Out of Control Book Summary, by Kevin Kelly

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Full Summary of Out of Control

Overview

I can imagine myself in 1994, having just heard of the Internet. I would have been amazed at how much it has changed life in 20 years. But Kevin Kelly’s predictions about that technology are amazing and he seems to be a psychic medium who could predict all those developments with remarkable accuracy.

This book is required reading for the cast of The Matrix, including Keanu Reeves. It also explains why artificial intelligence should scare us and how credit cards should have disappeared in the last 20 years. It even explains how a computer program could prove that Darwin didn’t get it all right when he described evolution through natural selection.

Big Idea #1: The future of technology will see the merging of natural and artificial characteristics.

Think back to the year 1994. If you were alive then, you may remember how technology was not as advanced as it is now. There weren’t social networks or camera phones, and the internet had just begun to catch on with people.

Back then, scientists and technologists were already asking the same questions we ask today about technology.

One way to drive technological progress is by learning lessons from nature. Artificial intelligence is one area in which we can apply these lessons. For example, if you program a machine to build a car door, it will be able to repeat that task over and over again without being reprogrammed; however, it cannot do anything else unless you reprogram it.

In nature, we find far more complex “technology” than in man-made machines. For example, the human brain learns new things and evolves based on what it experiences. This is known as vivid logic, and if we want to improve artificial intelligence (AI), we need to emulate this vivid logic in machines too. Yet learning from nature is just one lane in a two-way street: we can also add elements of technology to nature by enhancing natural systems with the help of technology.

Bioengineering is one example of this phenomenon. We can use it to modify plants and animals in a way that benefits mankind. For instance, we can breed cows so that their offspring produce more milk.

Even more, we can see how nature and technology are converging in bionic vivisystems. A great example of a natural vivisystem is a beehive, which has the ability to learn and adapt but isn’t an individual organism itself.

Big Idea #2: To take advantage of natural principles in technology, humanity must relinquish control.

Many of today’s technologies require a lot of supervision. However, as we begin to merge artificial and biological systems, it will become clear that humans must give up some control. Why? As technology becomes more advanced and adapts to nature, the latter will be in charge because it is at its core the foundation of all life on earth.

Nature is more efficient than man-made systems. For example, nature recycles nutrients from dead plants and animals in a way that’s better than any artificial system. In order to take advantage of the efficiency of natural processes, we must be willing to relinquish control over our lives. We must think like shepherds who guide their flock instead of acting as iron-fisted managers trying to control every single sheep. Such an approach would also allow us to develop machines according to three principles: autonomy (the ability for machines to act independently), creativity (the ability for machines invent new ways perform tasks) and adaptability (the ability for machines learn).

One of humanity’s main challenges for the 21st century will be to let artificial systems develop in a way that follows natural principles.

Big Idea #3: We can leverage the flexibility of bee swarms in our own technological networks.

Have you ever seen a swarm of bees? If so, you were probably amazed at how the group moved as one.

Humans can learn a lot from swarms. Swarms already exhibit characteristics that humans hope to attain in the future.

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For example, in a bee swarm there are no leaders. Each bee is autonomous and makes its own decisions. In the case of a bee swarm, it’s better that way because if one or more bees die then the whole system doesn’t collapse.

So, how can we use this to our advantage? By networking. Networking is like a bee swarm; it consists of nodes that are connected to each other in various ways.

Let’s say we have a network for communicating information from one node to another. Even if individual nodes malfunction, the information can still find alternative paths. This is why the internet cannot be totally knocked out – only individual sites or parts of it can.

A network of nodes can be expanded without any fundamental changes to the network itself. This is because each new node exponentially increases the number of connections for information to travel along, making it more robust. For example, if you had three nodes on a graph and then added one more node, you would now have six connections instead of just four!

Big Idea #4: Network thinking can transform the economy into a more ecological, consumer-friendly one.

Imagine a world where there was no big business, but instead everyone ran their own one-person company. That’s what the network economy would look like: each individual in the network would be responsible for a specific task. For example, you (node 1) would identify a general production manager (node 2), who would contact a designer (node 3). The designer would send that design to carpenter (node 4), who builds the chair and contacts logistics expert (node 5), who ships it to your home.

A chair might be created using a unique combination of nodes, and that same process will probably never be used again.

The network economy is far more efficient than the traditional one. This is because it’s based on sharing and exchanging goods, so there’s no overproduction of anything. The network economy also encourages recycling because when a person has finished using something, they can pass it along to someone else who might want to use or break it down into raw materials.

Consumers are more powerful because they can demand specific products. Also, consumers can participate in the production of goods and services. For example, people can create software like Firefox that is used by many people around the world.

Big Idea #5: In a networked economy, privacy requires that we encrypt information.

There are many benefits of being a part of a network. However, there are also concerns that arise from the fact that you have to share information with others in order for the network to function efficiently. Privacy is one concern because we wouldn’t want all our private information out there for anyone to see, so it’s important to protect privacy within these networks.

In order to protect our privacy, we need a way to remove information from the network. However, it’s not enough for this task to be left up to private companies; if they have that power, criminals could use it as well.

Therefore, encryption is a better option than deletion. It makes information unreadable to anyone who doesn’t have the key to decode it.

Electronic cash is an advanced application of encryption. It’s like credit cards, but it can be anonymous and transferred as easily as a payment from a credit card. Electronic cash requires strong encryption that makes it impossible for the merchant to decipher who exactly was the payer in any given transaction, unless the payer authorizes it.

Big Idea #6: Stable ecosystems cannot be designed, they can only emerge from natural randomness.

Aldo Leopold wanted to create his own ecosystem in the 1930s. He did this by introducing certain species into a particular environment and climatological conditions in order to recreate the prairie grasslands of North America.

Leopold found that a prairie ecosystem didn’t emerge even though he had the right climate and introduced the right plant and animal species to his farm.

When the researcher studied prairies, he found that they were in a state of decline. He realized that there was one crucial element missing from his study: fire. In nature, wildfires periodically ravage prairies and thereby regulate them.

This demonstrates that it’s difficult to artificially create ecosystems. No matter how scientists try, they’re unable to recreate the complex web of factors in natural ecosystems. Indeed, scientists have always failed at creating artificial ecosystems accurately.

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So, how do ecosystems develop if they’re not designed by a clear goal? They simply evolve over time through an evolutionary process.

Biotechnology is complex and difficult to control. It must arise from a process that’s equally as random, even if the end result isn’t known in advance. To do this, we need to let nature take over more than it currently does in biotechnology, just like Leopold did with his prairie ecosystem.

Big Idea #7: Artificial intelligence can emerge from artificial evolution.

As we know, life on earth has evolved as a result of Darwin’s natural selection and certain conditions like oxygen availability. This process created human intelligence.

Artificial intelligence is evolving. If we were to create a computer program that mimics the way life evolves, it could develop artificial intelligence. Such a program would be like our brain, with millions of interconnected neurons that work together and give us our personalities. We have no control over how this network develops or what kind of person we become.

Therefore, if we were to create a machine that works like the human brain, it would also learn and develop in the same way as our brains. We might not know whether this artificial intelligence is good or evil.

However, once we reach that point of a self-sustaining AI, it will be very close to artificial evolution. In a sense, we’ll be like God with one important distinction: We’ll have to share our universe with our creation and the unintended consequences are unknown.

Big Idea #8: By playing with artificial evolution, we can learn a lot about our own.

The previous point about artificial evolution is interesting because it could lead to artificial intelligence, but also because it can teach us a lot about how life on Earth evolved.

For example, if we create the right conditions in a computer program, we can watch and study how natural selection works. It’s also possible to alter those conditions so that it would be driven by something other than natural selection.

Natural selection is the driving force behind evolution on Earth. However, in an artificial setting, we can create conditions that could result in a different driving force for evolution.

Although we don’t know how life originated on earth, it’s safe to say that there must have been some higher power.

Some people believe that Darwin’s theory of evolution can be applied to artificial life forms. Post-Darwinism is the idea that artificial and natural evolution work in similar ways. For example, we might observe how mutations are not random but rather occur as a response to environmental signals.

We might see that the way life evolves is similar to the auto industry, which has settled on a standard four-wheeled model. Perhaps this is also true of evolution.

Big Idea #9: We can make predictions even about apparent chaos, but only for the short term.

The stock market and a balloon moving around in an unpredictable way have some things in common. A novice might think that the movements of the stock market are random, but as he learns more about how it works, he’ll find that there are patterns to its movements.

Similarly, if you untie a knot in a balloon and then try to catch it as it flies around the room, you’ll find yourself improving at catching it. It seems that there is some order in the apparent chaos after all.

We don’t really understand how chaos works, but we can make short-term predictions about it. These predictions are not 100% accurate, but they help us deal with the chaotic process in the short-term. In the long run though, these rules of thumb become outdated and inaccurate.

For example, in the stock market, an investor may observe that the price of oil has gone up consistently for many years. She then makes investments based on a rule of thumb that this trend will continue. This strategy works for a while but eventually she’ll lose money because prices change over time.

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Biotechnology is different from other systems because it can develop in unpredictable ways. Even though we may make short-term predictions, long-term ones are not possible because of our limited knowledge.

Scientists have been no better at predicting the future than if they made random guesses.

Thus, we should accept that we can only predict the immediate future. We cannot control what happens in the long term.

Out of Control Book Summary, by Kevin Kelly
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