Artificial Intelligence and Meaningful Work

“One of the most robust findings in the economics of happiness is that unemployment is highly damaging for people’s wellbeing. We find that this is true around the world.”

“Not only are the unemployed generally unhappier than those in work, but we also find that people generally do not adapt over time to becoming unemployed unlike their responses to many other shocks.”

Excerpts from “Happiness at work”, CentrePiece magazine, Autumn 2017, London School of Economics and Political Science

 

“Participating in the satisfying work of innovating enriches lives by endowing them with purpose, dignity, and the sheer joy of making progress in challenging endeavors. Imaginative problem-solving is part of human nature. Participating in it is essential to the good life – and no elite minority should have a monopoly on that.”

“The technologies our species is developing might either hold the keys to unlocking human potential — or to locking it up more tightly than ever.”

Excerpts from “Meaningful Work Should Not Be a Privilege of the Elite”, Harvard Business Review, 03 April, 2017

 

“Viewed narrowly, there seem to be almost as many definitions of intelligence as there were experts asked to define it.” – “Abilities Are Forms of Developing Expertise”, Robert J. Stenberg, Educational Researcher, April, 1998

 

 One day the AIs are going to look back on us the same way we look at fossil skeletons on the plains of Africa. An upright ape living in dust with crude language and tools, all set for extinction.” – Ex Machina (2014)

 

In Matilda, the children’s fantasy movie directed by Danny DeVito and based on Roald Dahl’s book of the same name, the lead character, six-and-a-half year old Matilda, on her first day of school correctly calculates the result of 13 times 379 in her head – much to the amazement of her teacher and classmates. If such an event had taken place in our classrooms, we too would have been astounded and many, if not all, of us would have described young Matilda as being intelligent or even a genius. Yet if we told you that we have a machine that can solve the very same problem in less than a nanosecond, would any of you describe it as being intelligent? We suspect not; although, some may describe the person who designed the machine as being intelligent.

What then is intelligence?

We conducted an informal experiment (read: an impromptu poll on Whatsapp) involving our school friends. We asked our friends a simple question: who was the most intelligent person in our year group at school? The experiment covered three different schools from three different cities. Without exception, the choice was unanimous for each school.

After collecting their responses, we asked each of our friends a follow-up question: what is intelligence? Some gave us the Oxford Dictionary definition, others referenced IQ or some other standardised test scores while others still bifurcated intelligence into ‘book smarts’ and ‘street smarts’. Each person’s interpretation of what intelligence is was somewhat unique. Despite that, each person came to the same conclusion of who the most intelligent person at school was.

Exploring some of the academic research available on the understanding of intelligence, we found that opinion of what intelligence is was just as, if not more, divided amongst academics and researchers.

While we, collectively as a race, may not have a unified understanding of what intelligence is this has not impeded our shared progress. We, however, do not have the luxury of not understanding artificial intelligence, its possible evolution from here on out and what it means for the future of employment.

Millions of blue-collar manufacturing jobs have already been automated away by machines. This was the low hanging fruit for artificial intelligence. It has been widely accepted for well over a decade that technology would gradually replace workers in process-oriented roles where the objectives are well defined and the operating environment is controlled. The development of artificial intelligence, however, now threatens to automate away non-routine jobs across a vast number of industries. PricewaterhouseCoopers has predicted that 38 per cent of American jobs could be automated by 2030. They identify jobs in industries such as human health and social work, financial & insurance, education, mining & quarrying and public administration & defence as those with high level of susceptibility to automation. McKinsey Global Institute is even more apocalyptic as it estimates that as many as 800 million workers worldwide may lose their jobs to robots and automation by the year 2030.

A survey of history reveals that many new technologies have been sub-optimally utilised for years, sometimes even decades, before a more effective use of the technology has been discovered. If artificial intelligence is being sub-optimally utilised, it may not be the case for long. In May this year, Google unveiled its AutoML project, which is based on the concept of an artificial algorithm becoming the architect of another artificial intelligence algorithm without the need for a human engineer. Facebook has also started incorporating AutoML into parts of its architecture while Microsoft invited teams to compete in an AutoML implementation competition.

Why does AutoML matter?

Until now, engineers and developers have used trial and error to choose the best algorithm or set of algorithms to solve problems. After model selection engineers are also heavily engaged in the iterative process of optimising the algorithm and its parameters to the specific problem at hand. This entire process is resource intensive. It requires hundreds, if not thousands, of man hours and mind-boggling levels of computing power. As a consequence, costs of solving a single problem can run into the millions of dollars.

AutoML, on the other hand, automates the entire process of model selection and optimisation; saving computational capacity by not having to optimise and re-optimise models; and significantly reducing development time from weeks and months to days. AutoML capabilities will only grow over time and the complexity of problems it is able to solve is also likely to increase.

The progress of AutoML has been rapid. Take the case of AlphaGo, the Go playing artificial intelligence developed by Google’s DeepMind, which defeated the world’s number one human Go player. AlphaGo was a technological marvel with 48 artificial intelligence processors and data from thousands of Go matches built into it. It was no match for AutoML, however. DeepMind developed AlphaGo Zero an algorithm that was only given the rules of Go and then proceeded to teach itself and create an algorithm to play Go – all without any additional human input. AlphaGo Zero defeated Alpha Go at its own game only 40 days later. In fact, during a period of 72 hours, AlphaGo Zero beat the original by a margin of 100 to 0. What is even more startling is that the AlphaGo Zero only utilises 4 artificial intelligence processors – a 12 fold improvement over AlphaGo in terms of processing power requirement.

If the example of AlphaGo Zero is a peek into the future of non-routine, dynamic capabilities of artificial intelligence then the role of humans in the workplace is at risk of being marginalised to oversight and system refinement.

Adoption of artificial intelligence outside the technology sector remains limited. Few companies have deployed it at scale. However, the business case for artificial intelligence adoption is strong. A performance gap between early adopters of the technology and laggards will become increasingly evident over the coming years. A widening of this gap is likely to result in an adopt-or-die type of scenario for the laggards. And may even lead to a “winner-takes-all” type of environment where even second-best is not good enough to survive.

As adoption increases, the implications for the human workforce are likely to be far reaching. To quote Wired: “The AI threat isn’t Skynet. It’s the end of the middle class.” The threat of artificial intelligence is seen as being so grave that many have toyed with the idea of a universal basic income – a guaranteed living wage paid by government – as a possible solution should artificial intelligence result in widespread job losses for the middle class. But what of human dignity and the meaning we find at work in solving problems and in collaborating with our colleagues? And what of our right to pursue happiness if we can no longer fulfil our ambitions and aspirations but rather live from one government hand-out to the next?

 

Investment Perspective

 

“We are subject to the processes and trials of evolution, to the struggle for existence and the survival of the fittest to survive. If some of us seem to escape the strife or the trials it is because our group protects us; but that group itself must meet the tests of survival.

So the first biological lesson of history is that life is competition. Competition is not only the life of trade, it is the trade of life – peaceful when food abounds, violent when the mouths outrun the food.” – The Lessons of History (1968), by Will and Ariel Durant

Away from capital markets, the personal investment implications of the development in artificial intelligence are far reaching. While we can be accused of being pessimistic, we do not want to be ignorant to the challenges artificial intelligence poses. We understand and acknowledge not only the benefits the technology could deliver to businesses but also in solving problems humans have struggled with for decades and centuries. Artificial intelligence may one day help us overcome cancer or develop early warning systems for natural disasters – such possibilities excite us. The blind, unchecked development of artificial intelligence, on the other hand, scares us as it could one day tear through the social fabric that binds us together and spread the venom of protectionism across the globe.

For those of us with children, we have many difficult decisions to make and challenges to overcome in helping our children prepare for the world that awaits them. In our humble opinion, the risk-reward for teaching and learning foreign languages is skewed to the upside. While Google and others may develop tools to reduce the friction of translation, one can never truly understand a culture without understanding its language. In a world where nations are becoming increasingly inward looking, we need to increase our cross-cultural understanding and there will be, in our opinion, great reward for those that can facilitate such understanding. Learning a foreign language is the first and most critical step in this process.

We encourage all of you to learn and to encourage your children to learn at least one foreign language.

On the capital markets side, we reiterate our earlier call that we are at the beginning of a long-term secular trend towards automation and recommend positioning in a basket of automation and robotics related companies through the ROBO Global Robotics and Automation Index ETF ($ROBO).

Follow us on Twitter @lxvresearch

 

This post should not be considered as investment advice or a recommendation to purchase any particular security, strategy or investment product. References to specific securities and issuers are not intended to be, and should not be interpreted as, recommendations to purchase or sell such securities. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.  

 

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