Sunday, March 5, 2017

Robots, work and the U.S. Presidential Election - Part 2

I admit to being a little taken aback when given credit for the election of US President Donald Trump, but, as one of my friends explained, it was all because of the robots. The loss of millions of manufacturing jobs in the US to offshoring was clearly a source of the discontent that propelled Donald Trump to the presidency, but the link to robots is less clear.

Manufacturing was offshored to countries with lower labour costs, but not, initially, because of robots. Indeed robots have been developed as a specific onshoring strategy to bring manufacturing back to the US. Australian Rodney Brooks, the inventor of the Roomba vacuum cleaning robot, founded Rethink Robotics with the aim of making American manufacturing more competitive. Rethink produces the Baxter robot, a cheap, flexible, human-safe collaborative robot.

Baxter is made in the US to convince companies that they can be competitive via onshoring 

Ironically, Bill Gates has recently called for robots who replace human workers to be taxed at the same rate that human workers are taxed. This seems fair, but how exactly will we know when robots have taken our jobs? While the US installed about 135,000 new industrial robots between 2010 and 2015, the number of employees in the automotive sector increased by 230,000 during the same period (IFR International Federation of Robotics) and there is a complex relationship between automation, employment growth and productivity (A3 White Paper).

Indeed it is automation rather than robots per se that are the biggest threat to human jobs, it's just that robots are the obvious, physical manifestation of artificial intelligence.


Source: "Uses of AI and Machine Learning", Gartner (October 2016)

Gartner recognises many other manifestations of AI and machine learning in their analysis of top strategic technology trends for 2017 - virtual assistants (chatbots) and operational applications (robotic process automation). Threats to job security may be invisible, as virtual and inconspicuous intelligent apps take over white collar work.

"Taking the robot out of the human," is how Leslie Wilcocks, London School of Economics, describes robotic process automation. Repetitive tasks are not a strong point for humans so why not let robots do that work? Unlike the job losses of the past, concentrated amongst blue collar workers, labour disruption caused by AI and machine learning will impact the invisible jobs of white collar workers and some commentators are predicting a future without work.

So how does this tie in to the US election result? When offshoring began in the US in the 1980s it mainly impacted blue collar workers. An interesting analysis, by Joan Williams (UC Hastings), of the US working class responsible for the political success of Donald Trump, suggests that it is the removal of jobs in certain regions that causes social upheaval. She observes that many of the people displaced by offshoring were never able to gain employment again, despite being willing to work. Many US working class families have admirably deep, strong ties to their local community, hence moving away from those networks to find employment is not an option.

In contrast, white collar or "professional" workers tend to have more broad but shallow networks, making them more mobile and resilient to job loss. Arguably it is easier for white collar workers to move and retrain for different jobs if displaced by "robots". Does this mean we shouldn't be worried about robots taking our jobs?

Although the effect of automation on the workplace may be overstated (see Jeff Borland's sensible study "Are our jobs being taken by robots") the discontent in the UK and US over job security and wealth distribution means that we should be worried, we should be very worried indeed.

In a recent opinion piece, the New York Times argued that robots aren't killing the American Dream - public policy is. We are living in an era where earnings inequality continues to grow. Over the past 40 years in Australia, wages have risen by 59% for the top 10th of the population and by only 15% for the bottom 10th. According to labour economist John Mangan, "the pay gap is now so vast that while people may be sharing the same geographical area, they may as well be living in a different society".

I'm an optimist, I believe that we will all benefit from the many amazing technologies currently being developed in robotics, computer vision, and AI, but these technologies will be disruptive. How will we deal with the disruption? Well, I also believe that humans are endlessly inventive and can adapt to any situation, but only if we maintain social cohesion. For us to enjoy the promise of Ray Kurzweil's Singularity we must all be able to share in the benefits wrought by new technologies. Unfortunately this is not a problem that we can program robots to solve for us.

Tuesday, February 28, 2017

CrikeyCon, cloud adoption and cloud security in the Asia Pacific region


It wasn't how I imagined my Saturday morning. Usually when I think of Saturdays I'm hoping for a 30 minute sleep in and enough time to read a few pages of the morning newspaper. Instead I found myself at CrikeyCon a community-led conference on information security held in SE Queensland.
What was I doing at CrikeyCon? Attempting the impossible, trying to get information out of information security professionals armed only with my clipboard and without an ISO 27001 certification. I was a little intimidated. I had heard the conference was very popular with an elite type of people known as "penetration testers". These are your "white hats" or ethical computer hackers, who can figure out your password, and the pin on your credit card, just by looking at you. I left my wallet and internet-enabled mobile device in the car, just in case.
I found myself at CrikeyCon as part of my Executive MBA with The University of Queensland. At the moment I'm like two people - robotic vision evangelist by day, cyber security enthusiast at night and on weekends.
UQ Business School has a partnership with the top ranking Wharton School at the University of Pennsylvania, called the Wharton Global Consulting Practicum (GCP). The GCP brings together selected UQ Business School and Wharton MBA students in international consulting teams to complete a “real life” market entry or expansion project for a company. In our case we are developing a growth strategy for a US cybersecurity firm looking to take advantage of the rapid adoption of public cloud in the Asia Pacific region.
Which brings me to CrikeyCon. I was hoping to survey a range of information security people about what was going on with the cloud, why were companies moving there and was it a good idea?
In our research so far it is clear that regulations in Australia play an important role in dictating the security measures taken by companies moving workloads to the cloud. The recent introduction of mandatory data breach notification laws in Australia will only accelerate this trend. Yet, strangely it is the most regulated industries that seem the keenest to adopt public cloud, suggesting that security shouldn't necessarily be seen as a weakness of cloud deployment.
The sun and coffee at the conference were both hot but CrikeyCon attendees were generous with their time and my clipboard and pen were put to good use - thanks to Sabina Janstrom for making it happen!
It was a good start, but to really get a handle on cloud adoption and cloud security in the Asia Pacific my team needs the help of all the wonderful IT professionals out there who intersect with cloud to participate in a short survey. It only takes about 10 minutes to complete and in return you can request a summary of the findings (after 12th March). We are keen to hear from people in India, Singapore, Japan, China and Australia
In case you missed it - a link to the survey is HERE.
CrikeyCon was a great way to spend a Saturday morning, indeed a whole Saturday, as well as Saturday night and the night before. You could even buy CrikeyCon lock picks! I would have purchased a set but missed the tutorial on how to use them, and I figured it wasn't a good idea to have such things lying around when you never know when a penetration tester might be nearby...

Sunday, February 26, 2017

Robots, work and the U.S. Presidential Election - Part 1

"Will robots take our jobs?" is one of the first questions I'm asked when I talk about my Centre's work in robotic vision (the application of computer vision to robotics). More recently the question has slightly changed to, "When will robots take our jobs?"

As someone who works in a lab full of robots, and sees the many limitations of robots every day, for example, our drinks-carrying robot - trained to avoid obstacles (including people who want a drink), it is easy to dismiss these concerns. I used to joke that if the job involved opening doors or going up or down stairs, humans were in no danger any time soon. One of my favourite videos is a compilation of robots failing to complete simple tasks while competing in the 2015 DARPA Robotics Challenge.


But while robots are a long way from having the balance and dexterity of humans, people do have cause for concern. The pace of technological change is overwhelming. Only 10 years ago the iPhone did not exist and the first autonomous vehicles bristled with so many sensors, and so much onboard computing hardware, they would have struggled to carry a passenger.

CMU's Tartan Racing Team won the DARPA Urban Challenge in 2007 with an extensively modified Chevrolet Tahoe. The first autonomous vehicle to navigate a 96 km course in less than 6 hours
Today your smart phone is orders of magnitude more powerful than the mainframe computers that put man on the moon, and autonomous vehicles are sleek and ready for passengers.

Google's Waymo in 2016 - no longer bristling with sensors and with more than 3 million km self-driven

While it takes human's 3-4 years to gain enough mastery of a subject to earn a university degree, IBM Watson can process 500Gb of data, the equivalent of reading a million books, per second. And while we humans can gain competence through our years of work experience, deep learning enables intelligent machines to also learn from their experiences, or indeed from ours (see robots learning to cook by watching YouTube videos). The only difference is that once one machine learns, that knowledge can be transferred to all networked machines, in much the same way that the IoT allows electric cars like Tesla to receive updates over the internet.

Imagine being able to share all the information you have gained from your life experiences with every other human on the planet. We can hope we are nearing Ray Kurzweil's Singularity, where we humans will transcend our biological limitations. In the meantime the transition to a time when there is no clear distinction between human and machine is likely to be tumultuous as robots do take some jobs and people struggle to redefine work and their place in the world.

For more on robots, work and what this has to do with the US presidential election see Part 2, coming soon.