Welcome
Today there are winners and losers. Sadly the losers outnumber the winners. This is because the world is changing so fast that few people keep up. The smartphone has wiped out thousands of camera stores. Kodak an industrial giant is now on it’s knees crawling through bankruptcy.
The internet, digital technologies, synthetic biology, nanotech, robots, and space travel, to name a few areas where technology is creating and killing companies. It is the unexpected rate of progress that defines and unites these advances in science and technology.
Humans have brains that expect linear rates of change. You through a rock twice as hard and it goes twice as far, but this linear rate of change no longer describes our world. Humanity has now entered the Exponential Age!
Let’s start off with an Allegory. It‘s about a grasshopper race and 2 kinds of energy food. The first food is called L-go and it produces a linear boost in energy. When the grasshopper eats this food it becomes so energized that it hops 1000 miles per day.
The second food is called E-go it is exponential, it causes a doubling of energy each day. But at first it makes the grasshopper rather sick.
After weeks of news coverage with all kinds of wild and unfounded claims and assertions it is decided to hold a 40 day race to test the two different energy foods.
Off to the races! There’s a big crowd with worldwide TV coverage and many gamblers, some betting large and others small amounts of money. Everyone is excited for the outcome of the race could have major impact on the world economy as humans start taking either L-go or E-g. It is said that over 1.5 trillion dollars is being gambled.
The grasshoppers are fed and the starting gun is fired. The grasshopper who was fed the L-go food leaps out of the starting gate and by the end of the day, has traveled 1000 miles. The second grasshopper eats the E-go food and collapses to the ground. Many people thought it dead but ever so slowly it drags itself forward and by the end of one day of struggle it had moved only one inch.
The next day was much the same with the L-go food enabling that grasshopper to travel an additional 1000 miles. Again the E-go food made the grasshopper sick and she moved only 2 inches. Day three the same 1000 miles and this time E-go made it 4 inches. and on the following day L-go have traveled a total of 4000 miles and this day E-go made it 8 inches before collapsing.
Now there were many very discouraged samples for those who bet on the E-g food saw no hope for that grasshopper to win. What do you think?
Lets take a vote, who will win after 40 days and by how much? It clear that L-go will have traveled 40,000 miles so how much do you think E-go will have moved?
a. 100 feet
b. 1000 feet
c. 1000 miles
d. 17 million miles
d. is the answer for on the 40th day E-go will have traveled over 17 million miles, winning the race by an overwhelming margin!
Enter a big chicken that points a wing at the audience and says, “I eat the sow grasshoppers alive! Are you going to keep up with the exponentially advancing technologies or are you going to become chicken feed?”
I've told this story because we are moving into an age where a number of newer technologies are advancing exponentially like the E-go eating grasshopper. This is a big heads up because I and many other people have a hard time making predictions about the future if it involves exponential rates of change. As the following chart shows things often start out so slow with many problems, this causes some people get blindsided when after many doubling things move at undreamed of speeds.
This graph shows the difference between linear and exponential progress.
Artificial Intelligence (AI) can serve as a real world example of exponential progress. Slow at first, but once one passes the knee of the curve watch out.
In 1950 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines with true intelligence. The Turing Test was the first serious proposal in the philosophy of artificial intelligence. Then in 1951, using the Ferranti Mark 1 machine of the University of Manchester, Christopher Strachey wrote a checkers program and Dietrich Prinz wrote one for chess. Arthur Samuel's checkers program, developed in the middle 50s and early 60s, eventually achieved sufficient skill to challenge a respectable amateur. Game AI would continue to be used as a measure of progress in AI throughout its history.
Progress was very slow, so slow in fact that in the 70s, AI was subject to pessimistic critics and financial setbacks. AI researchers had failed to appreciate the difficulty of the problems they faced. Their tremendous optimism had raised expectations impossibly high, and when the promised results failed to materialize, funding for AI disappeared.
Even though many people had given up on AI there were still some die hards doing further research. On 11 May 1997, Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov. In 2005, a Stanford robot won the DARPA Grand Challenge by driving autonomously for 131 miles along an unrehearsed desert trail. Two years later, a team from CMU won theDARPA Urban Challenge by autonomously navigating 55 miles in an Urban environment while adhering to traffic hazards and all traffic laws. In February 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin.
Once one passes the knee of the curve progress starts moving at impressive speeds. It took 60 years for computers to be able to play a winning game of Jeopardy. Now 3 years later IBM Watson is 24 times faster, and is 90 percent smaller. As of May 2014 Watson can read and process 500 gigabytes of text per second, this is equivalent of a million 200 page books, per second. The system has 2,880 POWER7 processor cores and 16 terabytes of RAM.
Using natural language processing and analytics, Watson processes information akin to how people think, representing a major shift in an organization’s ability to quickly analyze, understand and respond to Big Data. Watson’s ability to answer complex questions posed in natural language with speed, accuracy and confidence is transforming decision making across a variety of industries. This is way thousands of people like lawyers have lost their jobs for these new cognitive machines are faster cheaper and more skilled at understanding and extracting knowledge from billions of pages of text in multiple languages, and it is only at the knee of the exponential curve..
Is this intelligence? Yes and no, it is the kind of learning that one goes to school for and it is what IQ tests evaluate. It is not the kind of intelligence shown by people like Einstein who discover new knowledge.
Move over Einstein, Eureqa a science robot will now take over! Eureqa unveiled in April 2009 is descended from Lipson’s work on self-contemplating robots that figure out how to repair themselves. The same algorithms that guide the robots’ solution-finding computations have been customized for analyzing any type of data. Today there are problems in science that have eluding our understanding. When given these challenging problems Eureqa has been able solve some of them. Eureqa and other similar programs are advancing rapidly.
Eureqa returns equations that fit data, but refer to variables that are not yet understood. Eureqa finds new relationships. It’s predictive, it’s elegant, it has to be true. But we have no idea what it means. Said simply Eureqa, in the realm of scientific research, is now at times smarter than people, and it is only at the knee of the exponential curve.
Homework
Suggested Reading:
1. Brynjolfsson & McAfee - The Second Machine Age A revolution is under way.
In recent years, Google’s autonomous cars have logged thousands of miles on American highways and IBM’s Watson trounced the best human Jeopardy! players. Digital technologies—with hardware, software, and networks at their core—will in the near future diagnose diseases more accurately than doctors can, apply enormous data sets to transform retailing, and accomplish many tasks once considered uniquely human.
In The Second Machine Age MIT’s Erik Brynjolfsson and Andrew McAfee—two thinkers at the forefront of their field—reveal the forces driving the reinvention of our lives and our economy. As the full impact of digital technologies is felt, we will realize immense bounty in the form of dazzling personal technology, advanced infrastructure, and near-boundless access to the cultural items that enrich our lives.
Amid this bounty will also be wrenching change. Professions of all kinds—from lawyers to truck drivers—will be forever upended. Companies will be forced to transform or die. Recent economic indicators reflect this shift: fewer people are working, and wages are falling even as productivity and profits soar.
Drawing on years of research and up-to-the-minute trends, Brynjolfsson and McAfee identify the best strategies for survival and offer a new path to prosperity. These include revamping education so that it prepares people for the next economy instead of the last one, designing new collaborations that pair brute processing power with human ingenuity, and embracing policies that make sense in a radically transformed landscape. 
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