Certainly a growing and contentious policy debate on the role and purpose of autonomous robots in the “workplace”.

The potential of existential challenges to what we understand the “middle-class” to be [ in the context of a 20th Century definition of a  “labour-force], coupled with genuine and profound advances in autonomous and now artificial intelligence, gives one a moment [at least] of pause. Certainly minds [ such as Musk, Hawking, Gates and Omohundro ]   that closely consider these technologies and their implications would also agree. The existential challenges may extend far beyond the limited boundaries of what we call the “middle-class”.

As Prof. Stephen Hawking noted:

The real risk with AI isn’t malice but competence… a super intelligent AI will be extremely good at accomplishing its goals, and if those goals aren’t aligned with ours, we’re in trouble.

Consider the musings of Elon Musk and remember this phrase. Recursive self-improvement…:


Reforms and dislocations being witnessed in “modern” labour markets are perhaps the first symptoms of genuinely profound changes to what we understand work to be in the second half of this century. The implications of automation, “job-less growth” and widening skills and income inequality suggest profound changes in policy responses will be needed. Examples such as the development of “universal” or “basic income” schemes coupled with labour market and education reforms will no doubt begin to get more attention in the coming decade.

In January, the WEF estimated that over 5 million jobs will be lost globally due to technological change. Recently published figures from the International Federation of Robotics predict that annual global sales of industrial robots will almost double in volume by 2018.

The IFR also pointed out that:

By 2018, around 1.3 million industrial robots will be entering service in factories around the world. In the high-revenue automotive sector, global investments in industrial robots increased by a record-breaking 43 percent (2013-2014) within one year.

Global Robotic Density = 66

Global Robotic Density
[Source: IFR – Global Robotic Density]
Contentious? Certainly. A recent study by the VDMA Robotics and Automation Association shows that previous waves of automation have not made labor obsolete in the German context.  A case to “let it ride..”

German Automation
[Source: Bloomberg]
MIT Economist David Autor has some interesting things to say about similar changes. Simply put, Autor’s work suggest a bumpy ride, and the challenge is that:

human capital investment must be at the heart of any long-­term strategy for producing skills that are complemented rather than substituted by technology……Significantly, mastery of “middle skill” mathematics, life sciences, and analytical reasoning is indispensable for success in this training.

Certainly the changes in economic and social policy are “well behind the curve” currently. I see little indication that this trend will improve. Given the latest work from the World Economic Forum’s research on The Future of Jobs where they state:

Today, we are on the cusp of a Fourth Industrial Revolution. Developments in genetics, artificial intelligence, robotics, nanotechnology, 3D printing and biotechnology, to
name just a few, are all building on and amplifying one another. This will lay the foundation for a revolution more comprehensive and all-encompassing than anything we have ever seen. Smart systems—homes, factories, farms, grids or cities—will help tackle problems ranging from supply chain management to climate change. The rise of the sharing economy will allow people to monetize everything from their empty house to their car.

Perhaps most saliently, they note:

While the impending change holds great promise, the patterns of consumption, production and employment created by it also pose major challenges requiring proactive adaptation by corporations, governments and individuals. Concurrent to the technological revolution are a set of broader socio-economic, geopolitical and demographic drivers of change, each interacting in multiple directions and intensifying one another. As entire industries adjust, most occupations are undergoing a fundamental transformation.

Proactive adaptation by corporations, governments and individuals. Historically, a challenge at the best of times. Anticipating and proactively adapting to what the WEF considers the fourth industrial revolution?

Good luck with that…

When considering the global policy responses to Climate Change, a phenomenon that we have decades of evidence-based, scientifically verified research to support our understanding of both the near and long-term implications for humanity [with unprecedented consensus ] and yet adaptation, let alone proactive adaptation seems non-existent in the policy sphere, beyond the well-intentioned.

WEF founder Klaus Schwab and board member Richard Samans opined that:

To prevent a worst-case scenario — technological change accompanied by talent shortages, mass unemployment and growing inequality — re-skilling and up-skilling of today’s workers will be critical…

Now, for a response,  we turn to the Minister for Industry, Innovation and Science , the Minister for Employment, the Minister for Social Services, the Minister for Education and Training and the Minister for Vocational Education and Skills [ [ along with their State counter-parts of course ], for some proactive adaptation and hope we don’t get only recursive self-improvement



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  1. and this too came to pass…

    AlphGo, by Google’s DeepMind, defeats legendary Go player Lee Se-dol in historic victory

    Link: http://www.theverge.com/2016/3/9/11184362/google-alphago-go-deepmind-result

  2. Think about this for a moment…

    The game of “Go” originated in China more than 2,500 years ago. Confucius wrote about the game, and it is considered one of the four essential arts required of any true Chinese scholar. Played by more than 40 million people worldwide, the rules of the game are simple: Players take turns to place black or white stones on a board, trying to capture the opponent’s stones or surround empty space to make points of territory. The game is played primarily through intuition and feel, and because of its beauty, subtlety and intellectual depth it has captured the human imagination for centuries.

    But as simple as the rules are, Go is a game of profound complexity. There are
    1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 possible positions—that’s more than the number of atoms in the universe, and more than a googol times larger than chess.

    AlphaGo is Google’s AI and will face its ultimate challenge: a five-game challenge match in Seoul against the legendary Lee Sedol—the top Go player in the world over the past decade.

    Google says:

    We are thrilled to have mastered Go and thus achieved one of the grand challenges of AI. However, the most significant aspect of all this for us is that AlphaGo isn’t just an “expert” system built with hand-crafted rules; instead it uses general machine learning techniques to figure out for itself how to win at Go. While games are the perfect platform for developing and testing AI algorithms quickly and efficiently, ultimately we want to apply these techniques to important real-world problems. Because the methods we’ve used are general-purpose, our hope is that one day they could be extended to help us address some of society’s toughest and most pressing problems, from climate modelling to complex disease analysis. We’re excited to see what we can use this technology to tackle next!

    The link is: https://googleblog.blogspot.com.au/2016/01/alphago-machine-learning-game-go.html

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