1. “ All swans are white" had long been used as the standard example of a scientific truth. So what was the chance of seeing a black one? Impossible to calculate, or at least they were until 1697, when explorers found Cygnus in Australia.
2. About the author Taleb holds an MBA from the Wharton School at the University of Pennsylvania, and a Ph.D. in management science from the University of Paris (Dauphine). He is currently Distinguished Professor of Risk Engineering at NYU. "My major hobby is teasing people who take themselves and the quality of their knowledge too seriously and those who don’t have the guts to sometimes say: 'I don’t know...."
3. Warning of the Global Banking Crisis In 2006, in The Black Swan “ Globalization creates interlocking fragility, while reducing volatility and giving the appearance of stability. In other words it creates devastating Black Swans. We have never lived before under the threat of a global collapse. Financial Institutions have been merging into a smaller number of very large banks. Almost all banks are interrelated. So the financial ecology is swelling into gigantic, incestuous, bureaucratic banks – when one fails, they all fall. The increased concentration among banks seems to have the effect of making financial crisis less likely, but when they happen they are more global in scale and hit us very hard. We have moved from a diversified ecology of small banks, with varied lending policies, to a more homogeneous framework of firms that all resemble one another. True, we now have fewer failures, but when they occur ….I shiver at the thought. The government-sponsored institution Fannie Mae, when I look at its risks, seems to be sitting on a barrel of dynamite, vulnerable to the slightest hiccup. But not to worry: their large staff of scientists deem these events unlikely. ”
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6. Black Swan statistics are governed by Power Laws: whereas extreme events have essentially zero probability under Bell curve distributions, they are the essence of the Power Law distribution Example: at random, take two US people with a joint income of $1M/yr – what is the likely breakdown of their incomes? • Bell curve: $0.5M each • Power law: $50k and $950k While we plan and manage risk around Bell curve statistics, much of real world operates on a power law basis – In a Power Law world, what you don’t know is far more relevant than what you do know • It’s not how often you’re right, it’s your cumulative error that counts Statistics Behind
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14. Progress is accelerating • Increasingly powerful inexpensive computing power will yield $1000 computers with the processing equivalent of the human mind by ~2030 • Key issue is software development and the potential for strong AI – If successful, then we’ll see a singularity – Once one machine is our equal, we will be vastly surpassed the next day – Kurzweil has defined the singularity as the point when non-biological intelligence is a billion times more capable than all human intelligence today – and puts the date at 2045
