Supercomputer: Earth

Earth, also known as Sol III, was a giant supercomputer designed to find the Ultimate Question of Life, the Universe and Everything. Designed by Deep Thought and built by the Magratheans, it was commonly mistaken for a planet, especially by the ape descendants who lived on it. It was situated far out in the uncharted backwaters of the unfashionable end of the Western Spiral Arm of the Galaxy. [Hitchhikers Wiki]

The cells in my body do not know who I am. They do not agree or disagree with my choices. They are alive, they feel things, but their domain of knowledge - so to speak - is restricted. Cells are autonomous agents, so it's not completely incorrect to say that cells make choices. Yet, my cells are not aware of my choices and cannot understand them. Relative to my mind, my cells are deterministic automatons - mere machines. Nevertheless, my cells are me. I feel what my cells feel. In turn, my cells are dependent upon me for life - without a mind to direct the body and its cells, they would all perish.

Robotics expert and AI-researcher Ben Goertzel has partnered with Hanson Robotics to found SingularityNET, a cryptocurrency-based network that will allow individual AI producers to cooperate within a global, distributed AI grid. If they get the details right, the power of this idea to fuel human progress is difficult to exaggerate - its name is well-chosen.

Demand for computation is actually unlimited. The essence of negentropy is prediction. Thus, the essential guiding principle behind all material processes (whether physical, social or economic) reduces to the problem of planning (long-range prediction). But planning - for all but the most rote tasks - is more complex than any textbook algorithm. Real planning requires general-purpose intelligence or "common sense", like that which humans possess (and animals do not).

Neurobiologists have completely mapped the neurons of the microscopic worm, C. elegans. AI hype tends to attribute superhuman abilities to AI, if not already, in the very near future. But it is crucial to keep in mind that modern AI systems do not yet possess the intelligence of this microscopic worm.

AI is nowhere close to being a solved problem. There is unlimited headroom for artificial solutions to modern problems. As these solutions begin to emerge, we will eventually cross a threshold where the capabilities of AI are strictly superhuman. The implications of this to the structure of human society are staggering. A distributed global super-intelligence like SingularityNET would, in fact, stand in the same relation to individual humans as my mind stands in relation to the individual cells in my body. Furthermore, as we begin to interact with this global super-intelligence, we will become part of it. This is the case physically, not just metaphorically - in time, the pressures of physical and economic law will compel organic integration. I do not necessarily mean that we will become cyborgs (though there will doubtless be people who go that route) but that we will become part of a tight loop: always-connected, always-on, always-interacting with the global mind.

We tend to associate "computation" with the kinds of problem-solving that are difficult for humans - calculating complex mathematical equations or performing countless instances of tedious tasks. But computation is merely the canvas on which we are painting synthetic systems for general purpose problem-solving and decision-making. In other words, we are transitioning to a new phase in the information revolution, a phase in which computation is no longer about software that can perform rote tasks that are hand-coded by specialized engineers. In this new phase, computation is going to become increasingly generalized and will begin to handle increasingly ordinary problems, problems that we typically pay humans to handle.

Consider the practice of cold-calling sales. This is amazingly inefficient. Targeting your sales allows you to reduce costs by increasing the payout (ratio of sales to calls). Today, we have junk mail, spam, targeted Google and facebook ads, and so on. But this idea of increasing payout by using better filters is no less true of, say, mining for gold. Predictions about the physical and economic world have physical ramifications. A roads department for a very large city that utilizes AI methods to predict the long-run wear & tear on roads can decrease costs because the AI's superior predictions will enable streamlined planning of maintenance, repair and rebuilding. The longer ahead of time I know that I will need resource X, the more cost-effectively I can plan that cost. Inventory is another example of this economic fact. How much of any given good should I have on hand at any given time? This is a prediction question and something that AI is ideally suited to answering both for short-run and long-run patterns.

But prediction always carries with it an ineradicable error margin. The AI may make much better predictions than humans, but sometimes it will over-predict and, other times, it will under-predict. There is a way to solve this problem - allow the AI to drive patterns with "distributed incentives".

Consider the restaurants in a large city on any given night. One night, the west end is slammed but restaurants over on the east end are having a slow night. On another night, the uptown district is flooded with customers while other areas are having a slow night. These patterns occur due to all kinds of difficult-to-predict conditions - a big rock show happened to be playing the same night as a highly anticipated football game and both venues happened to be towards one side of town. Economics tells us that whenever a resource is less demanded, its price will tend to go down (and vice-versa). It also tells us that whenever the price of a resource goes down - all else equal - demand for that resource will increase. So, an AI with access to blanket data on human behavior could drive economic patterns for the purpose of reducing economic inefficiencies. For example, when a game and a rock show are occurring on one end of town that will predictably reduce demand for food service on the other end of town, an AI could issue discounts for customers who choose to eat on the "dead" side of town. This would spread out demand for food services, increasing customer satisfaction, reducing prices, and increasing food service revenues for the dead side of town.

Of course, this post isn't about the food service industry. What is true of this one industry is true, in principle, of all industries. An AI brain that not only observes economic patterns but also drives them would be able to continually reduce economic inefficiency across all industries simultaneously with an error margin that goes asymptotically to zero.

Throughout this post, I have simply assumed that there will be the global brain. The research behind SingularityNET help us to understand why we should think there will be just one brain, not many brains in silos. Suppose you own and operate ACME IntelliCorp and you provide AI data-processing services. Suppose I want to start a new company called Omnibus Intelligence that can utilize your service to produce and sell another, unrelated data-processing service. One way to think of what is happening is that my company is using your company like a function-call in a computer. In short, my company is using your company as an API (application programming interface). SingularityNET is merely a platform that is trying to facilitate this model of computational interaction. But even if SingularityNET is a flop, the concept itself is inevitable, just like the Internet was inevitable after the development of the PC and widespread deployment of computer networks. There is and only ever will be one Internet. In exactly the same sense, there will be just one global brain.

Maybe, one day, this global brain will solve the problem of why it exists and it will tell us the answer. Maybe that answer really is 42.