ree from annoying mass emails and save important messages.
However, this is far from the kind of “general intelligence” humans possess, and which has been the goal of AI research for decades. And when it comes to building a superintelligent machine that can learn and act without the guiding hand of a human, it may still be decades away. But advancements in the field are happening quickly, so it could be upon us faster than we think. Such a machine would have a lot of power over our lives. Its intelligence could even be dangerous, since it would be too smart for us to disable in the event of an emergency.
Since the invention of computers in 1940, scientists have been working to build a machine that can think. What progress has been made? One major advance in Artificial Intelligence (or AI) are man-made machines that mimic our own intelligence.
The story begins with the 1956 Dartmouth Summer Project, which endeavored to build intelligent machines that could do what humans do. Some machines could solve calculus problems, while others could write music and even drive cars. But there was a roadblock: inventors realized that the more complex the task, the more information the AI needed to process. Hardware to take on such difficult functions was unavailable.
By the mid-1970s, interest in AI had faded. But in the early ‘80s, Japan developed expert systems – rule-based programs that helped decision-makers by generating inferences based on data. However, this technology also encountered a problem: the huge banks of information required proved difficult to maintain, and interest dropped once again.
The ‘90s witnessed a new trend: machines that mimicked human biology by using technology to copy our neural and genetic structures. This process brings us up to the present day. Today, AI is present in everything from robots that conduct surgeries to smartphones to a simple Google search. The technology has improved to the point where it can beat the best human players at chess, Scrabble and Jeopardy!
But even our modern technology 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 behin