Predicting the Path to AI

As they say, hindsight has perfect vision. It is a simple matter to look back after an event takes place and recreate the path of steps that led up to that event. It is much more difficult to see that path in advance. When AI is born, assuming that such an event would be immediately apparent, and assuming that it has not yet occurred, what path will it have taken? Will cloud computing and data centers turn out to have been a crucial fork in the road?

IBM and Google have announced a joint initiative to build large data centers that will allow students and researchers to participate in remote "cloud computing," at term that refers to the combined use of thousands of processors, vast libraries of data, and specialized software that "scour the Web and other data sources in seconds or minutes for patterns and insights."

As these cloud computing centers are created and more and more resources are injected into their ever-increasing capacities, will a crucial threshold be attained? It has been theorized that consciousness is an inevitable outcome when a sufficient degree of processing complexity is reached. Are we on a path to the inevitable emergence of a conscious Internet? If this is indeed what happens, remember where you heard it first. Stay tuned.

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Exploring Swarm Theory

As promised, I have been searching the Internet for the latest news about emergent complexity. I found this fascinating article on National Geographic written by Peter Miller.

"Ants aren't smart," Gordon says. "Ant colonies are." A colony can solve problems unthinkable for individual ants, such as finding the shortest path to the best food source, allocating workers to different tasks, or defending a territory from neighbors. As individuals, ants might be tiny dummies, but as colonies they respond quickly and effectively to their environment. They do it with something called swarm intelligence.

Where this intelligence comes from raises a fundamental question in nature: How do the simple actions of individuals add up to the complex behavior of a group? How do hundreds of honeybees make a critical decision about their hive if many of them disagree? What enables a school of herring to coordinate its movements so precisely it can change direction in a flash, like a single, silvery organism? The collective abilities of such animals—none of which grasps the big picture, but each of which contributes to the group's success—seem miraculous even to the biologists who know them best. Yet during the past few decades, researchers have come up with intriguing insights.

One key to an ant colony, for example, is that no one's in charge. No generals command ant warriors. No managers boss ant workers. The queen plays no role except to lay eggs. Even with half a million ants, a colony functions just fine with no management at all—at least none that we would recognize. It relies instead upon countless interactions between individual ants, each of which is following simple rules of thumb. Scientists describe such a system as self-organizing.

That is fascinating, you say, but how does it affect the price of rice in China? It turns out that building computer models of ant behavior and swarm intelligence is allowing researchers to solve real-world problems.

In Houston, for example, a company named American Air Liquide has been using an ant-based strategy to manage a complex business problem. The company produces industrial and medical gases, mostly nitrogen, oxygen, and hydrogen, at about a hundred locations in the United States and delivers them to 6,000 sites, using pipelines, railcars, and 400 trucks. Deregulated power markets in some regions (the price of electricity changes every 15 minutes in parts of Texas) add yet another layer of complexity.

So they built a computer model based on the behavior of ants.

Ants had evolved an efficient method to find the best routes in their neighborhoods. Why not follow their example? So Air Liquide combined the ant approach with other artificial intelligence techniques to consider every permutation of plant scheduling, weather, and truck routing—millions of possible decisions and outcomes a day. Every night, forecasts of customer demand and manufacturing costs are fed into the model.

"It takes four hours to run, even with the biggest computers we have," Harper says. "But at six o'clock every morning we get a solution that says how we're going to manage our day."

For truck drivers, the new system took some getting used to. Instead of delivering gas from the plant closest to a customer, as they used to do, drivers were now asked to pick up shipments from whichever plant was making gas at the lowest delivered price, even if it was farther away.

"You want me to drive a hundred miles? To the drivers, it wasn't intuitive," Harper says. But for the company, the savings have been impressive. "It's huge. It's actually huge."

Another application of swarm intelligence uses the flocking actions of birds in flight.

A team of robots that could coordinate its actions like a flock of birds could offer significant advantages over a solitary robot. Spread out over a large area, a group could function as a powerful mobile sensor net, gathering information about what's out there. If the group encountered something unexpected, it could adjust and respond quickly, even if the robots in the group weren't very sophisticated, just as ants are able to come up with various options by trial and error. If one member of the group were to break down, others could take its place. And, most important, control of the group could be decentralized, not dependent on a leader.

This field is just getting started, and the applications to come defy imagination. How much do you want to bet that there's swarm intelligence in the brain's huge collection of neurons? Stay tuned.

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Emergent Complexity and Biological Engineering: 21st Century Science

I have decided to add two fields of scientific research and development to my roster of subjects to keep up with on my Singularity Blog. As we have established a beachhead on the shores of the 21st century, these two fields have become, as some would put it, the new new things. As I continue to comb through large quantities of articles on current developments in order to bring to my readers relevant and interesting news, I will be including these two topics: Emergent Complexity and Biological Engineering.

Whereas, not too long ago, creationists were able to convince many that the complexity of life could not be explained by science and must therefore remain the province of a Creator, this realm alas has joined so many others in falling to the advancing armies of scientific investigation. Emergent complexity is now understood to show that complex systems do emerge naturally out of a large number of simple interactions, with no need for a controlling entity. With a few very simple rules for each ant to follow, the complex behavior of the colony emerges. The complex shapes of snowflakes emerge out of the simple interactions of water molecules. A flock of birds moves and shifts in the sky in highly complex and coordinated fashion, using only the simple rules followed by each bird. These are examples of emergent complexity. What does this have to do with Singularity?

As we build computers that approach the level of complexity of the human brain, the question arises: Is consciousness the natural result of complexity? When we are able to build systems with as many connections between transistors as there are synapses in the brain, will the first sentient machine be born? No matter what anyone thinks is the answer, we will all find out soon enough.

Bioengineering is coming to the forefront of science only recently, since we are approaching a degree of understanding and technological wizardry wherein we can engineer and transform our own biology. Of course a nightmare scenario will immediately come to mind, thanks to Mary Shelley, but we hope for better things, such as the eradication of genetic disease and even death itself.

So onward we go, and I hope you'll come along for the ride.

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