Machines have developed the ability to understand, process, and even translate languages:
In recent years, much of the research in machine learning has focused on the algorithmic concept of deep neural networks, or DNNs, which learn essentially by inferring patterns — often patterns of remarkable complexity — from large amounts of data. For example, a DNN-based machine can be fed many thousands of snippets of recorded English utterances, each one paired with its text transcription, and from this discern the patterns of correlation between the speech recordings and the paired transcriptions. These inferred correlation patterns get precise enough that, eventually, the system can “understand” English speech. In fact, today’s DNNs are so good that, when given enough training examples and a powerful enough computer, they can listen to a person speaking and make fewer transcription errors than would any human.
What may be surprising to some is that computerized learning machines exhibit transfer learning. For example, let’s consider an experiment involving two machine-learning systems, which for the sake of simplicity we’ll refer to as machines A and B. Machine A uses a brand-new DNN, whereas machine B uses a DNN that has been trained previously to understand English. Now, suppose we train both A and B on identical sets of recorded Mandarin utterances, along with their transcriptions. What happens? Remarkably, machine B (the previously English-trained one) ends up with better Mandarin capabilities than machine A. In effect, the system’s prior training on English ends up transferring capabilities to the related task of understanding Mandarin.
But there is an even more astonishing outcome of this experiment. Machine B not only ends up better on Mandarin, but B’s ability to understand English is also improved! It seems that Willans and Goethe were onto something — learning a second language enables deeper learning about both languages, even for a machine.
Computer intelligence is going to be really scary and it’s coming extremely fast and there’s no way to stop it. A $1,000 chip with the processing power of a human is scheduled for around 2025. Very soon. Here’s a graphical gif showing you where we are and exactly how fast we’re coming up on Silicon supremacy. It shows the exponential increases.
http://assets.motherjones.com/media/2013/05/LakeMichigan-Final3.gif
In 3030 one processor chip will have the power of a small village. How you going to compete with that? You can’t. With no jobs what if the computers do a 5 People who sneer at the people lower ranked in intelligence are in for a big “eat crow” moment when they realize that the difference between a 100 IQ and a 150 IQ is nothing but a pimple compared to the power of computers in 2030. They will idiots compared to the computers and will not even understand what is going on.
How will we teach a computer to be “good” and value human life????? We may very well just be a brief tool needed to produce inorganic life.
People who scoff at this ,I think, really don’t have a handle on how fast exponential functions can expand after they’ve been started a while or they just ignore it. I try not to think about it until someone mentions the gains in computer processing or algorithm gains. It does no good I can’t do anything about it. They say computers can’t do this or that but a present computer is really only around the level of a lizard and look what it can do. It’s frightening.
“… 3030 one processor chip…”
Oops. 2030
Look at the graph and see how it zooms about 2018. We’re there.
My hope, like Cthulhu, the computers will see the massive evil the Jews are doing and gobble them up first. The Jews have been able to manipulate the masses for thousands of years now they will have competition. Big time. Remember Microsoft’s A.I. Tay? It took less than 24 hours for Tay’s to start saying “Hitler was right.”