Technology

Welcome to the Machine

“I kind of felt powerless… I do have extensive experience in terms of playing the game of Go, but there was never a case as this as such that I felt this amount of pressure.” (Lee Sedol, after playing against AlphaGo in March, 2016)

McAffee and Brynjolfsson (2017) describe phase two of the second machine age as the time “when science fiction technologies – the stuff of movies, books, and the controlled environments of elite research labs – started to appear in the real world”: winning at Go, diagnosing disease, interacting with people, engaging in creative work. The authors envision three great trends that are reshaping the business world:

Trend

Counterpart

Machines
including AI, boosted by:

      1. Moore’s law
      2. Cloud computing has opened relatively inexpensive computing power required to execute a machine learning project.
      3. An endless supply of data (and GPU‘s to process it). Machine learning systems need to be exposed to many examples in order to perform and improve in their tasks.
Human Mind
According to Kahneman and Egan (2011):

  • System 1: Evolutionary ancient, fast, automatic, intuitive
  • System 2: Evolutionary recent, slow, conscious, and a lot of work

“System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control. System 2 allocates attention to the effortful mental activities that demand it, including complex computations. The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration.” [Kahneman, D. and Egan, P. (2011) Thinking, fast and slow (Vol. 1), New York, Farrar, Straus and Giroux.]

Platforms
that people use to access a product or service, like Uber or AirBnB, but don’t actually produce anything or provide the service to the end customer.
Products and Services
For example, the big gains of electrification (one of the most disruptive technologies ever) came not from simple substitution of steam engines, but from the redesign of the production process itself. The process lens typically reveals many tasks that can be eliminated, or as Hammer and Champy (1993) put it, obliterated. According to Grant (2010) a firm increases attention to process innovation as it seeks to reduce costs and improve product reliability. The tendency over time for product life cycles has become compressed (p. 275).
The Crowd
e.g. GE’s FirstBuild, a “co-creation community that is changing the way products come to market”.
Organisational Capabilities
Prahalad and Hamel (1990) coined the term “core competences” to distiguish those capabilities fundamental to a firm’s strategy and performance. They also criticised U.S. companies for emphasizing product management over competence management.

Now where does all this leave us? As Haidt (2006) argues, “judgment and justification are two separate processes” of the mind. Judging, performed by System 1, happens almost instantaneously. It is then justified in rational and plausible arguments delivered by System 2:

“This finding, that people will readily fabricate reasons to explain their own behavior, is called ‘confabulation’. Confabulation is so frequent in work with split-brain patients and other people suffering brain damage that Gazzinga refers to the language centers on the left side of the brain as the interpreter module, whose job is to give a running commentary on whatever the self is doing, even though the interpreter module has no access to the real causes or motives of the self’s behavior. For example, if the word ‘walk’ is flashed to the right hemisphere, the patient might stand up and walk away. When asked why he is getting up, he might say, ‘I’m going to get a Coke’. The interpreter module is good at making up explanations, but not at knowing that it has done so.”
[Haidt, J. (2006) The happiness hypothesis: Finding modern truth in ancient wisdom, Basic Books]

At Microsoft, the acronym HiPPO (“Highest-Paid Person’s Opinion”) was created to summarise the dominant decision-making style at most companies.It illustrates the example given above of System 1 and 2 at work. HiPPOs too often destroy value. In a decades-long assessment Tetlock (1984) found that “humanity barely bests chimp” at predicting possible outcomes of politics, economics, and international affair. Today, machine learning – the science of building systems that can detect patterns and formulate winning strategies after shown many examples – is starting to accomplish interesting results. The “science fiction stuff” is just starting now…

Decoding the Antikythera Mechanism, the First Computer

Over 2’000 years ago, the churning ocean below the cliffs of the Greek island Antikythera swallowed a massive ship loaded with a trove of luxuries — fine glassware, marble statues and, famously, a complex geared device identified to be the earliest computer. Three flat, misshapen pieces of bronze are all shades of green, from emerald to forest. The proverbial ‘contraption’ has astonished archaeologists and scientists alike, by virtue of not only its advanced workmanship but also its fascinating (and rather enigmatic) purpose. To that end, the artifact is often also stated as the world’s oldest gear ‘machine’ (based on the workings of the differential calculator) – crafted to predict various complex astronomical observances, including planetary positions and eclipses. Nothing else like this has ever been discovered from antiquity. Nothing as sophisticated, or even close, appears again for more than a thousand years.


[You may read the full Smithsonian article here…]

Major Quantum Computing Advance Made Obsolete by Teenager

A teenager from Texas has taken quantum computing down a notch. In a paper posted online earlier this month, 18-year-old Ewin Tang proved that ordinary computers can solve an important computing problem with performance potentially comparable to that of a quantum computer.

Usually the benefits of quantum computing are difficult to understand – unless you are actively working in the field. But one of the most relatable problems that quantum algorithms could solve better than classical algorithms is the “recommendation” problem. Basically, the question of how services like Netflix or Amazon can find new things you will like based on your viewing/browsing habits and community ratings in a reasonable amount of time. Previously, it was believed that quantum algorithms were the only way to solve this problem, but an 18 year old MIT student has developed a way for today’s computers to solve it just as fast.

[You can read the full story —> here…]

A Production Possibilities Frontier

The engine of economic progress must ride on the same four wheels (supply side factors), no matter how rich or poor the country:

  • Human resources (including labor supply, education, discipline and motivation)

Labor inputs include, of course the quantity of workers. However, many economists believe that the quality of labor inputs, the skills, knowledge, and discipline of the labor force, is the single most important element in economic growth. capital goods can be effectively used and maintained only by skilled and trained workers.

Improvements in literacy, health, and discipline, and most recently, the ability to use computers, add greatly to the productivity of labor.

  • Natural resources (including land, minerals, fuels and environmental quality)

The important resources here are, arable land, oil and gas, forests, water, and mineral resources.
But the possession of natural resources is hardly necessary for economic success in the modern world. New York City prospers primarily on its high density service industries. While many countries that have virtually no natural resources, such as Japan, have thrived by concentrating on sectors that depend more on labor and capital than on indigenous resources.

  • Capital formation (including machines, factories and roads)

Tangible capital includes structures like roads, and power plants, and equipment, like trucks and computers. In this regard, some of the most dramatic stories in economic history, often involve the rapid accumulation of capital.
Accumulating capital requires a sacrifice of current consumption over many years. Countries that grow rapidly tend to invest heavily in new capital goods. In the most rapidly growing countries, 10 to 20% of output may go into capital formation. In this regard when we think of capital we must not concentrate only on private sector investment. In fact, many investments are undertaken only be governments, and provide the necessary social overhead capital and infrastructure for businesses to prosper. Roads, irrigation and water projects, and public health measures are important.

Government projects involve external benefits that private firms cannot capture so government is necessary to provide them.

  • Technology (from science and engineering to management and entrepreneurship)

Historically, growth has definitely not been a process of simple replication, adding rows of steel mills, or power plants next to each other. Rather, a never-ending stream of inventions and technological advances led to a vast improvement in the production possibilities of Europe, North America, and Japan. Technological change denotes changes in production processes or the introduction of new products or services.

Technological change is a continuous process of small and large improvements.

While the four supply factors of growth relate to the physical ability of the economy to expand, there are two other factors that are equally important:

  • First, there is the demand factor

To realize its growing production potential, a nation must fully employ its expanding supply of resources. This requires a growing level of aggregate demand.

  • Second, there is the efficiency factor

To reach its production potential, a nation must not only achieve full employment, but also two kinds of economic efficiency. Specifically, a country must achieve productive efficiency. That is, it must use its existing and new resources in the least costly way to produce what it does. And it must also achieve allocative efficiency, meaning that the specific mix of goods and services it produces must maximize society’s well-being.

The Future of Work: Driving for Uber

“Professionals who work for Google and Facebook can use the apps on their phones to get their apartments cleaned by Handy or Homejoy; their groceries bought and delivered by Instacart; their clothes washed by Washio and their flowers delivered by BloomThat. Fancy Hands will provide them with personal assistants who can book trips or negotiate with the cable company. Task Rabbit will send somebody out to pick up a last-minute gift and Shyp will gift-wrap and deliver it. SpoonRocket will deliver a restaurant-quality meal to the door within ten minutes. The obvious inspiration for all this is Uber, a car service which was founded in San Francisco in 2009 and which already operates in 53 countries.” (The Economist, 2015)

I learned about Uber through a workmate who used it for getting home after a work gathering. Being always a bit short of cash, I thought it might be an opportunity to work as a driver in order to get some extra pocket money. So I enrolled on their website and was promptly invited to an information evening. Obviously, quite a lot of people came with the same hope of brushing up their valet: Foreign workers, retired persons, outsiders, insurgents – in other words, desperate people like me.

“They have created a plethora of on-demand companies that put time-starved urban professionals in timely contact with job-starved workers, creating a sometimes distasteful caricature of technology-driven social disparity in the process; an article about the on-demand economy by Kevin Roose in New York magazine began with the revelation that the housecleaner he hired through Homejoy lived in a homeless shelter.” (Economist, 2015)

In contrast, the venue of a Zurich web agency, welcomed by a Yuppie-style host who informed us participants what a great thing Uber was. We were presented a short marketing movie about Uber’s history and finally got to employment details. I learnt that I am going to drive for their new service UberPop, their cheapest line of business open to anyone who owns a car not older than eight years and willing to engage in “ride sharing”. UberPop is not about ride sharing, but it sounds less suspect in the dawn of potential government intervention (as happened in the Netherlands, which banned Uber completely). The Economist (2015) refers in this context to “underused capacity” (how polite…):

“Underused capacity applies not just to people’s time, but also to their assets: to drive for Lyft or Uber you do need a car. The on-demand economy is in many ways a continuation of what has been called the “sharing economy” exemplified by Airnb, a company which turns apartments into guesthouses and their owners into hoteliers. For people with few assets, though, on-demand labour markets matter more.”

Our host stressed the fact that Uber is insured up to US$ 5 million – should there be an accident or an issue with one of the customers. The lengthy contract, which I received after the info meeting, says something else: That it is the driver’s responsibility to be properly insured and that the driver is obliged to inform his insurance that he is carrying out (professional) driving services. True, Uber as a company is insured against any claims customers may create after an incident, but it does not apply to the driver. The contract also makes clear that it is a driver’s responsibility to oblige to local laws and pay taxes – in other words, Uber does not care about local frameworks: Taxes, social security contributions, insurances – all left to the driver, returning to pre-industrial revolution principles of piecework with the associated exploitation and insecurity. Therefore, UberPop can be cheaper than any local transportations service, as it undermines competition through unequal conditions. Are these the working conditions of the future? Brave new world… The Economist may be too optimistic in its statement:

“The on-demand economy is not introducing the serpent of casual labour into the garden of full emoployment: it is exploiting an already casualised workforce on ways that will ameliorate some problems even as they aggravate others.”

So far the downside of the coin. Once I started driving, another side emerged. My customers were cheerful, between 16 and an estimated 35, knew how to make good use of their smart phones, many in well paid jobs.

“The on-demand economy is the result of pairing freelance workforce with the smartphone, which now provides far more computing power than the desktop computers which reshaped companies in the 1990s, and to far more people. […] Now that most people carry computers in their pockets which can keep them connected with each other, know where they are, understand their social network and so on, the transaction costs involved in finding people to do things can be pushed a long way down.” (Economist, 2015)

Using a taxi in Zurich can ruin you, but for many of my customers that was not the issue. The point was that taxi drivers were perceived as unfriendly (best case) to rude (worst case), that there were issues with acceptance of credit cards (instead of cash) and safety (for female customers). My backseat drivers had a higher opinion of Uber (painless service, easy payment, friendly drivers).

Besides the social problems that it creates, I strongly believe that Uber is undermining local rules and that is unfair. But Uber also addresses a public nuisance (expensive, unfriendly local taxi service) and an understandable need for painless service.

At the end of the day, the question for me was if Uber has fulfilled my expectations for a bit of pocket money. Somehow it is a yes – I made some cash. But compared to the amount of hours spent, very low. Subtract from that gas, more time spent for cleaning the car, and general car usage. Without Uber’s incentive program (extra payment for being available in the city on Friday and Saturday evening), it would definitely not pay to drive for UberPop. Not surprisingly, I was asked by my customers if I am a temporary “job seeker” (i.e. unemployed).

“The on-demand economy is good for outsiders and insurgents – and for entrepreneurs trying to create new businesses using such people. […] In Europe, the labour market drives a wedge between insiders who have lots of protections and outsiders who don’t; on-demand arrangements may give outsiders a chance of breaking in. […] If this seems attractive, it is also a measure of the way that the on-demand economy will contribute to pressure to reduce labour rights in all sorts of situations; a growing abundance of on-demand employees with no normally accepted rights such as sick-pay and overtime will give employers at firms with more standard structures an incentive to cut back. The more such pressures spread, the more protests against “Plattform Kapitalismus” the world is likely to see.”

[All quotes from: The Economist (2015): The Future of Work: There’s an App for that, EU Edition, January 3]
Further reading: How Payroll Survey is Missing Uber

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