ogy has issues: such AIs can only be programmed for one game and there’s no AI capable of mastering any game.
However, our children may see something much more advanced – the advent of superintelligence (or SI). In fact, according to a survey of international experts at The Second Conference on Artificial General Intelligence at the University of Memphis, in 2009, most experts think that machines as intelligent as humans will exist by 2075 and that superintelligence will exist within another 30 years.
It’s clear that imitating human intelligence is an effective way to build technology, but imitation comes in many forms. So, while some scientists are in favor of synthetically designing a machine that simulates humans (through AI, for instance), others stand by an exact imitation of human biology, a strategy that could be accomplished with techniques like Whole Brain Emulation (or WBE).
So what are the differences between the two?
AI mimics the way humans learn and think by calculating probability. Basically, AI uses logic to find simpler ways of imitating the complex abilities of humans. For instance, an AI programmed to play chess chooses the optimal move by first determining all possible moves and then picking the one with the highest probability of winning the game. But this strategy relies on a data bank that holds every possible chess move.
Therefore, an AI that does more than just play chess would need to access and process huge amounts of real world information. The problem is that present computers just can’t process the necessary amount of data fast enough.
But are there ways around this?
One potential solution is to build what the computer scientist Alan Turing called “the child machine,” a computer that comes with basic information and is designed to learn from experience.
Another option is WBE, which works by replicating the entire neural structure of the human brain to imitate its function. One advantage this method has over AI is that it doesn’t require a complete understanding of the processes behind the human brain – only the ability to duplicate its parts and the connections between them.
Most of the great discoveries of humanity were achieved either by a single scientist who reached a goal before others got there or through huge international collaborations. So, what would each route mean for the development of SI?
Well, if a single group of scientists were to rapidly find solutions to the issues preventing AI and WBE, it’s most likely their results would produce a single superintelligent machine. That’s because the field’s competitive nature might force such a group to work in secrecy.
Consider the Manhattan Project, the group that developed the atom bomb. The group’s activities were kept secret because the U.S. government feared that the USSR would use their research to build nuclear weapons of their own.
If SI developed like this, the first superintelligent machine would have a strategic advantage over all others. The danger is that a single SI might fall into nefarious hands and be used as a weapon of mass destruction. Or if a machine malfunctioned and tried to do something terrible – kill all humans, say – we’d have neither the intelligence nor the tools necessary to defend ourselves.
However, if multiple groups of scientists collaborated, sharing advances in technology, humankind would gradually build SI. A team effort like this might involve many scientists checking every step of the process, ensuring that the best choices have been made.
A good precedent for such collaboration is the Human Genome Project, an effort that brought together scientists from multiple countries to map human DNA. Another good technique would be public oversight – instating government safety regulations and funding stipulations that deter scientists from working independently.
So, while the rapid development of a single SI could still occur during such a slow collaborative process, an open team effort would be more likely to have safety protocols in place.
Check out my related post: Can AI redistribute wealth for us?