industrial revolutions happened roughly between the late 1700s through to the early 1900s. As jobs were displaced due to the advent of industrial power – jobs became more service-based.
This happened over a period of 100 to 200 years. Given the average working life is between 40 to 50 years, it’s reasonable to presume many people could continue working in their jobs until they retired.
Contrast that with the speed of disruption that happens today. Uber was started only nine years ago and now spans much of the globe, making the business models of incumbent taxi companies irrelevant.
Amazon, founded just over 24 years ago and spearhead of bricks and mortar retail disruption, is heavily investing in automation. So much so that it bought robotics company Kiva Systems in 2012 for US$775 million.
In today’s world, disruption will happen mid-career for many individuals.
In today’s world, disruption will happen mid-career for many individuals. As artificial intelligence brings cognitive capabilities to machinery, we will see customer service jobs, driving and analytical tasks increasingly being replaced. This will make redistribution of workers even more tricky.
I believe we will see “structural unemployment” where jobs are created in highly skilled/paid areas such as machine learning and software engineering whilst jobs are displaced in lower skilled such as truck driving. I expect this to happen in other sectors but that will result in partial replacement of a person’s tasks.
We are all just scratching the surface on these matters and getting a deeper understanding is a great place to start.
I recently participated in the Partnership on AI’s first working session on addressing issues around AI Labour and The Economy.
These organisations are valuable but will need to be truly global and diverse in their perspective.
The discussions included experts on the topic from non-profit and profit organisations (Google, University of Berkeley, IBM, UNICEF, Facebook and more) and are a great start. It’s clear that there isn’t a consensus yet, and everybody is still grappling with the challenges around artificial intelligence.
Giving AI a homogenous source of data risks creating biased and unfair artificial intelligence.
Artificial intelligence has the potential to become a fearsome big stick or a magic wand of miracles. The reality will likely be a bit of everything – but personally I’d like a magic wand more than a big stick.
We need to remind ourselves that we are one planet and one human race.
Collaborating on a global scale, sharing diverse perspectives, cultures and data – we have the opportunity to build human race benefiting artificial intelligence.
Data is the coal to the AI industrial engine, except we need our coal to be from diverse regions and backgrounds. Giving AI a homogenous source of data risks creating biased and unfair artificial intelligence.
At FaceMe we believe it all starts with us as individuals. When we value people, we can design technology in a way that reinforces and enhances this value.
We start by gaining a deep understanding of how AI will impact the people around us and our organisations. Let’s start conversations, projects and share our learnings with others.
Victor Yuen is head of product at FaceMe.
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