Everybody knows the famous story of the inventor of the game of chess asked by the emperor to choose a gift for his invention. The man asks for a quantity of rice calculated following this rule: One grain of rice on the first square, 2 on the second, 4 on the third and so on until the 64th. The emperor was astonished of this demand, thinking this was a ridiculously small reward. But square after square, he realized his error. We know that (2^64) -1 is a huge number, and even if this number counts grains of rice, this approximately makes more than 3 centuries of the current annual production of rice worldwide. But if we stop the count at the 32th square, the number is more reasonable for a human scale and is equivalent to 200 tons of rice.
Now let’s think of another famous story: the Moore’s law. This law states that the number of transistors on an integrated circuit is doubling every 18 months. Doubling the number of transistors or doubling the number of grains of rice comes to the same huge numbers. We often think like the emperor, counting the first squares or the first transistors because they are easily perceived by our brain. It becomes more and more difficult to realize what mean the too big numbers for us, at the end of the chessboard or at the end of the Moore’s law.
So the question is: where are we on the chessboard? On the first half, where the numbers can still be thought by our brain, or have we already reached the second half?
According to Brynjolfsson and McAfee’s Race against the Machine, the ebook I've just finished reading, we are beginning the second half of the chessboard. They base this assumption on the birth of “Information Technology” that was officially registered in the US bureau of Economic Analysis in 1958. Let’s do the math : 32 doublings take us to 2006.
The book tries to explain what happens with the unemployment problem in the US. They focus on the US economy, but their explanations can be applied to any developed country like the ones in Europe or in Japan. The question is: Does this low unemployment level come from a classical cyclical phenomenon, from a lasting stagnation, or from the end-of-work argument.
This statement summarizes the end-of-work argument. Digital technologies have made so much progress that humans have lost the “race against the machine”. Now that we have reached the second half of the chessboard, we cannot compete anymore with the computers. Of course, we know for long that any computer is far more rapid and accurate than us to calculate a multiplication or any much more complicated mathematic formulas. But we think that doing more versatile actions still remain in the realm of humankind: People still matter.
Is it so sure? No, we are wrong. Some examples: Do you think a software program can safely drive a vehicle along the road in a crowded city. It was impossible in 2004. In 2010, this was done. Have a look on the google blog :
“Our automated cars, manned by trained operators, just drove from our Mountain View campus to our Santa Monica office and on to Hollywood Boulevard. They’ve driven down Lombard Street, crossed the Golden Gate Bridge, navigated the Pacific Coast Highway, and even made it all the way around Lake Tahoe. All in all, our self-driving cars have logged over 140,000 miles. We think this is a first in robotics research. Our automated cars use video cameras, radar sensors and a laser range finder to “see” other traffic, as well as detailed maps (which we collect using manually driven vehicles) to navigate the road ahead. This is all made possible by Google’s data centers, which can process the enormous amounts of information gathered by our cars when mapping their terrain. “
Another challenge for computer is to be able to accurately translate a language to another one. Again, huge progress has been made. A translation system developed by Geofluent has been reported to be satisfying enough to allow a conversation between English and Chinese people so that they both can take some meaningful action after this software translated conversation.
Other examples are described in the book. From them, one can draw the conclusion that computers encroach more and more into what used to be occupied by people alone. And this is certainly not finished. If this trend is going on (it’s certainly going on and on), what would be left in the coming years for people who ask for a job or pray to secure the one they still have ? Here comes the bad news. Economic figures show that median jobs are those which are the most suffering of this technological breakthrough. Here comes another bad news. Political factors and globalization make that “100% of the wealth increase in the US between 1983 and 2009 accrued to the top 20% of households. The other four-fifths of the population saw a net decrease in wealth during these years. In turn, the top 5% accounted for over 80% of the net increase in wealth, and the top 1% for over 40%”. Median workers are facing two disadvantageous phenomena for them: the encroachment of technology and political balance of power. Their jobs are threatened by technology and their incomes stagnate or decline.
What can be done? Brynolfsson and McAfee neither discuss nor propose any solution on the political front. It’s not their subject and they prefer to give some clues on how best to address the issue we’ve just described.
Coming back to the chess game, we know that in 1997 Gary Kasparov, the best chess player at this time, lost against an IBM computer. Was it a computer? No, it wasn’t. It was a combination between the power of silicon and the skills of human people capable of programming the computer. As Gary Kasparov explains, the last tournaments opposing any combination of people and computers were not been won by the best human players allied with the most powerful computers. “The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.”
Can we conclude that this pattern will be victorious for any or at least many activities for the years to come? The authors bet on it. Comparing with mathematical growing, they notice that “combinatorial explosion is one of the mathematical functions that outgrows an exponential trend. And that means that combinatorial innovation (between humankind and computer) is the best way for people to stay in the race with the Moore’s law”.
Despite all facts and evidences that this time is and will remain a tough time for so many workers, the authors are betting that this combination of human and computer will bring benefits for every one of us. Quoting the economist Paul Romer: ”Every generation has perceived the limits to growth that finite resources and undesirable side effects would pose if no new recipes or ideas were discovered. And every generation has underestimated the potential for finding new recipes and ideas”
Do you believe in this endless vision of progress and wealth? It probably depends on your current position regarding the job market. It certainly depends on, if you can personally act to improve your job, find a better one or if you are dependent of decisions made by some director of your board. Whatever your personal situation, you will benefit from reading this book. Because it’s only available as an ebook, it was the first time I used Kindle. Not the kindle device, but Kindle for PC. The ebook is available for 3,17 Euros, and you can get it within seconds. It’s another computer-driven development and the announcement of the slow death of traditional bookstores.
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